From 80001740ba4e54a9c9a4c43a31576c72937e5e90 Mon Sep 17 00:00:00 2001
From: Ishika Roy <iroy@ipp1-3302.aselab.nvidia.com>
Date: Tue, 24 Feb 2026 16:24:27 -0800
Subject: [PATCH 1/2] add Pyomo example and QP example notebook

---
 Pyomo_integration_example/README.md           |   27 +
 .../p_median_problem.ipynb                    |  429 +++
 .../QP_portfolio_optimization.ipynb           | 2434 +++++++++++++++++
 portfolio_optimization/README.md              |    8 +-
 .../cvar_portfolio_optimization.ipynb         |    6 +-
 5 files changed, 2899 insertions(+), 5 deletions(-)
 create mode 100644 Pyomo_integration_example/README.md
 create mode 100644 Pyomo_integration_example/p_median_problem.ipynb
 create mode 100644 portfolio_optimization/QP_portfolio_optimization.ipynb

diff --git a/Pyomo_integration_example/README.md b/Pyomo_integration_example/README.md
new file mode 100644
index 0000000..6446e33
--- /dev/null
+++ b/Pyomo_integration_example/README.md
@@ -0,0 +1,27 @@
+# Pyomo Integration Example
+
+This folder contains a Jupyter Notebook demonstrating how to integrate NVIDIA cuOpt as a solver backend for optimization problems modeled with Pyomo.
+
+## About Pyomo
+
+[Pyomo](https://www.pyomo.org/) is a Python-based open-source software package that supports a diverse set of optimization capabilities for formulating, solving, and analyzing optimization models.
+
+## Using cuOpt with Pyomo
+
+Pyomo supports cuOpt as a backend solver, allowing you to leverage GPU-accelerated optimization while using Pyomo's intuitive modeling syntax. This integration provides:
+
+- **Familiar API**: Use Pyomo's pythonic syntax for modeling
+- **GPU Acceleration**: Benefit from cuOpt's high-performance GPU-based solving
+- **Easy Solver Switching**: Compare different solvers by simply changing the solver parameter
+
+## Example Notebook
+
+### `p_median_problem.ipynb`
+
+This notebook demonstrates the classic p-median problem:
+- **Problem**: Choosing facility locations to minimize the weighted distance while meeting assignment constraints.
+- **Approach**: Model the problem using Pyomo and solve with cuOpt
+- **Features**:
+  - Setting up decision variables and constraints with Pyomo
+  - Solving with setting `solver = pyo.SolverFactory("cuopt")` parameter
+  - Analyzing and visualizing results
\ No newline at end of file
diff --git a/Pyomo_integration_example/p_median_problem.ipynb b/Pyomo_integration_example/p_median_problem.ipynb
new file mode 100644
index 0000000..f0df362
--- /dev/null
+++ b/Pyomo_integration_example/p_median_problem.ipynb
@@ -0,0 +1,429 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "fMaKbZo6Afgd"
+   },
+   "source": [
+    "# P-median Problem Example with Pyomo\n",
+    "\n",
+    "cuOpt is NVIDIA's GPU accelerated solver that delivers massive speedups for real-world LP, MIP, and VRP workloads.\n",
+    "\n",
+    "cuOpt seemlessly integrates with modeling languages. You can drop cuOpt into existing models built with pyomo, PuLP, cvxpy and AMPL, with minimal refactoring. Let's take a look at an example solving a simple MIP problem with cuOpt.\n",
+    "\n",
+    "To run this in Google Colab, download the notebook and upload it to Google Colab. Make sure you are running this on a T4 GPU.\n",
+    "\n",
+    "If you are running this in the cuOpt container, you are good to go!\n",
+    "\n",
+    "\n",
+    "## 1. Install Dependencies\n",
+    "\n",
+    "To make sure we are good to go, let's install Pyomo and cuOpt.\n",
+    "\n",
+    "__[Pyomo](https://github.com/Pyomo/pyomo)__ is a popular linear and mixed integer programming modeler written in Python.\n",
+    "\n",
+    "\n",
+    "If you are running this notebook in Google Colab, or elsewhere outside the container where cuOpt is not yet installed, uncomment the pip install command to install cuOpt."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import subprocess\n",
+    "import html\n",
+    "from IPython.display import display, HTML\n",
+    "\n",
+    "def check_gpu():\n",
+    "    try:\n",
+    "        result = subprocess.run([\"nvidia-smi\"], capture_output=True, text=True, timeout=5)\n",
+    "        result.check_returncode()\n",
+    "        lines = result.stdout.splitlines()\n",
+    "        gpu_info = lines[2] if len(lines) > 2 else \"GPU detected\"\n",
+    "        gpu_info_escaped = html.escape(gpu_info)\n",
+    "        display(HTML(f\"\"\"\n",
+    "        <div style=\"border:2px solid #4CAF50;padding:10px;border-radius:10px;background:#e8f5e9;\">\n",
+    "            <h3>✅ GPU is enabled</h3>\n",
+    "            <pre>{gpu_info_escaped}</pre>\n",
+    "        </div>\n",
+    "        \"\"\"))\n",
+    "        return True\n",
+    "    except (subprocess.CalledProcessError, subprocess.TimeoutExpired, FileNotFoundError, IndexError) as e:\n",
+    "        display(HTML(\"\"\"\n",
+    "        <div style=\"border:2px solid red;padding:15px;border-radius:10px;background:#ffeeee;\">\n",
+    "            <h3>⚠️ GPU not detected!</h3>\n",
+    "            <p>This notebook requires a <b>GPU runtime</b>.</p>\n",
+    "            \n",
+    "            <h4>If running in Google Colab:</h4>\n",
+    "            <ol>\n",
+    "              <li>Click on <b>Runtime → Change runtime type</b></li>\n",
+    "              <li>Set <b>Hardware accelerator</b> to <b>GPU</b></li>\n",
+    "              <li>Then click <b>Save</b> and <b>Runtime → Restart runtime</b>.</li>\n",
+    "            </ol>\n",
+    "            \n",
+    "            <h4>If running in Docker:</h4>\n",
+    "            <ol>\n",
+    "              <li>Ensure you have <b>NVIDIA Docker runtime</b> installed (<code>nvidia-docker2</code>)</li>\n",
+    "              <li>Run container with GPU support: <code>docker run --gpus all ...</code></li>\n",
+    "              <li>Or use: <code>docker run --runtime=nvidia ...</code> for older Docker versions</li>\n",
+    "              <li>Verify GPU access: <code>docker run --gpus all nvidia/cuda:12.0.0-base-ubuntu22.04 nvidia-smi</code></li>\n",
+    "            </ol>\n",
+    "            \n",
+    "            <p><b>Additional resources:</b></p>\n",
+    "            <ul>\n",
+    "              <li><a href=\"https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html\" target=\"_blank\">NVIDIA Container Toolkit Installation Guide</a></li>\n",
+    "            </ul>\n",
+    "        </div>\n",
+    "        \"\"\"))\n",
+    "        return False\n",
+    "\n",
+    "check_gpu()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "T2L7jTld2Qqj"
+   },
+   "outputs": [],
+   "source": [
+    "!pip install pyomo==6.10.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "tFLzH53z2Qoc"
+   },
+   "outputs": [],
+   "source": [
+    "# Enable this in case you are running this in google colab or such places where cuOpt is not yet installed\n",
+    "#!pip uninstall -y cuda-python cuda-bindings cuda-core\n",
+    "#!pip install --upgrade --extra-index-url=https://pypi.nvidia.com cuopt-cu12 nvidia-nvjitlink-cu12 rapids-logger==0.1.19\n",
+    "#!pip install --upgrade --extra-index-url=https://pypi.nvidia.com cuopt-cu13 nvidia-nvjitlink-cu13 rapids-logger==0.1.19"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "VeTiQIUJEQbR"
+   },
+   "source": [
+    "## 2. Problem Setup\n",
+    "\n",
+    "A city wants to place p = 6 ambulance stations to serve 50 demand zones (neighborhood centroids). Every zone must be assigned to exactly one open station, and we want to minimize total (weighted) travel distance.\n",
+    "\n",
+    "This is the classic p-median: choose facility locations + assign demand points to them.\n",
+    "\n",
+    "- We have N=50 demand points (clients)\n",
+    "\n",
+    "- M=20 candidate facility sites\n",
+    "\n",
+    "- Open exactly p=6 sites\n",
+    "\n",
+    "- Assign every client to one open site\n",
+    "\n",
+    "- Objective: Minimize total weighted distance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Generate problem parameters\n",
+    "\n",
+    "import random, math\n",
+    "random.seed(7)\n",
+    "\n",
+    "# sizes\n",
+    "N = 50   # clients\n",
+    "M = 20   # candidate sites\n",
+    "p = 6    # number of facilities to open\n",
+    "\n",
+    "clients = list(range(N))\n",
+    "sites = list(range(M))\n",
+    "\n",
+    "# coordinates\n",
+    "client_xy = {i: (random.uniform(0, 20), random.uniform(0, 20)) for i in clients}\n",
+    "site_xy   = {j: (random.uniform(0, 20), random.uniform(0, 20)) for j in sites}\n",
+    "\n",
+    "# demand weights (e.g., expected calls)\n",
+    "w = {i: random.randint(1, 10) for i in clients}\n",
+    "\n",
+    "def euclid(a, b):\n",
+    "    return math.hypot(a[0] - b[0], a[1] - b[1])\n",
+    "\n",
+    "# distance matrix d[i,j]\n",
+    "d = {(i, j): euclid(client_xy[i], site_xy[j]) for i in clients for j in sites}\n",
+    "\n",
+    "print(\"Example weights:\", list(w.items())[:5])\n",
+    "print(\"Example distance d[0,0]:\", d[(0,0)])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Decision variables\n",
+    "\n",
+    "- y<sub>j</sub> ∈ {0,1} : open station at site j\n",
+    "\n",
+    "- x<sub>ij</sub> ∈ {0,1} : assign client i to site j\n",
+    "\n",
+    "\n",
+    "### Objective\n",
+    "Minimize weighted distance: \n",
+    "\n",
+    "&emsp; min⁡ ∑<sub>i∈I</sub> ∑<sub>j∈J</sub> w<sub>ij</sub> d<sub>ij</sub> x<sub>ij</sub>\n",
+    "\n",
+    "\n",
+    "### Constraints\n",
+    "1. Every client assigned to exactly one site:\n",
+    "  ∑<sub>j</sub> x<sub>ij</sub> = 1 ∀i\n",
+    "   \n",
+    "2. Assign only to opened sites:\n",
+    "  x<sub>ij</sub> ≤ y<sub>j</sub> ∀i,j\n",
+    "\n",
+    "3. Open exactly p sites:\n",
+    "  ∑<sub>j</sub> y<sub>j</sub> = p"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "0Xw4x3_W14TU"
+   },
+   "outputs": [],
+   "source": [
+    "# Model the problem\n",
+    "import pyomo.environ as pyo\n",
+    "\n",
+    "m = pyo.ConcreteModel()\n",
+    "\n",
+    "# Sets\n",
+    "m.I = pyo.Set(initialize=clients)  # clients\n",
+    "m.J = pyo.Set(initialize=sites)    # sites\n",
+    "\n",
+    "# Params\n",
+    "m.w = pyo.Param(m.I, initialize=w, within=pyo.PositiveIntegers)\n",
+    "m.d = pyo.Param(m.I, m.J, initialize=d, within=pyo.NonNegativeReals)\n",
+    "m.p = pyo.Param(initialize=p)\n",
+    "\n",
+    "# Decision variables\n",
+    "m.x = pyo.Var(m.I, m.J, domain=pyo.Binary)  # assignment\n",
+    "m.y = pyo.Var(m.J, domain=pyo.Binary)       # open facility\n",
+    "\n",
+    "# Objective\n",
+    "def obj_rule(m):\n",
+    "    return sum(m.w[i] * m.d[i, j] * m.x[i, j] for i in m.I for j in m.J)\n",
+    "m.obj = pyo.Objective(rule=obj_rule, sense=pyo.minimize)\n",
+    "\n",
+    "# Constraints\n",
+    "def assign_once_rule(m, i):\n",
+    "    return sum(m.x[i, j] for j in m.J) == 1\n",
+    "m.assign_once = pyo.Constraint(m.I, rule=assign_once_rule)\n",
+    "\n",
+    "def open_link_rule(m, i, j):\n",
+    "    return m.x[i, j] <= m.y[j]\n",
+    "m.open_link = pyo.Constraint(m.I, m.J, rule=open_link_rule)\n",
+    "\n",
+    "def open_p_rule(m):\n",
+    "    return sum(m.y[j] for j in m.J) == m.p\n",
+    "m.open_p = pyo.Constraint(rule=open_p_rule)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "OG02AqK2LpZ1"
+   },
+   "source": [
+    "## 3. Problem Solution\n",
+    "\n",
+    "Pyomo calls on the cuOpt solver, which finds the optimal site locations. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "UL0TM5pTLp_m"
+   },
+   "outputs": [],
+   "source": [
+    "# Solve the problem using CUOPT\n",
+    "solver = pyo.SolverFactory(\"cuopt\")\n",
+    "results = solver.solve(m)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "\n",
+    "# Print summary and plot results\n",
+    "\n",
+    "def print_summary(m):\n",
+    "    print(\"\\n=============== Summary ===============\")\n",
+    "    # opened sites\n",
+    "    opened = [j for j in m.J if pyo.value(m.y[j]) > 0.5]\n",
+    "    print(\"Opened sites:\", opened)\n",
+    "    \n",
+    "    # assignment per client\n",
+    "    assign = {}\n",
+    "    for i in m.I:\n",
+    "        for j in m.J:\n",
+    "            if pyo.value(m.x[i, j]) > 0.5:\n",
+    "                assign[i] = j\n",
+    "                break\n",
+    "    \n",
+    "    obj_val = pyo.value(m.obj)\n",
+    "    total_weight = sum(w.values())\n",
+    "    wavg_dist = sum(w[i] * d[(i, assign[i])] for i in clients) / total_weight\n",
+    "    max_dist = max(d[(i, assign[i])] for i in clients)\n",
+    "    \n",
+    "    print(f\"Objective (weighted distance): {obj_val:.4f}\")\n",
+    "    print(f\"Weighted avg distance: {wavg_dist:.4f}\")\n",
+    "    print(f\"Max distance: {max_dist:.4f}\")\n",
+    "    return opened, assign\n",
+    "\n",
+    "def plot_result(opened, assign):\n",
+    "    import matplotlib.pyplot as plt\n",
+    "    \n",
+    "    plt.figure()\n",
+    "    \n",
+    "    # clients (size by weight)\n",
+    "    cx = [client_xy[i][0] for i in clients]\n",
+    "    cy = [client_xy[i][1] for i in clients]\n",
+    "    cs = [25 + 10*w[i] for i in clients]\n",
+    "    plt.scatter(cx, cy, s=cs, marker=\"o\", label=\"Clients\")\n",
+    "    \n",
+    "    # all candidate sites\n",
+    "    sx = [site_xy[j][0] for j in sites]\n",
+    "    sy = [site_xy[j][1] for j in sites]\n",
+    "    plt.scatter(sx, sy, s=60, marker=\"^\", label=\"Candidate sites\")\n",
+    "    \n",
+    "    # opened sites\n",
+    "    ox = [site_xy[j][0] for j in opened]\n",
+    "    oy = [site_xy[j][1] for j in opened]\n",
+    "    plt.scatter(ox, oy, s=200, marker=\"^\", label=\"Opened\", edgecolors=\"black\")\n",
+    "    \n",
+    "    # assignment lines\n",
+    "    for i in clients:\n",
+    "        j = assign[i]\n",
+    "        plt.plot([client_xy[i][0], site_xy[j][0]],\n",
+    "                 [client_xy[i][1], site_xy[j][1]],\n",
+    "                 linewidth=0.5)\n",
+    "    \n",
+    "    plt.title(\"Pyomo p-Median: assignments to opened sites\")\n",
+    "    plt.legend()\n",
+    "    plt.show()\n",
+    "\n",
+    "opened, assign = print_summary(m)\n",
+    "plot_result(opened, assign)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Extension: Capacitated p-median\n",
+    "\n",
+    "If each station can handle at most K total demand weight:\n",
+    "\n",
+    "&emsp; ∑<sub>i</sub>w<sub>i</sub>x<sub>ij</sub> ≤ Ky<sub>j</sub> ∀j"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "K = 50\n",
+    "\n",
+    "# Add Constraint\n",
+    "def cap_rule(m, j):\n",
+    "    return sum(m.w[i] * m.x[i, j] for i in m.I) <= K * m.y[j]\n",
+    "m.capacity = pyo.Constraint(m.J, rule=cap_rule)\n",
+    "\n",
+    "# Re-optimize\n",
+    "result = solver.solve(m)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "# Print summary and plot results\n",
+    "opened, assign = print_summary(m)\n",
+    "plot_result(opened, assign)"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {
+    "vscode": {
+     "languageId": "raw"
+    }
+   },
+   "source": [
+    "SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.\n",
+    "\n",
+    "SPDX-License-Identifier: Apache-2.0\n",
+    "\n",
+    "Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+    "you may not use this file except in compliance with the License.\n",
+    "You may obtain a copy of the License at\n",
+    "\n",
+    "http://www.apache.org/licenses/LICENSE-2.0\n",
+    "\n",
+    "Unless required by applicable law or agreed to in writing, software\n",
+    "distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+    "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+    "See the License for the specific language governing permissions and\n",
+    "limitations under the License.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "gpuType": "T4",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/portfolio_optimization/QP_portfolio_optimization.ipynb b/portfolio_optimization/QP_portfolio_optimization.ipynb
new file mode 100644
index 0000000..6c2b287
--- /dev/null
+++ b/portfolio_optimization/QP_portfolio_optimization.ipynb
@@ -0,0 +1,2434 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "8055cf5b-4eb4-422a-b6fc-af885c4749c2",
+   "metadata": {},
+   "source": [
+    "# Portfolio Optimizer for Everyday Investors\n",
+    "\n",
+    "You have $10,000 in savings and want to invest across a few familiar assets. How do you balance higher expected return with the risk of large losses?\n",
+    "\n",
+    "This notebook builds a simple, realistic portfolio optimization workflow using **NVIDIA cuOpt's QP solver** for quadratic programming. We'll start with a small set of assets, compare equal-weight vs. optimized portfolios, and visualize the efficient frontier.\n",
+    "\n",
+    "**Key Concepts:**\n",
+    "- Mean-variance optimization (Markowitz portfolio theory)\n",
+    "- Efficient frontier visualization\n",
+    "- Interactive constraint exploration\n",
+    "\n",
+    "References:\n",
+    "- cuOpt LP/QP/MILP API Reference: https://docs.nvidia.com/cuopt/user-guide/latest/cuopt-python/lp-qp-milp/lp-qp-milp-api.html\n",
+    "- Portfolio Optimization: https://en.wikipedia.org/wiki/Portfolio_optimization"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fc98925c-ff8c-4767-bba0-16e193f97e23",
+   "metadata": {},
+   "source": [
+    "## Environment Setup and Installation\n",
+    "\n",
+    "### Install Required Dependencies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "96ea34e7-e559-47ce-93f4-daf2f6c34281",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "        <div style=\"border:2px solid #4CAF50;padding:10px;border-radius:10px;background:#e8f5e9;\">\n",
+       "            <h3>✅ GPU is enabled</h3>\n",
+       "            <pre>| NVIDIA-SMI 535.247.01             Driver Version: 535.247.01   CUDA Version: 12.2     |</pre>\n",
+       "        </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import subprocess\n",
+    "import html\n",
+    "from IPython.display import display, HTML\n",
+    "\n",
+    "def check_gpu():\n",
+    "    try:\n",
+    "        result = subprocess.run([\"nvidia-smi\"], capture_output=True, text=True, timeout=5)\n",
+    "        result.check_returncode()\n",
+    "        lines = result.stdout.splitlines()\n",
+    "        gpu_info = lines[2] if len(lines) > 2 else \"GPU detected\"\n",
+    "        gpu_info_escaped = html.escape(gpu_info)\n",
+    "        display(HTML(f\"\"\"\n",
+    "        <div style=\"border:2px solid #4CAF50;padding:10px;border-radius:10px;background:#e8f5e9;\">\n",
+    "            <h3>✅ GPU is enabled</h3>\n",
+    "            <pre>{gpu_info_escaped}</pre>\n",
+    "        </div>\n",
+    "        \"\"\"))\n",
+    "        return True\n",
+    "    except (subprocess.CalledProcessError, subprocess.TimeoutExpired, FileNotFoundError, IndexError) as e:\n",
+    "        display(HTML(\"\"\"\n",
+    "        <div style=\"border:2px solid red;padding:15px;border-radius:10px;background:#ffeeee;\">\n",
+    "            <h3>⚠️ GPU not detected!</h3>\n",
+    "            <p>This notebook requires a <b>GPU runtime</b>.</p>\n",
+    "            \n",
+    "            <h4>If running in Google Colab:</h4>\n",
+    "            <ol>\n",
+    "              <li>Click on <b>Runtime → Change runtime type</b></li>\n",
+    "              <li>Set <b>Hardware accelerator</b> to <b>GPU</b></li>\n",
+    "              <li>Then click <b>Save</b> and <b>Runtime → Restart runtime</b>.</li>\n",
+    "            </ol>\n",
+    "            \n",
+    "            <h4>If running in Docker:</h4>\n",
+    "            <ol>\n",
+    "              <li>Ensure you have <b>NVIDIA Docker runtime</b> installed (<code>nvidia-docker2</code>)</li>\n",
+    "              <li>Run container with GPU support: <code>docker run --gpus all ...</code></li>\n",
+    "              <li>Or use: <code>docker run --runtime=nvidia ...</code> for older Docker versions</li>\n",
+    "              <li>Verify GPU access: <code>docker run --gpus all nvidia/cuda:12.0.0-base-ubuntu22.04 nvidia-smi</code></li>\n",
+    "            </ol>\n",
+    "            \n",
+    "            <p><b>Additional resources:</b></p>\n",
+    "            <ul>\n",
+    "              <li><a href=\"https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html\" target=\"_blank\">NVIDIA Container Toolkit Installation Guide</a></li>\n",
+    "            </ul>\n",
+    "        </div>\n",
+    "        \"\"\"))\n",
+    "        return False\n",
+    "\n",
+    "check_gpu()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "18003f14-af20-44e8-b72a-381cc0312d3a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Enable this in case you are running this in google colab or such places where cuOpt is not yet installed\n",
+    "#!pip uninstall -y cuda-python cuda-bindings cuda-core\n",
+    "#!pip install --upgrade --extra-index-url=https://pypi.nvidia.com cuopt-cu12 nvidia-nvjitlink-cu12 rapids-logger==0.1.19\n",
+    "#!pip install --upgrade --extra-index-url=https://pypi.nvidia.com cuopt-cu13 nvidia-nvjitlink-cu13 rapids-logger==0.1.19"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "1a56950d-d08c-43cd-a135-29aa17830223",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Looking in indexes: https://pypi.org/simple, https://pypi.nvidia.com\n",
+      "Requirement already satisfied: numpy>=1.24.4 in /raid/iroy/miniforge3/envs/py313/lib/python3.13/site-packages (2.2.6)\n",
+      "Requirement already satisfied: pandas>=2.2.1 in /raid/iroy/miniforge3/envs/py313/lib/python3.13/site-packages (2.3.3)\n",
+      "Requirement already satisfied: python-dateutil>=2.8.2 in /raid/iroy/miniforge3/envs/py313/lib/python3.13/site-packages (from pandas>=2.2.1) (2.9.0.post0)\n",
+      "Requirement already satisfied: pytz>=2020.1 in /raid/iroy/miniforge3/envs/py313/lib/python3.13/site-packages (from pandas>=2.2.1) (2025.2)\n",
+      "Requirement already satisfied: tzdata>=2022.7 in /raid/iroy/miniforge3/envs/py313/lib/python3.13/site-packages (from pandas>=2.2.1) (2025.3)\n",
+      "Requirement already satisfied: six>=1.5 in /raid/iroy/miniforge3/envs/py313/lib/python3.13/site-packages (from python-dateutil>=2.8.2->pandas>=2.2.1) (1.17.0)\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install --extra-index-url https://pypi.nvidia.com \"numpy>=1.24.4\" \"pandas>=2.2.1\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e0987b2f-dbb0-4411-9a54-d1567247398c",
+   "metadata": {},
+   "source": [
+    "### Import Required Libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "449f51ca-33a0-463e-902f-2a1eecbedee7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Imports ready (using cuOpt QP solver)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from cuopt.linear_programming.problem import (\n",
+    "    Problem,\n",
+    "    QuadraticExpression,\n",
+    "    MINIMIZE,\n",
+    ")\n",
+    "\n",
+    "%config InlineBackend.figure_format = \"retina\"\n",
+    "\n",
+    "print(\"Imports ready (using cuOpt QP solver)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b68072a1-8497-4d93-b275-ce7b3729673f",
+   "metadata": {},
+   "source": [
+    "## **Step 1:** Data Setup - A Small, Realistic Universe\n",
+    "\n",
+    "We'll work with a compact set of assets an everyday investor might recognize:\n",
+    "- Cash or money market\n",
+    "- US equity ETF proxy\n",
+    "- International equity ETF proxy\n",
+    "- Bond ETF proxy\n",
+    "- Real-asset/REIT or gold ETF proxy\n",
+    "\n",
+    "We'll simulate monthly returns with realistic annual return/volatility assumptions and correlations, then estimate the annualized mean returns and covariance matrix.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "4beac288-fe86-40ee-9412-4b4004f233a3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style type=\"text/css\">\n",
+       "</style>\n",
+       "<table id=\"T_238ba\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th id=\"T_238ba_level0_col0\" class=\"col_heading level0 col0\" >Annualized Return</th>\n",
+       "      <th id=\"T_238ba_level0_col1\" class=\"col_heading level0 col1\" >Annualized Volatility</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_238ba_level0_row0\" class=\"row_heading level0 row0\" >Cash</th>\n",
+       "      <td id=\"T_238ba_row0_col0\" class=\"data row0 col0\" >2.36%</td>\n",
+       "      <td id=\"T_238ba_row0_col1\" class=\"data row0 col1\" >0.49%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_238ba_level0_row1\" class=\"row_heading level0 row1\" >US Equity</th>\n",
+       "      <td id=\"T_238ba_row1_col0\" class=\"data row1 col0\" >7.07%</td>\n",
+       "      <td id=\"T_238ba_row1_col1\" class=\"data row1 col1\" >14.50%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_238ba_level0_row2\" class=\"row_heading level0 row2\" >Intl Equity</th>\n",
+       "      <td id=\"T_238ba_row2_col0\" class=\"data row2 col0\" >7.50%</td>\n",
+       "      <td id=\"T_238ba_row2_col1\" class=\"data row2 col1\" >17.01%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_238ba_level0_row3\" class=\"row_heading level0 row3\" >Bond</th>\n",
+       "      <td id=\"T_238ba_row3_col0\" class=\"data row3 col0\" >5.26%</td>\n",
+       "      <td id=\"T_238ba_row3_col1\" class=\"data row3 col1\" >6.18%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_238ba_level0_row4\" class=\"row_heading level0 row4\" >REIT/Gold</th>\n",
+       "      <td id=\"T_238ba_row4_col0\" class=\"data row4 col0\" >7.24%</td>\n",
+       "      <td id=\"T_238ba_row4_col1\" class=\"data row4 col1\" >13.88%</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x7f7ca35c1010>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Simulate monthly returns with realistic assumptions\n",
+    "np.random.seed(7)\n",
+    "\n",
+    "assets = [\"Cash\", \"US Equity\", \"Intl Equity\", \"Bond\", \"REIT/Gold\"]\n",
+    "\n",
+    "annual_mean = np.array([0.02, 0.08, 0.075, 0.04, 0.06])\n",
+    "annual_vol = np.array([0.005, 0.16, 0.18, 0.06, 0.14])\n",
+    "\n",
+    "corr = np.array([\n",
+    "    [1.00, 0.05, 0.05, 0.10, 0.05],\n",
+    "    [0.05, 1.00, 0.80, -0.10, 0.55],\n",
+    "    [0.05, 0.80, 1.00, -0.05, 0.50],\n",
+    "    [0.10, -0.10, -0.05, 1.00, 0.00],\n",
+    "    [0.05, 0.55, 0.50, 0.00, 1.00],\n",
+    "])\n",
+    "\n",
+    "monthly_mean = annual_mean / 12.0\n",
+    "monthly_vol = annual_vol / np.sqrt(12.0)\n",
+    "monthly_cov = np.outer(monthly_vol, monthly_vol) * corr\n",
+    "\n",
+    "n_months = 120\n",
+    "returns = np.random.multivariate_normal(monthly_mean, monthly_cov, size=n_months)\n",
+    "\n",
+    "# Estimate annualized mean and covariance from the simulated data\n",
+    "mean_returns = returns.mean(axis=0) * 12.0\n",
+    "cov_matrix = np.cov(returns, rowvar=False) * 12.0\n",
+    "\n",
+    "summary = pd.DataFrame(\n",
+    "    {\n",
+    "        \"Annualized Return\": mean_returns,\n",
+    "        \"Annualized Volatility\": np.sqrt(np.diag(cov_matrix)),\n",
+    "    },\n",
+    "    index=assets,\n",
+    ")\n",
+    "\n",
+    "summary.style.format({\"Annualized Return\": \"{:.2%}\", \"Annualized Volatility\": \"{:.2%}\"})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "68a3cddf-0a15-4dd2-9477-715b475b3c01",
+   "metadata": {},
+   "source": [
+    "## **Step 2:** Baseline Portfolio - Equal-Weight\n",
+    "\n",
+    "Before optimizing, let's compute the equal-weight portfolio. This gives a simple baseline to compare against the optimized portfolios."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d6342042-e364-46e9-971b-344e3850766a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(             Weight\n",
+       " Asset              \n",
+       " Cash            0.2\n",
+       " US Equity       0.2\n",
+       " Intl Equity     0.2\n",
+       " Bond            0.2\n",
+       " REIT/Gold       0.2,\n",
+       " 0.058873523844686845,\n",
+       " np.float64(0.077468947707694))"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def portfolio_stats(weights, mean_vec, cov_mat):\n",
+    "    exp_return = float(weights @ mean_vec)\n",
+    "    variance = float(weights @ cov_mat @ weights)\n",
+    "    volatility = np.sqrt(variance)\n",
+    "    return exp_return, volatility, variance\n",
+    "\n",
+    "n_assets = len(assets)\n",
+    "weights_equal = np.repeat(1.0 / n_assets, n_assets)\n",
+    "\n",
+    "ret_eq, vol_eq, var_eq = portfolio_stats(weights_equal, mean_returns, cov_matrix)\n",
+    "\n",
+    "baseline_df = pd.DataFrame(\n",
+    "    {\n",
+    "        \"Weight\": weights_equal,\n",
+    "        \"Asset\": assets,\n",
+    "    }\n",
+    ").set_index(\"Asset\")\n",
+    "\n",
+    "baseline_df, ret_eq, vol_eq\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "660bc66a-6cbc-41b6-a0da-9c7eae657c11",
+   "metadata": {},
+   "source": [
+    "## **Step 3:** The Optimization Problem\n",
+    "\n",
+    "We translate the investor's goals into a quadratic program (QP):\n",
+    "\n",
+    "**Decision variables:** Portfolio weights $w_i$ for each asset $i$.\n",
+    "\n",
+    "**Objective:** Minimize portfolio variance (risk):\n",
+    "$$\\min \\frac{1}{2} w^\\top \\Sigma w$$\n",
+    "where $\\Sigma$ is the covariance matrix.\n",
+    "\n",
+    "**Constraints:**\n",
+    "- Fully invested: $\\sum_i w_i = 1$\n",
+    "- Long-only (no shorting): $w_i \\geq 0$\n",
+    "- Optional: target minimum return $\\mu^\\top w \\geq r_{target}$\n",
+    "- Optional: max allocation per asset $w_i \\leq w_{max}$\n",
+    "\n",
+    "### cuOpt QP Implementation\n",
+    "\n",
+    "cuOpt's QP solver allows us to express quadratic objectives using `Variable * Variable` syntax.\n",
+    "For the portfolio variance $w^\\top \\Sigma w$, we construct it as:\n",
+    "$$\\sum_i \\sum_j w_i \\cdot \\Sigma_{ij} \\cdot w_j$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "04dd2427-69da-4a4e-80fd-2d1e5f796a74",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Solver function defined (using cuOpt QP)\n"
+     ]
+    }
+   ],
+   "source": [
+    "def solve_min_variance_qp(\n",
+    "    cov_matrix,\n",
+    "    mean_returns,\n",
+    "    target_return=None,\n",
+    "    max_weight=None,\n",
+    "    min_safe_alloc=None,\n",
+    "    safe_indices=None,\n",
+    "):\n",
+    "    \"\"\"\n",
+    "    Solve the minimum-variance portfolio problem using cuOpt QP solver.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    cov_matrix : ndarray (n x n)\n",
+    "        Annualized covariance matrix\n",
+    "    mean_returns : ndarray (n,)\n",
+    "        Annualized expected returns\n",
+    "    target_return : float, optional\n",
+    "        Minimum expected return constraint\n",
+    "    max_weight : float, optional\n",
+    "        Maximum weight for any single asset\n",
+    "    min_safe_alloc : float, optional\n",
+    "        Minimum combined allocation to safe assets\n",
+    "    safe_indices : list of int, optional\n",
+    "        Indices of safe assets (e.g., Cash, Bonds)\n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    weights : ndarray\n",
+    "        Optimal portfolio weights\n",
+    "    portfolio_return : float\n",
+    "        Expected return of the optimal portfolio\n",
+    "    portfolio_vol : float\n",
+    "        Volatility of the optimal portfolio\n",
+    "    status : str\n",
+    "        Solver status\n",
+    "    \"\"\"\n",
+    "    n = len(mean_returns)\n",
+    "    \n",
+    "    # Create cuOpt Problem\n",
+    "    prob = Problem(\"Portfolio_Optimization\")\n",
+    "    \n",
+    "    # Decision variables: portfolio weights with bounds [0, upper_bound]\n",
+    "    upper_bound = max_weight if max_weight is not None else 1.0\n",
+    "    w = [prob.addVariable(lb=0.0, ub=upper_bound, name=f\"w_{i}\") for i in range(n)]\n",
+    "    \n",
+    "    # Build quadratic objective: minimize w' * cov_matrix * w\n",
+    "    quad_expr = None\n",
+    "    for i in range(n):\n",
+    "        for j in range(n):\n",
+    "            if abs(cov_matrix[i, j]) > 1e-12:\n",
+    "                if(i==0 and j==0):\n",
+    "                    quad_expr = float(cov_matrix[i, j]) * w[i] * w[j]\n",
+    "                else:\n",
+    "                    quad_expr += float(cov_matrix[i, j]) * w[i] * w[j]\n",
+    "    \n",
+    "    prob.setObjective(quad_expr, sense=MINIMIZE)\n",
+    "    \n",
+    "    # Constraint: Fully invested (sum of weights = 1)\n",
+    "    sum_weights = sum(w)\n",
+    "    prob.addConstraint(sum_weights == 1, name=\"fully_invested\")\n",
+    "    \n",
+    "    # Constraint: Minimum expected return\n",
+    "    if target_return is not None:\n",
+    "        expected_return_expr = sum(mean_returns[i] * w[i] for i in range(n))\n",
+    "        prob.addConstraint(expected_return_expr >= target_return, name=\"min_return\")\n",
+    "    \n",
+    "    # Constraint: Minimum allocation to safe assets\n",
+    "    if min_safe_alloc is not None and safe_indices is not None:\n",
+    "        safe_sum = sum(w[i] for i in safe_indices)\n",
+    "        prob.addConstraint(safe_sum >= min_safe_alloc, name=\"min_safe\")\n",
+    "    \n",
+    "    # Solve the problem\n",
+    "    prob.solve()\n",
+    "    \n",
+    "    # Extract solution\n",
+    "    weights = np.array([w[i].Value for i in range(n)])\n",
+    "    \n",
+    "    # Compute portfolio statistics\n",
+    "    portfolio_return = float(mean_returns @ weights)\n",
+    "    portfolio_vol = np.sqrt(float(weights @ cov_matrix @ weights))\n",
+    "    \n",
+    "    # Get solver status\n",
+    "    status = \"optimal\" if prob.Status == 1 else f\"status_{prob.Status}\"\n",
+    "    \n",
+    "    return weights, portfolio_return, portfolio_vol, status\n",
+    "\n",
+    "print(\"Solver function defined (using cuOpt QP)\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "519655b6-c792-4d52-842c-933e62632696",
+   "metadata": {},
+   "source": [
+    "## **Step 4:** Minimum-Variance Portfolio (No Return Target)\n",
+    "\n",
+    "First, let's find the portfolio with the lowest possible risk, without any constraint on expected return. This is the leftmost point on the efficient frontier."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "16df1281-0a32-48b0-bcbb-4ffcf6fc48c7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cuOpt version: 26.2.0, git hash: 96c11095, host arch: x86_64, device archs: 80-real\n",
+      "CPU: AMD EPYC 7642 48-Core Processor, threads (physical/logical): 96/96, RAM: 164.74 GiB\n",
+      "CUDA 12.9, device: NVIDIA A100 80GB PCIe (ID 0), VRAM: 79.33 GiB\n",
+      "CUDA device UUID: ffffff8a5affffff95ffffffc7-ffffffd05\n",
+      "\n",
+      "Problem has a quadratic objective. Using Barrier.\n",
+      "Solving a problem with 1 constraints, 5 variables (0 integers), and 5 nonzeros\n",
+      "Problem scaling:\n",
+      "Objective coefficents range:          [0e+00, 0e+00]\n",
+      "Constraint matrix coefficients range: [1e+00, 1e+00]\n",
+      "Constraint rhs / bounds range:        [0e+00, 1e+00]\n",
+      "Variable bounds range:                [0e+00, 1e+00]\n",
+      "\n",
+      "Third-party presolve is disabled, skipping\n",
+      "Objective offset 0.000000 scaling_factor 1.000000\n",
+      "Presolved problem: 1 constraints 5 variables 5 nonzeros\n",
+      "Skipping column scaling\n",
+      "Barrier solver: 1 constraints, 5 variables, 5 nonzeros\n",
+      "Quadratic objective matrix: 25 nonzeros\n",
+      "\n",
+      "\n",
+      "Upper bounds                : 5\n",
+      "Density estimator time      : 0.00s\n",
+      "Linear system               : augmented\n",
+      "cuDSS Version               : 0.7.1\n",
+      "cuDSS Threading layer       : libcudss_mtlayer_gomp.so.0\n",
+      "Reordering algorithm        : AMD\n",
+      "Reordering time             : 0.01s\n",
+      "Symbolic factorization time : 0.01s\n",
+      "Total symbolic time         : 0.02s\n",
+      "Symbolic nonzeros in factor : 1.80e+01\n",
+      "\n",
+      "\n",
+      "                  Objective                         Infeasibility        Time\n",
+      "Iter   Primal              Dual                Primal   Dual    Compl.   Elapsed\n",
+      "  0   +1.502020842960e+01 -2.002020842960e+01 5.00e+01 1.18e+00 1.10e+01 0.1\n",
+      "  1   +8.116317933600e-01 -2.610278408955e+00 5.81e+00 2.75e-01 5.01e-01 0.1\n",
+      "  2   +5.508720123496e-01 -3.733792686682e+00 3.53e+00 1.67e-01 3.45e-01 0.1\n",
+      "  3   +2.094183386605e-02 -1.607389653436e+00 3.53e-01 1.67e-02 4.58e-01 0.1\n",
+      "  4   +7.966856203550e-03 -2.704078921230e-01 3.57e-02 1.69e-03 6.49e-02 0.1\n",
+      "  5   +6.280527981394e-03 -3.566351416561e-02 5.29e-03 2.50e-04 4.62e-03 0.1\n",
+      "  6   +3.509462278556e-03 -8.043272893021e-03 1.37e-03 6.46e-05 1.66e-03 0.1\n",
+      "  7   +9.737719020591e-04 -1.894551124511e-03 1.74e-04 8.25e-06 7.75e-04 0.1\n",
+      "  8   +2.270364911982e-04 -2.951103286651e-04 1.74e-05 8.25e-07 1.27e-04 0.1\n",
+      "  9   +6.434100985271e-05 -3.163605023344e-05 1.91e-06 9.05e-08 2.39e-05 0.1\n",
+      " 10   +3.133026812835e-05 +1.369738017751e-05 1.97e-07 9.31e-09 4.72e-06 0.1\n",
+      " 11   +2.504128876428e-05 +2.179803092629e-05 1.97e-08 9.31e-10 8.85e-07 0.1\n",
+      " 12   +2.389471074490e-05 +2.332566789238e-05 1.98e-09 9.31e-11 1.65e-07 0.1\n",
+      " 13   +2.369223364640e-05 +2.358671257142e-05 3.06e-10 1.42e-11 3.19e-08 0.1\n",
+      " 14   +2.364419171111e-05 +2.362854845265e-05 3.59e-11 1.60e-12 5.49e-09 0.1\n",
+      "\n",
+      "\n",
+      "Optimal solution found in 14 iterations and 0.12s\n",
+      "Objective +2.36441917e-05\n",
+      "Primal infeasibility (abs/rel): 7.18e-11/3.59e-11\n",
+      "Dual infeasibility   (abs/rel): 1.60e-12/1.60e-12\n",
+      "Complementarity gap  (abs/rel): 5.49e-09/5.49e-09\n",
+      "\n",
+      "\n",
+      "Unscaled Primal infeasibility   (abs/rel): 7.18e-11/3.59e-11\n",
+      "Post-solve Primal infeasibility (abs/rel): 7.18e-11/3.59e-11\n",
+      "Barrier finished in 0.12 seconds\n",
+      "Status: optimal\n",
+      "\n",
+      "Minimum-Variance Portfolio:\n",
+      "  Expected Return: 2.38%\n",
+      "  Volatility:      0.49%\n",
+      "\n",
+      "Weights:\n",
+      "  Cash        :  99.6%\n",
+      "  US Equity   :   0.0%\n",
+      "  Intl Equity :   0.3%\n",
+      "  Bond        :   0.1%\n",
+      "  REIT/Gold   :   0.0%\n"
+     ]
+    }
+   ],
+   "source": [
+    "weights_mv, ret_mv, vol_mv, status_mv = solve_min_variance_qp(cov_matrix, mean_returns)\n",
+    "\n",
+    "print(f\"Status: {status_mv}\")\n",
+    "print(f\"\\nMinimum-Variance Portfolio:\")\n",
+    "print(f\"  Expected Return: {ret_mv:.2%}\")\n",
+    "print(f\"  Volatility:      {vol_mv:.2%}\")\n",
+    "print(f\"\\nWeights:\")\n",
+    "for asset, wt in zip(assets, weights_mv):\n",
+    "    print(f\"  {asset:<12}: {wt:6.1%}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "07ec7a0a-1521-4bea-94eb-38722be27218",
+   "metadata": {},
+   "source": [
+    "## **Step 5:** Efficient Frontier\n",
+    "\n",
+    "The efficient frontier shows all portfolios that offer the highest expected return for each level of risk. We trace it by solving QP problems for a range of target returns."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "f8947c1d-a409-4b54-98f9-a171e845d961",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cuOpt version: 26.2.0, git hash: 96c11095, host arch: x86_64, device archs: 80-real\n",
+      "CPU: AMD EPYC 7642 48-Core Processor, threads (physical/logical): 96/96, RAM: 165.75 GiB\n",
+      "CUDA 12.9, device: NVIDIA A100 80GB PCIe (ID 0), VRAM: 79.33 GiB\n",
+      "CUDA device UUID: ffffff8a5affffff95ffffffc7-ffffffd05\n",
+      "\n",
+      "Problem has a quadratic objective. Using Barrier.\n",
+      "Solving a problem with 2 constraints, 5 variables (0 integers), and 10 nonzeros\n",
+      "Problem scaling:\n",
+      "Objective coefficents range:          [0e+00, 0e+00]\n",
+      "Constraint matrix coefficients range: [2e-02, 1e+00]\n",
+      "Constraint rhs / bounds range:        [0e+00, 1e+00]\n",
+      "Variable bounds range:                [0e+00, 1e+00]\n",
+      "\n",
+      "Third-party presolve is disabled, skipping\n",
+      "Objective offset 0.000000 scaling_factor 1.000000\n",
+      "Presolved problem: 2 constraints 6 variables 11 nonzeros\n",
+      "Skipping column scaling\n",
+      "Barrier solver: 2 constraints, 6 variables, 11 nonzeros\n",
+      "Quadratic objective matrix: 25 nonzeros\n",
+      "\n",
+      "\n",
+      "Upper bounds                : 5\n",
+      "Density estimator time      : 0.00s\n",
+      "Linear system               : augmented\n",
+      "cuDSS Version               : 0.7.1\n",
+      "cuDSS Threading layer       : libcudss_mtlayer_gomp.so.0\n",
+      "Reordering algorithm        : AMD\n",
+      "Reordering time             : 0.00s\n",
+      "Symbolic factorization time : 0.01s\n",
+      "Total symbolic time         : 0.01s\n",
+      "Symbolic nonzeros in factor : 2.20e+01\n",
+      "\n",
+      "\n",
+      "                  Objective                         Infeasibility        Time\n",
+      "Iter   Primal              Dual                Primal   Dual    Compl.   Elapsed\n",
+      "  0   +3.842163103341e-05 -5.000038421631e+00 2.78e-17 1.00e+01 1.00e+00 0.0\n",
+      "  1   +3.841130629567e-05 -5.000339596431e-01 9.00e-10 1.00e+00 1.25e-05 0.0\n",
+      "  2   +3.830901399426e-05 -5.003312213897e-02 1.76e-10 1.00e-01 1.24e-05 0.0\n",
+      "  3   +3.736908280123e-05 -5.029419437216e-03 1.35e-11 1.01e-02 1.20e-05 0.0\n",
+      "  4   +3.225159545698e-05 -5.087329238139e-04 2.10e-10 1.08e-03 8.87e-06 0.0\n",
+      "  5   +2.608886999765e-05 -3.383434892714e-05 3.46e-10 1.15e-04 3.07e-06 0.0\n",
+      "  6   +2.413132881581e-05 +1.744114834034e-05 2.43e-10 1.19e-05 6.41e-07 0.0\n",
+      "  7   +2.372239892814e-05 +2.292425697517e-05 1.25e-10 1.26e-06 1.22e-07 0.0\n",
+      "  8   +2.365150610185e-05 +2.354353338821e-05 5.93e-11 1.45e-07 2.05e-08 0.0\n",
+      "  9   +2.363965326003e-05 +2.362681926190e-05 2.04e-11 1.45e-08 3.75e-09 0.0\n",
+      " 10   +2.363850356320e-05 +2.363636879759e-05 7.06e-12 2.45e-09 4.60e-10 0.0\n",
+      "\n",
+      "\n",
+      "Optimal solution found in 10 iterations and 0.03s\n",
+      "Objective +2.36385036e-05\n",
+      "Primal infeasibility (abs/rel): 1.41e-11/7.06e-12\n",
+      "Dual infeasibility   (abs/rel): 2.45e-09/2.45e-09\n",
+      "Complementarity gap  (abs/rel): 4.60e-10/4.60e-10\n",
+      "\n",
+      "\n",
+      "Unscaled Primal infeasibility   (abs/rel): 1.41e-11/7.06e-12\n",
+      "Post-solve Primal infeasibility (abs/rel): 1.41e-11/7.06e-12\n",
+      "Barrier finished in 0.04 seconds\n",
+      "cuOpt version: 26.2.0, git hash: 96c11095, host arch: x86_64, device archs: 80-real\n",
+      "CPU: AMD EPYC 7642 48-Core Processor, threads (physical/logical): 96/96, RAM: 166.76 GiB\n",
+      "CUDA 12.9, device: NVIDIA A100 80GB PCIe (ID 0), VRAM: 79.33 GiB\n",
+      "CUDA device UUID: ffffff8a5affffff95ffffffc7-ffffffd05\n",
+      "\n",
+      "Problem has a quadratic objective. Using Barrier.\n",
+      "Solving a problem with 2 constraints, 5 variables (0 integers), and 10 nonzeros\n",
+      "Problem scaling:\n",
+      "Objective coefficents range:          [0e+00, 0e+00]\n",
+      "Constraint matrix coefficients range: [2e-02, 1e+00]\n",
+      "Constraint rhs / bounds range:        [0e+00, 1e+00]\n",
+      "Variable bounds range:                [0e+00, 1e+00]\n",
+      "\n",
+      "Third-party presolve is disabled, skipping\n",
+      "Objective offset 0.000000 scaling_factor 1.000000\n",
+      "Presolved problem: 2 constraints 6 variables 11 nonzeros\n",
+      "Skipping column scaling\n",
+      "Barrier solver: 2 constraints, 6 variables, 11 nonzeros\n",
+      "Quadratic objective matrix: 25 nonzeros\n",
+      "\n",
+      "\n",
+      "Upper bounds                : 5\n",
+      "Density estimator time      : 0.00s\n",
+      "Linear system               : augmented\n",
+      "cuDSS Version               : 0.7.1\n",
+      "cuDSS Threading layer       : libcudss_mtlayer_gomp.so.0\n",
+      "Reordering algorithm        : AMD\n",
+      "Reordering time             : 0.00s\n",
+      "Symbolic factorization time : 0.01s\n",
+      "Total symbolic time         : 0.01s\n",
+      "Symbolic nonzeros in factor : 2.20e+01\n",
+      "\n",
+      "\n",
+      "                  Objective                         Infeasibility        Time\n",
+      "Iter   Primal              Dual                Primal   Dual    Compl.   Elapsed\n",
+      "  0   +1.502742918471e+01 -2.002742918471e+01 5.00e+01 1.00e+01 1.00e+02 0.0\n",
+      "  1   +1.541698214959e+00 -4.381109446336e+00 7.75e+00 3.10e+00 2.52e-01 0.0\n",
+      "  2   +1.128148347114e+00 -7.223091058021e+00 5.23e+00 2.09e+00 8.56e-01 0.0\n",
+      "  3   +2.050715616502e-02 -5.866183740948e+00 6.55e-01 2.62e-01 1.50e+00 0.0\n",
+      "  4   +6.683207617550e-03 -1.913588118132e+00 1.66e-01 6.65e-02 3.30e-01 0.0\n",
+      "  5   +5.890874559364e-03 -3.961531546376e-01 2.07e-02 8.27e-03 1.60e-01 0.0\n",
+      "  6   +5.376039427308e-03 -6.884302918090e-02 3.58e-03 1.43e-03 2.57e-02 0.0\n",
+      "  7   +2.854953955561e-03 -1.842630136251e-02 9.37e-04 3.75e-04 3.79e-03 0.0\n",
+      "  8   +1.183743286135e-03 -3.252862476038e-03 1.64e-04 6.56e-05 1.41e-03 0.0\n",
+      "  9   +3.895780767121e-04 -5.518051275662e-04 2.68e-05 1.07e-05 4.47e-04 0.0\n",
+      " 10   +1.205893915060e-04 -4.089981800464e-05 2.95e-06 1.18e-06 1.07e-04 0.0\n",
+      " 11   +5.740725607899e-05 +3.151087799295e-05 3.15e-07 1.26e-07 1.82e-05 0.0\n",
+      " 12   +4.480805852277e-05 +4.154837930777e-05 3.45e-08 1.38e-08 1.68e-06 0.0\n",
+      " 13   +4.322801306354e-05 +4.280923002739e-05 4.19e-09 1.68e-09 5.97e-08 0.0\n",
+      " 14   +4.304025995748e-05 +4.299565630553e-05 4.34e-10 1.74e-10 6.79e-09 0.0\n",
+      "\n",
+      "\n",
+      "Optimal solution found in 14 iterations and 0.05s\n",
+      "Objective +4.30402600e-05\n",
+      "Primal infeasibility (abs/rel): 8.68e-10/4.34e-10\n",
+      "Dual infeasibility   (abs/rel): 1.74e-10/1.74e-10\n",
+      "Complementarity gap  (abs/rel): 6.79e-09/6.79e-09\n",
+      "\n",
+      "\n",
+      "Unscaled Primal infeasibility   (abs/rel): 8.68e-10/4.34e-10\n",
+      "Post-solve Primal infeasibility (abs/rel): 8.68e-10/4.34e-10\n",
+      "Barrier finished in 0.05 seconds\n",
+      "cuOpt version: 26.2.0, git hash: 96c11095, host arch: x86_64, device archs: 80-real\n",
+      "CPU: AMD EPYC 7642 48-Core Processor, threads (physical/logical): 96/96, RAM: 167.73 GiB\n",
+      "CUDA 12.9, device: NVIDIA A100 80GB PCIe (ID 0), VRAM: 79.33 GiB\n",
+      "CUDA device UUID: ffffff8a5affffff95ffffffc7-ffffffd05\n",
+      "\n",
+      "Problem has a quadratic objective. Using Barrier.\n",
+      "Solving a problem with 2 constraints, 5 variables (0 integers), and 10 nonzeros\n",
+      "Problem scaling:\n",
+      "Objective coefficents range:          [0e+00, 0e+00]\n",
+      "Constraint matrix coefficients range: [2e-02, 1e+00]\n",
+      "Constraint rhs / bounds range:        [0e+00, 1e+00]\n",
+      "Variable bounds range:                [0e+00, 1e+00]\n",
+      "\n",
+      "Third-party presolve is disabled, skipping\n",
+      "Objective offset 0.000000 scaling_factor 1.000000\n",
+      "Presolved problem: 2 constraints 6 variables 11 nonzeros\n",
+      "Skipping column scaling\n",
+      "Barrier solver: 2 constraints, 6 variables, 11 nonzeros\n",
+      "Quadratic objective matrix: 25 nonzeros\n",
+      "\n",
+      "\n",
+      "Upper bounds                : 5\n",
+      "Density estimator time      : 0.00s\n",
+      "Linear system               : augmented\n",
+      "cuDSS Version               : 0.7.1\n",
+      "cuDSS Threading layer       : libcudss_mtlayer_gomp.so.0\n",
+      "Reordering algorithm        : AMD\n",
+      "Reordering time             : 0.00s\n",
+      "Symbolic factorization time : 0.01s\n",
+      "Total symbolic time         : 0.01s\n",
+      "Symbolic nonzeros in factor : 2.20e+01\n",
+      "\n",
+      "\n",
+      "                  Objective                         Infeasibility        Time\n",
+      "Iter   Primal              Dual                Primal   Dual    Compl.   Elapsed\n",
+      "  0   +1.505331680893e+01 -2.005331680893e+01 5.00e+01 1.00e+01 1.00e+02 0.0\n",
+      "  1   +1.460379831168e+00 -4.220852998771e+00 7.47e+00 2.99e+00 2.20e-01 0.0\n",
+      "  2   +6.100606494017e-01 -5.742897699177e+00 3.73e+00 1.49e+00 7.34e-01 0.0\n",
+      "  3   +2.380292049807e-02 -2.448948373282e+00 3.73e-01 1.49e-01 5.08e-01 0.0\n",
+      "  4   +1.017404860909e-02 -6.141945670814e-01 4.36e-02 1.74e-02 2.52e-01 0.0\n",
+      "  5   +9.035897495814e-03 -9.024382248308e-02 5.20e-03 2.08e-03 7.48e-02 0.0\n",
+      "  6   +6.284754962314e-03 -2.664081716060e-02 1.66e-03 6.65e-04 5.37e-03 0.0\n",
+      "  7   +2.276282325033e-03 -5.060314210233e-03 1.66e-04 6.65e-05 2.47e-03 0.0\n",
+      "  8   +9.165467368732e-04 -1.138861021330e-03 3.31e-05 1.32e-05 6.58e-04 0.0\n",
+      "  9   +2.631572762309e-04 -1.260242275450e-04 3.31e-06 1.32e-06 1.86e-04 0.0\n",
+      " 10   +1.195657529904e-04 +6.323884415030e-05 3.48e-07 1.39e-07 2.36e-05 0.0\n",
+      " 11   +1.016524474473e-04 +9.252900644151e-05 5.66e-08 2.26e-08 1.26e-06 0.0\n",
+      " 12   +9.874078077659e-05 +9.753763299414e-05 6.83e-09 2.73e-09 3.07e-07 0.0\n",
+      " 13   +9.834896696949e-05 +9.819693851136e-05 7.42e-10 2.97e-10 6.28e-08 0.0\n",
+      " 14   +9.829827933875e-05 +9.827810229704e-05 7.70e-11 3.09e-11 1.23e-08 0.0\n",
+      " 15   +9.829172608078e-05 +9.828885180253e-05 7.70e-12 3.13e-12 2.34e-09 0.0\n",
+      "\n",
+      "\n",
+      "Optimal solution found in 15 iterations and 0.05s\n",
+      "Objective +9.82917261e-05\n",
+      "Primal infeasibility (abs/rel): 1.54e-11/7.70e-12\n",
+      "Dual infeasibility   (abs/rel): 3.13e-12/3.13e-12\n",
+      "Complementarity gap  (abs/rel): 2.34e-09/2.34e-09\n",
+      "\n",
+      "\n",
+      "Unscaled Primal infeasibility   (abs/rel): 1.54e-11/7.70e-12\n",
+      "Post-solve Primal infeasibility (abs/rel): 1.54e-11/7.70e-12\n",
+      "Barrier finished in 0.06 seconds\n",
+      "cuOpt version: 26.2.0, git hash: 96c11095, host arch: x86_64, device archs: 80-real\n",
+      "CPU: AMD EPYC 7642 48-Core Processor, threads (physical/logical): 96/96, RAM: 167.90 GiB\n",
+      "CUDA 12.9, device: NVIDIA A100 80GB PCIe (ID 0), VRAM: 79.33 GiB\n",
+      "CUDA device UUID: ffffff8a5affffff95ffffffc7-ffffffd05\n",
+      "\n",
+      "Problem has a quadratic objective. Using Barrier.\n",
+      "Solving a problem with 2 constraints, 5 variables (0 integers), and 10 nonzeros\n",
+      "Problem scaling:\n",
+      "Objective coefficents range:          [0e+00, 0e+00]\n",
+      "Constraint matrix coefficients range: [2e-02, 1e+00]\n",
+      "Constraint rhs / bounds range:        [0e+00, 1e+00]\n",
+      "Variable bounds range:                [0e+00, 1e+00]\n",
+      "\n",
+      "Third-party presolve is disabled, skipping\n",
+      "Objective offset 0.000000 scaling_factor 1.000000\n",
+      "Presolved problem: 2 constraints 6 variables 11 nonzeros\n",
+      "Skipping column scaling\n",
+      "Barrier solver: 2 constraints, 6 variables, 11 nonzeros\n",
+      "Quadratic objective matrix: 25 nonzeros\n",
+      "\n",
+      "\n",
+      "Upper bounds                : 5\n",
+      "Density estimator time      : 0.00s\n",
+      "Linear system               : augmented\n",
+      "cuDSS Version               : 0.7.1\n",
+      "cuDSS Threading layer       : libcudss_mtlayer_gomp.so.0\n",
+      "Reordering algorithm        : AMD\n",
+      "Reordering time             : 0.00s\n",
+      "Symbolic factorization time : 0.01s\n",
+      "Total symbolic time         : 0.01s\n",
+      "Symbolic nonzeros in factor : 2.20e+01\n",
+      "\n",
+      "\n",
+      "                  Objective                         Infeasibility        Time\n",
+      "Iter   Primal              Dual                Primal   Dual    Compl.   Elapsed\n",
+      "  0   +1.508133859319e+01 -2.008133859319e+01 5.00e+01 1.00e+01 1.00e+02 0.0\n",
+      "  1   +1.396474142779e+00 -4.090196536907e+00 7.25e+00 2.90e+00 1.93e-01 0.0\n",
+      "  2   +3.460185576412e-01 -4.477705971449e+00 2.69e+00 1.07e+00 6.77e-01 0.0\n",
+      "  3   +2.173941068607e-02 -1.573352249955e+00 2.69e-01 1.07e-01 3.65e-01 0.0\n",
+      "  4   +1.113562522406e-02 -4.052481138604e-01 2.79e-02 1.12e-02 2.36e-01 0.0\n",
+      "  5   +9.878275571836e-03 -6.529149778574e-02 4.32e-03 1.73e-03 4.86e-02 0.0\n",
+      "  6   +6.213369991400e-03 -1.961323433571e-02 1.37e-03 5.49e-04 4.60e-03 0.0\n",
+      "  7   +2.753837784022e-03 -4.091524143227e-03 2.55e-04 1.02e-04 2.18e-03 0.0\n",
+      "  8   +8.816552093872e-04 -1.208051942755e-03 2.86e-05 1.15e-05 6.76e-04 0.0\n",
+      "  9   +3.328432347857e-04 -2.608355125524e-05 3.32e-06 1.33e-06 1.71e-04 0.0\n",
+      " 10   +2.041537352142e-04 +1.625201263960e-04 3.35e-07 1.34e-07 1.27e-05 0.0\n",
+      " 11   +1.915462685663e-04 +1.854400090055e-04 4.59e-08 1.84e-08 1.33e-06 0.0\n",
+      " 12   +1.895501304288e-04 +1.887776774556e-04 5.13e-09 2.05e-09 2.83e-07 0.0\n",
+      " 13   +1.892546773000e-04 +1.891586526727e-04 5.38e-10 2.15e-10 5.00e-08 0.0\n",
+      " 14   +1.892077492854e-04 +1.891971136588e-04 5.42e-11 2.18e-11 5.67e-09 0.0\n",
+      "\n",
+      "\n",
+      "Optimal solution found in 14 iterations and 0.05s\n",
+      "Objective +1.89207749e-04\n",
+      "Primal infeasibility (abs/rel): 1.08e-10/5.42e-11\n",
+      "Dual infeasibility   (abs/rel): 2.18e-11/2.18e-11\n",
+      "Complementarity gap  (abs/rel): 5.67e-09/5.67e-09\n",
+      "\n",
+      "\n",
+      "Unscaled Primal infeasibility   (abs/rel): 1.08e-10/5.42e-11\n",
+      "Post-solve Primal infeasibility (abs/rel): 1.08e-10/5.42e-11\n",
+      "Barrier finished in 0.05 seconds\n",
+      "cuOpt version: 26.2.0, git hash: 96c11095, host arch: x86_64, device archs: 80-real\n",
+      "CPU: AMD EPYC 7642 48-Core Processor, threads (physical/logical): 96/96, RAM: 168.87 GiB\n",
+      "CUDA 12.9, device: NVIDIA A100 80GB PCIe (ID 0), VRAM: 79.33 GiB\n",
+      "CUDA device UUID: ffffff8a5affffff95ffffffc7-ffffffd05\n",
+      "\n",
+      "Problem has a quadratic objective. Using Barrier.\n",
+      "Solving a problem with 2 constraints, 5 variables (0 integers), and 10 nonzeros\n",
+      "Problem scaling:\n",
+      "Objective coefficents range:          [0e+00, 0e+00]\n",
+      "Constraint matrix coefficients range: [2e-02, 1e+00]\n",
+      "Constraint rhs / bounds range:        [0e+00, 1e+00]\n",
+      "Variable bounds range:                [0e+00, 1e+00]\n",
+      "\n",
+      "Third-party presolve is disabled, skipping\n",
+      "Objective offset 0.000000 scaling_factor 1.000000\n",
+      "Presolved problem: 2 constraints 6 variables 11 nonzeros\n",
+      "Skipping column scaling\n",
+      "Barrier solver: 2 constraints, 6 variables, 11 nonzeros\n",
+      "Quadratic objective matrix: 25 nonzeros\n",
+      "\n",
+      "\n",
+      "Upper bounds                : 5\n",
+      "Density estimator time      : 0.00s\n",
+      "Linear system               : augmented\n",
+      "cuDSS Version               : 0.7.1\n",
+      "cuDSS Threading layer       : libcudss_mtlayer_gomp.so.0\n",
+      "Reordering algorithm        : AMD\n",
+      "Reordering time             : 0.00s\n",
+      "Symbolic factorization time : 0.01s\n",
+      "Total symbolic time         : 0.01s\n",
+      "Symbolic nonzeros in factor : 2.20e+01\n",
+      "\n",
+      "\n",
+      "                  Objective                         Infeasibility        Time\n",
+      "Iter   Primal              Dual                Primal   Dual    Compl.   Elapsed\n",
+      "  0   +1.510393544234e+01 -2.010393544234e+01 5.00e+01 1.00e+01 1.00e+02 0.0\n",
+      "  1   +1.333242185387e+00 -3.960795814572e+00 7.03e+00 2.81e+00 1.63e-01 0.0\n",
+      "  2   +2.019597554100e-01 -3.370808029717e+00 1.93e+00 7.71e-01 6.20e-01 0.0\n",
+      "  3   +1.973835605178e-02 -1.210158142048e+00 2.26e-01 9.02e-02 3.26e-01 0.0\n",
+      "  4   +1.117164531706e-02 -3.176860344685e-01 2.28e-02 9.13e-03 2.14e-01 0.0\n",
+      "  5   +9.865614060922e-03 -5.371200163128e-02 3.90e-03 1.56e-03 3.89e-02 0.0\n",
+      "  6   +5.717214928010e-03 -1.537024895140e-02 1.15e-03 4.60e-04 4.52e-03 0.0\n",
+      "  7   +2.924484228461e-03 -3.296754925010e-03 2.82e-04 1.13e-04 1.96e-03 0.0\n",
+      "  8   +9.132901760842e-04 -1.314976284683e-03 2.82e-05 1.13e-05 6.60e-04 0.0\n",
+      "  9   +4.518869562563e-04 +7.498309035824e-05 3.94e-06 1.57e-06 1.61e-04 0.0\n",
+      " 10   +3.354489370077e-04 +2.816353735678e-04 5.41e-07 2.16e-07 9.87e-06 0.0\n",
+      " 11   +3.187091008594e-04 +3.115052049844e-04 6.77e-08 2.71e-08 1.54e-06 0.0\n",
+      " 12   +3.162355471425e-04 +3.153647937008e-04 7.36e-09 2.95e-09 2.93e-07 0.0\n",
+      " 13   +3.158740329543e-04 +3.157774420129e-04 7.54e-10 3.02e-10 3.79e-08 0.0\n",
+      " 14   +3.158272667889e-04 +3.158175967381e-04 7.54e-11 3.02e-11 1.61e-09 0.0\n",
+      "\n",
+      "\n",
+      "Optimal solution found in 14 iterations and 0.05s\n",
+      "Objective +3.15827267e-04\n",
+      "Primal infeasibility (abs/rel): 1.51e-10/7.54e-11\n",
+      "Dual infeasibility   (abs/rel): 3.02e-11/3.02e-11\n",
+      "Complementarity gap  (abs/rel): 1.61e-09/1.61e-09\n",
+      "\n",
+      "\n",
+      "Unscaled Primal infeasibility   (abs/rel): 1.51e-10/7.54e-11\n",
+      "Post-solve Primal infeasibility (abs/rel): 1.51e-10/7.54e-11\n",
+      "Barrier finished in 0.05 seconds\n",
+      "cuOpt version: 26.2.0, git hash: 96c11095, host arch: x86_64, device archs: 80-real\n",
+      "CPU: AMD EPYC 7642 48-Core Processor, threads (physical/logical): 96/96, RAM: 169.18 GiB\n",
+      "CUDA 12.9, device: NVIDIA A100 80GB PCIe (ID 0), VRAM: 79.33 GiB\n",
+      "CUDA device UUID: ffffff8a5affffff95ffffffc7-ffffffd05\n",
+      "\n",
+      "Problem has a quadratic objective. Using Barrier.\n",
+      "Solving a problem with 2 constraints, 5 variables (0 integers), and 10 nonzeros\n",
+      "Problem scaling:\n",
+      "Objective coefficents range:          [0e+00, 0e+00]\n",
+      "Constraint matrix coefficients range: [2e-02, 1e+00]\n",
+      "Constraint rhs / bounds range:        [0e+00, 1e+00]\n",
+      "Variable bounds range:                [0e+00, 1e+00]\n",
+      "\n",
+      "Third-party presolve is disabled, skipping\n",
+      "Objective offset 0.000000 scaling_factor 1.000000\n",
+      "Presolved problem: 2 constraints 6 variables 11 nonzeros\n",
+      "Skipping column scaling\n",
+      "Barrier solver: 2 constraints, 6 variables, 11 nonzeros\n",
+      "Quadratic objective matrix: 25 nonzeros\n",
+      "\n",
+      "\n",
+      "Upper bounds                : 5\n",
+      "Density estimator time      : 0.00s\n",
+      "Linear system               : augmented\n",
+      "cuDSS Version               : 0.7.1\n",
+      "cuDSS Threading layer       : libcudss_mtlayer_gomp.so.0\n",
+      "Reordering algorithm        : AMD\n",
+      "Reordering time             : 0.00s\n",
+      "Symbolic factorization time : 0.01s\n",
+      "Total symbolic time         : 0.01s\n",
+      "Symbolic nonzeros in factor : 2.20e+01\n",
+      "\n",
+      "\n",
+      "                  Objective                         Infeasibility        Time\n",
+      "Iter   Primal              Dual                Primal   Dual    Compl.   Elapsed\n",
+      "  0   +1.512657603624e+01 -2.012657603624e+01 5.00e+01 1.00e+01 1.00e+02 0.0\n",
+      "  1   +1.270616299763e+00 -3.832013323724e+00 6.81e+00 2.73e+00 1.57e-01 0.0\n",
+      "  2   +1.298868640401e-01 -2.514136902958e+00 1.44e+00 5.75e-01 5.57e-01 0.0\n",
+      "  3   +1.689907707894e-02 -9.605711970241e-01 1.68e-01 6.71e-02 2.86e-01 0.0\n",
+      "  4   +1.109695500176e-02 -2.382622382087e-01 1.68e-02 6.71e-03 1.83e-01 0.0\n",
+      "  5   +9.694987573399e-03 -4.344441004257e-02 3.31e-03 1.32e-03 2.70e-02 0.0\n",
+      "  6   +4.966120941261e-03 -1.133171716008e-02 8.14e-04 3.25e-04 4.36e-03 0.0\n",
+      "  7   +2.874560244422e-03 -2.374290913744e-03 2.41e-04 9.62e-05 1.61e-03 0.0\n",
+      "  8   +9.187629921612e-04 -1.123468197022e-03 2.41e-05 9.62e-06 6.34e-04 0.0\n",
+      "  9   +5.742873295744e-04 +2.676528157210e-04 3.26e-06 1.30e-06 1.16e-04 0.0\n",
+      " 10   +4.941658796882e-04 +4.464244775474e-04 4.93e-07 1.97e-07 5.96e-06 0.0\n",
+      " 11   +4.804391335814e-04 +4.745025563531e-04 5.73e-08 2.29e-08 1.31e-06 0.0\n",
+      " 12   +4.784603690967e-04 +4.777821139805e-04 6.03e-09 2.42e-09 2.10e-07 0.0\n",
+      " 13   +4.781867186318e-04 +4.781173108467e-04 6.07e-10 2.43e-10 1.56e-08 0.0\n",
+      " 14   +4.781571832325e-04 +4.781502584744e-04 6.07e-11 2.43e-11 2.70e-10 0.0\n",
+      "\n",
+      "\n",
+      "Optimal solution found in 14 iterations and 0.05s\n",
+      "Objective +4.78157183e-04\n",
+      "Primal infeasibility (abs/rel): 1.21e-10/6.07e-11\n",
+      "Dual infeasibility   (abs/rel): 2.43e-11/2.43e-11\n",
+      "Complementarity gap  (abs/rel): 2.71e-10/2.70e-10\n",
+      "\n",
+      "\n",
+      "Unscaled Primal infeasibility   (abs/rel): 1.21e-10/6.07e-11\n",
+      "Post-solve Primal infeasibility (abs/rel): 1.21e-10/6.07e-11\n",
+      "Barrier finished in 0.05 seconds\n",
+      "cuOpt version: 26.2.0, git hash: 96c11095, host arch: x86_64, device archs: 80-real\n",
+      "CPU: AMD EPYC 7642 48-Core Processor, threads (physical/logical): 96/96, RAM: 169.18 GiB\n",
+      "CUDA 12.9, device: NVIDIA A100 80GB PCIe (ID 0), VRAM: 79.33 GiB\n",
+      "CUDA device UUID: ffffff8a5affffff95ffffffc7-ffffffd05\n",
+      "\n",
+      "Problem has a quadratic objective. Using Barrier.\n",
+      "Solving a problem with 2 constraints, 5 variables (0 integers), and 10 nonzeros\n",
+      "Problem scaling:\n",
+      "Objective coefficents range:          [0e+00, 0e+00]\n",
+      "Constraint matrix coefficients range: [2e-02, 1e+00]\n",
+      "Constraint rhs / bounds range:        [0e+00, 1e+00]\n",
+      "Variable bounds range:                [0e+00, 1e+00]\n",
+      "\n",
+      "Third-party presolve is disabled, skipping\n",
+      "Objective offset 0.000000 scaling_factor 1.000000\n",
+      "Presolved problem: 2 constraints 6 variables 11 nonzeros\n",
+      "Skipping column scaling\n",
+      "Barrier solver: 2 constraints, 6 variables, 11 nonzeros\n",
+      "Quadratic objective matrix: 25 nonzeros\n",
+      "\n",
+      "\n",
+      "Upper bounds                : 5\n",
+      "Density estimator time      : 0.00s\n",
+      "Linear system               : augmented\n",
+      "cuDSS Version               : 0.7.1\n",
+      "cuDSS Threading layer       : libcudss_mtlayer_gomp.so.0\n",
+      "Reordering algorithm        : AMD\n",
+      "Reordering time             : 0.00s\n",
+      "Symbolic factorization time : 0.01s\n",
+      "Total symbolic time         : 0.01s\n",
+      "Symbolic nonzeros in factor : 2.20e+01\n",
+      "\n",
+      "\n",
+      "                  Objective                         Infeasibility        Time\n",
+      "Iter   Primal              Dual                Primal   Dual    Compl.   Elapsed\n",
+      "  0   +1.514984849857e+01 -2.014984849857e+01 5.00e+01 1.00e+01 1.00e+02 0.0\n",
+      "  1   +1.208817177721e+00 -3.703922897095e+00 6.60e+00 2.64e+00 1.70e-01 0.0\n",
+      "  2   +9.381521346387e-02 -1.913007266340e+00 1.14e+00 4.54e-01 5.04e-01 0.0\n",
+      "  3   +1.505848193650e-02 -7.793498420948e-01 1.23e-01 4.91e-02 2.51e-01 0.0\n",
+      "  4   +1.116000968721e-02 -1.795084752459e-01 1.23e-02 4.91e-03 1.53e-01 0.0\n",
+      "  5   +9.590358257366e-03 -3.542692623435e-02 2.76e-03 1.10e-03 1.87e-02 0.0\n",
+      "  6   +4.179680195298e-03 -7.526206397935e-03 4.34e-04 1.74e-04 4.18e-03 0.0\n",
+      "  7   +2.739241462777e-03 -1.510500215556e-03 1.55e-04 6.18e-05 1.17e-03 0.0\n",
+      "  8   +9.473018258365e-04 -7.256446230424e-04 1.55e-05 6.18e-06 5.41e-04 0.0\n",
+      "  9   +7.274884522721e-04 +5.160879340455e-04 1.84e-06 7.35e-07 5.95e-05 0.0\n",
+      " 10   +6.843615236131e-04 +6.548743991659e-04 2.44e-07 9.75e-08 4.59e-06 0.0\n",
+      " 11   +6.773851388666e-04 +6.739050573756e-04 2.67e-08 1.07e-08 8.71e-07 0.0\n",
+      " 12   +6.763404861215e-04 +6.759684539399e-04 2.73e-09 1.09e-09 9.99e-08 0.0\n",
+      " 13   +6.762113064762e-04 +6.761741493209e-04 2.73e-10 1.10e-10 3.22e-09 0.0\n",
+      "\n",
+      "\n",
+      "Optimal solution found in 13 iterations and 0.05s\n",
+      "Objective +6.76211306e-04\n",
+      "Primal infeasibility (abs/rel): 5.46e-10/2.73e-10\n",
+      "Dual infeasibility   (abs/rel): 1.10e-10/1.10e-10\n",
+      "Complementarity gap  (abs/rel): 3.22e-09/3.22e-09\n",
+      "\n",
+      "\n",
+      "Unscaled Primal infeasibility   (abs/rel): 5.46e-10/2.73e-10\n",
+      "Post-solve Primal infeasibility (abs/rel): 5.46e-10/2.73e-10\n",
+      "Barrier finished in 0.05 seconds\n",
+      "cuOpt version: 26.2.0, git hash: 96c11095, host arch: x86_64, device archs: 80-real\n",
+      "CPU: AMD EPYC 7642 48-Core Processor, threads (physical/logical): 96/96, RAM: 168.71 GiB\n",
+      "CUDA 12.9, device: NVIDIA A100 80GB PCIe (ID 0), VRAM: 79.33 GiB\n",
+      "CUDA device UUID: ffffff8a5affffff95ffffffc7-ffffffd05\n",
+      "\n",
+      "Problem has a quadratic objective. Using Barrier.\n",
+      "Solving a problem with 2 constraints, 5 variables (0 integers), and 10 nonzeros\n",
+      "Problem scaling:\n",
+      "Objective coefficents range:          [0e+00, 0e+00]\n",
+      "Constraint matrix coefficients range: [2e-02, 1e+00]\n",
+      "Constraint rhs / bounds range:        [0e+00, 1e+00]\n",
+      "Variable bounds range:                [0e+00, 1e+00]\n",
+      "\n",
+      "Third-party presolve is disabled, skipping\n",
+      "Objective offset 0.000000 scaling_factor 1.000000\n",
+      "Presolved problem: 2 constraints 6 variables 11 nonzeros\n",
+      "Skipping column scaling\n",
+      "Barrier solver: 2 constraints, 6 variables, 11 nonzeros\n",
+      "Quadratic objective matrix: 25 nonzeros\n",
+      "\n",
+      "\n",
+      "Upper bounds                : 5\n",
+      "Density estimator time      : 0.00s\n",
+      "Linear system               : augmented\n",
+      "cuDSS Version               : 0.7.1\n",
+      "cuDSS Threading layer       : libcudss_mtlayer_gomp.so.0\n",
+      "Reordering algorithm        : AMD\n",
+      "Reordering time             : 0.00s\n",
+      "Symbolic factorization time : 0.01s\n",
+      "Total symbolic time         : 0.01s\n",
+      "Symbolic nonzeros in factor : 2.20e+01\n",
+      "\n",
+      "\n",
+      "                  Objective                         Infeasibility        Time\n",
+      "Iter   Primal              Dual                Primal   Dual    Compl.   Elapsed\n",
+      "  0   +1.517479878134e+01 -2.017479878134e+01 5.00e+01 1.00e+01 1.00e+02 0.0\n",
+      "  1   +1.148092268092e+00 -3.576625942896e+00 6.38e+00 2.55e+00 1.82e-01 0.0\n",
+      "  2   +7.373244614631e-02 -1.519097301397e+00 9.41e-01 3.76e-01 4.66e-01 0.0\n",
+      "  3   +1.405481789343e-02 -6.526450446579e-01 9.41e-02 3.76e-02 2.21e-01 0.0\n",
+      "  4   +1.129111268876e-02 -1.411430179235e-01 9.58e-03 3.83e-03 1.29e-01 0.0\n",
+      "  5   +9.569983652496e-03 -2.952392601877e-02 2.37e-03 9.46e-04 1.39e-02 0.0\n",
+      "  6   +3.746650518820e-03 -5.909422664397e-03 2.37e-04 9.46e-05 4.00e-03 0.0\n",
+      "  7   +2.637639839702e-03 -9.829665113148e-04 8.69e-05 3.48e-05 9.23e-04 0.0\n",
+      "  8   +1.077871978016e-03 -2.148465979347e-04 8.78e-06 3.51e-06 4.20e-04 0.0\n",
+      "  9   +9.432365932897e-04 +7.484578420154e-04 1.29e-06 5.16e-07 2.60e-05 0.0\n",
+      " 10   +9.152878173098e-04 +8.902997180768e-04 1.57e-07 6.30e-08 3.97e-06 0.0\n",
+      " 11   +9.107288072420e-04 +9.078947770245e-04 1.68e-08 6.72e-09 6.59e-07 0.0\n",
+      " 12   +9.100365166655e-04 +9.097460706874e-04 1.69e-09 6.79e-10 5.21e-08 0.0\n",
+      " 13   +9.099593933692e-04 +9.099303997400e-04 1.69e-10 6.79e-11 9.66e-10 0.0\n",
+      "\n",
+      "\n",
+      "Optimal solution found in 13 iterations and 0.05s\n",
+      "Objective +9.09959393e-04\n",
+      "Primal infeasibility (abs/rel): 3.38e-10/1.69e-10\n",
+      "Dual infeasibility   (abs/rel): 6.79e-11/6.79e-11\n",
+      "Complementarity gap  (abs/rel): 9.67e-10/9.66e-10\n",
+      "\n",
+      "\n",
+      "Unscaled Primal infeasibility   (abs/rel): 3.38e-10/1.69e-10\n",
+      "Post-solve Primal infeasibility (abs/rel): 3.38e-10/1.69e-10\n",
+      "Barrier finished in 0.05 seconds\n",
+      "cuOpt version: 26.2.0, git hash: 96c11095, host arch: x86_64, device archs: 80-real\n",
+      "CPU: AMD EPYC 7642 48-Core Processor, threads (physical/logical): 96/96, RAM: 168.05 GiB\n",
+      "CUDA 12.9, device: NVIDIA A100 80GB PCIe (ID 0), VRAM: 79.33 GiB\n",
+      "CUDA device UUID: ffffff8a5affffff95ffffffc7-ffffffd05\n",
+      "\n",
+      "Problem has a quadratic objective. Using Barrier.\n",
+      "Solving a problem with 2 constraints, 5 variables (0 integers), and 10 nonzeros\n",
+      "Problem scaling:\n",
+      "Objective coefficents range:          [0e+00, 0e+00]\n",
+      "Constraint matrix coefficients range: [2e-02, 1e+00]\n",
+      "Constraint rhs / bounds range:        [0e+00, 1e+00]\n",
+      "Variable bounds range:                [0e+00, 1e+00]\n",
+      "\n",
+      "Third-party presolve is disabled, skipping\n",
+      "Objective offset 0.000000 scaling_factor 1.000000\n",
+      "Presolved problem: 2 constraints 6 variables 11 nonzeros\n",
+      "Skipping column scaling\n",
+      "Barrier solver: 2 constraints, 6 variables, 11 nonzeros\n",
+      "Quadratic objective matrix: 25 nonzeros\n",
+      "\n",
+      "\n",
+      "Upper bounds                : 5\n",
+      "Density estimator time      : 0.00s\n",
+      "Linear system               : augmented\n",
+      "cuDSS Version               : 0.7.1\n",
+      "cuDSS Threading layer       : libcudss_mtlayer_gomp.so.0\n",
+      "Reordering algorithm        : AMD\n",
+      "Reordering time             : 0.00s\n",
+      "Symbolic factorization time : 0.01s\n",
+      "Total symbolic time         : 0.01s\n",
+      "Symbolic nonzeros in factor : 2.20e+01\n",
+      "\n",
+      "\n",
+      "                  Objective                         Infeasibility        Time\n",
+      "Iter   Primal              Dual                Primal   Dual    Compl.   Elapsed\n",
+      "  0   +1.519822927338e+01 -2.019822927338e+01 5.00e+01 1.00e+01 1.00e+02 0.0\n",
+      "  1   +1.088642319810e+00 -3.450260733500e+00 6.16e+00 2.47e+00 1.94e-01 0.0\n",
+      "  2   +9.103682359428e-02 -1.384143078061e+00 1.17e+00 4.69e-01 4.30e-01 0.0\n",
+      "  3   +1.460821084878e-02 -6.684180232329e-01 1.26e-01 5.04e-02 1.94e-01 0.0\n",
+      "  4   +1.116257159108e-02 -1.659507223274e-01 1.26e-02 5.04e-03 1.34e-01 0.0\n",
+      "  5   +9.819399452772e-03 -3.078565533805e-02 2.68e-03 1.07e-03 2.04e-02 0.0\n",
+      "  6   +4.883995819310e-03 -7.883886001876e-03 6.10e-04 2.44e-04 4.28e-03 0.0\n",
+      "  7   +2.796547384799e-03 -5.623503684105e-04 1.30e-04 5.22e-05 1.57e-03 0.0\n",
+      "  8   +1.457982670572e-03 +5.705801738194e-04 1.30e-05 5.22e-06 4.53e-04 0.0\n",
+      "  9   +1.223525277973e-03 +1.095160953841e-03 1.82e-06 7.26e-07 3.20e-05 0.0\n",
+      " 10   +1.185606104641e-03 +1.169795009750e-03 2.11e-07 8.44e-08 3.17e-06 0.0\n",
+      " 11   +1.180182564921e-03 +1.178441300336e-03 2.20e-08 8.81e-09 4.44e-07 0.0\n",
+      " 12   +1.179494175079e-03 +1.179319164882e-03 2.21e-09 8.84e-10 2.19e-08 0.0\n",
+      " 13   +1.179423664347e-03 +1.179406183542e-03 2.21e-10 8.84e-11 2.86e-10 0.0\n",
+      "\n",
+      "\n",
+      "Optimal solution found in 13 iterations and 0.05s\n",
+      "Objective +1.17942366e-03\n",
+      "Primal infeasibility (abs/rel): 4.42e-10/2.21e-10\n",
+      "Dual infeasibility   (abs/rel): 8.84e-11/8.84e-11\n",
+      "Complementarity gap  (abs/rel): 2.86e-10/2.86e-10\n",
+      "\n",
+      "\n",
+      "Unscaled Primal infeasibility   (abs/rel): 4.42e-10/2.21e-10\n",
+      "Post-solve Primal infeasibility (abs/rel): 4.42e-10/2.21e-10\n",
+      "Barrier finished in 0.05 seconds\n",
+      "cuOpt version: 26.2.0, git hash: 96c11095, host arch: x86_64, device archs: 80-real\n",
+      "CPU: AMD EPYC 7642 48-Core Processor, threads (physical/logical): 96/96, RAM: 167.11 GiB\n",
+      "CUDA 12.9, device: NVIDIA A100 80GB PCIe (ID 0), VRAM: 79.33 GiB\n",
+      "CUDA device UUID: ffffff8a5affffff95ffffffc7-ffffffd05\n",
+      "\n",
+      "Problem has a quadratic objective. Using Barrier.\n",
+      "Solving a problem with 2 constraints, 5 variables (0 integers), and 10 nonzeros\n",
+      "Problem scaling:\n",
+      "Objective coefficents range:          [0e+00, 0e+00]\n",
+      "Constraint matrix coefficients range: [2e-02, 1e+00]\n",
+      "Constraint rhs / bounds range:        [0e+00, 1e+00]\n",
+      "Variable bounds range:                [0e+00, 1e+00]\n",
+      "\n",
+      "Third-party presolve is disabled, skipping\n",
+      "Objective offset 0.000000 scaling_factor 1.000000\n",
+      "Presolved problem: 2 constraints 6 variables 11 nonzeros\n",
+      "Skipping column scaling\n",
+      "Barrier solver: 2 constraints, 6 variables, 11 nonzeros\n",
+      "Quadratic objective matrix: 25 nonzeros\n",
+      "\n",
+      "\n",
+      "Upper bounds                : 5\n",
+      "Density estimator time      : 0.00s\n",
+      "Linear system               : augmented\n",
+      "cuDSS Version               : 0.7.1\n",
+      "cuDSS Threading layer       : libcudss_mtlayer_gomp.so.0\n",
+      "Reordering algorithm        : AMD\n",
+      "Reordering time             : 0.00s\n",
+      "Symbolic factorization time : 0.01s\n",
+      "Total symbolic time         : 0.01s\n",
+      "Symbolic nonzeros in factor : 2.20e+01\n",
+      "\n",
+      "\n",
+      "                  Objective                         Infeasibility        Time\n",
+      "Iter   Primal              Dual                Primal   Dual    Compl.   Elapsed\n",
+      "  0   +1.522198048015e+01 -2.022198048015e+01 5.00e+01 1.00e+01 1.00e+02 0.0\n",
+      "  1   +1.030722113132e+00 -3.324932629490e+00 5.95e+00 2.38e+00 2.05e-01 0.0\n",
+      "  2   +1.210883272925e-01 -1.400611591234e+00 1.50e+00 5.99e-01 4.02e-01 0.0\n",
+      "  3   +1.616399035036e-02 -7.088829739567e-01 1.79e-01 7.15e-02 1.72e-01 0.0\n",
+      "  4   +1.114044029806e-02 -1.978332428797e-01 1.79e-02 7.15e-03 1.33e-01 0.0\n",
+      "  5   +1.019493828489e-02 -3.266858575161e-02 3.04e-03 1.22e-03 3.10e-02 0.0\n",
+      "  6   +6.157253383642e-03 -9.469655026885e-03 9.61e-04 3.84e-04 4.53e-03 0.0\n",
+      "  7   +2.898661906575e-03 -6.764700195963e-04 9.61e-05 3.84e-05 2.16e-03 0.0\n",
+      "  8   +2.169591556726e-03 +7.866731587962e-04 3.40e-05 1.36e-05 4.30e-04 0.0\n",
+      "  9   +1.556649619413e-03 +1.346162654355e-03 3.60e-06 1.44e-06 8.31e-05 0.0\n",
+      " 10   +1.494741771858e-03 +1.468442131272e-03 4.28e-07 1.71e-07 4.76e-06 0.0\n",
+      " 11   +1.485852838506e-03 +1.482942979710e-03 4.50e-08 1.80e-08 6.93e-07 0.0\n",
+      " 12   +1.484721320685e-03 +1.484428181482e-03 4.51e-09 1.81e-09 3.73e-08 0.0\n",
+      " 13   +1.484604797478e-03 +1.484575518201e-03 4.51e-10 1.81e-10 5.10e-10 0.0\n",
+      "\n",
+      "\n",
+      "Optimal solution found in 13 iterations and 0.05s\n",
+      "Objective +1.48460480e-03\n",
+      "Primal infeasibility (abs/rel): 9.03e-10/4.51e-10\n",
+      "Dual infeasibility   (abs/rel): 1.81e-10/1.81e-10\n",
+      "Complementarity gap  (abs/rel): 5.11e-10/5.10e-10\n",
+      "\n",
+      "\n",
+      "Unscaled Primal infeasibility   (abs/rel): 9.03e-10/4.51e-10\n",
+      "Post-solve Primal infeasibility (abs/rel): 9.03e-10/4.51e-10\n",
+      "Barrier finished in 0.05 seconds\n",
+      "cuOpt version: 26.2.0, git hash: 96c11095, host arch: x86_64, device archs: 80-real\n",
+      "CPU: AMD EPYC 7642 48-Core Processor, threads (physical/logical): 96/96, RAM: 166.04 GiB\n",
+      "CUDA 12.9, device: NVIDIA A100 80GB PCIe (ID 0), VRAM: 79.33 GiB\n",
+      "CUDA device UUID: ffffff8a5affffff95ffffffc7-ffffffd05\n",
+      "\n",
+      "Problem has a quadratic objective. Using Barrier.\n",
+      "Solving a problem with 2 constraints, 5 variables (0 integers), and 10 nonzeros\n",
+      "Problem scaling:\n",
+      "Objective coefficents range:          [0e+00, 0e+00]\n",
+      "Constraint matrix coefficients range: [2e-02, 1e+00]\n",
+      "Constraint rhs / bounds range:        [0e+00, 1e+00]\n",
+      "Variable bounds range:                [0e+00, 1e+00]\n",
+      "\n",
+      "Third-party presolve is disabled, skipping\n",
+      "Objective offset 0.000000 scaling_factor 1.000000\n",
+      "Presolved problem: 2 constraints 6 variables 11 nonzeros\n",
+      "Skipping column scaling\n",
+      "Barrier solver: 2 constraints, 6 variables, 11 nonzeros\n",
+      "Quadratic objective matrix: 25 nonzeros\n",
+      "\n",
+      "\n",
+      "Upper bounds                : 5\n",
+      "Density estimator time      : 0.00s\n",
+      "Linear system               : augmented\n",
+      "cuDSS Version               : 0.7.1\n",
+      "cuDSS Threading layer       : libcudss_mtlayer_gomp.so.0\n",
+      "Reordering algorithm        : AMD\n",
+      "Reordering time             : 0.00s\n",
+      "Symbolic factorization time : 0.01s\n",
+      "Total symbolic time         : 0.01s\n",
+      "Symbolic nonzeros in factor : 2.20e+01\n",
+      "\n",
+      "\n",
+      "                  Objective                         Infeasibility        Time\n",
+      "Iter   Primal              Dual                Primal   Dual    Compl.   Elapsed\n",
+      "  0   +1.524622925724e+01 -2.024622925724e+01 5.00e+01 1.00e+01 1.00e+02 0.0\n",
+      "  1   +9.745439733886e-01 -3.200723561906e+00 5.74e+00 2.30e+00 2.16e-01 0.0\n",
+      "  2   +8.223570425304e-02 -1.310528024826e+00 1.10e+00 4.41e-01 4.18e-01 0.0\n",
+      "  3   +1.423152671285e-02 -6.035760956834e-01 1.17e-01 4.70e-02 1.43e-01 0.0\n",
+      "  4   +1.150498155799e-02 -1.523339606740e-01 1.32e-02 5.29e-03 1.09e-01 0.0\n",
+      "  5   +1.040336378104e-02 -2.605415529542e-02 2.60e-03 1.04e-03 2.15e-02 0.0\n",
+      "  6   +5.947444352834e-03 -7.125444074227e-03 7.47e-04 2.99e-04 4.09e-03 0.0\n",
+      "  7   +2.974033076348e-03 +6.982926056997e-05 7.47e-05 2.99e-05 1.81e-03 0.0\n",
+      "  8   +2.392263896504e-03 +1.288355251272e-03 2.76e-05 1.10e-05 3.64e-04 0.0\n",
+      "  9   +1.887166953684e-03 +1.714020328251e-03 3.20e-06 1.28e-06 6.35e-05 0.0\n",
+      " 10   +1.833855500439e-03 +1.813017969433e-03 3.66e-07 1.46e-07 3.94e-06 0.0\n",
+      " 11   +1.826457093999e-03 +1.824223712591e-03 3.78e-08 1.51e-08 4.83e-07 0.0\n",
+      " 12   +1.825577441231e-03 +1.825354029663e-03 3.78e-09 1.51e-09 1.74e-08 0.0\n",
+      " 13   +1.825488761647e-03 +1.825466435415e-03 3.78e-10 1.51e-10 2.02e-10 0.0\n",
+      "\n",
+      "\n",
+      "Optimal solution found in 13 iterations and 0.05s\n",
+      "Objective +1.82548876e-03\n",
+      "Primal infeasibility (abs/rel): 7.56e-10/3.78e-10\n",
+      "Dual infeasibility   (abs/rel): 1.51e-10/1.51e-10\n",
+      "Complementarity gap  (abs/rel): 2.03e-10/2.02e-10\n",
+      "\n",
+      "\n",
+      "Unscaled Primal infeasibility   (abs/rel): 7.56e-10/3.78e-10\n",
+      "Post-solve Primal infeasibility (abs/rel): 7.56e-10/3.78e-10\n",
+      "Barrier finished in 0.05 seconds\n",
+      "cuOpt version: 26.2.0, git hash: 96c11095, host arch: x86_64, device archs: 80-real\n",
+      "CPU: AMD EPYC 7642 48-Core Processor, threads (physical/logical): 96/96, RAM: 165.35 GiB\n",
+      "CUDA 12.9, device: NVIDIA A100 80GB PCIe (ID 0), VRAM: 79.33 GiB\n",
+      "CUDA device UUID: ffffff8a5affffff95ffffffc7-ffffffd05\n",
+      "\n",
+      "Problem has a quadratic objective. Using Barrier.\n",
+      "Solving a problem with 2 constraints, 5 variables (0 integers), and 10 nonzeros\n",
+      "Problem scaling:\n",
+      "Objective coefficents range:          [0e+00, 0e+00]\n",
+      "Constraint matrix coefficients range: [2e-02, 1e+00]\n",
+      "Constraint rhs / bounds range:        [0e+00, 1e+00]\n",
+      "Variable bounds range:                [0e+00, 1e+00]\n",
+      "\n",
+      "Third-party presolve is disabled, skipping\n",
+      "Objective offset 0.000000 scaling_factor 1.000000\n",
+      "Presolved problem: 2 constraints 6 variables 11 nonzeros\n",
+      "Skipping column scaling\n",
+      "Barrier solver: 2 constraints, 6 variables, 11 nonzeros\n",
+      "Quadratic objective matrix: 25 nonzeros\n",
+      "\n",
+      "\n",
+      "Upper bounds                : 5\n",
+      "Density estimator time      : 0.00s\n",
+      "Linear system               : augmented\n",
+      "cuDSS Version               : 0.7.1\n",
+      "cuDSS Threading layer       : libcudss_mtlayer_gomp.so.0\n",
+      "Reordering algorithm        : AMD\n",
+      "Reordering time             : 0.00s\n",
+      "Symbolic factorization time : 0.01s\n",
+      "Total symbolic time         : 0.01s\n",
+      "Symbolic nonzeros in factor : 2.20e+01\n",
+      "\n",
+      "\n",
+      "                  Objective                         Infeasibility        Time\n",
+      "Iter   Primal              Dual                Primal   Dual    Compl.   Elapsed\n",
+      "  0   +1.527020397637e+01 -2.027020397637e+01 5.00e+01 1.00e+01 1.00e+02 0.0\n",
+      "  1   +9.203039706906e-01 -3.077728543129e+00 5.53e+00 2.21e+00 2.55e-01 0.0\n",
+      "  2   +4.911347341043e-02 -1.322780389161e+00 6.74e-01 2.69e-01 4.44e-01 0.0\n",
+      "  3   +1.348358606915e-02 -4.753270587424e-01 6.88e-02 2.75e-02 1.16e-01 0.0\n",
+      "  4   +1.226391550634e-02 -1.048478711776e-01 8.18e-03 3.27e-03 8.23e-02 0.0\n",
+      "  5   +1.089977959130e-02 -1.982960894753e-02 2.03e-03 8.12e-04 1.21e-02 0.0\n",
+      "  6   +5.092134767533e-03 -4.283450588425e-03 2.80e-04 1.12e-04 3.44e-03 0.0\n",
+      "  7   +2.948293376468e-03 +1.153178688177e-03 2.80e-05 1.12e-05 1.10e-03 0.0\n",
+      "  8   +2.488552154932e-03 +1.936365421811e-03 8.59e-06 3.44e-06 2.15e-04 0.0\n",
+      "  9   +2.236827199069e-03 +2.165706108653e-03 1.00e-06 4.00e-07 2.06e-05 0.0\n",
+      " 10   +2.206370833499e-03 +2.198305937478e-03 1.08e-07 4.30e-08 1.84e-06 0.0\n",
+      " 11   +2.202526399336e-03 +2.201703891549e-03 1.08e-08 4.34e-09 1.34e-07 0.0\n",
+      " 12   +2.202121899447e-03 +2.202039777566e-03 1.08e-09 4.34e-10 2.26e-09 0.0\n",
+      "\n",
+      "\n",
+      "Optimal solution found in 12 iterations and 0.04s\n",
+      "Objective +2.20212190e-03\n",
+      "Primal infeasibility (abs/rel): 2.17e-09/1.08e-09\n",
+      "Dual infeasibility   (abs/rel): 4.34e-10/4.34e-10\n",
+      "Complementarity gap  (abs/rel): 2.27e-09/2.26e-09\n",
+      "\n",
+      "\n",
+      "Unscaled Primal infeasibility   (abs/rel): 2.17e-09/1.08e-09\n",
+      "Post-solve Primal infeasibility (abs/rel): 2.17e-09/1.08e-09\n",
+      "Barrier finished in 0.05 seconds\n",
+      "cuOpt version: 26.2.0, git hash: 96c11095, host arch: x86_64, device archs: 80-real\n",
+      "CPU: AMD EPYC 7642 48-Core Processor, threads (physical/logical): 96/96, RAM: 164.80 GiB\n",
+      "CUDA 12.9, device: NVIDIA A100 80GB PCIe (ID 0), VRAM: 79.33 GiB\n",
+      "CUDA device UUID: ffffff8a5affffff95ffffffc7-ffffffd05\n",
+      "\n",
+      "Problem has a quadratic objective. Using Barrier.\n",
+      "Solving a problem with 2 constraints, 5 variables (0 integers), and 10 nonzeros\n",
+      "Problem scaling:\n",
+      "Objective coefficents range:          [0e+00, 0e+00]\n",
+      "Constraint matrix coefficients range: [2e-02, 1e+00]\n",
+      "Constraint rhs / bounds range:        [0e+00, 1e+00]\n",
+      "Variable bounds range:                [0e+00, 1e+00]\n",
+      "\n",
+      "Third-party presolve is disabled, skipping\n",
+      "Objective offset 0.000000 scaling_factor 1.000000\n",
+      "Presolved problem: 2 constraints 6 variables 11 nonzeros\n",
+      "Skipping column scaling\n",
+      "Barrier solver: 2 constraints, 6 variables, 11 nonzeros\n",
+      "Quadratic objective matrix: 25 nonzeros\n",
+      "\n",
+      "\n",
+      "Upper bounds                : 5\n",
+      "Density estimator time      : 0.00s\n",
+      "Linear system               : augmented\n",
+      "cuDSS Version               : 0.7.1\n",
+      "cuDSS Threading layer       : libcudss_mtlayer_gomp.so.0\n",
+      "Reordering algorithm        : AMD\n",
+      "Reordering time             : 0.00s\n",
+      "Symbolic factorization time : 0.01s\n",
+      "Total symbolic time         : 0.01s\n",
+      "Symbolic nonzeros in factor : 2.20e+01\n",
+      "\n",
+      "\n",
+      "                  Objective                         Infeasibility        Time\n",
+      "Iter   Primal              Dual                Primal   Dual    Compl.   Elapsed\n",
+      "  0   +1.529511056122e+01 -2.029511056122e+01 5.00e+01 1.00e+01 1.00e+02 0.6\n",
+      "  1   +8.682581221061e-01 -2.956103118309e+00 5.33e+00 2.13e+00 2.95e-01 0.6\n",
+      "  2   +6.475188707977e-02 -1.645133951334e+00 8.74e-01 3.50e-01 4.69e-01 0.6\n",
+      "  3   +1.415489958464e-02 -5.510228952495e-01 8.96e-02 3.59e-02 1.30e-01 0.6\n",
+      "  4   +1.259006578633e-02 -1.275394609469e-01 1.12e-02 4.47e-03 7.83e-02 0.6\n",
+      "  5   +1.164740173803e-02 -2.042367931952e-02 2.24e-03 8.97e-04 1.75e-02 0.6\n",
+      "  6   +7.793435726319e-03 -6.012585557822e-03 8.05e-04 3.22e-04 3.36e-03 0.6\n",
+      "  7   +3.658213766410e-03 -3.826550234567e-04 8.05e-05 3.22e-05 1.75e-03 0.6\n",
+      "  8   +2.903425452819e-03 +2.213230686080e-03 1.06e-05 4.23e-06 3.65e-04 0.6\n",
+      "  9   +2.691664792860e-03 +2.537143421696e-03 2.39e-06 9.56e-07 6.12e-05 0.6\n",
+      " 10   +2.623432206712e-03 +2.606117551593e-03 2.58e-07 1.03e-07 2.87e-06 0.6\n",
+      " 11   +2.615338190325e-03 +2.613562678865e-03 2.61e-08 1.05e-08 2.44e-07 0.6\n",
+      " 12   +2.614481278841e-03 +2.614303952490e-03 2.61e-09 1.05e-09 4.81e-09 0.6\n",
+      "\n",
+      "\n",
+      "Optimal solution found in 12 iterations and 0.58s\n",
+      "Objective +2.61448128e-03\n",
+      "Primal infeasibility (abs/rel): 5.22e-09/2.61e-09\n",
+      "Dual infeasibility   (abs/rel): 1.05e-09/1.05e-09\n",
+      "Complementarity gap  (abs/rel): 4.83e-09/4.81e-09\n",
+      "\n",
+      "\n",
+      "Unscaled Primal infeasibility   (abs/rel): 5.22e-09/2.61e-09\n",
+      "Post-solve Primal infeasibility (abs/rel): 5.22e-09/2.61e-09\n",
+      "Barrier finished in 0.58 seconds\n",
+      "cuOpt version: 26.2.0, git hash: 96c11095, host arch: x86_64, device archs: 80-real\n",
+      "CPU: AMD EPYC 7642 48-Core Processor, threads (physical/logical): 96/96, RAM: 170.18 GiB\n",
+      "CUDA 12.9, device: NVIDIA A100 80GB PCIe (ID 0), VRAM: 79.33 GiB\n",
+      "CUDA device UUID: ffffff8a5affffff95ffffffc7-ffffffd05\n",
+      "\n",
+      "Problem has a quadratic objective. Using Barrier.\n",
+      "Solving a problem with 2 constraints, 5 variables (0 integers), and 10 nonzeros\n",
+      "Problem scaling:\n",
+      "Objective coefficents range:          [0e+00, 0e+00]\n",
+      "Constraint matrix coefficients range: [2e-02, 1e+00]\n",
+      "Constraint rhs / bounds range:        [0e+00, 1e+00]\n",
+      "Variable bounds range:                [0e+00, 1e+00]\n",
+      "\n",
+      "Third-party presolve is disabled, skipping\n",
+      "Objective offset 0.000000 scaling_factor 1.000000\n",
+      "Presolved problem: 2 constraints 6 variables 11 nonzeros\n",
+      "Skipping column scaling\n",
+      "Barrier solver: 2 constraints, 6 variables, 11 nonzeros\n",
+      "Quadratic objective matrix: 25 nonzeros\n",
+      "\n",
+      "\n",
+      "Upper bounds                : 5\n",
+      "Density estimator time      : 0.00s\n",
+      "Linear system               : augmented\n",
+      "cuDSS Version               : 0.7.1\n",
+      "cuDSS Threading layer       : libcudss_mtlayer_gomp.so.0\n",
+      "Reordering algorithm        : AMD\n",
+      "Reordering time             : 0.00s\n",
+      "Symbolic factorization time : 0.01s\n",
+      "Total symbolic time         : 0.01s\n",
+      "Symbolic nonzeros in factor : 2.20e+01\n",
+      "\n",
+      "\n",
+      "                  Objective                         Infeasibility        Time\n",
+      "Iter   Primal              Dual                Primal   Dual    Compl.   Elapsed\n",
+      "  0   +1.539158841855e+01 -2.039158841855e+01 5.01e+01 1.00e+01 1.00e+02 0.0\n",
+      "  1   +8.222423946816e-01 -2.840480068426e+00 5.15e+00 2.05e+00 3.35e-01 0.0\n",
+      "  2   +9.840264957347e-02 -2.159253241132e+00 1.21e+00 4.82e-01 5.15e-01 0.0\n",
+      "  3   +1.643692694216e-02 -6.964955759785e-01 1.36e-01 5.43e-02 1.56e-01 0.0\n",
+      "  4   +1.311386957443e-02 -1.562719743967e-01 1.65e-02 6.59e-03 6.93e-02 0.0\n",
+      "  5   +1.261624115763e-02 -2.467998280669e-02 2.66e-03 1.06e-03 2.66e-02 0.0\n",
+      "  6   +1.026331880161e-02 -6.366838705073e-03 1.12e-03 4.47e-04 3.64e-03 0.0\n",
+      "  7   +4.277635184636e-03 -3.312730159188e-03 1.12e-04 4.47e-05 2.08e-03 0.0\n",
+      "  8   +3.493390496179e-03 +2.300440387459e-03 1.57e-05 6.25e-06 4.26e-04 0.0\n",
+      "  9   +3.179529045034e-03 +2.998477188940e-03 1.99e-06 7.95e-07 6.41e-05 0.0\n",
+      " 10   +3.107608526017e-03 +3.085234002890e-03 2.19e-07 8.76e-08 4.82e-06 0.0\n",
+      " 11   +3.096897646505e-03 +3.094404640747e-03 2.28e-08 9.13e-09 6.41e-07 0.0\n",
+      " 12   +3.095595011930e-03 +3.095331747353e-03 2.31e-09 9.25e-10 7.11e-08 0.0\n",
+      " 13   +3.095448087577e-03 +3.095421827971e-03 2.34e-10 9.25e-11 2.22e-09 0.0\n",
+      "\n",
+      "\n",
+      "Optimal solution found in 13 iterations and 0.05s\n",
+      "Objective +3.09544809e-03\n",
+      "Primal infeasibility (abs/rel): 4.67e-10/2.34e-10\n",
+      "Dual infeasibility   (abs/rel): 9.25e-11/9.25e-11\n",
+      "Complementarity gap  (abs/rel): 2.22e-09/2.22e-09\n",
+      "\n",
+      "\n",
+      "Unscaled Primal infeasibility   (abs/rel): 4.67e-10/2.34e-10\n",
+      "Post-solve Primal infeasibility (abs/rel): 4.67e-10/2.34e-10\n",
+      "Barrier finished in 0.05 seconds\n",
+      "cuOpt version: 26.2.0, git hash: 96c11095, host arch: x86_64, device archs: 80-real\n",
+      "CPU: AMD EPYC 7642 48-Core Processor, threads (physical/logical): 96/96, RAM: 169.82 GiB\n",
+      "CUDA 12.9, device: NVIDIA A100 80GB PCIe (ID 0), VRAM: 79.33 GiB\n",
+      "CUDA device UUID: ffffff8a5affffff95ffffffc7-ffffffd05\n",
+      "\n",
+      "Problem has a quadratic objective. Using Barrier.\n",
+      "Solving a problem with 2 constraints, 5 variables (0 integers), and 10 nonzeros\n",
+      "Problem scaling:\n",
+      "Objective coefficents range:          [0e+00, 0e+00]\n",
+      "Constraint matrix coefficients range: [2e-02, 1e+00]\n",
+      "Constraint rhs / bounds range:        [0e+00, 1e+00]\n",
+      "Variable bounds range:                [0e+00, 1e+00]\n",
+      "\n",
+      "Third-party presolve is disabled, skipping\n",
+      "Objective offset 0.000000 scaling_factor 1.000000\n",
+      "Presolved problem: 2 constraints 6 variables 11 nonzeros\n",
+      "Skipping column scaling\n",
+      "Barrier solver: 2 constraints, 6 variables, 11 nonzeros\n",
+      "Quadratic objective matrix: 25 nonzeros\n",
+      "\n",
+      "\n",
+      "Upper bounds                : 5\n",
+      "Density estimator time      : 0.00s\n",
+      "Linear system               : augmented\n",
+      "cuDSS Version               : 0.7.1\n",
+      "cuDSS Threading layer       : libcudss_mtlayer_gomp.so.0\n",
+      "Reordering algorithm        : AMD\n",
+      "Reordering time             : 0.00s\n",
+      "Symbolic factorization time : 0.01s\n",
+      "Total symbolic time         : 0.01s\n",
+      "Symbolic nonzeros in factor : 2.20e+01\n",
+      "\n",
+      "\n",
+      "                  Objective                         Infeasibility        Time\n",
+      "Iter   Primal              Dual                Primal   Dual    Compl.   Elapsed\n",
+      "  0   +1.545736587876e+01 -2.045736587876e+01 5.00e+01 1.00e+01 1.00e+02 0.0\n",
+      "  1   +8.482385625152e-01 -2.871008604319e+00 5.14e+00 2.06e+00 2.31e-01 0.0\n",
+      "  2   +4.457784867934e-02 -1.138815589197e+00 6.09e-01 2.44e-01 3.81e-01 0.0\n",
+      "  3   +1.363824292431e-02 -3.746098404758e-01 6.48e-02 2.59e-02 1.04e-01 0.0\n",
+      "  4   +1.321094372177e-02 -8.718157695723e-02 8.87e-03 3.55e-03 5.13e-02 0.0\n",
+      "  5   +1.234090535318e-02 -1.019458739369e-02 1.66e-03 6.66e-04 1.36e-02 0.0\n",
+      "  6   +8.897218445220e-03 -6.217783358345e-04 5.93e-04 2.37e-04 2.72e-03 0.0\n",
+      "  7   +5.050579146793e-03 +2.620287515633e-03 5.93e-05 2.37e-05 1.14e-03 0.0\n",
+      "  8   +4.287478183356e-03 +3.913902136995e-03 8.10e-06 3.24e-06 1.42e-04 0.0\n",
+      "  9   +4.134411674777e-03 +4.091331803009e-03 9.04e-07 3.62e-07 6.84e-06 0.0\n",
+      " 10   +4.117274497131e-03 +4.112886991594e-03 9.16e-08 3.67e-08 5.05e-07 0.0\n",
+      " 11   +4.115567619772e-03 +4.115129261349e-03 9.16e-09 3.67e-09 8.39e-09 0.0\n",
+      "\n",
+      "\n",
+      "Optimal solution found in 11 iterations and 0.04s\n",
+      "Objective +4.11556762e-03\n",
+      "Primal infeasibility (abs/rel): 1.83e-08/9.16e-09\n",
+      "Dual infeasibility   (abs/rel): 3.67e-09/3.67e-09\n",
+      "Complementarity gap  (abs/rel): 8.43e-09/8.39e-09\n",
+      "\n",
+      "\n",
+      "Unscaled Primal infeasibility   (abs/rel): 1.83e-08/9.16e-09\n",
+      "Post-solve Primal infeasibility (abs/rel): 1.83e-08/9.16e-09\n",
+      "Barrier finished in 0.05 seconds\n",
+      "cuOpt version: 26.2.0, git hash: 96c11095, host arch: x86_64, device archs: 80-real\n",
+      "CPU: AMD EPYC 7642 48-Core Processor, threads (physical/logical): 96/96, RAM: 169.65 GiB\n",
+      "CUDA 12.9, device: NVIDIA A100 80GB PCIe (ID 0), VRAM: 79.33 GiB\n",
+      "CUDA device UUID: ffffff8a5affffff95ffffffc7-ffffffd05\n",
+      "\n",
+      "Problem has a quadratic objective. Using Barrier.\n",
+      "Solving a problem with 2 constraints, 5 variables (0 integers), and 10 nonzeros\n",
+      "Problem scaling:\n",
+      "Objective coefficents range:          [0e+00, 0e+00]\n",
+      "Constraint matrix coefficients range: [2e-02, 1e+00]\n",
+      "Constraint rhs / bounds range:        [0e+00, 1e+00]\n",
+      "Variable bounds range:                [0e+00, 1e+00]\n",
+      "\n",
+      "Third-party presolve is disabled, skipping\n",
+      "Objective offset 0.000000 scaling_factor 1.000000\n",
+      "Presolved problem: 2 constraints 6 variables 11 nonzeros\n",
+      "Skipping column scaling\n",
+      "Barrier solver: 2 constraints, 6 variables, 11 nonzeros\n",
+      "Quadratic objective matrix: 25 nonzeros\n",
+      "\n",
+      "\n",
+      "Upper bounds                : 5\n",
+      "Density estimator time      : 0.00s\n",
+      "Linear system               : augmented\n",
+      "cuDSS Version               : 0.7.1\n",
+      "cuDSS Threading layer       : libcudss_mtlayer_gomp.so.0\n",
+      "Reordering algorithm        : AMD\n",
+      "Reordering time             : 0.00s\n",
+      "Symbolic factorization time : 0.01s\n",
+      "Total symbolic time         : 0.01s\n",
+      "Symbolic nonzeros in factor : 2.20e+01\n",
+      "\n",
+      "\n",
+      "                  Objective                         Infeasibility        Time\n",
+      "Iter   Primal              Dual                Primal   Dual    Compl.   Elapsed\n",
+      "  0   +1.557628786038e+01 -2.057628786038e+01 5.00e+01 1.00e+01 1.00e+02 0.0\n",
+      "  1   +8.659023347505e-01 -2.875681955974e+00 5.12e+00 2.05e+00 2.18e-01 0.0\n",
+      "  2   +7.167750560361e-02 -1.065293066373e+00 9.58e-01 3.83e-01 3.49e-01 0.0\n",
+      "  3   +1.405821491932e-02 -4.338529902428e-01 9.58e-02 3.83e-02 1.27e-01 0.0\n",
+      "  4   +1.301482674175e-02 -1.002477407248e-01 1.44e-02 5.77e-03 3.52e-02 0.0\n",
+      "  5   +1.301754794585e-02 -1.582036565924e-02 2.37e-03 9.48e-04 2.06e-02 0.0\n",
+      "  6   +1.114309894533e-02 +9.886304874267e-04 8.02e-04 3.21e-04 3.36e-03 0.0\n",
+      "  7   +6.636933040811e-03 +3.643468259823e-03 8.02e-05 3.21e-05 1.40e-03 0.0\n",
+      "  8   +5.933664965199e-03 +5.544609848139e-03 9.86e-06 3.95e-06 1.25e-04 0.0\n",
+      "  9   +5.814322724046e-03 +5.773597729160e-03 1.03e-06 4.12e-07 3.06e-06 0.0\n",
+      " 10   +5.801914618471e-03 +5.797841393491e-03 1.03e-07 4.12e-08 5.57e-08 0.0\n",
+      " 11   +5.800674192735e-03 +5.800266770107e-03 1.03e-08 4.12e-09 5.83e-10 0.0\n",
+      " 12   +5.800550037868e-03 +5.800509280366e-03 1.03e-09 4.12e-10 5.85e-12 0.0\n",
+      "\n",
+      "\n",
+      "Optimal solution found in 12 iterations and 0.04s\n",
+      "Objective +5.80055004e-03\n",
+      "Primal infeasibility (abs/rel): 2.06e-09/1.03e-09\n",
+      "Dual infeasibility   (abs/rel): 4.12e-10/4.12e-10\n",
+      "Complementarity gap  (abs/rel): 5.88e-12/5.85e-12\n",
+      "\n",
+      "\n",
+      "Unscaled Primal infeasibility   (abs/rel): 2.06e-09/1.03e-09\n",
+      "Post-solve Primal infeasibility (abs/rel): 2.06e-09/1.03e-09\n",
+      "Barrier finished in 0.05 seconds\n",
+      "cuOpt version: 26.2.0, git hash: 96c11095, host arch: x86_64, device archs: 80-real\n",
+      "CPU: AMD EPYC 7642 48-Core Processor, threads (physical/logical): 96/96, RAM: 169.27 GiB\n",
+      "CUDA 12.9, device: NVIDIA A100 80GB PCIe (ID 0), VRAM: 79.33 GiB\n",
+      "CUDA device UUID: ffffff8a5affffff95ffffffc7-ffffffd05\n",
+      "\n",
+      "Problem has a quadratic objective. Using Barrier.\n",
+      "Solving a problem with 2 constraints, 5 variables (0 integers), and 10 nonzeros\n",
+      "Problem scaling:\n",
+      "Objective coefficents range:          [0e+00, 0e+00]\n",
+      "Constraint matrix coefficients range: [2e-02, 1e+00]\n",
+      "Constraint rhs / bounds range:        [0e+00, 1e+00]\n",
+      "Variable bounds range:                [0e+00, 1e+00]\n",
+      "\n",
+      "Third-party presolve is disabled, skipping\n",
+      "Objective offset 0.000000 scaling_factor 1.000000\n",
+      "Presolved problem: 2 constraints 6 variables 11 nonzeros\n",
+      "Skipping column scaling\n",
+      "Barrier solver: 2 constraints, 6 variables, 11 nonzeros\n",
+      "Quadratic objective matrix: 25 nonzeros\n",
+      "\n",
+      "\n",
+      "Upper bounds                : 5\n",
+      "Density estimator time      : 0.00s\n",
+      "Linear system               : augmented\n",
+      "cuDSS Version               : 0.7.1\n",
+      "cuDSS Threading layer       : libcudss_mtlayer_gomp.so.0\n",
+      "Reordering algorithm        : AMD\n",
+      "Reordering time             : 0.00s\n",
+      "Symbolic factorization time : 0.01s\n",
+      "Total symbolic time         : 0.01s\n",
+      "Symbolic nonzeros in factor : 2.20e+01\n",
+      "\n",
+      "\n",
+      "                  Objective                         Infeasibility        Time\n",
+      "Iter   Primal              Dual                Primal   Dual    Compl.   Elapsed\n",
+      "  0   +1.569324326502e+01 -2.069324326502e+01 5.00e+01 1.00e+01 1.00e+02 0.0\n",
+      "  1   +8.889353282905e-01 -2.884493803380e+00 5.13e+00 2.05e+00 2.16e-01 0.0\n",
+      "  2   +1.103355033289e-01 -1.116845990343e+00 1.34e+00 5.37e-01 3.38e-01 0.0\n",
+      "  3   +1.528508108376e-02 -4.963825456806e-01 1.37e-01 5.46e-02 1.45e-01 0.0\n",
+      "  4   +1.296491880599e-02 -9.921448870723e-02 2.02e-02 8.09e-03 2.63e-02 0.0\n",
+      "  5   +1.401696582001e-02 -1.965664024994e-02 2.81e-03 1.12e-03 2.16e-02 0.0\n",
+      "  6   +1.255865587226e-02 +3.667807382478e-03 6.53e-04 2.61e-04 4.11e-03 0.0\n",
+      "  7   +9.013572922879e-03 +7.064574907964e-03 6.53e-05 2.61e-05 1.27e-03 0.0\n",
+      "  8   +8.241428131389e-03 +8.036849981935e-03 6.57e-06 2.63e-06 6.55e-05 0.0\n",
+      "  9   +8.159298357848e-03 +8.138846457327e-03 6.59e-07 2.64e-07 8.27e-07 0.0\n",
+      " 10   +8.151090800473e-03 +8.149045273471e-03 6.59e-08 2.64e-08 8.40e-09 0.0\n",
+      " 11   +8.150269442900e-03 +8.150064790376e-03 6.58e-09 2.64e-09 8.42e-11 0.0\n",
+      "\n",
+      "\n",
+      "Optimal solution found in 11 iterations and 0.04s\n",
+      "Objective +8.15026944e-03\n",
+      "Primal infeasibility (abs/rel): 1.32e-08/6.58e-09\n",
+      "Dual infeasibility   (abs/rel): 2.64e-09/2.64e-09\n",
+      "Complementarity gap  (abs/rel): 8.48e-11/8.42e-11\n",
+      "\n",
+      "\n",
+      "Unscaled Primal infeasibility   (abs/rel): 1.32e-08/6.58e-09\n",
+      "Post-solve Primal infeasibility (abs/rel): 1.32e-08/6.58e-09\n",
+      "Barrier finished in 0.04 seconds\n",
+      "cuOpt version: 26.2.0, git hash: 96c11095, host arch: x86_64, device archs: 80-real\n",
+      "CPU: AMD EPYC 7642 48-Core Processor, threads (physical/logical): 96/96, RAM: 168.69 GiB\n",
+      "CUDA 12.9, device: NVIDIA A100 80GB PCIe (ID 0), VRAM: 79.33 GiB\n",
+      "CUDA device UUID: ffffff8a5affffff95ffffffc7-ffffffd05\n",
+      "\n",
+      "Problem has a quadratic objective. Using Barrier.\n",
+      "Solving a problem with 2 constraints, 5 variables (0 integers), and 10 nonzeros\n",
+      "Problem scaling:\n",
+      "Objective coefficents range:          [0e+00, 0e+00]\n",
+      "Constraint matrix coefficients range: [2e-02, 1e+00]\n",
+      "Constraint rhs / bounds range:        [0e+00, 1e+00]\n",
+      "Variable bounds range:                [0e+00, 1e+00]\n",
+      "\n",
+      "Third-party presolve is disabled, skipping\n",
+      "Objective offset 0.000000 scaling_factor 1.000000\n",
+      "Presolved problem: 2 constraints 6 variables 11 nonzeros\n",
+      "Skipping column scaling\n",
+      "Barrier solver: 2 constraints, 6 variables, 11 nonzeros\n",
+      "Quadratic objective matrix: 25 nonzeros\n",
+      "\n",
+      "\n",
+      "Upper bounds                : 5\n",
+      "Density estimator time      : 0.00s\n",
+      "Linear system               : augmented\n",
+      "cuDSS Version               : 0.7.1\n",
+      "cuDSS Threading layer       : libcudss_mtlayer_gomp.so.0\n",
+      "Reordering algorithm        : AMD\n",
+      "Reordering time             : 0.00s\n",
+      "Symbolic factorization time : 0.01s\n",
+      "Total symbolic time         : 0.01s\n",
+      "Symbolic nonzeros in factor : 2.20e+01\n",
+      "\n",
+      "\n",
+      "                  Objective                         Infeasibility        Time\n",
+      "Iter   Primal              Dual                Primal   Dual    Compl.   Elapsed\n",
+      "  0   +1.581072518896e+01 -2.081072518896e+01 5.00e+01 1.00e+01 1.00e+02 0.0\n",
+      "  1   +9.171944797985e-01 -2.897547731585e+00 5.15e+00 2.06e+00 2.51e-01 0.0\n",
+      "  2   +1.383940670620e-01 -1.167543411320e+00 1.57e+00 6.29e-01 3.46e-01 0.0\n",
+      "  3   +1.641173715426e-02 -5.259369594647e-01 1.66e-01 6.63e-02 1.54e-01 0.0\n",
+      "  4   +1.308288200706e-02 -8.462785413487e-02 2.37e-02 9.46e-03 3.10e-02 0.0\n",
+      "  5   +1.524549024622e-02 -1.827807482086e-02 3.43e-03 1.37e-03 1.42e-02 0.0\n",
+      "  6   +1.415448154718e-02 +5.450683122898e-03 6.73e-04 2.69e-04 4.77e-03 0.0\n",
+      "  7   +1.295096437388e-02 +1.001622941609e-02 2.22e-04 8.89e-05 1.16e-03 0.0\n",
+      "  8   +1.133555004813e-02 +1.083410691317e-02 2.65e-05 1.06e-05 2.40e-04 0.0\n",
+      "  9   +1.118171065691e-02 +1.113109188056e-02 2.68e-06 1.07e-06 7.05e-06 0.0\n",
+      " 10   +1.116605007227e-02 +1.116098343772e-02 2.67e-07 1.07e-07 7.50e-08 0.0\n",
+      " 11   +1.116447904803e-02 +1.116397177011e-02 2.67e-08 1.07e-08 7.54e-10 0.0\n",
+      " 12   +1.116432138326e-02 +1.116427059189e-02 2.67e-09 1.07e-09 7.57e-12 0.0\n",
+      "\n",
+      "\n",
+      "Optimal solution found in 12 iterations and 0.04s\n",
+      "Objective +1.11643214e-02\n",
+      "Primal infeasibility (abs/rel): 5.34e-09/2.67e-09\n",
+      "Dual infeasibility   (abs/rel): 1.07e-09/1.07e-09\n",
+      "Complementarity gap  (abs/rel): 7.65e-12/7.57e-12\n",
+      "\n",
+      "\n",
+      "Unscaled Primal infeasibility   (abs/rel): 5.34e-09/2.67e-09\n",
+      "Post-solve Primal infeasibility (abs/rel): 5.34e-09/2.67e-09\n",
+      "Barrier finished in 0.05 seconds\n",
+      "cuOpt version: 26.2.0, git hash: 96c11095, host arch: x86_64, device archs: 80-real\n",
+      "CPU: AMD EPYC 7642 48-Core Processor, threads (physical/logical): 96/96, RAM: 168.20 GiB\n",
+      "CUDA 12.9, device: NVIDIA A100 80GB PCIe (ID 0), VRAM: 79.33 GiB\n",
+      "CUDA device UUID: ffffff8a5affffff95ffffffc7-ffffffd05\n",
+      "\n",
+      "Problem has a quadratic objective. Using Barrier.\n",
+      "Solving a problem with 2 constraints, 5 variables (0 integers), and 10 nonzeros\n",
+      "Problem scaling:\n",
+      "Objective coefficents range:          [0e+00, 0e+00]\n",
+      "Constraint matrix coefficients range: [2e-02, 1e+00]\n",
+      "Constraint rhs / bounds range:        [0e+00, 1e+00]\n",
+      "Variable bounds range:                [0e+00, 1e+00]\n",
+      "\n",
+      "Third-party presolve is disabled, skipping\n",
+      "Objective offset 0.000000 scaling_factor 1.000000\n",
+      "Presolved problem: 2 constraints 6 variables 11 nonzeros\n",
+      "Skipping column scaling\n",
+      "Barrier solver: 2 constraints, 6 variables, 11 nonzeros\n",
+      "Quadratic objective matrix: 25 nonzeros\n",
+      "\n",
+      "\n",
+      "Upper bounds                : 5\n",
+      "Density estimator time      : 0.00s\n",
+      "Linear system               : augmented\n",
+      "cuDSS Version               : 0.7.1\n",
+      "cuDSS Threading layer       : libcudss_mtlayer_gomp.so.0\n",
+      "Reordering algorithm        : AMD\n",
+      "Reordering time             : 0.00s\n",
+      "Symbolic factorization time : 0.01s\n",
+      "Total symbolic time         : 0.01s\n",
+      "Symbolic nonzeros in factor : 2.20e+01\n",
+      "\n",
+      "\n",
+      "                  Objective                         Infeasibility        Time\n",
+      "Iter   Primal              Dual                Primal   Dual    Compl.   Elapsed\n",
+      "  0   +1.592890286897e+01 -2.092890286897e+01 5.00e+01 1.00e+01 1.00e+02 0.0\n",
+      "  1   +9.509879095630e-01 -2.915278097713e+00 5.20e+00 2.08e+00 2.83e-01 0.0\n",
+      "  2   +1.714273335289e-01 -1.227609058513e+00 1.81e+00 7.23e-01 3.57e-01 0.0\n",
+      "  3   +1.761379308351e-02 -5.659243659774e-01 1.96e-01 7.83e-02 1.61e-01 0.0\n",
+      "  4   +1.316441992363e-02 -6.767628995385e-02 2.68e-02 1.07e-02 4.95e-02 0.0\n",
+      "  5   +1.662602390227e-02 -1.031846552591e-02 5.51e-03 2.20e-03 7.83e-03 0.0\n",
+      "  6   +1.707657476470e-02 +7.168532686553e-03 1.18e-03 4.72e-04 4.78e-03 0.0\n",
+      "  7   +1.573572513309e-02 +1.359744989972e-02 1.18e-04 4.72e-05 1.35e-03 0.0\n",
+      "  8   +1.514827700946e-02 +1.461435506411e-02 2.72e-05 1.09e-05 2.50e-04 0.0\n",
+      "  9   +1.488838290979e-02 +1.481141124768e-02 4.01e-06 1.60e-06 2.54e-05 0.0\n",
+      " 10   +1.484745370510e-02 +1.483974995630e-02 4.02e-07 1.61e-07 4.13e-07 0.0\n",
+      " 11   +1.484336964620e-02 +1.484259713333e-02 4.02e-08 1.61e-08 4.04e-09 0.0\n",
+      " 12   +1.484295926370e-02 +1.484288178485e-02 4.01e-09 1.61e-09 4.03e-11 0.0\n",
+      "\n",
+      "\n",
+      "Optimal solution found in 12 iterations and 0.05s\n",
+      "Objective +1.48429593e-02\n",
+      "Primal infeasibility (abs/rel): 8.03e-09/4.01e-09\n",
+      "Dual infeasibility   (abs/rel): 1.61e-09/1.61e-09\n",
+      "Complementarity gap  (abs/rel): 4.09e-11/4.03e-11\n",
+      "\n",
+      "\n",
+      "Unscaled Primal infeasibility   (abs/rel): 8.03e-09/4.01e-09\n",
+      "Post-solve Primal infeasibility (abs/rel): 8.03e-09/4.01e-09\n",
+      "Barrier finished in 0.05 seconds\n",
+      "cuOpt version: 26.2.0, git hash: 96c11095, host arch: x86_64, device archs: 80-real\n",
+      "CPU: AMD EPYC 7642 48-Core Processor, threads (physical/logical): 96/96, RAM: 167.73 GiB\n",
+      "CUDA 12.9, device: NVIDIA A100 80GB PCIe (ID 0), VRAM: 79.33 GiB\n",
+      "CUDA device UUID: ffffff8a5affffff95ffffffc7-ffffffd05\n",
+      "\n",
+      "Problem has a quadratic objective. Using Barrier.\n",
+      "Solving a problem with 2 constraints, 5 variables (0 integers), and 10 nonzeros\n",
+      "Problem scaling:\n",
+      "Objective coefficents range:          [0e+00, 0e+00]\n",
+      "Constraint matrix coefficients range: [2e-02, 1e+00]\n",
+      "Constraint rhs / bounds range:        [0e+00, 1e+00]\n",
+      "Variable bounds range:                [0e+00, 1e+00]\n",
+      "\n",
+      "Third-party presolve is disabled, skipping\n",
+      "Objective offset 0.000000 scaling_factor 1.000000\n",
+      "Presolved problem: 2 constraints 6 variables 11 nonzeros\n",
+      "Skipping column scaling\n",
+      "Barrier solver: 2 constraints, 6 variables, 11 nonzeros\n",
+      "Quadratic objective matrix: 25 nonzeros\n",
+      "\n",
+      "\n",
+      "Upper bounds                : 5\n",
+      "Density estimator time      : 0.00s\n",
+      "Linear system               : augmented\n",
+      "cuDSS Version               : 0.7.1\n",
+      "cuDSS Threading layer       : libcudss_mtlayer_gomp.so.0\n",
+      "Reordering algorithm        : AMD\n",
+      "Reordering time             : 0.00s\n",
+      "Symbolic factorization time : 0.01s\n",
+      "Total symbolic time         : 0.01s\n",
+      "Symbolic nonzeros in factor : 2.20e+01\n",
+      "\n",
+      "\n",
+      "                  Objective                         Infeasibility        Time\n",
+      "Iter   Primal              Dual                Primal   Dual    Compl.   Elapsed\n",
+      "  0   +1.637724249045e+01 -2.137775355618e+01 5.05e+01 1.00e+01 1.01e+02 0.0\n",
+      "  1   +1.009327908219e+00 -2.960496432529e+00 5.32e+00 2.10e+00 3.10e-01 0.0\n",
+      "  2   +2.055211533312e-01 -1.296078700636e+00 2.03e+00 8.02e-01 3.83e-01 0.0\n",
+      "  3   +1.866254449216e-02 -6.300274806275e-01 2.25e-01 8.90e-02 1.63e-01 0.0\n",
+      "  4   +1.336190661690e-02 -7.006679375187e-02 3.62e-02 1.43e-02 8.33e-02 0.0\n",
+      "  5   +1.779986132114e-02 +1.274846073199e-02 9.96e-03 3.94e-03 2.83e-02 0.0\n",
+      "  6   +2.484108766502e-02 +2.527187668379e-02 1.92e-03 7.61e-04 5.90e-03 0.0\n",
+      "  7   +2.836899074866e-02 +2.854995238954e-02 2.24e-04 8.86e-05 5.81e-04 0.0\n",
+      "  8   +2.887738461608e-02 +2.889675739448e-02 2.24e-05 8.88e-06 9.14e-06 0.0\n",
+      "  9   +2.892900298005e-02 +2.893095193795e-02 2.24e-06 8.88e-07 9.24e-08 0.0\n",
+      " 10   +2.893417061151e-02 +2.893436579742e-02 2.24e-07 8.88e-08 1.35e-09 0.0\n",
+      " 11   +2.893468760724e-02 +2.893470712721e-02 2.24e-08 8.88e-09 1.43e-11 0.0\n",
+      " 12   +2.893473930761e-02 +2.893474125962e-02 2.24e-09 8.88e-10 1.44e-13 0.0\n",
+      "\n",
+      "\n",
+      "Optimal solution found in 12 iterations and 0.04s\n",
+      "Objective +2.89347393e-02\n",
+      "Primal infeasibility (abs/rel): 4.49e-09/2.24e-09\n",
+      "Dual infeasibility   (abs/rel): 8.88e-10/8.88e-10\n",
+      "Complementarity gap  (abs/rel): 1.48e-13/1.44e-13\n",
+      "\n",
+      "\n",
+      "Unscaled Primal infeasibility   (abs/rel): 4.49e-09/2.24e-09\n",
+      "Post-solve Primal infeasibility (abs/rel): 4.49e-09/2.24e-09\n",
+      "Barrier finished in 0.05 seconds\n",
+      "Computed 20 points on the efficient frontier.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Compute efficient frontier by sweeping target returns\n",
+    "min_ret = ret_mv\n",
+    "max_ret = mean_returns.max()\n",
+    "target_returns = np.linspace(min_ret, max_ret, 20)\n",
+    "\n",
+    "frontier_vols = []\n",
+    "frontier_rets = []\n",
+    "frontier_weights = []\n",
+    "\n",
+    "for target in target_returns:\n",
+    "    w, r, v, _ = solve_min_variance_qp(cov_matrix, mean_returns, target_return=target)\n",
+    "    frontier_vols.append(v)\n",
+    "    frontier_rets.append(r)\n",
+    "    frontier_weights.append(w)\n",
+    "\n",
+    "frontier_vols = np.array(frontier_vols)\n",
+    "frontier_rets = np.array(frontier_rets)\n",
+    "\n",
+    "print(f\"Computed {len(frontier_rets)} points on the efficient frontier.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "ed46fea8-2410-4f87-9bcb-00099eda9300",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 900x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 588,
+       "width": 887
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(9, 6))\n",
+    "\n",
+    "# Efficient frontier\n",
+    "ax.plot(frontier_vols * 100, frontier_rets * 100, \"b-\", lw=2, label=\"Efficient Frontier\")\n",
+    "\n",
+    "# Individual assets\n",
+    "asset_vols = np.sqrt(np.diag(cov_matrix))\n",
+    "ax.scatter(asset_vols * 100, mean_returns * 100, s=80, c=\"gray\", marker=\"o\", zorder=3, label=\"Individual Assets\")\n",
+    "for i, asset in enumerate(assets):\n",
+    "    ax.annotate(asset, (asset_vols[i] * 100 + 0.3, mean_returns[i] * 100), fontsize=9)\n",
+    "\n",
+    "# Equal-weight portfolio\n",
+    "ax.scatter(vol_eq * 100, ret_eq * 100, s=120, c=\"orange\", marker=\"s\", zorder=4, label=\"Equal-Weight\")\n",
+    "\n",
+    "# Minimum-variance portfolio\n",
+    "ax.scatter(vol_mv * 100, ret_mv * 100, s=120, c=\"green\", marker=\"^\", zorder=4, label=\"Min-Variance\")\n",
+    "\n",
+    "ax.set_xlabel(\"Volatility (%)\")\n",
+    "ax.set_ylabel(\"Expected Return (%)\")\n",
+    "ax.set_title(\"Efficient Frontier: Risk vs. Return (cuOpt QP)\")\n",
+    "ax.legend(loc=\"lower right\")\n",
+    "ax.grid(True, alpha=0.3)\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2abad3d4-f354-4ffb-b516-74819d6a967f",
+   "metadata": {},
+   "source": [
+    "## **Step 6:** Constrained Portfolio - 5% Target Return + Diversification\n",
+    "\n",
+    "Now let's add practical constraints:\n",
+    "- Target at least 5% annual return\n",
+    "- No single asset > 50% (diversification)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "ba32fe1e-fed7-4220-a678-3ef709441f22",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cuOpt version: 26.2.0, git hash: 96c11095, host arch: x86_64, device archs: 80-real\n",
+      "CPU: AMD EPYC 7642 48-Core Processor, threads (physical/logical): 96/96, RAM: 167.74 GiB\n",
+      "CUDA 12.9, device: NVIDIA A100 80GB PCIe (ID 0), VRAM: 79.33 GiB\n",
+      "CUDA device UUID: ffffff8a5affffff95ffffffc7-ffffffd05\n",
+      "\n",
+      "Problem has a quadratic objective. Using Barrier.\n",
+      "Solving a problem with 2 constraints, 5 variables (0 integers), and 10 nonzeros\n",
+      "Problem scaling:\n",
+      "Objective coefficents range:          [0e+00, 0e+00]\n",
+      "Constraint matrix coefficients range: [2e-02, 1e+00]\n",
+      "Constraint rhs / bounds range:        [0e+00, 1e+00]\n",
+      "Variable bounds range:                [0e+00, 5e-01]\n",
+      "\n",
+      "Third-party presolve is disabled, skipping\n",
+      "Objective offset 0.000000 scaling_factor 1.000000\n",
+      "Presolved problem: 2 constraints 6 variables 11 nonzeros\n",
+      "Skipping column scaling\n",
+      "Barrier solver: 2 constraints, 6 variables, 11 nonzeros\n",
+      "Quadratic objective matrix: 25 nonzeros\n",
+      "\n",
+      "\n",
+      "Upper bounds                : 5\n",
+      "Density estimator time      : 0.00s\n",
+      "Linear system               : augmented\n",
+      "cuDSS Version               : 0.7.1\n",
+      "cuDSS Threading layer       : libcudss_mtlayer_gomp.so.0\n",
+      "Reordering algorithm        : AMD\n",
+      "Reordering time             : 0.00s\n",
+      "Symbolic factorization time : 0.01s\n",
+      "Total symbolic time         : 0.01s\n",
+      "Symbolic nonzeros in factor : 2.20e+01\n",
+      "\n",
+      "\n",
+      "                  Objective                         Infeasibility        Time\n",
+      "Iter   Primal              Dual                Primal   Dual    Compl.   Elapsed\n",
+      "  0   +3.587155347934e+01 -3.851550444719e+01 7.87e+01 1.00e+01 1.57e+02 0.0\n",
+      "  1   +5.067506558709e-01 -3.153955713295e+00 3.99e+00 1.01e+00 1.48e+00 0.0\n",
+      "  2   +2.232203683042e-02 -2.087895052501e+00 3.99e-01 1.01e-01 3.45e-01 0.0\n",
+      "  3   +9.452138738732e-03 -5.792642520708e-01 3.99e-02 1.01e-02 1.22e-01 0.0\n",
+      "  4   +9.272799588725e-03 -9.039922106078e-02 4.49e-03 1.14e-03 6.30e-02 0.0\n",
+      "  5   +7.681308461793e-03 -1.995179346922e-02 1.22e-03 3.10e-04 5.93e-03 0.0\n",
+      "  6   +4.362902291796e-03 -3.752060839684e-03 2.90e-04 7.37e-05 2.44e-03 0.0\n",
+      "  7   +2.891194357896e-03 +2.351247719196e-04 8.73e-05 2.22e-05 8.67e-04 0.0\n",
+      "  8   +1.931077930102e-03 +1.530335579382e-03 8.73e-06 2.22e-06 2.27e-04 0.0\n",
+      "  9   +1.768378608060e-03 +1.716379615938e-03 1.04e-06 2.63e-07 1.21e-05 0.0\n",
+      " 10   +1.744316691703e-03 +1.738610448922e-03 1.09e-07 2.78e-08 8.20e-07 0.1\n",
+      " 11   +1.741459082549e-03 +1.740881176226e-03 1.10e-08 2.80e-09 6.07e-08 0.1\n",
+      " 12   +1.741163854212e-03 +1.741106128826e-03 1.10e-09 2.80e-10 1.06e-09 0.1\n",
+      "\n",
+      "\n",
+      "Optimal solution found in 12 iterations and 0.05s\n",
+      "Objective +1.74116385e-03\n",
+      "Primal infeasibility (abs/rel): 2.20e-09/1.10e-09\n",
+      "Dual infeasibility   (abs/rel): 2.80e-10/2.80e-10\n",
+      "Complementarity gap  (abs/rel): 1.06e-09/1.06e-09\n",
+      "\n",
+      "\n",
+      "Unscaled Primal infeasibility   (abs/rel): 2.20e-09/1.10e-09\n",
+      "Post-solve Primal infeasibility (abs/rel): 2.20e-09/1.10e-09\n",
+      "Barrier finished in 0.06 seconds\n",
+      "Status: optimal\n",
+      "\n",
+      "Constrained Portfolio (Target >= 5%, Max Weight 50%):\n",
+      "  Expected Return: 5.00%\n",
+      "  Volatility:      4.17%\n",
+      "\n",
+      "Weights:\n",
+      "  Cash        :  25.1%\n",
+      "  US Equity   :  16.5%\n",
+      "  Intl Equity :   0.0%\n",
+      "  Bond        :  50.0%\n",
+      "  REIT/Gold   :   8.4%\n"
+     ]
+    }
+   ],
+   "source": [
+    "target_5pct = 0.05\n",
+    "max_wt = 0.50\n",
+    "\n",
+    "weights_5pct, ret_5pct, vol_5pct, status_5pct = solve_min_variance_qp(\n",
+    "    cov_matrix, mean_returns,\n",
+    "    target_return=target_5pct,\n",
+    "    max_weight=max_wt,\n",
+    ")\n",
+    "\n",
+    "print(f\"Status: {status_5pct}\")\n",
+    "print(f\"\\nConstrained Portfolio (Target >= 5%, Max Weight 50%):\")\n",
+    "print(f\"  Expected Return: {ret_5pct:.2%}\")\n",
+    "print(f\"  Volatility:      {vol_5pct:.2%}\")\n",
+    "print(f\"\\nWeights:\")\n",
+    "for asset, wt in zip(assets, weights_5pct):\n",
+    "    print(f\"  {asset:<12}: {wt:6.1%}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "241d2789-6009-4e8f-aa5c-90dd516a09bb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 488,
+       "width": 1188
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n",
+    "\n",
+    "# Bar chart of weights\n",
+    "x = np.arange(len(assets))\n",
+    "width = 0.35\n",
+    "\n",
+    "axes[0].bar(x - width/2, weights_equal * 100, width, label=\"Equal-Weight\", color=\"orange\")\n",
+    "axes[0].bar(x + width/2, weights_5pct * 100, width, label=\"Optimized (5%+ target)\", color=\"steelblue\")\n",
+    "axes[0].set_ylabel(\"Weight (%)\")\n",
+    "axes[0].set_xticks(x)\n",
+    "axes[0].set_xticklabels(assets, rotation=15)\n",
+    "axes[0].legend()\n",
+    "axes[0].set_title(\"Portfolio Allocation Comparison\")\n",
+    "\n",
+    "# Scatter: return vs vol\n",
+    "axes[1].scatter(vol_eq * 100, ret_eq * 100, s=150, c=\"orange\", marker=\"s\", label=f\"Equal-Weight ({ret_eq:.1%}, {vol_eq:.1%})\")\n",
+    "axes[1].scatter(vol_5pct * 100, ret_5pct * 100, s=150, c=\"steelblue\", marker=\"^\", label=f\"Optimized ({ret_5pct:.1%}, {vol_5pct:.1%})\")\n",
+    "axes[1].plot(frontier_vols * 100, frontier_rets * 100, \"k--\", alpha=0.4, label=\"Frontier\")\n",
+    "axes[1].set_xlabel(\"Volatility (%)\")\n",
+    "axes[1].set_ylabel(\"Expected Return (%)\")\n",
+    "axes[1].legend()\n",
+    "axes[1].grid(True, alpha=0.3)\n",
+    "\n",
+    "axes[1].set_title(\"Return vs. Volatility\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d0bd5fc4-037e-4c02-b8cc-927051b61de0",
+   "metadata": {},
+   "source": [
+    "## **Step 7:** Interactive Exploration - Adjust Your Preferences\n",
+    "\n",
+    "Now let's explore how changing your preferences affects the optimal portfolio. You can adjust:\n",
+    "- **Target return**: How much growth do you want?\n",
+    "- **Maximum weight per asset**: Avoid over-concentration\n",
+    "- **Minimum safe allocation**: Keep a floor in Cash + Bonds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "02c36d23-ae18-46fe-a4b3-c9bc65452424",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cuOpt version: 26.2.0, git hash: 96c11095, host arch: x86_64, device archs: 80-real\n",
+      "CPU: AMD EPYC 7642 48-Core Processor, threads (physical/logical): 96/96, RAM: 170.92 GiB\n",
+      "CUDA 12.9, device: NVIDIA A100 80GB PCIe (ID 0), VRAM: 79.33 GiB\n",
+      "CUDA device UUID: ffffff8a5affffff95ffffffc7-ffffffd05\n",
+      "\n",
+      "Problem has a quadratic objective. Using Barrier.\n",
+      "Solving a problem with 3 constraints, 5 variables (0 integers), and 12 nonzeros\n",
+      "Problem scaling:\n",
+      "Objective coefficents range:          [0e+00, 0e+00]\n",
+      "Constraint matrix coefficients range: [2e-02, 1e+00]\n",
+      "Constraint rhs / bounds range:        [0e+00, 1e+00]\n",
+      "Variable bounds range:                [0e+00, 6e-01]\n",
+      "\n",
+      "Third-party presolve is disabled, skipping\n",
+      "Objective offset 0.000000 scaling_factor 1.000000\n",
+      "Presolved problem: 3 constraints 7 variables 14 nonzeros\n",
+      "Skipping column scaling\n",
+      "Barrier solver: 3 constraints, 7 variables, 14 nonzeros\n",
+      "Quadratic objective matrix: 25 nonzeros\n",
+      "\n",
+      "\n",
+      "Upper bounds                : 5\n",
+      "Density estimator time      : 0.00s\n",
+      "Linear system               : augmented\n",
+      "cuDSS Version               : 0.7.1\n",
+      "cuDSS Threading layer       : libcudss_mtlayer_gomp.so.0\n",
+      "Reordering algorithm        : AMD\n",
+      "Reordering time             : 0.00s\n",
+      "Symbolic factorization time : 0.01s\n",
+      "Total symbolic time         : 0.01s\n",
+      "Symbolic nonzeros in factor : 2.80e+01\n",
+      "\n",
+      "\n",
+      "                  Objective                         Infeasibility        Time\n",
+      "Iter   Primal              Dual                Primal   Dual    Compl.   Elapsed\n",
+      "  0   +1.548744684335e+01 -1.848753907049e+01 5.00e+01 1.00e+01 1.04e+02 0.0\n",
+      "  1   +1.197922914774e+01 -2.147646235921e+01 2.19e+01 8.75e+00 1.21e+00 0.0\n",
+      "  2   +1.702352680531e+00 -1.327495860343e+01 8.40e+00 3.36e+00 3.67e+00 0.0\n",
+      "  3   +4.054146263100e-02 -6.509627179551e+00 1.32e+00 5.29e-01 6.32e+00 0.0\n",
+      "  4   +3.335565727001e-03 -2.268702185334e+00 3.71e-01 1.48e-01 1.79e+00 0.0\n",
+      "  5   +5.841029727822e-03 -2.344145665075e+00 1.15e-01 4.58e-02 3.14e-01 0.0\n",
+      "  6   +4.660285660828e-03 -4.805556743302e-01 1.97e-02 7.90e-03 9.36e-02 0.0\n",
+      "  7   +4.875793566180e-03 -7.089111491550e-02 2.51e-03 1.00e-03 3.22e-02 0.0\n",
+      "  8   +4.071306628726e-03 -9.243521245623e-03 4.33e-04 1.73e-04 2.70e-03 0.0\n",
+      "  9   +2.900357155089e-03 -1.136598909927e-03 1.24e-04 4.97e-05 9.65e-04 0.0\n",
+      " 10   +1.965539543876e-03 +1.295771690277e-03 1.44e-05 5.74e-06 3.40e-04 0.0\n",
+      " 11   +1.753639586900e-03 +1.678061851141e-03 1.49e-06 5.96e-07 3.00e-05 0.0\n",
+      " 12   +1.727056647227e-03 +1.719380036113e-03 1.51e-07 6.04e-08 7.20e-07 0.1\n",
+      " 13   +1.724339123344e-03 +1.723572472174e-03 1.51e-08 6.05e-09 8.61e-09 0.1\n",
+      " 14   +1.724067837032e-03 +1.723991184089e-03 1.51e-09 6.05e-10 8.71e-11 0.1\n",
+      "\n",
+      "\n",
+      "Optimal solution found in 14 iterations and 0.05s\n",
+      "Objective +1.72406784e-03\n",
+      "Primal infeasibility (abs/rel): 3.02e-09/1.51e-09\n",
+      "Dual infeasibility   (abs/rel): 6.05e-10/6.05e-10\n",
+      "Complementarity gap  (abs/rel): 8.72e-11/8.71e-11\n",
+      "\n",
+      "\n",
+      "Unscaled Primal infeasibility   (abs/rel): 3.02e-09/1.51e-09\n",
+      "Post-solve Primal infeasibility (abs/rel): 3.02e-09/1.51e-09\n",
+      "Barrier finished in 0.52 seconds\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 388,
+       "width": 1188
+      }
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Status: optimal\n",
+      "Expected Return: 5.00%  |  Volatility: 4.15%\n",
+      "Safe Assets (Cash + Bond): 77.7%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Interactive exploration function\n",
+    "# In Jupyter, uncomment the interact() call at the bottom to enable sliders\n",
+    "\n",
+    "def explore_portfolio(target_return_pct=5.0, max_weight_pct=60.0, min_safe_pct=10.0):\n",
+    "    \"\"\"Explore portfolio with given parameters.\"\"\"\n",
+    "    target_return = target_return_pct / 100.0\n",
+    "    max_weight = max_weight_pct / 100.0\n",
+    "    min_safe = min_safe_pct / 100.0\n",
+    "    safe_indices = [0, 3]  # Cash and Bond\n",
+    "    \n",
+    "    w, r, v, status = solve_min_variance_qp(\n",
+    "        cov_matrix, mean_returns,\n",
+    "        target_return=target_return,\n",
+    "        max_weight=max_weight,\n",
+    "        min_safe_alloc=min_safe,\n",
+    "        safe_indices=safe_indices,\n",
+    "    )\n",
+    "    \n",
+    "    fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n",
+    "    \n",
+    "    # Weights\n",
+    "    colors = [\"#2ecc71\" if i in safe_indices else \"#3498db\" for i in range(len(assets))]\n",
+    "    axes[0].barh(assets, w * 100, color=colors)\n",
+    "    axes[0].set_xlabel(\"Weight (%)\")\n",
+    "    axes[0].set_xlim(0, 70)\n",
+    "    axes[0].set_title(\"Optimal Allocation\")\n",
+    "    axes[0].axvline(max_weight * 100, color=\"red\", linestyle=\"--\", label=f\"Max {max_weight_pct:.0f}%\")\n",
+    "    axes[0].legend()\n",
+    "    \n",
+    "    # Frontier with current point\n",
+    "    axes[1].plot(frontier_vols * 100, frontier_rets * 100, \"b-\", lw=2, alpha=0.5)\n",
+    "    axes[1].scatter(v * 100, r * 100, s=200, c=\"red\", marker=\"*\", zorder=5, label=\"Your Portfolio\")\n",
+    "    axes[1].scatter(vol_eq * 100, ret_eq * 100, s=80, c=\"orange\", marker=\"s\", zorder=4, label=\"Equal-Weight\")\n",
+    "    axes[1].set_xlabel(\"Volatility (%)\")\n",
+    "    axes[1].set_ylabel(\"Expected Return (%)\")\n",
+    "    axes[1].set_title(\"Your Portfolio on the Frontier\")\n",
+    "    axes[1].legend()\n",
+    "    axes[1].grid(True, alpha=0.3)\n",
+    "    \n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "    \n",
+    "    print(f\"Status: {status}\")\n",
+    "    print(f\"Expected Return: {r:.2%}  |  Volatility: {v:.2%}\")\n",
+    "    print(f\"Safe Assets (Cash + Bond): {(w[0] + w[3]):.1%}\")\n",
+    "\n",
+    "# Demo with default parameters (static execution)\n",
+    "explore_portfolio(target_return_pct=5.0, max_weight_pct=60.0, min_safe_pct=10.0)\n",
+    "\n",
+    "# To enable interactive sliders in Jupyter, uncomment:\n",
+    "# from ipywidgets import interact, FloatSlider\n",
+    "# interact(\n",
+    "#     explore_portfolio,\n",
+    "#     target_return_pct=FloatSlider(value=5.0, min=2.0, max=8.0, step=0.5, description=\"Target Return %\"),\n",
+    "#     max_weight_pct=FloatSlider(value=60.0, min=25.0, max=100.0, step=5.0, description=\"Max Weight %\"),\n",
+    "#     min_safe_pct=FloatSlider(value=10.0, min=0.0, max=50.0, step=5.0, description=\"Min Safe %\"),\n",
+    "# )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e7b32527-5e24-4b71-afab-e075e80bfafb",
+   "metadata": {},
+   "source": [
+    "## **Step 8:** Interpretation - What It Means for You\n",
+    "\n",
+    "Let's put one scenario in plain English. Suppose you target a 6% annual return with at least 20% in safe assets."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "48f11500-0894-47a1-8dda-e5c06151379d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cuOpt version: 26.2.0, git hash: 96c11095, host arch: x86_64, device archs: 80-real\n",
+      "CPU: AMD EPYC 7642 48-Core Processor, threads (physical/logical): 96/96, RAM: 170.56 GiB\n",
+      "CUDA 12.9, device: NVIDIA A100 80GB PCIe (ID 0), VRAM: 79.33 GiB\n",
+      "CUDA device UUID: ffffff8a5affffff95ffffffc7-ffffffd05\n",
+      "\n",
+      "Problem has a quadratic objective. Using Barrier.\n",
+      "Solving a problem with 3 constraints, 5 variables (0 integers), and 12 nonzeros\n",
+      "Problem scaling:\n",
+      "Objective coefficents range:          [0e+00, 0e+00]\n",
+      "Constraint matrix coefficients range: [2e-02, 1e+00]\n",
+      "Constraint rhs / bounds range:        [0e+00, 1e+00]\n",
+      "Variable bounds range:                [0e+00, 1e+00]\n",
+      "\n",
+      "Third-party presolve is disabled, skipping\n",
+      "Objective offset 0.000000 scaling_factor 1.000000\n",
+      "Presolved problem: 3 constraints 7 variables 14 nonzeros\n",
+      "Skipping column scaling\n",
+      "Barrier solver: 3 constraints, 7 variables, 14 nonzeros\n",
+      "Quadratic objective matrix: 25 nonzeros\n",
+      "\n",
+      "\n",
+      "Upper bounds                : 5\n",
+      "Density estimator time      : 0.00s\n",
+      "Linear system               : augmented\n",
+      "cuDSS Version               : 0.7.1\n",
+      "cuDSS Threading layer       : libcudss_mtlayer_gomp.so.0\n",
+      "Reordering algorithm        : AMD\n",
+      "Reordering time             : 0.00s\n",
+      "Symbolic factorization time : 0.01s\n",
+      "Total symbolic time         : 0.01s\n",
+      "Symbolic nonzeros in factor : 2.80e+01\n",
+      "\n",
+      "\n",
+      "                  Objective                         Infeasibility        Time\n",
+      "Iter   Primal              Dual                Primal   Dual    Compl.   Elapsed\n",
+      "  0   +1.550142187102e+01 -2.050142187102e+01 5.03e+01 1.00e+01 1.05e+02 0.0\n",
+      "  1   +9.341560379055e+00 -2.494790318049e+01 1.96e+01 7.78e+00 1.96e+00 0.0\n",
+      "  2   +2.111362888662e-01 -1.461380165032e+01 3.55e+00 1.41e+00 1.15e+01 0.0\n",
+      "  3   +8.963752500084e-03 -4.614257805362e+00 1.04e+00 4.15e-01 4.72e+00 0.0\n",
+      "  4   +1.773863862279e-02 -4.653727425029e+00 2.70e-01 1.07e-01 1.22e+00 0.0\n",
+      "  5   +8.781733954736e-03 -8.401734910635e-01 3.02e-02 1.20e-02 2.34e-01 0.0\n",
+      "  6   +8.437570912594e-03 -1.201728774252e-01 3.46e-03 1.38e-03 4.27e-02 0.0\n",
+      "  7   +7.999243381885e-03 -1.479774260163e-02 5.91e-04 2.35e-04 5.86e-03 0.0\n",
+      "  8   +5.959453562596e-03 -1.522588233318e-03 1.77e-04 7.03e-05 1.63e-03 0.0\n",
+      "  9   +3.905148658193e-03 +2.505977932485e-03 1.77e-05 7.03e-06 5.40e-04 0.0\n",
+      " 10   +3.526936468726e-03 +3.296103585159e-03 2.72e-06 1.08e-06 4.75e-05 0.0\n",
+      " 11   +3.453129854611e-03 +3.423295211997e-03 3.17e-07 1.26e-07 1.07e-05 0.0\n",
+      " 12   +3.444326229591e-03 +3.440576962168e-03 3.39e-08 1.35e-08 1.92e-06 0.0\n",
+      " 13   +3.443447091108e-03 +3.443030130791e-03 3.48e-09 1.37e-09 2.21e-07 0.0\n",
+      " 14   +3.443370554202e-03 +3.443329087174e-03 3.55e-10 1.37e-10 7.24e-09 0.1\n",
+      "\n",
+      "\n",
+      "Optimal solution found in 14 iterations and 0.05s\n",
+      "Objective +3.44337055e-03\n",
+      "Primal infeasibility (abs/rel): 7.10e-10/3.55e-10\n",
+      "Dual infeasibility   (abs/rel): 1.37e-10/1.37e-10\n",
+      "Complementarity gap  (abs/rel): 7.26e-09/7.24e-09\n",
+      "\n",
+      "\n",
+      "Unscaled Primal infeasibility   (abs/rel): 7.10e-10/3.55e-10\n",
+      "Post-solve Primal infeasibility (abs/rel): 7.10e-10/3.55e-10\n",
+      "Barrier finished in 0.05 seconds\n",
+      "============================================================\n",
+      "YOUR RECOMMENDED PORTFOLIO\n",
+      "============================================================\n",
+      "\n",
+      "Target: At least 6% annual return\n",
+      "Constraint: Keep at least 20% in Cash + Bonds\n",
+      "\n",
+      "  Cash          0.0%  \n",
+      "  US Equity    22.1%  ████████\n",
+      "  Intl Equity   1.4%  \n",
+      "  Bond         61.0%  ████████████████████████\n",
+      "  REIT/Gold    15.6%  ██████\n",
+      "\n",
+      "→ Expected Return: 6.00%\n",
+      "→ Volatility:      5.87%\n",
+      "→ Safe Allocation: 61.0%\n",
+      "\n",
+      "============================================================\n",
+      "WHAT THIS MEANS\n",
+      "============================================================\n",
+      "\n",
+      "If you invest $10,000 according to this allocation:\n",
+      "  - Cash:        $0\n",
+      "  - US Equity:   $2,207\n",
+      "  - Intl Equity: $142\n",
+      "  - Bonds:       $6,096\n",
+      "  - REIT/Gold:   $1,555\n",
+      "\n",
+      "You can expect ~6.0% growth per year on average,\n",
+      "with a typical annual swing of about +/-5.9%.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "target_scenario = 0.06\n",
+    "min_safe_scenario = 0.20\n",
+    "safe_indices = [0, 3]\n",
+    "\n",
+    "w_scenario, r_scenario, v_scenario, _ = solve_min_variance_qp(\n",
+    "    cov_matrix, mean_returns,\n",
+    "    target_return=target_scenario,\n",
+    "    min_safe_alloc=min_safe_scenario,\n",
+    "    safe_indices=safe_indices,\n",
+    ")\n",
+    "\n",
+    "print(\"=\" * 60)\n",
+    "print(\"YOUR RECOMMENDED PORTFOLIO\")\n",
+    "print(\"=\" * 60)\n",
+    "print(f\"\\nTarget: At least {target_scenario:.0%} annual return\")\n",
+    "print(f\"Constraint: Keep at least {min_safe_scenario:.0%} in Cash + Bonds\\n\")\n",
+    "\n",
+    "for asset, wt in zip(assets, w_scenario):\n",
+    "    bar = \"█\" * int(wt * 40)\n",
+    "    print(f\"  {asset:<12} {wt:5.1%}  {bar}\")\n",
+    "\n",
+    "print(f\"\\n→ Expected Return: {r_scenario:.2%}\")\n",
+    "print(f\"→ Volatility:      {v_scenario:.2%}\")\n",
+    "print(f\"→ Safe Allocation: {(w_scenario[0] + w_scenario[3]):.1%}\")\n",
+    "\n",
+    "print(\"\\n\" + \"=\" * 60)\n",
+    "print(\"WHAT THIS MEANS\")\n",
+    "print(\"=\" * 60)\n",
+    "print(f\"\"\"\n",
+    "If you invest $10,000 according to this allocation:\n",
+    "  - Cash:        ${10000 * w_scenario[0]:,.0f}\n",
+    "  - US Equity:   ${10000 * w_scenario[1]:,.0f}\n",
+    "  - Intl Equity: ${10000 * w_scenario[2]:,.0f}\n",
+    "  - Bonds:       ${10000 * w_scenario[3]:,.0f}\n",
+    "  - REIT/Gold:   ${10000 * w_scenario[4]:,.0f}\n",
+    "\n",
+    "You can expect ~{r_scenario:.1%} growth per year on average,\n",
+    "with a typical annual swing of about +/-{v_scenario:.1%}.\n",
+    "\"\"\")\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/portfolio_optimization/README.md b/portfolio_optimization/README.md
index 9bdf4a7..830e952 100644
--- a/portfolio_optimization/README.md
+++ b/portfolio_optimization/README.md
@@ -10,7 +10,11 @@ The portfolio optimization notebook solves a portfolio optimization problem wher
 
 - The goal is to maximize the expected return of a portfolio while minimizing the risk.
 
-### 2. Advanced Portfolio Optimization
+### 2. Portfolio Optimization using QP
+
+- The aim is to balance expected return with the risk of losses
+
+### 3. Advanced Portfolio Optimization
 
 For advanced portfolio optimization examples including:
 - Efficient frontier construction
@@ -18,4 +22,4 @@ For advanced portfolio optimization examples including:
 - Turnover optimization
 - Mean-CVaR optimization with comprehensive workflows
 
-Please visit the **[NVIDIA Quantitative Portfolio Optimization repository](https://github.com/NVIDIA-AI-Blueprints/quantitative-portfolio-optimization)**
\ No newline at end of file
+Please visit the **[NVIDIA Quantitative Portfolio Optimization repository](https://github.com/NVIDIA-AI-Blueprints/quantitative-portfolio-optimization)**
diff --git a/portfolio_optimization/cvar_portfolio_optimization.ipynb b/portfolio_optimization/cvar_portfolio_optimization.ipynb
index ced1112..e07f534 100644
--- a/portfolio_optimization/cvar_portfolio_optimization.ipynb
+++ b/portfolio_optimization/cvar_portfolio_optimization.ipynb
@@ -663,7 +663,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "cuopt",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -677,9 +677,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.12"
+   "version": "3.13.11"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

From beac4826134b241cdff9b75b74cd65958825b2db Mon Sep 17 00:00:00 2001
From: Ishika Roy <iroy@ipp1-3302.aselab.nvidia.com>
Date: Wed, 25 Feb 2026 14:24:00 -0800
Subject: [PATCH 2/2] clear cell outputs

---
 .../QP_portfolio_optimization.ipynb           | 1788 +----------------
 1 file changed, 27 insertions(+), 1761 deletions(-)

diff --git a/portfolio_optimization/QP_portfolio_optimization.ipynb b/portfolio_optimization/QP_portfolio_optimization.ipynb
index 6c2b287..93f009b 100644
--- a/portfolio_optimization/QP_portfolio_optimization.ipynb
+++ b/portfolio_optimization/QP_portfolio_optimization.ipynb
@@ -33,38 +33,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "96ea34e7-e559-47ce-93f4-daf2f6c34281",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <div style=\"border:2px solid #4CAF50;padding:10px;border-radius:10px;background:#e8f5e9;\">\n",
-       "            <h3>✅ GPU is enabled</h3>\n",
-       "            <pre>| NVIDIA-SMI 535.247.01             Driver Version: 535.247.01   CUDA Version: 12.2     |</pre>\n",
-       "        </div>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import subprocess\n",
     "import html\n",
@@ -118,7 +90,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "18003f14-af20-44e8-b72a-381cc0312d3a",
    "metadata": {},
    "outputs": [],
@@ -131,24 +103,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "1a56950d-d08c-43cd-a135-29aa17830223",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Looking in indexes: https://pypi.org/simple, https://pypi.nvidia.com\n",
-      "Requirement already satisfied: numpy>=1.24.4 in /raid/iroy/miniforge3/envs/py313/lib/python3.13/site-packages (2.2.6)\n",
-      "Requirement already satisfied: pandas>=2.2.1 in /raid/iroy/miniforge3/envs/py313/lib/python3.13/site-packages (2.3.3)\n",
-      "Requirement already satisfied: python-dateutil>=2.8.2 in /raid/iroy/miniforge3/envs/py313/lib/python3.13/site-packages (from pandas>=2.2.1) (2.9.0.post0)\n",
-      "Requirement already satisfied: pytz>=2020.1 in /raid/iroy/miniforge3/envs/py313/lib/python3.13/site-packages (from pandas>=2.2.1) (2025.2)\n",
-      "Requirement already satisfied: tzdata>=2022.7 in /raid/iroy/miniforge3/envs/py313/lib/python3.13/site-packages (from pandas>=2.2.1) (2025.3)\n",
-      "Requirement already satisfied: six>=1.5 in /raid/iroy/miniforge3/envs/py313/lib/python3.13/site-packages (from python-dateutil>=2.8.2->pandas>=2.2.1) (1.17.0)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "!pip install --extra-index-url https://pypi.nvidia.com \"numpy>=1.24.4\" \"pandas>=2.2.1\""
    ]
@@ -163,18 +121,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "449f51ca-33a0-463e-902f-2a1eecbedee7",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Imports ready (using cuOpt QP solver)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import numpy as np\n",
     "import pandas as pd\n",
@@ -210,61 +160,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "4beac288-fe86-40ee-9412-4b4004f233a3",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style type=\"text/css\">\n",
-       "</style>\n",
-       "<table id=\"T_238ba\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th id=\"T_238ba_level0_col0\" class=\"col_heading level0 col0\" >Annualized Return</th>\n",
-       "      <th id=\"T_238ba_level0_col1\" class=\"col_heading level0 col1\" >Annualized Volatility</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th id=\"T_238ba_level0_row0\" class=\"row_heading level0 row0\" >Cash</th>\n",
-       "      <td id=\"T_238ba_row0_col0\" class=\"data row0 col0\" >2.36%</td>\n",
-       "      <td id=\"T_238ba_row0_col1\" class=\"data row0 col1\" >0.49%</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_238ba_level0_row1\" class=\"row_heading level0 row1\" >US Equity</th>\n",
-       "      <td id=\"T_238ba_row1_col0\" class=\"data row1 col0\" >7.07%</td>\n",
-       "      <td id=\"T_238ba_row1_col1\" class=\"data row1 col1\" >14.50%</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_238ba_level0_row2\" class=\"row_heading level0 row2\" >Intl Equity</th>\n",
-       "      <td id=\"T_238ba_row2_col0\" class=\"data row2 col0\" >7.50%</td>\n",
-       "      <td id=\"T_238ba_row2_col1\" class=\"data row2 col1\" >17.01%</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_238ba_level0_row3\" class=\"row_heading level0 row3\" >Bond</th>\n",
-       "      <td id=\"T_238ba_row3_col0\" class=\"data row3 col0\" >5.26%</td>\n",
-       "      <td id=\"T_238ba_row3_col1\" class=\"data row3 col1\" >6.18%</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_238ba_level0_row4\" class=\"row_heading level0 row4\" >REIT/Gold</th>\n",
-       "      <td id=\"T_238ba_row4_col0\" class=\"data row4 col0\" >7.24%</td>\n",
-       "      <td id=\"T_238ba_row4_col1\" class=\"data row4 col1\" >13.88%</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n"
-      ],
-      "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7f7ca35c1010>"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Simulate monthly returns with realistic assumptions\n",
     "np.random.seed(7)\n",
@@ -316,29 +215,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "d6342042-e364-46e9-971b-344e3850766a",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(             Weight\n",
-       " Asset              \n",
-       " Cash            0.2\n",
-       " US Equity       0.2\n",
-       " Intl Equity     0.2\n",
-       " Bond            0.2\n",
-       " REIT/Gold       0.2,\n",
-       " 0.058873523844686845,\n",
-       " np.float64(0.077468947707694))"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "def portfolio_stats(weights, mean_vec, cov_mat):\n",
     "    exp_return = float(weights @ mean_vec)\n",
@@ -391,18 +271,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "04dd2427-69da-4a4e-80fd-2d1e5f796a74",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Solver function defined (using cuOpt QP)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "def solve_min_variance_qp(\n",
     "    cov_matrix,\n",
@@ -506,91 +378,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "16df1281-0a32-48b0-bcbb-4ffcf6fc48c7",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "cuOpt version: 26.2.0, git hash: 96c11095, host arch: x86_64, device archs: 80-real\n",
-      "CPU: AMD EPYC 7642 48-Core Processor, threads (physical/logical): 96/96, RAM: 164.74 GiB\n",
-      "CUDA 12.9, device: NVIDIA A100 80GB PCIe (ID 0), VRAM: 79.33 GiB\n",
-      "CUDA device UUID: ffffff8a5affffff95ffffffc7-ffffffd05\n",
-      "\n",
-      "Problem has a quadratic objective. Using Barrier.\n",
-      "Solving a problem with 1 constraints, 5 variables (0 integers), and 5 nonzeros\n",
-      "Problem scaling:\n",
-      "Objective coefficents range:          [0e+00, 0e+00]\n",
-      "Constraint matrix coefficients range: [1e+00, 1e+00]\n",
-      "Constraint rhs / bounds range:        [0e+00, 1e+00]\n",
-      "Variable bounds range:                [0e+00, 1e+00]\n",
-      "\n",
-      "Third-party presolve is disabled, skipping\n",
-      "Objective offset 0.000000 scaling_factor 1.000000\n",
-      "Presolved problem: 1 constraints 5 variables 5 nonzeros\n",
-      "Skipping column scaling\n",
-      "Barrier solver: 1 constraints, 5 variables, 5 nonzeros\n",
-      "Quadratic objective matrix: 25 nonzeros\n",
-      "\n",
-      "\n",
-      "Upper bounds                : 5\n",
-      "Density estimator time      : 0.00s\n",
-      "Linear system               : augmented\n",
-      "cuDSS Version               : 0.7.1\n",
-      "cuDSS Threading layer       : libcudss_mtlayer_gomp.so.0\n",
-      "Reordering algorithm        : AMD\n",
-      "Reordering time             : 0.01s\n",
-      "Symbolic factorization time : 0.01s\n",
-      "Total symbolic time         : 0.02s\n",
-      "Symbolic nonzeros in factor : 1.80e+01\n",
-      "\n",
-      "\n",
-      "                  Objective                         Infeasibility        Time\n",
-      "Iter   Primal              Dual                Primal   Dual    Compl.   Elapsed\n",
-      "  0   +1.502020842960e+01 -2.002020842960e+01 5.00e+01 1.18e+00 1.10e+01 0.1\n",
-      "  1   +8.116317933600e-01 -2.610278408955e+00 5.81e+00 2.75e-01 5.01e-01 0.1\n",
-      "  2   +5.508720123496e-01 -3.733792686682e+00 3.53e+00 1.67e-01 3.45e-01 0.1\n",
-      "  3   +2.094183386605e-02 -1.607389653436e+00 3.53e-01 1.67e-02 4.58e-01 0.1\n",
-      "  4   +7.966856203550e-03 -2.704078921230e-01 3.57e-02 1.69e-03 6.49e-02 0.1\n",
-      "  5   +6.280527981394e-03 -3.566351416561e-02 5.29e-03 2.50e-04 4.62e-03 0.1\n",
-      "  6   +3.509462278556e-03 -8.043272893021e-03 1.37e-03 6.46e-05 1.66e-03 0.1\n",
-      "  7   +9.737719020591e-04 -1.894551124511e-03 1.74e-04 8.25e-06 7.75e-04 0.1\n",
-      "  8   +2.270364911982e-04 -2.951103286651e-04 1.74e-05 8.25e-07 1.27e-04 0.1\n",
-      "  9   +6.434100985271e-05 -3.163605023344e-05 1.91e-06 9.05e-08 2.39e-05 0.1\n",
-      " 10   +3.133026812835e-05 +1.369738017751e-05 1.97e-07 9.31e-09 4.72e-06 0.1\n",
-      " 11   +2.504128876428e-05 +2.179803092629e-05 1.97e-08 9.31e-10 8.85e-07 0.1\n",
-      " 12   +2.389471074490e-05 +2.332566789238e-05 1.98e-09 9.31e-11 1.65e-07 0.1\n",
-      " 13   +2.369223364640e-05 +2.358671257142e-05 3.06e-10 1.42e-11 3.19e-08 0.1\n",
-      " 14   +2.364419171111e-05 +2.362854845265e-05 3.59e-11 1.60e-12 5.49e-09 0.1\n",
-      "\n",
-      "\n",
-      "Optimal solution found in 14 iterations and 0.12s\n",
-      "Objective +2.36441917e-05\n",
-      "Primal infeasibility (abs/rel): 7.18e-11/3.59e-11\n",
-      "Dual infeasibility   (abs/rel): 1.60e-12/1.60e-12\n",
-      "Complementarity gap  (abs/rel): 5.49e-09/5.49e-09\n",
-      "\n",
-      "\n",
-      "Unscaled Primal infeasibility   (abs/rel): 7.18e-11/3.59e-11\n",
-      "Post-solve Primal infeasibility (abs/rel): 7.18e-11/3.59e-11\n",
-      "Barrier finished in 0.12 seconds\n",
-      "Status: optimal\n",
-      "\n",
-      "Minimum-Variance Portfolio:\n",
-      "  Expected Return: 2.38%\n",
-      "  Volatility:      0.49%\n",
-      "\n",
-      "Weights:\n",
-      "  Cash        :  99.6%\n",
-      "  US Equity   :   0.0%\n",
-      "  Intl Equity :   0.3%\n",
-      "  Bond        :   0.1%\n",
-      "  REIT/Gold   :   0.0%\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "weights_mv, ret_mv, vol_mv, status_mv = solve_min_variance_qp(cov_matrix, mean_returns)\n",
     "\n",
@@ -615,1231 +406,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "f8947c1d-a409-4b54-98f9-a171e845d961",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "cuOpt version: 26.2.0, git hash: 96c11095, host arch: x86_64, device archs: 80-real\n",
-      "CPU: AMD EPYC 7642 48-Core Processor, threads (physical/logical): 96/96, RAM: 165.75 GiB\n",
-      "CUDA 12.9, device: NVIDIA A100 80GB PCIe (ID 0), VRAM: 79.33 GiB\n",
-      "CUDA device UUID: ffffff8a5affffff95ffffffc7-ffffffd05\n",
-      "\n",
-      "Problem has a quadratic objective. Using Barrier.\n",
-      "Solving a problem with 2 constraints, 5 variables (0 integers), and 10 nonzeros\n",
-      "Problem scaling:\n",
-      "Objective coefficents range:          [0e+00, 0e+00]\n",
-      "Constraint matrix coefficients range: [2e-02, 1e+00]\n",
-      "Constraint rhs / bounds range:        [0e+00, 1e+00]\n",
-      "Variable bounds range:                [0e+00, 1e+00]\n",
-      "\n",
-      "Third-party presolve is disabled, skipping\n",
-      "Objective offset 0.000000 scaling_factor 1.000000\n",
-      "Presolved problem: 2 constraints 6 variables 11 nonzeros\n",
-      "Skipping column scaling\n",
-      "Barrier solver: 2 constraints, 6 variables, 11 nonzeros\n",
-      "Quadratic objective matrix: 25 nonzeros\n",
-      "\n",
-      "\n",
-      "Upper bounds                : 5\n",
-      "Density estimator time      : 0.00s\n",
-      "Linear system               : augmented\n",
-      "cuDSS Version               : 0.7.1\n",
-      "cuDSS Threading layer       : libcudss_mtlayer_gomp.so.0\n",
-      "Reordering algorithm        : AMD\n",
-      "Reordering time             : 0.00s\n",
-      "Symbolic factorization time : 0.01s\n",
-      "Total symbolic time         : 0.01s\n",
-      "Symbolic nonzeros in factor : 2.20e+01\n",
-      "\n",
-      "\n",
-      "                  Objective                         Infeasibility        Time\n",
-      "Iter   Primal              Dual                Primal   Dual    Compl.   Elapsed\n",
-      "  0   +3.842163103341e-05 -5.000038421631e+00 2.78e-17 1.00e+01 1.00e+00 0.0\n",
-      "  1   +3.841130629567e-05 -5.000339596431e-01 9.00e-10 1.00e+00 1.25e-05 0.0\n",
-      "  2   +3.830901399426e-05 -5.003312213897e-02 1.76e-10 1.00e-01 1.24e-05 0.0\n",
-      "  3   +3.736908280123e-05 -5.029419437216e-03 1.35e-11 1.01e-02 1.20e-05 0.0\n",
-      "  4   +3.225159545698e-05 -5.087329238139e-04 2.10e-10 1.08e-03 8.87e-06 0.0\n",
-      "  5   +2.608886999765e-05 -3.383434892714e-05 3.46e-10 1.15e-04 3.07e-06 0.0\n",
-      "  6   +2.413132881581e-05 +1.744114834034e-05 2.43e-10 1.19e-05 6.41e-07 0.0\n",
-      "  7   +2.372239892814e-05 +2.292425697517e-05 1.25e-10 1.26e-06 1.22e-07 0.0\n",
-      "  8   +2.365150610185e-05 +2.354353338821e-05 5.93e-11 1.45e-07 2.05e-08 0.0\n",
-      "  9   +2.363965326003e-05 +2.362681926190e-05 2.04e-11 1.45e-08 3.75e-09 0.0\n",
-      " 10   +2.363850356320e-05 +2.363636879759e-05 7.06e-12 2.45e-09 4.60e-10 0.0\n",
-      "\n",
-      "\n",
-      "Optimal solution found in 10 iterations and 0.03s\n",
-      "Objective +2.36385036e-05\n",
-      "Primal infeasibility (abs/rel): 1.41e-11/7.06e-12\n",
-      "Dual infeasibility   (abs/rel): 2.45e-09/2.45e-09\n",
-      "Complementarity gap  (abs/rel): 4.60e-10/4.60e-10\n",
-      "\n",
-      "\n",
-      "Unscaled Primal infeasibility   (abs/rel): 1.41e-11/7.06e-12\n",
-      "Post-solve Primal infeasibility (abs/rel): 1.41e-11/7.06e-12\n",
-      "Barrier finished in 0.04 seconds\n",
-      "cuOpt version: 26.2.0, git hash: 96c11095, host arch: x86_64, device archs: 80-real\n",
-      "CPU: AMD EPYC 7642 48-Core Processor, threads (physical/logical): 96/96, RAM: 166.76 GiB\n",
-      "CUDA 12.9, device: NVIDIA A100 80GB PCIe (ID 0), VRAM: 79.33 GiB\n",
-      "CUDA device UUID: ffffff8a5affffff95ffffffc7-ffffffd05\n",
-      "\n",
-      "Problem has a quadratic objective. Using Barrier.\n",
-      "Solving a problem with 2 constraints, 5 variables (0 integers), and 10 nonzeros\n",
-      "Problem scaling:\n",
-      "Objective coefficents range:          [0e+00, 0e+00]\n",
-      "Constraint matrix coefficients range: [2e-02, 1e+00]\n",
-      "Constraint rhs / bounds range:        [0e+00, 1e+00]\n",
-      "Variable bounds range:                [0e+00, 1e+00]\n",
-      "\n",
-      "Third-party presolve is disabled, skipping\n",
-      "Objective offset 0.000000 scaling_factor 1.000000\n",
-      "Presolved problem: 2 constraints 6 variables 11 nonzeros\n",
-      "Skipping column scaling\n",
-      "Barrier solver: 2 constraints, 6 variables, 11 nonzeros\n",
-      "Quadratic objective matrix: 25 nonzeros\n",
-      "\n",
-      "\n",
-      "Upper bounds                : 5\n",
-      "Density estimator time      : 0.00s\n",
-      "Linear system               : augmented\n",
-      "cuDSS Version               : 0.7.1\n",
-      "cuDSS Threading layer       : libcudss_mtlayer_gomp.so.0\n",
-      "Reordering algorithm        : AMD\n",
-      "Reordering time             : 0.00s\n",
-      "Symbolic factorization time : 0.01s\n",
-      "Total symbolic time         : 0.01s\n",
-      "Symbolic nonzeros in factor : 2.20e+01\n",
-      "\n",
-      "\n",
-      "                  Objective                         Infeasibility        Time\n",
-      "Iter   Primal              Dual                Primal   Dual    Compl.   Elapsed\n",
-      "  0   +1.502742918471e+01 -2.002742918471e+01 5.00e+01 1.00e+01 1.00e+02 0.0\n",
-      "  1   +1.541698214959e+00 -4.381109446336e+00 7.75e+00 3.10e+00 2.52e-01 0.0\n",
-      "  2   +1.128148347114e+00 -7.223091058021e+00 5.23e+00 2.09e+00 8.56e-01 0.0\n",
-      "  3   +2.050715616502e-02 -5.866183740948e+00 6.55e-01 2.62e-01 1.50e+00 0.0\n",
-      "  4   +6.683207617550e-03 -1.913588118132e+00 1.66e-01 6.65e-02 3.30e-01 0.0\n",
-      "  5   +5.890874559364e-03 -3.961531546376e-01 2.07e-02 8.27e-03 1.60e-01 0.0\n",
-      "  6   +5.376039427308e-03 -6.884302918090e-02 3.58e-03 1.43e-03 2.57e-02 0.0\n",
-      "  7   +2.854953955561e-03 -1.842630136251e-02 9.37e-04 3.75e-04 3.79e-03 0.0\n",
-      "  8   +1.183743286135e-03 -3.252862476038e-03 1.64e-04 6.56e-05 1.41e-03 0.0\n",
-      "  9   +3.895780767121e-04 -5.518051275662e-04 2.68e-05 1.07e-05 4.47e-04 0.0\n",
-      " 10   +1.205893915060e-04 -4.089981800464e-05 2.95e-06 1.18e-06 1.07e-04 0.0\n",
-      " 11   +5.740725607899e-05 +3.151087799295e-05 3.15e-07 1.26e-07 1.82e-05 0.0\n",
-      " 12   +4.480805852277e-05 +4.154837930777e-05 3.45e-08 1.38e-08 1.68e-06 0.0\n",
-      " 13   +4.322801306354e-05 +4.280923002739e-05 4.19e-09 1.68e-09 5.97e-08 0.0\n",
-      " 14   +4.304025995748e-05 +4.299565630553e-05 4.34e-10 1.74e-10 6.79e-09 0.0\n",
-      "\n",
-      "\n",
-      "Optimal solution found in 14 iterations and 0.05s\n",
-      "Objective +4.30402600e-05\n",
-      "Primal infeasibility (abs/rel): 8.68e-10/4.34e-10\n",
-      "Dual infeasibility   (abs/rel): 1.74e-10/1.74e-10\n",
-      "Complementarity gap  (abs/rel): 6.79e-09/6.79e-09\n",
-      "\n",
-      "\n",
-      "Unscaled Primal infeasibility   (abs/rel): 8.68e-10/4.34e-10\n",
-      "Post-solve Primal infeasibility (abs/rel): 8.68e-10/4.34e-10\n",
-      "Barrier finished in 0.05 seconds\n",
-      "cuOpt version: 26.2.0, git hash: 96c11095, host arch: x86_64, device archs: 80-real\n",
-      "CPU: AMD EPYC 7642 48-Core Processor, threads (physical/logical): 96/96, RAM: 167.73 GiB\n",
-      "CUDA 12.9, device: NVIDIA A100 80GB PCIe (ID 0), VRAM: 79.33 GiB\n",
-      "CUDA device UUID: ffffff8a5affffff95ffffffc7-ffffffd05\n",
-      "\n",
-      "Problem has a quadratic objective. Using Barrier.\n",
-      "Solving a problem with 2 constraints, 5 variables (0 integers), and 10 nonzeros\n",
-      "Problem scaling:\n",
-      "Objective coefficents range:          [0e+00, 0e+00]\n",
-      "Constraint matrix coefficients range: [2e-02, 1e+00]\n",
-      "Constraint rhs / bounds range:        [0e+00, 1e+00]\n",
-      "Variable bounds range:                [0e+00, 1e+00]\n",
-      "\n",
-      "Third-party presolve is disabled, skipping\n",
-      "Objective offset 0.000000 scaling_factor 1.000000\n",
-      "Presolved problem: 2 constraints 6 variables 11 nonzeros\n",
-      "Skipping column scaling\n",
-      "Barrier solver: 2 constraints, 6 variables, 11 nonzeros\n",
-      "Quadratic objective matrix: 25 nonzeros\n",
-      "\n",
-      "\n",
-      "Upper bounds                : 5\n",
-      "Density estimator time      : 0.00s\n",
-      "Linear system               : augmented\n",
-      "cuDSS Version               : 0.7.1\n",
-      "cuDSS Threading layer       : libcudss_mtlayer_gomp.so.0\n",
-      "Reordering algorithm        : AMD\n",
-      "Reordering time             : 0.00s\n",
-      "Symbolic factorization time : 0.01s\n",
-      "Total symbolic time         : 0.01s\n",
-      "Symbolic nonzeros in factor : 2.20e+01\n",
-      "\n",
-      "\n",
-      "                  Objective                         Infeasibility        Time\n",
-      "Iter   Primal              Dual                Primal   Dual    Compl.   Elapsed\n",
-      "  0   +1.505331680893e+01 -2.005331680893e+01 5.00e+01 1.00e+01 1.00e+02 0.0\n",
-      "  1   +1.460379831168e+00 -4.220852998771e+00 7.47e+00 2.99e+00 2.20e-01 0.0\n",
-      "  2   +6.100606494017e-01 -5.742897699177e+00 3.73e+00 1.49e+00 7.34e-01 0.0\n",
-      "  3   +2.380292049807e-02 -2.448948373282e+00 3.73e-01 1.49e-01 5.08e-01 0.0\n",
-      "  4   +1.017404860909e-02 -6.141945670814e-01 4.36e-02 1.74e-02 2.52e-01 0.0\n",
-      "  5   +9.035897495814e-03 -9.024382248308e-02 5.20e-03 2.08e-03 7.48e-02 0.0\n",
-      "  6   +6.284754962314e-03 -2.664081716060e-02 1.66e-03 6.65e-04 5.37e-03 0.0\n",
-      "  7   +2.276282325033e-03 -5.060314210233e-03 1.66e-04 6.65e-05 2.47e-03 0.0\n",
-      "  8   +9.165467368732e-04 -1.138861021330e-03 3.31e-05 1.32e-05 6.58e-04 0.0\n",
-      "  9   +2.631572762309e-04 -1.260242275450e-04 3.31e-06 1.32e-06 1.86e-04 0.0\n",
-      " 10   +1.195657529904e-04 +6.323884415030e-05 3.48e-07 1.39e-07 2.36e-05 0.0\n",
-      " 11   +1.016524474473e-04 +9.252900644151e-05 5.66e-08 2.26e-08 1.26e-06 0.0\n",
-      " 12   +9.874078077659e-05 +9.753763299414e-05 6.83e-09 2.73e-09 3.07e-07 0.0\n",
-      " 13   +9.834896696949e-05 +9.819693851136e-05 7.42e-10 2.97e-10 6.28e-08 0.0\n",
-      " 14   +9.829827933875e-05 +9.827810229704e-05 7.70e-11 3.09e-11 1.23e-08 0.0\n",
-      " 15   +9.829172608078e-05 +9.828885180253e-05 7.70e-12 3.13e-12 2.34e-09 0.0\n",
-      "\n",
-      "\n",
-      "Optimal solution found in 15 iterations and 0.05s\n",
-      "Objective +9.82917261e-05\n",
-      "Primal infeasibility (abs/rel): 1.54e-11/7.70e-12\n",
-      "Dual infeasibility   (abs/rel): 3.13e-12/3.13e-12\n",
-      "Complementarity gap  (abs/rel): 2.34e-09/2.34e-09\n",
-      "\n",
-      "\n",
-      "Unscaled Primal infeasibility   (abs/rel): 1.54e-11/7.70e-12\n",
-      "Post-solve Primal infeasibility (abs/rel): 1.54e-11/7.70e-12\n",
-      "Barrier finished in 0.06 seconds\n",
-      "cuOpt version: 26.2.0, git hash: 96c11095, host arch: x86_64, device archs: 80-real\n",
-      "CPU: AMD EPYC 7642 48-Core Processor, threads (physical/logical): 96/96, RAM: 167.90 GiB\n",
-      "CUDA 12.9, device: NVIDIA A100 80GB PCIe (ID 0), VRAM: 79.33 GiB\n",
-      "CUDA device UUID: ffffff8a5affffff95ffffffc7-ffffffd05\n",
-      "\n",
-      "Problem has a quadratic objective. Using Barrier.\n",
-      "Solving a problem with 2 constraints, 5 variables (0 integers), and 10 nonzeros\n",
-      "Problem scaling:\n",
-      "Objective coefficents range:          [0e+00, 0e+00]\n",
-      "Constraint matrix coefficients range: [2e-02, 1e+00]\n",
-      "Constraint rhs / bounds range:        [0e+00, 1e+00]\n",
-      "Variable bounds range:                [0e+00, 1e+00]\n",
-      "\n",
-      "Third-party presolve is disabled, skipping\n",
-      "Objective offset 0.000000 scaling_factor 1.000000\n",
-      "Presolved problem: 2 constraints 6 variables 11 nonzeros\n",
-      "Skipping column scaling\n",
-      "Barrier solver: 2 constraints, 6 variables, 11 nonzeros\n",
-      "Quadratic objective matrix: 25 nonzeros\n",
-      "\n",
-      "\n",
-      "Upper bounds                : 5\n",
-      "Density estimator time      : 0.00s\n",
-      "Linear system               : augmented\n",
-      "cuDSS Version               : 0.7.1\n",
-      "cuDSS Threading layer       : libcudss_mtlayer_gomp.so.0\n",
-      "Reordering algorithm        : AMD\n",
-      "Reordering time             : 0.00s\n",
-      "Symbolic factorization time : 0.01s\n",
-      "Total symbolic time         : 0.01s\n",
-      "Symbolic nonzeros in factor : 2.20e+01\n",
-      "\n",
-      "\n",
-      "                  Objective                         Infeasibility        Time\n",
-      "Iter   Primal              Dual                Primal   Dual    Compl.   Elapsed\n",
-      "  0   +1.508133859319e+01 -2.008133859319e+01 5.00e+01 1.00e+01 1.00e+02 0.0\n",
-      "  1   +1.396474142779e+00 -4.090196536907e+00 7.25e+00 2.90e+00 1.93e-01 0.0\n",
-      "  2   +3.460185576412e-01 -4.477705971449e+00 2.69e+00 1.07e+00 6.77e-01 0.0\n",
-      "  3   +2.173941068607e-02 -1.573352249955e+00 2.69e-01 1.07e-01 3.65e-01 0.0\n",
-      "  4   +1.113562522406e-02 -4.052481138604e-01 2.79e-02 1.12e-02 2.36e-01 0.0\n",
-      "  5   +9.878275571836e-03 -6.529149778574e-02 4.32e-03 1.73e-03 4.86e-02 0.0\n",
-      "  6   +6.213369991400e-03 -1.961323433571e-02 1.37e-03 5.49e-04 4.60e-03 0.0\n",
-      "  7   +2.753837784022e-03 -4.091524143227e-03 2.55e-04 1.02e-04 2.18e-03 0.0\n",
-      "  8   +8.816552093872e-04 -1.208051942755e-03 2.86e-05 1.15e-05 6.76e-04 0.0\n",
-      "  9   +3.328432347857e-04 -2.608355125524e-05 3.32e-06 1.33e-06 1.71e-04 0.0\n",
-      " 10   +2.041537352142e-04 +1.625201263960e-04 3.35e-07 1.34e-07 1.27e-05 0.0\n",
-      " 11   +1.915462685663e-04 +1.854400090055e-04 4.59e-08 1.84e-08 1.33e-06 0.0\n",
-      " 12   +1.895501304288e-04 +1.887776774556e-04 5.13e-09 2.05e-09 2.83e-07 0.0\n",
-      " 13   +1.892546773000e-04 +1.891586526727e-04 5.38e-10 2.15e-10 5.00e-08 0.0\n",
-      " 14   +1.892077492854e-04 +1.891971136588e-04 5.42e-11 2.18e-11 5.67e-09 0.0\n",
-      "\n",
-      "\n",
-      "Optimal solution found in 14 iterations and 0.05s\n",
-      "Objective +1.89207749e-04\n",
-      "Primal infeasibility (abs/rel): 1.08e-10/5.42e-11\n",
-      "Dual infeasibility   (abs/rel): 2.18e-11/2.18e-11\n",
-      "Complementarity gap  (abs/rel): 5.67e-09/5.67e-09\n",
-      "\n",
-      "\n",
-      "Unscaled Primal infeasibility   (abs/rel): 1.08e-10/5.42e-11\n",
-      "Post-solve Primal infeasibility (abs/rel): 1.08e-10/5.42e-11\n",
-      "Barrier finished in 0.05 seconds\n",
-      "cuOpt version: 26.2.0, git hash: 96c11095, host arch: x86_64, device archs: 80-real\n",
-      "CPU: AMD EPYC 7642 48-Core Processor, threads (physical/logical): 96/96, RAM: 168.87 GiB\n",
-      "CUDA 12.9, device: NVIDIA A100 80GB PCIe (ID 0), VRAM: 79.33 GiB\n",
-      "CUDA device UUID: ffffff8a5affffff95ffffffc7-ffffffd05\n",
-      "\n",
-      "Problem has a quadratic objective. Using Barrier.\n",
-      "Solving a problem with 2 constraints, 5 variables (0 integers), and 10 nonzeros\n",
-      "Problem scaling:\n",
-      "Objective coefficents range:          [0e+00, 0e+00]\n",
-      "Constraint matrix coefficients range: [2e-02, 1e+00]\n",
-      "Constraint rhs / bounds range:        [0e+00, 1e+00]\n",
-      "Variable bounds range:                [0e+00, 1e+00]\n",
-      "\n",
-      "Third-party presolve is disabled, skipping\n",
-      "Objective offset 0.000000 scaling_factor 1.000000\n",
-      "Presolved problem: 2 constraints 6 variables 11 nonzeros\n",
-      "Skipping column scaling\n",
-      "Barrier solver: 2 constraints, 6 variables, 11 nonzeros\n",
-      "Quadratic objective matrix: 25 nonzeros\n",
-      "\n",
-      "\n",
-      "Upper bounds                : 5\n",
-      "Density estimator time      : 0.00s\n",
-      "Linear system               : augmented\n",
-      "cuDSS Version               : 0.7.1\n",
-      "cuDSS Threading layer       : libcudss_mtlayer_gomp.so.0\n",
-      "Reordering algorithm        : AMD\n",
-      "Reordering time             : 0.00s\n",
-      "Symbolic factorization time : 0.01s\n",
-      "Total symbolic time         : 0.01s\n",
-      "Symbolic nonzeros in factor : 2.20e+01\n",
-      "\n",
-      "\n",
-      "                  Objective                         Infeasibility        Time\n",
-      "Iter   Primal              Dual                Primal   Dual    Compl.   Elapsed\n",
-      "  0   +1.510393544234e+01 -2.010393544234e+01 5.00e+01 1.00e+01 1.00e+02 0.0\n",
-      "  1   +1.333242185387e+00 -3.960795814572e+00 7.03e+00 2.81e+00 1.63e-01 0.0\n",
-      "  2   +2.019597554100e-01 -3.370808029717e+00 1.93e+00 7.71e-01 6.20e-01 0.0\n",
-      "  3   +1.973835605178e-02 -1.210158142048e+00 2.26e-01 9.02e-02 3.26e-01 0.0\n",
-      "  4   +1.117164531706e-02 -3.176860344685e-01 2.28e-02 9.13e-03 2.14e-01 0.0\n",
-      "  5   +9.865614060922e-03 -5.371200163128e-02 3.90e-03 1.56e-03 3.89e-02 0.0\n",
-      "  6   +5.717214928010e-03 -1.537024895140e-02 1.15e-03 4.60e-04 4.52e-03 0.0\n",
-      "  7   +2.924484228461e-03 -3.296754925010e-03 2.82e-04 1.13e-04 1.96e-03 0.0\n",
-      "  8   +9.132901760842e-04 -1.314976284683e-03 2.82e-05 1.13e-05 6.60e-04 0.0\n",
-      "  9   +4.518869562563e-04 +7.498309035824e-05 3.94e-06 1.57e-06 1.61e-04 0.0\n",
-      " 10   +3.354489370077e-04 +2.816353735678e-04 5.41e-07 2.16e-07 9.87e-06 0.0\n",
-      " 11   +3.187091008594e-04 +3.115052049844e-04 6.77e-08 2.71e-08 1.54e-06 0.0\n",
-      " 12   +3.162355471425e-04 +3.153647937008e-04 7.36e-09 2.95e-09 2.93e-07 0.0\n",
-      " 13   +3.158740329543e-04 +3.157774420129e-04 7.54e-10 3.02e-10 3.79e-08 0.0\n",
-      " 14   +3.158272667889e-04 +3.158175967381e-04 7.54e-11 3.02e-11 1.61e-09 0.0\n",
-      "\n",
-      "\n",
-      "Optimal solution found in 14 iterations and 0.05s\n",
-      "Objective +3.15827267e-04\n",
-      "Primal infeasibility (abs/rel): 1.51e-10/7.54e-11\n",
-      "Dual infeasibility   (abs/rel): 3.02e-11/3.02e-11\n",
-      "Complementarity gap  (abs/rel): 1.61e-09/1.61e-09\n",
-      "\n",
-      "\n",
-      "Unscaled Primal infeasibility   (abs/rel): 1.51e-10/7.54e-11\n",
-      "Post-solve Primal infeasibility (abs/rel): 1.51e-10/7.54e-11\n",
-      "Barrier finished in 0.05 seconds\n",
-      "cuOpt version: 26.2.0, git hash: 96c11095, host arch: x86_64, device archs: 80-real\n",
-      "CPU: AMD EPYC 7642 48-Core Processor, threads (physical/logical): 96/96, RAM: 169.18 GiB\n",
-      "CUDA 12.9, device: NVIDIA A100 80GB PCIe (ID 0), VRAM: 79.33 GiB\n",
-      "CUDA device UUID: ffffff8a5affffff95ffffffc7-ffffffd05\n",
-      "\n",
-      "Problem has a quadratic objective. Using Barrier.\n",
-      "Solving a problem with 2 constraints, 5 variables (0 integers), and 10 nonzeros\n",
-      "Problem scaling:\n",
-      "Objective coefficents range:          [0e+00, 0e+00]\n",
-      "Constraint matrix coefficients range: [2e-02, 1e+00]\n",
-      "Constraint rhs / bounds range:        [0e+00, 1e+00]\n",
-      "Variable bounds range:                [0e+00, 1e+00]\n",
-      "\n",
-      "Third-party presolve is disabled, skipping\n",
-      "Objective offset 0.000000 scaling_factor 1.000000\n",
-      "Presolved problem: 2 constraints 6 variables 11 nonzeros\n",
-      "Skipping column scaling\n",
-      "Barrier solver: 2 constraints, 6 variables, 11 nonzeros\n",
-      "Quadratic objective matrix: 25 nonzeros\n",
-      "\n",
-      "\n",
-      "Upper bounds                : 5\n",
-      "Density estimator time      : 0.00s\n",
-      "Linear system               : augmented\n",
-      "cuDSS Version               : 0.7.1\n",
-      "cuDSS Threading layer       : libcudss_mtlayer_gomp.so.0\n",
-      "Reordering algorithm        : AMD\n",
-      "Reordering time             : 0.00s\n",
-      "Symbolic factorization time : 0.01s\n",
-      "Total symbolic time         : 0.01s\n",
-      "Symbolic nonzeros in factor : 2.20e+01\n",
-      "\n",
-      "\n",
-      "                  Objective                         Infeasibility        Time\n",
-      "Iter   Primal              Dual                Primal   Dual    Compl.   Elapsed\n",
-      "  0   +1.512657603624e+01 -2.012657603624e+01 5.00e+01 1.00e+01 1.00e+02 0.0\n",
-      "  1   +1.270616299763e+00 -3.832013323724e+00 6.81e+00 2.73e+00 1.57e-01 0.0\n",
-      "  2   +1.298868640401e-01 -2.514136902958e+00 1.44e+00 5.75e-01 5.57e-01 0.0\n",
-      "  3   +1.689907707894e-02 -9.605711970241e-01 1.68e-01 6.71e-02 2.86e-01 0.0\n",
-      "  4   +1.109695500176e-02 -2.382622382087e-01 1.68e-02 6.71e-03 1.83e-01 0.0\n",
-      "  5   +9.694987573399e-03 -4.344441004257e-02 3.31e-03 1.32e-03 2.70e-02 0.0\n",
-      "  6   +4.966120941261e-03 -1.133171716008e-02 8.14e-04 3.25e-04 4.36e-03 0.0\n",
-      "  7   +2.874560244422e-03 -2.374290913744e-03 2.41e-04 9.62e-05 1.61e-03 0.0\n",
-      "  8   +9.187629921612e-04 -1.123468197022e-03 2.41e-05 9.62e-06 6.34e-04 0.0\n",
-      "  9   +5.742873295744e-04 +2.676528157210e-04 3.26e-06 1.30e-06 1.16e-04 0.0\n",
-      " 10   +4.941658796882e-04 +4.464244775474e-04 4.93e-07 1.97e-07 5.96e-06 0.0\n",
-      " 11   +4.804391335814e-04 +4.745025563531e-04 5.73e-08 2.29e-08 1.31e-06 0.0\n",
-      " 12   +4.784603690967e-04 +4.777821139805e-04 6.03e-09 2.42e-09 2.10e-07 0.0\n",
-      " 13   +4.781867186318e-04 +4.781173108467e-04 6.07e-10 2.43e-10 1.56e-08 0.0\n",
-      " 14   +4.781571832325e-04 +4.781502584744e-04 6.07e-11 2.43e-11 2.70e-10 0.0\n",
-      "\n",
-      "\n",
-      "Optimal solution found in 14 iterations and 0.05s\n",
-      "Objective +4.78157183e-04\n",
-      "Primal infeasibility (abs/rel): 1.21e-10/6.07e-11\n",
-      "Dual infeasibility   (abs/rel): 2.43e-11/2.43e-11\n",
-      "Complementarity gap  (abs/rel): 2.71e-10/2.70e-10\n",
-      "\n",
-      "\n",
-      "Unscaled Primal infeasibility   (abs/rel): 1.21e-10/6.07e-11\n",
-      "Post-solve Primal infeasibility (abs/rel): 1.21e-10/6.07e-11\n",
-      "Barrier finished in 0.05 seconds\n",
-      "cuOpt version: 26.2.0, git hash: 96c11095, host arch: x86_64, device archs: 80-real\n",
-      "CPU: AMD EPYC 7642 48-Core Processor, threads (physical/logical): 96/96, RAM: 169.18 GiB\n",
-      "CUDA 12.9, device: NVIDIA A100 80GB PCIe (ID 0), VRAM: 79.33 GiB\n",
-      "CUDA device UUID: ffffff8a5affffff95ffffffc7-ffffffd05\n",
-      "\n",
-      "Problem has a quadratic objective. Using Barrier.\n",
-      "Solving a problem with 2 constraints, 5 variables (0 integers), and 10 nonzeros\n",
-      "Problem scaling:\n",
-      "Objective coefficents range:          [0e+00, 0e+00]\n",
-      "Constraint matrix coefficients range: [2e-02, 1e+00]\n",
-      "Constraint rhs / bounds range:        [0e+00, 1e+00]\n",
-      "Variable bounds range:                [0e+00, 1e+00]\n",
-      "\n",
-      "Third-party presolve is disabled, skipping\n",
-      "Objective offset 0.000000 scaling_factor 1.000000\n",
-      "Presolved problem: 2 constraints 6 variables 11 nonzeros\n",
-      "Skipping column scaling\n",
-      "Barrier solver: 2 constraints, 6 variables, 11 nonzeros\n",
-      "Quadratic objective matrix: 25 nonzeros\n",
-      "\n",
-      "\n",
-      "Upper bounds                : 5\n",
-      "Density estimator time      : 0.00s\n",
-      "Linear system               : augmented\n",
-      "cuDSS Version               : 0.7.1\n",
-      "cuDSS Threading layer       : libcudss_mtlayer_gomp.so.0\n",
-      "Reordering algorithm        : AMD\n",
-      "Reordering time             : 0.00s\n",
-      "Symbolic factorization time : 0.01s\n",
-      "Total symbolic time         : 0.01s\n",
-      "Symbolic nonzeros in factor : 2.20e+01\n",
-      "\n",
-      "\n",
-      "                  Objective                         Infeasibility        Time\n",
-      "Iter   Primal              Dual                Primal   Dual    Compl.   Elapsed\n",
-      "  0   +1.514984849857e+01 -2.014984849857e+01 5.00e+01 1.00e+01 1.00e+02 0.0\n",
-      "  1   +1.208817177721e+00 -3.703922897095e+00 6.60e+00 2.64e+00 1.70e-01 0.0\n",
-      "  2   +9.381521346387e-02 -1.913007266340e+00 1.14e+00 4.54e-01 5.04e-01 0.0\n",
-      "  3   +1.505848193650e-02 -7.793498420948e-01 1.23e-01 4.91e-02 2.51e-01 0.0\n",
-      "  4   +1.116000968721e-02 -1.795084752459e-01 1.23e-02 4.91e-03 1.53e-01 0.0\n",
-      "  5   +9.590358257366e-03 -3.542692623435e-02 2.76e-03 1.10e-03 1.87e-02 0.0\n",
-      "  6   +4.179680195298e-03 -7.526206397935e-03 4.34e-04 1.74e-04 4.18e-03 0.0\n",
-      "  7   +2.739241462777e-03 -1.510500215556e-03 1.55e-04 6.18e-05 1.17e-03 0.0\n",
-      "  8   +9.473018258365e-04 -7.256446230424e-04 1.55e-05 6.18e-06 5.41e-04 0.0\n",
-      "  9   +7.274884522721e-04 +5.160879340455e-04 1.84e-06 7.35e-07 5.95e-05 0.0\n",
-      " 10   +6.843615236131e-04 +6.548743991659e-04 2.44e-07 9.75e-08 4.59e-06 0.0\n",
-      " 11   +6.773851388666e-04 +6.739050573756e-04 2.67e-08 1.07e-08 8.71e-07 0.0\n",
-      " 12   +6.763404861215e-04 +6.759684539399e-04 2.73e-09 1.09e-09 9.99e-08 0.0\n",
-      " 13   +6.762113064762e-04 +6.761741493209e-04 2.73e-10 1.10e-10 3.22e-09 0.0\n",
-      "\n",
-      "\n",
-      "Optimal solution found in 13 iterations and 0.05s\n",
-      "Objective +6.76211306e-04\n",
-      "Primal infeasibility (abs/rel): 5.46e-10/2.73e-10\n",
-      "Dual infeasibility   (abs/rel): 1.10e-10/1.10e-10\n",
-      "Complementarity gap  (abs/rel): 3.22e-09/3.22e-09\n",
-      "\n",
-      "\n",
-      "Unscaled Primal infeasibility   (abs/rel): 5.46e-10/2.73e-10\n",
-      "Post-solve Primal infeasibility (abs/rel): 5.46e-10/2.73e-10\n",
-      "Barrier finished in 0.05 seconds\n",
-      "cuOpt version: 26.2.0, git hash: 96c11095, host arch: x86_64, device archs: 80-real\n",
-      "CPU: AMD EPYC 7642 48-Core Processor, threads (physical/logical): 96/96, RAM: 168.71 GiB\n",
-      "CUDA 12.9, device: NVIDIA A100 80GB PCIe (ID 0), VRAM: 79.33 GiB\n",
-      "CUDA device UUID: ffffff8a5affffff95ffffffc7-ffffffd05\n",
-      "\n",
-      "Problem has a quadratic objective. Using Barrier.\n",
-      "Solving a problem with 2 constraints, 5 variables (0 integers), and 10 nonzeros\n",
-      "Problem scaling:\n",
-      "Objective coefficents range:          [0e+00, 0e+00]\n",
-      "Constraint matrix coefficients range: [2e-02, 1e+00]\n",
-      "Constraint rhs / bounds range:        [0e+00, 1e+00]\n",
-      "Variable bounds range:                [0e+00, 1e+00]\n",
-      "\n",
-      "Third-party presolve is disabled, skipping\n",
-      "Objective offset 0.000000 scaling_factor 1.000000\n",
-      "Presolved problem: 2 constraints 6 variables 11 nonzeros\n",
-      "Skipping column scaling\n",
-      "Barrier solver: 2 constraints, 6 variables, 11 nonzeros\n",
-      "Quadratic objective matrix: 25 nonzeros\n",
-      "\n",
-      "\n",
-      "Upper bounds                : 5\n",
-      "Density estimator time      : 0.00s\n",
-      "Linear system               : augmented\n",
-      "cuDSS Version               : 0.7.1\n",
-      "cuDSS Threading layer       : libcudss_mtlayer_gomp.so.0\n",
-      "Reordering algorithm        : AMD\n",
-      "Reordering time             : 0.00s\n",
-      "Symbolic factorization time : 0.01s\n",
-      "Total symbolic time         : 0.01s\n",
-      "Symbolic nonzeros in factor : 2.20e+01\n",
-      "\n",
-      "\n",
-      "                  Objective                         Infeasibility        Time\n",
-      "Iter   Primal              Dual                Primal   Dual    Compl.   Elapsed\n",
-      "  0   +1.517479878134e+01 -2.017479878134e+01 5.00e+01 1.00e+01 1.00e+02 0.0\n",
-      "  1   +1.148092268092e+00 -3.576625942896e+00 6.38e+00 2.55e+00 1.82e-01 0.0\n",
-      "  2   +7.373244614631e-02 -1.519097301397e+00 9.41e-01 3.76e-01 4.66e-01 0.0\n",
-      "  3   +1.405481789343e-02 -6.526450446579e-01 9.41e-02 3.76e-02 2.21e-01 0.0\n",
-      "  4   +1.129111268876e-02 -1.411430179235e-01 9.58e-03 3.83e-03 1.29e-01 0.0\n",
-      "  5   +9.569983652496e-03 -2.952392601877e-02 2.37e-03 9.46e-04 1.39e-02 0.0\n",
-      "  6   +3.746650518820e-03 -5.909422664397e-03 2.37e-04 9.46e-05 4.00e-03 0.0\n",
-      "  7   +2.637639839702e-03 -9.829665113148e-04 8.69e-05 3.48e-05 9.23e-04 0.0\n",
-      "  8   +1.077871978016e-03 -2.148465979347e-04 8.78e-06 3.51e-06 4.20e-04 0.0\n",
-      "  9   +9.432365932897e-04 +7.484578420154e-04 1.29e-06 5.16e-07 2.60e-05 0.0\n",
-      " 10   +9.152878173098e-04 +8.902997180768e-04 1.57e-07 6.30e-08 3.97e-06 0.0\n",
-      " 11   +9.107288072420e-04 +9.078947770245e-04 1.68e-08 6.72e-09 6.59e-07 0.0\n",
-      " 12   +9.100365166655e-04 +9.097460706874e-04 1.69e-09 6.79e-10 5.21e-08 0.0\n",
-      " 13   +9.099593933692e-04 +9.099303997400e-04 1.69e-10 6.79e-11 9.66e-10 0.0\n",
-      "\n",
-      "\n",
-      "Optimal solution found in 13 iterations and 0.05s\n",
-      "Objective +9.09959393e-04\n",
-      "Primal infeasibility (abs/rel): 3.38e-10/1.69e-10\n",
-      "Dual infeasibility   (abs/rel): 6.79e-11/6.79e-11\n",
-      "Complementarity gap  (abs/rel): 9.67e-10/9.66e-10\n",
-      "\n",
-      "\n",
-      "Unscaled Primal infeasibility   (abs/rel): 3.38e-10/1.69e-10\n",
-      "Post-solve Primal infeasibility (abs/rel): 3.38e-10/1.69e-10\n",
-      "Barrier finished in 0.05 seconds\n",
-      "cuOpt version: 26.2.0, git hash: 96c11095, host arch: x86_64, device archs: 80-real\n",
-      "CPU: AMD EPYC 7642 48-Core Processor, threads (physical/logical): 96/96, RAM: 168.05 GiB\n",
-      "CUDA 12.9, device: NVIDIA A100 80GB PCIe (ID 0), VRAM: 79.33 GiB\n",
-      "CUDA device UUID: ffffff8a5affffff95ffffffc7-ffffffd05\n",
-      "\n",
-      "Problem has a quadratic objective. Using Barrier.\n",
-      "Solving a problem with 2 constraints, 5 variables (0 integers), and 10 nonzeros\n",
-      "Problem scaling:\n",
-      "Objective coefficents range:          [0e+00, 0e+00]\n",
-      "Constraint matrix coefficients range: [2e-02, 1e+00]\n",
-      "Constraint rhs / bounds range:        [0e+00, 1e+00]\n",
-      "Variable bounds range:                [0e+00, 1e+00]\n",
-      "\n",
-      "Third-party presolve is disabled, skipping\n",
-      "Objective offset 0.000000 scaling_factor 1.000000\n",
-      "Presolved problem: 2 constraints 6 variables 11 nonzeros\n",
-      "Skipping column scaling\n",
-      "Barrier solver: 2 constraints, 6 variables, 11 nonzeros\n",
-      "Quadratic objective matrix: 25 nonzeros\n",
-      "\n",
-      "\n",
-      "Upper bounds                : 5\n",
-      "Density estimator time      : 0.00s\n",
-      "Linear system               : augmented\n",
-      "cuDSS Version               : 0.7.1\n",
-      "cuDSS Threading layer       : libcudss_mtlayer_gomp.so.0\n",
-      "Reordering algorithm        : AMD\n",
-      "Reordering time             : 0.00s\n",
-      "Symbolic factorization time : 0.01s\n",
-      "Total symbolic time         : 0.01s\n",
-      "Symbolic nonzeros in factor : 2.20e+01\n",
-      "\n",
-      "\n",
-      "                  Objective                         Infeasibility        Time\n",
-      "Iter   Primal              Dual                Primal   Dual    Compl.   Elapsed\n",
-      "  0   +1.519822927338e+01 -2.019822927338e+01 5.00e+01 1.00e+01 1.00e+02 0.0\n",
-      "  1   +1.088642319810e+00 -3.450260733500e+00 6.16e+00 2.47e+00 1.94e-01 0.0\n",
-      "  2   +9.103682359428e-02 -1.384143078061e+00 1.17e+00 4.69e-01 4.30e-01 0.0\n",
-      "  3   +1.460821084878e-02 -6.684180232329e-01 1.26e-01 5.04e-02 1.94e-01 0.0\n",
-      "  4   +1.116257159108e-02 -1.659507223274e-01 1.26e-02 5.04e-03 1.34e-01 0.0\n",
-      "  5   +9.819399452772e-03 -3.078565533805e-02 2.68e-03 1.07e-03 2.04e-02 0.0\n",
-      "  6   +4.883995819310e-03 -7.883886001876e-03 6.10e-04 2.44e-04 4.28e-03 0.0\n",
-      "  7   +2.796547384799e-03 -5.623503684105e-04 1.30e-04 5.22e-05 1.57e-03 0.0\n",
-      "  8   +1.457982670572e-03 +5.705801738194e-04 1.30e-05 5.22e-06 4.53e-04 0.0\n",
-      "  9   +1.223525277973e-03 +1.095160953841e-03 1.82e-06 7.26e-07 3.20e-05 0.0\n",
-      " 10   +1.185606104641e-03 +1.169795009750e-03 2.11e-07 8.44e-08 3.17e-06 0.0\n",
-      " 11   +1.180182564921e-03 +1.178441300336e-03 2.20e-08 8.81e-09 4.44e-07 0.0\n",
-      " 12   +1.179494175079e-03 +1.179319164882e-03 2.21e-09 8.84e-10 2.19e-08 0.0\n",
-      " 13   +1.179423664347e-03 +1.179406183542e-03 2.21e-10 8.84e-11 2.86e-10 0.0\n",
-      "\n",
-      "\n",
-      "Optimal solution found in 13 iterations and 0.05s\n",
-      "Objective +1.17942366e-03\n",
-      "Primal infeasibility (abs/rel): 4.42e-10/2.21e-10\n",
-      "Dual infeasibility   (abs/rel): 8.84e-11/8.84e-11\n",
-      "Complementarity gap  (abs/rel): 2.86e-10/2.86e-10\n",
-      "\n",
-      "\n",
-      "Unscaled Primal infeasibility   (abs/rel): 4.42e-10/2.21e-10\n",
-      "Post-solve Primal infeasibility (abs/rel): 4.42e-10/2.21e-10\n",
-      "Barrier finished in 0.05 seconds\n",
-      "cuOpt version: 26.2.0, git hash: 96c11095, host arch: x86_64, device archs: 80-real\n",
-      "CPU: AMD EPYC 7642 48-Core Processor, threads (physical/logical): 96/96, RAM: 167.11 GiB\n",
-      "CUDA 12.9, device: NVIDIA A100 80GB PCIe (ID 0), VRAM: 79.33 GiB\n",
-      "CUDA device UUID: ffffff8a5affffff95ffffffc7-ffffffd05\n",
-      "\n",
-      "Problem has a quadratic objective. Using Barrier.\n",
-      "Solving a problem with 2 constraints, 5 variables (0 integers), and 10 nonzeros\n",
-      "Problem scaling:\n",
-      "Objective coefficents range:          [0e+00, 0e+00]\n",
-      "Constraint matrix coefficients range: [2e-02, 1e+00]\n",
-      "Constraint rhs / bounds range:        [0e+00, 1e+00]\n",
-      "Variable bounds range:                [0e+00, 1e+00]\n",
-      "\n",
-      "Third-party presolve is disabled, skipping\n",
-      "Objective offset 0.000000 scaling_factor 1.000000\n",
-      "Presolved problem: 2 constraints 6 variables 11 nonzeros\n",
-      "Skipping column scaling\n",
-      "Barrier solver: 2 constraints, 6 variables, 11 nonzeros\n",
-      "Quadratic objective matrix: 25 nonzeros\n",
-      "\n",
-      "\n",
-      "Upper bounds                : 5\n",
-      "Density estimator time      : 0.00s\n",
-      "Linear system               : augmented\n",
-      "cuDSS Version               : 0.7.1\n",
-      "cuDSS Threading layer       : libcudss_mtlayer_gomp.so.0\n",
-      "Reordering algorithm        : AMD\n",
-      "Reordering time             : 0.00s\n",
-      "Symbolic factorization time : 0.01s\n",
-      "Total symbolic time         : 0.01s\n",
-      "Symbolic nonzeros in factor : 2.20e+01\n",
-      "\n",
-      "\n",
-      "                  Objective                         Infeasibility        Time\n",
-      "Iter   Primal              Dual                Primal   Dual    Compl.   Elapsed\n",
-      "  0   +1.522198048015e+01 -2.022198048015e+01 5.00e+01 1.00e+01 1.00e+02 0.0\n",
-      "  1   +1.030722113132e+00 -3.324932629490e+00 5.95e+00 2.38e+00 2.05e-01 0.0\n",
-      "  2   +1.210883272925e-01 -1.400611591234e+00 1.50e+00 5.99e-01 4.02e-01 0.0\n",
-      "  3   +1.616399035036e-02 -7.088829739567e-01 1.79e-01 7.15e-02 1.72e-01 0.0\n",
-      "  4   +1.114044029806e-02 -1.978332428797e-01 1.79e-02 7.15e-03 1.33e-01 0.0\n",
-      "  5   +1.019493828489e-02 -3.266858575161e-02 3.04e-03 1.22e-03 3.10e-02 0.0\n",
-      "  6   +6.157253383642e-03 -9.469655026885e-03 9.61e-04 3.84e-04 4.53e-03 0.0\n",
-      "  7   +2.898661906575e-03 -6.764700195963e-04 9.61e-05 3.84e-05 2.16e-03 0.0\n",
-      "  8   +2.169591556726e-03 +7.866731587962e-04 3.40e-05 1.36e-05 4.30e-04 0.0\n",
-      "  9   +1.556649619413e-03 +1.346162654355e-03 3.60e-06 1.44e-06 8.31e-05 0.0\n",
-      " 10   +1.494741771858e-03 +1.468442131272e-03 4.28e-07 1.71e-07 4.76e-06 0.0\n",
-      " 11   +1.485852838506e-03 +1.482942979710e-03 4.50e-08 1.80e-08 6.93e-07 0.0\n",
-      " 12   +1.484721320685e-03 +1.484428181482e-03 4.51e-09 1.81e-09 3.73e-08 0.0\n",
-      " 13   +1.484604797478e-03 +1.484575518201e-03 4.51e-10 1.81e-10 5.10e-10 0.0\n",
-      "\n",
-      "\n",
-      "Optimal solution found in 13 iterations and 0.05s\n",
-      "Objective +1.48460480e-03\n",
-      "Primal infeasibility (abs/rel): 9.03e-10/4.51e-10\n",
-      "Dual infeasibility   (abs/rel): 1.81e-10/1.81e-10\n",
-      "Complementarity gap  (abs/rel): 5.11e-10/5.10e-10\n",
-      "\n",
-      "\n",
-      "Unscaled Primal infeasibility   (abs/rel): 9.03e-10/4.51e-10\n",
-      "Post-solve Primal infeasibility (abs/rel): 9.03e-10/4.51e-10\n",
-      "Barrier finished in 0.05 seconds\n",
-      "cuOpt version: 26.2.0, git hash: 96c11095, host arch: x86_64, device archs: 80-real\n",
-      "CPU: AMD EPYC 7642 48-Core Processor, threads (physical/logical): 96/96, RAM: 166.04 GiB\n",
-      "CUDA 12.9, device: NVIDIA A100 80GB PCIe (ID 0), VRAM: 79.33 GiB\n",
-      "CUDA device UUID: ffffff8a5affffff95ffffffc7-ffffffd05\n",
-      "\n",
-      "Problem has a quadratic objective. Using Barrier.\n",
-      "Solving a problem with 2 constraints, 5 variables (0 integers), and 10 nonzeros\n",
-      "Problem scaling:\n",
-      "Objective coefficents range:          [0e+00, 0e+00]\n",
-      "Constraint matrix coefficients range: [2e-02, 1e+00]\n",
-      "Constraint rhs / bounds range:        [0e+00, 1e+00]\n",
-      "Variable bounds range:                [0e+00, 1e+00]\n",
-      "\n",
-      "Third-party presolve is disabled, skipping\n",
-      "Objective offset 0.000000 scaling_factor 1.000000\n",
-      "Presolved problem: 2 constraints 6 variables 11 nonzeros\n",
-      "Skipping column scaling\n",
-      "Barrier solver: 2 constraints, 6 variables, 11 nonzeros\n",
-      "Quadratic objective matrix: 25 nonzeros\n",
-      "\n",
-      "\n",
-      "Upper bounds                : 5\n",
-      "Density estimator time      : 0.00s\n",
-      "Linear system               : augmented\n",
-      "cuDSS Version               : 0.7.1\n",
-      "cuDSS Threading layer       : libcudss_mtlayer_gomp.so.0\n",
-      "Reordering algorithm        : AMD\n",
-      "Reordering time             : 0.00s\n",
-      "Symbolic factorization time : 0.01s\n",
-      "Total symbolic time         : 0.01s\n",
-      "Symbolic nonzeros in factor : 2.20e+01\n",
-      "\n",
-      "\n",
-      "                  Objective                         Infeasibility        Time\n",
-      "Iter   Primal              Dual                Primal   Dual    Compl.   Elapsed\n",
-      "  0   +1.524622925724e+01 -2.024622925724e+01 5.00e+01 1.00e+01 1.00e+02 0.0\n",
-      "  1   +9.745439733886e-01 -3.200723561906e+00 5.74e+00 2.30e+00 2.16e-01 0.0\n",
-      "  2   +8.223570425304e-02 -1.310528024826e+00 1.10e+00 4.41e-01 4.18e-01 0.0\n",
-      "  3   +1.423152671285e-02 -6.035760956834e-01 1.17e-01 4.70e-02 1.43e-01 0.0\n",
-      "  4   +1.150498155799e-02 -1.523339606740e-01 1.32e-02 5.29e-03 1.09e-01 0.0\n",
-      "  5   +1.040336378104e-02 -2.605415529542e-02 2.60e-03 1.04e-03 2.15e-02 0.0\n",
-      "  6   +5.947444352834e-03 -7.125444074227e-03 7.47e-04 2.99e-04 4.09e-03 0.0\n",
-      "  7   +2.974033076348e-03 +6.982926056997e-05 7.47e-05 2.99e-05 1.81e-03 0.0\n",
-      "  8   +2.392263896504e-03 +1.288355251272e-03 2.76e-05 1.10e-05 3.64e-04 0.0\n",
-      "  9   +1.887166953684e-03 +1.714020328251e-03 3.20e-06 1.28e-06 6.35e-05 0.0\n",
-      " 10   +1.833855500439e-03 +1.813017969433e-03 3.66e-07 1.46e-07 3.94e-06 0.0\n",
-      " 11   +1.826457093999e-03 +1.824223712591e-03 3.78e-08 1.51e-08 4.83e-07 0.0\n",
-      " 12   +1.825577441231e-03 +1.825354029663e-03 3.78e-09 1.51e-09 1.74e-08 0.0\n",
-      " 13   +1.825488761647e-03 +1.825466435415e-03 3.78e-10 1.51e-10 2.02e-10 0.0\n",
-      "\n",
-      "\n",
-      "Optimal solution found in 13 iterations and 0.05s\n",
-      "Objective +1.82548876e-03\n",
-      "Primal infeasibility (abs/rel): 7.56e-10/3.78e-10\n",
-      "Dual infeasibility   (abs/rel): 1.51e-10/1.51e-10\n",
-      "Complementarity gap  (abs/rel): 2.03e-10/2.02e-10\n",
-      "\n",
-      "\n",
-      "Unscaled Primal infeasibility   (abs/rel): 7.56e-10/3.78e-10\n",
-      "Post-solve Primal infeasibility (abs/rel): 7.56e-10/3.78e-10\n",
-      "Barrier finished in 0.05 seconds\n",
-      "cuOpt version: 26.2.0, git hash: 96c11095, host arch: x86_64, device archs: 80-real\n",
-      "CPU: AMD EPYC 7642 48-Core Processor, threads (physical/logical): 96/96, RAM: 165.35 GiB\n",
-      "CUDA 12.9, device: NVIDIA A100 80GB PCIe (ID 0), VRAM: 79.33 GiB\n",
-      "CUDA device UUID: ffffff8a5affffff95ffffffc7-ffffffd05\n",
-      "\n",
-      "Problem has a quadratic objective. Using Barrier.\n",
-      "Solving a problem with 2 constraints, 5 variables (0 integers), and 10 nonzeros\n",
-      "Problem scaling:\n",
-      "Objective coefficents range:          [0e+00, 0e+00]\n",
-      "Constraint matrix coefficients range: [2e-02, 1e+00]\n",
-      "Constraint rhs / bounds range:        [0e+00, 1e+00]\n",
-      "Variable bounds range:                [0e+00, 1e+00]\n",
-      "\n",
-      "Third-party presolve is disabled, skipping\n",
-      "Objective offset 0.000000 scaling_factor 1.000000\n",
-      "Presolved problem: 2 constraints 6 variables 11 nonzeros\n",
-      "Skipping column scaling\n",
-      "Barrier solver: 2 constraints, 6 variables, 11 nonzeros\n",
-      "Quadratic objective matrix: 25 nonzeros\n",
-      "\n",
-      "\n",
-      "Upper bounds                : 5\n",
-      "Density estimator time      : 0.00s\n",
-      "Linear system               : augmented\n",
-      "cuDSS Version               : 0.7.1\n",
-      "cuDSS Threading layer       : libcudss_mtlayer_gomp.so.0\n",
-      "Reordering algorithm        : AMD\n",
-      "Reordering time             : 0.00s\n",
-      "Symbolic factorization time : 0.01s\n",
-      "Total symbolic time         : 0.01s\n",
-      "Symbolic nonzeros in factor : 2.20e+01\n",
-      "\n",
-      "\n",
-      "                  Objective                         Infeasibility        Time\n",
-      "Iter   Primal              Dual                Primal   Dual    Compl.   Elapsed\n",
-      "  0   +1.527020397637e+01 -2.027020397637e+01 5.00e+01 1.00e+01 1.00e+02 0.0\n",
-      "  1   +9.203039706906e-01 -3.077728543129e+00 5.53e+00 2.21e+00 2.55e-01 0.0\n",
-      "  2   +4.911347341043e-02 -1.322780389161e+00 6.74e-01 2.69e-01 4.44e-01 0.0\n",
-      "  3   +1.348358606915e-02 -4.753270587424e-01 6.88e-02 2.75e-02 1.16e-01 0.0\n",
-      "  4   +1.226391550634e-02 -1.048478711776e-01 8.18e-03 3.27e-03 8.23e-02 0.0\n",
-      "  5   +1.089977959130e-02 -1.982960894753e-02 2.03e-03 8.12e-04 1.21e-02 0.0\n",
-      "  6   +5.092134767533e-03 -4.283450588425e-03 2.80e-04 1.12e-04 3.44e-03 0.0\n",
-      "  7   +2.948293376468e-03 +1.153178688177e-03 2.80e-05 1.12e-05 1.10e-03 0.0\n",
-      "  8   +2.488552154932e-03 +1.936365421811e-03 8.59e-06 3.44e-06 2.15e-04 0.0\n",
-      "  9   +2.236827199069e-03 +2.165706108653e-03 1.00e-06 4.00e-07 2.06e-05 0.0\n",
-      " 10   +2.206370833499e-03 +2.198305937478e-03 1.08e-07 4.30e-08 1.84e-06 0.0\n",
-      " 11   +2.202526399336e-03 +2.201703891549e-03 1.08e-08 4.34e-09 1.34e-07 0.0\n",
-      " 12   +2.202121899447e-03 +2.202039777566e-03 1.08e-09 4.34e-10 2.26e-09 0.0\n",
-      "\n",
-      "\n",
-      "Optimal solution found in 12 iterations and 0.04s\n",
-      "Objective +2.20212190e-03\n",
-      "Primal infeasibility (abs/rel): 2.17e-09/1.08e-09\n",
-      "Dual infeasibility   (abs/rel): 4.34e-10/4.34e-10\n",
-      "Complementarity gap  (abs/rel): 2.27e-09/2.26e-09\n",
-      "\n",
-      "\n",
-      "Unscaled Primal infeasibility   (abs/rel): 2.17e-09/1.08e-09\n",
-      "Post-solve Primal infeasibility (abs/rel): 2.17e-09/1.08e-09\n",
-      "Barrier finished in 0.05 seconds\n",
-      "cuOpt version: 26.2.0, git hash: 96c11095, host arch: x86_64, device archs: 80-real\n",
-      "CPU: AMD EPYC 7642 48-Core Processor, threads (physical/logical): 96/96, RAM: 164.80 GiB\n",
-      "CUDA 12.9, device: NVIDIA A100 80GB PCIe (ID 0), VRAM: 79.33 GiB\n",
-      "CUDA device UUID: ffffff8a5affffff95ffffffc7-ffffffd05\n",
-      "\n",
-      "Problem has a quadratic objective. Using Barrier.\n",
-      "Solving a problem with 2 constraints, 5 variables (0 integers), and 10 nonzeros\n",
-      "Problem scaling:\n",
-      "Objective coefficents range:          [0e+00, 0e+00]\n",
-      "Constraint matrix coefficients range: [2e-02, 1e+00]\n",
-      "Constraint rhs / bounds range:        [0e+00, 1e+00]\n",
-      "Variable bounds range:                [0e+00, 1e+00]\n",
-      "\n",
-      "Third-party presolve is disabled, skipping\n",
-      "Objective offset 0.000000 scaling_factor 1.000000\n",
-      "Presolved problem: 2 constraints 6 variables 11 nonzeros\n",
-      "Skipping column scaling\n",
-      "Barrier solver: 2 constraints, 6 variables, 11 nonzeros\n",
-      "Quadratic objective matrix: 25 nonzeros\n",
-      "\n",
-      "\n",
-      "Upper bounds                : 5\n",
-      "Density estimator time      : 0.00s\n",
-      "Linear system               : augmented\n",
-      "cuDSS Version               : 0.7.1\n",
-      "cuDSS Threading layer       : libcudss_mtlayer_gomp.so.0\n",
-      "Reordering algorithm        : AMD\n",
-      "Reordering time             : 0.00s\n",
-      "Symbolic factorization time : 0.01s\n",
-      "Total symbolic time         : 0.01s\n",
-      "Symbolic nonzeros in factor : 2.20e+01\n",
-      "\n",
-      "\n",
-      "                  Objective                         Infeasibility        Time\n",
-      "Iter   Primal              Dual                Primal   Dual    Compl.   Elapsed\n",
-      "  0   +1.529511056122e+01 -2.029511056122e+01 5.00e+01 1.00e+01 1.00e+02 0.6\n",
-      "  1   +8.682581221061e-01 -2.956103118309e+00 5.33e+00 2.13e+00 2.95e-01 0.6\n",
-      "  2   +6.475188707977e-02 -1.645133951334e+00 8.74e-01 3.50e-01 4.69e-01 0.6\n",
-      "  3   +1.415489958464e-02 -5.510228952495e-01 8.96e-02 3.59e-02 1.30e-01 0.6\n",
-      "  4   +1.259006578633e-02 -1.275394609469e-01 1.12e-02 4.47e-03 7.83e-02 0.6\n",
-      "  5   +1.164740173803e-02 -2.042367931952e-02 2.24e-03 8.97e-04 1.75e-02 0.6\n",
-      "  6   +7.793435726319e-03 -6.012585557822e-03 8.05e-04 3.22e-04 3.36e-03 0.6\n",
-      "  7   +3.658213766410e-03 -3.826550234567e-04 8.05e-05 3.22e-05 1.75e-03 0.6\n",
-      "  8   +2.903425452819e-03 +2.213230686080e-03 1.06e-05 4.23e-06 3.65e-04 0.6\n",
-      "  9   +2.691664792860e-03 +2.537143421696e-03 2.39e-06 9.56e-07 6.12e-05 0.6\n",
-      " 10   +2.623432206712e-03 +2.606117551593e-03 2.58e-07 1.03e-07 2.87e-06 0.6\n",
-      " 11   +2.615338190325e-03 +2.613562678865e-03 2.61e-08 1.05e-08 2.44e-07 0.6\n",
-      " 12   +2.614481278841e-03 +2.614303952490e-03 2.61e-09 1.05e-09 4.81e-09 0.6\n",
-      "\n",
-      "\n",
-      "Optimal solution found in 12 iterations and 0.58s\n",
-      "Objective +2.61448128e-03\n",
-      "Primal infeasibility (abs/rel): 5.22e-09/2.61e-09\n",
-      "Dual infeasibility   (abs/rel): 1.05e-09/1.05e-09\n",
-      "Complementarity gap  (abs/rel): 4.83e-09/4.81e-09\n",
-      "\n",
-      "\n",
-      "Unscaled Primal infeasibility   (abs/rel): 5.22e-09/2.61e-09\n",
-      "Post-solve Primal infeasibility (abs/rel): 5.22e-09/2.61e-09\n",
-      "Barrier finished in 0.58 seconds\n",
-      "cuOpt version: 26.2.0, git hash: 96c11095, host arch: x86_64, device archs: 80-real\n",
-      "CPU: AMD EPYC 7642 48-Core Processor, threads (physical/logical): 96/96, RAM: 170.18 GiB\n",
-      "CUDA 12.9, device: NVIDIA A100 80GB PCIe (ID 0), VRAM: 79.33 GiB\n",
-      "CUDA device UUID: ffffff8a5affffff95ffffffc7-ffffffd05\n",
-      "\n",
-      "Problem has a quadratic objective. Using Barrier.\n",
-      "Solving a problem with 2 constraints, 5 variables (0 integers), and 10 nonzeros\n",
-      "Problem scaling:\n",
-      "Objective coefficents range:          [0e+00, 0e+00]\n",
-      "Constraint matrix coefficients range: [2e-02, 1e+00]\n",
-      "Constraint rhs / bounds range:        [0e+00, 1e+00]\n",
-      "Variable bounds range:                [0e+00, 1e+00]\n",
-      "\n",
-      "Third-party presolve is disabled, skipping\n",
-      "Objective offset 0.000000 scaling_factor 1.000000\n",
-      "Presolved problem: 2 constraints 6 variables 11 nonzeros\n",
-      "Skipping column scaling\n",
-      "Barrier solver: 2 constraints, 6 variables, 11 nonzeros\n",
-      "Quadratic objective matrix: 25 nonzeros\n",
-      "\n",
-      "\n",
-      "Upper bounds                : 5\n",
-      "Density estimator time      : 0.00s\n",
-      "Linear system               : augmented\n",
-      "cuDSS Version               : 0.7.1\n",
-      "cuDSS Threading layer       : libcudss_mtlayer_gomp.so.0\n",
-      "Reordering algorithm        : AMD\n",
-      "Reordering time             : 0.00s\n",
-      "Symbolic factorization time : 0.01s\n",
-      "Total symbolic time         : 0.01s\n",
-      "Symbolic nonzeros in factor : 2.20e+01\n",
-      "\n",
-      "\n",
-      "                  Objective                         Infeasibility        Time\n",
-      "Iter   Primal              Dual                Primal   Dual    Compl.   Elapsed\n",
-      "  0   +1.539158841855e+01 -2.039158841855e+01 5.01e+01 1.00e+01 1.00e+02 0.0\n",
-      "  1   +8.222423946816e-01 -2.840480068426e+00 5.15e+00 2.05e+00 3.35e-01 0.0\n",
-      "  2   +9.840264957347e-02 -2.159253241132e+00 1.21e+00 4.82e-01 5.15e-01 0.0\n",
-      "  3   +1.643692694216e-02 -6.964955759785e-01 1.36e-01 5.43e-02 1.56e-01 0.0\n",
-      "  4   +1.311386957443e-02 -1.562719743967e-01 1.65e-02 6.59e-03 6.93e-02 0.0\n",
-      "  5   +1.261624115763e-02 -2.467998280669e-02 2.66e-03 1.06e-03 2.66e-02 0.0\n",
-      "  6   +1.026331880161e-02 -6.366838705073e-03 1.12e-03 4.47e-04 3.64e-03 0.0\n",
-      "  7   +4.277635184636e-03 -3.312730159188e-03 1.12e-04 4.47e-05 2.08e-03 0.0\n",
-      "  8   +3.493390496179e-03 +2.300440387459e-03 1.57e-05 6.25e-06 4.26e-04 0.0\n",
-      "  9   +3.179529045034e-03 +2.998477188940e-03 1.99e-06 7.95e-07 6.41e-05 0.0\n",
-      " 10   +3.107608526017e-03 +3.085234002890e-03 2.19e-07 8.76e-08 4.82e-06 0.0\n",
-      " 11   +3.096897646505e-03 +3.094404640747e-03 2.28e-08 9.13e-09 6.41e-07 0.0\n",
-      " 12   +3.095595011930e-03 +3.095331747353e-03 2.31e-09 9.25e-10 7.11e-08 0.0\n",
-      " 13   +3.095448087577e-03 +3.095421827971e-03 2.34e-10 9.25e-11 2.22e-09 0.0\n",
-      "\n",
-      "\n",
-      "Optimal solution found in 13 iterations and 0.05s\n",
-      "Objective +3.09544809e-03\n",
-      "Primal infeasibility (abs/rel): 4.67e-10/2.34e-10\n",
-      "Dual infeasibility   (abs/rel): 9.25e-11/9.25e-11\n",
-      "Complementarity gap  (abs/rel): 2.22e-09/2.22e-09\n",
-      "\n",
-      "\n",
-      "Unscaled Primal infeasibility   (abs/rel): 4.67e-10/2.34e-10\n",
-      "Post-solve Primal infeasibility (abs/rel): 4.67e-10/2.34e-10\n",
-      "Barrier finished in 0.05 seconds\n",
-      "cuOpt version: 26.2.0, git hash: 96c11095, host arch: x86_64, device archs: 80-real\n",
-      "CPU: AMD EPYC 7642 48-Core Processor, threads (physical/logical): 96/96, RAM: 169.82 GiB\n",
-      "CUDA 12.9, device: NVIDIA A100 80GB PCIe (ID 0), VRAM: 79.33 GiB\n",
-      "CUDA device UUID: ffffff8a5affffff95ffffffc7-ffffffd05\n",
-      "\n",
-      "Problem has a quadratic objective. Using Barrier.\n",
-      "Solving a problem with 2 constraints, 5 variables (0 integers), and 10 nonzeros\n",
-      "Problem scaling:\n",
-      "Objective coefficents range:          [0e+00, 0e+00]\n",
-      "Constraint matrix coefficients range: [2e-02, 1e+00]\n",
-      "Constraint rhs / bounds range:        [0e+00, 1e+00]\n",
-      "Variable bounds range:                [0e+00, 1e+00]\n",
-      "\n",
-      "Third-party presolve is disabled, skipping\n",
-      "Objective offset 0.000000 scaling_factor 1.000000\n",
-      "Presolved problem: 2 constraints 6 variables 11 nonzeros\n",
-      "Skipping column scaling\n",
-      "Barrier solver: 2 constraints, 6 variables, 11 nonzeros\n",
-      "Quadratic objective matrix: 25 nonzeros\n",
-      "\n",
-      "\n",
-      "Upper bounds                : 5\n",
-      "Density estimator time      : 0.00s\n",
-      "Linear system               : augmented\n",
-      "cuDSS Version               : 0.7.1\n",
-      "cuDSS Threading layer       : libcudss_mtlayer_gomp.so.0\n",
-      "Reordering algorithm        : AMD\n",
-      "Reordering time             : 0.00s\n",
-      "Symbolic factorization time : 0.01s\n",
-      "Total symbolic time         : 0.01s\n",
-      "Symbolic nonzeros in factor : 2.20e+01\n",
-      "\n",
-      "\n",
-      "                  Objective                         Infeasibility        Time\n",
-      "Iter   Primal              Dual                Primal   Dual    Compl.   Elapsed\n",
-      "  0   +1.545736587876e+01 -2.045736587876e+01 5.00e+01 1.00e+01 1.00e+02 0.0\n",
-      "  1   +8.482385625152e-01 -2.871008604319e+00 5.14e+00 2.06e+00 2.31e-01 0.0\n",
-      "  2   +4.457784867934e-02 -1.138815589197e+00 6.09e-01 2.44e-01 3.81e-01 0.0\n",
-      "  3   +1.363824292431e-02 -3.746098404758e-01 6.48e-02 2.59e-02 1.04e-01 0.0\n",
-      "  4   +1.321094372177e-02 -8.718157695723e-02 8.87e-03 3.55e-03 5.13e-02 0.0\n",
-      "  5   +1.234090535318e-02 -1.019458739369e-02 1.66e-03 6.66e-04 1.36e-02 0.0\n",
-      "  6   +8.897218445220e-03 -6.217783358345e-04 5.93e-04 2.37e-04 2.72e-03 0.0\n",
-      "  7   +5.050579146793e-03 +2.620287515633e-03 5.93e-05 2.37e-05 1.14e-03 0.0\n",
-      "  8   +4.287478183356e-03 +3.913902136995e-03 8.10e-06 3.24e-06 1.42e-04 0.0\n",
-      "  9   +4.134411674777e-03 +4.091331803009e-03 9.04e-07 3.62e-07 6.84e-06 0.0\n",
-      " 10   +4.117274497131e-03 +4.112886991594e-03 9.16e-08 3.67e-08 5.05e-07 0.0\n",
-      " 11   +4.115567619772e-03 +4.115129261349e-03 9.16e-09 3.67e-09 8.39e-09 0.0\n",
-      "\n",
-      "\n",
-      "Optimal solution found in 11 iterations and 0.04s\n",
-      "Objective +4.11556762e-03\n",
-      "Primal infeasibility (abs/rel): 1.83e-08/9.16e-09\n",
-      "Dual infeasibility   (abs/rel): 3.67e-09/3.67e-09\n",
-      "Complementarity gap  (abs/rel): 8.43e-09/8.39e-09\n",
-      "\n",
-      "\n",
-      "Unscaled Primal infeasibility   (abs/rel): 1.83e-08/9.16e-09\n",
-      "Post-solve Primal infeasibility (abs/rel): 1.83e-08/9.16e-09\n",
-      "Barrier finished in 0.05 seconds\n",
-      "cuOpt version: 26.2.0, git hash: 96c11095, host arch: x86_64, device archs: 80-real\n",
-      "CPU: AMD EPYC 7642 48-Core Processor, threads (physical/logical): 96/96, RAM: 169.65 GiB\n",
-      "CUDA 12.9, device: NVIDIA A100 80GB PCIe (ID 0), VRAM: 79.33 GiB\n",
-      "CUDA device UUID: ffffff8a5affffff95ffffffc7-ffffffd05\n",
-      "\n",
-      "Problem has a quadratic objective. Using Barrier.\n",
-      "Solving a problem with 2 constraints, 5 variables (0 integers), and 10 nonzeros\n",
-      "Problem scaling:\n",
-      "Objective coefficents range:          [0e+00, 0e+00]\n",
-      "Constraint matrix coefficients range: [2e-02, 1e+00]\n",
-      "Constraint rhs / bounds range:        [0e+00, 1e+00]\n",
-      "Variable bounds range:                [0e+00, 1e+00]\n",
-      "\n",
-      "Third-party presolve is disabled, skipping\n",
-      "Objective offset 0.000000 scaling_factor 1.000000\n",
-      "Presolved problem: 2 constraints 6 variables 11 nonzeros\n",
-      "Skipping column scaling\n",
-      "Barrier solver: 2 constraints, 6 variables, 11 nonzeros\n",
-      "Quadratic objective matrix: 25 nonzeros\n",
-      "\n",
-      "\n",
-      "Upper bounds                : 5\n",
-      "Density estimator time      : 0.00s\n",
-      "Linear system               : augmented\n",
-      "cuDSS Version               : 0.7.1\n",
-      "cuDSS Threading layer       : libcudss_mtlayer_gomp.so.0\n",
-      "Reordering algorithm        : AMD\n",
-      "Reordering time             : 0.00s\n",
-      "Symbolic factorization time : 0.01s\n",
-      "Total symbolic time         : 0.01s\n",
-      "Symbolic nonzeros in factor : 2.20e+01\n",
-      "\n",
-      "\n",
-      "                  Objective                         Infeasibility        Time\n",
-      "Iter   Primal              Dual                Primal   Dual    Compl.   Elapsed\n",
-      "  0   +1.557628786038e+01 -2.057628786038e+01 5.00e+01 1.00e+01 1.00e+02 0.0\n",
-      "  1   +8.659023347505e-01 -2.875681955974e+00 5.12e+00 2.05e+00 2.18e-01 0.0\n",
-      "  2   +7.167750560361e-02 -1.065293066373e+00 9.58e-01 3.83e-01 3.49e-01 0.0\n",
-      "  3   +1.405821491932e-02 -4.338529902428e-01 9.58e-02 3.83e-02 1.27e-01 0.0\n",
-      "  4   +1.301482674175e-02 -1.002477407248e-01 1.44e-02 5.77e-03 3.52e-02 0.0\n",
-      "  5   +1.301754794585e-02 -1.582036565924e-02 2.37e-03 9.48e-04 2.06e-02 0.0\n",
-      "  6   +1.114309894533e-02 +9.886304874267e-04 8.02e-04 3.21e-04 3.36e-03 0.0\n",
-      "  7   +6.636933040811e-03 +3.643468259823e-03 8.02e-05 3.21e-05 1.40e-03 0.0\n",
-      "  8   +5.933664965199e-03 +5.544609848139e-03 9.86e-06 3.95e-06 1.25e-04 0.0\n",
-      "  9   +5.814322724046e-03 +5.773597729160e-03 1.03e-06 4.12e-07 3.06e-06 0.0\n",
-      " 10   +5.801914618471e-03 +5.797841393491e-03 1.03e-07 4.12e-08 5.57e-08 0.0\n",
-      " 11   +5.800674192735e-03 +5.800266770107e-03 1.03e-08 4.12e-09 5.83e-10 0.0\n",
-      " 12   +5.800550037868e-03 +5.800509280366e-03 1.03e-09 4.12e-10 5.85e-12 0.0\n",
-      "\n",
-      "\n",
-      "Optimal solution found in 12 iterations and 0.04s\n",
-      "Objective +5.80055004e-03\n",
-      "Primal infeasibility (abs/rel): 2.06e-09/1.03e-09\n",
-      "Dual infeasibility   (abs/rel): 4.12e-10/4.12e-10\n",
-      "Complementarity gap  (abs/rel): 5.88e-12/5.85e-12\n",
-      "\n",
-      "\n",
-      "Unscaled Primal infeasibility   (abs/rel): 2.06e-09/1.03e-09\n",
-      "Post-solve Primal infeasibility (abs/rel): 2.06e-09/1.03e-09\n",
-      "Barrier finished in 0.05 seconds\n",
-      "cuOpt version: 26.2.0, git hash: 96c11095, host arch: x86_64, device archs: 80-real\n",
-      "CPU: AMD EPYC 7642 48-Core Processor, threads (physical/logical): 96/96, RAM: 169.27 GiB\n",
-      "CUDA 12.9, device: NVIDIA A100 80GB PCIe (ID 0), VRAM: 79.33 GiB\n",
-      "CUDA device UUID: ffffff8a5affffff95ffffffc7-ffffffd05\n",
-      "\n",
-      "Problem has a quadratic objective. Using Barrier.\n",
-      "Solving a problem with 2 constraints, 5 variables (0 integers), and 10 nonzeros\n",
-      "Problem scaling:\n",
-      "Objective coefficents range:          [0e+00, 0e+00]\n",
-      "Constraint matrix coefficients range: [2e-02, 1e+00]\n",
-      "Constraint rhs / bounds range:        [0e+00, 1e+00]\n",
-      "Variable bounds range:                [0e+00, 1e+00]\n",
-      "\n",
-      "Third-party presolve is disabled, skipping\n",
-      "Objective offset 0.000000 scaling_factor 1.000000\n",
-      "Presolved problem: 2 constraints 6 variables 11 nonzeros\n",
-      "Skipping column scaling\n",
-      "Barrier solver: 2 constraints, 6 variables, 11 nonzeros\n",
-      "Quadratic objective matrix: 25 nonzeros\n",
-      "\n",
-      "\n",
-      "Upper bounds                : 5\n",
-      "Density estimator time      : 0.00s\n",
-      "Linear system               : augmented\n",
-      "cuDSS Version               : 0.7.1\n",
-      "cuDSS Threading layer       : libcudss_mtlayer_gomp.so.0\n",
-      "Reordering algorithm        : AMD\n",
-      "Reordering time             : 0.00s\n",
-      "Symbolic factorization time : 0.01s\n",
-      "Total symbolic time         : 0.01s\n",
-      "Symbolic nonzeros in factor : 2.20e+01\n",
-      "\n",
-      "\n",
-      "                  Objective                         Infeasibility        Time\n",
-      "Iter   Primal              Dual                Primal   Dual    Compl.   Elapsed\n",
-      "  0   +1.569324326502e+01 -2.069324326502e+01 5.00e+01 1.00e+01 1.00e+02 0.0\n",
-      "  1   +8.889353282905e-01 -2.884493803380e+00 5.13e+00 2.05e+00 2.16e-01 0.0\n",
-      "  2   +1.103355033289e-01 -1.116845990343e+00 1.34e+00 5.37e-01 3.38e-01 0.0\n",
-      "  3   +1.528508108376e-02 -4.963825456806e-01 1.37e-01 5.46e-02 1.45e-01 0.0\n",
-      "  4   +1.296491880599e-02 -9.921448870723e-02 2.02e-02 8.09e-03 2.63e-02 0.0\n",
-      "  5   +1.401696582001e-02 -1.965664024994e-02 2.81e-03 1.12e-03 2.16e-02 0.0\n",
-      "  6   +1.255865587226e-02 +3.667807382478e-03 6.53e-04 2.61e-04 4.11e-03 0.0\n",
-      "  7   +9.013572922879e-03 +7.064574907964e-03 6.53e-05 2.61e-05 1.27e-03 0.0\n",
-      "  8   +8.241428131389e-03 +8.036849981935e-03 6.57e-06 2.63e-06 6.55e-05 0.0\n",
-      "  9   +8.159298357848e-03 +8.138846457327e-03 6.59e-07 2.64e-07 8.27e-07 0.0\n",
-      " 10   +8.151090800473e-03 +8.149045273471e-03 6.59e-08 2.64e-08 8.40e-09 0.0\n",
-      " 11   +8.150269442900e-03 +8.150064790376e-03 6.58e-09 2.64e-09 8.42e-11 0.0\n",
-      "\n",
-      "\n",
-      "Optimal solution found in 11 iterations and 0.04s\n",
-      "Objective +8.15026944e-03\n",
-      "Primal infeasibility (abs/rel): 1.32e-08/6.58e-09\n",
-      "Dual infeasibility   (abs/rel): 2.64e-09/2.64e-09\n",
-      "Complementarity gap  (abs/rel): 8.48e-11/8.42e-11\n",
-      "\n",
-      "\n",
-      "Unscaled Primal infeasibility   (abs/rel): 1.32e-08/6.58e-09\n",
-      "Post-solve Primal infeasibility (abs/rel): 1.32e-08/6.58e-09\n",
-      "Barrier finished in 0.04 seconds\n",
-      "cuOpt version: 26.2.0, git hash: 96c11095, host arch: x86_64, device archs: 80-real\n",
-      "CPU: AMD EPYC 7642 48-Core Processor, threads (physical/logical): 96/96, RAM: 168.69 GiB\n",
-      "CUDA 12.9, device: NVIDIA A100 80GB PCIe (ID 0), VRAM: 79.33 GiB\n",
-      "CUDA device UUID: ffffff8a5affffff95ffffffc7-ffffffd05\n",
-      "\n",
-      "Problem has a quadratic objective. Using Barrier.\n",
-      "Solving a problem with 2 constraints, 5 variables (0 integers), and 10 nonzeros\n",
-      "Problem scaling:\n",
-      "Objective coefficents range:          [0e+00, 0e+00]\n",
-      "Constraint matrix coefficients range: [2e-02, 1e+00]\n",
-      "Constraint rhs / bounds range:        [0e+00, 1e+00]\n",
-      "Variable bounds range:                [0e+00, 1e+00]\n",
-      "\n",
-      "Third-party presolve is disabled, skipping\n",
-      "Objective offset 0.000000 scaling_factor 1.000000\n",
-      "Presolved problem: 2 constraints 6 variables 11 nonzeros\n",
-      "Skipping column scaling\n",
-      "Barrier solver: 2 constraints, 6 variables, 11 nonzeros\n",
-      "Quadratic objective matrix: 25 nonzeros\n",
-      "\n",
-      "\n",
-      "Upper bounds                : 5\n",
-      "Density estimator time      : 0.00s\n",
-      "Linear system               : augmented\n",
-      "cuDSS Version               : 0.7.1\n",
-      "cuDSS Threading layer       : libcudss_mtlayer_gomp.so.0\n",
-      "Reordering algorithm        : AMD\n",
-      "Reordering time             : 0.00s\n",
-      "Symbolic factorization time : 0.01s\n",
-      "Total symbolic time         : 0.01s\n",
-      "Symbolic nonzeros in factor : 2.20e+01\n",
-      "\n",
-      "\n",
-      "                  Objective                         Infeasibility        Time\n",
-      "Iter   Primal              Dual                Primal   Dual    Compl.   Elapsed\n",
-      "  0   +1.581072518896e+01 -2.081072518896e+01 5.00e+01 1.00e+01 1.00e+02 0.0\n",
-      "  1   +9.171944797985e-01 -2.897547731585e+00 5.15e+00 2.06e+00 2.51e-01 0.0\n",
-      "  2   +1.383940670620e-01 -1.167543411320e+00 1.57e+00 6.29e-01 3.46e-01 0.0\n",
-      "  3   +1.641173715426e-02 -5.259369594647e-01 1.66e-01 6.63e-02 1.54e-01 0.0\n",
-      "  4   +1.308288200706e-02 -8.462785413487e-02 2.37e-02 9.46e-03 3.10e-02 0.0\n",
-      "  5   +1.524549024622e-02 -1.827807482086e-02 3.43e-03 1.37e-03 1.42e-02 0.0\n",
-      "  6   +1.415448154718e-02 +5.450683122898e-03 6.73e-04 2.69e-04 4.77e-03 0.0\n",
-      "  7   +1.295096437388e-02 +1.001622941609e-02 2.22e-04 8.89e-05 1.16e-03 0.0\n",
-      "  8   +1.133555004813e-02 +1.083410691317e-02 2.65e-05 1.06e-05 2.40e-04 0.0\n",
-      "  9   +1.118171065691e-02 +1.113109188056e-02 2.68e-06 1.07e-06 7.05e-06 0.0\n",
-      " 10   +1.116605007227e-02 +1.116098343772e-02 2.67e-07 1.07e-07 7.50e-08 0.0\n",
-      " 11   +1.116447904803e-02 +1.116397177011e-02 2.67e-08 1.07e-08 7.54e-10 0.0\n",
-      " 12   +1.116432138326e-02 +1.116427059189e-02 2.67e-09 1.07e-09 7.57e-12 0.0\n",
-      "\n",
-      "\n",
-      "Optimal solution found in 12 iterations and 0.04s\n",
-      "Objective +1.11643214e-02\n",
-      "Primal infeasibility (abs/rel): 5.34e-09/2.67e-09\n",
-      "Dual infeasibility   (abs/rel): 1.07e-09/1.07e-09\n",
-      "Complementarity gap  (abs/rel): 7.65e-12/7.57e-12\n",
-      "\n",
-      "\n",
-      "Unscaled Primal infeasibility   (abs/rel): 5.34e-09/2.67e-09\n",
-      "Post-solve Primal infeasibility (abs/rel): 5.34e-09/2.67e-09\n",
-      "Barrier finished in 0.05 seconds\n",
-      "cuOpt version: 26.2.0, git hash: 96c11095, host arch: x86_64, device archs: 80-real\n",
-      "CPU: AMD EPYC 7642 48-Core Processor, threads (physical/logical): 96/96, RAM: 168.20 GiB\n",
-      "CUDA 12.9, device: NVIDIA A100 80GB PCIe (ID 0), VRAM: 79.33 GiB\n",
-      "CUDA device UUID: ffffff8a5affffff95ffffffc7-ffffffd05\n",
-      "\n",
-      "Problem has a quadratic objective. Using Barrier.\n",
-      "Solving a problem with 2 constraints, 5 variables (0 integers), and 10 nonzeros\n",
-      "Problem scaling:\n",
-      "Objective coefficents range:          [0e+00, 0e+00]\n",
-      "Constraint matrix coefficients range: [2e-02, 1e+00]\n",
-      "Constraint rhs / bounds range:        [0e+00, 1e+00]\n",
-      "Variable bounds range:                [0e+00, 1e+00]\n",
-      "\n",
-      "Third-party presolve is disabled, skipping\n",
-      "Objective offset 0.000000 scaling_factor 1.000000\n",
-      "Presolved problem: 2 constraints 6 variables 11 nonzeros\n",
-      "Skipping column scaling\n",
-      "Barrier solver: 2 constraints, 6 variables, 11 nonzeros\n",
-      "Quadratic objective matrix: 25 nonzeros\n",
-      "\n",
-      "\n",
-      "Upper bounds                : 5\n",
-      "Density estimator time      : 0.00s\n",
-      "Linear system               : augmented\n",
-      "cuDSS Version               : 0.7.1\n",
-      "cuDSS Threading layer       : libcudss_mtlayer_gomp.so.0\n",
-      "Reordering algorithm        : AMD\n",
-      "Reordering time             : 0.00s\n",
-      "Symbolic factorization time : 0.01s\n",
-      "Total symbolic time         : 0.01s\n",
-      "Symbolic nonzeros in factor : 2.20e+01\n",
-      "\n",
-      "\n",
-      "                  Objective                         Infeasibility        Time\n",
-      "Iter   Primal              Dual                Primal   Dual    Compl.   Elapsed\n",
-      "  0   +1.592890286897e+01 -2.092890286897e+01 5.00e+01 1.00e+01 1.00e+02 0.0\n",
-      "  1   +9.509879095630e-01 -2.915278097713e+00 5.20e+00 2.08e+00 2.83e-01 0.0\n",
-      "  2   +1.714273335289e-01 -1.227609058513e+00 1.81e+00 7.23e-01 3.57e-01 0.0\n",
-      "  3   +1.761379308351e-02 -5.659243659774e-01 1.96e-01 7.83e-02 1.61e-01 0.0\n",
-      "  4   +1.316441992363e-02 -6.767628995385e-02 2.68e-02 1.07e-02 4.95e-02 0.0\n",
-      "  5   +1.662602390227e-02 -1.031846552591e-02 5.51e-03 2.20e-03 7.83e-03 0.0\n",
-      "  6   +1.707657476470e-02 +7.168532686553e-03 1.18e-03 4.72e-04 4.78e-03 0.0\n",
-      "  7   +1.573572513309e-02 +1.359744989972e-02 1.18e-04 4.72e-05 1.35e-03 0.0\n",
-      "  8   +1.514827700946e-02 +1.461435506411e-02 2.72e-05 1.09e-05 2.50e-04 0.0\n",
-      "  9   +1.488838290979e-02 +1.481141124768e-02 4.01e-06 1.60e-06 2.54e-05 0.0\n",
-      " 10   +1.484745370510e-02 +1.483974995630e-02 4.02e-07 1.61e-07 4.13e-07 0.0\n",
-      " 11   +1.484336964620e-02 +1.484259713333e-02 4.02e-08 1.61e-08 4.04e-09 0.0\n",
-      " 12   +1.484295926370e-02 +1.484288178485e-02 4.01e-09 1.61e-09 4.03e-11 0.0\n",
-      "\n",
-      "\n",
-      "Optimal solution found in 12 iterations and 0.05s\n",
-      "Objective +1.48429593e-02\n",
-      "Primal infeasibility (abs/rel): 8.03e-09/4.01e-09\n",
-      "Dual infeasibility   (abs/rel): 1.61e-09/1.61e-09\n",
-      "Complementarity gap  (abs/rel): 4.09e-11/4.03e-11\n",
-      "\n",
-      "\n",
-      "Unscaled Primal infeasibility   (abs/rel): 8.03e-09/4.01e-09\n",
-      "Post-solve Primal infeasibility (abs/rel): 8.03e-09/4.01e-09\n",
-      "Barrier finished in 0.05 seconds\n",
-      "cuOpt version: 26.2.0, git hash: 96c11095, host arch: x86_64, device archs: 80-real\n",
-      "CPU: AMD EPYC 7642 48-Core Processor, threads (physical/logical): 96/96, RAM: 167.73 GiB\n",
-      "CUDA 12.9, device: NVIDIA A100 80GB PCIe (ID 0), VRAM: 79.33 GiB\n",
-      "CUDA device UUID: ffffff8a5affffff95ffffffc7-ffffffd05\n",
-      "\n",
-      "Problem has a quadratic objective. Using Barrier.\n",
-      "Solving a problem with 2 constraints, 5 variables (0 integers), and 10 nonzeros\n",
-      "Problem scaling:\n",
-      "Objective coefficents range:          [0e+00, 0e+00]\n",
-      "Constraint matrix coefficients range: [2e-02, 1e+00]\n",
-      "Constraint rhs / bounds range:        [0e+00, 1e+00]\n",
-      "Variable bounds range:                [0e+00, 1e+00]\n",
-      "\n",
-      "Third-party presolve is disabled, skipping\n",
-      "Objective offset 0.000000 scaling_factor 1.000000\n",
-      "Presolved problem: 2 constraints 6 variables 11 nonzeros\n",
-      "Skipping column scaling\n",
-      "Barrier solver: 2 constraints, 6 variables, 11 nonzeros\n",
-      "Quadratic objective matrix: 25 nonzeros\n",
-      "\n",
-      "\n",
-      "Upper bounds                : 5\n",
-      "Density estimator time      : 0.00s\n",
-      "Linear system               : augmented\n",
-      "cuDSS Version               : 0.7.1\n",
-      "cuDSS Threading layer       : libcudss_mtlayer_gomp.so.0\n",
-      "Reordering algorithm        : AMD\n",
-      "Reordering time             : 0.00s\n",
-      "Symbolic factorization time : 0.01s\n",
-      "Total symbolic time         : 0.01s\n",
-      "Symbolic nonzeros in factor : 2.20e+01\n",
-      "\n",
-      "\n",
-      "                  Objective                         Infeasibility        Time\n",
-      "Iter   Primal              Dual                Primal   Dual    Compl.   Elapsed\n",
-      "  0   +1.637724249045e+01 -2.137775355618e+01 5.05e+01 1.00e+01 1.01e+02 0.0\n",
-      "  1   +1.009327908219e+00 -2.960496432529e+00 5.32e+00 2.10e+00 3.10e-01 0.0\n",
-      "  2   +2.055211533312e-01 -1.296078700636e+00 2.03e+00 8.02e-01 3.83e-01 0.0\n",
-      "  3   +1.866254449216e-02 -6.300274806275e-01 2.25e-01 8.90e-02 1.63e-01 0.0\n",
-      "  4   +1.336190661690e-02 -7.006679375187e-02 3.62e-02 1.43e-02 8.33e-02 0.0\n",
-      "  5   +1.779986132114e-02 +1.274846073199e-02 9.96e-03 3.94e-03 2.83e-02 0.0\n",
-      "  6   +2.484108766502e-02 +2.527187668379e-02 1.92e-03 7.61e-04 5.90e-03 0.0\n",
-      "  7   +2.836899074866e-02 +2.854995238954e-02 2.24e-04 8.86e-05 5.81e-04 0.0\n",
-      "  8   +2.887738461608e-02 +2.889675739448e-02 2.24e-05 8.88e-06 9.14e-06 0.0\n",
-      "  9   +2.892900298005e-02 +2.893095193795e-02 2.24e-06 8.88e-07 9.24e-08 0.0\n",
-      " 10   +2.893417061151e-02 +2.893436579742e-02 2.24e-07 8.88e-08 1.35e-09 0.0\n",
-      " 11   +2.893468760724e-02 +2.893470712721e-02 2.24e-08 8.88e-09 1.43e-11 0.0\n",
-      " 12   +2.893473930761e-02 +2.893474125962e-02 2.24e-09 8.88e-10 1.44e-13 0.0\n",
-      "\n",
-      "\n",
-      "Optimal solution found in 12 iterations and 0.04s\n",
-      "Objective +2.89347393e-02\n",
-      "Primal infeasibility (abs/rel): 4.49e-09/2.24e-09\n",
-      "Dual infeasibility   (abs/rel): 8.88e-10/8.88e-10\n",
-      "Complementarity gap  (abs/rel): 1.48e-13/1.44e-13\n",
-      "\n",
-      "\n",
-      "Unscaled Primal infeasibility   (abs/rel): 4.49e-09/2.24e-09\n",
-      "Post-solve Primal infeasibility (abs/rel): 4.49e-09/2.24e-09\n",
-      "Barrier finished in 0.05 seconds\n",
-      "Computed 20 points on the efficient frontier.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Compute efficient frontier by sweeping target returns\n",
     "min_ret = ret_mv\n",
@@ -1864,26 +434,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "ed46fea8-2410-4f87-9bcb-00099eda9300",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-      "text/plain": [
-       "<Figure size 900x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 588,
-       "width": 887
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "fig, ax = plt.subplots(figsize=(9, 6))\n",
     "\n",
@@ -1925,89 +479,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "ba32fe1e-fed7-4220-a678-3ef709441f22",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "cuOpt version: 26.2.0, git hash: 96c11095, host arch: x86_64, device archs: 80-real\n",
-      "CPU: AMD EPYC 7642 48-Core Processor, threads (physical/logical): 96/96, RAM: 167.74 GiB\n",
-      "CUDA 12.9, device: NVIDIA A100 80GB PCIe (ID 0), VRAM: 79.33 GiB\n",
-      "CUDA device UUID: ffffff8a5affffff95ffffffc7-ffffffd05\n",
-      "\n",
-      "Problem has a quadratic objective. Using Barrier.\n",
-      "Solving a problem with 2 constraints, 5 variables (0 integers), and 10 nonzeros\n",
-      "Problem scaling:\n",
-      "Objective coefficents range:          [0e+00, 0e+00]\n",
-      "Constraint matrix coefficients range: [2e-02, 1e+00]\n",
-      "Constraint rhs / bounds range:        [0e+00, 1e+00]\n",
-      "Variable bounds range:                [0e+00, 5e-01]\n",
-      "\n",
-      "Third-party presolve is disabled, skipping\n",
-      "Objective offset 0.000000 scaling_factor 1.000000\n",
-      "Presolved problem: 2 constraints 6 variables 11 nonzeros\n",
-      "Skipping column scaling\n",
-      "Barrier solver: 2 constraints, 6 variables, 11 nonzeros\n",
-      "Quadratic objective matrix: 25 nonzeros\n",
-      "\n",
-      "\n",
-      "Upper bounds                : 5\n",
-      "Density estimator time      : 0.00s\n",
-      "Linear system               : augmented\n",
-      "cuDSS Version               : 0.7.1\n",
-      "cuDSS Threading layer       : libcudss_mtlayer_gomp.so.0\n",
-      "Reordering algorithm        : AMD\n",
-      "Reordering time             : 0.00s\n",
-      "Symbolic factorization time : 0.01s\n",
-      "Total symbolic time         : 0.01s\n",
-      "Symbolic nonzeros in factor : 2.20e+01\n",
-      "\n",
-      "\n",
-      "                  Objective                         Infeasibility        Time\n",
-      "Iter   Primal              Dual                Primal   Dual    Compl.   Elapsed\n",
-      "  0   +3.587155347934e+01 -3.851550444719e+01 7.87e+01 1.00e+01 1.57e+02 0.0\n",
-      "  1   +5.067506558709e-01 -3.153955713295e+00 3.99e+00 1.01e+00 1.48e+00 0.0\n",
-      "  2   +2.232203683042e-02 -2.087895052501e+00 3.99e-01 1.01e-01 3.45e-01 0.0\n",
-      "  3   +9.452138738732e-03 -5.792642520708e-01 3.99e-02 1.01e-02 1.22e-01 0.0\n",
-      "  4   +9.272799588725e-03 -9.039922106078e-02 4.49e-03 1.14e-03 6.30e-02 0.0\n",
-      "  5   +7.681308461793e-03 -1.995179346922e-02 1.22e-03 3.10e-04 5.93e-03 0.0\n",
-      "  6   +4.362902291796e-03 -3.752060839684e-03 2.90e-04 7.37e-05 2.44e-03 0.0\n",
-      "  7   +2.891194357896e-03 +2.351247719196e-04 8.73e-05 2.22e-05 8.67e-04 0.0\n",
-      "  8   +1.931077930102e-03 +1.530335579382e-03 8.73e-06 2.22e-06 2.27e-04 0.0\n",
-      "  9   +1.768378608060e-03 +1.716379615938e-03 1.04e-06 2.63e-07 1.21e-05 0.0\n",
-      " 10   +1.744316691703e-03 +1.738610448922e-03 1.09e-07 2.78e-08 8.20e-07 0.1\n",
-      " 11   +1.741459082549e-03 +1.740881176226e-03 1.10e-08 2.80e-09 6.07e-08 0.1\n",
-      " 12   +1.741163854212e-03 +1.741106128826e-03 1.10e-09 2.80e-10 1.06e-09 0.1\n",
-      "\n",
-      "\n",
-      "Optimal solution found in 12 iterations and 0.05s\n",
-      "Objective +1.74116385e-03\n",
-      "Primal infeasibility (abs/rel): 2.20e-09/1.10e-09\n",
-      "Dual infeasibility   (abs/rel): 2.80e-10/2.80e-10\n",
-      "Complementarity gap  (abs/rel): 1.06e-09/1.06e-09\n",
-      "\n",
-      "\n",
-      "Unscaled Primal infeasibility   (abs/rel): 2.20e-09/1.10e-09\n",
-      "Post-solve Primal infeasibility (abs/rel): 2.20e-09/1.10e-09\n",
-      "Barrier finished in 0.06 seconds\n",
-      "Status: optimal\n",
-      "\n",
-      "Constrained Portfolio (Target >= 5%, Max Weight 50%):\n",
-      "  Expected Return: 5.00%\n",
-      "  Volatility:      4.17%\n",
-      "\n",
-      "Weights:\n",
-      "  Cash        :  25.1%\n",
-      "  US Equity   :  16.5%\n",
-      "  Intl Equity :   0.0%\n",
-      "  Bond        :  50.0%\n",
-      "  REIT/Gold   :   8.4%\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "target_5pct = 0.05\n",
     "max_wt = 0.50\n",
@@ -2029,26 +504,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "id": "241d2789-6009-4e8f-aa5c-90dd516a09bb",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x500 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 488,
-       "width": 1188
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n",
     "\n",
@@ -2094,103 +553,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "id": "02c36d23-ae18-46fe-a4b3-c9bc65452424",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "cuOpt version: 26.2.0, git hash: 96c11095, host arch: x86_64, device archs: 80-real\n",
-      "CPU: AMD EPYC 7642 48-Core Processor, threads (physical/logical): 96/96, RAM: 170.92 GiB\n",
-      "CUDA 12.9, device: NVIDIA A100 80GB PCIe (ID 0), VRAM: 79.33 GiB\n",
-      "CUDA device UUID: ffffff8a5affffff95ffffffc7-ffffffd05\n",
-      "\n",
-      "Problem has a quadratic objective. Using Barrier.\n",
-      "Solving a problem with 3 constraints, 5 variables (0 integers), and 12 nonzeros\n",
-      "Problem scaling:\n",
-      "Objective coefficents range:          [0e+00, 0e+00]\n",
-      "Constraint matrix coefficients range: [2e-02, 1e+00]\n",
-      "Constraint rhs / bounds range:        [0e+00, 1e+00]\n",
-      "Variable bounds range:                [0e+00, 6e-01]\n",
-      "\n",
-      "Third-party presolve is disabled, skipping\n",
-      "Objective offset 0.000000 scaling_factor 1.000000\n",
-      "Presolved problem: 3 constraints 7 variables 14 nonzeros\n",
-      "Skipping column scaling\n",
-      "Barrier solver: 3 constraints, 7 variables, 14 nonzeros\n",
-      "Quadratic objective matrix: 25 nonzeros\n",
-      "\n",
-      "\n",
-      "Upper bounds                : 5\n",
-      "Density estimator time      : 0.00s\n",
-      "Linear system               : augmented\n",
-      "cuDSS Version               : 0.7.1\n",
-      "cuDSS Threading layer       : libcudss_mtlayer_gomp.so.0\n",
-      "Reordering algorithm        : AMD\n",
-      "Reordering time             : 0.00s\n",
-      "Symbolic factorization time : 0.01s\n",
-      "Total symbolic time         : 0.01s\n",
-      "Symbolic nonzeros in factor : 2.80e+01\n",
-      "\n",
-      "\n",
-      "                  Objective                         Infeasibility        Time\n",
-      "Iter   Primal              Dual                Primal   Dual    Compl.   Elapsed\n",
-      "  0   +1.548744684335e+01 -1.848753907049e+01 5.00e+01 1.00e+01 1.04e+02 0.0\n",
-      "  1   +1.197922914774e+01 -2.147646235921e+01 2.19e+01 8.75e+00 1.21e+00 0.0\n",
-      "  2   +1.702352680531e+00 -1.327495860343e+01 8.40e+00 3.36e+00 3.67e+00 0.0\n",
-      "  3   +4.054146263100e-02 -6.509627179551e+00 1.32e+00 5.29e-01 6.32e+00 0.0\n",
-      "  4   +3.335565727001e-03 -2.268702185334e+00 3.71e-01 1.48e-01 1.79e+00 0.0\n",
-      "  5   +5.841029727822e-03 -2.344145665075e+00 1.15e-01 4.58e-02 3.14e-01 0.0\n",
-      "  6   +4.660285660828e-03 -4.805556743302e-01 1.97e-02 7.90e-03 9.36e-02 0.0\n",
-      "  7   +4.875793566180e-03 -7.089111491550e-02 2.51e-03 1.00e-03 3.22e-02 0.0\n",
-      "  8   +4.071306628726e-03 -9.243521245623e-03 4.33e-04 1.73e-04 2.70e-03 0.0\n",
-      "  9   +2.900357155089e-03 -1.136598909927e-03 1.24e-04 4.97e-05 9.65e-04 0.0\n",
-      " 10   +1.965539543876e-03 +1.295771690277e-03 1.44e-05 5.74e-06 3.40e-04 0.0\n",
-      " 11   +1.753639586900e-03 +1.678061851141e-03 1.49e-06 5.96e-07 3.00e-05 0.0\n",
-      " 12   +1.727056647227e-03 +1.719380036113e-03 1.51e-07 6.04e-08 7.20e-07 0.1\n",
-      " 13   +1.724339123344e-03 +1.723572472174e-03 1.51e-08 6.05e-09 8.61e-09 0.1\n",
-      " 14   +1.724067837032e-03 +1.723991184089e-03 1.51e-09 6.05e-10 8.71e-11 0.1\n",
-      "\n",
-      "\n",
-      "Optimal solution found in 14 iterations and 0.05s\n",
-      "Objective +1.72406784e-03\n",
-      "Primal infeasibility (abs/rel): 3.02e-09/1.51e-09\n",
-      "Dual infeasibility   (abs/rel): 6.05e-10/6.05e-10\n",
-      "Complementarity gap  (abs/rel): 8.72e-11/8.71e-11\n",
-      "\n",
-      "\n",
-      "Unscaled Primal infeasibility   (abs/rel): 3.02e-09/1.51e-09\n",
-      "Post-solve Primal infeasibility (abs/rel): 3.02e-09/1.51e-09\n",
-      "Barrier finished in 0.52 seconds\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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DNWjQQM2aNdOQIUO0fPlyzZ8/X8ePH1efPn00d+7cDPuQP5Ck//znPzp48KAqV66siRMnOrw/eQSzrKbXL1u2rJYtW2Z5Qv7999/P0E4e4dY/wIwdOzZDgZSZv79/hidXFy9ebPMY5BFutWDBAstsZSNHjsy0H3lUOLhq3Id88myOfHa3V3bLFT388MOWpbHPnj2rb775JlfnQ97Jzb2xPbi+eD57713sxfWlcHDltcF8LkfupXJ6LrjHm2++qc8++0xSWgHMrFmzcn3MrK5FxYsX15IlS1SyZElJ0ieffGJXfsG1goODs1yFoGfPnpowYYIkKS4uTp9//nmOzpOTawzXl4Ljyy+/lJRW4Hbvvfc65Zj56fpCkRQKpOLFi1te2zM1n/mpZ3um/APMHMmz9E/Wk2eeLTIyUp07d9aVK1fk7e2tRYsW6a677sq0P3mE7IwYMUKDBg1Samqqxo8fr8uXL1vayB8cPHhQb775pqS0gfn005/bizyCvapXr6577rlHknT06FGdPXvW0kYeIX0OSFLnzp0z7duxY0f5+KRNXL1z506bxyCPcCt7B+DIo8LBVeM+5JPncvSzuzM9/PDDltfmJSpQ8GR1b2wPri+eLy/+eJgdri8FnyuvDeZzOXIvldNzwfU+/vhjvfzyy5KkunXr6vvvv8/RuKGjgoODNWTIEElpeWOexQoFy0MPPWQppMrp75OcXGO4vhQMO3bs0MGDByVJvXv3zraQ21lceX2hSAoFkp+fn0qVKiUpbVrbrFy5csVy8TWvuwrYo2LFipbX2eXZqVOnLK/JM8919uxZderUSWfPnpXJZNIXX3yhPn36ZLkPeQR7mPPoxo0b+vHHHy3byR+8++67SkxMVPXq1RUXF6dFixZZff3555+W/hs2bLBsN9//kEdwRL169Syvz5w5Y3lNHqFo0aIqU6aM5fusfrZ+fn4qXbq0JCk6OtqynTxCZnbt2qUDBw5ISnuqtUSJEpn2JY8KB1eN+5BPniknn92dKbP7KRQ8uflZcn3xbI7cuzgT15eCz5XXBvO5bty4oatXr9p1rjJlymRYeg/509dff63HHntMklSlShWtW7fO8hncFbgWFXyhoaGWz1s5/RmarzHZXcukf68x3OcUDPPnz7e8dtZSe/Zy1fXFJ8+ODOSxevXqacuWLTp69KiSk5MtTyrfylzpKEm33Xabq8KDB0h/IU6fR7aQZ57v4sWLuueee3T8+HFJaTO62HNzQB7BHun/6Hzy5EnL69q1a8vb21spKSnkTyFlnor4+PHjGjp0aLb9X3/9dcvryMhIBQYGch2CQzKbjps8giTVr19fmzZtkiSlpKRk2dfcnv5zGnmEzKQfgMtuuRryqPBwxbgP+eR5cvrZ3ZmyWt4EBUtufpY5ub74+PioVq1aOT4nXMeRexdn4vpS8Lny3qNevXqWZRkPHjyoli1b2uyXnJysY8eO5fg8cK1Vq1bp/vvvV2pqqsqVK6f169dnKL5zBa5FniG3P8d69epp9+7dunbtms6dO6ewsDCb/aKiohQTEyOJa0xBkJSUpEWLFklKK6br2rWrS8/vqusLM0mhwGrTpo2ktCr43bt3Z9ov/TSBrVu3zvO44DmqVaum8uXLS8p+uslffvlFklShQgVVrVo1r0ODi127dk1dunSxPCH21ltvady4cXbtSx7BHukr4tNPOevr66vmzZtLkrZt25blGszm/CpatKiaNm2aR5GiIOI6BEeYf9dJsuSNRB4hTfplisx/fLYlJiZGFy9elJSWB2bkEWxJPwBXpkwZdevWLcv+5FHh4YpxH/LJs+Tms7szZXY/hYInNz/LZs2aydfXV1LW15fExERt377dsk+RIkVyEClcydF7F2fi+lLwufLew3wvld25du3aZZmVk7+h5W/r16/X4MGDlZycrFKlSmndunWqUaOGy+PgWlTwRUdHW8ZtcvoztPcaw9/pC5Y1a9bo0qVLkqT77rsv04eV8oqrri8USaHA6tu3r+X1nDlzbPZJTU21PNUREhKiDh06uCI0eAiTyWSZjv3gwYOWAYtbbd++3fJUR58+faii9zBxcXHq0aOH9uzZI0l65ZVX9MILL9i9P3kEeyxdutTyOjw8PEOb+fddTEyMli9fbnP/06dP6+eff5YkdezY0bIeOAq+uXPnyjCMLL8mTpxo6b9x40bLdvMAGtch2CsyMlLr1q2TJNWoUSNDcQt5BEkaMGCA5fWKFSsy7bdixQoZhiFJatu2rWU7eQRbfvjhB8uyjPYMwJFHhYcrxn3IJ8+R28/uzvTxxx9bXrdr184tMSD3sro3tkfx4sXVsWNHSdLPP/+c6VI0y5cvt8yu0K9fv1xEDFdx9N7Fmbi+FHyuvPdo3769goODJUnz5s2zfEa71dy5cy2vuQ7lXxEREerTp48SEhIUHBysn376SfXr13d5HNeuXbMUigYEBPCwbgH1ySefWK4JOf190rt3b3l5pZWaZPZ5Tfr3GuPl5aXevXvn6FxwHXfNlim5+PpiAAVY27ZtDUmGj4+PERERYdU+bdo0Q5IhyZg4caLrA0S+EhkZacmHkSNH2rXPoUOHDG9vb0OS0bRpUyMuLi5De1xcnNG0aVNLHh4+fDgPIoe7JCQkGJ07d7bkzRNPPJGj45BHhdecOXOM+Pj4LPu88847lhyrVq2akZycnKH90qVLRnBwsCHJqFKlinHx4sUM7cnJyUavXr0sx9i4caOz3wbyuYkTJ2b78+c6hFWrVhlJSUmZtp87d85o3LixJZf++9//WvUhj2AYhtGtWzdDkuHl5WX8/PPPVu1RUVFGxYoVDUmGr6+vcfr06Qzt5BFuNWDAAMu1Z/fu3XbtQx4VHrkd99m4cWO24wDkU8HnrM/uc+bMyTKf/vjjD+PIkSNZHuPjjz+2HCMsLMyIjY3NUSzIW864N84uXwzDMNavX2/p07t3b6vP+9HR0UblypUNSUZISIhx+fLlXL0vuEZO7l24vngud/7NYeTIkdmOB7322muWPtOmTbNqj4iIMHx8fAxJRrt27eyKH7mTk5zZu3evERISYkgyAgMDjV9//TVH527Xrp3l3JGRkVbtP/zwg1U+pnf9+vUM91yPP/54juKA/RzNl8jISGPPnj1Z9lm9erXh6+trSDL8/f2txm3MsssXwzCMESNGWPosXbrUqn3JkiUO5ztyLifXl/QuXbpkyY3w8HCH9i1o1xfXzo8FONl7772n1q1bKz4+Xp07d9bLL7+sDh06KD4+XosWLdInn3wiSapdu7aeeeYZN0cLV/v111919OhRy/fmqSMl6ejRoxmekJCkUaNGWR2jdu3aeu655/TWW29p165dat26tV544QXVqFFDx44d09tvv629e/dKkp577jnVqlUrT94L3GPo0KFau3atJOnuu+/WmDFj9Oeff2ba39fXV7Vr17baTh4VXpMmTdIzzzyjAQMGqE2bNqpRo4aKFSum69eva//+/Vq4cKG2bt0qKS1/PvnkE3l7e2c4RsmSJfX222/rkUce0cmTJ9WiRQu98sorCg8P19mzZzVz5kxt3LhRUlrOtm/f3tVvEwUA1yE8/vjjSkpK0oABA9SqVStVrVpV/v7+unjxojZt2qSPP/7Ycq/Upk0bm0vTkEeQpJkzZ2rbtm26evWqevbsqSeffFLdu3eXv7+/duzYoTfffNMyU8Lrr79uNesCeYT0rly5ou+++06S1KBBA91xxx127UceFR6uGPchnwo+Z312z87u3bs1duxYdejQQd26dVN4eLhKlSql5ORkHTx4UAsXLrTE4e3trU8++USBgYE5e1PIU864N7bH3XffrSFDhmjRokVatWqV7rnnHj355JMqX7689u/frzfeeEP//POPJOntt99WiRIlnPYekTdyeu+SHa4vBUdB+5vDc889p8WLF+vw4cN6/vnndfToUQ0ZMkT+/v7auHGj/vOf/yg5OVn+/v6aOXNmjs+DzOU2Z44dO6YuXbro6tWrkqSpU6cqODg4y3ud0NBQhYaGOhzrW2+9pWHDhql///4ZxrKvXbumiIgIffTRR5bfW3Xq1NGkSZMcPgeyltt8OXHihDp06KBWrVqpV69eatiwoSUXjh8/rmXLlmnZsmWWWaRmzJjh8GyZ6b3xxhv68ccfFR0draFDh2rXrl3q2bOnJOm7777Tf//7X0lpy9NOnTo1x+eBbc74nZTeokWLlJiYKMn5s0jlu+tLnpZgAS6watUqIygoyFJZeOtX7dq1s30KA54p/ZMU9nxlJiUlxXjggQey3HfMmDFGSkqKC98dXMGR/NH/zfKTGfKocKpSpYpduVOxYkVj7dq1WR5rwoQJhslkyvQY3bt3z3bWKngme2aSMgyuQ4WdvdejAQMGGFeuXMn0OOQRDMMwtmzZYpQtWzbTHDCZTMarr76a6f7kEcxmz55t+ZnberI9K+RR4ZGbcR97ZpIyDPKpoHPWZ/fsZnpJ357VV6lSpYyVK1fm7ZtGrjjj3tiemaQMI21GmO7du2d6Di8vL1ZAKEByeu/C9cVz5Ke/Odgzk5RhGMaRI0eMWrVqZXqeoKAgY/Xq1bn5Z0EWcpsz9l4f0n9l9nslu5le0rdn9dWuXbtMZx9C7uQ2X9J//snqKyAgwPj444+zjMWemaQMwzC2b99uhIWFZXqusLAwY/v27bn9p4ENzvqdZNaiRQtDkuHt7W1ERUU5FEtBu74wkxQKvF69eumPP/7Qe++9pzVr1uj06dPy9fVVzZo1NWjQII0fP14BAQHuDhMFmJeXlz7//HMNGDBAn3zyiXbu3KmLFy+qdOnSatasmR5++GF169bN3WEinyOPCqeffvpJa9as0datW3X06FGdP39ely5dkr+/v0JDQ9WoUSP17NlTgwcPzvZ31eTJk9WlSxfNmjVLW7Zs0fnz5xUSEqKGDRtq9OjRGjp0qIveFQoqrkOF27x587R582Zt27ZNx48f18WLFxUTE6NixYqpUqVKuvPOOzVy5Ei1atUqy+OQR5DSZlT466+/9P7772vlypWKjIxUYmKiypUrp/bt2+vxxx9X48aNM92fPILZl19+KSltRoRhw4Y5tC95VHi4YtyHfII9unfvrs8//1zbtm3T3r17LZ/vDMNQyZIl1bBhQ3Xt2lWjRo1SUFCQu8NFFpx1b2wPf39/rVmzRl999ZXmzp2rffv26erVqypbtqzatm2r8ePHO+U8cI3c3LtkhetL4ePKe4+aNWtq7969mjVrlpYuXaqjR48qMTFRlSpVUvfu3fXEE0+oSpUqTjkXCrYZM2Zo/fr12rZtmw4dOqSLFy/q6tWrCggIUPny5dWiRQsNHTpUnTt3lslkcne4sKFJkyZasGCBtm3bpl27dikqKkoXL15UcnKySpQoofr166tjx44aO3ZsjmYbs6VFixbav3+/3nvvPa1cuVInTpyQJFWrVk19+vTRk08+qVKlSjnlXMg7R44c0W+//SZJuueeexQWFubU4+e364vJMP5vPjUAAAAAAAAAAAAAAAAA8EBe7g4AAAAAAAAAAAAAAAAAAPISRVIAAAAAAAAAAAAAAAAAPBpFUgAAAAAAAAAAAAAAAAA8GkVSAAAAAAAAAAAAAAAAADwaRVIAAAAAAAAAAAAAAAAAPBpFUgAAAAAAAAAAAAAAAAA8GkVSAAAAAAAAAAAAAAAAADwaRVIAAAAAAAAAAAAAAAAAPBpFUgAAAAAAAAAAAAAAAAA8GkVSAAAAAAAAAAAAAAAAADwaRVIAAAAAAAAAAAAAAAAAPBpFUgAAAAAAAAAAAAAAAAA8GkVSAAAAAAAAAAAAAAAAADwaRVIAAAAAAAAAAAAAAAAAPBpFUgAAAAAAAAAAAAAAAAA8GkVSAAAAAAAAAAAAAAAAADwaRVIAAADIsUmTJslkMslkMuXZOTZt2mQ5x6ZNm/LsPLmxYcMGmUwmlS1bVnFxcW6JIS4uTqGhofn63wkAAAAAAOTeqFGjZDKZVLVqVXeHIkmWcZtJkyZZtWU3ruOKsaW8cujQIfn6+srPz09nzpxxSwyGYSg8PFwmk0lz5sxxSwwAABQkFEkBAADkEykpKQoKCpLJZNIdd9yRZV/DMFSqVCnLINIXX3yRZf958+ZZ+s6ePduZYRd6qampevLJJyVJzz77rAICAqz6XL9+XS+88IKqVaumokWLqmLFiho3bpwuXryY7fFfeOEFmUwmjRgxIst+AQEBevrppyVJTz75pAzDcPzNAAAAAAAAKw8//LBlXGXDhg0O7bt27VrLvk888UQeRQh3ePrpp5WUlKQxY8aoQoUKVu1JSUl68803VbduXRUtWlRly5bViBEjdOLEiWyPPXv2bJlMJt11111Z9jOZTHrllVckSa+88opu3LiRo/cCAEBhQZEUAABAPuHt7a0777xTkrRv3z7FxMRk2vevv/7S5cuXLd9v2bIly2Onb89ucMVT5dWTiYsWLdL+/ftVunRpPfbYY1btN2/e1N13361p06bpxIkTSkxM1JkzZ/Thhx+qVatWunTpUqbH/vvvv/Xuu+8qODhY06dPzzaWcePGqWTJktq3b5+WLl2aq/cFAAAAAADS3H///ZbXCxYscGjfL7/80uZx8ruqVavKZDJp1KhRbo2jffv2MplMat++vVvjuFVERIS+//57+fr66sUXX7RqNwxDAwYM0Msvv6xDhw4pMTFRFy5c0IIFC9S8eXMdPXo002NHR0frlVdekY+Pj2bNmpVtLIMHD1adOnUUFRVlV38AAAoziqQAAADyEXMBU2pqqiIiIjLtZy568vb2zvB9dv1Lly6tevXqOSNUSWmFR4ZhFOpZi9544w1JaU+VBgYGWrVPnz5du3btUpEiRfTmm28qIiJCH374oYoXL66jR4/aHEgzGz9+vJKSkjR58mSFhYVlG0vx4sX10EMPSZKmTp2aw3cEAAAAAADSa926tWrUqCFJ+uabbxQfH2/Xfjdu3NCKFSskSfXr11eTJk3yLEZ3Mo8N2VpuLzsFdWzJPO4yaNAgVapUyap9wYIFWr16tUwmk5577jn9+uuv+vLLLxUWFqbo6Gg9+uijmR77hRde0JUrVzR+/HiFh4dnG4uXl5eeeuopSdKMGTN08+bNHL4rAAA8H0VSAAAA+Uj6WZ5++eWXTPuZ2wYNGiRJOnbsmM6ePWuz74ULF3T48GFJUps2bZw+k1Jhtm7dOh04cECSNHz4cJt95s6dK0maPHmyXnzxRbVq1UqPPvqoPv74Y0nSwoULlZSUZLXfokWLtGHDBoWHh2vcuHF2x3TfffdJkvbv369NmzY58G4AAAAAAEBmRowYIUmKiYnRt99+a9c+y5cvtyx/Zt4fBd+hQ4f0448/Ssp+POjBBx/UtGnT1Lp1aw0fPlzLly+XJP388886ffq01X7btm3T3LlzFRYWpsmTJ9sd06BBg1SkSBFFR0dr0aJFDr4jAAAKD4qkAAAA8pFmzZrJz89PUtazQ5nbBg4caHmSMbP+LLWXdz7//HNJ0h133KG6detatV+/fl3Hjx+XJA0dOjRD26BBg+Tj46P4+HgdOnQoQ1tsbKyeffZZSdKsWbPk4+Njd0zh4eGWpwzN8QEAAAAAgNwZMWKE5cEze5fcMy+15+XllWkxDQqeOXPmyDAMhYaGqlOnTjb7/P7775Ksx4NatWqlKlWqZOhjlpKSonHjxskwDE2fPl1BQUF2x1SyZEl17dpVEuNBAABkhSIpAACAfKRo0aJq3ry5JGnnzp1KSEiw6hMZGakzZ85ISpsZqk2bNpJyVyS1cuVKDRo0SJUrV5afn59CQkLUtGlTTZ48WVeuXMk03kmTJslkMmU7O9Wvv/6qAQMGKCwsTH5+fqpevboeeeQRHT16VJLUvn17mUwmtW/fPsvjmC1ZskQdO3ZUmTJl5O/vrzp16uj555/X5cuXrfrOnTtXJpMpw9N35pjTf504ccKuc5vdvHlTq1atkiQNGDDAZp9r165ZXpcrVy5Dm4+Pj0qVKmXVT0r7dz1z5oyGDx+utm3bOhRX+nhWrlzJFOsAAAAAADhB9erV1bp1a0nSTz/9pAsXLmTZ/+zZs1q/fr0k6e6771aFChUytMfGxuqtt95Sq1atVLJkSRUtWlQVK1bUwIED9d133+Uq1hs3bmjx4sUaO3asGjVqpODgYBUpUkRlypRRu3btNGPGDMXGxtrc1zxGc/LkSUnSvHnzrMZQbh2/MW/P6XJ7tsaWRo0aJZPJpM2bN0uSNm/ebBVH1apVJUn/+9//LNu2b9+e7TkHDBggk8mkkiVL5mjcZMmSJZKkPn36ZPpgm3ms59bxIEkKCwvL0Mds9uzZ2rt3r9q2bZujojrzeNDWrVt16tQph/cHAKAwoEgKAAAgnzEXMiUkJOi3336zajcvtVerVi2VLVvWUiSV2fJ85iKpoKAgNWrUKEPblStX1LFjR/Xr10/Lli3TqVOnlJCQoGvXrmn37t2aNGmS6tata9cAU2befvtt3XXXXVq+fLnOnz+vhIQERUZG6uOPP9Ydd9yhtWvX2n2s1NRUjRgxQvfee682bNigixcv6ubNmzp8+LCmT5+uFi1a6Ny5czmO1RG//fab4uPjJUktW7a02Sf9E3+3xpWcnKxLly5JkoKDgy3bDxw4oPfee09BQUGaPn16jmIzxxMbG5vljGQAAAAAAMB+999/v6S0z/TZLWn21VdfKTU1NcN+Znv37lWdOnX00ksvafv27bpy5YoSExN15swZffPNN+rVq5cGDBiQ4wefevTooSFDhujzzz/Xvn37FBMTo+TkZF28eFG//PKLnnvuOd1+++06ePBgjo6f3wwfPlxFixaV9O8yd5m5ePGiVq9eLUm67777LDO62+vkyZOKjIyUlPl4kPTvWI+tcSrztvTjQRcuXNBrr70mb29vzZo1y6GYzMzxGIZhWQ4QAABkRJEUAABAPpN+tidbBS7mbebiKPN///zzT6tZn65fv659+/ZJku688055e3tb2hISEtSpUydt2LBB3t7eGjFihL7++mtt375dW7Zs0RtvvKFSpUrpwoUL6t69u+UJQkcsWbJEL774ogzDUMmSJfX2228rIiJCERERevvtt+Xj46MhQ4YoKirKruO99tprWrBggfr27avly5dr9+7d+v7779WjRw9J0tGjR/XUU09l2Kdv377av3+/Hn30Ucu2/fv3W33d+kRndsw/B5PJpCZNmtjsExQUpGrVqkmSli5dmqFt2bJlSk5Olr+/v2rXrm3ZPm7cOCUnJ2vy5MmWJwsdZZ6NTJLliUsAAAAAAJA7gwcPthTVmJfSy4y5vVixYurfv79l+5kzZ9SxY0edPXtWJpNJo0eP1k8//aRdu3Zp/vz5atiwoSRp+fLlGjVqVI7iTE5OVnh4uF555RWtWLFCv/32m7Zv367FixdryJAh8vLyUmRkpPr27WtViDVnzhzt379f5cuXl5Q2W9KtYyhz5szJUVyOeOONN7R//341bdpUktS0aVOrOMwP3pUsWdLyb7xo0SLLQ222LFy4UElJSZKkBx54wOG40o/VNWvWLNN+5p/jreNB27dvt4yxmftI0vPPP6+rV69q/PjxCg8PdzguSapdu7ZCQkIkMR4EAEBmbM8BCQAAALdp1aqVfHx8lJycnGWRlHkZtrp166p06dK6ePGitm7dqp49e1r6RkREKCUlRZL1UntTpkzRnj17FBISop9//tmq0KdNmzYaNmyYWrVqpaioKL388stauHCh3e8jISFB/+///T9JUunSpbVt2zbVrFkzw/vs27evWrVqpcOHD9t1zIiICE2dOlWvvPJKhu1du3ZV165dtXbtWi1btkz/+9//VKZMGUlSSEiIQkJCFBoaaunfoEEDu99HVrFIadPtp3/y71YjRozQlClT9Oqrr8owDLVt21b79u3TCy+8IEkaOnSofH19JaU9Zbpp0yaFh4dr/PjxOY6tRIkSqlatmiIjI7V79+4cHwcAAAAAAPwrODhYvXv31pIlS7Rr1y4dOnRIderUser3xx9/6I8//pAk9e/fX4GBgZa2J5980vKQ26effqoxY8ZY2po0aaLBgwerW7du2rhxoxYvXqyRI0eqW7duDsU5Z84c1apVy2p7ixYtNHjwYI0ZM0ZdunTRoUOHtHDhwgwxmB/2KlKkiKS0cRVnjKM4qkKFCqpQoYLl3y4wMDDLOMaOHauvv/5a165d04oVK3TffffZ7Gcu8GrYsKHuuOMOh+Myjwf5+vqqXr16mfYbMWKENm7cqNmzZysoKEg9e/bUiRMn9Nxzz0lKW4KxUqVKktKWx5s/f77CwsI0ZcoUh2MyM5lMaty4sTZu3Mh4EAAAmWAmKQAAgHymWLFiaty4saSMRU5S2tTb5oIi8wxSktS6dWtJ1jNPpf8+fZFUbGysZeru119/PdOZkKpUqaLXXntNUtqTbzdu3LD7faxcuVLnz5+XJE2aNClDgZRZ7dq1NXHiRLuP2aRJE7388stW200mk55++mlJaU9Lbtu2ze5j5tTp06clKUPxlS0vvPCCGjZsqISEBD3//PNq1aqVHnnkEV27dk3Vq1fX22+/LSlt1q9nn31WkjRr1iz5+OTueQZzXMePH8/VcQAAAAAAwL/SL52X2WxS6ben73/27FmtWLFCUtoDX+mLk8yKFi2qL774wjIu8MEHHzgco60CqfQ6deqk3r17S0obv/EEHTp0UI0aNSQp05mu9uzZY5lxPSezSEn/jgeVKlUqw4zttxo5cqS6dOmi1NRUvfnmm2rdurWGDRums2fPqlSpUpo9e7YkKSUlRePGjZNhGJo2bZqCgoJyFJeZeTwoMjJShmHk6lgAAHgiiqQAAADyIXNB0/Xr1/X7779btv/yyy+SpLJly2YY8DIXTJnbzcxFUn5+fhmmAN+8ebOuXbsmSRo4cKBdsSQlJTn0FNrPP/8sSfLy8tKwYcMy7Td8+HCZTCa7jnnfffdl2jd9oZcrCoOio6Mlpc3alJWAgABt3rxZzzzzjCpXrqwiRYqofPnyeuSRR7Rt2zaVLl1akjRx4kRFRUVp+PDhllnCYmJi9Oyzz6pq1aoqWrSoqlSpoueee07Xr1/PNr6SJUtKks6dO5ebtwkAAAAAANLp0qWLypYtKylt6bZbC1FSU1P11VdfSZIqVqyoDh06WNo2bdpkeRjOVoGUWdWqVXXPPfdY7ZNT0dHROnLkiP7880/Ll3kGbnPRUEFnMpkshU8bNmzQP//8Y9XHXDzl6+ub5VhVVuwdD/Ly8tK3336rKVOmqFatWvL19VWZMmV03333aceOHapdu7aktAfl9u3bp7Zt22rEiBGSpMTERE2dOlV16tRR0aJFVa5cOT300EOWhxGzYh4PSkhI0NWrV3P0HgEA8GQUSQEAAORD5iIZKeNsUObX6WeRSt9/9+7dio+Pl5Q2oLJjxw5JadOpm5d0k6Rdu3ZZXpcrV04mkynTr/RTmTtScPPnn39KSluOLiQkJNN+JUuWVPXq1e06Zt26dbM8jpk9RUS5dfnyZUnZD4pJadPxz5gxQydPnlRiYqLOnDmj2bNnW57u279/v95//30FBQVp+vTpkqT4+Hi1b99e//3vfxUVFaUaNWro3LlzmjFjhu6++27dvHkzy3Oa43Jk9i8AAAAAAJA1Hx8fy1JuJ06c0K+//pqhff369Tp79qwkadiwYfLy+vdPceaxEiltrCYr5va4uLgcPQy2detW3XvvvSpVqpRCQ0NVu3ZthYeHW74+/fRTSdLFixcdPnZ+NWrUKHl7eys1NVXz5s3L0JaQkGApXuvTp49KlSqVo3M4Mh5UtGhRvfbaazp8+LASEhJ04cIFLVy40DIOdv78eU2YMEHe3t6WGcMMw1Dfvn312muv6dixY6pevbpiYmL06aefqlWrVpYircykj4sxIQAArFEkBQAAkA+1bdvWMmOSPUVSd9xxhwICApSUlKTt27dLknbu3GkppEm/1J6UtmxfTsTFxdnd98qVK5JkeTIxK/b0kdJmZcpM+kHH3D5haQ8/Pz9JshSl5ca4ceOUnJysyZMnKywsTJI0bdo07d27V3Xr1lVkZKQOHDigEydOqG7dutq1a5dmzJiR5THNcRUpUiTX8QEAAAAAgH9lteReZkvtSf8W2Ej/LouWGfP4wK372WPSpElq06aNlixZku2+zhjXyC/Kly+v7t27S5Lmzp2bYZavb7/91vJvkdOl9iTnjgc9++yzunbtmsaPH6/bb79dkjR//nz98MMPKlu2rP744w/9/fffOnfunNq0aaPIyEi99NJLWR4zfVyMCQEAYI0iKQAAgHyoZMmSql+/vqR/C6NiYmIsU6DfWiRVpEgRNW/eXNK/S+6lL666tUgqfRHRnj17tH//fru++vbt69w3WoCZC7scHai81ZdffqktW7YoPDxc48ePt2yfP3++JOmNN95Q+fLlJaXN+jV16lRJaYN9WTHHldUsXgAAAAAAwHGNGjVSeHi4JGnp0qVKSEiQlDZzz/LlyyVJTZo0Ub169TI9hvnhOGdbv369Jk+eLCltdu8PP/xQf/zxh65evaqkpCQZhiHDMPTaa6/lyfndbezYsZKk48ePW8bIpH+X2qtYsaI6d+6c4+M7azxoy5YtWrBggcqWLWv5eUn/jgc9//zzlvwpXry4Zs6cKUn66quvlJiYmOlx08cVHBycqxgBAPBEPu4OAAAAALbddddd+vPPPxUdHa2DBw8qMjJSqampKlasmBo3bmzVv02bNtq0aZOlOMo8EFSkSBG1atUqQ9/0U4qXKVNGFStWdHr85um9s5sG3N4++U2ZMmV07Ngxy4xZOXHt2jU9//zzkqQPPvhAPj5pt+fXr1+3TKXfunXrDPuYvz927JiuX7+u4sWL2zy2Oa7KlSvnOD4AAAAAAGDb/fffr+eee05Xr17V6tWrNXDgQK1YscKyxNmts0hJaQ/FmZ0/f16VKlXK9Pjnzp2zuV92zMvolShRQtu3b8909u7cFvnkVz169FC5cuUUFRWlOXPmqF27djpz5ozWrl0rSRo5cmSG2cgdZf73zM14UHJyssaNGydJmj59eoZipt9//12S9XhQkyZN5Ofnp/j4eB0+fFgNGjSweWxzXKGhoZZZrwAAwL+YSQoAACCfatu2reX1li1bLMVPLVu2lLe3t1V/8+xS27dvV0JCgiIiIiSlLcUXGBiYoW/6IqutW7c6PXZJlpmwjh8/nuXA0eXLly0FQXnJ2U9omp8YPXbsmFJTU3N0jAkTJujcuXMaNmxYhtm+rl27Znl961N/6WeGiomJsXnc1NRUy7+p+ecAAAAAAACcZ9iwYZbxmQULFkj6d6m9IkWKaOjQoVb7pC9s+e2337I8/o4dOyRJAQEBql69ut1x/fXXX5KkDh06ZFogJUm7du3K8jh5NdOVoxyNw9vbW6NGjZIkLVu2TLGxsZo3b55SU1NlMpk0evToXMVjHg+6du2aLly4kKNjvP/++9q/f7/atGmjESNGZGgzjwnZmgXKvC39uNGtDh8+LInxIAAAMkORFAAAQD6Vvmjml19+scwMdetSe2atWrWSt7e3bty4oblz51oGTG5dak+SOnXqpICAAEnS//73PxmG4ezw1bFjR0lpBTtfffVVpv0WLFiQJ+e/Vfqn58zT4OeGuYgtNjZWf//9t8P7//HHH5o1a5aCgoI0Y8aMDG1BQUGW16dPn87QdurUKZv90jtw4IBiY2MlSS1atHA4NgAAAAAAkLVy5cqpU6dOkqTvv/9ef/75p9avXy9J6tq1q80Cpfbt21sKq7744otMj/3PP/9o3bp1VvvYIzk5WZIsM1rZsnfv3myLtMzjKM4YQ8mNnMQxZswYmUwm3bhxQ4sXL9bcuXMlpY2R1ahRI1fxpH+ocefOnQ7vHxUVpUmTJsnb21uzZs2yajcXQt06HpSQkGCZiT2z8aCYmBgdOnRIEuNBAABkhiIpAACAfKp8+fKWgZuNGzdanvBLPxiTXlBQkOVptmnTplm22yqSCgkJ0fjx4yVJEREReuqpp7KcDen8+fP67LPPHIq/X79+Cg0NlSRNmjRJx44ds+pz5MgRTZ482aHj5lS5cuUsr23F4qj0Pwfz0532MgxD48aNU0pKiiZNmqSwsLAM7UFBQapWrZokaeHChRnazAVn1atXz3SpvfTxdO7c2aHYAAAAAACAfcxL6iUlJWnIkCFKSUnJsP1W5cuXV79+/SRJP/zwg+bNm2fVJzExUQ888ICSkpIkyTJ+Y69atWpJkn799VcdPXrUqj06Otpq9iJbzOMozhhDyQ1zHMePH7f7IbsaNWqoffv2kqRXX31VR44ckSQ98MADuY6nefPmKlq0qCTHx4Mk6dlnn1VMTIzGjRun22+/3aq9YcOGkqzHgxYtWqTU1FT5+fmpTp06No+9a9cuy78R40EAANhGkRQAAEA+Zi7EOXPmjBISEuTj46OWLVtm2t88y5R5qTUvL69MZ56aMmWK5amy9957T3fccYdmzZqlrVu36vfff9fGjRv1wQcfqG/fvqpcubI++ugjh2L38/PTzJkzJUkXL15UixYtNH36dG3fvl3bt2/XtGnT1LJlS6WmploG8PJyKvc777zT8vqpp57SL7/8oiNHjujo0aM6evSo5UlLe1WtWtUymGV+UtRe8+fP16+//qoGDRro8ccft9ln5MiRkqSpU6fqrbfeUkREhKZNm6apU6dKynzANX08DRs2tBRbAQAAAAAA5+rXr5/lASbzMnclSpRQr169Mt3n3XffVYkSJSSlFe08+OCD+vnnn7V7924tXLhQLVq0sHyuHzx4sLp16+ZQTObxghs3bqhdu3Z6//33FRERoYiICM2YMUMNGzbUgQMH1KpVqyyPYx5H2blzp9566y3t27fPMoZy5swZh2LKDXMcFy5c0NNPP63du3db4jh58mSm+40dO1aSdO7cOUlpD6QNHDgw1/EULVpUXbp0keT4eNDmzZv11VdfqWz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-      "text/plain": [
-       "<Figure size 1200x400 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 388,
-       "width": 1188
-      }
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Status: optimal\n",
-      "Expected Return: 5.00%  |  Volatility: 4.15%\n",
-      "Safe Assets (Cash + Bond): 77.7%\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Interactive exploration function\n",
     "# In Jupyter, uncomment the interact() call at the bottom to enable sliders\n",
@@ -2263,110 +629,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "id": "48f11500-0894-47a1-8dda-e5c06151379d",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "cuOpt version: 26.2.0, git hash: 96c11095, host arch: x86_64, device archs: 80-real\n",
-      "CPU: AMD EPYC 7642 48-Core Processor, threads (physical/logical): 96/96, RAM: 170.56 GiB\n",
-      "CUDA 12.9, device: NVIDIA A100 80GB PCIe (ID 0), VRAM: 79.33 GiB\n",
-      "CUDA device UUID: ffffff8a5affffff95ffffffc7-ffffffd05\n",
-      "\n",
-      "Problem has a quadratic objective. Using Barrier.\n",
-      "Solving a problem with 3 constraints, 5 variables (0 integers), and 12 nonzeros\n",
-      "Problem scaling:\n",
-      "Objective coefficents range:          [0e+00, 0e+00]\n",
-      "Constraint matrix coefficients range: [2e-02, 1e+00]\n",
-      "Constraint rhs / bounds range:        [0e+00, 1e+00]\n",
-      "Variable bounds range:                [0e+00, 1e+00]\n",
-      "\n",
-      "Third-party presolve is disabled, skipping\n",
-      "Objective offset 0.000000 scaling_factor 1.000000\n",
-      "Presolved problem: 3 constraints 7 variables 14 nonzeros\n",
-      "Skipping column scaling\n",
-      "Barrier solver: 3 constraints, 7 variables, 14 nonzeros\n",
-      "Quadratic objective matrix: 25 nonzeros\n",
-      "\n",
-      "\n",
-      "Upper bounds                : 5\n",
-      "Density estimator time      : 0.00s\n",
-      "Linear system               : augmented\n",
-      "cuDSS Version               : 0.7.1\n",
-      "cuDSS Threading layer       : libcudss_mtlayer_gomp.so.0\n",
-      "Reordering algorithm        : AMD\n",
-      "Reordering time             : 0.00s\n",
-      "Symbolic factorization time : 0.01s\n",
-      "Total symbolic time         : 0.01s\n",
-      "Symbolic nonzeros in factor : 2.80e+01\n",
-      "\n",
-      "\n",
-      "                  Objective                         Infeasibility        Time\n",
-      "Iter   Primal              Dual                Primal   Dual    Compl.   Elapsed\n",
-      "  0   +1.550142187102e+01 -2.050142187102e+01 5.03e+01 1.00e+01 1.05e+02 0.0\n",
-      "  1   +9.341560379055e+00 -2.494790318049e+01 1.96e+01 7.78e+00 1.96e+00 0.0\n",
-      "  2   +2.111362888662e-01 -1.461380165032e+01 3.55e+00 1.41e+00 1.15e+01 0.0\n",
-      "  3   +8.963752500084e-03 -4.614257805362e+00 1.04e+00 4.15e-01 4.72e+00 0.0\n",
-      "  4   +1.773863862279e-02 -4.653727425029e+00 2.70e-01 1.07e-01 1.22e+00 0.0\n",
-      "  5   +8.781733954736e-03 -8.401734910635e-01 3.02e-02 1.20e-02 2.34e-01 0.0\n",
-      "  6   +8.437570912594e-03 -1.201728774252e-01 3.46e-03 1.38e-03 4.27e-02 0.0\n",
-      "  7   +7.999243381885e-03 -1.479774260163e-02 5.91e-04 2.35e-04 5.86e-03 0.0\n",
-      "  8   +5.959453562596e-03 -1.522588233318e-03 1.77e-04 7.03e-05 1.63e-03 0.0\n",
-      "  9   +3.905148658193e-03 +2.505977932485e-03 1.77e-05 7.03e-06 5.40e-04 0.0\n",
-      " 10   +3.526936468726e-03 +3.296103585159e-03 2.72e-06 1.08e-06 4.75e-05 0.0\n",
-      " 11   +3.453129854611e-03 +3.423295211997e-03 3.17e-07 1.26e-07 1.07e-05 0.0\n",
-      " 12   +3.444326229591e-03 +3.440576962168e-03 3.39e-08 1.35e-08 1.92e-06 0.0\n",
-      " 13   +3.443447091108e-03 +3.443030130791e-03 3.48e-09 1.37e-09 2.21e-07 0.0\n",
-      " 14   +3.443370554202e-03 +3.443329087174e-03 3.55e-10 1.37e-10 7.24e-09 0.1\n",
-      "\n",
-      "\n",
-      "Optimal solution found in 14 iterations and 0.05s\n",
-      "Objective +3.44337055e-03\n",
-      "Primal infeasibility (abs/rel): 7.10e-10/3.55e-10\n",
-      "Dual infeasibility   (abs/rel): 1.37e-10/1.37e-10\n",
-      "Complementarity gap  (abs/rel): 7.26e-09/7.24e-09\n",
-      "\n",
-      "\n",
-      "Unscaled Primal infeasibility   (abs/rel): 7.10e-10/3.55e-10\n",
-      "Post-solve Primal infeasibility (abs/rel): 7.10e-10/3.55e-10\n",
-      "Barrier finished in 0.05 seconds\n",
-      "============================================================\n",
-      "YOUR RECOMMENDED PORTFOLIO\n",
-      "============================================================\n",
-      "\n",
-      "Target: At least 6% annual return\n",
-      "Constraint: Keep at least 20% in Cash + Bonds\n",
-      "\n",
-      "  Cash          0.0%  \n",
-      "  US Equity    22.1%  ████████\n",
-      "  Intl Equity   1.4%  \n",
-      "  Bond         61.0%  ████████████████████████\n",
-      "  REIT/Gold    15.6%  ██████\n",
-      "\n",
-      "→ Expected Return: 6.00%\n",
-      "→ Volatility:      5.87%\n",
-      "→ Safe Allocation: 61.0%\n",
-      "\n",
-      "============================================================\n",
-      "WHAT THIS MEANS\n",
-      "============================================================\n",
-      "\n",
-      "If you invest $10,000 according to this allocation:\n",
-      "  - Cash:        $0\n",
-      "  - US Equity:   $2,207\n",
-      "  - Intl Equity: $142\n",
-      "  - Bonds:       $6,096\n",
-      "  - REIT/Gold:   $1,555\n",
-      "\n",
-      "You can expect ~6.0% growth per year on average,\n",
-      "with a typical annual swing of about +/-5.9%.\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "target_scenario = 0.06\n",
     "min_safe_scenario = 0.20\n",