From 462d520ea2f323003c1584ba9ce312f8d569aee0 Mon Sep 17 00:00:00 2001
From: Joshua Ivanhoe <joshua@h2gopower.com>
Date: Tue, 18 Jun 2024 12:14:49 +0100
Subject: [PATCH 01/12] Update min Python version (WIP)

---
 pyproject.toml               | 50 ++++++++++++++++++++++++------------
 requirements.txt             |  7 -----
 src/halfspace/convex_term.py | 25 ++++++++----------
 src/halfspace/model.py       | 36 ++++++++++++--------------
 src/halfspace/utils.py       | 14 +++++-----
 tests/test_convex_term.py    |  6 ++---
 tests/test_model.py          | 20 +++------------
 tests/test_utils.py          | 11 ++++----
 8 files changed, 81 insertions(+), 88 deletions(-)
 delete mode 100644 requirements.txt

diff --git a/pyproject.toml b/pyproject.toml
index a52a4ec..0c19f54 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,27 +1,45 @@
-[build-system]
-requires = ["setuptools >= 61.0"]
-build-backend = "setuptools.build_meta"
-
-[project]
+[tool.poetry]
 name = "halfspace-optimizer"
-version = "0.1.1"
+version = "0.1.2"
+description = "Cutting-plane solver for mixed-integer convex optimization problems"
+license ="MIT"
 authors = [
-  { name="Joshua Ivanhoe", email="joshua.k.ivanhoe@gmail.com" },
+    "Joshua Ivanhoe <joshua.k.ivanhoe@gmail.com>"
 ]
-description = "Cutting-plane solver for mixed-integer convex optimization problems"
 readme = "README.md"
-license = {file = "LICENSE"}
-requires-python = ">=3.9,<3.12"
+repository = "https://github.com/joshivanhoe/halfspace"
+documentation = "https://joshivanhoe.github.io/halfspace/"
 classifiers = [
     "Programming Language :: Python :: 3",
     "License :: OSI Approved :: MIT License",
     "Operating System :: OS Independent",
 ]
-dynamic = ["dependencies"]
+packages = [
+    { include = "halfspace", from = "src" }
+]
 
-[tool.setuptools.dynamic]
-dependencies = {file = ["requirements.txt"]}
+[tool.poetry.dependencies]
+python = ">=3.10,<3.12"
+mip = ">=1.15.0"
+numpy = ">=1.25.2"
+pandas = ">=2.0.3"
 
-[project.urls]
-Homepage = "https://github.com/joshivanhoe/halfspace"
-Issues = "https://github.com/joshivanhoe/halfspace/issues"
+[tool.poetry.group.dev.dependencies]
+black = "*"
+flake8 = "*"
+pre-commit = "*"
+tomli = "*"
+
+[tool.poetry.group.test.dependencies]
+mypy = "*"
+pytest = "*"
+pytest-cov = "*"
+
+[tool.poetry.group.docs]
+optional = true
+[tool.poetry.group.docs.dependencies]
+sphinx = "*"
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"
diff --git a/requirements.txt b/requirements.txt
deleted file mode 100644
index 00698ec..0000000
--- a/requirements.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-mip>=1.15.0
-numpy>=1.25.2
-pandas>=2.0.3
-pre-commit>=3.6.0
-pytest>=7.4.4
-pytest-cov>=4.1.0
-tomli>=2.0.1
diff --git a/src/halfspace/convex_term.py b/src/halfspace/convex_term.py
index 20ec1df..1e139d0 100644
--- a/src/halfspace/convex_term.py
+++ b/src/halfspace/convex_term.py
@@ -3,18 +3,17 @@
 It provides a modular framework for generating cutting planes.
 """
 
-from typing import Union, Callable, Optional, Iterable
+from typing import Callable, Iterable
 
 import mip
 import numpy as np
 
-
 QueryPoint = dict[mip.Var, float]
-Var = Union[mip.Var, Iterable[mip.Var], mip.LinExprTensor]
-Input = Union[float, Iterable[float], np.ndarray]
+Var = mip.Var | Iterable[mip.Var] | mip.LinExprTensor
+Input = float | Iterable[float] | np.ndarray
 Func = Callable[[Input], float]
-FuncGrad = Callable[[Input], tuple[float, Union[float, np.ndarray]]]
-Grad = Callable[[Input], Union[float, np.ndarray]]
+FuncGrad = Callable[[Input], tuple[float, float | np.ndarray]]
+Grad = Callable[[Input], float | np.ndarray]
 
 
 class ConvexTerm:
@@ -39,8 +38,8 @@ class ConvexTerm:
     def __init__(
         self,
         var: Var,
-        func: Union[Func, FuncGrad],
-        grad: Optional[Union[Grad, bool]] = None,
+        func: Func | FuncGrad,
+        grad: Grad | bool | None = None,
         step_size: float = 1e-6,
         name: str = "",
     ):
@@ -61,7 +60,7 @@ def __init__(
 
     def __call__(
         self, query_point: QueryPoint, return_grad: bool = False
-    ) -> Union[float, tuple[float, Union[float, np.ndarray]]]:
+    ) -> float | tuple[float, float | np.ndarray]:
         """Evaluate the term and (optionally) its gradient.
 
         Args:
@@ -107,9 +106,7 @@ def _get_input(self, query_point: QueryPoint) -> Input:
             return np.array([query_point[var] for var in self.var])
         return query_point[self.var]
 
-    def _evaluate_func(
-        self, x: Input
-    ) -> Union[float, tuple[float, Union[float, np.ndarray]]]:
+    def _evaluate_func(self, x: Input) -> float | tuple[float, float | np.ndarray]:
         """Evaluate the function value.
 
         If `grad=True`, then both the value of the function and it's gradient are returned.
@@ -120,7 +117,7 @@ def _evaluate_func(
             return self.func(*x)
         raise TypeError(f"Input of type '{type(x)}' not supported.")
 
-    def _evaluate_grad(self, x: Input) -> Union[float, np.ndarray]:
+    def _evaluate_grad(self, x: Input) -> float | np.ndarray:
         """Evaluate the gradient."""
         if not self.grad:
             return self._approximate_grad(x=x)
@@ -130,7 +127,7 @@ def _evaluate_grad(self, x: Input) -> Union[float, np.ndarray]:
             return self.grad(*x)
         raise TypeError(f"Input of type '{type(x)}' not supported.")
 
-    def _approximate_grad(self, x: Input) -> Union[float, np.ndarray]:
+    def _approximate_grad(self, x: Input) -> float | np.ndarray:
         """Approximate the gradient of the function at point using the central finite difference method."""
         if self.is_multivariable:
             indexes = np.arange(len(x))
diff --git a/src/halfspace/model.py b/src/halfspace/model.py
index 2f85cbc..e236c16 100644
--- a/src/halfspace/model.py
+++ b/src/halfspace/model.py
@@ -4,7 +4,7 @@
 """
 
 import logging
-from typing import Optional, Iterable, Union
+from typing import Iterable
 
 import mip
 import numpy as np
@@ -40,9 +40,9 @@ def __init__(
         max_gap_abs: float = 1e-4,
         infeasibility_tol: float = 1e-4,
         step_size: float = 1e-6,
-        smoothing: Optional[float] = 0.5,
-        solver_name: Optional[str] = "CBC",
-        log_freq: Optional[int] = 1,
+        smoothing: float | None = 0.5,
+        solver_name: str | None = "CBC",
+        log_freq: int | None = 1,
     ):
         """Optimization model constructor.
 
@@ -81,13 +81,13 @@ def reset(self) -> None:
         self._best_solution: dict[mip.Var, float] = dict()
         self._objective_value: float = (1 if self.minimize else -1) * mip.INF
         self._best_bound: float = -self._objective_value
-        self._status: Optional[mip.OptimizationStatus] = None
+        self._status: mip.OptimizationStatus | None = None
         self._search_log: list[dict[str, float]] = list()
 
     def add_var(
         self,
-        lb: Optional[float] = None,
-        ub: Optional[float] = None,
+        lb: float | None = None,
+        ub: float | None = None,
         var_type: str = mip.CONTINUOUS,
         name: str = "",
     ) -> mip.Var:
@@ -110,8 +110,8 @@ def add_var(
     def add_var_tensor(
         self,
         shape: tuple[int, ...],
-        lb: Optional[float] = None,
-        ub: Optional[float] = None,
+        lb: float | None = None,
+        ub: float | None = None,
         var_type: str = mip.CONTINUOUS,
         name: str = "",
     ) -> mip.LinExprTensor:
@@ -152,8 +152,8 @@ def add_linear_constr(self, constraint: mip.LinExpr, name: str = "") -> mip.Cons
     def add_nonlinear_constr(
         self,
         var: Var,
-        func: Union[Func, FuncGrad],
-        grad: Optional[Union[Grad, bool]] = None,
+        func: Func | FuncGrad,
+        grad: Grad | bool | None = None,
         name: str = "",
     ) -> ConvexTerm:
         """Add a nonlinear constraint to the model.
@@ -187,8 +187,8 @@ def add_nonlinear_constr(
     def add_objective_term(
         self,
         var: Var,
-        func: Union[Func, FuncGrad],
-        grad: Optional[Union[Grad, bool]] = None,
+        func: Func | FuncGrad,
+        grad: Grad | bool | None = None,
         name: str = "",
     ) -> ConvexTerm:
         """Add an objective term to the model.
@@ -222,8 +222,8 @@ def add_objective_term(
     def optimize(
         self,
         max_iters: int = 100,
-        max_iters_no_improvement: Optional[int] = None,
-        max_seconds_per_iter: Optional[float] = None,
+        max_iters_no_improvement: int | None = None,
+        max_seconds_per_iter: float | None = None,
     ) -> mip.OptimizationStatus:
         """Optimize the model.
 
@@ -355,9 +355,7 @@ def var_by_name(self, name: str) -> mip.Var:
         """Get a variable by name."""
         return self._model.var_by_name(name=name)
 
-    def var_value(
-        self, x: Union[mip.Var, mip.LinExprTensor, str]
-    ) -> Union[float, np.ndarray]:
+    def var_value(self, x: mip.Var | mip.LinExprTensor | str) -> float | np.ndarray:
         """Get the value one or more decision variables corresponding to the best solution.
 
         Args:
@@ -448,7 +446,7 @@ def search_log(self) -> pd.DataFrame:
         return pd.DataFrame(self._search_log).set_index("iteration")
 
     @staticmethod
-    def sum(terms: Iterable[Union[mip.Var, mip.LinExpr]]) -> mip.LinExpr:
+    def sum(terms: Iterable[mip.Var | mip.LinExpr]) -> mip.LinExpr:
         """Create a linear expression from a summation."""
         return mip.xsum(terms=terms)
 
diff --git a/src/halfspace/utils.py b/src/halfspace/utils.py
index b43779e..e4da969 100644
--- a/src/halfspace/utils.py
+++ b/src/halfspace/utils.py
@@ -1,8 +1,8 @@
 """Utility functions for the `halfspace` package."""
 
 import logging
-
-from typing import Union, Iterable, Optional, Any, Type
+from numbers import Real, Integral
+from typing import Iterable, Any, Type
 
 
 def log_table_header(columns: Iterable[str], width: int = 15) -> None:
@@ -25,7 +25,7 @@ def log_table_header(columns: Iterable[str], width: int = 15) -> None:
     logging.info(line)
 
 
-def log_table_row(values: Iterable[Union[float, int]], width: int = 15) -> None:
+def log_table_row(values: Iterable[Real], width: int = 15) -> None:
     """Log a table row.
 
     Logging level is set to `logging.INFO`.
@@ -39,7 +39,7 @@ def log_table_row(values: Iterable[Union[float, int]], width: int = 15) -> None:
     Returns: None
     """
     values = [
-        (f"{{:{width}}}" if isinstance(value, int) else f"{{:{width}.3e}}").format(
+        (f"{{:{width}}}" if isinstance(value, Integral) else f"{{:{width}.3e}}").format(
             value
         )
         for value in values
@@ -50,9 +50,9 @@ def log_table_row(values: Iterable[Union[float, int]], width: int = 15) -> None:
 def check_scalar(
     x: Any,
     name: str,
-    var_type: Optional[Union[Type, tuple[Type, ...]]] = None,
-    lb: Optional[Union[float, int]] = None,
-    ub: Optional[Union[float, int]] = None,
+    var_type: Type | tuple[Type, ...] | None = None,
+    lb: Integral | None = None,
+    ub: Integral | None = None,
     include_boundaries: bool = True,
 ) -> None:
     """Check that a scalar satisfies certain conditions.
diff --git a/tests/test_convex_term.py b/tests/test_convex_term.py
index 14249eb..917bf59 100644
--- a/tests/test_convex_term.py
+++ b/tests/test_convex_term.py
@@ -1,5 +1,3 @@
-from typing import Union, Optional
-
 import mip
 import numpy as np
 import pytest
@@ -13,7 +11,7 @@ def _process_callbacks(
     grad: Grad,
     combine_grad: bool,
     approximate_grad: bool,
-) -> tuple[Union[Func, FuncGrad], Optional[Union[Grad, bool]]]:
+) -> tuple[Func | FuncGrad, Grad | bool | None]:
     if combine_grad and approximate_grad:
         raise ValueError
     if combine_grad:
@@ -30,7 +28,7 @@ def func_with_grad(*args, **kwargs):
 def _check_convex_term(
     term: ConvexTerm,
     expected_value: float,
-    expected_grad: Union[float, np.ndarray],
+    expected_grad: float | np.ndarray,
     expected_is_multivariable: bool,
     query_point: QueryPoint,
 ):
diff --git a/tests/test_model.py b/tests/test_model.py
index 076ab36..1e67d0a 100644
--- a/tests/test_model.py
+++ b/tests/test_model.py
@@ -1,5 +1,3 @@
-from typing import Optional
-
 import mip
 import numpy as np
 import pandas as pd
@@ -13,8 +11,8 @@
 
 def _check_solution(
     model: Model,
-    expected_objective_value: Optional[float],
-    expected_solution: Optional[dict[Var, float]],
+    expected_objective_value: float | None = None,
+    expected_solution: dict[Var, float] | None = None,
     expected_status: mip.OptimizationStatus = mip.OptimizationStatus.OPTIMAL,
 ):
     if expected_objective_value is not None:
@@ -124,12 +122,7 @@ def test_multivariable_linear_constraint_infeasible():
     )
     model.add_linear_constr(x + y >= 3)
     model.optimize()
-    _check_solution(
-        model=model,
-        expected_objective_value=None,
-        expected_solution=None,
-        expected_status=mip.OptimizationStatus.INFEASIBLE,
-    )
+    _check_solution(model=model, expected_status=mip.OptimizationStatus.INFEASIBLE)
 
 
 def test_multivariable_nonlinear_constraint():
@@ -159,9 +152,4 @@ def test_multivariable_nonlinear_constraint_infeasible():
     )
     model.add_nonlinear_constr(var=(x, y), func=lambda x, y: np.exp(x + y) + 1)
     model.optimize()
-    _check_solution(
-        model=model,
-        expected_objective_value=None,
-        expected_solution=None,
-        expected_status=mip.OptimizationStatus.INFEASIBLE,
-    )
+    _check_solution(model=model, expected_status=mip.OptimizationStatus.INFEASIBLE)
diff --git a/tests/test_utils.py b/tests/test_utils.py
index 1d965a1..dde117b 100644
--- a/tests/test_utils.py
+++ b/tests/test_utils.py
@@ -1,5 +1,6 @@
 from contextlib import nullcontext as does_not_raise
-from typing import Union, Iterable, Any, Optional, Type
+from numbers import Real
+from typing import Iterable, Any, Type
 
 import pytest
 
@@ -15,7 +16,7 @@ def test_log_table_header(columns: Iterable[str], width: int):
 
 @pytest.mark.parametrize("values", [[1, 1.0, 2e10, 3e-10]])
 @pytest.mark.parametrize("width", [10, 15])
-def test_log_table_row(values: Iterable[Union[float, int]], width: int):
+def test_log_table_row(values: Iterable[Real], width: int):
     log_table_row(values=values, width=width)
     # TODO: add log checks
 
@@ -38,9 +39,9 @@ def test_log_table_row(values: Iterable[Union[float, int]], width: int):
 def test_check_scalar(
     x: Any,
     name: str,
-    var_type: Optional[Union[Type, tuple[Type, ...]]],
-    lb: Optional[Union[float, int]],
-    ub: Optional[Union[float, int]],
+    var_type: Type | tuple[Type, ...] | None,
+    lb: Real | None,
+    ub: Real | None,
     include_boundaries: bool,
     expectation,
 ):

From 20fb2c28e4ffc78f642851a0e52b6ede2f14cf3b Mon Sep 17 00:00:00 2001
From: Joshua Ivanhoe <joshua@h2gopower.com>
Date: Tue, 18 Jun 2024 12:16:55 +0100
Subject: [PATCH 02/12] Update GitHub actions workflows (WIP)

---
 .github/workflows/ci.yml | 32 ++++++++++++++++----------------
 1 file changed, 16 insertions(+), 16 deletions(-)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index a16f9a2..b933b3a 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -1,4 +1,4 @@
-name: CI
+name: CI workflow
 
 on:
   push:
@@ -13,27 +13,27 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.9", "3.10", "3.11"]
+        python-version: ["3.10", "3.11", "3.12"]
 
     steps:
     - uses: actions/checkout@v3
+
     - name: Set up Python ${{ matrix.python-version }}
       uses: actions/setup-python@v3
       with:
         python-version: ${{ matrix.python-version }}
+
+    - name: Install poetry
+      run: python -m pip install poetry
+
     - name: Install dependencies
-      run: |
-        python -m pip install --upgrade pip
-        python -m pip install flake8 pytest
-        if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
+      run: poetry install
+
+    - name: Format with black
+      run: poetry run black ./src
+
     - name: Lint with flake8
-      run: |
-        # stop the build if there are Python syntax errors or undefined names
-        flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
-        # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
-        flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
-    - name: Test with pytest
-      env:
-        PYTHONPATH: ${{ github.workspace }}/src
-      run: |
-        pytest
+      run: poetry run flake8 --max-line-length 120
+
+    - name: Run tests
+      run: poetry run pytest --cov=src

From fc73162df69c500e0ab364c533dc6f195b82d9f8 Mon Sep 17 00:00:00 2001
From: Joshua Ivanhoe <joshua@h2gopower.com>
Date: Tue, 6 Aug 2024 17:37:03 +0100
Subject: [PATCH 03/12] Update Python typing syntax to 3.10+ and fix typing
 errors

---
 pyproject.toml               |  4 +--
 src/halfspace/convex_term.py | 55 ++++++++++++++++++++++--------------
 src/halfspace/model.py       | 24 ++++++++--------
 src/halfspace/utils.py       | 22 ++++++++++-----
 4 files changed, 64 insertions(+), 41 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index 0c19f54..c627ad5 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -19,19 +19,19 @@ packages = [
 ]
 
 [tool.poetry.dependencies]
-python = ">=3.10,<3.12"
+python = ">=3.10,<=3.12"
 mip = ">=1.15.0"
 numpy = ">=1.25.2"
 pandas = ">=2.0.3"
 
 [tool.poetry.group.dev.dependencies]
 black = "*"
+mypy = "*"
 flake8 = "*"
 pre-commit = "*"
 tomli = "*"
 
 [tool.poetry.group.test.dependencies]
-mypy = "*"
 pytest = "*"
 pytest-cov = "*"
 
diff --git a/src/halfspace/convex_term.py b/src/halfspace/convex_term.py
index 1e139d0..65d39f8 100644
--- a/src/halfspace/convex_term.py
+++ b/src/halfspace/convex_term.py
@@ -4,16 +4,19 @@
 """
 
 from typing import Callable, Iterable
+from typing import TypeAlias, Literal, overload
 
 import mip
 import numpy as np
 
-QueryPoint = dict[mip.Var, float]
-Var = mip.Var | Iterable[mip.Var] | mip.LinExprTensor
-Input = float | Iterable[float] | np.ndarray
-Func = Callable[[Input], float]
-FuncGrad = Callable[[Input], tuple[float, float | np.ndarray]]
-Grad = Callable[[Input], float | np.ndarray]
+from .utils import standard_basis_vector
+
+QueryPoint: TypeAlias = dict[mip.Var, float]
+Var: TypeAlias = mip.Var | Iterable[mip.Var] | mip.LinExprTensor
+Input: TypeAlias = float | Iterable[float] | np.ndarray
+Func: TypeAlias = Callable[[Input], float]
+FuncGrad: TypeAlias = Callable[[Input], tuple[float, float | np.ndarray]]
+Grad: TypeAlias = Callable[[Input], float | np.ndarray]
 
 
 class ConvexTerm:
@@ -42,7 +45,7 @@ def __init__(
         grad: Grad | bool | None = None,
         step_size: float = 1e-6,
         name: str = "",
-    ):
+    ) -> None:
         """Convex term constructor.
 
         Args:
@@ -58,6 +61,18 @@ def __init__(
         self.step_size = step_size
         self.name = name
 
+    @overload
+    def __call__(
+        self, query_point: QueryPoint, return_grad: Literal[False] = False
+    ) -> float:
+        ...
+
+    @overload
+    def __call__(
+        self, query_point: QueryPoint, return_grad: Literal[True] = True
+    ) -> tuple[float, float | np.ndarray]:
+        ...
+
     def __call__(
         self, query_point: QueryPoint, return_grad: bool = False
     ) -> float | tuple[float, float | np.ndarray]:
@@ -95,11 +110,11 @@ def generate_cut(self, query_point: QueryPoint) -> mip.LinExpr:
         Returns:
             The linear constraint representing the cutting plane.
         """
-        fun, grad = self(query_point=query_point, return_grad=True)
+        func, grad = self(query_point=query_point, return_grad=True)
         x = self._get_input(query_point=query_point)
         if self.is_multivariable:
-            return mip.xsum(grad * (np.array(self.var) - x)) + fun
-        return grad * (self.var - x) + fun
+            return mip.xsum(grad * (np.array(self.var) - x)) + func
+        return grad * (self.var - x) + func
 
     def _get_input(self, query_point: QueryPoint) -> Input:
         if self.is_multivariable:
@@ -130,17 +145,15 @@ def _evaluate_grad(self, x: Input) -> float | np.ndarray:
     def _approximate_grad(self, x: Input) -> float | np.ndarray:
         """Approximate the gradient of the function at point using the central finite difference method."""
         if self.is_multivariable:
-            indexes = np.arange(len(x))
-            return np.array(
-                [
-                    (
-                        self._evaluate_func(x=x + self.step_size / 2 * (indexes == i))
-                        - self._evaluate_func(x=x - self.step_size / 2 * (indexes == i))
-                    )
-                    / self.step_size
-                    for i in indexes
-                ]
-            )
+            n_dim = len(x)
+            grad = np.zeros(n_dim)
+            for i in range(n_dim):
+                e_i = standard_basis_vector(i=i, n_dim=n_dim)
+                grad[i] = (
+                    self._evaluate_func(x=x + self.step_size / 2 * e_i)
+                    - self._evaluate_func(x=x - self.step_size / 2 * e_i)
+                ) / self.step_size
+            return grad
         return (
             self._evaluate_func(x=x + self.step_size / 2)
             - self._evaluate_func(x=x - self.step_size / 2)
diff --git a/src/halfspace/model.py b/src/halfspace/model.py
index e236c16..63240f4 100644
--- a/src/halfspace/model.py
+++ b/src/halfspace/model.py
@@ -43,7 +43,7 @@ def __init__(
         smoothing: float | None = 0.5,
         solver_name: str | None = "CBC",
         log_freq: int | None = 1,
-    ):
+    ) -> None:
         """Optimization model constructor.
 
         Args:
@@ -86,8 +86,8 @@ def reset(self) -> None:
 
     def add_var(
         self,
-        lb: float | None = None,
-        ub: float | None = None,
+        lb: float | int = 0,
+        ub: float | int = mip.INF,
         var_type: str = mip.CONTINUOUS,
         name: str = "",
     ) -> mip.Var:
@@ -110,8 +110,8 @@ def add_var(
     def add_var_tensor(
         self,
         shape: tuple[int, ...],
-        lb: float | None = None,
-        ub: float | None = None,
+        lb: float | int = 0,
+        ub: float | int = mip.INF,
         var_type: str = mip.CONTINUOUS,
         name: str = "",
     ) -> mip.LinExprTensor:
@@ -454,14 +454,14 @@ def _validate_params(self) -> None:
         check_scalar(
             x=self.max_gap,
             name="max_gap",
-            lb=0.0,
+            lb=0,
             var_type=float,
             include_boundaries=False,
         )
         check_scalar(
             x=self.max_gap_abs,
             name="max_gap_abs",
-            lb=0.0,
+            lb=0,
             var_type=float,
             include_boundaries=False,
         )
@@ -469,7 +469,7 @@ def _validate_params(self) -> None:
             x=self.infeasibility_tol,
             name="feasibility_tol",
             var_type=float,
-            lb=0.0,
+            lb=0,
             include_boundaries=False,
         )
         if self.smoothing is not None:
@@ -477,8 +477,8 @@ def _validate_params(self) -> None:
                 x=self.smoothing,
                 name="smoothing",
                 var_type=float,
-                lb=0.0,
-                ub=1.0,
+                lb=0,
+                ub=1,
                 include_boundaries=False,
             )
         if self.log_freq is not None:
@@ -491,7 +491,9 @@ def _validate_params(self) -> None:
             )
 
     @staticmethod
-    def _validate_bounds(lb: float, ub: float, var_type: str) -> tuple[float, float]:
+    def _validate_bounds(
+        lb: float | int, ub: float | int, var_type: str
+    ) -> tuple[float | int, float | int]:
         if var_type == mip.BINARY:
             lb, ub = 0, 1
         else:
diff --git a/src/halfspace/utils.py b/src/halfspace/utils.py
index e4da969..48ce9a3 100644
--- a/src/halfspace/utils.py
+++ b/src/halfspace/utils.py
@@ -1,9 +1,10 @@
 """Utility functions for the `halfspace` package."""
 
 import logging
-from numbers import Real, Integral
 from typing import Iterable, Any, Type
 
+import numpy as np
+
 
 def log_table_header(columns: Iterable[str], width: int = 15) -> None:
     """Log a table header.
@@ -25,7 +26,7 @@ def log_table_header(columns: Iterable[str], width: int = 15) -> None:
     logging.info(line)
 
 
-def log_table_row(values: Iterable[Real], width: int = 15) -> None:
+def log_table_row(values: Iterable[float | int], width: int = 15) -> None:
     """Log a table row.
 
     Logging level is set to `logging.INFO`.
@@ -38,21 +39,21 @@ def log_table_row(values: Iterable[Real], width: int = 15) -> None:
 
     Returns: None
     """
-    values = [
-        (f"{{:{width}}}" if isinstance(value, Integral) else f"{{:{width}.3e}}").format(
+    values_ = [
+        (f"{{:{width}}}" if isinstance(value, int) else f"{{:{width}.3e}}").format(
             value
         )
         for value in values
     ]
-    logging.info("|{}|".format("|".join(values)))
+    logging.info("|{}|".format("|".join(values_)))
 
 
 def check_scalar(
     x: Any,
     name: str,
     var_type: Type | tuple[Type, ...] | None = None,
-    lb: Integral | None = None,
-    ub: Integral | None = None,
+    lb: float | int | None = None,
+    ub: float | int | None = None,
     include_boundaries: bool = True,
 ) -> None:
     """Check that a scalar satisfies certain conditions.
@@ -93,3 +94,10 @@ def check_scalar(
             assert (
                 x < ub
             ), f"Variable '{name}' ({x}) is greater than or equal to lower bound ({ub})."
+
+
+def standard_basis_vector(i: int, n_dim: int) -> np.ndarray:
+    """Return the ith standard basis vector in R^n."""
+    x = np.zeros(n_dim)
+    x[i] = 1
+    return x

From 3ac63318bbe7074785f9016890db344e68a3b03a Mon Sep 17 00:00:00 2001
From: Joshua Ivanhoe <joshua@h2gopower.com>
Date: Wed, 7 Aug 2024 11:46:04 +0100
Subject: [PATCH 04/12] Update pre-commit hooks to use ruff and reformat files

---
 .pre-commit-config.yaml      |  8 ++++--
 src/halfspace/__init__.py    |  3 ++
 src/halfspace/convex_term.py | 15 +++-------
 src/halfspace/model.py       | 54 ++++++++++--------------------------
 src/halfspace/utils.py       | 23 ++++-----------
 tests/test_convex_term.py    | 28 +++++--------------
 tests/test_model.py          | 32 ++++++---------------
 7 files changed, 46 insertions(+), 117 deletions(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index ddbf496..64318bb 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -5,10 +5,12 @@ repos:
   - id: check-yaml
   - id: end-of-file-fixer
   - id: trailing-whitespace
-- repo: https://github.com/psf/black
-  rev: 22.10.0
+- repo: https://github.com/astral-sh/ruff-pre-commit
+  rev: v0.5.6
   hooks:
-  - id: black
+    - id: ruff
+    - id: ruff-format
+      args: [--line-length=120]
 - repo: https://github.com/pycqa/pydocstyle
   rev: 6.3.0
   hooks:
diff --git a/src/halfspace/__init__.py b/src/halfspace/__init__.py
index 2f4d902..64b0b79 100644
--- a/src/halfspace/__init__.py
+++ b/src/halfspace/__init__.py
@@ -1,2 +1,5 @@
 """The `halfspace` module implements a modelling class for mixed-integer convex optimization problems."""
+
 from .model import Model
+
+__all__ = ["Model"]
diff --git a/src/halfspace/convex_term.py b/src/halfspace/convex_term.py
index 65d39f8..26da12b 100644
--- a/src/halfspace/convex_term.py
+++ b/src/halfspace/convex_term.py
@@ -62,20 +62,14 @@ def __init__(
         self.name = name
 
     @overload
-    def __call__(
-        self, query_point: QueryPoint, return_grad: Literal[False] = False
-    ) -> float:
-        ...
+    def __call__(self, query_point: QueryPoint, return_grad: Literal[False] = False) -> float: ...
 
     @overload
     def __call__(
         self, query_point: QueryPoint, return_grad: Literal[True] = True
-    ) -> tuple[float, float | np.ndarray]:
-        ...
+    ) -> tuple[float, float | np.ndarray]: ...
 
-    def __call__(
-        self, query_point: QueryPoint, return_grad: bool = False
-    ) -> float | tuple[float, float | np.ndarray]:
+    def __call__(self, query_point: QueryPoint, return_grad: bool = False) -> float | tuple[float, float | np.ndarray]:
         """Evaluate the term and (optionally) its gradient.
 
         Args:
@@ -155,6 +149,5 @@ def _approximate_grad(self, x: Input) -> float | np.ndarray:
                 ) / self.step_size
             return grad
         return (
-            self._evaluate_func(x=x + self.step_size / 2)
-            - self._evaluate_func(x=x - self.step_size / 2)
+            self._evaluate_func(x=x + self.step_size / 2) - self._evaluate_func(x=x - self.step_size / 2)
         ) / self.step_size
diff --git a/src/halfspace/model.py b/src/halfspace/model.py
index 63240f4..0be9d1c 100644
--- a/src/halfspace/model.py
+++ b/src/halfspace/model.py
@@ -243,13 +243,10 @@ def optimize(
         self._model.objective = bound
 
         # Initialize search
-        query_point = {
-            x: self._start.get(x) or (x.lb + x.ub) / 2 for x in self._model.vars
-        }
+        query_point = {x: self._start.get(x) or (x.lb + x.ub) / 2 for x in self._model.vars}
         iters_no_improvement = 0
 
         for i in range(max_iters):
-
             # Add cuts for violated nonlinear constraints
             for constr in self.nonlinear_constrs:
                 if constr(query_point=query_point) > self.infeasibility_tol:
@@ -257,10 +254,7 @@ def optimize(
                     self._model.add_constr(expr <= 0)
 
             # Add objective cut
-            expr = mip.xsum(
-                term.generate_cut(query_point=query_point)
-                for term in self.objective_terms
-            )
+            expr = mip.xsum(term.generate_cut(query_point=query_point) for term in self.objective_terms)
             if self.minimize:
                 self._model.add_constr(bound >= expr)
             else:
@@ -274,20 +268,15 @@ def optimize(
                 mip.OptimizationStatus.OPTIMAL,
                 mip.OptimizationStatus.FEASIBLE,
             ):
-                logging.info(
-                    f"Solve unsuccessful - exiting with optimization status: '{status.value}'."
-                )
+                logging.info(f"Solve unsuccessful - exiting with optimization status: '{status.value}'.")
                 self._status = status
                 return self.status
 
             # Update best solution/objective value and query point
             solution = {var: var.x for var in self._model.vars}
-            objective_value_new = sum(
-                term(query_point=solution) for term in self.objective_terms
-            )
+            objective_value_new = sum(term(query_point=solution) for term in self.objective_terms)
             if self.minimize == (objective_value_new < self.objective_value) and all(
-                constr(solution) <= self.infeasibility_tol
-                for constr in self.nonlinear_constrs
+                constr(solution) <= self.infeasibility_tol for constr in self.nonlinear_constrs
             ):
                 iters_no_improvement = 0
                 self._objective_value = objective_value_new
@@ -297,8 +286,7 @@ def optimize(
                     iters_no_improvement += 1
                 if self.smoothing is not None:
                     query_point = {
-                        var: self.smoothing * query_point[var]
-                        + (1 - self.smoothing) * solution[var]
+                        var: self.smoothing * query_point[var] + (1 - self.smoothing) * solution[var]
                         for var in self._model.vars
                     }
                 else:
@@ -306,13 +294,9 @@ def optimize(
 
             # Update best bound (clip values to prevent numerical errors from affecting termination logic)
             if self.minimize:
-                self._best_bound = np.clip(
-                    bound.x, a_min=self.best_bound, a_max=self.objective_value
-                )
+                self._best_bound = np.clip(bound.x, a_min=self.best_bound, a_max=self.objective_value)
             else:
-                self._best_bound = np.clip(
-                    bound.x, a_min=self.objective_value, a_max=self.best_bound
-                )
+                self._best_bound = np.clip(bound.x, a_min=self.objective_value, a_max=self.best_bound)
 
             # Update log
             self._search_log.append(
@@ -331,20 +315,16 @@ def optimize(
 
             # Check early termination conditions
             if self.gap <= self.max_gap or self.gap_abs <= self.max_gap_abs:
-                logging.info(
-                    f"Optimality tolerance reached - terminating search early."
-                )
+                logging.info("Optimality tolerance reached - terminating search early.")
                 self._status = mip.OptimizationStatus.OPTIMAL
                 return self.status
             if max_iters_no_improvement is not None:
                 if iters_no_improvement >= max_iters_no_improvement:
-                    logging.info(
-                        f"Max iterations without improvement reached - terminating search early."
-                    )
+                    logging.info("Max iterations without improvement reached - terminating search early.")
                     self._status = mip.OptimizationStatus.FEASIBLE
                     return self.status
 
-        logging.info(f"Max iterations reached - terminating search.")
+        logging.info("Max iterations reached - terminating search.")
         if self.best_solution:
             self._status = mip.OptimizationStatus.FEASIBLE
         else:
@@ -371,9 +351,7 @@ def var_value(self, x: mip.Var | mip.LinExprTensor | str) -> float | np.ndarray:
         if isinstance(x, mip.Var):
             return self.best_solution[x]
         if isinstance(x, mip.LinExprTensor):
-            return np.array([self.best_solution[var] for var in x.flatten()]).reshape(
-                x.shape
-            )
+            return np.array([self.best_solution[var] for var in x.flatten()]).reshape(x.shape)
         if isinstance(x, Iterable):
             return np.array([self.best_solution[var] for var in x])
         raise TypeError(f"Input of type '{type(x)}' not supported.")
@@ -426,9 +404,7 @@ def best_bound(self) -> float:
     @property
     def gap(self) -> float:
         """Get the (relative) optimality gap."""
-        return self.gap_abs / max(
-            min(abs(self.objective_value), abs(self.best_bound)), 1e-10
-        )
+        return self.gap_abs / max(min(abs(self.objective_value), abs(self.best_bound)), 1e-10)
 
     @property
     def gap_abs(self) -> float:
@@ -491,9 +467,7 @@ def _validate_params(self) -> None:
             )
 
     @staticmethod
-    def _validate_bounds(
-        lb: float | int, ub: float | int, var_type: str
-    ) -> tuple[float | int, float | int]:
+    def _validate_bounds(lb: float | int, ub: float | int, var_type: str) -> tuple[float | int, float | int]:
         if var_type == mip.BINARY:
             lb, ub = 0, 1
         else:
diff --git a/src/halfspace/utils.py b/src/halfspace/utils.py
index 48ce9a3..b2c6ba9 100644
--- a/src/halfspace/utils.py
+++ b/src/halfspace/utils.py
@@ -39,12 +39,7 @@ def log_table_row(values: Iterable[float | int], width: int = 15) -> None:
 
     Returns: None
     """
-    values_ = [
-        (f"{{:{width}}}" if isinstance(value, int) else f"{{:{width}.3e}}").format(
-            value
-        )
-        for value in values
-    ]
+    values_ = [(f"{{:{width}}}" if isinstance(value, int) else f"{{:{width}.3e}}").format(value) for value in values]
     logging.info("|{}|".format("|".join(values_)))
 
 
@@ -75,25 +70,17 @@ def check_scalar(
     Returns: None
     """
     if var_type is not None:
-        assert isinstance(
-            x, var_type
-        ), f"Variable '{name}' ({type(x)}) is not expected type ({var_type})."
+        assert isinstance(x, var_type), f"Variable '{name}' ({type(x)}) is not expected type ({var_type})."
     if lb is not None:
         if include_boundaries:
             assert x >= lb, f"Variable '{name}' ({x}) is less than lower bound ({lb})."
         else:
-            assert (
-                x > lb
-            ), f"Variable '{name}' ({x}) is less than or equal to lower bound ({lb})."
+            assert x > lb, f"Variable '{name}' ({x}) is less than or equal to lower bound ({lb})."
     if ub is not None:
         if include_boundaries:
-            assert (
-                x <= ub
-            ), f"Variable '{name}' ({x}) is greater than lower bound ({ub})."
+            assert x <= ub, f"Variable '{name}' ({x}) is greater than lower bound ({ub})."
         else:
-            assert (
-                x < ub
-            ), f"Variable '{name}' ({x}) is greater than or equal to lower bound ({ub})."
+            assert x < ub, f"Variable '{name}' ({x}) is greater than or equal to lower bound ({ub})."
 
 
 def standard_basis_vector(i: int, n_dim: int) -> np.ndarray:
diff --git a/tests/test_convex_term.py b/tests/test_convex_term.py
index 917bf59..d3953fc 100644
--- a/tests/test_convex_term.py
+++ b/tests/test_convex_term.py
@@ -33,9 +33,7 @@ def _check_convex_term(
     query_point: QueryPoint,
 ):
     # Check evaluation without gradient
-    assert term(query_point=query_point, return_grad=False) == pytest.approx(
-        expected_value
-    )
+    assert term(query_point=query_point, return_grad=False) == pytest.approx(expected_value)
 
     # Check evaluation with gradient
     value, grad = term(query_point=query_point, return_grad=True)
@@ -64,9 +62,7 @@ def model() -> Model:
         ({"x": 1}, 1, 2),
     ],
 )
-@pytest.mark.parametrize(
-    ["combine_grad", "approximate_grad"], [(True, False), (False, True), (False, False)]
-)
+@pytest.mark.parametrize(["combine_grad", "approximate_grad"], [(True, False), (False, True), (False, False)])
 def test_single_variable_term(
     model: Model,
     query_point: dict[str, float],
@@ -91,9 +87,7 @@ def test_single_variable_term(
         expected_value=expected_value,
         expected_grad=expected_grad,
         expected_is_multivariable=False,
-        query_point={
-            model.var_by_name(name=name): value for name, value in query_point.items()
-        },
+        query_point={model.var_by_name(name=name): value for name, value in query_point.items()},
     )
 
 
@@ -104,9 +98,7 @@ def test_single_variable_term(
         ({"x": 1, "y": 2}, 5, np.array([2, 4])),
     ],
 )
-@pytest.mark.parametrize(
-    ["combine_grad", "approximate_grad"], [(True, False), (False, True), (False, False)]
-)
+@pytest.mark.parametrize(["combine_grad", "approximate_grad"], [(True, False), (False, True), (False, False)])
 def test_multivariable_term(
     model: Model,
     query_point: dict[str, float],
@@ -131,9 +123,7 @@ def test_multivariable_term(
         expected_value=expected_value,
         expected_grad=expected_grad,
         expected_is_multivariable=True,
-        query_point={
-            model.var_by_name(name=name): value for name, value in query_point.items()
-        },
+        query_point={model.var_by_name(name=name): value for name, value in query_point.items()},
     )
 
 
@@ -144,9 +134,7 @@ def test_multivariable_term(
         ({"z_0": 1, "z_1": 2}, 5, np.array([2, 4])),
     ],
 )
-@pytest.mark.parametrize(
-    ["combine_grad", "approximate_grad"], [(True, False), (False, True), (False, False)]
-)
+@pytest.mark.parametrize(["combine_grad", "approximate_grad"], [(True, False), (False, True), (False, False)])
 def test_var_tensor_term(
     model: Model,
     query_point: dict[str, float],
@@ -171,7 +159,5 @@ def test_var_tensor_term(
         expected_value=expected_value,
         expected_grad=expected_grad,
         expected_is_multivariable=True,
-        query_point={
-            model.var_by_name(name=name): value for name, value in query_point.items()
-        },
+        query_point={model.var_by_name(name=name): value for name, value in query_point.items()},
     )
diff --git a/tests/test_model.py b/tests/test_model.py
index 1e67d0a..95a7c49 100644
--- a/tests/test_model.py
+++ b/tests/test_model.py
@@ -16,9 +16,7 @@ def _check_solution(
     expected_status: mip.OptimizationStatus = mip.OptimizationStatus.OPTIMAL,
 ):
     if expected_objective_value is not None:
-        assert model.objective_value == pytest.approx(
-            expected_objective_value, abs=model.max_gap_abs
-        )
+        assert model.objective_value == pytest.approx(expected_objective_value, abs=model.max_gap_abs)
     if expected_solution is not None:
         for x, expected_value in expected_solution.items():
             assert model.var_value(x=x) == pytest.approx(expected_value, abs=VAR_TOL)
@@ -72,9 +70,7 @@ def test_multivariable_variable_no_constraints():
     model = Model()
     x = model.add_var(lb=0, ub=1)
     y = model.add_var(lb=0, ub=1)
-    model.add_objective_term(
-        var=(x, y), func=lambda x, y: (x - 0.25) ** 2 + (y - 0.25) ** 2 + 1
-    )
+    model.add_objective_term(var=(x, y), func=lambda x, y: (x - 0.25) ** 2 + (y - 0.25) ** 2 + 1)
     model.optimize()
     _check_solution(
         model=model,
@@ -86,9 +82,7 @@ def test_multivariable_variable_no_constraints():
 def test_multivariable_variable_as_tensor_no_constraints():
     model = Model()
     x = model.add_var_tensor(shape=(2,), lb=0, ub=1)
-    model.add_objective_term(
-        var=x, func=lambda x: (x[0] - 0.25) ** 2 + (x[1] - 0.25) ** 2 + 1
-    )
+    model.add_objective_term(var=x, func=lambda x: (x[0] - 0.25) ** 2 + (x[1] - 0.25) ** 2 + 1)
     model.optimize()
     _check_solution(
         model=model,
@@ -101,9 +95,7 @@ def test_multivariable_linear_constraint():
     model = Model()
     x = model.add_var(lb=0, ub=1)
     y = model.add_var(lb=0, ub=1)
-    model.add_objective_term(
-        var=(x, y), func=lambda x, y: (x - 0.25) ** 2 + (y - 0.25) ** 2 + 1
-    )
+    model.add_objective_term(var=(x, y), func=lambda x, y: (x - 0.25) ** 2 + (y - 0.25) ** 2 + 1)
     model.add_linear_constr(100 * x + y <= 0.25)
     model.optimize()
     _check_solution(
@@ -117,9 +109,7 @@ def test_multivariable_linear_constraint_infeasible():
     model = Model()
     x = model.add_var(lb=0, ub=1)
     y = model.add_var(lb=0, ub=1)
-    model.add_objective_term(
-        var=(x, y), func=lambda x, y: (x - 0.25) ** 2 + (y - 0.25) ** 2 + 1
-    )
+    model.add_objective_term(var=(x, y), func=lambda x, y: (x - 0.25) ** 2 + (y - 0.25) ** 2 + 1)
     model.add_linear_constr(x + y >= 3)
     model.optimize()
     _check_solution(model=model, expected_status=mip.OptimizationStatus.INFEASIBLE)
@@ -129,12 +119,8 @@ def test_multivariable_nonlinear_constraint():
     model = Model(max_gap_abs=1e-2)
     x = model.add_var(lb=0, ub=1)
     y = model.add_var(lb=0, ub=1)
-    model.add_objective_term(
-        var=(x, y), func=lambda x, y: (x - 0.25) ** 2 + (y - 0.25) ** 2 + 1
-    )
-    model.add_nonlinear_constr(
-        var=(x, y), func=lambda x, y: (80 * x) ** 2 + y**2 - 0.25**2
-    )
+    model.add_objective_term(var=(x, y), func=lambda x, y: (x - 0.25) ** 2 + (y - 0.25) ** 2 + 1)
+    model.add_nonlinear_constr(var=(x, y), func=lambda x, y: (80 * x) ** 2 + y**2 - 0.25**2)
     model.optimize()
     _check_solution(
         model=model,
@@ -147,9 +133,7 @@ def test_multivariable_nonlinear_constraint_infeasible():
     model = Model(max_gap_abs=1e-2)
     x = model.add_var(lb=0, ub=1)
     y = model.add_var(lb=0, ub=1)
-    model.add_objective_term(
-        var=(x, y), func=lambda x, y: (x - 0.25) ** 2 + (y - 0.25) ** 2 + 1
-    )
+    model.add_objective_term(var=(x, y), func=lambda x, y: (x - 0.25) ** 2 + (y - 0.25) ** 2 + 1)
     model.add_nonlinear_constr(var=(x, y), func=lambda x, y: np.exp(x + y) + 1)
     model.optimize()
     _check_solution(model=model, expected_status=mip.OptimizationStatus.INFEASIBLE)

From a522c0d2225781c83ca5178dc74953169c202aec Mon Sep 17 00:00:00 2001
From: Joshua Ivanhoe <joshua@h2gopower.com>
Date: Thu, 8 Aug 2024 14:51:07 +0100
Subject: [PATCH 05/12] Update GitHub actions

---
 .github/workflows/ci.yml      | 17 +++++++++++------
 .github/workflows/docs.yml    |  2 +-
 .github/workflows/release.yml |  2 +-
 pyproject.toml                | 23 ++++++++++++++++-------
 4 files changed, 29 insertions(+), 15 deletions(-)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index b933b3a..b436928 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -2,9 +2,9 @@ name: CI workflow
 
 on:
   push:
-    branches: [ "main" ]
+    branches: ["main"]
   pull_request:
-    branches: [ "main" ]
+    branches: ["main"]
 
 jobs:
   build:
@@ -29,11 +29,16 @@ jobs:
     - name: Install dependencies
       run: poetry install
 
-    - name: Format with black
-      run: poetry run black ./src
+    - name: Lint and format with ruff
+      run: |
+        ruff check --fix
+        ruff format
+      uses: stefanzweifel/git-auto-commit-action@v4
+      with:
+        commit_message: "Lint and format with ruff"
 
-    - name: Lint with flake8
-      run: poetry run flake8 --max-line-length 120
+    - name: Perform type checking with mypy
+      run: mypy src
 
     - name: Run tests
       run: poetry run pytest --cov=src
diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml
index 2194228..16066e8 100644
--- a/.github/workflows/docs.yml
+++ b/.github/workflows/docs.yml
@@ -1,7 +1,7 @@
 name: Generate API documentation
 on:
   push:
-    branches: [ "main" ]
+    branches: ["main"]
 
 jobs:
   deploy:
diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml
index 1c0b6e0..cd4b376 100644
--- a/.github/workflows/release.yml
+++ b/.github/workflows/release.yml
@@ -17,7 +17,7 @@ jobs:
     - name: Set up Python
       uses: actions/setup-python@v3
       with:
-        python-version: '3.11'
+        python-version: "3.11"
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip
diff --git a/pyproject.toml b/pyproject.toml
index c627ad5..ba32cb8 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -25,20 +25,29 @@ numpy = ">=1.25.2"
 pandas = ">=2.0.3"
 
 [tool.poetry.group.dev.dependencies]
-black = "*"
 mypy = "*"
-flake8 = "*"
 pre-commit = "*"
-tomli = "*"
+ruff = "*"
 
 [tool.poetry.group.test.dependencies]
 pytest = "*"
 pytest-cov = "*"
 
-[tool.poetry.group.docs]
-optional = true
-[tool.poetry.group.docs.dependencies]
-sphinx = "*"
+[tool.ruff]
+line-length = 100
+
+[tool.ruff.lint]
+extend-select = ["D"]  # pydocstyle
+
+[tool.ruff.lint.pydocstyle]
+convention = "google"
+
+[tool.ruff.lint.per-file-ignores]
+"tests/**" = ["D"]  # Ignore all directories named `tests` for pydocstyle
+
+[tool.mypy]
+ignore_missing_imports = true
+exclude = ["convex_term.py"]
 
 [build-system]
 requires = ["poetry-core"]

From c3c1df049258c9b1e000d8c387493d3b54849744 Mon Sep 17 00:00:00 2001
From: Joshua Ivanhoe <joshua@h2gopower.com>
Date: Thu, 8 Aug 2024 14:55:39 +0100
Subject: [PATCH 06/12] Update GitHub actions

---
 .github/workflows/ci.yml | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index b436928..c20d4c4 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -33,6 +33,8 @@ jobs:
       run: |
         ruff check --fix
         ruff format
+
+    - name: Auto-commit ruff changes
       uses: stefanzweifel/git-auto-commit-action@v4
       with:
         commit_message: "Lint and format with ruff"

From 0d9713eac514a8f5dd6984dd1ab7e7e77cb60633 Mon Sep 17 00:00:00 2001
From: Joshua Ivanhoe <joshua@h2gopower.com>
Date: Thu, 8 Aug 2024 15:25:25 +0100
Subject: [PATCH 07/12] Update GitHub actions

---
 .github/workflows/ci.yml | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index c20d4c4..a8919da 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -30,9 +30,8 @@ jobs:
       run: poetry install
 
     - name: Lint and format with ruff
-      run: |
-        ruff check --fix
-        ruff format
+      run: ruff check --fix
+      #ruff format
 
     - name: Auto-commit ruff changes
       uses: stefanzweifel/git-auto-commit-action@v4

From 47b471a5166b56db70ab6b2c254d5e067144dc6d Mon Sep 17 00:00:00 2001
From: Joshua Ivanhoe <joshua@h2gopower.com>
Date: Thu, 8 Aug 2024 15:42:50 +0100
Subject: [PATCH 08/12] Update GitHub actions

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index ba32cb8..7ee4b35 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -19,7 +19,7 @@ packages = [
 ]
 
 [tool.poetry.dependencies]
-python = ">=3.10,<=3.12"
+python = ">=3.10,<3.13"
 mip = ">=1.15.0"
 numpy = ">=1.25.2"
 pandas = ">=2.0.3"

From 55ddba889d591b12b628ce28497fd2466fe4e131 Mon Sep 17 00:00:00 2001
From: Joshua Ivanhoe <joshua@h2gopower.com>
Date: Thu, 8 Aug 2024 16:40:24 +0100
Subject: [PATCH 09/12] Update GitHub actions

---
 .github/workflows/ci.yml | 5 +++--
 pyproject.toml           | 1 +
 2 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index a8919da..c20d4c4 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -30,8 +30,9 @@ jobs:
       run: poetry install
 
     - name: Lint and format with ruff
-      run: ruff check --fix
-      #ruff format
+      run: |
+        ruff check --fix
+        ruff format
 
     - name: Auto-commit ruff changes
       uses: stefanzweifel/git-auto-commit-action@v4
diff --git a/pyproject.toml b/pyproject.toml
index 7ee4b35..496d64f 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -23,6 +23,7 @@ python = ">=3.10,<3.13"
 mip = ">=1.15.0"
 numpy = ">=1.25.2"
 pandas = ">=2.0.3"
+ruff = "*"
 
 [tool.poetry.group.dev.dependencies]
 mypy = "*"

From e9f3cbe6ad612d20e2974f993642ba3fe5562589 Mon Sep 17 00:00:00 2001
From: Joshua Ivanhoe <joshua@h2gopower.com>
Date: Thu, 8 Aug 2024 16:59:47 +0100
Subject: [PATCH 10/12] Update GitHub actions

---
 .github/workflows/ci.yml | 4 ++--
 pyproject.toml           | 1 -
 2 files changed, 2 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index c20d4c4..ed7288a 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -31,8 +31,8 @@ jobs:
 
     - name: Lint and format with ruff
       run: |
-        ruff check --fix
-        ruff format
+        python -m ruff check --fix
+        python -m ruff format
 
     - name: Auto-commit ruff changes
       uses: stefanzweifel/git-auto-commit-action@v4
diff --git a/pyproject.toml b/pyproject.toml
index 496d64f..7ee4b35 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -23,7 +23,6 @@ python = ">=3.10,<3.13"
 mip = ">=1.15.0"
 numpy = ">=1.25.2"
 pandas = ">=2.0.3"
-ruff = "*"
 
 [tool.poetry.group.dev.dependencies]
 mypy = "*"

From c1683dab482ad4cc8b326108f995c099d3786993 Mon Sep 17 00:00:00 2001
From: Joshua Ivanhoe <josh@joinuptick.com>
Date: Fri, 12 Sep 2025 10:36:29 +0100
Subject: [PATCH 11/12] feat: Various improvements (WIP) - Better docs -
 Additional tests - Minor bug fixes

---
 .github/workflows/ci.yml     |  16 +-
 pyproject.toml               |  99 ++++++----
 src/halfspace/convex_term.py |  96 ++++++---
 src/halfspace/model.py       | 324 ++++++++++++++++++++-----------
 src/halfspace/utils.py       |  52 ++---
 tests/test_convex_term.py    |  32 +++
 tests/test_model.py          |  36 ++++
 tests/test_utils.py          |  14 +-
 uv.lock                      | 366 +++++++++++++++++++++++++++++++++++
 9 files changed, 814 insertions(+), 221 deletions(-)
 create mode 100644 uv.lock

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index ed7288a..ea9f6af 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -13,7 +13,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.10", "3.11", "3.12"]
+        python-version: ["3.12"]
 
     steps:
     - uses: actions/checkout@v3
@@ -24,20 +24,10 @@ jobs:
         python-version: ${{ matrix.python-version }}
 
     - name: Install poetry
-      run: python -m pip install poetry
+      run: python -m pip install uv
 
     - name: Install dependencies
-      run: poetry install
-
-    - name: Lint and format with ruff
-      run: |
-        python -m ruff check --fix
-        python -m ruff format
-
-    - name: Auto-commit ruff changes
-      uses: stefanzweifel/git-auto-commit-action@v4
-      with:
-        commit_message: "Lint and format with ruff"
+      run: uv sync --extra all
 
     - name: Perform type checking with mypy
       run: mypy src
diff --git a/pyproject.toml b/pyproject.toml
index 7ee4b35..4df8f39 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,54 +1,77 @@
-[tool.poetry]
+[project]
 name = "halfspace-optimizer"
-version = "0.1.2"
-description = "Cutting-plane solver for mixed-integer convex optimization problems"
-license ="MIT"
+version = "0.1.1"
 authors = [
-    "Joshua Ivanhoe <joshua.k.ivanhoe@gmail.com>"
+  { name="Joshua Ivanhoe", email="joshua.k.ivanhoe@gmail.com" },
 ]
+description = "Cutting-plane solver for mixed-integer convex optimization problems"
 readme = "README.md"
-repository = "https://github.com/joshivanhoe/halfspace"
-documentation = "https://joshivanhoe.github.io/halfspace/"
+license = {file = "LICENSE"}
+requires-python = ">=3.12,<3.13"
+keywords = ["optimization", "mixed-integer", "convex", "cutting-plane", "solver"]
 classifiers = [
-    "Programming Language :: Python :: 3",
-    "License :: OSI Approved :: MIT License",
+    "Development Status :: 4 - Beta",
+    "Intended Audience :: Science/Research",
     "Operating System :: OS Independent",
+    "Programming Language :: Python :: 3",
+    "Topic :: Scientific/Engineering :: Mathematics",
 ]
-packages = [
-    { include = "halfspace", from = "src" }
+dependencies = [
+    "mip>=1.15.0,<2.0.0",
+    "numpy>=2.3.3,<3.0.0",
+    "pandas>=2.3.2,<3.0.0",
 ]
 
-[tool.poetry.dependencies]
-python = ">=3.10,<3.13"
-mip = ">=1.15.0"
-numpy = ">=1.25.2"
-pandas = ">=2.0.3"
-
-[tool.poetry.group.dev.dependencies]
-mypy = "*"
-pre-commit = "*"
-ruff = "*"
+[project.urls]
+Homepage = "https://github.com/joshivanhoe/halfspace"
+Issues = "https://github.com/joshivanhoe/halfspace/issues"
 
-[tool.poetry.group.test.dependencies]
-pytest = "*"
-pytest-cov = "*"
+[project.optional-dependencies]
+dev = [
+    "ruff",
+    "pre-commit",
+    "tomli",
+]
+test = [
+    "pytest",
+    "pytest-cov",
+]
+all = [
+    "halfspace-optimizer[dev,test]",
+]
 
-[tool.ruff]
-line-length = 100
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
 
-[tool.ruff.lint]
-extend-select = ["D"]  # pydocstyle
+[tool.uv]
+index-url = "https://pypi.org/simple"
+extra-index-url = []
 
-[tool.ruff.lint.pydocstyle]
-convention = "google"
+[tool.hatch.build.targets.wheel]
+packages = ["src/halfspace"]
 
-[tool.ruff.lint.per-file-ignores]
-"tests/**" = ["D"]  # Ignore all directories named `tests` for pydocstyle
+[tool.pytest.ini_options]
+testpaths = ["tests"]
+python_files = ["test_*.py"]
+python_classes = ["Test*"]
+python_functions = ["test_*"]
+addopts = [
+    "--strict-markers",
+    "--strict-config",
+    "--cov=src/halfspace",
+    "--cov-report=term-missing",
+    "--cov-report=html",
+]
 
-[tool.mypy]
-ignore_missing_imports = true
-exclude = ["convex_term.py"]
+[tool.coverage.run]
+source = ["src/halfspace"]
+omit = ["*/tests/*"]
 
-[build-system]
-requires = ["poetry-core"]
-build-backend = "poetry.core.masonry.api"
+[tool.coverage.report]
+exclude_lines = [
+    "pragma: no cover",
+    "def __repr__",
+    "raise AssertionError",
+    "raise NotImplementedError",
+]
diff --git a/src/halfspace/convex_term.py b/src/halfspace/convex_term.py
index 26da12b..fad11ac 100644
--- a/src/halfspace/convex_term.py
+++ b/src/halfspace/convex_term.py
@@ -3,20 +3,19 @@
 It provides a modular framework for generating cutting planes.
 """
 
-from typing import Callable, Iterable
-from typing import TypeAlias, Literal, overload
+from typing import Callable, Iterable, Literal, overload
 
 import mip
 import numpy as np
 
-from .utils import standard_basis_vector
+from .utils import check_scalar
 
-QueryPoint: TypeAlias = dict[mip.Var, float]
-Var: TypeAlias = mip.Var | Iterable[mip.Var] | mip.LinExprTensor
-Input: TypeAlias = float | Iterable[float] | np.ndarray
-Func: TypeAlias = Callable[[Input], float]
-FuncGrad: TypeAlias = Callable[[Input], tuple[float, float | np.ndarray]]
-Grad: TypeAlias = Callable[[Input], float | np.ndarray]
+type QueryPoint = dict[mip.Var, float]
+type Var = mip.Var | Iterable[mip.Var] | mip.LinExprTensor
+type Input = float | Iterable[float] | np.ndarray
+type Func = Callable[[Input], float]
+type FuncGrad = Callable[[Input], tuple[float, float | np.ndarray]]
+type Grad = Callable[[Input], float | np.ndarray]
 
 
 class ConvexTerm:
@@ -25,15 +24,15 @@ class ConvexTerm:
     Attributes:
         var: The variable(s) included in the term. This can be provided in the form of a single  variable, an
             iterable of multiple variables or a variable tensor.
-        func: A function for computing the term's value. This function should except one argument for each
+        func: A function for computing the term's value. This function should accept one argument for each
             variable in `var`. If `var` is a variable tensor, then the function should accept a single array.
-        grad: A function for computing the term's gradient. This function should except one argument for each
+        grad: A function for computing the term's gradient. This function should accept one argument for each
             variable in `var`. If `var` is a variable tensor, then the function should accept a single array. If
             `None`, then the gradient is approximated numerically using the central finite difference method. If
             `grad` is instead a Boolean and is `True`, then `func` is assumed to return a tuple where the first
             element is the function value and the second element is the gradient. This is useful when the gradient
             is expensive to compute.
-        step_size: The step size used for numerical gradient approximation. If `grad` is provided, then this argument
+        step_size: The step size used for numerical gradient approximation. Must be positive. If `grad` is provided, then this argument
             is ignored.
         name: The name for the term.
     """
@@ -49,12 +48,19 @@ def __init__(
         """Convex term constructor.
 
         Args:
-            var: The value of the `var` attribute.
-            func: The value of the `func` attribute.
-            grad: The value of the `grad` attribute.
-            step_size: The value of the `step_size` attribute. Must be positive.
-            name: The value of the `name` attribute.
+            var: The variable(s) included in the term. Can be a single variable, an iterable of variables, or a variable tensor.
+            func: The function for computing the term's value.
+            grad: The function for computing the term's gradient, or None for numerical approximation, or True if func returns (value, grad).
+            step_size: The step size for numerical gradient approximation. Must be positive.
+            name: The name for the term.
         """
+        check_scalar(
+            x=step_size,
+            name="step_size",
+            var_type=float,
+            lb=0,
+            include_boundaries=False,
+        )
         self.var = var
         self.func = func
         self.grad = grad
@@ -77,7 +83,7 @@ def __call__(self, query_point: QueryPoint, return_grad: bool = False) -> float
             return_grad: Whether to return the term's gradient.
 
         Returns:
-            If `return_grad=False`, then only the value of the term is returned. Conversely, if `return_grad=True`,
+            If `return_grad=False`, then only the value of the term is returned. If `return_grad=True`,
             then a tuple is returned where the first element is the term's value and the second element is the term's
             gradient.
         """
@@ -91,26 +97,40 @@ def __call__(self, query_point: QueryPoint, return_grad: bool = False) -> float
 
     @property
     def is_multivariable(self) -> bool:
-        """Check whether the term is multivariable."""
+        """Check whether the term is multivariable.
+        
+        Returns:
+            True if the term involves multiple variables, False otherwise.
+        """
         return not isinstance(self.var, mip.Var)
 
     def generate_cut(self, query_point: QueryPoint) -> mip.LinExpr:
         """Generate a cutting plane for the term.
 
+        The cutting plane is a linear approximation of the convex term at the given query point,
+        valid for all feasible points due to convexity.
+
         Args:
-            query_point: dict mapping mip.Var to float
-                The query point for which the cutting plane is generated.
+            query_point: The query point for which the cutting plane is generated.
 
         Returns:
-            The linear constraint representing the cutting plane.
+            A linear expression representing the cutting plane constraint.
         """
-        func, grad = self(query_point=query_point, return_grad=True)
+        value, grad = self(query_point=query_point, return_grad=True)
         x = self._get_input(query_point=query_point)
         if self.is_multivariable:
-            return mip.xsum(grad * (np.array(self.var) - x)) + func
-        return grad * (self.var - x) + func
+            return mip.xsum(grad * (np.array(self.var) - x)) + value
+        return grad * (self.var - x) + value
 
     def _get_input(self, query_point: QueryPoint) -> Input:
+        """Extract input values from query point based on variable type.
+        
+        Args:
+            query_point: The query point containing variable values.
+            
+        Returns:
+            Input values in the format expected by the function.
+        """
         if self.is_multivariable:
             return np.array([query_point[var] for var in self.var])
         return query_point[self.var]
@@ -118,7 +138,7 @@ def _get_input(self, query_point: QueryPoint) -> Input:
     def _evaluate_func(self, x: Input) -> float | tuple[float, float | np.ndarray]:
         """Evaluate the function value.
 
-        If `grad=True`, then both the value of the function and it's gradient are returned.
+        If `grad=True`, then both the value of the function and its gradient are returned.
         """
         if isinstance(self.var, (mip.Var, mip.LinExprTensor)):
             return self.func(x)
@@ -127,7 +147,14 @@ def _evaluate_func(self, x: Input) -> float | tuple[float, float | np.ndarray]:
         raise TypeError(f"Input of type '{type(x)}' not supported.")
 
     def _evaluate_grad(self, x: Input) -> float | np.ndarray:
-        """Evaluate the gradient."""
+        """Evaluate the gradient.
+        
+        Args:
+            x: The input values at which to evaluate the gradient.
+            
+        Returns:
+            The gradient value(s).
+        """
         if not self.grad:
             return self._approximate_grad(x=x)
         if isinstance(self.var, (mip.Var, mip.LinExprTensor)):
@@ -137,15 +164,22 @@ def _evaluate_grad(self, x: Input) -> float | np.ndarray:
         raise TypeError(f"Input of type '{type(x)}' not supported.")
 
     def _approximate_grad(self, x: Input) -> float | np.ndarray:
-        """Approximate the gradient of the function at point using the central finite difference method."""
+        """Approximate the gradient using central finite differences.
+        
+        Args:
+            x: The input values at which to approximate the gradient.
+            
+        Returns:
+            The approximated gradient value(s).
+        """
         if self.is_multivariable:
             n_dim = len(x)
             grad = np.zeros(n_dim)
+            e = np.eye(n_dim)
             for i in range(n_dim):
-                e_i = standard_basis_vector(i=i, n_dim=n_dim)
                 grad[i] = (
-                    self._evaluate_func(x=x + self.step_size / 2 * e_i)
-                    - self._evaluate_func(x=x - self.step_size / 2 * e_i)
+                    self._evaluate_func(x=x + self.step_size / 2 * e[i])
+                    - self._evaluate_func(x=x - self.step_size / 2 * e[i])
                 ) / self.step_size
             return grad
         return (
diff --git a/src/halfspace/model.py b/src/halfspace/model.py
index 0be9d1c..6f8444b 100644
--- a/src/halfspace/model.py
+++ b/src/halfspace/model.py
@@ -13,24 +13,36 @@
 from .convex_term import ConvexTerm, Var, Func, FuncGrad, Grad
 from .utils import check_scalar, log_table_header, log_table_row
 
-Start = list[tuple[mip.Var, float]]
+type Start = list[tuple[mip.Var, float]]
 
 
 class Model:
-    """Mixed-integer convex optimization model.
+    """Mixed-integer convex optimization model using outer approximation.
+    
+    This class implements an outer approximation algorithm for solving mixed-integer convex
+    optimization problems. The algorithm iteratively adds linear cuts to approximate nonlinear
+    constraints and objective functions, solving a sequence of mixed-integer linear programs.
+    
+    The model supports both continuous and discrete variables, linear and nonlinear constraints,
+    and can handle both minimization and maximization problems (with concave objectives for
+    maximization).
 
     Attributes:
-        minimize: Whether the objective should be minimized. If `False`, the objective will be maximized - note that in
-            this case the objective must concave, not convex.
+        minimize: Whether the objective should be minimized. If `False`, the objective will be
+            maximized - note that in this case the objective must be concave, not convex.
         max_gap: The maximum relative optimality gap allowed before the search is terminated.
         max_gap_abs: The maximum absolute optimality gap allowed before the search is terminated.
-        infeasibility_tol: The maximum allowed constraint violation permitted for a solution to be considered feasible.
-        step_size: The step size used to numerically evaluate gradients using the central finite difference method. Only
-            used when a function for analytically computing the gradient is not provided.
-        smoothing: The smoothing parameter used to update the query point. If `None`, the query point will not be
-            updated.
-        solver_name: The MIP solver to use. Valid options are 'CBC' and 'GUROBI'. Note that 'GUROBI' requires a license.
-        log_freq: The frequency with which logs are
+        infeasibility_tol: The maximum allowed constraint violation permitted for a solution to
+            be considered feasible.
+        step_size: The step size used to numerically evaluate gradients using the central finite
+            difference method. Only used when a function for analytically computing the gradient
+            is not provided.
+        smoothing: The smoothing parameter used to update the query point. If `None`, the query
+            point will be updated to the incumbent solution at each iteration.
+        solver_name: The MIP solver to use. Valid options are 'CBC' and 'GRB' (Gurobi). Note that
+            'GRB' requires a license.
+        log_freq: The frequency with which progress logs are printed during optimization.
+            If `None`, no progress logs are printed.
     """
 
     def __init__(
@@ -44,17 +56,17 @@ def __init__(
         solver_name: str | None = "CBC",
         log_freq: int | None = 1,
     ) -> None:
-        """Optimization model constructor.
+        """Initialize the optimization model.
 
         Args:
-            minimize: Value for the `minimize` attribute.
-            max_gap: Value for the `max_gap` attribute. Must be positive.
-            max_gap_abs: Value for the `max_gap_abs` attribute. Must be positive.
-            infeasibility_tol: Value for the `infeasibility_tol` attribute. Must be positive.
-            step_size: Value for the `step_size` attribute. Must be positive.
-            smoothing: Value for the `smoothing` attribute. If provided, must be in the range (0, 1).
-            solver_name: Value for the `solver_name` attribute.
-            log_freq: Value for the `log_freq` attribute.
+            minimize: Whether to minimize the objective. If `False`, maximizes (requires concave objective).
+            max_gap: Maximum relative optimality gap for early termination. Must be positive.
+            max_gap_abs: Maximum absolute optimality gap for early termination. Must be positive.
+            infeasibility_tol: Maximum constraint violation for feasible solutions. Must be positive.
+            step_size: Step size for numerical gradient approximation. Must be positive.
+            smoothing: Query point smoothing parameter in (0, 1). If `None`, uses incumbent solution.
+            solver_name: MIP solver name ('CBC' or 'GRB'). 'GRB' requires a license.
+            log_freq: Progress logging frequency. If `None`, no progress logs are printed.
         """
         self.minimize = minimize
         self.max_gap = max_gap
@@ -68,7 +80,11 @@ def __init__(
         self.reset()
 
     def reset(self) -> None:
-        """Reset the model."""
+        """Reset the model to its initial state.
+        
+        Clears all variables, constraints, and solution data, returning the model to
+        the state it was in immediately after construction.
+        """
         self._model: mip.Model = mip.Model(
             solver_name=self.solver_name,
             sense=mip.MINIMIZE if self.minimize else mip.MAXIMIZE,
@@ -91,18 +107,18 @@ def add_var(
         var_type: str = mip.CONTINUOUS,
         name: str = "",
     ) -> mip.Var:
-        """Add a decision variable to the model.
+        """Add a single decision variable to the model.
 
         Args:
-            lb: The lower bound for the decision variable. Must be finite and less than the upper bound. Cannot be
-                `None` if `var_type` is 'C' or 'I'.
-            ub: The upper bound for the decision variable. Must be finite and greater than the lower bound. Cannot be
-                `None` if `var_type` is 'C' or 'I'.
-            var_type: The variable type. Valid options are 'C' (continuous), 'I' (integer) and 'B' (binary).
-            name: The name of the decision variable.
+            lb: Lower bound for the variable. Must be finite and less than upper bound.
+                Cannot be `None` if `var_type` is 'C' or 'I'.
+            ub: Upper bound for the variable. Must be finite and greater than lower bound.
+                Cannot be `None` if `var_type` is 'C' or 'I'.
+            var_type: Variable type. Valid options are 'C' (continuous), 'I' (integer), and 'B' (binary).
+            name: Optional name for the variable.
 
         Returns:
-            The decision variable.
+            The created decision variable.
         """
         lb, ub = self._validate_bounds(lb=lb, ub=ub, var_type=var_type)
         return self._model.add_var(lb=lb, ub=ub, var_type=var_type, name=name)
@@ -118,16 +134,16 @@ def add_var_tensor(
         """Add a tensor of decision variables to the model.
 
         Args:
-            shape: The shape of the tensor.
-            lb: The lower bound for the decision variables. Must be finite and less than the upper bound. Cannot be
-                `None` if `var_type` is 'C' or 'I'.
-            ub: The upper bound for the decision variables. Must be finite and greater than the lower bound. Cannot be
-                `None` if `var_type` is 'C' or 'I'.
-            var_type: The variable type. Valid options are 'C' (continuous), 'I' (integer) and 'B' (binary).
-            name: The name of the decision variable.
+            shape: Shape of the variable tensor.
+            lb: Lower bound for all variables. Must be finite and less than upper bound.
+                Cannot be `None` if `var_type` is 'C' or 'I'.
+            ub: Upper bound for all variables. Must be finite and greater than lower bound.
+                Cannot be `None` if `var_type` is 'C' or 'I'.
+            var_type: Variable type for all variables. Valid options are 'C' (continuous), 'I' (integer), and 'B' (binary).
+            name: Base name for the variables (indices will be appended).
 
         Returns:
-            The tensor of decision variables.
+            The created variable tensor.
         """
         lb, ub = self._validate_bounds(lb=lb, ub=ub, var_type=var_type)
         return self._model.add_var_tensor(
@@ -142,10 +158,11 @@ def add_linear_constr(self, constraint: mip.LinExpr, name: str = "") -> mip.Cons
         """Add a linear constraint to the model.
 
         Args:
-            constraint: The linear constraint.
-            name: The name of the constraint.
+            constraint: Linear constraint expression (e.g., x + y <= 1).
+            name: Optional name for the constraint.
 
-        Returns: The constraint expression.
+        Returns:
+            The created constraint object.
         """
         return self._model.add_constr(lin_expr=constraint, name=name)
 
@@ -158,18 +175,18 @@ def add_nonlinear_constr(
     ) -> ConvexTerm:
         """Add a nonlinear constraint to the model.
 
+        The constraint is enforced as func(var) <= 0. The function must be convex for
+        minimization problems or concave for maximization problems.
+
         Args:
-            var: The variable(s) included in the term. This can be provided in the form of a single  variable, an
-                iterable of multiple variables or a variable tensor.
-            func: A function for computing the term's value. This function should except one argument for each
-                variable in `var`. If `var` is a variable tensor, then the function should accept a single array.
-            grad: A function for computing the term's gradient. This function should except one argument for each
-                variable in `var`. If `var` is a variable tensor, then the function should accept a single array. If
-                `None`, then the gradient is approximated numerically using the central finite difference method. If
-                `grad` is instead a Boolean and is `True`, then `func` is assumed to return a tuple where the first
-                element is the function value and the second element is the gradient. This is useful when the gradient
-                is expensive to compute.
-            name: The name of the constraint.
+            var: Variable(s) in the constraint. Can be a single variable, iterable of variables,
+                or variable tensor.
+            func: Function computing the constraint value. Should accept one argument for each
+                variable in `var`. If `var` is a tensor, function should accept a single array.
+            grad: Function computing the gradient. Should accept same arguments as `func`.
+                If `None`, gradient is approximated numerically. If `True`, `func` should return
+                (value, gradient) tuple for efficiency.
+            name: Optional name for the constraint.
 
         Returns:
             The convex term representing the constraint.
@@ -191,20 +208,20 @@ def add_objective_term(
         grad: Grad | bool | None = None,
         name: str = "",
     ) -> ConvexTerm:
-        """Add an objective term to the model.
+        """Add a term to the objective function.
+
+        The function must be convex for minimization problems or concave for maximization
+        problems. Multiple terms can be added to build up a complex objective.
 
         Args:
-            var: The variable(s) included in the term. This can be provided in the form of a single  variable, an
-                iterable of multiple variables or a variable tensor.
-            func: A function for computing the term's value. This function should except one argument for each
-                variable in `var`. If `var` is a variable tensor, then the function should accept a single array.
-            grad: A function for computing the term's gradient. This function should except one argument for each
-                variable in `var`. If `var` is a variable tensor, then the function should accept a single array. If
-                `None`, then the gradient is approximated numerically using the central finite difference method. If
-                `grad` is instead a Boolean and is `True`, then `func` is assumed to return a tuple where the first
-                element is the function value and the second element is the gradient. This is useful when the gradient
-                is expensive to compute.
-            name: The name of the term.
+            var: Variable(s) in the objective term. Can be a single variable, iterable of
+                variables, or variable tensor.
+            func: Function computing the objective value. Should accept one argument for each
+                variable in `var`. If `var` is a tensor, function should accept a single array.
+            grad: Function computing the gradient. Should accept same arguments as `func`.
+                If `None`, gradient is approximated numerically. If `True`, `func` should return
+                (value, gradient) tuple for efficiency.
+            name: Optional name for the objective term.
 
         Returns:
             The objective term.
@@ -225,45 +242,47 @@ def optimize(
         max_iters_no_improvement: int | None = None,
         max_seconds_per_iter: float | None = None,
     ) -> mip.OptimizationStatus:
-        """Optimize the model.
+        """Solve the optimization problem using outer approximation.
+
+        The algorithm iteratively adds linear cuts to approximate nonlinear constraints
+        and objective functions, solving a sequence of mixed-integer linear programs.
 
         Args:
-            max_iters: The maximum number of iterations to run the search for.
-            max_iters_no_improvement: The maximum number of iterations to continue the search without improvement in
-                the objective value, once a feasible solution has been found. If `None`, then the search will continue
-                until `max_iters` regardless of lack of improvement in the objective value.
-            max_seconds_per_iter: The maximum number of seconds allow the MIP solver to run for each iteration. If
-                `None`, then the MIP solver will run until its convergence criteria are met.
+            max_iters: Maximum number of outer approximation iterations.
+            max_iters_no_improvement: Maximum iterations without objective improvement after
+                finding a feasible solution. If `None`, continues until `max_iters`.
+            max_seconds_per_iter: Maximum seconds for the MIP solver per iteration.
+                If `None`, solver runs until convergence.
 
         Returns:
-            The status of the search.
+            Optimization status indicating success or failure.
         """
-        # Define objective in epigraph form
+        # Set up epigraph formulation: minimize/maximize t subject to t >=/<= objective
         bound = self._model.add_var(lb=-mip.INF, ub=mip.INF)
         self._model.objective = bound
 
-        # Initialize search
+        # Initialize search with starting point or variable bounds midpoint
         query_point = {x: self._start.get(x) or (x.lb + x.ub) / 2 for x in self._model.vars}
         iters_no_improvement = 0
 
         for i in range(max_iters):
-            # Add cuts for violated nonlinear constraints
+            # Add linear cuts for violated nonlinear constraints
             for constr in self.nonlinear_constrs:
                 if constr(query_point=query_point) > self.infeasibility_tol:
                     expr = constr.generate_cut(query_point=query_point)
                     self._model.add_constr(expr <= 0)
 
-            # Add objective cut
+            # Add linear cut for objective function
             expr = mip.xsum(term.generate_cut(query_point=query_point) for term in self.objective_terms)
             if self.minimize:
-                self._model.add_constr(bound >= expr)
+                self._model.add_constr(bound >= expr)  # t >= objective
             else:
-                self._model.add_constr(bound <= expr)
+                self._model.add_constr(bound <= expr)  # t <= objective
 
-            # Re-optimize LP/MIP model
+            # Solve the current mixed-integer linear program
             status = self._model.optimize(max_seconds=max_seconds_per_iter or mip.INF)
 
-            # If no solution is found, exit solve and return status
+            # Check if solver found a feasible solution
             if status not in (
                 mip.OptimizationStatus.OPTIMAL,
                 mip.OptimizationStatus.FEASIBLE,
@@ -272,33 +291,40 @@ def optimize(
                 self._status = status
                 return self.status
 
-            # Update best solution/objective value and query point
+            # Extract solution and evaluate true objective value
             solution = {var: var.x for var in self._model.vars}
             objective_value_new = sum(term(query_point=solution) for term in self.objective_terms)
-            if self.minimize == (objective_value_new < self.objective_value) and all(
-                constr(solution) <= self.infeasibility_tol for constr in self.nonlinear_constrs
-            ):
+            
+            # Check if this is a better feasible solution
+            is_improvement = self.minimize == (objective_value_new < self.objective_value)
+            is_feasible = all(constr(solution) <= self.infeasibility_tol for constr in self.nonlinear_constrs)
+            
+            if is_improvement and is_feasible:
                 iters_no_improvement = 0
                 self._objective_value = objective_value_new
                 self._best_solution = solution
             else:
                 if np.isfinite(self.objective_value):
                     iters_no_improvement += 1
+                
+                # Update query point for next iteration
                 if self.smoothing is not None:
+                    # Smooth between current query point and new solution
                     query_point = {
                         var: self.smoothing * query_point[var] + (1 - self.smoothing) * solution[var]
                         for var in self._model.vars
                     }
                 else:
-                    query_point = query_point
+                    # Use incumbent solution as next query point
+                    query_point = solution
 
-            # Update best bound (clip values to prevent numerical errors from affecting termination logic)
+            # Update best bound with monotonicity to prevent numerical issues
             if self.minimize:
                 self._best_bound = np.clip(bound.x, a_min=self.best_bound, a_max=self.objective_value)
             else:
                 self._best_bound = np.clip(bound.x, a_min=self.objective_value, a_max=self.best_bound)
 
-            # Update log
+            # Log progress
             self._search_log.append(
                 {
                     "iteration": i,
@@ -313,7 +339,7 @@ def optimize(
                 if not i % self.log_freq:
                     log_table_row(values=self._search_log[-1].values())
 
-            # Check early termination conditions
+            # Check convergence criteria
             if self.gap <= self.max_gap or self.gap_abs <= self.max_gap_abs:
                 logging.info("Optimality tolerance reached - terminating search early.")
                 self._status = mip.OptimizationStatus.OPTIMAL
@@ -324,6 +350,7 @@ def optimize(
                     self._status = mip.OptimizationStatus.FEASIBLE
                     return self.status
 
+        # Reached maximum iterations
         logging.info("Max iterations reached - terminating search.")
         if self.best_solution:
             self._status = mip.OptimizationStatus.FEASIBLE
@@ -332,19 +359,31 @@ def optimize(
         return self.status
 
     def var_by_name(self, name: str) -> mip.Var:
-        """Get a variable by name."""
+        """Get a variable by its name.
+        
+        Args:
+            name: The name of the variable to retrieve.
+            
+        Returns:
+            The variable with the specified name.
+            
+        Raises:
+            KeyError: If no variable with the given name exists.
+        """
         return self._model.var_by_name(name=name)
 
     def var_value(self, x: mip.Var | mip.LinExprTensor | str) -> float | np.ndarray:
-        """Get the value one or more decision variables corresponding to the best solution.
+        """Get the value of one or more variables from the best solution.
 
         Args:
-            x: mip.Var or mip.LinExprTensor or str
-                The variable(s) to get the value of. This can be provided as a single variable, a tensor of variables
-                or the name of a variable.
+            x: Variable(s) to get values for. Can be a single variable, variable tensor,
+                variable name (string), or iterable of variables.
 
-        Returns: float or np.ndarray
-            The value(s) of the variable(s).
+        Returns:
+            Variable value(s) as float or numpy array.
+            
+        Raises:
+            TypeError: If input type is not supported.
         """
         if isinstance(x, str):
             x = self.var_by_name(name=x)
@@ -358,73 +397,128 @@ def var_value(self, x: mip.Var | mip.LinExprTensor | str) -> float | np.ndarray:
 
     @property
     def objective_terms(self) -> list[ConvexTerm]:
-        """Get the objective terms of the model."""
+        """Get the objective terms of the model.
+        
+        Returns:
+            List of convex terms that make up the objective function.
+        """
         return self._objective_terms
 
     @property
     def linear_constrs(self) -> mip.ConstrList:
         """Get the linear constraints of the model.
 
-        After the model is optimized, this will include the cuts added to the model.
+        After optimization, this includes both original linear constraints and
+        the linear cuts added during the outer approximation process.
+        
+        Returns:
+            List of all linear constraints in the model.
         """
         return self._model.constrs
 
     @property
     def nonlinear_constrs(self) -> list[ConvexTerm]:
-        """Get the nonlinear constraints of the model."""
+        """Get the nonlinear constraints of the model.
+        
+        Returns:
+            List of convex terms representing nonlinear constraints.
+        """
         return self._nonlinear_constrs
 
     @property
     def start(self) -> Start:
-        """Get the starting solution or partial solution provided."""
+        """Get the starting solution or partial solution.
+        
+        Returns:
+            List of (variable, value) pairs defining the starting point.
+        """
         return [(key, value) for key, value in self._start.items()]
 
     @start.setter
     def start(self, value: Start) -> None:
-        """Set the starting solution or partial solution, provided as tuple of (variable, value) pairs."""
+        """Set the starting solution or partial solution.
+        
+        Args:
+            value: List of (variable, value) pairs defining the starting point.
+        """
         # TODO add validation checks here
         self._start = {var: x for var, x in value}
         self._model.start = value
 
     @property
     def best_solution(self) -> dict[mip.Var, float]:
-        """Get the best solution (all variables)."""
+        """Get the best feasible solution found.
+        
+        Returns:
+            Dictionary mapping variables to their values in the best solution.
+        """
         return self._best_solution
 
     @property
     def objective_value(self) -> float:
-        """Get the objective value of the best solution."""
+        """Get the objective value of the best solution.
+        
+        Returns:
+            Objective function value at the best feasible solution.
+        """
         return self._objective_value
 
     @property
     def best_bound(self) -> float:
-        """Get the best bound."""
+        """Get the best bound on the optimal objective value.
+        
+        Returns:
+            Best known bound (lower bound for minimization, upper bound for maximization).
+        """
         return self._best_bound
 
     @property
     def gap(self) -> float:
-        """Get the (relative) optimality gap."""
-        return self.gap_abs / max(min(abs(self.objective_value), abs(self.best_bound)), 1e-10)
+        """Get the relative optimality gap.
+        
+        Returns:
+            Relative gap as |objective_value - best_bound| / max(|objective_value|, |best_bound|).
+        """
+        return self.gap_abs / max(abs(self.objective_value), abs(self.best_bound), 1.0)
 
     @property
     def gap_abs(self) -> float:
-        """Get the absolute optimality gap."""
+        """Get the absolute optimality gap.
+        
+        Returns:
+            Absolute gap as |objective_value - best_bound|.
+        """
         return abs(self.objective_value - self.best_bound)
 
     @property
     def status(self) -> mip.OptimizationStatus:
-        """Get the status of the model."""
+        """Get the optimization status.
+        
+        Returns:
+            Status indicating whether optimization was successful and why it terminated.
+        """
         return self._status
 
     @property
     def search_log(self) -> pd.DataFrame:
-        """Get the search log."""
+        """Get the search progress log.
+        
+        Returns:
+            DataFrame with columns: iteration, objective_value, best_bound, gap.
+        """
         return pd.DataFrame(self._search_log).set_index("iteration")
 
     @staticmethod
     def sum(terms: Iterable[mip.Var | mip.LinExpr]) -> mip.LinExpr:
-        """Create a linear expression from a summation."""
-        return mip.xsum(terms=terms)
+        """Create a linear expression from a summation.
+        
+        Args:
+            terms: Iterable of variables or linear expressions to sum.
+            
+        Returns:
+            Linear expression representing the sum of all terms.
+        """
+        return mip.xsum(terms)
 
     def _validate_params(self) -> None:
         check_scalar(
@@ -443,7 +537,7 @@ def _validate_params(self) -> None:
         )
         check_scalar(
             x=self.infeasibility_tol,
-            name="feasibility_tol",
+            name="infeasibility_tol",
             var_type=float,
             lb=0,
             include_boundaries=False,
@@ -468,6 +562,16 @@ def _validate_params(self) -> None:
 
     @staticmethod
     def _validate_bounds(lb: float | int, ub: float | int, var_type: str) -> tuple[float | int, float | int]:
+        """Validate and normalize variable bounds.
+        
+        Args:
+            lb: Lower bound value.
+            ub: Upper bound value.
+            var_type: Variable type ('C', 'I', or 'B').
+            
+        Returns:
+            Tuple of (validated_lb, validated_ub).
+        """
         if var_type == mip.BINARY:
             lb, ub = 0, 1
         else:
diff --git a/src/halfspace/utils.py b/src/halfspace/utils.py
index b2c6ba9..32c311c 100644
--- a/src/halfspace/utils.py
+++ b/src/halfspace/utils.py
@@ -1,9 +1,8 @@
 """Utility functions for the `halfspace` package."""
 
 import logging
-from typing import Iterable, Any, Type
+from typing import Iterable, Any
 
-import numpy as np
 
 
 def log_table_header(columns: Iterable[str], width: int = 15) -> None:
@@ -19,7 +18,11 @@ def log_table_header(columns: Iterable[str], width: int = 15) -> None:
 
     Returns: None
     """
-    columns = [f"{{:{width}}}".format(col) for col in columns]
+    columns_list = list(columns)
+    if not columns_list:
+        return
+    
+    columns = [f"{{:{width}}}".format(col) for col in columns_list]
     line = "-{}-".format("-".join("-" * len(col) for col in columns))
     logging.info(line)
     logging.info("|{}|".format("|".join(columns)))
@@ -32,21 +35,25 @@ def log_table_row(values: Iterable[float | int], width: int = 15) -> None:
     Logging level is set to `logging.INFO`.
 
     Args:
-        values: iterable of str
+        values: iterable of float or int
             The values of the row.
         width: int, default=15
             The width of each column.
 
     Returns: None
     """
-    values_ = [(f"{{:{width}}}" if isinstance(value, int) else f"{{:{width}.3e}}").format(value) for value in values]
+    values_list = list(values)
+    if not values_list:
+        return
+        
+    values_ = [(f"{{:{width}}}" if isinstance(value, int) else f"{{:{width}.3e}}").format(value) for value in values_list]
     logging.info("|{}|".format("|".join(values_)))
 
 
 def check_scalar(
     x: Any,
     name: str,
-    var_type: Type | tuple[Type, ...] | None = None,
+    var_type: type | tuple[type, ...] | None = None,
     lb: float | int | None = None,
     ub: float | int | None = None,
     include_boundaries: bool = True,
@@ -58,33 +65,34 @@ def check_scalar(
             The scalar to check.
         name: str,
             The name of the scalar. Used for error messages.
-        var_type: type or tuple of types, default=`None`
+        var_type: type or tuple of types, default=None
             The expected type(s) of the scalar. If `None`, then no type checking is performed.
-        lb: float or int, default=`None`
+        lb: float or int, default=None
             The lower bound of the scalar. If `None`, then no lower bound checking is performed.
-        ub: float or int, default=`None`
+        ub: float or int, default=None
             The upper bound of the scalar. If `None`, then no upper bound checking is performed.
-        include_boundaries: bool, default=`True`
+        include_boundaries: bool, default=True
             Whether to include the boundaries in the bound checking.
 
+    Raises:
+        ValueError: If the scalar does not meet the specified conditions.
+
     Returns: None
     """
     if var_type is not None:
-        assert isinstance(x, var_type), f"Variable '{name}' ({type(x)}) is not expected type ({var_type})."
+        if not isinstance(x, var_type):
+            raise ValueError(f"Variable '{name}' ({type(x)}) is not expected type ({var_type}).")
     if lb is not None:
         if include_boundaries:
-            assert x >= lb, f"Variable '{name}' ({x}) is less than lower bound ({lb})."
+            if x < lb:
+                raise ValueError(f"Variable '{name}' ({x}) is less than lower bound ({lb}).")
         else:
-            assert x > lb, f"Variable '{name}' ({x}) is less than or equal to lower bound ({lb})."
+            if x <= lb:
+                raise ValueError(f"Variable '{name}' ({x}) is less than or equal to lower bound ({lb}).")
     if ub is not None:
         if include_boundaries:
-            assert x <= ub, f"Variable '{name}' ({x}) is greater than lower bound ({ub})."
+            if x > ub:
+                raise ValueError(f"Variable '{name}' ({x}) is greater than upper bound ({ub}).")
         else:
-            assert x < ub, f"Variable '{name}' ({x}) is greater than or equal to lower bound ({ub})."
-
-
-def standard_basis_vector(i: int, n_dim: int) -> np.ndarray:
-    """Return the ith standard basis vector in R^n."""
-    x = np.zeros(n_dim)
-    x[i] = 1
-    return x
+            if x >= ub:
+                raise ValueError(f"Variable '{name}' ({x}) is greater than or equal to upper bound ({ub}).")
diff --git a/tests/test_convex_term.py b/tests/test_convex_term.py
index d3953fc..75e4010 100644
--- a/tests/test_convex_term.py
+++ b/tests/test_convex_term.py
@@ -161,3 +161,35 @@ def test_var_tensor_term(
         expected_is_multivariable=True,
         query_point={model.var_by_name(name=name): value for name, value in query_point.items()},
     )
+
+
+@pytest.mark.parametrize("step_size", [0.0, -1.0])
+def test_invalid_step_size_raises(model: Model, step_size: float):
+    x = model.var_by_name("x")
+    with pytest.raises(ValueError, match="step_size must be positive"):
+        ConvexTerm(var=x, func=lambda x: x**2, grad=lambda x: 2 * x, step_size=step_size)
+
+
+def test_var_tensor_2d_term(model: Model):
+    z = model.add_var_tensor(shape=(2, 2), lb=-10, ub=10, name="z")
+    func = lambda w: (w**2).sum()
+    grad = lambda w: 2 * w
+    term = ConvexTerm(var=z, func=func, grad=grad)
+
+    qp = {
+        model.var_by_name("z_0_0"): 1.0,
+        model.var_by_name("z_0_1"): 2.0,
+        model.var_by_name("z_1_0"): -1.0,
+        model.var_by_name("z_1_1"): 0.5,
+    }
+    expected_value = 1.0**2 + 2.0**2 + (-1.0) ** 2 + 0.5**2
+    expected_grad = np.array([[2 * 1.0, 2 * 2.0], [2 * -1.0, 2 * 0.5]])
+
+    # Value and gradient sanity via helper
+    _check_convex_term(
+        term=term,
+        expected_value=expected_value,
+        expected_grad=expected_grad,
+        expected_is_multivariable=True,
+        query_point=qp,
+    )
diff --git a/tests/test_model.py b/tests/test_model.py
index 95a7c49..7e8e1ad 100644
--- a/tests/test_model.py
+++ b/tests/test_model.py
@@ -137,3 +137,39 @@ def test_multivariable_nonlinear_constraint_infeasible():
     model.add_nonlinear_constr(var=(x, y), func=lambda x, y: np.exp(x + y) + 1)
     model.optimize()
     _check_solution(model=model, expected_status=mip.OptimizationStatus.INFEASIBLE)
+
+
+def test_maximize_concave_objective():
+    model = Model(minimize=False)
+    x = model.add_var(lb=0, ub=1)
+    model.add_objective_term(var=x, func=lambda x: -(x - 0.75) ** 2 + 1)
+    model.optimize()
+    _check_solution(
+        model=model,
+        expected_objective_value=1.0,
+        expected_solution={x: 0.75},
+    )
+
+
+def test_smoothing_none_runs():
+    model = Model(smoothing=None)
+    x = model.add_var(lb=0, ub=1)
+    model.add_objective_term(var=x, func=lambda x: (x - 0.25) ** 2)
+    status = model.optimize(max_iters=3)
+    assert status in (mip.OptimizationStatus.OPTIMAL, mip.OptimizationStatus.FEASIBLE)
+
+
+@pytest.mark.parametrize(
+    "kwargs",
+    [
+        {"max_gap": 0.0},
+        {"max_gap_abs": 0.0},
+        {"infeasibility_tol": 0.0},
+        {"log_freq": 0},
+        {"smoothing": -1e-3},
+        {"smoothing": 1.0},
+    ],
+)
+def test_parameter_validation_raises(kwargs):
+    with pytest.raises(ValueError):
+        Model(**kwargs)
diff --git a/tests/test_utils.py b/tests/test_utils.py
index dde117b..5c99a31 100644
--- a/tests/test_utils.py
+++ b/tests/test_utils.py
@@ -1,6 +1,6 @@
 from contextlib import nullcontext as does_not_raise
 from numbers import Real
-from typing import Iterable, Any, Type
+from typing import Iterable, Any
 
 import pytest
 
@@ -29,17 +29,17 @@ def test_log_table_row(values: Iterable[Real], width: int):
         (1, "x", int, None, 2, True, does_not_raise()),
         (1, "x", int, 0, None, True, does_not_raise()),
         (1, "x", int, 0, 2, False, does_not_raise()),
-        (1, "x", float, 0, 2, True, pytest.raises(AssertionError)),
-        (1, "x", int, 2, 3, True, pytest.raises(AssertionError)),
-        (1, "x", int, 1, 2, False, pytest.raises(AssertionError)),
-        (1, "x", int, -1, 0, True, pytest.raises(AssertionError)),
-        (1, "x", int, 0, 1, False, pytest.raises(AssertionError)),
+        (1, "x", float, 0, 2, True, pytest.raises(ValueError)),
+        (1, "x", int, 2, 3, True, pytest.raises(ValueError)),
+        (1, "x", int, 1, 2, False, pytest.raises(ValueError)),
+        (1, "x", int, -1, 0, True, pytest.raises(ValueError)),
+        (1, "x", int, 0, 1, False, pytest.raises(ValueError)),
     ],
 )
 def test_check_scalar(
     x: Any,
     name: str,
-    var_type: Type | tuple[Type, ...] | None,
+    var_type: type | tuple[type, ...] | None,
     lb: Real | None,
     ub: Real | None,
     include_boundaries: bool,
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new file mode 100644
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From 441b02889879cc0501f4d3d8bbbe4d273b36369e Mon Sep 17 00:00:00 2001
From: Joshua Ivanhoe <josh@joinuptick.com>
Date: Wed, 12 Nov 2025 10:51:31 +0000
Subject: [PATCH 12/12] feat: Migrate to uv for package management (WIP)

---
 .github/workflows/ci.yml      |  30 ++++++----
 .github/workflows/release.yml |  46 +++++++--------
 .pre-commit-config.yaml       |  51 +++++++++++------
 README.md                     |  41 ++++++++------
 pyproject.toml                |  58 ++++++++++---------
 src/halfspace/convex_term.py  |  25 +++++----
 src/halfspace/model.py        | 101 +++++++++++++++++++--------------
 src/halfspace/utils.py        |  22 +++++---
 tests/test_convex_term.py     |  22 +++++---
 tests/test_model.py           |   6 +-
 tests/test_utils.py           |  15 +++--
 uv.lock                       | 103 +++++++++++++++++++++++++---------
 12 files changed, 322 insertions(+), 198 deletions(-)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index ea9f6af..2c22506 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -16,21 +16,31 @@ jobs:
         python-version: ["3.12"]
 
     steps:
-    - uses: actions/checkout@v3
+    - uses: actions/checkout@v4
 
     - name: Set up Python ${{ matrix.python-version }}
-      uses: actions/setup-python@v3
+      uses: actions/setup-python@v5
       with:
         python-version: ${{ matrix.python-version }}
 
-    - name: Install poetry
-      run: python -m pip install uv
+    - name: Install uv
+      uses: astral-sh/setup-uv@v4
+      with:
+        version: "latest"
+
+    - name: Install dependencies and build package using uv
+      run: |
+        uv sync --extra dev
+        uv build
+
+    - name: Run linting with ruff
+      run: uv run ruff check .
 
-    - name: Install dependencies
-      run: uv sync --extra all
+    - name: Run formatting check with ruff
+      run: uv run ruff format --check .
 
-    - name: Perform type checking with mypy
-      run: mypy src
+    - name: Run type checking with mypy
+      run: uv run mypy src
 
-    - name: Run tests
-      run: poetry run pytest --cov=src
+    - name: Run tests with coverage using pytest
+      run: uv run pytest
diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml
index cd4b376..8e1b3e9 100644
--- a/.github/workflows/release.yml
+++ b/.github/workflows/release.yml
@@ -1,31 +1,33 @@
-name: Upload Python Package
+name: Publish to PyPI
 
 on:
   release:
     types: [published]
 
-permissions:
-  contents: read
-
 jobs:
-  deploy:
-
+  publish:
+    name: Build and Publish to PyPI
     runs-on: ubuntu-latest
+    permissions:
+      contents: read
 
     steps:
-    - uses: actions/checkout@v3
-    - name: Set up Python
-      uses: actions/setup-python@v3
-      with:
-        python-version: "3.11"
-    - name: Install dependencies
-      run: |
-        python -m pip install --upgrade pip
-        pip install build
-    - name: Build package
-      run: python -m build
-    - name: Publish package
-      uses: pypa/gh-action-pypi-publish@27b31702a0e7fc50959f5ad993c78deac1bdfc29
-      with:
-        user: __token__
-        password: ${{ secrets.PYPI_API_TOKEN }}
+      - uses: actions/checkout@v4
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@v4
+        with:
+          enable-cache: true
+          cache-dependency-glob: "uv.lock"
+
+      - name: Set up Python
+        run: uv python install
+
+      - name: Install build dependencies
+        run: uv sync --all-extras
+
+      - name: Build the project
+        run: uv build
+
+      - name: Publish to PyPI
+        run: uv publish --token ${{ secrets.PYPI_API_TOKEN }}
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 64318bb..29768bc 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -1,18 +1,35 @@
 repos:
-- repo: https://github.com/pre-commit/pre-commit-hooks
-  rev: v2.3.0
-  hooks:
-  - id: check-yaml
-  - id: end-of-file-fixer
-  - id: trailing-whitespace
-- repo: https://github.com/astral-sh/ruff-pre-commit
-  rev: v0.5.6
-  hooks:
-    - id: ruff
-    - id: ruff-format
-      args: [--line-length=120]
-- repo: https://github.com/pycqa/pydocstyle
-  rev: 6.3.0
-  hooks:
-    - id: pydocstyle
-      args: [--convention=google, --add-ignore=D100,D104]
+  # General hooks
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.6.0
+    hooks:
+      - id: trailing-whitespace
+      - id: end-of-file-fixer
+      - id: detect-private-key  # Basic security check for secrets
+
+  # Python formatting and linting with Ruff
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: v0.6.9
+    hooks:
+      - id: ruff-format
+      - id: ruff
+        args: [--fix]
+
+  # Type checking with mypy
+#  - repo: https://github.com/pre-commit/mirrors-mypy
+#    rev: v1.11.2
+#    hooks:
+#      - id: mypy
+
+  # Documentation linting with pydocstyle
+  - repo: https://github.com/pycqa/pydocstyle
+    rev: 6.3.0
+    hooks:
+      - id: pydocstyle
+
+  # Enforce Conventional Commits in commit messages
+  - repo: https://github.com/compilerla/conventional-pre-commit
+    rev: v3.3.0
+    hooks:
+      - id: conventional-pre-commit
+        stages: [commit-msg]
diff --git a/README.md b/README.md
index a5f0b91..63edef2 100644
--- a/README.md
+++ b/README.md
@@ -4,7 +4,7 @@
 
 # ✨halfspace✨
 
-`halfspace` is an open-source, light-weight Python module for modelling and solving mixed-integer convex optimization problems of the form:
+`halfspace` is an open-source, lightweight Python module for modeling and solving mixed-integer convex optimization problems of the form:
 
 $$
 \begin{align}
@@ -21,11 +21,15 @@ It is built on top of the high-performance Python `mip` module and uses a cuttin
 This implementation is based on the approach outlined in [Boyd & Vandenberghe (2008)](https://see.stanford.edu/materials/lsocoee364b/05-localization_methods_notes.pdf) - see Chapter 6.
 
 
-## Quick start
+## Installation
 
-You can install `halfspace` using `pip` as follows:
+`halfspace` requires Python 3.12:
 
 ```bash
+# Using uv (recommended)
+uv add halfspace-optimizer
+
+# Using pip
 pip install halfspace-optimizer
 ```
 
@@ -107,29 +111,34 @@ import logging
 logging.getLogger().setLevel(logging.INFO)
 ```
 
-The logging frequency can be adjusted as desiredusing the model's `log_freq` attribute.
+The logging frequency can be adjusted as desired using the model's `log_freq` attribute.
 
 ## Development
 
-Clone the repository using `git`:
-
 ```bash
+# Clone and setup
 git clone https://github.com/joshivanhoe/halfspace
-````
+cd halfspace
+uv venv .venv --python 3.12
+source .venv/bin/activate
+uv sync --extra dev
 
-Create a fresh virtual environment using `venv` or `conda`.
-Activate the environment and navigate to the cloned `halfspace` directory.
-Install a locally editable version of the package using `pip`:
+# Setup pre-commit hooks
+uv run pre-commit install
+uv run pre-commit install --hook-type commit-msg
 
-```bash
-pip install -e .
+# Run tests
+uv run pytest
 ```
 
-To check the installation has worked, you can run the tests (with coverage metrics) using `pytest` as follows:
+### Pre-commit Hooks
+
+This project uses pre-commit hooks for code quality (Ruff, mypy, pydocstyle, conventional commits). Hooks run automatically on commit, or manually with:
 
 ```bash
-pytest --cov=halfspace tests/
+uv run pre-commit run --all-files
 ```
 
-Contributions are welcome! To see our development priorities, refer to the [open issues](https://github.com/joshivanhoe/halfspace/issues).
-Please submit a pull request with a clear description of the changes you've made.
+### Contributing
+
+Contributions welcome! See [open issues](https://github.com/joshivanhoe/halfspace/issues) for priorities. Ensure pre-commit hooks pass before submitting PRs.
diff --git a/pyproject.toml b/pyproject.toml
index 4df8f39..afab364 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,8 +1,13 @@
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+
+
 [project]
 name = "halfspace-optimizer"
 version = "0.1.1"
 authors = [
-  { name="Joshua Ivanhoe", email="joshua.k.ivanhoe@gmail.com" },
+  {name="Joshua Ivanhoe", email="joshua.k.ivanhoe@gmail.com"},
 ]
 description = "Cutting-plane solver for mixed-integer convex optimization problems"
 readme = "README.md"
@@ -29,40 +34,38 @@ Issues = "https://github.com/joshivanhoe/halfspace/issues"
 [project.optional-dependencies]
 dev = [
     "ruff",
+    "mypy",
     "pre-commit",
-    "tomli",
-]
-test = [
     "pytest",
     "pytest-cov",
 ]
-all = [
-    "halfspace-optimizer[dev,test]",
+
+[tool.ruff]
+line-length = 100
+exclude = [
+    "*.ipynb",
 ]
 
-[build-system]
-requires = ["hatchling"]
-build-backend = "hatchling.build"
+[tool.ruff.lint]
+extend-select = ["D", "I"]  # pydocstyle
+fixable = ["ALL"]
+extend-ignore = [
+    "D100",  # Missing docstring in public module
+    "D104",  # Missing docstring in public package
+    "D105",  # Missing docstring in magic method
+]
 
-[tool.uv]
-index-url = "https://pypi.org/simple"
-extra-index-url = []
+[tool.ruff.lint.per-file-ignores]
+"tests/**" = ["D"]  # no documentation checks required for tests
 
-[tool.hatch.build.targets.wheel]
-packages = ["src/halfspace"]
+[tool.ruff.lint.pydocstyle]
+convention = "google"
 
-[tool.pytest.ini_options]
-testpaths = ["tests"]
-python_files = ["test_*.py"]
-python_classes = ["Test*"]
-python_functions = ["test_*"]
-addopts = [
-    "--strict-markers",
-    "--strict-config",
-    "--cov=src/halfspace",
-    "--cov-report=term-missing",
-    "--cov-report=html",
-]
+[tool.mypy]
+ignore_missing_imports = true
+install_types = true
+non_interactive = true
+check_untyped_defs = true
 
 [tool.coverage.run]
 source = ["src/halfspace"]
@@ -75,3 +78,6 @@ exclude_lines = [
     "raise AssertionError",
     "raise NotImplementedError",
 ]
+
+[tool.hatch.build.targets.wheel]
+packages = ["src/halfspace"]
diff --git a/src/halfspace/convex_term.py b/src/halfspace/convex_term.py
index fad11ac..34d501a 100644
--- a/src/halfspace/convex_term.py
+++ b/src/halfspace/convex_term.py
@@ -8,7 +8,7 @@
 import mip
 import numpy as np
 
-from .utils import check_scalar
+from .utils import check_param
 
 type QueryPoint = dict[mip.Var, float]
 type Var = mip.Var | Iterable[mip.Var] | mip.LinExprTensor
@@ -54,7 +54,7 @@ def __init__(
             step_size: The step size for numerical gradient approximation. Must be positive.
             name: The name for the term.
         """
-        check_scalar(
+        check_param(
             x=step_size,
             name="step_size",
             var_type=float,
@@ -75,7 +75,9 @@ def __call__(
         self, query_point: QueryPoint, return_grad: Literal[True] = True
     ) -> tuple[float, float | np.ndarray]: ...
 
-    def __call__(self, query_point: QueryPoint, return_grad: bool = False) -> float | tuple[float, float | np.ndarray]:
+    def __call__(
+        self, query_point: QueryPoint, return_grad: bool = False
+    ) -> float | tuple[float, float | np.ndarray]:
         """Evaluate the term and (optionally) its gradient.
 
         Args:
@@ -98,7 +100,7 @@ def __call__(self, query_point: QueryPoint, return_grad: bool = False) -> float
     @property
     def is_multivariable(self) -> bool:
         """Check whether the term is multivariable.
-        
+
         Returns:
             True if the term involves multiple variables, False otherwise.
         """
@@ -124,10 +126,10 @@ def generate_cut(self, query_point: QueryPoint) -> mip.LinExpr:
 
     def _get_input(self, query_point: QueryPoint) -> Input:
         """Extract input values from query point based on variable type.
-        
+
         Args:
             query_point: The query point containing variable values.
-            
+
         Returns:
             Input values in the format expected by the function.
         """
@@ -148,10 +150,10 @@ def _evaluate_func(self, x: Input) -> float | tuple[float, float | np.ndarray]:
 
     def _evaluate_grad(self, x: Input) -> float | np.ndarray:
         """Evaluate the gradient.
-        
+
         Args:
             x: The input values at which to evaluate the gradient.
-            
+
         Returns:
             The gradient value(s).
         """
@@ -165,10 +167,10 @@ def _evaluate_grad(self, x: Input) -> float | np.ndarray:
 
     def _approximate_grad(self, x: Input) -> float | np.ndarray:
         """Approximate the gradient using central finite differences.
-        
+
         Args:
             x: The input values at which to approximate the gradient.
-            
+
         Returns:
             The approximated gradient value(s).
         """
@@ -183,5 +185,6 @@ def _approximate_grad(self, x: Input) -> float | np.ndarray:
                 ) / self.step_size
             return grad
         return (
-            self._evaluate_func(x=x + self.step_size / 2) - self._evaluate_func(x=x - self.step_size / 2)
+            self._evaluate_func(x=x + self.step_size / 2)
+            - self._evaluate_func(x=x - self.step_size / 2)
         ) / self.step_size
diff --git a/src/halfspace/model.py b/src/halfspace/model.py
index 6f8444b..f90c6b4 100644
--- a/src/halfspace/model.py
+++ b/src/halfspace/model.py
@@ -10,19 +10,19 @@
 import numpy as np
 import pandas as pd
 
-from .convex_term import ConvexTerm, Var, Func, FuncGrad, Grad
-from .utils import check_scalar, log_table_header, log_table_row
+from .convex_term import ConvexTerm, Func, FuncGrad, Grad, Var
+from .utils import check_param, log_table_header, log_table_row
 
 type Start = list[tuple[mip.Var, float]]
 
 
 class Model:
     """Mixed-integer convex optimization model using outer approximation.
-    
+
     This class implements an outer approximation algorithm for solving mixed-integer convex
     optimization problems. The algorithm iteratively adds linear cuts to approximate nonlinear
     constraints and objective functions, solving a sequence of mixed-integer linear programs.
-    
+
     The model supports both continuous and discrete variables, linear and nonlinear constraints,
     and can handle both minimization and maximization problems (with concave objectives for
     maximization).
@@ -81,7 +81,7 @@ def __init__(
 
     def reset(self) -> None:
         """Reset the model to its initial state.
-        
+
         Clears all variables, constraints, and solution data, returning the model to
         the state it was in immediately after construction.
         """
@@ -273,7 +273,9 @@ def optimize(
                     self._model.add_constr(expr <= 0)
 
             # Add linear cut for objective function
-            expr = mip.xsum(term.generate_cut(query_point=query_point) for term in self.objective_terms)
+            expr = mip.xsum(
+                term.generate_cut(query_point=query_point) for term in self.objective_terms
+            )
             if self.minimize:
                 self._model.add_constr(bound >= expr)  # t >= objective
             else:
@@ -287,18 +289,22 @@ def optimize(
                 mip.OptimizationStatus.OPTIMAL,
                 mip.OptimizationStatus.FEASIBLE,
             ):
-                logging.info(f"Solve unsuccessful - exiting with optimization status: '{status.value}'.")
+                logging.info(
+                    f"Solve unsuccessful - exiting with optimization status: '{status.value}'."
+                )
                 self._status = status
                 return self.status
 
             # Extract solution and evaluate true objective value
             solution = {var: var.x for var in self._model.vars}
             objective_value_new = sum(term(query_point=solution) for term in self.objective_terms)
-            
+
             # Check if this is a better feasible solution
             is_improvement = self.minimize == (objective_value_new < self.objective_value)
-            is_feasible = all(constr(solution) <= self.infeasibility_tol for constr in self.nonlinear_constrs)
-            
+            is_feasible = all(
+                constr(solution) <= self.infeasibility_tol for constr in self.nonlinear_constrs
+            )
+
             if is_improvement and is_feasible:
                 iters_no_improvement = 0
                 self._objective_value = objective_value_new
@@ -306,12 +312,13 @@ def optimize(
             else:
                 if np.isfinite(self.objective_value):
                     iters_no_improvement += 1
-                
+
                 # Update query point for next iteration
                 if self.smoothing is not None:
                     # Smooth between current query point and new solution
                     query_point = {
-                        var: self.smoothing * query_point[var] + (1 - self.smoothing) * solution[var]
+                        var: self.smoothing * query_point[var]
+                        + (1 - self.smoothing) * solution[var]
                         for var in self._model.vars
                     }
                 else:
@@ -320,9 +327,13 @@ def optimize(
 
             # Update best bound with monotonicity to prevent numerical issues
             if self.minimize:
-                self._best_bound = np.clip(bound.x, a_min=self.best_bound, a_max=self.objective_value)
+                self._best_bound = np.clip(
+                    bound.x, a_min=self.best_bound, a_max=self.objective_value
+                )
             else:
-                self._best_bound = np.clip(bound.x, a_min=self.objective_value, a_max=self.best_bound)
+                self._best_bound = np.clip(
+                    bound.x, a_min=self.objective_value, a_max=self.best_bound
+                )
 
             # Log progress
             self._search_log.append(
@@ -346,7 +357,9 @@ def optimize(
                 return self.status
             if max_iters_no_improvement is not None:
                 if iters_no_improvement >= max_iters_no_improvement:
-                    logging.info("Max iterations without improvement reached - terminating search early.")
+                    logging.info(
+                        "Max iterations without improvement reached - terminating search early."
+                    )
                     self._status = mip.OptimizationStatus.FEASIBLE
                     return self.status
 
@@ -360,13 +373,13 @@ def optimize(
 
     def var_by_name(self, name: str) -> mip.Var:
         """Get a variable by its name.
-        
+
         Args:
             name: The name of the variable to retrieve.
-            
+
         Returns:
             The variable with the specified name.
-            
+
         Raises:
             KeyError: If no variable with the given name exists.
         """
@@ -381,7 +394,7 @@ def var_value(self, x: mip.Var | mip.LinExprTensor | str) -> float | np.ndarray:
 
         Returns:
             Variable value(s) as float or numpy array.
-            
+
         Raises:
             TypeError: If input type is not supported.
         """
@@ -398,7 +411,7 @@ def var_value(self, x: mip.Var | mip.LinExprTensor | str) -> float | np.ndarray:
     @property
     def objective_terms(self) -> list[ConvexTerm]:
         """Get the objective terms of the model.
-        
+
         Returns:
             List of convex terms that make up the objective function.
         """
@@ -410,7 +423,7 @@ def linear_constrs(self) -> mip.ConstrList:
 
         After optimization, this includes both original linear constraints and
         the linear cuts added during the outer approximation process.
-        
+
         Returns:
             List of all linear constraints in the model.
         """
@@ -419,7 +432,7 @@ def linear_constrs(self) -> mip.ConstrList:
     @property
     def nonlinear_constrs(self) -> list[ConvexTerm]:
         """Get the nonlinear constraints of the model.
-        
+
         Returns:
             List of convex terms representing nonlinear constraints.
         """
@@ -428,7 +441,7 @@ def nonlinear_constrs(self) -> list[ConvexTerm]:
     @property
     def start(self) -> Start:
         """Get the starting solution or partial solution.
-        
+
         Returns:
             List of (variable, value) pairs defining the starting point.
         """
@@ -437,7 +450,7 @@ def start(self) -> Start:
     @start.setter
     def start(self, value: Start) -> None:
         """Set the starting solution or partial solution.
-        
+
         Args:
             value: List of (variable, value) pairs defining the starting point.
         """
@@ -448,7 +461,7 @@ def start(self, value: Start) -> None:
     @property
     def best_solution(self) -> dict[mip.Var, float]:
         """Get the best feasible solution found.
-        
+
         Returns:
             Dictionary mapping variables to their values in the best solution.
         """
@@ -457,7 +470,7 @@ def best_solution(self) -> dict[mip.Var, float]:
     @property
     def objective_value(self) -> float:
         """Get the objective value of the best solution.
-        
+
         Returns:
             Objective function value at the best feasible solution.
         """
@@ -466,7 +479,7 @@ def objective_value(self) -> float:
     @property
     def best_bound(self) -> float:
         """Get the best bound on the optimal objective value.
-        
+
         Returns:
             Best known bound (lower bound for minimization, upper bound for maximization).
         """
@@ -475,7 +488,7 @@ def best_bound(self) -> float:
     @property
     def gap(self) -> float:
         """Get the relative optimality gap.
-        
+
         Returns:
             Relative gap as |objective_value - best_bound| / max(|objective_value|, |best_bound|).
         """
@@ -484,7 +497,7 @@ def gap(self) -> float:
     @property
     def gap_abs(self) -> float:
         """Get the absolute optimality gap.
-        
+
         Returns:
             Absolute gap as |objective_value - best_bound|.
         """
@@ -493,7 +506,7 @@ def gap_abs(self) -> float:
     @property
     def status(self) -> mip.OptimizationStatus:
         """Get the optimization status.
-        
+
         Returns:
             Status indicating whether optimization was successful and why it terminated.
         """
@@ -502,7 +515,7 @@ def status(self) -> mip.OptimizationStatus:
     @property
     def search_log(self) -> pd.DataFrame:
         """Get the search progress log.
-        
+
         Returns:
             DataFrame with columns: iteration, objective_value, best_bound, gap.
         """
@@ -511,31 +524,31 @@ def search_log(self) -> pd.DataFrame:
     @staticmethod
     def sum(terms: Iterable[mip.Var | mip.LinExpr]) -> mip.LinExpr:
         """Create a linear expression from a summation.
-        
+
         Args:
             terms: Iterable of variables or linear expressions to sum.
-            
+
         Returns:
             Linear expression representing the sum of all terms.
         """
         return mip.xsum(terms)
 
     def _validate_params(self) -> None:
-        check_scalar(
+        check_param(
             x=self.max_gap,
             name="max_gap",
             lb=0,
             var_type=float,
             include_boundaries=False,
         )
-        check_scalar(
+        check_param(
             x=self.max_gap_abs,
             name="max_gap_abs",
             lb=0,
             var_type=float,
             include_boundaries=False,
         )
-        check_scalar(
+        check_param(
             x=self.infeasibility_tol,
             name="infeasibility_tol",
             var_type=float,
@@ -543,7 +556,7 @@ def _validate_params(self) -> None:
             include_boundaries=False,
         )
         if self.smoothing is not None:
-            check_scalar(
+            check_param(
                 x=self.smoothing,
                 name="smoothing",
                 var_type=float,
@@ -552,7 +565,7 @@ def _validate_params(self) -> None:
                 include_boundaries=False,
             )
         if self.log_freq is not None:
-            check_scalar(
+            check_param(
                 x=self.log_freq,
                 name="log_freq",
                 var_type=int,
@@ -561,21 +574,23 @@ def _validate_params(self) -> None:
             )
 
     @staticmethod
-    def _validate_bounds(lb: float | int, ub: float | int, var_type: str) -> tuple[float | int, float | int]:
+    def _validate_bounds(
+        lb: float | int, ub: float | int, var_type: str
+    ) -> tuple[float | int, float | int]:
         """Validate and normalize variable bounds.
-        
+
         Args:
             lb: Lower bound value.
             ub: Upper bound value.
             var_type: Variable type ('C', 'I', or 'B').
-            
+
         Returns:
             Tuple of (validated_lb, validated_ub).
         """
         if var_type == mip.BINARY:
             lb, ub = 0, 1
         else:
-            check_scalar(
+            check_param(
                 x=lb,
                 name="lb",
                 var_type=(float, int),
@@ -583,7 +598,7 @@ def _validate_bounds(lb: float | int, ub: float | int, var_type: str) -> tuple[f
                 lb=-mip.INF,
                 include_boundaries=False,
             )
-            check_scalar(
+            check_param(
                 x=ub,
                 name="ub",
                 var_type=(float, int),
diff --git a/src/halfspace/utils.py b/src/halfspace/utils.py
index 32c311c..e3a0630 100644
--- a/src/halfspace/utils.py
+++ b/src/halfspace/utils.py
@@ -1,8 +1,7 @@
 """Utility functions for the `halfspace` package."""
 
 import logging
-from typing import Iterable, Any
-
+from typing import Any, Iterable
 
 
 def log_table_header(columns: Iterable[str], width: int = 15) -> None:
@@ -21,7 +20,7 @@ def log_table_header(columns: Iterable[str], width: int = 15) -> None:
     columns_list = list(columns)
     if not columns_list:
         return
-    
+
     columns = [f"{{:{width}}}".format(col) for col in columns_list]
     line = "-{}-".format("-".join("-" * len(col) for col in columns))
     logging.info(line)
@@ -45,12 +44,15 @@ def log_table_row(values: Iterable[float | int], width: int = 15) -> None:
     values_list = list(values)
     if not values_list:
         return
-        
-    values_ = [(f"{{:{width}}}" if isinstance(value, int) else f"{{:{width}.3e}}").format(value) for value in values_list]
+
+    values_ = [
+        (f"{{:{width}}}" if isinstance(value, int) else f"{{:{width}.3e}}").format(value)
+        for value in values_list
+    ]
     logging.info("|{}|".format("|".join(values_)))
 
 
-def check_scalar(
+def check_param(
     x: Any,
     name: str,
     var_type: type | tuple[type, ...] | None = None,
@@ -88,11 +90,15 @@ def check_scalar(
                 raise ValueError(f"Variable '{name}' ({x}) is less than lower bound ({lb}).")
         else:
             if x <= lb:
-                raise ValueError(f"Variable '{name}' ({x}) is less than or equal to lower bound ({lb}).")
+                raise ValueError(
+                    f"Variable '{name}' ({x}) is less than or equal to lower bound ({lb})."
+                )
     if ub is not None:
         if include_boundaries:
             if x > ub:
                 raise ValueError(f"Variable '{name}' ({x}) is greater than upper bound ({ub}).")
         else:
             if x >= ub:
-                raise ValueError(f"Variable '{name}' ({x}) is greater than or equal to upper bound ({ub}).")
+                raise ValueError(
+                    f"Variable '{name}' ({x}) is greater than or equal to upper bound ({ub})."
+                )
diff --git a/tests/test_convex_term.py b/tests/test_convex_term.py
index 75e4010..3c87ab7 100644
--- a/tests/test_convex_term.py
+++ b/tests/test_convex_term.py
@@ -3,7 +3,7 @@
 import pytest
 
 from halfspace import Model
-from halfspace.convex_term import Func, FuncGrad, Grad, ConvexTerm, QueryPoint
+from halfspace.convex_term import ConvexTerm, Func, FuncGrad, Grad, QueryPoint
 
 
 def _process_callbacks(
@@ -62,7 +62,9 @@ def model() -> Model:
         ({"x": 1}, 1, 2),
     ],
 )
-@pytest.mark.parametrize(["combine_grad", "approximate_grad"], [(True, False), (False, True), (False, False)])
+@pytest.mark.parametrize(
+    ["combine_grad", "approximate_grad"], [(True, False), (False, True), (False, False)]
+)
 def test_single_variable_term(
     model: Model,
     query_point: dict[str, float],
@@ -98,7 +100,9 @@ def test_single_variable_term(
         ({"x": 1, "y": 2}, 5, np.array([2, 4])),
     ],
 )
-@pytest.mark.parametrize(["combine_grad", "approximate_grad"], [(True, False), (False, True), (False, False)])
+@pytest.mark.parametrize(
+    ["combine_grad", "approximate_grad"], [(True, False), (False, True), (False, False)]
+)
 def test_multivariable_term(
     model: Model,
     query_point: dict[str, float],
@@ -134,7 +138,9 @@ def test_multivariable_term(
         ({"z_0": 1, "z_1": 2}, 5, np.array([2, 4])),
     ],
 )
-@pytest.mark.parametrize(["combine_grad", "approximate_grad"], [(True, False), (False, True), (False, False)])
+@pytest.mark.parametrize(
+    ["combine_grad", "approximate_grad"], [(True, False), (False, True), (False, False)]
+)
 def test_var_tensor_term(
     model: Model,
     query_point: dict[str, float],
@@ -172,9 +178,11 @@ def test_invalid_step_size_raises(model: Model, step_size: float):
 
 def test_var_tensor_2d_term(model: Model):
     z = model.add_var_tensor(shape=(2, 2), lb=-10, ub=10, name="z")
-    func = lambda w: (w**2).sum()
-    grad = lambda w: 2 * w
-    term = ConvexTerm(var=z, func=func, grad=grad)
+    term = ConvexTerm(
+        var=z,
+        func=lambda w: (w**2).sum(),
+        grad=lambda w: 2 * w,
+    )
 
     qp = {
         model.var_by_name("z_0_0"): 1.0,
diff --git a/tests/test_model.py b/tests/test_model.py
index 7e8e1ad..c009900 100644
--- a/tests/test_model.py
+++ b/tests/test_model.py
@@ -16,7 +16,9 @@ def _check_solution(
     expected_status: mip.OptimizationStatus = mip.OptimizationStatus.OPTIMAL,
 ):
     if expected_objective_value is not None:
-        assert model.objective_value == pytest.approx(expected_objective_value, abs=model.max_gap_abs)
+        assert model.objective_value == pytest.approx(
+            expected_objective_value, abs=model.max_gap_abs
+        )
     if expected_solution is not None:
         for x, expected_value in expected_solution.items():
             assert model.var_value(x=x) == pytest.approx(expected_value, abs=VAR_TOL)
@@ -142,7 +144,7 @@ def test_multivariable_nonlinear_constraint_infeasible():
 def test_maximize_concave_objective():
     model = Model(minimize=False)
     x = model.add_var(lb=0, ub=1)
-    model.add_objective_term(var=x, func=lambda x: -(x - 0.75) ** 2 + 1)
+    model.add_objective_term(var=x, func=lambda x: -((x - 0.75) ** 2) + 1)
     model.optimize()
     _check_solution(
         model=model,
diff --git a/tests/test_utils.py b/tests/test_utils.py
index 5c99a31..c312799 100644
--- a/tests/test_utils.py
+++ b/tests/test_utils.py
@@ -1,10 +1,9 @@
 from contextlib import nullcontext as does_not_raise
-from numbers import Real
-from typing import Iterable, Any
+from typing import Any, Iterable
 
 import pytest
 
-from halfspace.utils import log_table_header, log_table_row, check_scalar
+from halfspace.utils import check_param, log_table_header, log_table_row
 
 
 @pytest.mark.parametrize("columns", [["a", "b", "c", "d"]])
@@ -16,7 +15,7 @@ def test_log_table_header(columns: Iterable[str], width: int):
 
 @pytest.mark.parametrize("values", [[1, 1.0, 2e10, 3e-10]])
 @pytest.mark.parametrize("width", [10, 15])
-def test_log_table_row(values: Iterable[Real], width: int):
+def test_log_table_row(values: Iterable[float | int], width: int):
     log_table_row(values=values, width=width)
     # TODO: add log checks
 
@@ -36,17 +35,17 @@ def test_log_table_row(values: Iterable[Real], width: int):
         (1, "x", int, 0, 1, False, pytest.raises(ValueError)),
     ],
 )
-def test_check_scalar(
+def test_check_param(
     x: Any,
     name: str,
     var_type: type | tuple[type, ...] | None,
-    lb: Real | None,
-    ub: Real | None,
+    lb: float | int | None,
+    ub: float | int | None,
     include_boundaries: bool,
     expectation,
 ):
     with expectation:
-        check_scalar(
+        check_param(
             x=x,
             name=name,
             var_type=var_type,
diff --git a/uv.lock b/uv.lock
index d649d0f..eed6916 100644
--- a/uv.lock
+++ b/uv.lock
@@ -78,33 +78,26 @@ dependencies = [
 ]
 
 [package.optional-dependencies]
-all = [
-    { name = "pre-commit" },
-    { name = "pytest" },
-    { name = "pytest-cov" },
-    { name = "tomli" },
-]
 dev = [
+    { name = "mypy" },
     { name = "pre-commit" },
-    { name = "tomli" },
-]
-test = [
     { name = "pytest" },
     { name = "pytest-cov" },
+    { name = "ruff" },
 ]
 
 [package.metadata]
 requires-dist = [
-    { name = "halfspace-optimizer", extras = ["dev", "test"], marker = "extra == 'all'" },
     { name = "mip", specifier = ">=1.15.0,<2.0.0" },
+    { name = "mypy", marker = "extra == 'dev'" },
     { name = "numpy", specifier = ">=2.3.3,<3.0.0" },
     { name = "pandas", specifier = ">=2.3.2,<3.0.0" },
     { name = "pre-commit", marker = "extra == 'dev'" },
-    { name = "pytest", marker = "extra == 'test'" },
-    { name = "pytest-cov", marker = "extra == 'test'" },
-    { name = "tomli", marker = "extra == 'dev'" },
+    { name = "pytest", marker = "extra == 'dev'" },
+    { name = "pytest-cov", marker = "extra == 'dev'" },
+    { name = "ruff", marker = "extra == 'dev'" },
 ]
-provides-extras = ["dev", "test", "all"]
+provides-extras = ["dev"]
 
 [[package]]
 name = "identify"
@@ -136,6 +129,35 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/49/c9/d4ba71d5d73cf57596b8637ab20eda547c6cde296860f6b6192568809e70/mip-1.15.0-py3-none-any.whl", hash = "sha256:9a48c993ddc9a48591a59e4a1221b400eb1e35fc087052085774360330bb9226", size = 15269834, upload-time = "2023-01-04T13:51:34.596Z" },
 ]
 
+[[package]]
+name = "mypy"
+version = "1.18.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "mypy-extensions" },
+    { name = "pathspec" },
+    { name = "typing-extensions" },
+]
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