Merge pull request #263 from SciML/polyalgorithms

Create GalacticPolyalgorithms.jl

Author: ChrisRackauckas

.github/workflows/CI.yml

@@ -30,6 +30,7 @@ jobs:
           - GalacticOptimisers
           - GalacticQuadDIRECT
           - GalacticSpeedMapping
+          - GalacticPolyalgorithms
         version:
           - '1'
           - '1.6'

lib/GalacticPolyalgorithms/LICENSE

@@ -0,0 +1,19 @@
+Copyright (c) 2022 Julia Computing
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

lib/GalacticPolyalgorithms/Project.toml

@@ -0,0 +1,22 @@
+name = "GalacticPolyalgorithms"
+uuid = "8202cac9-28d3-4ced-94fb-69829a1553b4"
+authors = ["Vaibhav Dixit <vaibhavyashdixit@gmail.com> and contributors"]
+version = "0.1.0"
+
+[deps]
+GalacticOptim = "a75be94c-b780-496d-a8a9-0878b188d577"
+GalacticOptimJL = "9d3c5eb1-403b-401b-8c0f-c11105342e6b"
+GalacticOptimisers = "86b7a833-eb4b-49e2-87ed-89357ad7afa2"
+
+[compat]
+GalacticOptim = "3"
+GalacticOptimJL = "0.1"
+GalacticOptimisers = "0.1"
+julia = "1.6"
+
+[extras]
+ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
+Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+
+[targets]
+test = ["ForwardDiff", "Test"]

lib/GalacticPolyalgorithms/src/GalacticPolyalgorithms.jl

@@ -0,0 +1,42 @@
+module GalacticPolyalgorithms
+
+using GalacticOptim, GalacticOptim.SciMLBase, GalacticOptimJL, GalacticOptimisers
+
+struct PolyOpt end
+
+function SciMLBase.__solve(prob::OptimizationProblem,
+                           opt::PolyOpt,
+                           args...;
+                           maxiters = nothing,
+                           kwargs...)
+
+    loss, θ = x -> prob.f(x, prob.p), prob.u0
+    deterministic = first(loss(θ)) == first(loss(θ))
+
+    if (!isempty(args) || !deterministic) && maxiters === nothing
+        error("Automatic optimizer determination requires deterministic loss functions (and no data) or maxiters must be specified.")
+    end
+
+    if isempty(args) && deterministic && prob.lb === nothing && prob.ub === nothing
+        # If determinsitic then ADAM -> finish with BFGS
+        if maxiters === nothing
+            res1 = GalacticOptim.solve(prob, Optimisers.ADAM(0.01), args...; maxiters=300, kwargs...)
+        else
+            res1 = GalacticOptim.solve(prob, Optimisers.ADAM(0.01), args...; maxiters, kwargs...)
+        end
+
+        optprob2 = remake(prob,u0 = res1.u)
+        res1 = GalacticOptim.solve(
+            optprob2, BFGS(initial_stepnorm=0.01), args...; maxiters, kwargs...)
+    elseif isempty(args) && deterministic
+        res1 = GalacticOptim.solve(
+            prob, BFGS(initial_stepnorm=0.01), args...; maxiters, kwargs...)
+    else
+        res1 = GalacticOptim.solve(prob, Optimisers.ADAM(0.1), args...; maxiters, kwargs...)
+    end
+
+end
+
+export PolyOpt
+
+end

lib/GalacticPolyalgorithms/test/runtests.jl

@@ -0,0 +1,14 @@
+using GalacticPolyalgorithms, GalacticOptim, ForwardDiff
+using Test
+
+@testset "GalacticPolyalgorithms.jl" begin
+    rosenbrock(x, p) = (p[1] - x[1])^2 + p[2] * (x[2] - x[1]^2)^2
+    x0 = zeros(2)
+    _p = [1.0, 100.0]
+    l1 = rosenbrock(x0, _p)
+
+    optprob = OptimizationFunction(rosenbrock, GalacticOptim.AutoForwardDiff())
+    prob = OptimizationProblem(optprob, x0, _p)
+    sol = GalacticOptim.solve(prob, PolyOpt(), maxiters=1000)
+    @test 10 * sol.minimum < l1
+end