Add GalacticMultistartOptimization

Author: Vaibhavdixit02

GalacticMultistartOptimization/Project.toml

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+name = "GalacticMultistartOptimization"
+uuid = "969b6d83-0cb9-4e98-85e3-a6a04e06aec6"
+authors = ["Vaibhav Dixit <[email protected]> and contributors"]
+version = "0.1.0"
+
+[deps]
+GalacticOptim = "a75be94c-b780-496d-a8a9-0878b188d577"
+MultistartOptimization = "3933049c-43be-478e-a8bb-6e0f7fd53575"
+
+[compat]
+julia = "1"
+
+[extras]
+NLopt = "76087f3c-5699-56af-9a33-bf431cd00edd"
+Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+
+[targets]
+test = ["Test"]

GalacticMultistartOptimization/src/GalacticMultistartOptimization.jl

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+module GalacticMultistartOptimization
+
+using MultistartOptimization, GalacticOptim, GalacticOptim.SciMLBase
+
+function SciMLBase.__solve(prob::OptimizationProblem, multiopt::MultistartOptimization.TikTak, opt;
+    use_threads=true,
+    kwargs...)
+    local x, _loss
+
+    _loss = function (θ)
+        x = prob.f(θ, prob.p)
+        return first(x)
+    end
+
+    opt_setup = MultistartOptimization.MinimizationProblem(_loss, prob.lb, prob.ub)
+
+    _local_optimiser = function (pb, θ0, prob)
+        prob_tmp = remake(prob, u0=θ0)
+        res = solve(prob_tmp, opt;
+            kwargs...)
+        return (value=res.minimum, location=res.minimizer, ret=res.retcode)
+    end
+
+    local_optimiser(pb, θ0) = _local_optimiser(pb, θ0, prob)
+
+    t0 = time()
+    opt_res = MultistartOptimization.multistart_minimization(multiopt, local_optimiser, opt_setup; use_threads=use_threads)
+    t1 = time()
+    opt_ret = hasproperty(opt_res, :ret) ? opt_res.ret : nothing
+
+    SciMLBase.build_solution(prob, (multiopt, opt), opt_res.location, opt_res.value; (isnothing(opt_ret) ? (; original=opt_res) : (; original=opt_res, retcode=opt_ret))...)
+end
+
+end

GalacticMultistartOptimization/test/runtests.jl

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+using GalacticMultistartOptimization, GalacticOptim, MultistartOptimization, NLopt, ForwardDiff
+using Test
+
+@testset "GalacticMultistartOptimization.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)
+    f = OptimizationFunction(rosenbrock, GalacticOptim.AutoForwardDiff())
+    prob = GalacticOptim.OptimizationProblem(f, x0, _p, lb=[-1.0, -1.0], ub=[1.5, 1.5])
+    sol = solve(prob, MultistartOptimization.TikTak(100), NLopt.LD_LBFGS())
+    @test 10 * sol.minimum < l1
+end