Merge pull request #471 from SciML/evoup

Update OptimizationEvolutionary to support constraints and pass additional kwargs

Author: ChrisRackauckas

lib/OptimizationEvolutionary/Project.toml

@@ -15,7 +15,8 @@ Optimization = "3.9"
 Reexport = "1.2"
 
 [extras]
+Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
-test = ["Test"]
+test = ["Random", "Test"]

lib/OptimizationEvolutionary/src/OptimizationEvolutionary.jl

@@ -5,6 +5,7 @@ using Reexport
 using Optimization.SciMLBase
 
 SciMLBase.allowsbounds(opt::Evolutionary.AbstractOptimizer) = true
+SciMLBase.allowsconstraints(opt::Evolutionary.AbstractOptimizer) = true
 
 decompose_trace(trace::Evolutionary.OptimizationTrace) = last(trace)
 decompose_trace(trace::Evolutionary.OptimizationTraceRecord) = trace
@@ -20,8 +21,9 @@ function __map_optimizer_args(prob::OptimizationProblem,
                               maxiters::Union{Number, Nothing} = nothing,
                               maxtime::Union{Number, Nothing} = nothing,
                               abstol::Union{Number, Nothing} = nothing,
-                              reltol::Union{Number, Nothing} = nothing)
-    mapped_args = (;)
+                              reltol::Union{Number, Nothing} = nothing,
+                              kwargs...)
+    mapped_args = (;kwargs...)
 
     if !isnothing(callback)
         mapped_args = (; mapped_args..., callback = callback)
@@ -74,6 +76,8 @@ function SciMLBase.__solve(prob::OptimizationProblem, opt::Evolutionary.Abstract
     maxiters = Optimization._check_and_convert_maxiters(maxiters)
     maxtime = Optimization._check_and_convert_maxtime(maxtime)
 
+    f = Optimization.instantiate_function(prob.f, prob.u0, prob.f.adtype, prob.p,
+                                          prob.ucons === nothing ? 0 : length(prob.ucons))
     _loss = function (θ)
         x = prob.f(θ, prob.p, cur...)
         return first(x)
@@ -84,10 +88,21 @@ function SciMLBase.__solve(prob::OptimizationProblem, opt::Evolutionary.Abstract
                                     kwargs...)
 
     t0 = time()
-    if isnothing(prob.ub) || isnothing(prob.ub)
-        opt_res = Evolutionary.optimize(_loss, prob.u0, opt, opt_args)
+    if isnothing(prob.lb) || isnothing(prob.ub)
+        if !isnothing(f.cons)
+            c = x -> (res = zeros(length(prob.lcons)); f.cons(res, x); res)
+            cons = WorstFitnessConstraints(Float64[], Float64[], prob.lcons, prob.ucons, c)
+            opt_res = Evolutionary.optimize(_loss, cons, prob.u0, opt, opt_args)
+        else
+            opt_res = Evolutionary.optimize(_loss, prob.u0, opt, opt_args)
+        end
     else
-        cons = Evolutionary.BoxConstraints(prob.lb, prob.ub)
+        if !isnothing(f.cons)
+            c = x -> (res = zeros(length(prob.lcons)); f.cons(res, x); res)
+            cons = WorstFitnessConstraints(prob.lb, prob.ub, prob.lcons, prob.ucons, c)
+        else
+            cons = BoxConstraints(prob.lb, prob.ub)
+        end
         opt_res = Evolutionary.optimize(_loss, cons, prob.u0, opt, opt_args)
     end
     t1 = time()

lib/OptimizationEvolutionary/test/runtests.jl

@@ -1,6 +1,7 @@
-using OptimizationEvolutionary, Optimization
+using OptimizationEvolutionary, Optimization, Random
 using Test
 
+Random.seed!(1234)
 @testset "OptimizationEvolutionary.jl" begin
     rosenbrock(x, p) = (p[1] - x[1])^2 + p[2] * (x[2] - x[1]^2)^2
     x0 = zeros(2)
@@ -10,4 +11,26 @@ using Test
     prob = Optimization.OptimizationProblem(optprob, x0, _p)
     sol = solve(prob, CMAES(μ = 40, λ = 100), abstol = 1e-15)
     @test 10 * sol.objective < l1
+
+    x0 = [-0.7, 0.3]
+    prob = Optimization.OptimizationProblem(optprob, x0, _p, lb = [0.0, 0.0],
+                                            ub = [0.5, 0.5])
+    sol = solve(prob, CMAES(μ = 50, λ = 60))
+    @test sol.u == zeros(2)
+
+    x0 = zeros(2)
+    cons_circ = (res, x, p) -> res .= [x[1]^2 + x[2]^2]
+    optprob = OptimizationFunction(rosenbrock; cons = cons_circ)
+    prob = OptimizationProblem(optprob, x0, _p, lcons = [-Inf], ucons = [0.25^2])
+    sol = solve(prob, CMAES(μ = 40, λ = 100))
+    res = zeros(1)
+    cons_circ(res, sol.u, nothing)
+    @test res[1]≈0.0625 atol=1e-5
+    @test sol.objective < l1
+
+    prob = OptimizationProblem(optprob, x0, _p, lcons = [-Inf], ucons = [5.0], lb = [0.0, 1.0], ub = [Inf, Inf])
+    sol = solve(prob, CMAES(μ = 40, λ = 100))
+    res = zeros(1)
+    cons_circ(res, sol.u, nothing)
+    @test sol.objective < l1
 end