Run optimization tests and add constructors for it

Project.toml

@@ -21,6 +21,7 @@ Markdown = "d6f4376e-aef5-505a-96c1-9c027394607a"
 PrecompileTools = "aea7be01-6a6a-4083-8856-8a6e6704d82a"
 Preferences = "21216c6a-2e73-6563-6e65-726566657250"
 Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
+QuasiMonteCarlo = "8a4e6c94-4038-4cdc-81c3-7e6ffdb2a71b"
 RecipesBase = "3cdcf5f2-1ef4-517c-9805-6587b60abb01"
 RecursiveArrayTools = "731186ca-8d62-57ce-b412-fbd966d074cd"
 Reexport = "189a3867-3050-52da-a836-e630ba90ab69"

src/ensemble/ensemble_problems.jl

@@ -44,6 +44,23 @@ function EnsembleProblem(; prob,
     EnsembleProblem(prob; prob_func, output_func, reduction, u_init, safetycopy)
 end
 
+#since NonlinearProblem might want to use this dispatch as well
+function SciMLBase.EnsembleProblem(prob::AbstractSciMLProblem, u0s::Vector{Vector{T}}; kwargs...) where {T}
+    prob_func = (prob, i, repeat = nothing) -> remake(prob, u0 = u0s[i])
+    return SciMLBase.EnsembleProblem(prob; prob_func, kwargs...)
+end
+
+#only makes sense for OptimizationProblem, might make sense for IntervalNonlinearProblem
+function SciMLBase.EnsembleProblem(prob::OptimizationProblem, trajectories::Int; kwargs...)
+    if prob.lb !== nothing && prob.ub !== nothing
+        u0s = QuasiMonteCarlo.sample(trajectories, prob.lb, prob.ub, LatinHypercubeSample())
+        prob_func = (prob, i, repeat = nothing) -> remake(prob, u0 = u0s[:, i])
+    else
+        error("EnsembleProblem with `trajectories` as second argument requires lower and upper bounds to be defined in the `OptimizationProblem`.")
+    end
+    return SciMLBase.EnsembleProblem(prob; prob_func, kwargs...)
+end
+
 struct WeightedEnsembleProblem{T1 <: AbstractEnsembleProblem, T2 <: AbstractVector} <:
        AbstractEnsembleProblem
     ensembleprob::T1

test/downstream/ensemble_nondes.jl

@@ -23,4 +23,16 @@ sol = Optimization.solve(ensembleprob, OptimizationOptimJL.BFGS(), EnsembleThrea
 @test findmin(i -> sol[i].objective, 1:4)[1] < sol1.objective
 
 sol = Optimization.solve(ensembleprob, OptimizationOptimJL.BFGS(), EnsembleDistributed(), trajectories = 5, maxiters = 5)
-@test findmin(i -> sol[i].objective, 1:4)[1] < sol1.objective
+@test findmin(i -> sol[i].objective, 1:4)[1] < sol1.objective
+
+using NonlinearSolve
+
+f(u, p) = u .* u .- p
+u0 = [1.0, 1.0]
+p = 2.0
+prob = NonlinearProblem(f, u0, p)
+ensembleprob = EnsembleProblem(prob, [u0, u0 .+ rand(2), u0 .+ rand(2), u0 .+ rand(2)])
+
+sol = solve(ensembleprob, EnsembleThreads(), trajectories = 4, maxiters = 100)
+
+sol = solve(ensembleprob, EnsembleDistributed(), trajectories = 4, maxiters = 100)

test/runtests.jl

@@ -78,9 +78,9 @@ end
         @time @safetestset "Ensemble solution statistics" begin
             include("downstream/ensemble_stats.jl")
         end
-        # @time @safetestset "Ensemble Optimization and Nonlinear problems" begin
-        #     include("downstream/ensemble_nondes.jl")
-        # end
+        @time @safetestset "Ensemble Optimization and Nonlinear problems" begin
+            include("downstream/ensemble_nondes.jl")
+        end
         @time @safetestset "Ensemble with DifferentialEquations automatic algorithm selection" begin
             include("downstream/ensemble_diffeq.jl")
         end