Merge branch 'master' into GCMAES

Author: ChrisRackauckas

docs/src/optimization_packages/speedmapping.md

@@ -0,0 +1,26 @@
+# SpeedMapping.jl
+[`SpeedMapping`](https://github.com/NicolasL-S/SpeedMapping.jl) accelerates the convergence of a mapping to a fixed point by the Alternating cyclic extrapolation algorithm which can also perform multivariate optimization based on the gradient function.
+
+The SpeedMapping algorithm is called by `SpeedMappingOpt()`
+
+## Global Optimizer
+### Without Constraint Equations
+
+The method in [`SpeedMapping`](https://github.com/NicolasL-S/SpeedMapping.jl) is performing optimization on problems without
+constraint equations. Lower and upper constraints set by `lb` and `ub` in the `OptimizationProblem` are optional.
+
+If no AD backend is defined via `OptimizationFunction` the gradient is calculated via `SpeedMapping`'s ForwardDiff AD backend.
+
+The Rosenbrock function can be optimized using the `SpeedMappingOpt()` with and without bound as follows:
+
+```julia
+rosenbrock(x, p) =  (p[1] - x[1])^2 + p[2] * (x[2] - x[1]^2)^2
+x0 = zeros(2)
+p  = [1.0, 100.0]
+f = OptimizationFunction(rosenbrock, GalacticOptim.AutoForwardDiff())
+prob = OptimizationProblem(f, x0, _p)
+sol = solve(prob,SpeedMappingOpt())
+
+prob = OptimizationProblem(f, x0, _p;lb=[0.0,0.0], ub=[1.0,1.0])
+sol = solve(prob,SpeedMappingOpt())
+```

src/GalacticOptim.jl

@@ -44,6 +44,7 @@ function __init__()
         end
     end
     @require Metaheuristics="bcdb8e00-2c21-11e9-3065-2b553b22f898" include("solve/metaheuristics.jl")
+    @require SpeedMapping="f1835b91-879b-4a3f-a438-e4baacf14412" include("solve/speedmapping.jl")
 
     # AD backends
     @require FiniteDiff="6a86dc24-6348-571c-b903-95158fe2bd41" include("function/finitediff.jl")

src/solve/speedmapping.jl

@@ -0,0 +1,68 @@
+export SpeedMappingOpt
+
+struct SpeedMappingOpt end
+
+function __map_optimizer_args(prob::OptimizationProblem, opt::SpeedMappingOpt; 
+    cb=nothing, 
+    maxiters::Union{Number, Nothing}=nothing, 
+    maxtime::Union{Number, Nothing}=nothing, 
+    abstol::Union{Number, Nothing}=nothing, 
+    reltol::Union{Number, Nothing}=nothing,
+    kwargs...)
+
+    # add optimiser options from kwargs
+    mapped_args = (; kwargs...)
+
+    if !(isnothing(maxiters))
+        @info "maxiters defines maximum gradient calls for $(opt)"
+        mapped_args = (; mapped_args..., maps_limit = maxiters)
+    end
+
+    if !(isnothing(maxtime))
+        mapped_args = (; mapped_args..., time_limit = maxtime)
+    end
+
+    if !isnothing(abstol)
+        @warn "common abstol is currently not used by $(opt)"
+    end
+
+    if !isnothing(reltol)
+        @warn "common reltol is currently not used by $(opt)"
+    end
+
+    return mapped_args
+end
+
+
+function __solve(prob::OptimizationProblem, opt::SpeedMappingOpt;
+                 maxiters::Union{Number, Nothing} = nothing,
+                 maxtime::Union{Number, Nothing} = nothing,
+                 abstol::Union{Number, Nothing}=nothing,
+                 reltol::Union{Number, Nothing}=nothing,
+                 progress = false,
+                 kwargs...)
+    local x
+
+    maxiters = _check_and_convert_maxiters(maxiters)
+    maxtime = _check_and_convert_maxtime(maxtime)
+
+    f = instantiate_function(prob.f,prob.u0,prob.f.adtype,prob.p)
+
+    _loss = function(θ)
+        x = f.f(θ, prob.p)
+        return first(x)
+    end
+    
+    if isnothing(f.grad)
+        @info "SpeedMapping's ForwardDiff AD backend is used to calculate the gradient information."
+    end 
+
+    opt_args = _map_optimizer_args(prob,opt, maxiters=maxiters, maxtime=maxtime, abstol=abstol, reltol=reltol; kwargs...)
+    
+    t0 = time()
+    opt_res = SpeedMapping.speedmapping(prob.u0; f = _loss, g! =f.grad, lower=prob.lb, upper=prob.ub, opt_args...)
+    t1 = time()
+    opt_ret = Symbol(opt_res.converged)
+
+    SciMLBase.build_solution(prob, opt, opt_res.minimizer, _loss(opt_res.minimizer); original=opt_res, retcode=opt_ret)
+end

test/Project.toml

@@ -31,6 +31,7 @@ Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 ReverseDiff = "37e2e3b7-166d-5795-8a7a-e32c996b4267"
 SafeTestsets = "1bc83da4-3b8d-516f-aca4-4fe02f6d838f"
+SpeedMapping = "f1835b91-879b-4a3f-a438-e4baacf14412"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 Tracker = "9f7883ad-71c0-57eb-9f7f-b5c9e6d3789c"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
@@ -65,6 +66,7 @@ Optim = ">= 1.4.1"
 OrdinaryDiffEq = ">= 5"
 ReverseDiff = ">= 1.9.0"
 SafeTestsets = ">= 0.0.1"
+SpeedMapping = ">= 0.3.0"
 Tracker = ">= 0.2"
 Zygote = ">= 0.5"
 julia = "1.5"

test/rosenbrock.jl

@@ -173,7 +173,7 @@ end
 # sol = solve(prob, QuadDirect(); splits = ([-0.5, 0.0, 0.5],[-0.5, 0.0, 0.5]))
 # @test 10*sol.minimum < l1
 
-@testset "Evolutionary, BlackBoxOptim, Metaheuristics, Nonconvex, GCMAES" begin
+@testset "Evolutionary, BlackBoxOptim, Metaheuristics, Nonconvex, GCMAES, SpeedMapping" begin
     optprob = OptimizationFunction(rosenbrock, GalacticOptim.AutoZygote())
     using Evolutionary
     prob = GalacticOptim.OptimizationProblem(optprob, x0, _p)
@@ -426,7 +426,7 @@ end
 
     sol = solve(prob, BayesOptAlg(NLoptAlg(:LN_NELDERMEAD)), sub_options=(;maxeval=100))
     @test 10*sol.minimum < l1
-
+  
     using GCMAES
     f_ad = OptimizationFunction(rosenbrock, GalacticOptim.AutoForwardDiff())
     f_noad = OptimizationFunction(rosenbrock)
@@ -438,4 +438,19 @@ end
     prob = GalacticOptim.OptimizationProblem(f_noad, x0, _p, lb=[-1.0, -1.0], ub=[1.0, 1.0])
     sol = solve(prob, GCMAESOpt(), maxiters=1000)
     @test 10*sol.minimum < l1
+
+    using SpeedMapping
+    f = OptimizationFunction(rosenbrock, GalacticOptim.AutoForwardDiff())
+    prob = OptimizationProblem(f, x0, _p)
+    sol = solve(prob,SpeedMappingOpt())
+
+    prob = OptimizationProblem(f, x0, _p;lb=[0.0,0.0], ub=[1.0,1.0])
+    sol = solve(prob,SpeedMappingOpt())
+
+    f = OptimizationFunction(rosenbrock)
+    prob = OptimizationProblem(f, x0, _p)
+    sol = solve(prob,SpeedMappingOpt())
+
+    prob = OptimizationProblem(f, x0, _p;lb=[0.0,0.0], ub=[1.0,1.0])
+    sol = solve(prob,SpeedMappingOpt())
 end