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Add constraints support for NLopt
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@Vaibhavdixit02
Vaibhavdixit02 committed Aug 12, 2024
1 parent 977630e commit 2a78d49
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Showing 3 changed files with 106 additions and 12 deletions.
1 change: 1 addition & 0 deletions lib/OptimizationNLopt/Project.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -6,6 +6,7 @@ version = "0.2.2"
[deps]
NLopt = "76087f3c-5699-56af-9a33-bf431cd00edd"
Optimization = "7f7a1694-90dd-40f0-9382-eb1efda571ba"
Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
Reexport = "189a3867-3050-52da-a836-e630ba90ab69"

[compat]
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67 changes: 61 additions & 6 deletions lib/OptimizationNLopt/src/OptimizationNLopt.jl
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Expand Up @@ -36,6 +36,24 @@ function SciMLBase.requiresconsjac(opt::NLopt.Algorithm) #https://github.com/Jul
end
end

function SciMLBase.allowsconstraints(opt::NLopt.Algorithm)
str_opt = string(opt)
if occursin("AUGLAG", str_opt) || occursin("CCSA", str_opt) || occursin("MMA", str_opt) || occursin("COBYLA", str_opt) || occursin("ISRES", str_opt) || occursin("AGS", str_opt) || occursin("ORIG_DIRECT", str_opt) || occursin("SLSQP", str_opt)
return true
else
return false
end
end

function SciMLBase.__init(prob::SciMLBase.OptimizationProblem, opt::NLopt.Algorithm,
data = Optimization.DEFAULT_DATA; cons_tol = 1e-6,
callback = (args...) -> (false),
progress = false, kwargs...)
return OptimizationCache(prob, opt, data; cons_tol, callback, progress,
kwargs...)
end


function __map_optimizer_args!(cache::OptimizationCache, opt::NLopt.Opt;
callback = nothing,
maxiters::Union{Number, Nothing} = nothing,
Expand Down Expand Up @@ -76,7 +94,9 @@ function __map_optimizer_args!(cache::OptimizationCache, opt::NLopt.Opt;

# add optimiser options from kwargs
for j in kwargs
eval(Meta.parse("NLopt." * string(j.first) * "!"))(opt, j.second)
if j.first != :cons_tol
eval(Meta.parse("NLopt." * string(j.first) * "!"))(opt, j.second)
end
end

if cache.ub !== nothing
Expand Down Expand Up @@ -170,14 +190,18 @@ function SciMLBase.__solve(cache::OptimizationCache{
return x[1]
end

fg! = function (θ, G)
if length(G) > 0
cache.f.grad(G, θ)
if !hasfield(typeof(cache.f), :fg) || cache.f.fg === nothing
fg! = function (θ, G)
if length(G) > 0
cache.f.grad(G, θ)
end
return _loss(θ)
end

return _loss(θ)
else
fg! = cache.f.fg
end


opt_setup = if isa(cache.opt, NLopt.Opt)
if ndims(cache.opt) != length(cache.u0)
error("Passed NLopt.Opt optimization dimension does not match OptimizationProblem dimension.")
Expand All @@ -193,6 +217,37 @@ function SciMLBase.__solve(cache::OptimizationCache{
NLopt.min_objective!(opt_setup, fg!)
end

if cache.f.cons !== nothing
eqinds = map((y) -> y[1]==y[2], zip(cache.lcons, cache.ucons))
ineqinds = map((y) -> y[1]!=y[2], zip(cache.lcons, cache.ucons))
if sum(ineqinds) > 0
ineqcons = function (res, θ, J)
cons_cache = zeros(eltype(res), sum(eqinds)+sum(ineqinds))
cache.f.cons(cons_cache, θ)
res .= @view(cons_cache[ineqinds])
if length(J) > 0
Jcache = zeros(eltype(J), sum(ineqinds)+sum(eqinds), length(θ))
cache.f.cons_j(Jcache, θ)
J .= @view(Jcache[ineqinds, :])'
end
end
NLopt.inequality_constraint!(opt_setup, ineqcons, [cache.solver_args.cons_tol for i in 1:sum(ineqinds)])
end
if sum(eqinds) > 0
eqcons = function (res, θ, J)
cons_cache = zeros(eltype(res), sum(eqinds)+sum(ineqinds))
cache.f.cons(cons_cache, θ)
res .= @view(cons_cache[eqinds])
if length(J) > 0
Jcache = zeros(eltype(res), sum(eqinds)+sum(ineqinds), length(θ))
cache.f.cons_j(Jcache, θ)
J .= @view(Jcache[eqinds, :])'
end
end
NLopt.equality_constraint!(opt_setup, eqcons, [cache.solver_args.cons_tol for i in 1:sum(eqinds)])
end
end

maxiters = Optimization._check_and_convert_maxiters(cache.solver_args.maxiters)
maxtime = Optimization._check_and_convert_maxtime(cache.solver_args.maxtime)

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50 changes: 44 additions & 6 deletions lib/OptimizationNLopt/test/runtests.jl
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Original file line number Diff line number Diff line change
@@ -1,5 +1,5 @@
using OptimizationNLopt, Optimization, Zygote
using Test
using Test, Random

@testset "OptimizationNLopt.jl" begin
rosenbrock(x, p) = (p[1] - x[1])^2 + p[2] * (x[2] - x[1]^2)^2
Expand All @@ -16,7 +16,7 @@ using Test
optprob = OptimizationFunction(rosenbrock, Optimization.AutoZygote())
prob = OptimizationProblem(optprob, x0, _p)

sol = solve(prob, NLopt.Opt(:LN_BOBYQA, 2))
sol = solve(prob, NLopt.Opt(:LD_LBFGS, 2))
@test sol.retcode == ReturnCode.Success
@test 10 * sol.objective < l1

Expand All @@ -26,10 +26,6 @@ using Test
@test sol.retcode == ReturnCode.Success
@test 10 * sol.objective < l1

sol = solve(prob, NLopt.Opt(:LD_LBFGS, 2))
@test sol.retcode == ReturnCode.Success
@test 10 * sol.objective < l1

sol = solve(prob, NLopt.Opt(:G_MLSL_LDS, 2), local_method = NLopt.Opt(:LD_LBFGS, 2),
maxiters = 10000)
@test sol.retcode == ReturnCode.MaxIters
Expand Down Expand Up @@ -82,4 +78,46 @@ using Test
#nlopt gives the last best not the one where callback stops
@test sol.objective < 0.8
end

@testset "constrained" begin
cons = (res, x, p) -> res .= [x[1]^2 + x[2]^2 - 1.0]
x0 = zeros(2)
optprob = OptimizationFunction(rosenbrock, Optimization.AutoZygote();
cons = cons)
prob = OptimizationProblem(optprob, x0, _p, lcons = [0.0], ucons = [0.0])
sol = solve(prob, NLopt.LN_COBYLA())
@test sol.retcode == ReturnCode.Success
@test 10 * sol.objective < l1

Random.seed!(1)
prob = OptimizationProblem(optprob, rand(2), _p,
lcons = [0.0], ucons = [0.0])

sol = solve(prob, NLopt.LD_SLSQP())
@test sol.retcode == ReturnCode.Success
@test 10 * sol.objective < l1

Random.seed!(1)
prob = OptimizationProblem(optprob, rand(2), _p,
lcons = [0.0], ucons = [0.0])
sol = solve(prob, NLopt.AUGLAG(), local_method = NLopt.LD_LBFGS())
@test sol.retcode == ReturnCode.Success
@test 10 * sol.objective < l1

function con2_c(res, x, p)
res .= [x[1]^2 + x[2]^2 - 1.0, x[2] * sin(x[1]) - x[1] - 2.0]
end

optprob = OptimizationFunction(rosenbrock, Optimization.AutoForwardDiff();cons = con2_c)
Random.seed!(1)
prob = OptimizationProblem(optprob, rand(2), _p, lcons = [0.0, -Inf], ucons = [0.0, 0.0])
sol = solve(prob, NLopt.LD_AUGLAG(), local_method = NLopt.LD_LBFGS())
@test sol.retcode == ReturnCode.Success
@test 10 * sol.objective < l1

prob = OptimizationProblem(optprob, rand(2), _p, lcons = [-Inf, -Inf], ucons = [0.0, 0.0], lb = [-1.0, -1.0], ub = [1.0, 1.0])
sol = solve(prob, NLopt.GN_ISRES(), maxiters = 1000)
@test sol.retcode == ReturnCode.MaxIters
@test 10 * sol.objective < l1
end
end

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