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mohdibntarekmohdibntarek
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move from Nonconvex
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‎Project.toml‎

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@@ -3,7 +3,22 @@ uuid = "4296f080-e499-44ba-a02c-ae40015c44d5"
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authors = ["Mohamed Tarek <[email protected]> and contributors"]
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version = "0.1.0"
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[deps]
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ADNLPModels = "54578032-b7ea-4c30-94aa-7cbd1cce6c9a"
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LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
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NLPModelsModifiers = "e01155f1-5c6f-4375-a9d8-616dd036575f"
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NonconvexCore = "035190e5-69f1-488f-aaab-becca2889735"
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Parameters = "d96e819e-fc66-5662-9728-84c9c7592b0a"
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Percival = "01435c0c-c90d-11e9-3788-63660f8fbccc"
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Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
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[compat]
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ADNLPModels = "0.1, 0.2, 0.3"
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NLPModelsModifiers = "0.1, 0.2, 0.4"
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NonconvexCore = "0.1.4"
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Parameters = "0.12"
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Percival = "0.3.1"
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Reexport = "1"
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julia = "1"
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[extras]

‎src/NonconvexPercival.jl‎

Lines changed: 164 additions & 1 deletion
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@@ -1,5 +1,168 @@
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module NonconvexPercival
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3-
# Write your package code here.
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export PercivalAlg, PercivalOptions, AugLag, AugLagOptions
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import Percival, NLPModelsModifiers, ADNLPModels
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using Reexport, Parameters
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@reexport using NonconvexCore
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using NonconvexCore: @params, VecModel, AbstractOptimizer
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using NonconvexCore: AbstractResult, CountingFunction, getnconstraints
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import NonconvexCore: Workspace, reset!, optimize!
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struct PercivalAlg <: AbstractOptimizer end
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const AugLag = PercivalAlg
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@params struct PercivalOptions
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nt::NamedTuple
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end
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function PercivalOptions(; first_order = true, memory = 5, kwargs...)
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return PercivalOptions(
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(;first_order = first_order,
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memory = memory, inity = ones,
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kwargs...),
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)
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end
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const AugLagOptions = PercivalOptions
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@params mutable struct PercivalWorkspace <: Workspace
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model::VecModel
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problem::Percival.NLPModels.AbstractNLPModel
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x0::AbstractVector
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options::PercivalOptions
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counter::Base.RefValue{Int}
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end
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function PercivalWorkspace(
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model::VecModel, x0::AbstractVector = getinit(model);
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options = PercivalOptions(), kwargs...,
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)
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problem, counter = get_percival_problem(model, copy(x0))
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return PercivalWorkspace(model, problem, copy(x0), options, counter)
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end
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@params struct PercivalResult <: AbstractResult
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minimizer
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minimum
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problem
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result
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fcalls
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end
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const AugLagWorkspace = PercivalWorkspace
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function optimize!(workspace::PercivalWorkspace)
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@unpack problem, options, x0, counter = workspace
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counter[] = 0
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m = getnconstraints(workspace.model)
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problem.meta.x0 .= x0
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result = _percival(problem; options.nt...,
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inity = options.nt.inity(eltype(x0), m))
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result.solution = result.solution[1:length(x0)]
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return PercivalResult(
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copy(result.solution), result.objective, problem, result, counter[],
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)
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end
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function _percival(nlp;
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T = eltype(nlp.meta.x0),
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μ::Real = convert(T, 10),
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max_iter::Int = 1000, max_time::Real = convert(T, Inf),
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max_eval::Int = 100000, atol::Real = convert(T, 1e-6),
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rtol::Real = convert(T, 1e-6), ctol::Real = convert(T, 1e-6),
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first_order = true, memory = 5,
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subsolver_logger::Percival.AbstractLogger = Percival.NullLogger(),
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inity = nothing, max_cgiter = 100, subsolver_max_eval = 200, kwargs...,
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)
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modifier = m -> begin
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op = NLPModelsModifiers.LinearOperators.LBFGSOperator(T, m.meta.nvar; mem = memory)
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return NLPModelsModifiers.LBFGSModel(m.meta, m, op)
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end
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_kwargs = (
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max_iter = max_iter, max_time = max_time,
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max_eval = max_eval, atol = atol, rtol = rtol,
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subsolver_logger = subsolver_logger,
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subproblem_modifier = first_order ? modifier : identity,
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subsolver_max_eval = subsolver_max_eval,
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subsolver_kwargs = Dict(:max_cgiter => max_cgiter),
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)
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if Percival.unconstrained(nlp) || Percival.bound_constrained(nlp)
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return Percival.percival(
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Val(:tron), nlp; _kwargs...,
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)
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elseif Percival.equality_constrained(nlp)
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return Percival.percival(
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Val(:equ), nlp; inity = inity, _kwargs...,
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)
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else # has inequalities
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return Percival.percival(
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Val(:ineq), nlp; inity = inity, _kwargs...,
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)
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end
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end
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function Workspace(model::VecModel, optimizer::PercivalAlg, args...; kwargs...,)
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return PercivalWorkspace(model, args...; kwargs...)
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end
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function reset!(w::AugLagWorkspace, x0 = nothing)
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w.counter[] = 0
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if x0 !== nothing
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w.x0 .= x0
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end
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return w
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end
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function get_percival_problem(model::VecModel, x0::AbstractVector)
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eq = if length(model.eq_constraints.fs) == 0
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nothing
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else
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model.eq_constraints
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end
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ineq = if length(model.ineq_constraints.fs) == 0
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nothing
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else
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model.ineq_constraints
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end
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obj = CountingFunction(getobjective(model))
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return get_percival_problem(
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obj,
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ineq,
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eq,
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x0,
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getmin(model),
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getmax(model),
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), obj.counter
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end
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function get_percival_problem(obj, ineq_constr, eq_constr, x0, xlb, xub)
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T = eltype(x0)
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nvars = length(x0)
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if ineq_constr !== nothing
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ineqval = ineq_constr(x0)
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ineq_nconstr = length(ineqval)
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else
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ineqval = T[]
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ineq_nconstr = 0
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end
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if eq_constr !== nothing
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eqval = eq_constr(x0)
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eq_nconstr = length(eqval)
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else
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eqval = T[]
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eq_nconstr = 0
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end
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c = x -> begin
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if ineq_constr !== nothing
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v1 = ineq_constr(x)
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else
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v1 = eltype(x)[]
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end
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if eq_constr !== nothing
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v2 = eq_constr(x)
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else
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v2 = eltype(x)[]
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end
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return [v1; v2]
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end
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lcon = [fill(convert(T, -Inf), ineq_nconstr); zeros(T, eq_nconstr)]
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ucon = zeros(T, ineq_nconstr + eq_nconstr)
165+
return ADNLPModels.ADNLPModel(obj, x0, xlb, xub, c, lcon, ucon, adbackend = ADNLPModels.ZygoteAD())
166+
end
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end

‎test/runtests.jl‎

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Original file line numberDiff line numberDiff line change
@@ -1,6 +1,107 @@
1-
using NonconvexPercival
2-
using Test
1+
using NonconvexPercival, LinearAlgebra, Test
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4-
@testset "NonconvexPercival.jl" begin
5-
# Write your tests here.
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f(x::AbstractVector) = sqrt(x[2])
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g(x::AbstractVector, a, b) = (a*x[1] + b)^3 - x[2]
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6+
alg = AugLag()
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8+
@testset "Simple constraints" begin
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m = Model(f)
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addvar!(m, [0.0, 0.0], [10.0, 10.0])
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add_ineq_constraint!(m, x -> g(x, 2, 0))
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add_ineq_constraint!(m, x -> g(x, -1, 1))
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for first_order in (true, false)
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options = AugLagOptions(first_order = first_order)
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r = optimize(m, alg, [1.234, 2.345], options = options)
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@test abs(r.minimum - sqrt(8/27)) < 1e-6
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@test norm(r.minimizer - [1/3, 8/27]) < 1e-6
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end
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end
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@testset "Equality constraints" begin
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m = Model(f)
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addvar!(m, [0.0, 0.0], [10.0, 10.0])
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add_ineq_constraint!(m, x -> g(x, 2, 0))
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add_ineq_constraint!(m, x -> g(x, -1, 1))
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add_eq_constraint!(m, x -> sum(x) - 1/3 - 8/27)
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for first_order in (true, false)
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options = AugLagOptions(first_order = first_order)
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r = optimize(m, alg, [1.234, 2.345], options = options)
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@test abs(r.minimum - sqrt(8/27)) < 1e-6
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@test norm(r.minimizer - [1/3, 8/27]) < 1e-6
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end
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end
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@testset "Equality constraints BigFloat" begin
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T = BigFloat
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m = Model(f)
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addvar!(m, T.([0.0, 0.0]), T.([10.0, 10.0]))
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add_ineq_constraint!(m, x -> g(x, T(2), T(0)))
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add_ineq_constraint!(m, x -> g(x, T(-1), T(1)))
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add_eq_constraint!(m, x -> sum(x) - T(1/3) - T(8/27))
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for first_order in (true, false)
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options = AugLagOptions(first_order = first_order)
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r = optimize(m, alg, T.([1.234, 2.345]), options = options)
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@test abs(r.minimum - sqrt(T(8/27))) < T(1e-6)
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@test norm(r.minimizer - T.([1/3, 8/27])) < T(1e-6)
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@test r.minimum isa T
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@test eltype(r.minimizer) <: T
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end
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end
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@testset "Block constraints" begin
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m = Model(f)
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addvar!(m, [0.0, 0.0], [10.0, 10.0])
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add_ineq_constraint!(m, FunctionWrapper(x -> [g(x, 2, 0), g(x, -1, 1)], 2))
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for first_order in (true, false)
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options = AugLagOptions(first_order = first_order)
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r = optimize(m, alg, [1.234, 2.345], options = options)
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@test abs(r.minimum - sqrt(8/27)) < 1e-6
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@test norm(r.minimizer - [1/3, 8/27]) < 1e-6
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end
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end
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@testset "Infinite bounds" begin
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@testset "Infinite upper bound" begin
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m = Model(f)
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addvar!(m, [0.0, 0.0], [Inf, Inf])
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add_ineq_constraint!(m, x -> g(x, 2, 0))
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add_ineq_constraint!(m, x -> g(x, -1, 1))
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72+
for first_order in (true, false)
73+
options = AugLagOptions(first_order = first_order)
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r = optimize(m, alg, [1.234, 2.345], options = options)
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@test abs(r.minimum - sqrt(8/27)) < 1e-6
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@test norm(r.minimizer - [1/3, 8/27]) < 1e-6
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end
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end
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#=
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@testset "Infinite lower bound" begin
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m = Model(f)
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addvar!(m, [-Inf, -Inf], [10, 10])
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add_ineq_constraint!(m, x -> g(x, 2, 0))
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add_ineq_constraint!(m, x -> g(x, -1, 1))
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for first_order in (true, false)
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options = AugLagOptions(first_order = first_order)
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r = optimize(m, alg, [1.234, 2.345], options = options)
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@test abs(r.minimum - sqrt(8/27)) < 1e-6
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@test norm(r.minimizer - [1/3, 8/27]) < 1e-6
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end
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end
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@testset "Infinite upper and lower bound" begin
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m = Model(f)
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addvar!(m, [-Inf, -Inf], [Inf, Inf])
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add_ineq_constraint!(m, x -> g(x, 2, 0))
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add_ineq_constraint!(m, x -> g(x, -1, 1))
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for first_order in (true, false)
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options = AugLagOptions(first_order = first_order)
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r = optimize(m, alg, [1.234, 2.345], options = options)
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@test abs(r.minimum - sqrt(8/27)) < 1e-6
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@test norm(r.minimizer - [1/3, 8/27]) < 1e-6
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end
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end
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=#
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end

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