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Update to MathOptInterface v1.0 #218

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Mar 24, 2022
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Update to MathOptInterface v1.0 #218

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ec25e80
Update to MathOptInterface v1.0
odow Mar 21, 2022
7fcb5f4
Update src/solve/moi.jl
odow Mar 21, 2022
c7d5d98
Variety of misc fixes
odow Mar 22, 2022
4eba2e8
Merge branch 'od/moi1' of https://github.com/odow/GalacticOptim.jl in…
odow Mar 22, 2022
72155b6
Remove unneeded hessian call
odow Mar 22, 2022
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231 changes: 142 additions & 89 deletions src/solve/moi.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -4,22 +4,95 @@ const MOI = MathOptInterface
struct MOIOptimizationProblem{T,F<:OptimizationFunction,uType,P} <: MOI.AbstractNLPEvaluator
f::F
u0::uType
p::P
p::P
J::Matrix{T}
H::Matrix{T}
cons_H::Vector{Matrix{T}}
end

MOI.eval_objective(moiproblem::MOIOptimizationProblem, x) = moiproblem.f(x, moiproblem.p)
function MOIOptimizationProblem(prob::OptimizationProblem)
num_cons = prob.ucons === nothing ? 0 : length(prob.ucons)
f = instantiate_function(prob.f, prob.u0, prob.f.adtype, prob.p, num_cons)
T = eltype(prob.u0)
n = length(prob.u0)
return MOIOptimizationProblem(
f,
prob.u0,
prob.p,
zeros(T, num_cons, n),
zeros(T, n, n),
Matrix{T}[zeros(T, n, n) for i in 1:num_cons],
)
end

MOI.features_available(::MOIOptimizationProblem) = [:Grad, :Hess, :Jac]

function MOI.initialize(
moiproblem::MOIOptimizationProblem,
requested_features::Vector{Symbol},
)
available_features = MOI.features_available(moiproblem)
for feat in requested_features
if !(feat in available_features)
error("Unsupported feature $feat")
# TODO: implement Jac-vec and Hess-vec products
# for solvers that need them
end
end
return
end

function MOI.eval_objective(moiproblem::MOIOptimizationProblem, x)
return moiproblem.f(x, moiproblem.p)
end

MOI.eval_constraint(moiproblem::MOIOptimizationProblem, g, x) = g .= moiproblem.f.cons(x)
function MOI.eval_constraint(moiproblem::MOIOptimizationProblem, g, x)
g .= moiproblem.f.cons(x)
return
end

MOI.eval_objective_gradient(moiproblem::MOIOptimizationProblem, G, x) = moiproblem.f.grad(G, x)
function MOI.eval_objective_gradient(moiproblem::MOIOptimizationProblem, G, x)
moiproblem.f.grad(G, x)
return
end

function MOI.eval_hessian_lagrangian(moiproblem::MOIOptimizationProblem{T}, h, x, σ, μ) where {T}
# This structure assumes the calculation of moiproblem.J is dense.
function MOI.jacobian_structure(moiproblem::MOIOptimizationProblem)
rows, cols = size(moiproblem.J)
return Tuple{Int,Int}[(i, j) for j in 1:cols for i in 1:rows]
end

function MOI.eval_constraint_jacobian(moiproblem::MOIOptimizationProblem, j, x)
if isempty(j)
return
elseif moiproblem.f.cons_j === nothing
error(
"Use OptimizationFunction to pass the derivatives or " *
"automatically generate them with one of the autodiff backends",
)
end
moiproblem.f.cons_j(moiproblem.J, x)
for i in eachindex(j)
j[i] = moiproblem.J[i]
end
return
end

# Because the Hessian is symmetrical, we choose to store the upper-triangular
# component. We also assume that it is dense.
function MOI.hessian_lagrangian_structure(moiproblem::MOIOptimizationProblem)
num_vars = length(moiproblem.u0)
return Tuple{Int,Int}[(row, col) for col in 1:num_vars for row in 1:col]
end

function MOI.eval_hessian_lagrangian(
moiproblem::MOIOptimizationProblem{T},
h,
x,
σ,
μ,
) where {T}
n = length(moiproblem.u0)
a = zeros(n, n)
moiproblem.f.hess(a, x)
if iszero(σ)
fill!(h, zero(T))
else
Expand Down Expand Up @@ -47,126 +120,99 @@ function MOI.eval_hessian_lagrangian(moiproblem::MOIOptimizationProblem{T}, h, x
return
end

function MOI.eval_constraint_jacobian(moiproblem::MOIOptimizationProblem, j, x)
isempty(j) && return
moiproblem.f.cons_j === nothing && error("Use OptimizationFunction to pass the derivatives or automatically generate them with one of the autodiff backends")
n = length(moiproblem.u0)
moiproblem.f.cons_j(moiproblem.J, x)
for i in eachindex(j)
j[i] = moiproblem.J[i]
end
end

function MOI.jacobian_structure(moiproblem::MOIOptimizationProblem)
return Tuple{Int,Int}[(con, var) for con in 1:size(moiproblem.J,1) for var in 1:size(moiproblem.J,2)]
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end

function MOI.hessian_lagrangian_structure(moiproblem::MOIOptimizationProblem)
return Tuple{Int,Int}[(row, col) for col in 1:length(moiproblem.u0) for row in 1:col]
end

function MOI.initialize(moiproblem::MOIOptimizationProblem, requested_features::Vector{Symbol})
for feat in requested_features
if !(feat in MOI.features_available(moiproblem))
error("Unsupported feature $feat")
# TODO: implement Jac-vec and Hess-vec products
# for solvers that need them
end
end
end

MOI.features_available(moiproblem::MOIOptimizationProblem) = [:Grad, :Hess, :Jac]

function make_moi_problem(prob::OptimizationProblem)
num_cons = prob.ucons === nothing ? 0 : length(prob.ucons)
f = instantiate_function(prob.f,prob.u0,prob.f.adtype,prob.p,num_cons)
T = eltype(prob.u0)
n = length(prob.u0)
moiproblem = MOIOptimizationProblem(f,prob.u0,prob.p,zeros(T,num_cons,n),zeros(T,n,n),Matrix{T}[zeros(T,n,n) for i in 1:num_cons])
return moiproblem
end
_create_new_optimizer(opt::MOI.AbstractOptimizer) = opt
_create_new_optimizer(opt::MOI.OptimizerWithAttributes) = MOI.instantiate(opt)

function __map_optimizer_args(prob::OptimizationProblem, opt::Union{MOI.AbstractOptimizer, MOI.OptimizerWithAttributes};
function __map_optimizer_args(
prob::OptimizationProblem,
opt::Union{MOI.AbstractOptimizer, MOI.OptimizerWithAttributes};
maxiters::Union{Number, Nothing}=nothing,
maxtime::Union{Number, Nothing}=nothing,
abstol::Union{Number, Nothing}=nothing,
abstol::Union{Number, Nothing}=nothing,
reltol::Union{Number, Nothing}=nothing,
kwargs...)

mapped_args = Vector{Pair{String, Any}}[]
mapped_args = [mapped_args..., [Pair(string(j.first),j.second) for j = kwargs]...]

if isa(opt, MOI.AbstractOptimizer)
if length(mapped_args) > 0
opt = MOI.OptimizerWithAttributes(typeof(opt), mapped_args...)
else
opt = typeof(opt)
end
kwargs...,
)
optimizer = _create_new_optimizer(opt)
for (key, value) in kwargs
MOI.set(optimizer, MOI.RawOptimizerAttribute("$(key)"), value)
end

optimizer = MOI.instantiate(opt)

if !isnothing(maxtime)
MOI.set(optimizer, MOI.TimeLimitSec(), maxtime)
end

if !isnothing(reltol)
@warn "common reltol argument is currently not used by $(optimizer). Set tolerances via optimizer specific keyword aguments."
end

if !isnothing(abstol)
@warn "common abstol argument is currently not used by $(optimizer). Set tolerances via optimizer specific keyword aguments."
end

if !isnothing(maxiters)
@warn "common maxiters argument is currently not used by $(optimizer). Set number of interations via optimizer specific keyword aguments."
end

return optimizer
end

function __solve(prob::OptimizationProblem, opt::Union{MOI.AbstractOptimizer, MOI.OptimizerWithAttributes};
function __solve(
prob::OptimizationProblem,
opt::Union{MOI.AbstractOptimizer, MOI.OptimizerWithAttributes};
maxiters::Union{Number, Nothing}=nothing,
maxtime::Union{Number, Nothing}=nothing,
abstol::Union{Number, Nothing}=nothing,
abstol::Union{Number, Nothing}=nothing,
reltol::Union{Number, Nothing}=nothing,
kwargs...)

kwargs...,
)
maxiters = _check_and_convert_maxiters(maxiters)
maxtime = _check_and_convert_maxtime(maxtime)

opt_setup = __map_optimizer_args(prob, opt; abstol=abstol, reltol=reltol, maxiters=maxiters, maxtime=maxtime, kwargs...)

opt_setup = __map_optimizer_args(
prob,
opt;
abstol=abstol,
reltol=reltol,
maxiters=maxiters,
maxtime=maxtime,
kwargs...,
)
num_variables = length(prob.u0)
θ = MOI.add_variables(opt_setup, num_variables)
if prob.lb !== nothing
θ = MOI.add_variables(opt_setup, num_variables)
if prob.lb !== nothing
@assert eachindex(prob.lb) == Base.OneTo(num_variables)
for i in 1:num_variables
MOI.add_constraint(opt_setup, MOI.SingleVariable(θ[i]), MOI.GreaterThan(prob.lb[i]))
for i in 1:num_variables
if prob.lb[i] > -Inf
MOI.add_constraint(opt_setup, θ[i], MOI.GreaterThan(prob.lb[i]))
end
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end
end
if prob.ub !== nothing
if prob.ub !== nothing
@assert eachindex(prob.ub) == Base.OneTo(num_variables)
for i in 1:num_variables
MOI.add_constraint(opt_setup, MOI.SingleVariable(θ[i]), MOI.LessThan(prob.ub[i]))
for i in 1:num_variables
if prob.ub[i] < Inf
MOI.add_constraint(opt_setup, θ[i], MOI.LessThan(prob.ub[i]))
end
end
end
@assert eachindex(prob.u0) == Base.OneTo(num_variables)
for i in 1:num_variables
MOI.set(opt_setup, MOI.VariablePrimalStart(), θ[i], prob.u0[i])
end
MOI.set(opt_setup, MOI.ObjectiveSense(), prob.sense === MaxSense ? MOI.MAX_SENSE : MOI.MIN_SENSE)
if MOI.supports(opt_setup, MOI.VariablePrimalStart(), MOI.VariableIndex)
@assert eachindex(prob.u0) == Base.OneTo(num_variables)
for i in 1:num_variables
MOI.set(opt_setup, MOI.VariablePrimalStart(), θ[i], prob.u0[i])
end
end
MOI.set(
opt_setup,
MOI.ObjectiveSense(),
prob.sense === MaxSense ? MOI.MAX_SENSE : MOI.MIN_SENSE,
)
if prob.lcons === nothing
@assert prob.ucons === nothing
con_bounds = MOI.NLPBoundsPair[]
else
@assert prob.ucons !== nothing
con_bounds = MOI.NLPBoundsPair.(prob.lcons, prob.ucons)
end
MOI.set(opt_setup, MOI.NLPBlock(), MOI.NLPBlockData(con_bounds, make_moi_problem(prob), true))

MOI.optimize!(opt_setup)

MOI.set(
opt_setup,
MOI.NLPBlock(),
MOI.NLPBlockData(con_bounds, MOIOptimizationProblem(prob), true),
)
MOI.optimize!(opt_setup)
if MOI.get(opt_setup, MOI.ResultCount()) >= 1
minimizer = MOI.get(opt_setup, MOI.VariablePrimal(), θ)
minimum = MOI.get(opt_setup, MOI.ObjectiveValue())
Expand All @@ -176,5 +222,12 @@ function __solve(prob::OptimizationProblem, opt::Union{MOI.AbstractOptimizer, MO
minimum = NaN
opt_ret= :Default
end
SciMLBase.build_solution(prob, opt, minimizer, minimum; original=opt_setup, retcode=opt_ret)
return SciMLBase.build_solution(
prob,
opt,
minimizer,
minimum;
original=opt_setup,
retcode=opt_ret,
)
end
2 changes: 1 addition & 1 deletion test/Project.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -48,7 +48,7 @@ ForwardDiff = ">= 0.10.19"
GCMAES = ">= 0.1.25"
Ipopt = ">= 0.7.0"
IterTools = ">= 1.3.0"
MathOptInterface = ">= 0.9.22"
MathOptInterface = ">= 1"
Metaheuristics = ">=3.0.2"
ModelingToolkit = ">= 6.4.7"
MultistartOptimization = ">= 0.1.2"
Expand Down