cuPDLPx.jl

Julia interface for cuPDLPx.

Use with JuMP

To use cuPDLPx with JuMP, use cuPDLPx.Optimizer:

using JuMP, cuPDLPx
model = Model(cuPDLPx.Optimizer)

Setting solver parameters

cuPDLPx.jl supports setting solver parameters via set_optimizer_attribute.

using JuMP
using cuPDLPx

model = read_from_file("2club200v15p5scn.mps.gz")
undo = relax_integrality(model)

println("Read MPS succeed.")
set_optimizer(model, cuPDLPx.Optimizer)

set_optimizer_attribute(model, "verbose", true)
set_optimizer_attribute(model, "l_inf_ruiz_iterations", 0)
set_optimizer_attribute(model, "iteration_limit", 200)

optimize!(model)
println(solution_summary(model))

Supported parameters

All of the following attributes are supported.

PDHG parameters

name	type
l_inf_ruiz_iterations	Int
has_pock_chambolle_alpha	Bool
pock_chambolle_alpha	Float64
bound_objective_rescaling	Bool
verbose	Bool
termination_evaluation_frequency	Int
sv_max_iter	Int
sv_tol	Float64
reflection_coefficient	Float64
feasibility_polishing	Bool
presolve	Bool
artificial_restart_threshold	Float64
sufficient_reduction_for_restart	Float64
necessary_reduction_for_restart	Float64
k_p	Float64
k_i	Float64
k_d	Float64
i_smooth	Float64

Termination criteria

name	type
eps_optimal_relative	Float64
eps_feasible_relative	Float64
eps_feas_polish_relative	Float64
eps_infeasible	Float64
time_sec_limit	Float64
iteration_limit	Int