Xpress.jl

Xpress.jl is a wrapper for the FICO Xpress Solver.

It has two components:

• a thin wrapper around the complete C API
• an interface to MathOptInterface

The Xpress wrapper for Julia is community driven and not officially supported by FICO Xpress. If you are a commercial customer interested in official support for Julia from FICO Xpress, let them know.

If you need help, please ask a question on the JuMP community forum.

If you have a reproducible example of a bug, please open a GitHub issue.

Xpress.jl is licensed under the MIT License.

The underlying solver is a closed-source commercial product for which you must purchase a license.

First, obtain a license of Xpress and install Xpress solver, following the instructions on the FICO website. Ensure that the XPRESSDIR license variable is set to the install location by checking the output of:

julia> ENV["XPRESSDIR"]

Then, install this package using:

import Pkg
Pkg.add("Xpress")

If you encounter an error, make sure that the XPRESSDIR environmental variable is set to the path of the Xpress directory. This should be part of a standard installation. The Xpress library will be searched for in XPRESSDIR/lib on Unix platforms and XPRESSDIR/bin on Windows.

For example, on macOS, you may need:

ENV["XPRESSDIR"] = "/Applications/FICO Xpress/xpressmp/"
import Pkg
Pkg.add("Xpress")

By default, building Xpress.jl will fail if the Xpress library is not found.

This may not be desirable in certain cases, for example when part of a package's test suite uses Xpress as an optional test dependency, but Xpress cannot be installed on a CI server running the test suite.

To skip the error, set the XPRESS_JL_SKIP_LIB_CHECK environment variable to true to make Xpress.jl installable (but not usable).

ENV["XPRESS_JL_SKIP_LIB_CHECK"] = true
import Pkg
Pkg.add("Xpress")

Instead of manually installing Xpress, you can use the binaries provided by the Xpress_jll.jl package.

By using Xpress_jll, you agree to certain license conditions. See the Xpress_jll.jl README for more details.

import Xpress_jll
# This environment variable must be set _before_ loading Xpress.jl
ENV["XPRESS_JL_LIBRARY"] = Xpress_jll.libxprs
# Point to your xpauth.xpr license file
ENV["XPAUTH_PATH"] = "/path/to/xpauth.xpr"
using Xpress

If you plan to use Xpress_jll, Pkg.add("Xpress") will fail because it cannot find a local installation of Xpress. Therefore, you should set XPRESS_JL_SKIP_LIB_CHECK before installing.

To use Xpress with JuMP, use:

using JuMP, Xpress
model = Model(Xpress.Optimizer)
set_optimizer(model, "PRESOLVE", 0)

For other parameters see the Xpress Optimizer manual.

If logfile is set to "", the log file is disabled and output is printed to the console (there might be issues with console output on windows (it is manually implemented with callbacks)).

If logfile is set to a file's path, output is printed to that file. By default, logfile = "" (console).

• MOI_POST_SOLVE::Bool: set this attribute to false to skip XPRSpostsolve. This is most useful in older versions of Xpress that throw an error if the model is infeasible.
• MOI_IGNORE_START::Bool: set this attribute to true to skip setting MOI.VariablePrimalStart
• MOI_WARNINGS::Bool: set this attribute to false to turn off the various warnings printed by the MathOptInterface wrapper
• MOI_SOLVE_MODE::String: set the flags argument to lpoptimize
• XPRESS_WARNING_WINDOWS::Bool: set this attribute to false to turn of warnings on Windows.

Here is an example using Xpress's solver-specific callbacks.

using JuMP, Xpress, Test

model = direct_model(Xpress.Optimizer())
@variable(model, 0 <= x <= 2.5, Int)
@variable(model, 0 <= y <= 2.5, Int)
@objective(model, Max, y)
function my_callback_function(cb_data)
    prob = cb_data.model
    p_value = Ref{Cint}(0)
    ret = Xpress.Lib.XPRSgetintattrib(prob, Xpress.Lib.XPRS_MIPINFEAS, p_value)
    if p_value[] > 0
        return  # There are integer infeasibilities. The solution is fractional.
    end
    p_obj, p_bound = Ref{Cdouble}(), Ref{Cdouble}()
    Xpress.Lib.XPRSgetdblattrib(prob, Xpress.Lib.XPRS_MIPBESTOBJVAL, p_obj)
    Xpress.Lib.XPRSgetdblattrib(prob, Xpress.Lib.XPRS_BESTBOUND, p_bound)
    rel_gap = abs((p_obj[] - p_bound[]) / p_obj[])
    @info "Relative gap = $rel_gap"
    # Before querying `callback_value`, you must call:
    Xpress.get_cb_solution(unsafe_backend(model), cb_data.model)
    x_val = callback_value(cb_data, x)
    y_val = callback_value(cb_data, y)
    # You can submit solver-independent MathOptInterface attributes such as
    # lazy constraints, user-cuts, and heuristic solutions.
    if y_val - x_val > 1 + 1e-6
        con = @build_constraint(y - x <= 1)
        MOI.submit(model, MOI.LazyConstraint(cb_data), con)
    elseif y_val + x_val > 3 + 1e-6
        con = @build_constraint(y + x <= 3)
        MOI.submit(model, MOI.LazyConstraint(cb_data), con)
    end
    if rand() < 0.1
        # You can terminate the callback as follows:
        Xpress.Lib.XPRSinterrupt(cb_data.model, 1234)
    end
    return
end
set_attribute(model, Xpress.CallbackFunction(), my_callback_function)
set_attribute(model, "HEUREMPHASIS", 0)
optimize!(model)
@test termination_status(model) == MOI.OPTIMAL
@test primal_status(model) == MOI.FEASIBLE_POINT
@test value(x) == 1
@test value(y) == 2

• XPRESS_JL_SKIP_LIB_CHECK: Used to skip build lib check as previously described.
• XPRESS_JL_NO_INFO: Disable license info log.
• XPRESS_JL_NO_DEPS_ERROR: Disable error when do deps.jl file is found.
• XPRESS_JL_NO_AUTO_INIT: Disable automatic run of Xpress.initialize(). Specially useful for explicitly loading the dynamic library.
• XPRESS_JL_LIBRARY: Provide a custom path to libxprs
• XPAUTH_PATH: Provide a custom path to the license file

The C API can be accessed via Xpress.Lib.XPRSxx functions, where the names and arguments are identical to the C API.

See the Xpress documentation for details.

For more information, consult the FICO optimizer manual.