GLPK.jl is a wrapper for the GNU Linear Programming Kit library.

It has two components:

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

The C API can be accessed via GLPK.glp_XXX functions, where the names and arguments are identical to the C API. See the /tests folder for inspiration.

The package is registered in the General registry and so can be installed with Pkg.add.

import Pkg
Pkg.add("GLPK")

In addition to installing the GLPK.jl package, this will also download and install the GLPK binaries. (You do not need to install GLPK separately.) If you require a custom build of GLPK, see the Custom Installation instructions below.

To install custom built GLPK binaries, use the environmental variable JULIA_GLPK_LIBRARY_PATH to point to the path at which you installed GLPK (the folder containing libglpk). For example, on Mac, after installing GLPK with brew install glpk, use:

ENV["JULIA_GLPK_LIBRARY_PATH"] = "/usr/local/Cellar/glpk/4.65/lib"
import Pkg
Pkg.add("GLPK")
Pkg.build("GLPK")

Replace "/usr/local/Cellar/glpk/4.65/lib" with a different path as appropriate.

You must have JULIA_GLPK_LIBRARY_PATH set every time you run using GLPK, not just when you install it.

Switch back to the default binaries as follows:

delete!(ENV, "JULIA_GLPK_LIBRARY_PATH")
import Pkg
Pkg.build("GLPK")

We highly recommend that you use GLPK.jl with higher level packages such as JuMP.jl.

This can be done using the GLPK.Optimizer object. Here is how to create a JuMP model that uses GLPK as the solver.

using JuMP, GLPK

model = Model(GLPK.Optimizer)
set_optimizer_attribute(model, "tm_lim", 60 * 1_000)
set_optimizer_attribute(model, "msg_lev", GLPK.GLP_MSG_OFF)

If the model is primal or dual infeasible, GLPK will attempt to find a certificate of infeasibility. This can be expensive, particularly if you do not intend to use the certificate. If this is the case, use:

model = Model() -> GLPK.Optimizer(want_infeasibility_certificates = false))

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

using JuMP, GLPK, Test

model = Model(GLPK.Optimizer)
@variable(model, 0 <= x <= 2.5, Int)
@variable(model, 0 <= y <= 2.5, Int)
@objective(model, Max, y)
reasons = UInt8[]
function my_callback_function(cb_data)
    reason = GLPK.glp_ios_reason(cb_data.tree)
    push!(reasons, reason)
    if reason != GLPK.GLP_IROWGEN
        return
    end
    x_val = callback_value(cb_data, x)
    y_val = callback_value(cb_data, y)
    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 <= 1)
        MOI.submit(model, MOI.LazyConstraint(cb_data), con)
    end
end
MOI.set(model, GLPK.CallbackFunction(), my_callback_function)
optimize!(model)
@test termination_status(model) == MOI.OPTIMAL
@test primal_status(model) == MOI.FEASIBLE_POINT
@test value(x) == 1
@test value(y) == 2
@show reasons