GLPK wrapper module for Julia
56 Stars
Updated Last
28 Days Ago
Started In
February 2013


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

It has two components:

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.

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

import Pkg

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.

Custom Installation

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

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:

import Pkg"GLPK")

Use with JuMP

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
    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)
MOI.set(model, GLPK.CallbackFunction(), my_callback_function)
@test termination_status(model) == MOI.OPTIMAL
@test primal_status(model) == MOI.FEASIBLE_POINT
@test value(x) == 1
@test value(y) == 2
@show reasons