GLPK.jl
GLPK.jl is a wrapper for the GNU Linear Programming Kit library.
The wrapper 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.
Installation
Install GLPK using 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.)
Use with JuMP
To use GLPK with JuMP, use GLPK.Optimizer
:
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() do
return GLPK.Optimizer(want_infeasibility_certificates = false)
end
Callbacks
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