Julia interface for the CPLEX optimization software
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October 2013

CPLEX.jl underwent a major rewrite between versions 0.6.6 and 0.7.0. Users of JuMP should see no breaking changes, but if you used the lower-level C API (e.g., for callbacks), you will need to update your code accordingly. For a full description of the changes, read this discourse post.

To revert to the old API, use:

import Pkg
Pkg.add(Pkg.PackageSpec(name = "CPLEX", version = v"0.6"))

Then restart Julia for the change to take effect.


CPLEX.jl is a wrapper for the IBM® ILOG® CPLEX® Optimization Studio

You cannot use CPLEX.jl without having purchased and installed a copy of CPLEX Optimization Studio from IBM. However, CPLEX is available for free to academics and students.

CPLEX.jl has two components:

The C API can be accessed via CPLEX.CPXxx functions, where the names and arguments are identical to the C API. See the CPLEX documentation for details.

Note: This wrapper is maintained by the JuMP community and is not officially supported by IBM. However, we thank IBM for providing us with a CPLEX license to test CPLEX.jl on GitHub. If you are a commercial customer interested in official support for CPLEX in Julia, let them know!.


Minimum version requirement: CPLEX.jl requires CPLEX version 12.10 or 20.1.

First, obtain a license of CPLEX and install CPLEX solver, following the instructions on IBM's website. Then, set the CPLEX_STUDIO_BINARIES environment variable as appropriate and run Pkg.add("CPLEX"), then Pkg.build("CPLEX"). For example:

# On Windows, this might be
ENV["CPLEX_STUDIO_BINARIES"] = "C:\\Program Files\\CPLEX_Studio1210\\cplex\\bin\\x86-64_win\\"
import Pkg

# On OSX, this might be
ENV["CPLEX_STUDIO_BINARIES"] = "/Applications/CPLEX_Studio1210/cplex/bin/x86-64_osx/"
import Pkg

# On Unix, this might be
ENV["CPLEX_STUDIO_BINARIES"] = "/opt/CPLEX_Studio1210/cplex/bin/x86-64_linux/"
import Pkg

Note: your path may differ. Check which folder you installed CPLEX in, and update the path accordingly.

Use with JuMP

We highly recommend that you use the CPLEX.jl package with higher level packages such as JuMP.jl.

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

using JuMP, CPLEX

model = Model(CPLEX.Optimizer)
set_optimizer_attribute(model, "CPX_PARAM_EPINT", 1e-8)

Parameters match those of the C API in the CPLEX documentation.


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

using JuMP, CPLEX, Test

model = direct_model(CPLEX.Optimizer())

# This is very, very important!!! Only use callbacks in single-threaded mode.
MOI.set(model, MOI.NumberOfThreads(), 1)

@variable(model, 0 <= x <= 2.5, Int)
@variable(model, 0 <= y <= 2.5, Int)
@objective(model, Max, y)
cb_calls = Clong[]
function my_callback_function(cb_data::CPLEX.CallbackContext, context_id::Clong)
    # You can reference variables outside the function as normal
    push!(cb_calls, context_id)
    # You can select where the callback is run
    ispoint_p = Ref{Cint}()
    ret = CPXcallbackcandidateispoint(cb_data, ispoint_p)
    if ret != 0 || ispoint_p[] == 0
        return  # No candidate point available or error
    # You can query CALLBACKINFO items
    valueP = Ref{Cdouble}()
    ret = CPXcallbackgetinfodbl(cb_data, CPXCALLBACKINFO_BEST_BND, valueP)
    @info "Best bound is currently: $(valueP[])"
    # As well as any other C API
    x_p = Vector{Cdouble}(undef, 2)
    obj_p = Ref{Cdouble}()
    ret = CPXcallbackgetincumbent(cb_data, x_p, 0, 1, obj_p)
    if ret == 0
        @info "Objective incumbent is: $(obj_p[])"
        @info "Incumbent solution is: $(x_p)"
        # Use CPLEX.column to map between variable references and the 1-based
        # column.
        x_col = CPLEX.column(cb_data, index(x))
        @info "x = $(x_p[x_col])"
        # Unable to query incumbent.

    # Before querying `callback_value`, you must call:
    CPLEX.load_callback_variable_primal(cb_data, context_id)
    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)
MOI.set(model, CPLEX.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