CPLEXCP.jl is a wrapper for the IBM® ILOG® CPLEX® CP Optimizer.
You cannot use CPLEXCP.jl without having purchased and installed a copy of CPLEX Optimization Studio from IBM. However, CPLEX is available for free to academics and students.
CPLEXCP.jl has two components:
- a thin wrapper around the complete Java API
- an interface to MathOptInterface and ConstraintProgrammingExtensions.jl
The Java API can be accessed via CPLEXCP.cpo_java_xxx functions, where the names and
arguments are built from the Java API. See the CPLEX documentation
for details.
Note: This wrapper is maintained by the community and is not officially supported by IBM. If you are a commercial customer interested in official support for CPLEX CP Optimizer in Julia, let them know!
Minimum version requirement: CPLEXCP.jl requires CPLEX version 20.1. Other versions may work, but are not tested. Versions 12.9 and 12.10 are automatically detected, though.
First, obtain a license of CPLEX and install CPLEX CP optimizer, following the
instructions on IBM's website. Then, set the
CPLEX_STUDIO_DIR environment variable as appropriate and run
Pkg.add("CPLEXCP"), then Pkg.build("CPLEXCP"). For example:
# On Windows, this might be
ENV["CPLEX_STUDIO_DIR"] = "C:\\Program Files\\CPLEX_Studio1210\\"
import Pkg
Pkg.add("CPLEXCP")
Pkg.build("CPLEXCP")
# On macOS, this might be
ENV["CPLEX_STUDIO_DIR"] = "/Applications/CPLEX_Studio1210/"
import Pkg
Pkg.add("CPLEXCP")
Pkg.build("CPLEXCP")
# On Unix, this might be
ENV["CPLEX_STUDIO_DIR"] = "/opt/CPLEX_Studio1210/"
import Pkg
Pkg.add("CPLEXCP")
Pkg.build("CPLEXCP")Note: your path may differ. Check which folder you installed CPLEX in, and update the path accordingly.
Here is an example of use of CPLEXCP.jl through MOI and CP:
using MathOptInterface
using ConstraintProgrammingExtensions
using CPLEXCP
const MOI = MathOptInterface
const CP = ConstraintProgrammingExtensions
model = CPLEXCP.Optimizer()
# Create the variables: six countriers; the value is the colour number for each country
belgium, _ = MOI.add_constrained_variable(model, MOI.Integer())
denmark, _ = MOI.add_constrained_variable(model, MOI.Integer())
france, _ = MOI.add_constrained_variable(model, MOI.Integer())
germany, _ = MOI.add_constrained_variable(model, MOI.Integer())
luxembourg, _ = MOI.add_constrained_variable(model, MOI.Integer())
netherlands, _ = MOI.add_constrained_variable(model, MOI.Integer())
# Constrain the colours to be in {0, 1, 2, 3}
MOI.add_constraint(model, belgium, MOI.Interval(0, 3))
MOI.add_constraint(model, denmark, MOI.Interval(0, 3))
MOI.add_constraint(model, france, MOI.Interval(0, 3))
MOI.add_constraint(model, germany, MOI.Interval(0, 3))
MOI.add_constraint(model, luxembourg, MOI.Interval(0, 3))
MOI.add_constraint(model, netherlands, MOI.Interval(0, 3))
# Two adjacent countries must have different colours.
countries(c1, c2) = MOI.ScalarAffineFunction(MOI.ScalarAffineTerm.([1, -1], [c1, c2]), 0)
MOI.add_constraint(model, countries(belgium, france), CP.DifferentFrom(0))
MOI.add_constraint(model, countries(belgium, germany), CP.DifferentFrom(0))
MOI.add_constraint(model, countries(belgium, netherlands), CP.DifferentFrom(0))
MOI.add_constraint(model, countries(belgium, luxembourg), CP.DifferentFrom(0))
MOI.add_constraint(model, countries(denmark, germany), CP.DifferentFrom(0))
MOI.add_constraint(model, countries(france, germany), CP.DifferentFrom(0))
MOI.add_constraint(model, countries(france, luxembourg), CP.DifferentFrom(0))
MOI.add_constraint(model, countries(germany, luxembourg), CP.DifferentFrom(0))
MOI.add_constraint(model, countries(germany, netherlands), CP.DifferentFrom(0))
# Solve the model.
MOI.optimize!(model)
# Check if the solution is optimum.
@assert MOI.get(model, MOI.TerminationStatus()) == MOI.OPTIMAL
# Get the solution
@show MOI.get(model, MOI.VariablePrimal(), belgium)
@show MOI.get(model, MOI.VariablePrimal(), denmark)
@show MOI.get(model, MOI.VariablePrimal(), france)
@show MOI.get(model, MOI.VariablePrimal(), germany)
@show MOI.get(model, MOI.VariablePrimal(), luxembourg)
@show MOI.get(model, MOI.VariablePrimal(), netherlands)We highly recommend that you use the CPLEXCP.jl package with higher level packages such as JuMP.jl. However, for now, JuMP hasn't caught up with MOI 0.10; you will not be able to use JuMP with the latest version of CPLEXCP.jl.
This can be done using a CPLEXCP.Optimizer object. Here is how to create a
JuMP model that uses Chuffed as solver.
using JuMP, CPLEXCP
model = Model(CPLEXCP.Optimizer)