COIN-OR Linear Programming Interface (Clp.jl)

Clp.jl is an interface to the COIN-OR Linear Programming solver. It provides a complete interface to the low-level C API, as well as an implementation of the solver-independent MathOptInterface API.

Note: This wrapper is maintained by the JuMP community and is not a COIN-OR project.

Installation

The package can be installed with Pkg.add.

julia> import Pkg; Pkg.add("Clp")

Clp.jl will use BinaryProvider.jl to automatically install the Clp binaries. This should work for both the official Julia binaries and source-builds.

Custom Installation

To install custom built Clp binaries set the environmental variable JULIA_CLP_LIBRARY_PATH and call import Pkg; Pkg.build("Clp"). For instance, if the libraries are installed in /opt/lib, then call

ENV["JULIA_CLP_LIBRARY_PATH"] = "/opt/lib"
import Pkg; Pkg.build("Clp")

If you do not want BinaryProvider to download the default binaries on install, set JULIA_CLP_LIBRARY_PATH before calling import Pkg; Pkg.add("Clp").

To switch back to the default binaries clear JULIA_CLP_LIBRARY_PATH and call import Pkg; Pkg.build("Clp").

Using with JuMP

Due to some restrictions in Clp's C api, the Clp's MathOptInterface wrapper does not support directly modifying a problem after it has been created, e.g., changing variable bounds or modifying constraints coefficients.

Therefore, we highly recommend that you use the Clp.jl package with higher-level package such as JuMP.jl. This can be done with following syntax:

using JuMP, Clp

model = Model(Clp.Optimizer)
set_optimizer_attribute(model, "LogLevel", 1)
set_optimizer_attribute(model, "Algorithm", 4)

See the list of options below.

Furthermore, the following features are not supported:

• Querying the dual bound via JuMP.objective_bound (not in the C API)
• Setting a time limit (the C API behaves inconsistently, see #65)
• Setting the number of threads used (not in the C API)
• Quadratic objective (not supported yet)

Solver options

The following options are available to get/set via JuMP.set_optimizer_attribute or MOI.RawParameter.