Installation Guide

This guide explains how to install Julia and JuMP. If you have installation troubles, read the Common installation issues section below.

Install Julia

JuMP is a package for Julia. To use JuMP, first download and install Julia.

Choosing a version

You can install the "Current stable release" or the "Long-term support (LTS) release."

• The "Current stable release" is the latest release of Julia. It has access to newer features, and is likely faster.
• The "Long-term support release" is an older version of Julia that has continued to receive bug and security fixes. However, it may not have the latest features or performance improvements.

For most users, you should install the "Current stable release," and whenever Julia releases a new version of the current stable release, you should update your version of Julia. Note that any code you write on one version of the current stable release will continue to work on all subsequent releases.

For users in restricted software environments (for example, your enterprise IT controls what software you can install), you may be better off installing the long-term support release because you will not have to update Julia as frequently.

Install JuMP

JuMP is installed using the built-in Julia package manager. Launch Julia, and then enter the following at the julia> prompt:

julia> import Pkg

julia> Pkg.add("JuMP")

When we release a new version of JuMP, you can update with:

julia> import Pkg

julia> Pkg.update("JuMP")

Install a solver

JuMP depends on solvers to solve optimization problems. Therefore, you will need to install one before you can solve problems with JuMP.

Install a solver using the Julia package manager, replacing "HiGHS" by the Julia package name as appropriate.

julia> import Pkg

julia> Pkg.add("HiGHS")

Once installed, you can use HiGHS as a solver with JuMP as follows, using set_attribute to set solver-specific options:

julia> using JuMP

julia> using HiGHS

julia> model = Model(HiGHS.Optimizer);

julia> set_attribute(model, "output_flag" => false)

julia> set_attribute(model, "primal_feasibility_tolerance" => 1e-8)

Supported solvers

Most solvers are not written in Julia, and some require commercial licenses to use, so installation is often more complex.

• If a solver has Manual in the Installation column, the solver requires a manual installation step, such as downloading and installing a binary, or obtaining a commercial license. Consult the README of the relevant Julia package for more information.
• If the solver has Manualᴹ in the Installation column, the solver requires an installation of MATLAB.
• If the Installation column is missing an entry, installing the Julia package will download and install any relevant solver binaries automatically, and you shouldn't need to do anything other than Pkg.add.

Solvers with a missing entry in the Julia Package column are written in Julia. The link in the Solver column is the corresponding Julia package.

Where:

• LP = Linear programming
• QP = Quadratic programming
• SOCP = Second-order conic programming (including problems with convex quadratic constraints or objective)
• MCP = Mixed-complementarity programming
• NLP = Nonlinear programming
• SDP = Semidefinite programming
• (MI)XXX = Mixed-integer equivalent of problem type XXX
• CP-SAT = Constraint programming and Boolean satisfiability

AMPL-based solvers

Use AmplNLWriter to access solvers that support the NL format.

Some solvers, such as Bonmin, Couenne and SHOT can be installed via the Julia package manager. Others need to be manually installed.

Consult the AMPL documentation for a complete list of supported solvers.

GAMS-based solvers

Use GAMS.jl to access solvers available through GAMS. Such solvers include: AlphaECP, Antigone, BARON, CONOPT, Couenne, LocalSolver, PATHNLP, SHOT, SNOPT, SoPlex. See a complete list here.

NEOS-based solvers

Use NEOSServer.jl to access solvers available through the NEOS Server.

Common installation issues

Check the version of your packages

Each package is versioned with a three-part number of the form vX.Y.Z. You can check which versions you have installed with import Pkg; Pkg.status().

This should almost always be the most-recent release. You can check the releases of a package by going to the relevant GitHub page, and navigating to the "releases" page. For example, the list of JuMP releases is available at: https://github.com/jump-dev/JuMP.jl/releases.

If you post on the community forum, please include the output of Pkg.status().

Unsatisfiable requirements detected

Did you get an error like Unsatisfiable requirements detected for package JuMP? The Pkg documentation has a section on how to understand and manage these conflicts.

Installing new packages can make JuMP downgrade to an earlier version

Another common complaint is that after adding a new package, code that previously worked no longer works.

This usually happens because the new package is not compatible with the latest version of JuMP. Therefore, the package manager rolls-back JuMP to an earlier version. Here's an example.

First, we add JuMP:

(jump_example) pkg> add JuMP
  Resolving package versions...
Updating `~/jump_example/Project.toml`
  [4076af6c] + JuMP v0.21.5
Updating `~/jump_example/Manifest.toml`
  ... lines omitted ...

The + JuMP v0.21.5 line indicates that JuMP has been added at version 0.21.5. However, watch what happens when we add JuMPeR:

(jump_example) pkg> add JuMPeR
  Resolving package versions...
Updating `~/jump_example/Project.toml`
  [4076af6c] ↓ JuMP v0.21.5 ⇒ v0.18.6
  [707a9f91] + JuMPeR v0.6.0
Updating `~/jump_example/Manifest.toml`
  ... lines omitted ...

JuMPeR gets added at version 0.6.0 (+ JuMPeR v0.6.0), but JuMP gets downgraded from 0.21.5 to 0.18.6 (↓ JuMP v0.21.5 ⇒ v0.18.6)! The reason for this is that JuMPeR doesn't support a version of JuMP newer than 0.18.6.