This guide explains how to install Julia and JuMP. If you have installation troubles, read the Common installation issues section below.
JuMP is a package for Julia. To use JuMP, first download and install Julia.
If you are new to Julia, read our Getting started with Julia tutorial.
Which version should I pick?
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 (e.g., 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.
From Julia, JuMP is installed using the built-in package manager:
import Pkg Pkg.add("JuMP")
We recommend you create a Pkg environment for each project you use JuMP for, instead of adding lots of packages to the global environment. The Pkg manager documentation has more information on this topic.
When we release a new version of JuMP, you can update with:
import Pkg 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
"Clp" by the Julia package name as appropriate.
import Pkg Pkg.add("Clp")
Once installed, you can use Clp as a solver with JuMP as follows, using
set_attribute to set solver-specific options:
using JuMP using Clp model = Model(Clp.Optimizer) set_attribute(model, "LogLevel" => 1) set_attribute(model, "PrimalTolerance" => 1e-7)
Most packages follow the
ModuleName.Optimizer naming convention, but exceptions may exist. See the README of the Julia package's GitHub repository for more details on how to use a particular solver, including any solver-specific options.
Most solvers are not written in Julia, and some require commercial licenses to use, so installation is often more complex.
- If a solver has
Installationcolumn, 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
Installationcolumn, the solver requires an installation of MATLAB.
- If the
Installationcolumn 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
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.
|Artelys Knitro||KNITRO.jl||Manual||Comm.||(MI)LP, (MI)SOCP, (MI)NLP|
|CDCS||CDCS.jl||Manualᴹ||GPL||LP, SOCP, SDP|
|Clarabel.jl||Apache||LP, QP, SOCP, SDP|
|COPT||COPT.jl||Comm.||(MI)LP, SOCP, SDP|
|COSMO.jl||Apache||LP, QP, SOCP, SDP|
|FICO Xpress||Xpress.jl||Manual||Comm.||(MI)LP, (MI)SOCP|
|Hypatia.jl||MIT||LP, SOCP, SDP|
|Ipopt||Ipopt.jl||EPL||LP, QP, NLP|
|MadNLP.jl||MIT||LP, QP, NLP|
|MOSEK||MosekTools.jl||Manual||Comm.||(MI)LP, (MI)SOCP, SDP|
|NLopt||NLopt.jl||GPL||LP, QP, NLP|
|Pajarito.jl||MPL-2||(MI)NLP, (MI)SOCP, (MI)SDP|
|ProxSDP.jl||MIT||LP, SOCP, SDP|
|SCS||SCS.jl||MIT||LP, SOCP, SDP|
|SDPA||SDPA.jl, SDPAFamily.jl||GPL||LP, SDP|
|SDPNAL||SDPNAL.jl||Manualᴹ||CC BY-SA||LP, SDP|
|SDPT3||SDPT3.jl||Manualᴹ||GPL||LP, SOCP, SDP|
|SeDuMi||SeDuMi.jl||Manualᴹ||GPL||LP, SOCP, SDP|
- 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
Developed a solver or solver wrapper? This table is open for new contributions! Start by making a pull request to edit the installation.md file.
Developing a solver or solver wrapper? See Models and the MathOptInterface docs for more details on how JuMP interacts with solvers. Please get in touch via the Developer Chatroom with any questions about connecting new solvers with JuMP.
Use AmplNLWriter to access solvers that support the NL format.
Some solvers, such as Bonmin and Couenne 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.
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.
GAMS.jl requires an installation of the commercial software GAMS for which a free community license exists.
Use NEOSServer.jl to access solvers available through the NEOS Server.
Common installation issues
When in doubt, run
import Pkg; Pkg.update() to see if updating your packages fixes the issue. Remember you will need to exit Julia and start a new session for the changes to take effect.
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
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 ...
+ 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
+ JuMPeR v0.6.0), but JuMP gets downgraded from
↓ JuMP v0.21.5 ⇒ v0.18.6)! The reason for this is that JuMPeR doesn't support a version of JuMP newer than
Pay careful attention to the output of the package manager when adding new packages, especially when you see a package being downgraded!