- https://jump.dev/
- Started in 2012, shortly after Julia was created
- Significant breaking changes in Jump v0.19 in Feb 2019 to fix things based on lessons learnt
- Last release v0.21.2 from April 2020
- Solvers: supports many open-source and commercial solvers, need to be installed separately
- 2013 JuMP paper
- 2017 JuMP paper
- Examples: https://github.com/JuliaOpt/JuMP.jl/tree/master/examples
- Tutorials: https://github.com/JuliaOpt/JuMPTutorials.jl
- Very modern implementation, nice API
- Most active open-source discrete optimization project? (yearly workshops, Numfocus support)
- Still work in progress, not fully stable or feature-complete yet (see JuMP 1.0 roadmap)
- No JuMP conda package, have to use Julia package manager (try Conda.jl?)
- It's Julia, and our team so far only uses Python, need to build up some expertise
I used Homebrew to install Julia:
brew cask install julia
Package install is pretty fast:
$ julia
import Pkg
Pkg.add("JuMP")
Pkg.add("GLPK")
Pkg.add("Cbc")
Pkg.add("Clp")
Pkg.add("Ipopt")
First-time import will trigger pre-compilation, also pretty fast:
$ julia
using JuMP
using GLPK
using Cbc
using Clp
using Ipopt
If you want to use Julia from Jupyter Lab:
$ julia
import Pkg
Pkg.add("IJulia")
and then start jupyter lab as usual, and select Julia from the launcher screen.