JuMP is a modeling interface and a collection of supporting packages for mathematical optimization that is embedded in Julia. With JuMP, users formulate various classes of optimization problems with easy-to-read code, and then solve these problems using state-of-the-art open-source and commercial solvers. JuMP also makes advanced optimization techniques easily accessible from a high-level language.
JuMP will be participating as a sub-organization in NumFOCUS's application for Google Summer of Code 2026.
For more information about this application see: NumFOCUS's main GSoC page
MathOptAI.jl is a package for embedding trained machine learning predictors into JuMP models. The field is moving fast, and many new models and formulations are being proposed. The goal is this project is to add support for new predictors to MathOptAI.jl so that it remains state-of-the-art.
The contributor will conduct a literature review of the field to construct a prioritized list new formulations that are of interest to the research community. Then, as time permits, they will add new predictors to MathOptAI.jl.
For each new predictor added to MathOptAI.jl, the contributor must add:
- tests that achieve 100% code coverage
- integration with relevant package extensions
- documentation, including new tutorials that use the predictor
- Knowledge of mathematical optimization
- Basic knowledge of Python, Julia, and JuMP
- Basic knowledge of machine learning frameworks such as PyTorch
- 175 hours (Medium): at least four (4) new predictors
- 350 hours (Large): at least eight (8) new predictors
