MathOptInterface: a data structure for mathematical optimization problems
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Abstract (arXiv)
We introduce MathOptInterface, an abstract data structure for representing mathematical optimization problems based on combining pre-defined functions and sets. MathOptInterface is significantly more general than existing data structures in the literature, encompassing, for example, a spectrum of problems classes from integer programming with indicator constraints to bilinear semidefinite programming. We also outline an automated rewriting system between equivalent formulations of a constraint. MathOptInterface has been implemented in practice, forming the foundation of a recent rewrite of JuMP, an open-source algebraic modeling language in the Julia language. The regularity of the MathOptInterface representation leads naturally to a general file format for mathematical optimization we call MathOptFormat. In addition, the automated rewriting system provides modeling power to users while making it easy to connect new solvers to JuMP.
Key facts. Cited by 9 papers in the map (data through 13 Oct 2026). First posted on arXiv 9 Feb 2020.
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Citing papers per year: 2021: 2; 2022: 2; 2023: 2; 2024: 0; 2025: 1; 2026: 2.
9 papers.
- 2022Flexible Differentiable Optimization via Model Transformationscited by 16
- 2023Synthesis of Mathematical programs from Natural Language Specificationscited by 8
- 2021SeaPearl: A Constraint Programming Solver guided by Reinforcement Learningcited by 6
- 2022Convex mixed-integer optimization with Frank-Wolfe methodscited by 3
- 2021AbstractDifferentiation.jl: Backend-Agnostic Differentiable Programming in Juliacited by 2
- 2023Stabilizing reinforcement learning control: A modular framework for optimizing over all stable behaviorcited by 2
- 2025MathOptAI.jl: Embed trained machine learning predictors into JuMP modelscited by 2
- 2026FLARE: Verifying MILP Reformulations with LLM-Based Theorem Provingcited by 1
- 2026TorchDCM: A Unified PyTorch-Native Package for Discrete Choice Modelingcited by 0
- 2026FLARE: Verifying MILP Reformulations with LLM-Based Theorem Proving25 Aug 2026
- 2026TorchDCM: A Unified PyTorch-Native Package for Discrete Choice Modeling4 Aug 2026
- 2025MathOptAI.jl: Embed trained machine learning predictors into JuMP models3 Jul 2025
- 2023Stabilizing reinforcement learning control: A modular framework for optimizing over all stable behavior21 Oct 2023
- 2023Synthesis of Mathematical programs from Natural Language Specifications30 Mar 2023
- 2022Convex mixed-integer optimization with Frank-Wolfe methods23 Aug 2022
- 2022Flexible Differentiable Optimization via Model Transformations10 Jun 2022
- 2021AbstractDifferentiation.jl: Backend-Agnostic Differentiable Programming in Julia25 Sep 2021
- 2021SeaPearl: A Constraint Programming Solver guided by Reinforcement Learning18 Feb 2021