# JuMP 1.0: Recent improvements to a modeling language for mathematical optimization

arXiv: https://arxiv.org/abs/2206.03866 · HTML: https://mlsanity.com/paper/2206.03866/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jun 2022.

## Built on by (most cited first, 30 of 37)

- [Diagnosing Infeasible Optimization Problems Using Large Language Models](https://mlsanity.com/paper/2308.12923/) (2023)
- [Evolving Scientific Discovery by Unifying Data and Background Knowledge with AI Hilbert](https://mlsanity.com/paper/2308.09474/) (2023)
- [PGLearn -- An Open-Source Learning Toolkit for Optimal Power Flow](https://mlsanity.com/paper/2505.22825/) (2025)
- [DualSchool: How Reliable are LLMs for Optimization Education?](https://mlsanity.com/paper/2505.21775/) (2025)
- [A General and Streamlined Differentiable Optimization Framework](https://mlsanity.com/paper/2510.25986/) (2025)
- [Let's Have a Conversation: Designing and Evaluating LLM Agents for Interactive Optimization](https://mlsanity.com/paper/2604.02666/) (2026)
- [Discrete distributions are learnable from metastable samples](https://mlsanity.com/paper/2410.13800/) (2024)
- [Certifiably Optimal Anisotropic Rotation Averaging](https://mlsanity.com/paper/2503.07353/) (2025)
- [Sobolev Training of End-to-End Optimization Proxies](https://mlsanity.com/paper/2505.11342/) (2025)
- [MathOptAI.jl: Embed trained machine learning predictors into JuMP models](https://mlsanity.com/paper/2507.03159/) (2025)
- [Active Inverse Learning in Stackelberg Trajectory Games](https://mlsanity.com/paper/2308.08017/) (2023)
- [Sequential transport maps using SoS density estimation and α-divergences](https://mlsanity.com/paper/2402.17943/) (2024)
- [Cons-training Tensor Networks: Embedding and Optimization Over Discrete Linear Constraints](https://mlsanity.com/paper/2405.09005/) (2024)
- [Formulations and scalability of neural network surrogates in nonlinear optimization problems](https://mlsanity.com/paper/2412.11403/) (2024)
- [Adaptive Preference Aggregation](https://mlsanity.com/paper/2503.10215/) (2025)
- [Differentiable Optimization for Deep Learning-Enhanced DC Approximation of AC Optimal Power Flow](https://mlsanity.com/paper/2504.01970/) (2025)
- [Learning Parametric Distributions from Samples and Preferences](https://mlsanity.com/paper/2505.23557/) (2025)
- [Reservoir Predictive Path Integral Control for Unknown Nonlinear Dynamics](https://mlsanity.com/paper/2509.03839/) (2025)
- [Nonlinear Optimization with GPU-Accelerated Neural Network Constraints](https://mlsanity.com/paper/2509.22462/) (2025)
- [MDP modeling for multi-stage stochastic programs](https://mlsanity.com/paper/2509.22981/) (2025)
- [Tractable hierarchies of convex relaxations for polynomial optimization on the nonnegative orthant](https://mlsanity.com/paper/2209.06175/) (2022)
- [Bayesian Regret Minimization in Offline Bandits](https://mlsanity.com/paper/2306.01237/) (2023)
- [Optimization meets Machine Learning: An Exact Algorithm for Semi-Supervised Support Vector Machines](https://mlsanity.com/paper/2312.09789/) (2023)
- [Combinatorial optimization of the coefficient of determination](https://mlsanity.com/paper/2410.09316/) (2024)
- [Mixed-feature Logistic Regression Robust to Distribution Shifts](https://mlsanity.com/paper/2503.12012/) (2025)
- [Self-Certifying Primal-Dual Optimization Proxies for Large-Scale Batch Economic Dispatch](https://mlsanity.com/paper/2510.15850/) (2025)
- [Physics-Informed Machine Learning for Characterizing System Stability](https://mlsanity.com/paper/2511.08831/) (2025)
- [Learning Dynamics from Infrequent Output Measurements for Uncertainty-Aware Optimal Control](https://mlsanity.com/paper/2512.08013/) (2025)
- [Generating adversarial inputs for a graph neural network model of AC power flow](https://mlsanity.com/paper/2602.17975/) (2026)
- [Accelerating Optimization and Machine Learning through Decentralization](https://mlsanity.com/paper/2604.19518/) (2026)

## Abstract (arXiv)

JuMP is an algebraic modeling language embedded in the Julia programming language. JuMP allows users to model optimization problems of a variety of kinds, including linear programming, integer programming, conic optimization, semidefinite programming, and nonlinear programming, and handles the low-level details of communicating with solvers. After nearly 10 years in development, JuMP 1.0 was released in March, 2022. In this short communication, we highlight the improvements to JuMP from recent releases up to and including 1.0.