mlsanityData to 14 Oct 2026

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

arXiv 2206.03866 ↗First posted 1 Jun 2022cs.PLcited by 37

Not on the map. Why?

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.

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

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 5; 2024: 5; 2025: 16; 2026: 10.

37 papers.

Sort:

By area:

  1. 2023Diagnosing Infeasible Optimization Problems Using Large Language Modelscited by 27
  2. 2023Evolving Scientific Discovery by Unifying Data and Background Knowledge with AI Hilbertcited by 18
  3. 2025PGLearn -- An Open-Source Learning Toolkit for Optimal Power Flowcited by 11
  4. 2025DualSchool: How Reliable are LLMs for Optimization Education?cited by 4
  5. 2025A General and Streamlined Differentiable Optimization Frameworkcited by 3
  6. 2026Let's Have a Conversation: Designing and Evaluating LLM Agents for Interactive Optimizationcited by 3
  7. 2024Discrete distributions are learnable from metastable samplescited by 2
  8. 2025Certifiably Optimal Anisotropic Rotation Averagingcited by 2
  9. 2025Sobolev Training of End-to-End Optimization Proxiescited by 2
  10. 2025MathOptAI.jl: Embed trained machine learning predictors into JuMP modelscited by 2
  11. 2023Active Inverse Learning in Stackelberg Trajectory Gamescited by 1
  12. 2024Sequential transport maps using SoS density estimation and α-divergencescited by 1
25 more
  1. 2024Cons-training Tensor Networks: Embedding and Optimization Over Discrete Linear Constraintscited by 1
  2. 2024Formulations and scalability of neural network surrogates in nonlinear optimization problemscited by 1
  3. 2025Adaptive Preference Aggregationcited by 1
  4. 2025Differentiable Optimization for Deep Learning-Enhanced DC Approximation of AC Optimal Power Flowcited by 1
  5. 2025Learning Parametric Distributions from Samples and Preferencescited by 1
  6. 2025Reservoir Predictive Path Integral Control for Unknown Nonlinear Dynamicscited by 1
  7. 2025Nonlinear Optimization with GPU-Accelerated Neural Network Constraintscited by 1
  8. 2025MDP modeling for multi-stage stochastic programscited by 1
  9. 2022Tractable hierarchies of convex relaxations for polynomial optimization on the nonnegative orthantcited by 0
  10. 2023Bayesian Regret Minimization in Offline Banditscited by 0
  11. 2023Optimization meets Machine Learning: An Exact Algorithm for Semi-Supervised Support Vector Machinescited by 0
  12. 2024Combinatorial optimization of the coefficient of determinationcited by 0
  13. 2025Mixed-feature Logistic Regression Robust to Distribution Shiftscited by 0
  14. 2025Self-Certifying Primal-Dual Optimization Proxies for Large-Scale Batch Economic Dispatchcited by 0
  15. 2025Physics-Informed Machine Learning for Characterizing System Stabilitycited by 0
  16. 2025Learning Dynamics from Infrequent Output Measurements for Uncertainty-Aware Optimal Controlcited by 0
  17. 2026Generating adversarial inputs for a graph neural network model of AC power flowcited by 0
  18. 2026Accelerating Optimization and Machine Learning through Decentralizationcited by 0
  19. 2026Feasibility-Aware Security-Constrained Unit Commitment via Hybrid Soft Actor-Critic with Quantum-Sampled Featurescited by 0
  20. 2026SNAP-FM: Sparse Nonlinear Accelerated Projection for Physics-Constrained Generative Modelingcited by 0
  21. 2026Feasibility-Aware Security-Constrained Unit Commitment via Hybrid Soft Actor-Critic with Quantum-Sampled Featurescited by 0
  22. 2026Input convex neural networks as surrogates in mathematical optimisationcited by 0
  23. 2026Disciplined Bilevel Programmingcited by 0
  24. 2026Learning structural balance of graphs from quantum spectral featurescited by 0
  25. 2026Learning Distributions from Samples and Qualitiescited by 0