mlsanityData to 7 Oct 2026

JuMP: A Modeling Language for Mathematical Optimization

arXiv 1508.01982 ↗First posted 9 Aug 2015math.OC cs.MScited by 49

Not on the map. Why?

Abstract (arXiv)

JuMP is an open-source modeling language that allows users to express a wide range of optimization problems (linear, mixed-integer, quadratic, conic-quadratic, semidefinite, and nonlinear) in a high-level, algebraic syntax. JuMP takes advantage of advanced features of the Julia programming language to offer unique functionality while achieving performance on par with commercial modeling tools for standard tasks. In this work we will provide benchmarks, present the novel aspects of the implementation, and discuss how JuMP can be extended to new problem classes and composed with state-of-the-art tools for visualization and interactivity.

Key facts. Cited by 49 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Aug 2015.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 14; 2022: 14; 2023: 9; 2024: 7; 2025: 1; 2026: 4.

49 papers.

Sort:

By area:

  1. 2022Tutorial on amortized optimizationcited by 83
  2. 2023End-to-End Feasible Optimization Proxies for Large-Scale Economic Dispatchcited by 35
  3. 2021Learning Optimization Proxies for Large-Scale Security-Constrained Economic Dispatchcited by 29
  4. 2021OPF-Learn: An Open-Source Framework for Creating Representative AC Optimal Power Flow Datasetscited by 17
  5. 2022Flexible Differentiable Optimization via Model Transformationscited by 16
  6. 2022Pushing the limits of fairness impossibility: Who's the fairest of them all?cited by 15
  7. 2022Data-Driven Sample Average Approximation with Covariate Informationcited by 14
  8. 2022Confidence-Aware Graph Neural Networks for Learning Reliability Assessment Commitmentscited by 12
  9. 2024Differentiation Through Black-Box Quadratic Programming Solverscited by 10
  10. 2021Modeling Design and Control Problems Involving Neural Network Surrogatescited by 8
  11. 2022Motion Planning and Control for Multi Vehicle Autonomous Racing at High Speedscited by 8
  12. 2021Slowly Varying Regression under Sparsitycited by 7
37 more
  1. 2021SeaPearl: A Constraint Programming Solver guided by Reinforcement Learningcited by 6
  2. 2021Learning Continuous Exponential Families Beyond Gaussiancited by 6
  3. 2021Sparse Bayesian Learning via Stepwise Regressioncited by 6
  4. 2022Reachable Polyhedral Marching (RPM): An Exact Analysis Tool for Deep-Learned Control Systemscited by 6
  5. 2021Community Detection in the Stochastic Block Model by Mixed Integer Programmingcited by 5
  6. 2021Advances in Trajectory Optimization for Space Vehicle Controlcited by 5
  7. 2023Robust expected improvement for Bayesian optimizationcited by 5
  8. 2024PMLBmini: A Tabular Classification Benchmark Suite for Data-Scarce Applicationscited by 5
  9. 2024A Tutorial on the Design, Experimentation and Application of Metaheuristic Algorithms to Real-World Optimization Problemscited by 5
  10. 2022Chordal Sparsity for SDP-based Neural Network Verificationcited by 4
  11. 2023A Physics-Informed Machine Learning for Electricity Markets: A NYISO Case Studycited by 4
  12. 2023A Neural Separation Algorithm for the Rounded Capacity Inequalitiescited by 4
  13. 2023Data-driven decision-focused surrogate modelingcited by 4
  14. 2025Sparser, Better, Faster, Stronger: Sparsity Detection for Efficient Automatic Differentiationcited by 4
  15. 2021Convolutional Neural Nets in Chemical Engineering: Foundations, Computations, and Applicationscited by 3
  16. 2022Efficient Learning of Decision-Making Models: A Penalty Block Coordinate Descent Algorithm for Data-Driven Inverse Optimizationcited by 3
  17. 2023Cost-Sensitive Best Subset Selection for Logistic Regression: A Mixed-Integer Conic Optimization Perspectivecited by 3
  18. 2023Randomized Forward Mode of Automatic Differentiation For Optimization Algorithmscited by 3
  19. 2021Application-Driven Learning: A Closed-Loop Prediction and Optimization Approach Applied to Dynamic Reserves and Demand Forecastingcited by 2
  20. 2021AbstractDifferentiation.jl: Backend-Agnostic Differentiable Programming in Juliacited by 2
  21. 2022Solving infinite-horizon POMDPs with memoryless stochastic policies in state-action spacecited by 2
  22. 2023Ensemble Modeling for Time Series Forecasting: an Adaptive Robust Optimization Approachcited by 2
  23. 2026OR-Space: A Full-Lifecycle Workspace Benchmark for Industrial Optimization Agentscited by 2
  24. 2021Geostatistical Learning: Challenges and Opportunitiescited by 1
  25. 2022Randomized Policy Optimization for Optimal Stoppingcited by 1
  26. 2023Soft-Bellman Equilibrium in Affine Markov Games: Forward Solutions and Inverse Learningcited by 1
  27. 2024Squeezing Lemons with Hammers: An Evaluation of AutoML and Tabular Deep Learning for Data-Scarce Classification Applicationscited by 1
  28. 2024Robotic warehousing operations: a learn-then-optimize approach to large-scale neighborhood searchcited by 1
  29. 2026Distributionally Robust PAC-Bayesian Controlcited by 1
  30. 2021Minimax Estimation of Partially-Observed Vector AutoRegressionscited by 0
  31. 2022Minimizing Entropy to Discover Good Solutions to Recurrent Mixed Integer Programscited by 0
  32. 2022Sparse Polynomial Optimization: Theory and Practicecited by 0
  33. 2022A General Stochastic Optimization Framework for Convergence Biddingcited by 0
  34. 2024Distributionally and Adversarially Robust Logistic Regression via Intersecting Wasserstein Ballscited by 0
  35. 2024The State of Julia for Scientific Machine Learningcited by 0
  36. 2026Uncovering expert objectives in production planning via inverse optimization: An industrial case studycited by 0
  37. 2026Disciplined Bilevel Programmingcited by 0