mlsanityData to 7 Oct 2026

OMLT: Optimization & Machine Learning Toolkit

arXiv 2202.02414 ↗First posted 4 Feb 2022stat.ML cs.AI cs.LGcited by 34

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Abstract (arXiv)

The optimization and machine learning toolkit (OMLT) is an open-source software package incorporating neural network and gradient-boosted tree surrogate models, which have been trained using machine learning, into larger optimization problems. We discuss the advances in optimization technology that made OMLT possible and show how OMLT seamlessly integrates with the algebraic modeling language Pyomo. We demonstrate how to use OMLT for solving decision-making problems in both computer science and engineering.

Key facts. Cited by 34 papers in the map (data through 7 Oct 2026). Builds on 20 of its 37 references that are in the map. First posted on arXiv 4 Feb 2022.

Builds on

20 of 37 references.

References · 20

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2019Minimally distorted Adversarial Examples with a Fast Adaptive Boundary Attackcited by 333
  3. 2018Semidefinite relaxations for certifying robustness to adversarial examplescited by 178
  4. 2017An approach to reachability analysis for feed-forward ReLU neural networkscited by 126
  5. 2018Strong mixed-integer programming formulations for trained neural networkscited by 115
  6. 2019Meta-Learning Acquisition Functions for Transfer Learning in Bayesian Optimizationcited by 70
  7. 2020Enabling certification of verification-agnostic networks via memory-efficient semidefinite programmingcited by 65
  8. 2020The Convex Relaxation Barrier, Revisited: Tightened Single-Neuron Relaxations for Neural Network Verificationcited by 50
  9. 2021Partition-based formulations for mixed-integer optimization of trained ReLU neural networkscited by 39
  10. 2020Lossless Compression of Deep Neural Networkscited by 34
  11. 2018Global Deterministic Optimization with Artificial Neural Networks Embeddedcited by 32
  12. 2019JANOS: An Integrated Predictive and Prescriptive Modeling Frameworkcited by 27
8 more
  1. 2017Optimization of Tree Ensemblescited by 26
  2. 2017The ALAMO approach to machine learningcited by 18
  3. 2022Maximizing information from chemical engineering data sets: Applications to machine learningcited by 17
  4. 2018Mixed-Integer Convex Nonlinear Optimization with Gradient-Boosted Trees Embeddedcited by 16
  5. 2020ENTMOOT: A Framework for Optimization over Ensemble Tree Modelscited by 13
  6. 2021Multi-Objective Constrained Optimization for Energy Applications via Tree Ensemblescited by 12
  7. 2021Between steps: Intermediate relaxations between big-M and convex hull formulationscited by 11
  8. 2021Modeling Design and Control Problems Involving Neural Network Surrogatescited by 8

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 9; 2024: 6; 2025: 9; 2026: 7.

34 papers.

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  1. 2023When Deep Learning Meets Polyhedral Theory: A Surveycited by 39
  2. 2024Physics-Informed Neural Networks with Hard Linear Equality Constraintscited by 30
  3. 2022Neur2SP: Neural Two-Stage Stochastic Programmingcited by 16
  4. 2022Tree ensemble kernels for Bayesian optimization with known constraints over mixed-feature spacescited by 14
  5. 2024Optimization Over Trained Neural Networks: Taking a Relaxing Walkcited by 10
  6. 2024Verifying message-passing neural networks via topology-based bounds tighteningcited by 10
  7. 2023PySCIPOpt-ML: Embedding Trained Machine Learning Models into Mixed-Integer Programscited by 8
  8. 2022P-split formulations: A class of intermediate formulations between big-M and convex hull for disjunctive constraintscited by 7
  9. 2023Computational Tradeoffs of Optimization-Based Bound Tightening in ReLU Networkscited by 6
  10. 2024Generating Likely Counterfactuals Using Sum-Product Networkscited by 6
  11. 2025Reinforcement learning with combinatorial actions for coupled restless banditscited by 5
  12. 2025Molecular Machine Learning in Chemical Process Designcited by 5
22 more
  1. 2023Data-driven decision-focused surrogate modelingcited by 4
  2. 2023Global Optimization: A Machine Learning Approachcited by 4
  3. 2025A Simultaneous Approach for Training Neural Differential-Algebraic Systems of Equationscited by 4
  4. 2025Training Neural ODEs Using Fully Discretized Simultaneous Optimizationcited by 3
  5. 2023Model-based feature selection for neural networks: A mixed-integer programming approachcited by 2
  6. 2023A Constraint Enforcement Deep Reinforcement Learning Framework for Optimal Energy Storage Systems Dispatchcited by 2
  7. 2025Survey and Tutorial of Reinforcement Learning Methods in Process Systems Engineeringcited by 2
  8. 2023Physics Informed Piecewise Linear Neural Networks for Process Optimizationcited by 1
  9. 2024LEVIS: Large Exact Verifiable Input Spaces for Neural Networkscited by 1
  10. 2024Formulations and scalability of neural network surrogates in nonlinear optimization problemscited by 1
  11. 2025Nonlinear Optimization with GPU-Accelerated Neural Network Constraintscited by 1
  12. 2026Relaxation-Informed Training of Neural Network Surrogate Modelscited by 1
  13. 2023Optimizing the switching operation in monoclonal antibody production: Economic MPC and reinforcement learningcited by 0
  14. 2025Optimization over Trained (and Sparse) Neural Networks: A Surrogate within a Surrogatecited by 0
  15. 2025Conformal Mixed-Integer Constraint Learning with Feasibility Guaranteescited by 0
  16. 2025Neural Two-Stage Stochastic Optimization for Solving Unit Commitment Problemcited by 0
  17. 2026Convex Hybrid Modeling: An Operator-Based Approachcited by 0
  18. 2026Generative Robust Optimisationcited by 0
  19. 2026Optimization with Dynamic Constraint Learning (DCL)cited by 0
  20. 2026Reduced-Space Multi-Fidelity Bayesian Optimization of Process Simulation Modelscited by 0
  21. 2026Representation Learning for Exact Preimagescited by 0
  22. 2026DCEmbed: Scalable Optimization over Neural Surrogatescited by 0