mlsanityData to 14 Oct 2026

End-to-End Feasible Optimization Proxies for Large-Scale Economic Dispatch

arXiv 2304.11726 ↗First posted 23 Apr 2023math.OC cs.LGcited by 35

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Abstract (arXiv)

The paper proposes a novel End-to-End Learning and Repair (E2ELR) architecture for training optimization proxies for economic dispatch problems. E2ELR combines deep neural networks with closed-form, differentiable repair layers, thereby integrating learning and feasibility in an end-to-end fashion. E2ELR is also trained with self-supervised learning, removing the need for labeled data and the solving of numerous optimization problems offline. E2ELR is evaluated on industry-size power grids with tens of thousands of buses using an economic dispatch that co-optimizes energy and reserves. The results demonstrate that the self-supervised E2ELR achieves state-of-the-art performance, with optimality gaps that outperform other baselines by at least an order of magnitude.

Key facts. Cited by 35 papers in the map (data through 14 Oct 2026). Builds on 33 of its 58 references that are in the map. First posted on arXiv 23 Apr 2023.

Builds on

33 of 58 references.

References · 33

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 7,005
  3. 2019Differentiable Convex Optimization Layerscited by 465
  4. 2021DC3: A learning method for optimization with hard constraintscited by 184
  5. 2019Predicting AC Optimal Power Flows: Combining Deep Learning and Lagrangian Dual Methodscited by 94
  6. 2019Learning Optimal Solutions for Extremely Fast AC Optimal Power Flowcited by 77
  7. 2019The Power Grid Library for Benchmarking AC Optimal Power Flow Algorithmscited by 65
  8. 2019Optimal Power Flow Using Graph Neural Networkscited by 63
  9. 2022Self-Supervised Primal-Dual Learning for Constrained Optimizationcited by 61
  10. 2021Physics-Informed Neural Networks for AC Optimal Power Flowcited by 54
  11. 2019DeepOPF: A Deep Neural Network Approach for Security-Constrained DC Optimal Power Flowcited by 50
  12. 2015JuMP: A Modeling Language for Mathematical Optimizationcited by 49
21 more
  1. 2020DeepOPF: A Feasibility-Optimized Deep Neural Network Approach for AC Optimal Power Flow Problemscited by 47
  2. 2021Learning Optimization Proxies for Large-Scale Security-Constrained Economic Dispatchcited by 29
  3. 2022Topology-aware Graph Neural Networks for Learning Feasible and Adaptive ac-OPF Solutionscited by 28
  4. 2019Verification of Neural Network Behaviour: Formal Guarantees for Power System Applicationscited by 26
  5. 2019Machine Learning for AC Optimal Power Flowcited by 24
  6. 2021Physics-Informed Neural Networks for Minimising Worst-Case Violations in DC Optimal Power Flowcited by 23
  7. 2022Learning to Solve Optimization Problems with Hard Linear Constraintscited by 23
  8. 2021Leveraging power grid topology in machine learning assisted optimal power flowcited by 22
  9. 2016AC Power Flow Data in MATPOWER and QCQP Format: iTesla, RTE Snapshots, and PEGASEcited by 17
  10. 2021Spatial Network Decomposition for Fast and Scalable AC-OPF Learningcited by 15
  11. 2022Unsupervised Optimal Power Flow Using Graph Neural Networkscited by 14
  12. 2018Statistical Learning For DC Optimal Power Flowcited by 12
  13. 2022Reduced Optimal Power Flow Using Graph Neural Networkcited by 11
  14. 2023Compact Optimization Learning for AC Optimal Power Flowcited by 11
  15. 2021DNN-based Policies for Stochastic AC OPFcited by 7
  16. 2021Ensuring DNN Solution Feasibility for Optimization Problems with Convex Constraints and Its Application to DC Optimal Power Flow Problemscited by 6
  17. 2023A Physics-Informed Machine Learning for Electricity Markets: A NYISO Case Studycited by 4
  18. 2023Optimal Power Flow Based on Physical-Model-Integrated Neural Network with Worth-Learning Data Generationcited by 3
  19. 2022Restoring AC Power Flow Feasibility from Relaxed and Approximated Optimal Power Flow Modelscited by 2
  20. 2022Just-In-Time Learning for Operational Risk Assessment in Power Gridscited by 2
  21. 2022DeepOPF-AL: Augmented Learning for Solving AC-OPF Problems with Multiple Load-Solution Mappingscited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 7; 2024: 6; 2025: 18; 2026: 4.

35 papers.

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  1. 2023A Survey of Contextual Optimization Methods for Decision Making under Uncertaintycited by 82
  2. 2024HardNet: Hard-Constrained Neural Networks with Universal Approximation Guaranteescited by 31
  3. 2023Dual Conic Proxies for AC Optimal Power Flowcited by 11
  4. 2024Dual Lagrangian Learning for Conic Optimizationcited by 11
  5. 2025PGLearn -- An Open-Source Learning Toolkit for Optimal Power Flowcited by 11
  6. 2025Enforcing Hard Linear Constraints in Deep Learning Models with Decision Rulescited by 10
  7. 2024Learning to Optimize for Mixed-Integer Non-linear Programming with Feasibility Guaranteescited by 8
  8. 2023AI4OPT: AI Institute for Advances in Optimizationcited by 6
  9. 2024Dual Interior Point Optimization Learningcited by 4
  10. 2025Towards graph neural networks for provably solving convex optimization problemscited by 4
  11. 2025PROPEL: Supervised and Reinforcement Learning for Large-Scale Supply Chain Planningcited by 4
  12. 2023Optimization-based Learning for Dynamic Load Planning in Trucking Service Networkscited by 3
23 more
  1. 2023Self-Supervised Learning for Large-Scale Preventive Security Constrained DC Optimal Power Flowcited by 3
  2. 2024Compact Optimality Verification for Optimization Proxiescited by 2
  3. 2025Tightening optimality gap with confidence through conformal predictioncited by 2
  4. 2025Sobolev Training of End-to-End Optimization Proxiescited by 2
  5. 2025Smart Surrogate Losses for Contextual Stochastic Linear Optimization with Robust Constraintscited by 2
  6. 2024RL-SPH: Learning to Achieve Feasible Solutions for Integer Linear Programscited by 1
  7. 2025Dual Conic Proxy for Semidefinite Relaxation of AC Optimal Power Flowcited by 1
  8. 2025Differentiable Optimization for Deep Learning-Enhanced DC Approximation of AC Optimal Power Flowcited by 1
  9. 2025Partially-Supervised Neural Network Model For Quadratic Multiparametric Programmingcited by 1
  10. 2025ID-PaS+ : Identity-Aware Predict-and-Search for General Mixed-Integer Linear Programscited by 1
  11. 2023Operational risk quantification of power grids using graph neural network surrogates of the DC OPFcited by 0
  12. 2023Toward Rapid, Optimal, and Feasible Power Dispatch through Generalized Neural Mappingcited by 0
  13. 2025Impact of Data Poisoning Attacks on Feasibility and Optimality of Neural Power System Optimizerscited by 0
  14. 2025Navigating Demand Uncertainty in Container Shipping: Deep Reinforcement Learning for Enabling Adaptive and Feasible Master Stowage Planningcited by 0
  15. 2025Learning-Augmented Power System Operations: A Unified Optimization Viewcited by 0
  16. 2025Learning to Solve Constrained Bilevel Control Co-Design Problemscited by 0
  17. 2025OptiProxy-NAS: Optimization Proxy based End-to-End Neural Architecture Searchcited by 0
  18. 2025Self-Certifying Primal-Dual Optimization Proxies for Large-Scale Batch Economic Dispatchcited by 0
  19. 2025A Neural Network Framework for Discovering Closed-form Solutions to Quadratic Programs with Linear Constraintscited by 0
  20. 2026Geometry-Preserving Neural Architectures on Manifolds with Boundarycited by 0
  21. 2026Fusing Backdoors, Machine Learning, and Optimization for Large-Scale Parametric Mixed-Integer Programscited by 0
  22. 2026Learning Optimization Proxies for Sequential Contextual Stochastic Programs: An Order Fulfillment Applicationcited by 0
  23. 2026Smooth Learning with Hard Constraints via Legendre-Regularized Policiescited by 0