{"total":29,"rows":[{"id":"2104.12225","title":"DC3: A learning method for optimization with hard constraints","created":"2021-04-25","cited_by_count":184,"area":null},{"id":"2202.00665","title":"Tutorial on amortized optimization","created":"2022-02-01","cited_by_count":83,"area":null},{"id":"2309.07835","title":"Learning to Warm-Start Fixed-Point Optimization Algorithms","created":"2023-09-14","cited_by_count":32,"area":null},{"id":"2205.10129","title":"Topology-aware Graph Neural Networks for Learning Feasible and Adaptive ac-OPF Solutions","created":"2022-05-16","cited_by_count":28,"area":null},{"id":"2110.00306","title":"Leveraging power grid topology in machine learning assisted optimal power flow","created":"2021-10-01","cited_by_count":22,"area":null},{"id":"2111.01228","title":"OPF-Learn: An Open-Source Framework for Creating Representative AC Optimal Power Flow Datasets","created":"2021-11-01","cited_by_count":18,"area":null},{"id":"2101.06768","title":"Spatial Network Decomposition for Fast and Scalable AC-OPF Learning","created":"2021-01-17","cited_by_count":15,"area":null},{"id":"2211.15755","title":"Confidence-Aware Graph Neural Networks for Learning Reliability Assessment Commitments","created":"2022-11-28","cited_by_count":12,"area":null},{"id":"2301.08840","title":"Compact Optimization Learning for AC Optimal Power Flow","created":"2023-01-21","cited_by_count":11,"area":null},{"id":"2205.03787","title":"Learning Regionally Decentralized AC Optimal Power Flows with ADMM","created":"2022-05-08","cited_by_count":7,"area":null},{"id":"2112.08091","title":"Ensuring DNN Solution Feasibility for Optimization Problems with Convex Constraints and Its Application to DC Optimal Power Flow Problems","created":"2021-12-15","cited_by_count":6,"area":null},{"id":"2510.06860","title":"Towards Generalization of Graph Neural Networks for AC Optimal Power Flow","created":"2025-10-08","cited_by_count":6,"area":null},{"id":"2106.10529","title":"Graph Neural Networks for Learning Real-Time Prices in Electricity Market","created":"2021-06-19","cited_by_count":5,"area":null},{"id":"2401.06820","title":"QCQP-Net: Reliably Learning Feasible Alternating Current Optimal Power Flow Solutions Under Constraints","created":"2024-01-11","cited_by_count":5,"area":null},{"id":"2404.13831","title":"Data-Driven Performance Guarantees for Classical and Learned Optimizers","created":"2024-04-22","cited_by_count":5,"area":null},{"id":"2411.02158","title":"Learning Multiple Initial Solutions to Optimization Problems","created":"2024-11-04","cited_by_count":4,"area":null},{"id":"2111.11980","title":"Scalable Learning for Optimal Load Shedding Under Power Grid Emergency Operations","created":"2021-11-23","cited_by_count":3,"area":null},{"id":"2206.06789","title":"Grid-SiPhyR: An end-to-end learning to optimize framework for combinatorial problems in power systems","created":"2022-06-11","cited_by_count":1,"area":null},{"id":"2606.08993","title":"LEAF: A Learning-Enabled ADMM Framework for Accelerated Convex Optimization","created":"2026-06-08","cited_by_count":1,"area":null},{"id":"2306.10080","title":"AI Driven Near Real-time Locational Marginal Pricing Method: A Feasibility and Robustness Study","created":"2023-06-16","cited_by_count":0,"area":null},{"id":"2310.08601","title":"Unit Commitment Predictor With a Performance Guarantee: A Support Vector Machine Classifier","created":"2023-10-07","cited_by_count":0,"area":null},{"id":"2509.19233","title":"Study Design and Demystification of Physics Informed Neural Networks for Power Flow Simulation","created":"2025-09-23","cited_by_count":0,"area":null},{"id":"2510.16064","title":"Residual Correction Models for AC Optimal Power Flow Using DC Optimal Power Flow Solutions","created":"2025-10-17","cited_by_count":0,"area":null},{"id":"2603.08283","title":"Learning efficient representations of complex constraints for scalable optimization","created":"2026-03-09","cited_by_count":0,"area":null},{"id":"2605.05728","title":"WARP: A Benchmark for Primal-Dual Warm-Starting of Interior-Point Solvers","created":"2026-05-07","cited_by_count":0,"area":null},{"id":"2607.12775","title":"Learning-enabled Acceleration of Scenario-based Model Predictive Control","created":"2026-07-14","cited_by_count":0,"area":null},{"id":"2607.27169","title":"When Do Learned Diffusion Proposals Help Constraint Solving? A Controlled Study on Continuous Algebraic Systems","created":"2026-07-29","cited_by_count":0,"area":null},{"id":"2608.16494","title":"Graph Machine Learning: An Opportunity for Power Systems","created":"2026-08-17","cited_by_count":0,"area":null},{"id":"2608.25095","title":"Scalable Self-Supervised Learning for Multiphase AC-OPF in Distribution Systems with Topology Reconfiguration","created":"2026-08-25","cited_by_count":0,"area":null}],"next_cursor":null}