Cover Image

Self-Supervised Learning for Large-Scale Preventive Security Constrained DC Optimal Power Flow

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Park, Seonho, Van Hentenryck, Pascal
Format:	Preprint
Published:	2023
Subjects:	Machine Learning Optimization and Control
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Differentiable Optimization for Deep Learning-Enhanced DC Approximation of AC Optimal Power Flow
by: Rosemberg, Andrew, et al.
Published: (2025)

Dual Conic Proxies for AC Optimal Power Flow
by: Qiu, Guancheng, et al.
Published: (2023)

Dual Conic Proxy for Semidefinite Relaxation of AC Optimal Power Flow
by: Qiu, Guancheng, et al.
Published: (2025)

PGLearn -- An Open-Source Learning Toolkit for Optimal Power Flow
by: Klamkin, Michael, et al.
Published: (2025)

Self-Certifying Primal-Dual Optimization Proxies for Large-Scale Batch Economic Dispatch
by: Klamkin, Michael, et al.
Published: (2025)

Dual Lagrangian Learning for Conic Optimization
by: Tanneau, Mathieu, et al.
Published: (2024)

A Fair OR-ML Framework for Resource Substitution in Large-Scale Networks
by: Mohan, Ved, et al.
Published: (2025)

Dual Interior Point Optimization Learning
by: Klamkin, Michael, et al.
Published: (2024)

Transit Network Design with Two-Level Demand Uncertainties: A Machine Learning and Contextual Stochastic Optimization Framework
by: Guan, Hongzhao, et al.
Published: (2026)

Boosting Column Generation with Graph Neural Networks for Joint Rider Trip Planning and Crew Shift Scheduling
by: Lu, Jiawei, et al.
Published: (2024)

A Rolling-Space Branch-and-Price Algorithm for the Multi-Compartment Vehicle Routing Problem with Multiple Time Windows
by: Raqabi, El Mehdi Er, et al.
Published: (2026)

Self-Supervised Learning of Iterative Solvers for Constrained Optimization
by: Lüken, Lukas, et al.
Published: (2024)

Sobolev Training of End-to-End Optimization Proxies
by: Rosemberg, Andrew W., et al.
Published: (2025)

Bucketized Active Sampling for Learning ACOPF
by: Klamkin, Michael, et al.
Published: (2022)

Self-Supervised Penalty-Based Learning for Robust Constrained Optimization
by: Benslimane, Wyame, et al.
Published: (2025)

Efficiently Training Deep-Learning Parametric Policies using Lagrangian Duality
by: Rosemberg, Andrew, et al.
Published: (2024)

Optimization Learning
by: Van Hentenryck, Pascal
Published: (2025)

Self-Supervised Learning of Parametric Approximation for Security-Constrained DC-OPF
by: Anrrango, Anderson, et al.
Published: (2026)

Multiscale Supervised Unbalanced Optimal Transport Flow Matching
by: Peng, Qiangwei, et al.
Published: (2026)

Beyond the Neural Fog: Interpretable Learning for AC Optimal Power Flow
by: Pineda, Salvador, et al.
Published: (2024)

DualSchool: How Reliable are LLMs for Optimization Education?
by: Klamkin, Michael, et al.
Published: (2025)

SPOT: Spatio-Temporal Pattern Mining and Optimization for Load Consolidation in Freight Transportation Networks
by: Cheng, Sikai, et al.
Published: (2025)

Bilevel Optimization and Heuristic Algorithms for Integrating Latent Demand into the Design of Large-Scale Transit Systems
by: Guan, Hongzhao, et al.
Published: (2022)

QCQP-Net: Reliably Learning Feasible Alternating Current Optimal Power Flow Solutions Under Constraints
by: Zeng, Sihan, et al.
Published: (2024)

Uncertainty-Aware Delivery Delay Duration Prediction via Multi-Task Deep Learning
by: Faulkner, Stefan, et al.
Published: (2026)

Route Optimization Over Scheduled Services For Large-Scale Package Delivery Networks
by: Ahmed, Mohammed Faisal, et al.
Published: (2025)

A General and Streamlined Differentiable Optimization Framework
by: Rosemberg, Andrew W., et al.
Published: (2025)

GPU Accelerated Security Constrained Optimal Power Flow
by: Degleris, Anthony, et al.
Published: (2024)

A Data-Driven Real-Time Optimal Power Flow Algorithm Using Local Feedback
by: Liang, Heng, et al.
Published: (2025)

A Theoretical Analysis of Self-Supervised Learning for Vision Transformers
by: Huang, Yu, et al.
Published: (2024)

Rate-Optimal Regret for the Safe Learning-based Control of the Constrained Linear Quadratic Regulator
by: Hutchinson, Spencer, et al.
Published: (2026)

A Sinkhorn-type Algorithm for Constrained Optimal Transport
by: Tang, Xun, et al.
Published: (2024)

Near-Optimal Solutions of Constrained Learning Problems
by: Elenter, Juan, et al.
Published: (2024)

Chance-Constrained DC Optimal Power Flow Using Constraint-Informed Statistical Estimation
by: Yi, Tianyang, et al.
Published: (2025)

gridfm-datakit-v1: A Python Library for Scalable and Realistic Power Flow and Optimal Power Flow Data Generation
by: Puech, Alban, et al.
Published: (2025)

Learning to Optimally Dispatch Power: Performance on a Nation-Wide Real-World Dataset
by: Boero, Ignacio, et al.
Published: (2025)

Parallel Batch Scheduling With Incompatible Job Families Via Constraint Programming
by: Huertas, Jorge A., et al.
Published: (2024)

Fast and Large-Scale Unbalanced Optimal Transport via its Semi-Dual and Adaptive Gradient Methods
by: Genans, Ferdinand
Published: (2026)

PINNs in PDE Constrained Optimal Control Problems: Direct vs Indirect Methods
by: Zhang, Zhen, et al.
Published: (2026)

Optimizing Autonomous Transfer Hub Networks: Quantifying the Potential Impact of Self-Driving Trucks
by: Lee, Chungjae, et al.
Published: (2023)