Cover Image

Dual Lagrangian Learning for Conic Optimization

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Tanneau, Mathieu, Van Hentenryck, Pascal
Format:	Preprint
Published:	2024
Subjects:	Optimization and Control Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

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)

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

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

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

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

On the Viability of Stochastic Economic Dispatch for Real-Time Energy Market Clearing
by: Zhao, Haoruo, et al.
Published: (2023)

Volt/VAR Optimization in Transmission Networks with Discrete-Control Devices
by: Tong, Shuaicheng, et al.
Published: (2026)

Compact Optimality Verification for Optimization Proxies
by: Chen, Wenbo, et al.
Published: (2024)

Self-Supervised Learning for Large-Scale Preventive Security Constrained DC Optimal Power Flow
by: Park, Seonho, et al.
Published: (2023)

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

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

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

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

A Hybrid Decomposition Approach for Stochastic Unit Commitment with Combined-Cycle Generators
by: Barrass, Rosemary, et al.
Published: (2026)

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

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

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

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

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)

Bound Tightening using Rolling-Horizon Decomposition for Neural Network Verification
by: Zhao, Haoruo, et al.
Published: (2024)

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

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

Learning Constrained Optimization with Deep Augmented Lagrangian Methods
by: Kotary, James, et al.
Published: (2024)

Predicting Accurate Lagrangian Multipliers for Mixed Integer Linear Programs
by: Demelas, Francesco, et al.
Published: (2023)

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

SOC-ICNN: From Polyhedral to Conic Geometry for Learning Convex Surrogate Functions
by: Liu, Kang, et al.
Published: (2026)

AI4OPT: AI Institute for Advances in Optimization
by: Van Hentenryck, Pascal, et al.
Published: (2023)

Dual Optimistic Ascent (PI Control) is the Augmented Lagrangian Method in Disguise
by: Ramirez, Juan, et al.
Published: (2025)

Developing Lagrangian-based Methods for Nonsmooth Nonconvex Optimization
by: Xiao, Nachuan, et al.
Published: (2024)

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

Strong Mixed-Integer Formulations for Transmission Expansion Planning with FACTS Devices
by: Wu, Kevin, et al.
Published: (2023)

Admission Control of Quasi-Reversible Queueing Systems: Optimization and Reinforcement Learning
by: Comte, Céline, et al.
Published: (2025)

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

Deep Learning for the Multiple Optimal Stopping Problem
by: Laurière, Mathieu, et al.
Published: (2025)

Bregman Linearized Augmented Lagrangian Method for Nonconvex Constrained Stochastic Zeroth-order Optimization
by: Shi, Qiankun, et al.
Published: (2025)

Practice-Based Optimization for the Strategic Locomotive Assignment Problem
by: Kim, Yunji, et al.
Published: (2025)

Recent Developments in Machine Learning Methods for Stochastic Control and Games
by: Hu, Ruimeng, et al.
Published: (2023)

Population-Aware Imitation Learning in Mean-field Games with Common Noise
by: Lambrecht, Grégoire, et al.
Published: (2026)

Constrained Bi-Level Optimization: Proximal Lagrangian Value function Approach and Hessian-free Algorithm
by: Yao, Wei, et al.
Published: (2024)