Cover Image

Beyond Discretization: Learning the Optimal Solution Path

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Dong, Qiran, Grigas, Paul, Gupta, Vishal
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

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

Smart Surrogate Losses for Contextual Stochastic Linear Optimization with Robust Constraints
by: Im, Hyungki, et al.
Published: (2025)

Active Learning For Contextual Linear Optimization: A Margin-Based Approach
by: Liu, Mo, et al.
Published: (2023)

Decision-Focused Learning with Directional Gradients
by: Huang, Michael, et al.
Published: (2024)

Near-Optimal Algorithms for Group Distributionally Robust Optimization and Beyond
by: Soma, Tasuku, et al.
Published: (2022)

End-to-End Learning Framework for Solving Non-Markovian Optimal Control
by: Zhang, Xiaole, et al.
Published: (2025)

Optimal Design of Volt/VAR Control Rules of Inverters using Deep Learning
by: Gupta, Sarthak, et al.
Published: (2022)

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

Mutual Information Optimal Control of Discrete-Time Linear Systems
by: Enami, Shoju, et al.
Published: (2025)

Unifying Entropy Regularization in Optimal Control: From and Back to Classical Objectives via Iterated Soft Policies and Path Integral Solutions
by: Bhole, Ajinkya, et al.
Published: (2025)

Convergence for Discrete Parameter Update Schemes
by: Wilson, Paul, et al.
Published: (2025)

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

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

A Barrier-Metric First-Order Method for Linearly Constrained Bilevel Optimization
by: Hong, Tenglong, et al.
Published: (2026)

DOGE-Train: Discrete Optimization on GPU with End-to-end Training
by: Abbas, Ahmed, et al.
Published: (2022)

Zero-Shot Transferable Solution Method for Parametric Optimal Control Problems
by: Li, Xingjian, et al.
Published: (2025)

Differentiable Discrete Event Simulation for Queuing Network Control
by: Che, Ethan, et al.
Published: (2024)

Learning to Adapt: In-Context Learning Beyond Stationarity
by: Qin, Zhen, et al.
Published: (2026)

Direct Spectral Acceleration of First-Order Methods for Saddle Point Problems with Bilinear Coupling
by: Li, Meng, et al.
Published: (2026)

Model Predictive Path Integral PID Control for Learning-Based Path Following
by: Kato, Teruki, et al.
Published: (2026)

Reinforcement Learning for a Discrete-Time Linear-Quadratic Control Problem with an Application
by: Li, Lucky
Published: (2024)

Learning to Stop: Deep Learning for Mean Field Optimal Stopping
by: Magnino, Lorenzo, et al.
Published: (2024)

Learning Fair Policies for Infectious Diseases Mitigation using Path Integral Control
by: Jia, Zhuangzhuang, et al.
Published: (2025)

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

Optimal Data Splitting in Distributed Optimization for Machine Learning
by: Medyakov, Daniil, et al.
Published: (2024)

Learning Optimal Classification Trees Robust to Distribution Shifts
by: Justin, Nathan, et al.
Published: (2023)

Achieve Performatively Optimal Policy for Performative Reinforcement Learning
by: Chen, Ziyi, et al.
Published: (2025)

An Efficient On-Policy Deep Learning Framework for Stochastic Optimal Control
by: Hua, Mengjian, et al.
Published: (2024)

Model-Free Learning for the Linear Quadratic Regulator over Rate-Limited Channels
by: Ye, Lintao, et al.
Published: (2024)

Corruption-Tolerant Asynchronous Q-Learning with Near-Optimal Rates
by: Maity, Sreejeet, et al.
Published: (2025)

ODTlearn: A Package for Learning Optimal Decision Trees for Prediction and Prescription
by: Vossler, Patrick, et al.
Published: (2023)

Learning Decentralized Linear Quadratic Regulators with $\sqrt{T}$ Regret
by: Ye, Lintao, et al.
Published: (2022)

Reheated Gradient-based Discrete Sampling for Combinatorial Optimization
by: Li, Muheng, et al.
Published: (2025)

Traversing Pareto Optimal Policies: Provably Efficient Multi-Objective Reinforcement Learning
by: Qiu, Shuang, et al.
Published: (2024)

Can Learning Be Explained By Local Optimality In Robust Low-rank Matrix Recovery?
by: Ma, Jianhao, et al.
Published: (2023)

Machine Learning Guided Optimal Transmission Switching to Mitigate Wildfire Ignition Risk
by: Huang, Weimin, et al.
Published: (2025)

Quotient-Categorical Representations for Bellman-Compatible Average-Reward Distributional Reinforcement Learning
by: Kaya, Ege C., et al.
Published: (2026)

Training Neural ODEs Using Fully Discretized Simultaneous Optimization
by: Shapovalova, Mariia, et al.
Published: (2025)

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

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