Cover Image

Self-Supervised Penalty-Based Learning for Robust Constrained Optimization

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Benslimane, Wyame, Grigas, Paul
Format:	Preprint
Published:	2025
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

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

Decision-Focused On-Policy Learning for Contextual Linear Optimization with Partial Feedback
by: Benslimane, Wyame, et al.
Published: (2026)

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

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

Beyond Discretization: Learning the Optimal Solution Path
by: Dong, Qiran, et al.
Published: (2024)

Penalty-Based First-Order Methods for Bilevel Optimization with Minimax and Constrained Lower-Level Problems
by: Shen, Yiyang, et al.
Published: (2026)

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

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

On Penalty Methods for Nonconvex Bilevel Optimization and First-Order Stochastic Approximation
by: Kwon, Jeongyeol, et al.
Published: (2023)

A Primal-Dual-Assisted Penalty Approach to Bilevel Optimization with Coupled Constraints
by: Jiang, Liuyuan, et al.
Published: (2024)

Position: Adopt Constraints Over Fixed Penalties in Deep Learning
by: Ramirez, Juan, et al.
Published: (2025)

Principled Penalty-based Methods for Bilevel Reinforcement Learning and RLHF
by: Shen, Han, et al.
Published: (2024)

Accelerating Multi-Block Constrained Optimization Through Learning to Optimize
by: Liang, Ling, et al.
Published: (2024)

Proximal Operators of Sorted Nonconvex Penalties
by: Gagneux, Anne, et al.
Published: (2025)

Nonconvex Optimization Framework for Group-Sparse Feedback Linear-Quadratic Optimal Control: Penalty Approach
by: Feng, Lechen, et al.
Published: (2025)

Self-supervised Equality Embedded Deep Lagrange Dual for Approximate Constrained Optimization
by: Kim, Minsoo, et al.
Published: (2023)

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

Efficient Penalty-Based Bilevel Methods: Improved Analysis, Novel Updates, and Flatness Condition
by: Jiang, Liuyuan, et al.
Published: (2025)

Nonconvex Optimization Framework for Group-Sparse Feedback Linear-Quadratic Optimal Control: Non-Penalty Approach
by: Feng, Lechen, et al.
Published: (2025)

On Penalty-based Bilevel Gradient Descent Method
by: Shen, Han, et al.
Published: (2023)

Distributionally-Robust Learning to Optimize
by: Ranjan, Vinit, et al.
Published: (2026)

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

Learning to Solve Optimization Problems Constrained with Partial Differential Equations
by: Guven, Yusuf, et al.
Published: (2025)

Robust Information Selection for Hypothesis Testing with Misclassification Penalties
by: Bhargav, Jayanth, et al.
Published: (2025)

Accelerated Gradient Methods for Sparse Statistical Learning with Nonconvex Penalties
by: Yang, Kai, et al.
Published: (2020)

SPAP: Structured Pruning via Alternating Optimization and Penalty Methods
by: Hu, Hanyu, et al.
Published: (2025)

High-Dimensional Bayesian Optimization Using Both Random and Supervised Embeddings
by: Priem, Rémy, et al.
Published: (2025)

HUANet: Hard-Constrained Unrolled ADMM for Constrained Convex Optimization
by: Tran, Trinh, et al.
Published: (2026)

CLASP: An online learning algorithm for Convex Losses And Squared Penalties
by: Ferreira, Ricardo N., et al.
Published: (2026)

Double Duality: Variational Primal-Dual Policy Optimization for Constrained Reinforcement Learning
by: Li, Zihao, et al.
Published: (2024)

Towards Robust Learning to Optimize with Theoretical Guarantees
by: Song, Qingyu, et al.
Published: (2025)

Progressively Sampled Equality-Constrained Optimization
by: Curtis, Frank E., et al.
Published: (2025)

Stochastic-Constrained Stochastic Optimization with Markovian Data
by: Kim, Yeongjong, et al.
Published: (2023)

A Penalty Approach for Differentiation Through Black-Box Quadratic Programming Solvers
by: Linghu, Yuxuan, et al.
Published: (2026)

Optimization meets Machine Learning: An Exact Algorithm for Semi-Supervised Support Vector Machines
by: Piccialli, Veronica, et al.
Published: (2023)

The ADMM-PINNs Algorithmic Framework for Nonsmooth PDE-Constrained Optimization: A Deep Learning Approach
by: Song, Yongcun, et al.
Published: (2023)

Conformal Predictive Programming for Chance Constrained Optimization
by: Zhao, Yiqi, et al.
Published: (2024)

Learning Rate Annealing Improves Tuning Robustness in Stochastic Optimization
by: Attia, Amit, et al.
Published: (2025)

Machine Learning for K-adaptability in Two-stage Robust Optimization
by: Julien, Esther, et al.
Published: (2022)

Wasserstein Distributionally Robust Optimization: Theory and Applications in Machine Learning
by: Kuhn, Daniel, et al.
Published: (2019)