Cover Image

Learning solutions to some toy constrained optimization problems in infinite dimensional Hilbert spaces

Fuente: arXiv

Saved in:

Bibliographic Details
Main Author:	Mandal, Pinak
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

Learning based convex approximation for constrained parametric optimization
by: Liu, Kang, et al.
Published: (2025)

Bayesian Quality-Diversity approaches for constrained optimization problems with mixed continuous, discrete and categorical variables
by: Brevault, Loic, et al.
Published: (2023)

Exact augmented Lagrangians for constrained optimization problems in Hilbert spaces I: Theory
by: Dolgopolik, M. V.
Published: (2022)

Exact augmented Lagrangians for constrained optimization problems in Hilbert spaces II: Applications
by: Dolgopolik, M. V.
Published: (2023)

Learning zeros of Fokker-Planck operators
by: Mandal, Pinak, et al.
Published: (2023)

The inexact power augmented Lagrangian method for constrained nonconvex optimization
by: Bodard, Alexander, et al.
Published: (2024)

A constrained optimization approach to improve robustness of neural networks
by: Zhao, Shudian, et al.
Published: (2024)

Safe exploration in reproducing kernel Hilbert spaces
by: Tokmak, Abdullah, et al.
Published: (2025)

Approximate solution of stochastic infinite horizon optimal control problems for constrained linear uncertain systems
by: Joa, Eunhyek, et al.
Published: (2024)

Block majorization-minimization with diminishing radius for constrained nonsmooth nonconvex optimization
by: Lyu, Hanbaek, et al.
Published: (2020)

Group-blind optimal transport to group parity and its constrained variants
by: Zhou, Quan, et al.
Published: (2023)

Convergence and complexity of block majorization-minimization for constrained block-Riemannian optimization
by: Li, Yuchen, et al.
Published: (2023)

On exploration of an interior mirror descent flow for stochastic nonconvex constrained problem
by: Ding, Kuangyu, et al.
Published: (2025)

A successive approximation method in functional spaces for hierarchical optimal control problems and its application to learning
by: Befekadu, Getachew K.
Published: (2024)

Towards a connection between the capacitated vehicle routing problem and the constrained centroid-based clustering
by: Abdellaoui, Abdelhakim, et al.
Published: (2024)

"Over-optimizing" for Normality: Budget-constrained Uncertainty Quantification for Contextual Decision-making
by: Wang, Yanyuan, et al.
Published: (2025)

Non-geodesically-convex optimization in the Wasserstein space
by: Luu, Hoang Phuc Hau, et al.
Published: (2024)

Accelerating optimization over the space of probability measures
by: Chen, Shi, et al.
Published: (2023)

Generalized infinite dimensional Alpha-Procrustes based geometries
by: Goomanee, Salvish, et al.
Published: (2025)

Manifold constrained steepest descent
by: Yang, Kaiwei, et al.
Published: (2026)

Formulations and scalability of neural network surrogates in nonlinear optimization problems
by: Parker, Robert B., et al.
Published: (2024)

On characterizing optimal learning trajectories in a class of learning problems
by: Befekadu, Getachew K
Published: (2025)

An analysis of optimization problems involving ReLU neural networks
by: Plate, Christoph, et al.
Published: (2025)

Worst-case generation via minimax optimization in Wasserstein space
by: Cheng, Xiuyuan, et al.
Published: (2025)

Sparse Polyak with optimal thresholding operators for high-dimensional M-estimation
by: Qiao, Tianqi, et al.
Published: (2025)

Sinkhorn algorithms and linear programming solvers for optimal partial transport problems
by: Bai, Yikun
Published: (2024)

Gromov-Wasserstein and optimal transport: from assignment problems to probabilistic numeric
by: Seyedi, Iman, et al.
Published: (2025)

Learning to optimize: A tutorial for continuous and mixed-integer optimization
by: Chen, Xiaohan, et al.
Published: (2024)

Solving a class of stochastic optimal control problems by physics-informed neural networks
by: Jiao, Zhe, et al.
Published: (2024)

Time-optimal neural feedback control of nilpotent systems as a binary classification problem
by: Bicego, Sara, et al.
Published: (2025)

Distributed Thompson sampling under constrained communication
by: Zerefa, Saba, et al.
Published: (2024)

Alignment of large language models with constrained learning
by: Zhang, Botong, et al.
Published: (2025)

A safe exploration approach to constrained Markov decision processes
by: Ni, Tingting, et al.
Published: (2023)

Deep learning enhanced mixed integer optimization: Learning to reduce model dimensionality
by: Triantafyllou, Niki, et al.
Published: (2024)

On improving generalization in a class of learning problems with the method of small parameters for weakly-controlled optimal gradient systems
by: Befekadu, Getachew K.
Published: (2024)

A stochastic smoothing framework for nonconvex-nonconcave min-sum-max problems with applications to Wasserstein distributionally robust optimization
by: Liu, Wei, et al.
Published: (2025)

Control and optimization for Neural Partial Differential Equations in Supervised Learning
by: Bensoussan, Alain, et al.
Published: (2025)

Understanding the training of infinitely deep and wide ResNets with Conditional Optimal Transport
by: Barboni, Raphaël, et al.
Published: (2024)

Data-Driven Stochastic Optimal Control in Reproducing Kernel Hilbert Spaces
by: Hoischen, Nicolas, et al.
Published: (2024)

SplitVAEs: Decentralized scenario generation from siloed data for stochastic optimization problems
by: Islam, H M Mohaimanul, et al.
Published: (2024)