:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Author:	Gonzalo, Valdes
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Optimization and Control
Online Access:	https://arxiv.org/abs/2406.10703
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Optimality-Informed Neural Networks for Solving Parametric Optimization Problems
by: Hoffmann, Matthias K., et al.
Published: (2025)

Solving Elliptic Optimal Control Problems via Neural Networks and Optimality System
by: Dai, Yongcheng, et al.
Published: (2023)

Exploring the Potential of Bilevel Optimization for Calibrating Neural Networks
by: Sanguin, Gabriele, et al.
Published: (2025)

Optimal Depth of Neural Networks
by: Qi, Qian
Published: (2025)

Contraction-Guided Adaptive Partitioning for Reachability Analysis of Neural Network Controlled Systems
by: Harapanahalli, Akash, et al.
Published: (2023)

A Nonlinear Separation Principle via Contraction Theory: Applications to Neural Networks, Control, and Learning
by: Gokhale, Anand, et al.
Published: (2026)

Discrete-time Contraction-based Control of Nonlinear Systems with Parametric Uncertainties using Neural Networks
by: Wei, Lai, et al.
Published: (2021)

Physics-Informed Neural Networks with Hard Linear Equality Constraints
by: Chen, Hao, et al.
Published: (2024)

On Integer Programming for the Binarized Neural Network Verification Problem
by: Kim, Woojin, et al.
Published: (2025)

Learning Neural Contracting Dynamics: Extended Linearization and Global Guarantees
by: Jaffe, Sean, et al.
Published: (2024)

Neural Contraction Metrics with Formal Guarantees for Discrete-Time Nonlinear Dynamical Systems
by: Li, Haoyu, et al.
Published: (2025)

Deep Operator Neural Network Model Predictive Control
by: de Jong, Thomas Oliver, et al.
Published: (2025)

Towards Optimal Branching of Linear and Semidefinite Relaxations for Neural Network Robustness Certification
by: Anderson, Brendon G., et al.
Published: (2021)

A Guaranteed-Stable Neural Network Approach for Optimal Control of Nonlinear Systems
by: Li, Anran, et al.
Published: (2025)

Bayesian Optimization of a Lightweight and Accurate Neural Network for Aerodynamic Performance Prediction
by: Shihua, James M., et al.
Published: (2025)

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

Riemannian Neural Optimal Transport
by: Micheli, Alessandro, et al.
Published: (2026)

Solving Max-Cut to Global Optimality via Feasibility-Preserving Graph Neural Networks
by: Chen, Hao, et al.
Published: (2026)

Scalable Solution of the Stochastic Multi-path Traveling Salesman Problem via Neural Networks
by: Chou, Xiaochen, et al.
Published: (2026)

Analyzing Neural Network-Based Generative Diffusion Models through Convex Optimization
by: Zhang, Fangzhao, et al.
Published: (2024)

Assessing and Enhancing Graph Neural Networks for Combinatorial Optimization: Novel Approaches and Application in Maximum Independent Set Problems
by: Hu, Chenchuhui
Published: (2024)

Solving Inverse Problems with Deep Linear Neural Networks: Global Convergence Guarantees for Gradient Descent with Weight Decay
by: Laus, Hannah, et al.
Published: (2025)

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

Deterministic Trajectory Optimization through Probabilistic Optimal Control
by: Filabadi, Mohammad Mahmoudi, et al.
Published: (2024)

On the Convergence of Overparameterized Problems: Inherent Properties of the Compositional Structure of Neural Networks
by: de Oliveira, Arthur Castello Branco, et al.
Published: (2025)

An Optimal Transport Approach for Network Regression
by: Zalles, Alex G., et al.
Published: (2024)

Neural Collapse versus Low-rank Bias: Is Deep Neural Collapse Really Optimal?
by: Súkeník, Peter, et al.
Published: (2024)

Heuristic Optimal Transport in Branching Networks
by: Andrecut, M.
Published: (2023)

Graph Neural Networks for the Offline Nanosatellite Task Scheduling Problem
by: Pacheco, Bruno Machado, et al.
Published: (2023)

Closing the Gaps: Optimality of Sample Average Approximation for Data-Driven Newsvendor Problems
by: Lyu, Jiameng, et al.
Published: (2024)

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

Computational Tradeoffs of Optimization-Based Bound Tightening in ReLU Networks
by: Badilla, Fabian, et al.
Published: (2023)

Asymptotically Optimal Regret for Black-Box Predict-then-Optimize
by: Tan, Samuel, et al.
Published: (2024)

Learning Optimal Control and Dynamical Structure of Global Trajectory Search Problems with Diffusion Models
by: Graebner, Jannik, et al.
Published: (2024)

More Optimal Fractional-Order Stochastic Gradient Descent for Non-Convex Optimization Problems
by: Partohaghighi, Mohammad, et al.
Published: (2025)

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

Tightening convex relaxations of trained neural networks: a unified approach for convex and S-shaped activations
by: Carrasco, Pablo, et al.
Published: (2024)

Concentration of Contractive Stochastic Approximation: Additive and Multiplicative Noise
by: Chen, Zaiwei, et al.
Published: (2023)

Adjoint Matching through the Lens of the Stochastic Maximum Principle in Optimal Control
by: Domingo-Enrich, Carles, et al.
Published: (2026)

Applications of 0-1 Neural Networks in Prescription and Prediction
by: Patil, Vrishabh, et al.
Published: (2024)