Optimization over Trained Neural Networks: Going Large with Gradient-Based Algorithms
Fuente:
arXiv
Saved in:
| Main Authors: | Tong, Jiatai, Zhu, Yilin, Serra, Thiago, Burer, Samuel |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Optimization Over Trained Neural Networks: Taking a Relaxing Walk
by: Tong, Jiatai, et al.
Published: (2024)
by: Tong, Jiatai, et al.
Published: (2024)
Optimization over Trained (and Sparse) Neural Networks: A Surrogate within a Surrogate
by: Pham, Hung, et al.
Published: (2025)
by: Pham, Hung, et al.
Published: (2025)
An Extended Validity Domain for Constraint Learning
by: Zhu, Yilin, et al.
Published: (2024)
by: Zhu, Yilin, et al.
Published: (2024)
What is the Best Way to Do Something? A Discreet Tour of Discrete Optimization
by: Serra, Thiago
Published: (2025)
by: Serra, Thiago
Published: (2025)
Optimization over Trained Neural Networks: Difference-of-Convex Algorithm and Application to Data Center Scheduling
by: Liu, Xinwei, et al.
Published: (2025)
by: Liu, Xinwei, et al.
Published: (2025)
Computational Tradeoffs of Optimization-Based Bound Tightening in ReLU Networks
by: Badilla, Fabian, et al.
Published: (2023)
by: Badilla, Fabian, et al.
Published: (2023)
A Semidefinite Relaxation for Sums of Heterogeneous Quadratic Forms on the Stiefel Manifold
by: Gilman, Kyle, et al.
Published: (2022)
by: Gilman, Kyle, et al.
Published: (2022)
On the Semidefinite Representability of Continuous Quadratic Submodular Minimization With Applications to Moment Problems
by: Burer, Samuel, et al.
Published: (2025)
by: Burer, Samuel, et al.
Published: (2025)
ADMM-Tracking Gradient for Distributed Optimization over Asynchronous and Unreliable Networks
by: Carnevale, Guido, et al.
Published: (2023)
by: Carnevale, Guido, et al.
Published: (2023)
Towards Guided Descent: Optimization Algorithms for Training Neural Networks At Scale
by: Nagwekar, Ansh
Published: (2025)
by: Nagwekar, Ansh
Published: (2025)
On Linear Convergence of Distributed Stochastic Bilevel Optimization over Undirected Networks via Gradient Aggregation
by: Tak, Ajay, et al.
Published: (2025)
by: Tak, Ajay, et al.
Published: (2025)
Distributed Adaptive Gradient Algorithm with Gradient Tracking for Stochastic Non-Convex Optimization
by: Han, Dongyu, et al.
Published: (2024)
by: Han, Dongyu, et al.
Published: (2024)
A Gradient Sampling Algorithm for Noisy Nonsmooth Optimization
by: Berahas, Albert S., et al.
Published: (2026)
by: Berahas, Albert S., et al.
Published: (2026)
Compressed Gradient Tracking Algorithms for Distributed Nonconvex Optimization
by: Xu, Lei, et al.
Published: (2023)
by: Xu, Lei, et al.
Published: (2023)
A Fundamental Convergence Rate Bound for Gradient Based Online Optimization Algorithms with Exact Tracking
by: Wu, Alex Xinting, et al.
Published: (2025)
by: Wu, Alex Xinting, et al.
Published: (2025)
Optimization with First Order Algorithms
by: Dossal, Charles, et al.
Published: (2024)
by: Dossal, Charles, et al.
Published: (2024)
FAB: A First-Order AB-based Gradient Algorithm for Distributed Bilevel Optimization over Time-Varying Directed Graphs
by: Ma, Yaoshuai, et al.
Published: (2026)
by: Ma, Yaoshuai, et al.
Published: (2026)
S-DIGing: A Stochastic Gradient Tracking Algorithm for Distributed Optimization
by: Li, Huaqing, et al.
Published: (2019)
by: Li, Huaqing, et al.
Published: (2019)
A Proximal Gradient Method with an Explicit Line search for Multiobjective Optimization
by: Bello-Cruz, Yunier, et al.
Published: (2024)
by: Bello-Cruz, Yunier, et al.
Published: (2024)
Regularized Gradient Clipping Provably Trains Wide and Deep Neural Networks
by: Tucat, Matteo, et al.
Published: (2024)
by: Tucat, Matteo, et al.
Published: (2024)
Distributed Zeroth-Order Optimization with Rademacher Perturbations and Momentum Gradient Tracking
by: Su, Yanxu, et al.
Published: (2026)
by: Su, Yanxu, et al.
Published: (2026)
Variable Smoothing Alternating Proximal Gradient Algorithm for Coupled Composite Optimization
by: Long, Xian-Jun, et al.
Published: (2025)
by: Long, Xian-Jun, et al.
Published: (2025)
Efficient Gradient Tracking Algorithms for Distributed Optimization Problems with Inexact Communication
by: Zhao, Shengchao, et al.
Published: (2025)
by: Zhao, Shengchao, et al.
Published: (2025)
Dual Natural Gradient Descent for Scalable Training of Physics-Informed Neural Networks
by: Jnini, Anas, et al.
Published: (2025)
by: Jnini, Anas, et al.
Published: (2025)
A Distributed Gradient-based Algorithm for Optimization Problems with Coupled Equality Constraints
by: Qiu, Chenyang, et al.
Published: (2025)
by: Qiu, Chenyang, et al.
Published: (2025)
Mean-Field Limits for Two-Layer Neural Networks Trained with Consensus-Based Optimization
by: De Deyn, William, et al.
Published: (2025)
by: De Deyn, William, et al.
Published: (2025)
Accelerated Gradient Methods for Geodesically Convex Optimization: Tractable Algorithms and Convergence Analysis
by: Kim, Jungbin, et al.
Published: (2022)
by: Kim, Jungbin, et al.
Published: (2022)
A Communication-Efficient Stochastic Gradient Descent Algorithm for Distributed Nonconvex Optimization
by: Xie, Antai, et al.
Published: (2024)
by: Xie, Antai, et al.
Published: (2024)
A Decentralized Proximal Gradient Tracking Algorithm for Composite Optimization on Riemannian Manifolds
by: Wang, Lei, et al.
Published: (2024)
by: Wang, Lei, et al.
Published: (2024)
Convergence of Implicit Gradient Descent for Training Two-Layer Physics-Informed Neural Networks
by: Xu, Xianliang, et al.
Published: (2024)
by: Xu, Xianliang, et al.
Published: (2024)
Shuffling Momentum Gradient Algorithm for Convex Optimization
by: Tran, Trang H., et al.
Published: (2024)
by: Tran, Trang H., et al.
Published: (2024)
Multi-Revolution Low-Thrust Trajectory Optimization With Very Sparse Mesh Pseudospectral Method
by: Zou, Yilin, et al.
Published: (2025)
by: Zou, Yilin, et al.
Published: (2025)
Almost Sure Convergence of Networked Policy Gradient over Time-Varying Networks in Markov Potential Games
by: Aydin, Sarper, et al.
Published: (2024)
by: Aydin, Sarper, et al.
Published: (2024)
Multiobjective Balanced Gradient Flow: A Dynamical Perspective on a Class of Optimization Algorithms
by: Yin, Yingdong
Published: (2025)
by: Yin, Yingdong
Published: (2025)
A Single-Loop Gradient Algorithm for Pessimistic Bilevel Optimization via Smooth Approximation
by: Cao, Qichao, et al.
Published: (2025)
by: Cao, Qichao, et al.
Published: (2025)
Smoothing Gradient Tracking for Decentralized Optimization over the Stiefel Manifold with Non-smooth Regularizers
by: Wang, Lei, et al.
Published: (2023)
by: Wang, Lei, et al.
Published: (2023)
Nonlinear Splitting for Gradient-Based Unconstrained and Adjoint Optimization
by: Tran, Brian K., et al.
Published: (2025)
by: Tran, Brian K., et al.
Published: (2025)
A Single-Loop Stochastic Gradient Algorithm for Minimax Optimization with Nonlinear Coupled Constraints
by: Cao, Qichao, et al.
Published: (2026)
by: Cao, Qichao, et al.
Published: (2026)
First-Order Algorithms Without Lipschitz Gradient: A Sequential Local Optimization Approach
by: Zhang, Junyu, et al.
Published: (2020)
by: Zhang, Junyu, et al.
Published: (2020)
Distributed Normal Map-based Stochastic Proximal Gradient Methods over Networks
by: Huang, Kun, et al.
Published: (2024)
by: Huang, Kun, et al.
Published: (2024)
Similar Items
-
Optimization Over Trained Neural Networks: Taking a Relaxing Walk
by: Tong, Jiatai, et al.
Published: (2024) -
Optimization over Trained (and Sparse) Neural Networks: A Surrogate within a Surrogate
by: Pham, Hung, et al.
Published: (2025) -
An Extended Validity Domain for Constraint Learning
by: Zhu, Yilin, et al.
Published: (2024) -
What is the Best Way to Do Something? A Discreet Tour of Discrete Optimization
by: Serra, Thiago
Published: (2025) -
Optimization over Trained Neural Networks: Difference-of-Convex Algorithm and Application to Data Center Scheduling
by: Liu, Xinwei, et al.
Published: (2025)