Cover Image

KKT-Informed Neural Network

Fuente: arXiv

Saved in:

Bibliographic Details
Main Author:	Femine, Carmine Delle
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

Karush-Kuhn-Tucker Condition-Trained Neural Networks (KKT Nets)
by: Arvind, Shreya, et al.
Published: (2024)

Limitations of Physics-Informed Neural Networks: a Study on Smart Grid Surrogation
by: Cestero, Julen, et al.
Published: (2025)

Relaxation-Informed Training of Neural Network Surrogate Models
by: Tsay, Calvin
Published: (2026)

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

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

Online Traffic Density Estimation using Physics-Informed Neural Networks
by: Wilkman, Dennis, et al.
Published: (2025)

Physics-Informed Graph Neural Network for Dynamic Reconfiguration of Power Systems
by: Authier, Jules, et al.
Published: (2023)

Dual Natural Gradient Descent for Scalable Training of Physics-Informed Neural Networks
by: Jnini, Anas, et al.
Published: (2025)

Physics-Informed Neural Network Lyapunov Functions: PDE Characterization, Learning, and Verification
by: Liu, Jun, et al.
Published: (2023)

Convergence of Implicit Gradient Descent for Training Two-Layer Physics-Informed Neural Networks
by: Xu, Xianliang, et al.
Published: (2024)

Enhancing Stability of Physics-Informed Neural Network Training Through Saddle-Point Reformulation
by: Bylinkin, Dmitry, et al.
Published: (2025)

Verifiable Error Bounds for Physics-Informed Neural Network Solutions of Lyapunov and Hamilton-Jacobi-Bellman Equations
by: Liu, Jun
Published: (2026)

Optimizing the Optimizer for Physics-Informed Neural Networks and Kolmogorov-Arnold Networks
by: Kiyani, Elham, et al.
Published: (2025)

Formally Verified Physics-Informed Neural Control Lyapunov Functions
by: Liu, Jun, et al.
Published: (2024)

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

Curse of Dimensionality in Neural Network Optimization
by: Na, Sanghoon, et al.
Published: (2025)

On the Topology of Neural Network Superlevel Sets
by: Gharesifard, Bahman
Published: (2026)

Learning Neural Networks by Neuron Pursuit
by: Kumar, Akshay, et al.
Published: (2025)

AutoBalance: An Automatic Balancing Framework for Training Physics-Informed Neural Networks
by: An, Kang, et al.
Published: (2025)

Wide Neural Networks Trained with Weight Decay Provably Exhibit Neural Collapse
by: Jacot, Arthur, et al.
Published: (2024)

Optimizing Energy Management of Smart Grid using Reinforcement Learning aided by Surrogate models built using Physics-informed Neural Networks
by: Cestero, Julen, et al.
Published: (2025)

Deconstructing the Goldilocks Zone of Neural Network Initialization
by: Vysogorets, Artem, et al.
Published: (2024)

A Recovery Guarantee for Sparse Neural Networks
by: Fridovich-Keil, Sara, et al.
Published: (2025)

Towards Quantifying the Hessian Structure of Neural Networks
by: Dong, Zhaorui, et al.
Published: (2025)

A Unified Representation of Neural Networks Architectures
by: Prieur, Christophe, et al.
Published: (2025)

Forward Invariance in Neural Network Controlled Systems
by: Harapanahalli, Akash, et al.
Published: (2023)

Randomized Geometric Algebra Methods for Convex Neural Networks
by: Wang, Yifei, et al.
Published: (2024)

Wasserstein Distributionally Robust Shallow Convex Neural Networks
by: Pallage, Julien, et al.
Published: (2024)

SGD with Partial Hessian for Deep Neural Networks Optimization
by: Sun, Ying, et al.
Published: (2024)

Rethinking the Capacity of Graph Neural Networks for Branching Strategy
by: Chen, Ziang, et al.
Published: (2024)

Regularized Gauss-Newton for Optimizing Overparameterized Neural Networks
by: Adeoye, Adeyemi D., et al.
Published: (2024)

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

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

Adaptive Momentum and Nonlinear Damping for Neural Network Training
by: Karoni, Aikaterini, et al.
Published: (2026)

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

Chordal Sparsity for SDP-based Neural Network Verification
by: Xue, Anton, et al.
Published: (2022)

Distributed Control of Network Systems in the Space of Stabilizing Graph Neural Network Policies
by: Cao, John, et al.
Published: (2025)

Improved Scalable Lipschitz Bounds for Deep Neural Networks
by: Syed, Usman, et al.
Published: (2025)

Loss Landscape Characterization of Neural Networks without Over-Parametrization
by: Islamov, Rustem, et al.
Published: (2024)

Compression-aware Training of Neural Networks using Frank-Wolfe
by: Zimmer, Max, et al.
Published: (2022)