Cover Image

Neural incomplete factorization: learning preconditioners for the conjugate gradient method

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Häusner, Paul, Öktem, Ozan, Sjölund, Jens
Format:	Preprint
Published:	2023
Subjects:	Optimization and Control Machine Learning Numerical Analysis
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Learning incomplete factorization preconditioners for GMRES
by: Häusner, Paul, et al.
Published: (2024)

Learned iterative networks: An operator learning perspective
by: Hauptmann, Andreas, et al.
Published: (2025)

Learning rate adaptive stochastic gradient descent optimization methods: numerical simulations for deep learning methods for partial differential equations and convergence analyses
by: Dereich, Steffen, et al.
Published: (2024)

Natural Riemannian gradient for learning functional tensor networks
by: Klug, Nikolas, et al.
Published: (2026)

Learning to accelerate distributed ADMM using graph neural networks
by: Doerks, Henri, et al.
Published: (2025)

Flattened one-bit stochastic gradient descent: compressed distributed optimization with controlled variance
by: Stollenwerk, Alexander, et al.
Published: (2024)

Convergence of two-timescale gradient descent ascent dynamics: finite-dimensional and mean-field perspectives
by: An, Jing, et al.
Published: (2025)

A note on continuous-time online learning
by: Ying, Lexing
Published: (2024)

Non-asymptotic convergence analysis of the stochastic gradient Hamiltonian Monte Carlo algorithm with discontinuous stochastic gradient with applications to training of ReLU neural networks
by: Liang, Luxu, et al.
Published: (2024)

Continuum-marginal optimal transport: a mesh-free kernel method
by: Nakano, Yumiharu
Published: (2026)

A distributed semismooth Newton based augmented Lagrangian method for distributed optimization
by: Ma, Qihao, et al.
Published: (2026)

First-order methods for stochastic and finite-sum convex optimization with deterministic constraints
by: Lu, Zhaosong, et al.
Published: (2025)

RGNMR: A Gauss-Newton method for robust matrix completion with theoretical guarantees
by: Laufer, Eilon Vaknin, et al.
Published: (2025)

Multi-level Optimal Control with Neural Surrogate Models
by: Kalise, Dante, et al.
Published: (2024)

Efficient parallel inversion of ParaOpt preconditioners
by: Bonte, Corentin, et al.
Published: (2024)

PADAM: Parallel averaged Adam reduces the error for stochastic optimization in scientific machine learning
by: Jentzen, Arnulf, et al.
Published: (2025)

Control, Optimal Transport and Neural Differential Equations in Supervised Learning
by: Phung, Minh-Nhat, et al.
Published: (2025)

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

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

Shape Derivative-Informed Neural Operators with Application to Risk-Averse Shape Optimization
by: Gong, Xindi, et al.
Published: (2026)

Real-time optimal control of high-dimensional parametrized systems by deep learning-based reduced order models
by: Tomasetto, Matteo, et al.
Published: (2024)

Latent feedback control of distributed systems in multiple scenarios through deep learning-based reduced order models
by: Tomasetto, Matteo, et al.
Published: (2024)

A Natural Primal-Dual Hybrid Gradient Method for Adversarial Neural Network Training on Solving Partial Differential Equations
by: Liu, Shu, et al.
Published: (2024)

Langevin dynamics based algorithm e-TH$\varepsilon$O POULA for stochastic optimization problems with discontinuous stochastic gradient
by: Lim, Dong-Young, et al.
Published: (2022)

Dual Cone Gradient Descent for Training Physics-Informed Neural Networks
by: Hwang, Youngsik, et al.
Published: (2024)

On the Width Scaling of Neural Optimizers Under Matrix Operator Norms I: Row/Column Normalization and Hyperparameter Transfer
by: Xu, Ruihan, et al.
Published: (2026)

Nonparametric Filtering, Estimation and Classification using Neural Jump ODEs
by: Heiss, Jakob, et al.
Published: (2024)

Limited memory gradient methods for unconstrained optimization
by: Ferrandi, Giulia, et al.
Published: (2023)

Scalable Acceleration for Classification-Based Derivative-Free Optimization
by: Han, Tianyi, et al.
Published: (2023)

Error Feedback Can Accurately Compress Preconditioners
by: Modoranu, Ionut-Vlad, et al.
Published: (2023)

Linear convergence of forward-backward accelerated algorithms without knowledge of the modulus of strong convexity
by: Li, Bowen, et al.
Published: (2023)

Gradient is All You Need? How Consensus-Based Optimization can be Interpreted as a Stochastic Relaxation of Gradient Descent
by: Riedl, Konstantin, et al.
Published: (2023)

Enhanced Adaptive Gradient Algorithms for Nonconvex-PL Minimax Optimization
by: Huang, Feihu, et al.
Published: (2023)

Provably Faster Gradient Descent via Long Steps
by: Grimmer, Benjamin
Published: (2023)

On the Convergence of the Gradient Descent Method with Stochastic Fixed-point Rounding Errors under the Polyak-Lojasiewicz Inequality
by: Xia, Lu, et al.
Published: (2023)

Convergence of Adam for Non-convex Objectives: Relaxed Hyperparameters and Non-ergodic Case
by: He, Meixuan, et al.
Published: (2023)

Adaptive Proximal Gradient Method for Convex Optimization
by: Malitsky, Yura, et al.
Published: (2023)

A Block Coordinate Descent Method for Nonsmooth Composite Optimization under Orthogonality Constraints
by: Yuan, Ganzhao
Published: (2023)

UAdam: Unified Adam-Type Algorithmic Framework for Non-Convex Stochastic Optimization
by: Jiang, Yiming, et al.
Published: (2023)

Dynamic Proximal Gradient Algorithms for Schatten-$p$ Quasi-Norm Regularized Problems
by: Shen, Weiping, et al.
Published: (2026)