Copertina

Learning to Relax: Setting Solver Parameters Across a Sequence of Linear System Instances

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Khodak, Mikhail, Chow, Edmond, Balcan, Maria-Florina, Talwalkar, Ameet
Natura:	Preprint
Pubblicazione:	2023
Soggetti:	Machine Learning Artificial Intelligence Numerical Analysis
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Provably tuning the ElasticNet across instances
di: Balcan, Maria-Florina, et al.
Pubblicazione: (2022)

CodePDE: An Inference Framework for LLM-driven PDE Solver Generation
di: Li, Shanda, et al.
Pubblicazione: (2025)

Breakeven complexity: A new perspective on neural partial differential equation solvers
di: Zhang, Yijing, et al.
Pubblicazione: (2026)

One-shot acceleration of transient PDE solvers via online-learned preconditioners
di: Khodak, Mikhail, et al.
Pubblicazione: (2025)

Deep Learning-Enhanced Preconditioning for Efficient Conjugate Gradient Solvers in Large-Scale PDE Systems
di: Li, Rui, et al.
Pubblicazione: (2024)

UGrid: An Efficient-And-Rigorous Neural Multigrid Solver for Linear PDEs
di: Han, Xi, et al.
Pubblicazione: (2024)

Better Neural PDE Solvers Through Data-Free Mesh Movers
di: Hu, Peiyan, et al.
Pubblicazione: (2023)

Unisolver: PDE-Conditional Transformers Towards Universal Neural PDE Solvers
di: Zhou, Hang, et al.
Pubblicazione: (2024)

SymMaP: Improving Computational Efficiency in Linear Solvers through Symbolic Preconditioning
di: Wang, Hong, et al.
Pubblicazione: (2025)

Algorithm Configuration for Structured Pfaffian Settings
di: Balcan, Maria-Florina, et al.
Pubblicazione: (2024)

Robust Non-Linear Correlations via Polynomial Regression
di: Giuliani, Luca, et al.
Pubblicazione: (2025)

Critical Sampling for Robust Evolution Operator Learning of Unknown Dynamical Systems
di: Zhang, Ce, et al.
Pubblicazione: (2023)

Leveraging Gauge Freedom for Learning Non-Gradient Population Dynamics of Stochastic Systems
di: Berman, Jules, et al.
Pubblicazione: (2026)

LieSolver: A PDE-constrained solver for IBVPs using Lie symmetries
di: Klausen, René P., et al.
Pubblicazione: (2025)

Specialized Foundation Models Struggle to Beat Supervised Baselines
di: Xu, Zongzhe, et al.
Pubblicazione: (2024)

Reward Learning from Best-of-$N$ Preference Data: Targets, Tradeoffs, and Design Principles
di: Pukdee, Rattana, et al.
Pubblicazione: (2026)

Posterior Covariance Structures in Gaussian Processes
di: Cai, Difeng, et al.
Pubblicazione: (2024)

Blending Neural Operators and Relaxation Methods in PDE Numerical Solvers
di: Zhang, Enrui, et al.
Pubblicazione: (2022)

Fractal Language Modelling by Universal Sequence Maps (USM)
di: Almeida, Jonas S, et al.
Pubblicazione: (2025)

Enhancing Forex Forecasting Accuracy: The Impact of Hybrid Variable Sets in Cognitive Algorithmic Trading Systems
di: King, Juan C., et al.
Pubblicazione: (2025)

A Mathematical Guide to Operator Learning
di: Boullé, Nicolas, et al.
Pubblicazione: (2023)

Learning to Discover Iterative Spectral Algorithms
di: Liu, Zihang, et al.
Pubblicazione: (2026)

Projection Methods for Operator Learning and Universal Approximation
di: Zappala, Emanuele
Pubblicazione: (2024)

Mixture of Experts Softens the Curse of Dimensionality in Operator Learning
di: Kratsios, Anastasis, et al.
Pubblicazione: (2024)

Parametrizing Convex Sets Using Sublinear Neural Networks
di: Martinet, Eloi
Pubblicazione: (2026)

Graph Neural Network as Computationally Efficient Emulator of Ice-sheet and Sea-level System Model (ISSM)
di: Koo, Younghyun, et al.
Pubblicazione: (2024)

Learning from Integral Losses in Physics Informed Neural Networks
di: Saleh, Ehsan, et al.
Pubblicazione: (2023)

Kinetic-based regularization: Learning spatial derivatives and PDE applications
di: Ganguly, Abhisek, et al.
Pubblicazione: (2026)

Learning Semilinear Neural Operators : A Unified Recursive Framework For Prediction And Data Assimilation
di: Singh, Ashutosh, et al.
Pubblicazione: (2024)

CFO: Learning Continuous-Time PDE Dynamics via Flow-Matched Neural Operators
di: Hou, Xianglong, et al.
Pubblicazione: (2025)

Physics-Informed Neural Networks for High-Frequency and Multi-Scale Problems using Transfer Learning
di: Mustajab, Abdul Hannan, et al.
Pubblicazione: (2024)

ELM-DeepONets: Backpropagation-Free Training of Deep Operator Networks via Extreme Learning Machines
di: Son, Hwijae
Pubblicazione: (2025)

V-ABFT: Variance-Based Adaptive Threshold for Fault-Tolerant Matrix Multiplication in Mixed-Precision Deep Learning
di: Gao, Yiheng, et al.
Pubblicazione: (2026)

DeepContour: A Hybrid Deep Learning Framework for Accelerating Generalized Eigenvalue Problem Solving via Efficient Contour Design
di: Chen, Yeqiu, et al.
Pubblicazione: (2025)

Principled Approaches for Extending Neural Architectures to Function Spaces for Operator Learning
di: Berner, Julius, et al.
Pubblicazione: (2025)

Graph-Instructed Neural Networks for parametric problems with varying boundary conditions
di: Della Santa, Francesco, et al.
Pubblicazione: (2026)

On-device Computation of Single-lead ECG Parameters for Real-time Remote Cardiac Health Assessment: A Real-world Validation Study
di: Fan, Sumei, et al.
Pubblicazione: (2025)

HomPINNs: homotopy physics-informed neural networks for solving the inverse problems of nonlinear differential equations with multiple solutions
di: Zheng, Haoyang, et al.
Pubblicazione: (2023)

AttNS: Attention-Inspired Numerical Solving For Limited Data Scenarios
di: Huang, Zhongzhan, et al.
Pubblicazione: (2023)

Moving Sampling Physics-informed Neural Networks induced by Moving Mesh PDE
di: Yang, Yu, et al.
Pubblicazione: (2023)