Learning Sparse Approximate Inverse Preconditioners for Conjugate Gradient Solvers on GPUs
Fuente:
arXiv
Saved in:
| Main Authors: | Yang, Zherui, Li, Zhehao, Lyu, Kangbo, Li, Yixuan, Du, Tao, Liu, Ligang |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Learning from Linear Algebra: A Graph Neural Network Approach to Preconditioner Design for Conjugate Gradient Solvers
by: Trifonov, Vladislav, et al.
Published: (2024)
by: Trifonov, Vladislav, et al.
Published: (2024)
Hybrid Iterative Solvers with Geometry-Aware Neural Preconditioners for Parametric PDEs
by: Lee, Youngkyu, et al.
Published: (2025)
by: Lee, Youngkyu, et al.
Published: (2025)
Deep Learning-Enhanced Preconditioning for Efficient Conjugate Gradient Solvers in Large-Scale PDE Systems
by: Li, Rui, et al.
Published: (2024)
by: Li, Rui, et al.
Published: (2024)
Graph Neural Preconditioners for Iterative Solutions of Sparse Linear Systems
by: Chen, Jie
Published: (2024)
by: Chen, Jie
Published: (2024)
Accelerating Conjugate Gradient Solvers for Homogenization Problems with Unitary Neural Operators
by: Herb, Julius, et al.
Published: (2025)
by: Herb, Julius, et al.
Published: (2025)
Accelerating Legacy Numerical Solvers by Non-intrusive Gradient-based Meta-solving
by: Arisaka, Sohei, et al.
Published: (2024)
by: Arisaka, Sohei, et al.
Published: (2024)
PEARL: Preconditioner Enhancement through Actor-critic Reinforcement Learning
by: Millard, David, et al.
Published: (2025)
by: Millard, David, et al.
Published: (2025)
Multi-Level GNN Preconditioner for Solving Large Scale Problems
by: Nastorg, Matthieu, et al.
Published: (2024)
by: Nastorg, Matthieu, et al.
Published: (2024)
Efficient Differentiable Approximation of Generalized Low-rank Regularization
by: Li, Naiqi, et al.
Published: (2025)
by: Li, Naiqi, et al.
Published: (2025)
Matrix-Free Least Squares Solvers: Values, Gradients, and What to Do With Them
by: Roy, Hrittik, et al.
Published: (2025)
by: Roy, Hrittik, et al.
Published: (2025)
Quantifying Training Difficulty and Accelerating Convergence in Neural Network-Based PDE Solvers
by: Chen, Chuqi, et al.
Published: (2024)
by: Chen, Chuqi, et al.
Published: (2024)
Matrix Phylogeny: Compact Spectral Fingerprints for Trap-Robust Preconditioner Selection
by: Baek, Jinwoo
Published: (2025)
by: Baek, Jinwoo
Published: (2025)
An Improved Multi-Stage Preconditioner on GPUs for Compositional Reservoir Simulation
by: Zhao, Li, et al.
Published: (2022)
by: Zhao, Li, et al.
Published: (2022)
Fine-Tune Language Models as Multi-Modal Differential Equation Solvers
by: Yang, Liu, et al.
Published: (2023)
by: Yang, Liu, et al.
Published: (2023)
Holistic Physics Solver: Learning PDEs in a Unified Spectral-Physical Space
by: Yue, Xihang, et al.
Published: (2024)
by: Yue, Xihang, et al.
Published: (2024)
Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification
by: Solomon, Jack Michael, et al.
Published: (2026)
by: Solomon, Jack Michael, et al.
Published: (2026)
Error Feedback Can Accurately Compress Preconditioners
by: Modoranu, Ionut-Vlad, et al.
Published: (2023)
by: Modoranu, Ionut-Vlad, et al.
Published: (2023)
Singularity Formation: Synergy in Theoretical, Numerical and Machine Learning Approaches
by: Wang, Yixuan
Published: (2026)
by: Wang, Yixuan
Published: (2026)
Gradient Flows for Sampling: Mean-Field Models, Gaussian Approximations and Affine Invariance
by: Chen, Yifan, et al.
Published: (2023)
by: Chen, Yifan, et al.
Published: (2023)
Second Order Ensemble Langevin Method for Sampling and Inverse Problems
by: Liu, Ziming, et al.
Published: (2022)
by: Liu, Ziming, et al.
Published: (2022)
Inverse Evolution Layers: Physics-informed Regularizers for Deep Neural Networks
by: Liu, Chaoyu, et al.
Published: (2023)
by: Liu, Chaoyu, et al.
Published: (2023)
Learning Neural PDE Solvers with Convergence Guarantees
by: Hsieh, Jun-Ting, et al.
Published: (2019)
by: Hsieh, Jun-Ting, et al.
Published: (2019)
PDE Solvers Should Be Local: Fast, Stable Rollouts with Learned Local Stencils
by: Cheng, Chun-Wun, et al.
Published: (2025)
by: Cheng, Chun-Wun, et al.
Published: (2025)
Learn to Evolve: Self-supervised Neural JKO Operator for Wasserstein Gradient Flow
by: Feng, Xue, et al.
Published: (2026)
by: Feng, Xue, et al.
Published: (2026)
Scale-Consistent Learning for Partial Differential Equations
by: Li, Zongyi, et al.
Published: (2025)
by: Li, Zongyi, et al.
Published: (2025)
Monte Carlo Neural PDE Solver for Learning PDEs via Probabilistic Representation
by: Zhang, Rui, et al.
Published: (2023)
by: Zhang, Rui, et al.
Published: (2023)
SciML Agents: Write the Solver, Not the Solution
by: Gaonkar, Saarth, et al.
Published: (2025)
by: Gaonkar, Saarth, et al.
Published: (2025)
An Empirical Study of Conjugate Gradient Preconditioners for Solving Symmetric Positive Definite Systems of Linear Equations
by: Tunnell, Marc A., et al.
Published: (2025)
by: Tunnell, Marc A., et al.
Published: (2025)
Parallel-in-Time Probabilistic Numerical ODE Solvers
by: Bosch, Nathanael, et al.
Published: (2023)
by: Bosch, Nathanael, et al.
Published: (2023)
High precision PINNs in unbounded domains: application to singularity formulation in PDEs
by: Wang, Yixuan, et al.
Published: (2025)
by: Wang, Yixuan, et al.
Published: (2025)
Are Deep Learning Based Hybrid PDE Solvers Reliable? Why Training Paradigms and Update Strategies Matter
by: Wu, Yuhan, et al.
Published: (2026)
by: Wu, Yuhan, et al.
Published: (2026)
Preconditioned One-Step Generative Modeling for Bayesian Inverse Problems in Function Spaces
by: Cheng, Zilan, et al.
Published: (2026)
by: Cheng, Zilan, et al.
Published: (2026)
Solving the Wide-band Inverse Scattering Problem via Equivariant Neural Networks
by: Zhang, Borong, et al.
Published: (2022)
by: Zhang, Borong, et al.
Published: (2022)
Higher Order Approximation Rates for ReLU CNNs in Korobov Spaces
by: Li, Yuwen, et al.
Published: (2025)
by: Li, Yuwen, et al.
Published: (2025)
Machine Learning Based Optimization Workflow for Tuning Numerical Settings of Differential Equation Solvers for Boundary Value Problems
by: Victor, Viny Saajan, et al.
Published: (2024)
by: Victor, Viny Saajan, et al.
Published: (2024)
Supervised and Unsupervised Neural Network Solver for First Order Hyperbolic Nonlinear PDEs
by: Baba, Zakaria, et al.
Published: (2026)
by: Baba, Zakaria, et al.
Published: (2026)
Back-Projection Diffusion: Solving the Wideband Inverse Scattering Problem with Diffusion Models
by: Zhang, Borong, et al.
Published: (2024)
by: Zhang, Borong, et al.
Published: (2024)
Learned Finite Element-based Regularization of the Inverse Problem in Electrocardiographic Imaging
by: Haas, Manuel, et al.
Published: (2026)
by: Haas, Manuel, et al.
Published: (2026)
Affine Tracing: A New Paradigm for Probabilistic Linear Solvers
by: Hegde, Disha, et al.
Published: (2026)
by: Hegde, Disha, et al.
Published: (2026)
Transolver: A Fast Transformer Solver for PDEs on General Geometries
by: Wu, Haixu, et al.
Published: (2024)
by: Wu, Haixu, et al.
Published: (2024)
Similar Items
-
Learning from Linear Algebra: A Graph Neural Network Approach to Preconditioner Design for Conjugate Gradient Solvers
by: Trifonov, Vladislav, et al.
Published: (2024) -
Hybrid Iterative Solvers with Geometry-Aware Neural Preconditioners for Parametric PDEs
by: Lee, Youngkyu, et al.
Published: (2025) -
Deep Learning-Enhanced Preconditioning for Efficient Conjugate Gradient Solvers in Large-Scale PDE Systems
by: Li, Rui, et al.
Published: (2024) -
Graph Neural Preconditioners for Iterative Solutions of Sparse Linear Systems
by: Chen, Jie
Published: (2024) -
Accelerating Conjugate Gradient Solvers for Homogenization Problems with Unitary Neural Operators
by: Herb, Julius, et al.
Published: (2025)