Fast Randomized Iteration: Diffusion Monte Carlo through the Lens of Numerical Linear Algebra
DOI10.1137/15M1040827zbMath1371.65005arXiv1508.06104OpenAlexW3098761958MaRDI QIDQ5348329
Publication date: 15 August 2017
Published in: SIAM Review (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1508.06104
convergencenumerical examplesdimension reductioniterative algorithmsrandomized algorithmdata assimilationrare event simulation
Numerical computation of eigenvalues and eigenvectors of matrices (65F15) Monte Carlo methods (65C05) Iterative numerical methods for linear systems (65F10) Numerical computation of matrix exponential and similar matrix functions (65F60)
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Cites Work
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- A patch that imparts unconditional stability to explicit integrators for Langevin-like equations
- A randomized algorithm for the decomposition of matrices
- A fast randomized algorithm for the approximation of matrices
- A randomized Kaczmarz algorithm with exponential convergence
- Approximate solutions for large transfer matrix problems
- Random choice solution of hyperbolic systems
- A new iterative Monte Carlo approach for inverse matrix problem
- Random-walk interpretations of classical iteration methods
- Sequential Monto Carlo techniques for the solution of linear systems
- Matrix Algorithms
- Compressed plane waves yield a compactly supported multiresolution basis for the Laplace operator
- Randomized algorithms for the low-rank approximation of matrices
- A fast randomized algorithm for overdetermined linear least-squares regression
- A Fast Randomized Algorithm for Orthogonal Projection
- Importance Sampling for a Monte Carlo Matrix Multiplication Algorithm, with Application to Information Retrieval
- On the Control of an Interacting Particle Estimation of Schrödinger Ground States
- A Randomized Algorithm for Principal Component Analysis
- MONTE CARLO METHODS FOR SOLVING MULTIVARIABLE PROBLEMS
- Nuclear norm of higher-order tensors
- Sparse dynamics for partial differential equations
- Compressed modes for variational problems in mathematics and physics
- Advanced Lectures on Machine Learning
- Fast monte-carlo algorithms for finding low-rank approximations
- Multiscale Methods
- Fast Monte Carlo Algorithms for Matrices I: Approximating Matrix Multiplication
- Fast Monte Carlo Algorithms for Matrices II: Computing a Low-Rank Approximation to a Matrix
- Fast Monte Carlo Algorithms for Matrices III: Computing a Compressed Approximate Matrix Decomposition
- A Retrospective and Prospective Survey of the Monte Carlo Method
- A Note on the Inversion of Matrices by Random Walks
- A Stochastic Approximation Method
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