Low-Rank Matrix Iteration Using Polynomial-Filtered Subspace Extraction
DOI10.1137/19M1259444zbMath1452.65069arXiv1904.10585OpenAlexW3035425337MaRDI QIDQ3300851
Yongfeng Li, ZaiWen Wen, Haoyang Liu, Ya-Xiang Yuan
Publication date: 30 July 2020
Published in: SIAM Journal on Scientific Computing (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1904.10585
polynomial filtereigenvalue decompositionsubspace extractioninexact optimization methodlow-rank matrix iteration
Numerical computation of eigenvalues and eigenvectors of matrices (65F15) Semidefinite programming (90C22) Convex programming (90C25) Large-scale problems in mathematical programming (90C06) Numerical methods for low-rank matrix approximation; matrix compression (65F55)
Related Items (2)
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Conic optimization via operator splitting and homogeneous self-dual embedding
- Alternating direction augmented Lagrangian methods for semidefinite programming
- Self-consistent-field calculations using Chebyshev-filtered subspace iteration
- A regularized semi-smooth Newton method with projection steps for composite convex programs
- Spectral operators of matrices
- Twice Differentiable Spectral Functions
- Low-Rank Spectral Optimization via Gauge Duality
- Subspace Acceleration for Large-Scale Parameter-Dependent Hermitian Eigenproblems
- Low-Rank Matrix Completion by Riemannian Optimization
- A Subspace Method for Large-Scale Eigenvalue Optimization
- On the Numerical Solution of Heat Conduction Problems in Two and Three Space Variables
- Chebyshev Acceleration Techniques for Solving Nonsymmetric Eigenvalue Problems
- A Quadratically Convergent Newton Method for Computing the Nearest Correlation Matrix
- Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming
- A SemiSmooth Newton Method for Semidefinite Programs and its Applications in Electronic Structure Calculations
- Subspace Acceleration for the Crawford Number and Related Eigenvalue Optimization Problems
- Derivatives of Spectral Functions
- Convergence Rate Analysis of Several Splitting Schemes
- The Rotation of Eigenvectors by a Perturbation. III
- Numerical Determination of Fundamental Modes
- Convex analysis and monotone operator theory in Hilbert spaces
This page was built for publication: Low-Rank Matrix Iteration Using Polynomial-Filtered Subspace Extraction