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

zbMATH Keywords

polynomial filtereigenvalue decompositionsubspace extractioninexact optimization methodlow-rank matrix iteration

Mathematics Subject Classification ID

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)

Efficient proximal mapping computation for low-rank inducing norms ⋮ Efficient semidefinite programming with approximate ADMM

Uses Software

• SCS

Cites Work

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• 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

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