Title
Low-Rank Updates and a Divide-And-Conquer Method for Linear Matrix Equations.
Abstract
Linear matrix equations, such as the Sylvester and Lyapunov equations, play an important role in various applications, including the stability analysis and dimensionality reduction of linear dynamical control systems and the solution of partial differential equations. In this work, we present and analyze a new algorithm, based on tensorized Krylov subspaces, for quickly updating the solution of such a matrix equation when its coefficients undergo low-rank changes. We demonstrate how our algorithm can be utilized to accelerate the Newton method for solving continuous-time algebraic Riccati equations. Our algorithm also forms the basis of a new divide-and-conquer approach for linear matrix equations with coefficients that feature hierarchical low-rank structure, such as hierarchically off-diagonal low-rank structures, hierarchically semiseparable, and banded matrices. Numerical experiments demonstrate the advantages of divide-and-conquer over existing approaches, in terms of computational time and memory consumption.
Year
DOI
Venue
2019
10.1137/17M1161038
SIAM JOURNAL ON SCIENTIFIC COMPUTING
Keywords
Field
DocType
Sylvester equation,Lyapunov equation,low-rank update,divide-and-conquer,hierarchical matrices
Lyapunov function,Applied mathematics,Dimensionality reduction,Algebraic number,Mathematical analysis,Matrix (mathematics),Linear subspace,Divide and conquer algorithms,Partial differential equation,Mathematics,Newton's method
Journal
Volume
Issue
ISSN
41
2
1064-8275
Citations 
PageRank 
References 
1
0.35
0
Authors
3
Name
Order
Citations
PageRank
Daniel Kressner144948.01
Stefano Massei263.15
Leonardo Robol3307.04