학술논문

PLSS: a projected linear systems solver.
Document Type
Journal
Author
Brust, Johannes J. (1-UCSD) AMS Author Profile; Saunders, Michael A. (1-STF-MGE) AMS Author Profile
Source
SIAM Journal on Scientific Computing (SIAM J. Sci. Comput.) (20230101), 45, no.~2, A1012-A1037. ISSN: 1064-8275 (print).eISSN: 1095-7197.
Subject
15 Linear and multilinear algebra; matrix theory -- 15A Basic linear algebra
  15A06 Linear equations

15 Linear and multilinear algebra; matrix theory -- 15B Special matrices
  15B52 Random matrices

65 Numerical analysis -- 65Y Computer aspects of numerical algorithms
  65Y20 Complexity and performance of numerical algorithms

90 Operations research, mathematical programming -- 90C Mathematical programming
  90C20 Quadratic programming
Language
English
Abstract
Summary: ``We propose iterative projection methods for solving square or rectangular consistent linear systems $\bf Ax = b$. Existing projection methods use sketching matrices (possibly randomized) to generate a sequence of small projected subproblems, but even the smaller systems can be costly. We develop a process that appends one column to the sketching matrix each iteration and converges in a finite number of iterations whether the sketch is random or deterministic. In general, our process generates orthogonal updates to the approximate solution ${\bf x}_k$. By choosing the sketch to be the set of all previous residuals, we obtain a simple recursive update and convergence in at most rank$(\bf A)$ iterations (in exact arithmetic). By choosing a sequence of identity columns for the sketch, we develop a generalization of the Kaczmarz method. In experiments on large sparse systems, our method (PLSS) with residual sketches is competitive with LSQR and LSMR and with residual and identity sketches compares favorably with state-of-the-art randomized methods.''MR