A Non-Krylov subspace Method for Solving Large and Sparse Linear System of Equations

TL;DR

This paper introduces a novel non-Krylov subspace iterative method, the APAP, which uses a variable projection matrix and demonstrates improved convergence and effectiveness on large, ill-conditioned systems compared to traditional methods.

Contribution

The paper proposes the accumulated projection (AP) and accelerated AP (APAP) methods that differ from traditional Krylov approaches by using a varying projection matrix, enhancing convergence.

Findings

APAP shows significantly improved convergence over traditional methods.

APAP effectively solves large, ill-conditioned systems like Hilbert matrices.

Numerical experiments confirm the advantages of APAP in various scenarios.

Abstract

Most current prevalent iterative methods can be classified into the so-called extended Krylov subspace methods, a class of iterative methods which do not fall into this category are also proposed in this paper. Comparing with traditional Krylov subspace methods which always depend on the matrix-vector multiplication with a fixed matrix, the newly introduced methods(the so-called (progressively) accumulated projection methods, or AP (PAP) for short) use a projection matrix which varies in every iteration to form a subspace from which an approximate solution is sought. More importantly an accelerative approach(called APAP) is introduced to improve the convergence of PAP method. Numerical experiments demonstrate some surprisingly improved convergence behavior. Comparison between benchmark extended Krylov subspace methods(Block Jacobi and GMRES) are made and one can also see remarkable advantage of APAP in some examples. APAP is also used to solve systems with extremely ill-conditioned coefficient matrix (the Hilbert matrix) and numerical experiments shows that it can bring very satisfactory results even when the size of system is up to a few thousands.