Structure of the preconditioned system in various preconditioned conjugate gradient squared algorithms

TL;DR

This paper analyzes the structure of preconditioned systems in various PCGS algorithms, demonstrating how initial settings influence the system direction and comparing improved methods' performance.

Contribution

It clarifies the relationship between PCGS algorithm operations and the preconditioned system direction, introducing insights into algorithm construction and initial vector settings.

Findings

The improved PCGS algorithm acts as a coordinator of left-preconditioned systems.

The direction of the preconditioned system depends on $ ext{α}_k$ and $ ext{β}_k$ operations.

Initial shadow residual vector setting can switch the system's preconditioning direction.

Abstract

An improved preconditioned conjugate gradient squared (PCGS) algorithm has recently been proposed, and it performs much better than the conventional PCGS algorithm. In this paper, the improved PCGS algorithm is verified as a coordinative to the left-preconditioned system, and it has the advantages of both the conventional and the left-PCGS; this is done by comparing, analyzing, and executing numerical examinations of various PCGS algorithms, including another improved one. We show that the direction of the preconditioned system for the CGS method is determined by the operations of $\alpha_k$ and $\beta_k$ in the PCGS algorithm. By comparing the logical structures of these algorithms, we show that the direction of the preconditioned system can be switched by the construction and setting of the initial shadow residual vector.