Analysis of the rational Krylov subspace projection method for large-scale algebraic Riccati equations

TL;DR

This paper provides a theoretical analysis of the rational Krylov subspace projection method for solving large-scale algebraic Riccati equations, offering new insights and relations that support recent methodological improvements.

Contribution

It derives new relations for approximate solutions and residuals, enhancing understanding of the method's theoretical foundations and supporting recent modifications.

Findings

New relations for approximate solutions and residuals.

Insights into the role of the matrix A - B B* X.

Theoretical support for modifications of projection methods.

Abstract

In the numerical solution of the algebraic Riccati equation $A^* X + X A - X BB^* X + C^* C =0$, where $A$ is large, sparse and stable, and $B$, $C$ have low rank, projection methods have recently emerged as a possible alternative to the more established Newton-Kleinman iteration. In spite of convincing numerical experiments, a systematic matrix analysis of this class of methods is still lacking. We derive new relations for the approximate solution, the residual and the error matrices, giving new insights into the role of the matrix $A-BB^*X$ and of its approximations in the numerical procedure. The new results provide theoretical ground for recently proposed modifications of projection methods onto rational Krylov subspaces.