An increasing rank Riemannian method for generalized Lyapunov equations

TL;DR

This paper introduces IRRLyap, a novel Riemannian optimization algorithm with increasing rank technique for efficiently approximating solutions to large-scale generalized Lyapunov equations, outperforming existing methods.

Contribution

The paper proposes a new increasing rank Riemannian method combined with a line-search-based inexact Newton's method for generalized Lyapunov equations, including a specialized preconditioner.

Findings

IRRLyap outperforms state-of-the-art methods in low-rank solutions.

The Riemannian inexact Newton's method has guaranteed global and local superlinear convergence.

Numerical experiments demonstrate superior efficiency and accuracy of the proposed approach.

Abstract

In this paper, we consider finding a low-rank approximation to the solution of a large-scale generalized Lyapunov matrix equation in the form of $A X M + M X A = C$, where $A$ and $M$ are symmetric positive definite matrices. An algorithm called an Increasing Rank Riemannian Method for Generalized Lyapunov Equation (IRRLyap) is proposed by merging the increasing rank technique and Riemannian optimization techniques on the quotient manifold $\mathbb{R}_*^{n \times p} / \mathcal{O}_p$. To efficiently solve the optimization problem on $\mathbb{R}_*^{n \times p} / \mathcal{O}_p$, a line-search-based Riemannian inexact Newton's method is developed with its global convergence and local superlinear convergence rate guaranteed. Moreover, we derive a preconditioner which takes $M \neq I$ into consideration. Numerical experiments show that the proposed Riemannian inexact Newton's method and preconditioner has superior performance and IRRLyap is preferable compared to the tested state-of-the-art methods when the lowest rank solution is desired.