Computationally enhanced projection methods for symmetric Sylvester and Lyapunov matrix equations

TL;DR

This paper introduces a computationally efficient approach for evaluating residuals in projection methods solving large-scale symmetric Sylvester and Lyapunov equations, improving performance especially for symmetric data.

Contribution

It presents a new residual norm computation method that reduces cost and memory usage, making classical Krylov methods more competitive for large symmetric problems.

Findings

Residual norm computation is significantly faster for symmetric problems.

The new method reduces memory requirements in Krylov subspace methods.

Numerical experiments confirm improved efficiency and competitiveness.

Abstract

In the numerical treatment of large-scale Sylvester and Lyapunov equations, projection methods require solving a reduced problem to check convergence. As the approximation space expands, this solution takes an increasing portion of the overall computational effort. When data are symmetric, we show that the Frobenius norm of the residual matrix can be computed at significantly lower cost than with available methods, without explicitly solving the reduced problem. For certain classes of problems, the new residual norm expression combined with a memory-reducing device make classical Krylov strategies competitive with respect to more recent projection methods. Numerical experiments illustrate the effectiveness of the new implementation for standard and extended Krylov subspace methods.