Computationally enhanced projection methods for symmetric Sylvester and Lyapunov matrix equations
For researchers solving large-scale symmetric matrix equations, this provides a practical efficiency improvement, though it is incremental in nature.
This work reduces the computational cost of checking convergence in projection methods for symmetric Sylvester and Lyapunov equations by deriving a cheaper expression for the Frobenius norm of the residual, making classical Krylov methods competitive with newer approaches.
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.