Recycling singular and projection subspaces for pseudospectra computation

Computing matrix pseudospectra over a prescribed region requires evaluating the smallest singular value of $C-zI$ at a large number of grid points, which can be prohibitively expensive for large-scale matrices.

We develop a recycling-based framework for accelerating such computations for both dense and sparse matrices.

The main idea is to exploit the correlation between singular value problems at neighboring grid points by adaptively recycling singular subspaces computed at previously visited points by an iterative SVD solver.

We develop fast Rayleigh-Ritz-SVD procedures for extracting Ritz singular pairs from the recycled singular subspaces, together with fast residual evaluation procedures, with an overall cost that scales linearly with the number of recycled samples.

When the iterative SVD solver admits preconditioning, we propose using a two-level preconditioner whose projection subspaces are recycled.

Numerical experiments demonstrate that the proposed recycling strategies yield substantial speedups over existing methods while maintaining the accuracy of the computed pseudospectra.