Speaker Francesco Hrobat will talk about; 'Lanczos with compression for symmetric matrix Lyapunov equations'
Large-scale symmetric matrix Lyapunov equations arise in control theory, model order reduction, and the discretization of PDEs. State-of-the-art algorithms, such as standard and rational Krylov methods, aim to approximate the solution with a low-rank matrix. However, the standard polynomial Krylov method (also referred to as the Lanczos method) often converges slowly and faces a memory bottleneck as the dimension of the Lanczos basis grows. Conversely, rational Krylov alternatives, while effective for low-rank approximations, require the solution of expensive shifted linear systems involving a large coefficient matrix.
In this talk, I will present a low-memory variant of the Lanczos algorithm for solving symmetric Lyapunov equations. Our approach leverages a polynomial Krylov subspace while employing rational subspaces associated with small matrices to compress the Lanczos basis. This method accesses the large coefficient matrix exclusively through matrix-vector products and maintains fixed storage requirements. The resulting low-rank solution has a rank that is independent of the dimension of the underlying polynomial Krylov subspace.