Spectral optimization of admissible entries in fixed-support FSAI preconditioners, using projected Krylov gradients and a detached Rayleigh surrogate, improves performance over Frobenius-based selection on finite-element problems, especially indefinite saddle-point systems.
Chen , Graph neural preconditioners for iterative solutions of sparse linear systems , arXiv preprint arXiv:2406.00809, (2024)
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Factored Sparse Approximate Inverse Preconditioning via Spectral Optimization
Spectral optimization of admissible entries in fixed-support FSAI preconditioners, using projected Krylov gradients and a detached Rayleigh surrogate, improves performance over Frobenius-based selection on finite-element problems, especially indefinite saddle-point systems.