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.
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The Neural Green's Operator matches exact coarse-solve iteration counts in two-level preconditioners for diffusion and advection-diffusion problems when inputs are integrated against the output basis.
RL4RLA is a reinforcement learning framework that discovers interpretable symbolic randomized linear algebra algorithms by combining curriculum learning and graph-based search to overcome sparse rewards and large search spaces.
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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.
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When can a neural operator replace a coarse solve? Architectural principles for two-level preconditioning
The Neural Green's Operator matches exact coarse-solve iteration counts in two-level preconditioners for diffusion and advection-diffusion problems when inputs are integrated against the output basis.
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RL4RLA: Teaching ML to Discover Randomized Linear Algebra Algorithms Through Curriculum Design and Graph-Based Search
RL4RLA is a reinforcement learning framework that discovers interpretable symbolic randomized linear algebra algorithms by combining curriculum learning and graph-based search to overcome sparse rewards and large search spaces.