RINS: Residual-Image Neural Subspace Solvers for Large Sparse Linear Systems

Abstract:Large sparse linear systems from PDE discretizations require correction subspaces whose operator images explain the current residual. We study this residual-image viewpoint and propose Gate-RINS, a neural subspace solver that generates polynomial correction bases from cached residual probes and modulates them with a lightweight residual- and coordinate-dependent pointwise gate. The projected least-squares update remains unchanged, so the neural component only chooses the expansion directions while the numerical closure tests them through \(\operatorname{range}(AQ_t)\). We also introduce a hybrid controller schedule that composes GRANS-style graph controllers with Gate-RINS under the same projected solver. Across six PDE-derived benchmark tasks and two scales, Gate-RINS reaches fixed relative-residual thresholds faster in synchronized wall-clock time than GMRES and a recent graph-only neural baseline in most settings, and hybrid schedules further improve the residual trajectory. Difficult-mode diagnostics and trajectory visualizations support the interpretation that these gains are associated with operator-image subspaces that align more effectively with the current residual.

Computational Engineering, Finance, and Science (cs.CE); Machine Learning (cs.LG)

arXiv:2610.02217 [cs.CE]

(or arXiv:2610.02217v1 [cs.CE] for this version)

https://doi.org/10.48550/arXiv.2610.02217 Focus to learn more arXiv-issued DOI via DataCite

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