Author

Ryne Beeson

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Preprint Jul 2026

Complexity Bounds and Approaches to Learning Projected Gradient Descent Solver Iterates

The approach proposed in this paper enables a more capable DDDAS paradigm by improving the efficiency of the data-model-optimization loop by extracting a generalization bound based on Rademacher complexity that reveals the role of the $k-neighborhoods and related parameters.

Anjian Li, Ryne Beeson · 0 citations