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Overparameterized DMD achieves exponential speedups in convergence for low-rank matrix optimization, even when the target rank is uncertain.
AngularMuown not only enhances optimization stability but also outperforms its predecessor in competitive benchmarks, redefining expectations for matrix-aware optimizers.
Finally, a reinforcement learning algorithm, PGP, can provably find near-optimal policies that respect safety and resource constraints, even when the policy space is non-convex.
