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math.OC2026
Randomized Subspace Nesterov Accelerated Gradient
Gaku Omiya, Pierre-Louis Poirion, Akiko Takeda
Randomized-subspace methods reduce the cost of first-order optimization by using only low-dimensional projected-gradient information, a feature that is attractive in forward-mode a…
math.OC2026
Convergence Analysis of Randomized Subspace Normalized SGD under Heavy-Tailed Noise
Gaku Omiya, Pierre-Louis Poirion, Akiko Takeda
Randomized subspace methods reduce per-iteration cost; however, in nonconvex optimization, most analyses are expectation-based, and high-probability bounds remain scarce even under…