Randomized Subspace Nesterov Accelerated Gradient
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 automatic differentiation and communication-limited settings. While Nesterov acceleration is well understood for full-gradient and coordinate-based methods, obtaining accelerated methods for general subspace sketches that use only projected-gradient information and can improve over full-dimensional Nesterov acceleration in oracle complexity is technically nontrivial. We develop randomized-subspace Nesterov accelerated gradient methods for smooth convex and smooth strongly convex optimization under matrix smoothne...
| Authors | Gaku Omiya, Pierre-Louis Poirion, Akiko Takeda |
|---|---|
| Primary category | math.OC |
| Published | 1 May 2026 |
| Last updated at source | 1 May 2026 |
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