The Nesterov-Spokoiny Acceleration Achieves Strict $o(1/k^2)$ Convergence

Fuente: arXiv
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Autori principali: Peng, Weibin, Liu, Yu, Wang, Tianyu
Natura: Preprint
Pubblicazione: 2023
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author Peng, Weibin
Liu, Yu
Wang, Tianyu
author_facet Peng, Weibin
Liu, Yu
Wang, Tianyu
contents This paper studies the Nesterov-Spokoiny Acceleration (NSA), a variant of the accelerated gradient method by Nesterov and Spokoiny. For smooth convex optimization, NSA achieves a strict $o(1/k^2)$ convergence rate in function value and an $o(1/(k^3 \log k))$ rate in squared gradient norm, while ensuring monotonic descent of the objective. We further study a zeroth-order version of NSA that handles inexact gradients, and extends NSA to composite optimization problems, in each case establishing $o(1/k^2)$ convergence in function value. A continuous-time analysis reveals connections to high-resolution ODEs known to underlie acceleration phenomena.
format Preprint
id arxiv_https___arxiv_org_abs_2308_14314
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle The Nesterov-Spokoiny Acceleration Achieves Strict $o(1/k^2)$ Convergence
Peng, Weibin
Liu, Yu
Wang, Tianyu
Optimization and Control
This paper studies the Nesterov-Spokoiny Acceleration (NSA), a variant of the accelerated gradient method by Nesterov and Spokoiny. For smooth convex optimization, NSA achieves a strict $o(1/k^2)$ convergence rate in function value and an $o(1/(k^3 \log k))$ rate in squared gradient norm, while ensuring monotonic descent of the objective. We further study a zeroth-order version of NSA that handles inexact gradients, and extends NSA to composite optimization problems, in each case establishing $o(1/k^2)$ convergence in function value. A continuous-time analysis reveals connections to high-resolution ODEs known to underlie acceleration phenomena.
title The Nesterov-Spokoiny Acceleration Achieves Strict $o(1/k^2)$ Convergence
topic Optimization and Control
url https://arxiv.org/abs/2308.14314