English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Stochastic approximation is a foundation for many algorithms found in machinelearning and optimization. It is in general slow to converge: the mean squareerror vanishes as $O(n^{-1})$. A deterministic counterpart known asquasi-stochastic approximation (QSA) is a viable alternative in manyapplications, including gradient free optimization and reinforcement learning.It was assumed in recent prior research that the optimal achievable convergencerate is $O(n^{-2})$. It is shown in this paper that through design it ispossible to obtain far faster convergence, of order $O(n^{-4+\delta})$, with$\delta>0$ arbitrary. Two acceleration techniques are introduced for the firsttime to achieve this rate of convergence. The theory is also specialized withinthe context of gradient-free optimization, and tested on standard benchmarks.The main results are based on a combination of recent results from numbertheory and techniques adapted from stochastic approximation theory.

Extremely Fast Convergence Rates for Extremum Seeking Control with Polyak-Ruppert Averaging