On strict convergence of stochastic gradients

TL;DR

This paper investigates the conditions under which stochastic gradient algorithms converge strictly, providing theoretical insights into their convergence behavior.

Contribution

It introduces new conditions that guarantee the strict convergence of stochastic gradient algorithms, advancing theoretical understanding.

Findings

Established sufficient conditions for strict convergence

Provided theoretical proofs for convergence guarantees

Enhanced understanding of stochastic gradient behavior

Abstract

We discuss conditions ensuring the (strict) convergence of stochastic gradient algorithms.