… the primary method analyzed in this paper as stochastic trust-region optimization with random
models (… STORM is a stochastic variance-reducing trust-region method, essentially a minor …

… method, which we analyze in this paper and which we will refer to as STORM (Stochastic
trust region method with Random … where the increments of a supermartingale depend on the …

… convergence rate of the trust region method in [2], which we will refer to as STORM (Stochastic
trust region method with Random … where the increments of a supermartingale depend on …

… In Section 4, we present some theoretical results concerning the convergence of the method.
… Stochastic optimization using a trust-region method and random models. Mathematical …

… using only gradient information. In this work, we study the performance of stochastic quasi-…
This study fills a gap concerning the real performance of both updates in the minibatch …

… We present an enhanced stochastic trust-region optimization with adaptive sampling (ASTRO-DF) …
“Convergence rate analysis of a stochastic trust-region method via supermartingales”. …

… on the convergence rate analysis of stochastic variants of the … -search methods analyzed in
[17], using a supermartingale-… convergence rate analysis of a stochastic trust-region method. …

… We provide convergence analysis for all … trust-region (TR) algorithm [2] adapted to the
stochastic framework where the step and the candidate point for the next iterate are obtained using …

… trust-region (TR) algorithm [2] adapted to the stochastic framework where the step and the
candidate point for the next iterate are obtained using … We provide convergence analysis for all …

… minimization using SGD with line-search and study stochastic momentum techniques under
… -search and trust-region methods to improve stochastic optimization for machine learning. …