Stochastic Optimization and Data Science
Optimization and Control
2026-05-19 v1
Abstract
This paper aims to motivate stochastic optimization problems from a statistical perspective and a statistical learning perspective, where the goal is to maximize the log-likelihood or minimize the population risk. We briefly describe the two main approaches: offline (Monte Carlo / Sample Average Approximation) and online (Stochastic Approximation) approaches -- to solve the expectation minimization problems.
Cite
@article{arxiv.2605.16875,
title = {Stochastic Optimization and Data Science},
author = {Arutyun Avetisyan and Darina Dvinskikh and Alexander Gasnikov and Vladimir Temlyakov and Nazarii Tupitsa and Denis Turdakov},
journal= {arXiv preprint arXiv:2605.16875},
year = {2026}
}