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arXiv · 2605.16875

Stochastic Optimization and Data Science

Arutyun Avetisyan·Darina Dvinskikh·Alexander Gasnikov·Vladimir Temlyakov·Nazarii Tupitsa·Denis Turdakov
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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.

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Arutyun Avetisyan, Darina Dvinskikh, Alexander Gasnikov, Vladimir Temlyakov, Nazarii Tupitsa, Denis Turdakov. 2026-05-16. Stochastic Optimization and Data Science. https://arxiv.org/abs/2605.16875

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