Author

E. Gladin

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Preprint Sep 2026

Sampled-Max Subgradient Method for Convex Finite-Max Optimization

We study the Sampled-Max Subgradient Method (SMax-SGM) for large convex finite-max problems. Each iteration maximizes over a fresh random subset of the $N$ components and takes one subgradient of the sampled maximizer. The method is therefore stochastic subgradient descent on a sampled-max surrogate. We bound the surro...

E. Gladin, Анна Фёдорровна Попова, Georgii Babinskii · 0 citations

Review Jul 2026

Mathematical methods of reinforcement learning

Reinforcement learning (RL) is increasingly grounded in tools from probability, optimization, and operator theory. This survey organizes the mathematical structures that underpin the design and analysis of modern algorithms in RL. We begin from Markov decision processes (MDPs) and Bellman operators, emphasizing contrac...

D. Belomestny, Alexander V. Gasnikov, E. Gladin et al. · Успехи математических наук · 0 citations