Georgii Babinskii

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...