Smooth Monotone Stochastic Variational Inequalities and Saddle Point Problems: A Survey
It provides a comprehensive overview for researchers in optimization and machine learning, but is incremental as it is a survey.
This paper surveys methods for solving smooth monotone stochastic variational inequalities and saddle point problems, reviewing deterministic foundations, stochastic formulations, finite sum setups, and recent algorithmic advances.
This paper is a survey of methods for solving smooth (strongly) monotone stochastic variational inequalities. To begin with, we give the deterministic foundation from which the stochastic methods eventually evolved. Then we review methods for the general stochastic formulation, and look at the finite sum setup. The last parts of the paper are devoted to various recent (not necessarily stochastic) advances in algorithms for variational inequalities.