Variance-Reduced Splitting Schemes for Monotone Stochastic Generalized Equations
This work addresses uncertainty in stochastic optimization and variational inequality problems, offering improved convergence rates and efficiency, though it appears incremental as it builds on existing splitting schemes with variance reduction.
The paper tackles the challenge of solving monotone inclusion problems with expectation-valued operators by proposing a variance-reduced stochastic modified forward-backward splitting scheme (vr-SMFBS), achieving almost sure convergence with linear rates for strongly monotone cases and O(1/k) rates for monotone cases, along with optimal oracle complexity bounds.
We consider monotone inclusion problems where the operators may be expectation-valued, a class of problems that subsumes convex stochastic optimization problems as well as subclasses of stochastic variational inequality and equilibrium problems. A direct application of splitting schemes is complicated by the need to resolve problems with expectation-valued maps at each step, a concern that is addressed by using sampling. Accordingly, we propose an avenue for addressing uncertainty in the mapping: Variance-reduced stochastic modified forward-backward splitting scheme (vr-SMFBS). In constrained settings, we consider structured settings when the map can be decomposed into an expectation-valued map A and a maximal monotone map B with a tractable resolvent. We show that the proposed schemes are equipped with a.s. convergence guarantees, linear (strongly monotone A) and O(1/k) (monotone A) rates of convergence while achieving optimal oracle complexity bounds. The rate statements in monotone regimes appear to be amongst the first and rely on leveraging the Fitzpatrick gap function for monotone inclusions. Furthermore, the schemes rely on weaker moment requirements on noise and allow for weakening unbiasedness requirements on oracles in strongly monotone regimes. Preliminary numerics on a class of two-stage stochastic variational inequality problems reflect these findings and show that the variance-reduced schemes outperform stochastic approximation schemes and sample-average approximation approaches. The benefits of attaining deterministic rates of convergence become even more salient when resolvent computation is expensive.