A Primal-Dual Algorithm for Faster Distributionally Robust Optimization
CoRR(2024)
摘要
We consider the penalized distributionally robust optimization (DRO) problem
with a closed, convex uncertainty set, a setting that encompasses the f-DRO,
Wasserstein-DRO, and spectral/L-risk formulations used in practice. We
present Drago, a stochastic primal-dual algorithm that achieves a
state-of-the-art linear convergence rate on strongly convex-strongly concave
DRO problems. The method combines both randomized and cyclic components with
mini-batching, which effectively handles the unique asymmetric nature of the
primal and dual problems in DRO. We support our theoretical results with
numerical benchmarks in classification and regression.
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