Projection-Free Variance Reduction Methods for Stochastic Constrained Multi-Level Compositional Optimization
Abstract
This paper investigates projection-free algorithms for stochastic constrained multi-level optimization. In this context, the objective function is a nested composition of several smooth functions, and the decision set is closed and convex. Existing projection-free algorithms for solving this problem suffer from two limitations: 1) they solely focus on the gradient mapping criterion and fail to match the optimal sample complexities in unconstrained settings; 2) their analysis is exclusively applicable to non-convex functions, without considering convex and strongly convex objectives. To address these issues, we introduce novel projection-free variance reduction algorithms and analyze their complexities under different criteria. For gradient mapping, our complexities improve existing results and match the optimal rates for unconstrained problems. For the widely-used Frank-Wolfe gap criterion, we provide theoretical guarantees that align with those for single-level problems. Additionally, by using a stage-wise adaptation, we further obtain complexities for convex and strongly convex functions. Finally, numerical experiments on different tasks demonstrate the effectiveness of our methods.
Cite
@article{arxiv.2406.03787,
title = {Projection-Free Variance Reduction Methods for Stochastic Constrained Multi-Level Compositional Optimization},
author = {Wei Jiang and Sifan Yang and Wenhao Yang and Yibo Wang and Yuanyu Wan and Lijun Zhang},
journal= {arXiv preprint arXiv:2406.03787},
year = {2024}
}