Enhancing Stochastic Optimization for Statistical Efficiency Using ROOT-SGD with Diminishing Stepsize

Abstract

In this paper, we revisit ROOT-SGD, an innovative method for stochastic optimization to bridge the gap between stochastic optimization and statistical efficiency. The proposed method enhances the performance and reliability of ROOT-SGD by integrating a carefully designed diminishing stepsize strategy. This approach addresses key challenges in optimization, providing robust theoretical guarantees and practical benefits. Our analysis demonstrates that ROOT-SGD with diminishing achieves optimal convergence rates while maintaining computational efficiency. By dynamically adjusting the learning rate, ROOT-SGD ensures improved stability and precision throughout the optimization process. The findings of this study offer valuable insights for developing advanced optimization algorithms that are both efficient and statistically robust.

Tasks

Reproductions