Enhancing Stochastic Optimization for Statistical Efficiency Using ROOT-SGD with Diminishing Stepsize
<|reference_start|>Enhancing Stochastic Optimization for Statistical Efficiency Using ROOT-SGD with Diminishing Stepsize: In this paper, we revisit \textsf{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 \textsf{ROOT-SGD} by integrating a carefully designed \emph{diminishing stepsize strategy}. This approach addresses key challenges in optimization, providing robust theoretical guarantees and practical benefits. Our analysis demonstrates that \textsf{ROOT-SGD} with diminishing achieves optimal convergence rates while maintaining computational efficiency. By dynamically adjusting the learning rate, \textsf{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.<|reference_end|>
@article{li2024enhancing,
    title={Enhancing Stochastic Optimization for Statistical Efficiency Using
  ROOT-SGD with Diminishing Stepsize},
    author={Chris Junchi Li},
    journal={arXiv preprint arXiv:2407.10955},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.10955},
    primaryClass={stat.ML cs.LG math.OC}
}