提出改进版稀疏Polyak算法，实现高维估计中更稀疏精准解。
Sparse Polyak with optimal thresholding operators for high-dimensional M-estimation
- 引入最优阈值算子，保持维度扩展下的稳定性能
- 在高维场景下获得更稀疏且更准确的解
- 适合需要稀疏性与统计精度的高维估计任务
本文提出一种高维M估计问题的稀疏Polyak变体。稀疏Polyak通过自适应步长规则，能有效估计高维场景下的问题曲率，保证算法性能不随环境维度增加而退化。但原有方法需牺牲解的稀疏性和统计精度以获得收敛性保障。本工作提出的新变体，在保持维度扩展优势的同时，实现了更稀疏、更精确的解，突破了原有权衡限制。
原文摘要 · Abstract (English)
We propose and analyze a variant of Sparse Polyak for high dimensional M-estimation problems. Sparse Polyak proposes a novel adaptive step-size rule tailored to suitably estimate the problem's curvature in the high-dimensional setting, guaranteeing that the algorithm's performance does not deteriorate when the ambient dimension increases. However, convergence guarantees can only be obtained by sacrificing solution sparsity and statistical accuracy. In this work, we introduce a variant of Sparse Polyak that retains its desirable scaling properties with respect to the ambient dimension while obtaining sparser and more accurate solutions.
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