弹性网络正则化：误差估计与活动集方法
Elastic-Net Regularization: Error estimates and Active Set Methods
AI总结：
本文研究弹性网络正则化的理论性质与数值算法，证明了先验和后验参数选择规则的收敛率，并提出了两种活动集迭代算法。
摘要：
本文研究了源自统计学的所谓弹性网络正则化的理论性质和高效数值算法，该正则化同时施加 l^1 和 l^2 正则化。研究了极小元的稳定性及其一致性，并建立了先验和后验参数选择规则的收敛率。提出了两种活动集类型的迭代数值算法，并讨论了它们的收敛性质。给出了数值结果以说明泛函和算法的特征。
英文摘要：
This paper investigates theoretical properties and efficient numerical algorithms for the so-called elastic-net regularization originating from statistics, which enforces simultaneously l^1 and l^2 regularization. The stability of the minimizer and its consistency are studied, and convergence rates for both a priori and a posteriori parameter choice rules are established. Two iterative numerical algorithms of active set type are proposed, and their convergence properties are discussed. Numerical results are presented to illustrate the features of the functional and algorithms.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
