西安工程大学学报2017，Vol.31Issue(1)：135-140,6.DOI:10.13338/j.issn.1674-649x.2017.01.024
基于稀疏鲁棒M-投资选择模型的鲁棒Half算法
Robust half threshold algorithms based on the sparse robust M-Portfolios model
摘要
Abstract
The sparse and robust M-portfolio model is proposed to obtain robust and sparse portfolio,based on L1/2 regularization theory and the half threshold algorithm,the robust Half threshold algorithm is designed for numerically solving the sparse and robust M-portfolio problems.Numerical experiments show that this algorithm not only converges much faster than Lasso but also obtains a much less and much stabler risk obtained when the expectation value is fixed.关键词
稀疏投资选择模型/Half阈值算法/稀疏鲁棒M-投资选择/L1/2正则化/鲁棒Half阈值算法Key words
sparse portfolio selection model/Half threshold algorithm/sparse robust M-Portfolios model/L1/2 regularization/robust Half threshold algorithm分类
数理科学引用本文复制引用
张亚飞,张成毅,罗双华..基于稀疏鲁棒M-投资选择模型的鲁棒Half算法[J].西安工程大学学报,2017,31(1):135-140,6.基金项目
国家自然科学基金资助项目(11201362) （11201362）
陕西省教育厅自然科学专项基金资助项目(14JK1305) （14JK1305）
