HONES: A Fast and Tuning-free Homotopy Method For Online Newton Step

被引:0
|
作者
Ye, Yuting [1 ]
Ju, Cheng [1 ]
Lei, Lihua [1 ]
机构
[1] Univ Calif Berkeley, Berkeley, CA 94720 USA
关键词
STANDARD QUADRATIC OPTIMIZATION; PORTFOLIO SELECTION; GRADIENT; DESCENT;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this article, we develop and analyze a homotopy continuation method, referred to as HONES, for solving the sequential generalized projections in Online Newton Step (Hazan et al., 2006b), as well as the generalized problem known as sequential standard quadratic programming. HONES is fast, tuning-free, error-free (up to machine error) and adaptive to the solution sparsity. This is confirmed by both careful theoretical analysis and extensive experiments on both synthetic and real data.
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页数:10
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