Multi-Domain Adaptive Learning Based on Feasibility Splitting and Adaptive Projected Subgradient Method

被引:15
|
作者
Yukawa, Masahiro [1 ]
Slavakis, Konstantinos [3 ]
Yamada, Isao [2 ]
机构
[1] RIKEN, BSI, Lab Math Neurosci, Wako, Saitama 3510198, Japan
[2] Tokyo Inst Technol, Dept Commun & Integrated Syst, Tokyo 1528552, Japan
[3] Univ Peloponnese, Dept Telecommun Sci & Technol, Tripolis, Greece
关键词
adaptive algorithm; convex projection; projected gradient method; convex feasibility problem; FIXED-POINT SET; ALGORITHM; SYSTEMS; MINIMIZATION; SUPPRESSION;
D O I
10.1587/transfun.E93.A.456
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
We propose the multi-domain adaptive learning that enables us to find a point meeting possibly time-varying specifications simultaneously in multiple domains. e.g. space, time, frequency, etc. The novel concept is based on the idea of feasibility splitting - dealing with feasibility in each individual domain. We show that the adaptive projected subgradient method (Yamada, 2003) realizes the multi-domain adaptive learning by employing (i) a projected gradient operator with respect to a 'fixed' proximity function reflecting the time-invariant specifications and (ii) a subgradient projection with respect to 'time-varying' objective functions reflecting the time-varying specifications. The resulting algorithm is Suitable for real-time implementation, because it requires no more than metric projections onto closed convex sets each of which accommodates the specification in each domain. A convergence analysis and numerical examples are presented.
引用
收藏
页码:456 / 466
页数:11
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