A Convergence Analysis of G-NPAST Algorithm for Extracting the First Principal Generalized Eigenvector
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概要
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In our previous work, we established the convergence analysis of the normalized projection approximation subspace tracking (NPAST) algorithm, for extracting the first principal eigenvector of an autocorrelation matrix, via a deterministic discrete time (DDT) approach. In this technical report, we extend the analysis to the convergence analysis of an algorithm (we call G-NPAST) by J. Yang et al.('06), which is developed for generalized symmetric eigenvalue problem. Indeed, we have shown that G-NPAST is nothing but a generalization of NPAST. The proposed analysis shows that G-NPAST can be applied to the general case where the generalized eigenvalues are not necessarily distinct. Numerical examples further confirm the results.
- 2012-03-01
著者
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Yamada Isao
Departmen Of Communications And Integrated Systems Tokyo Institute Of Technology
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NGUYEN Tuan
Department of Communications and Integrated Systems, Tokyo Institute of Technology
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YAMADA Isao
Department of Communications and Integrated Systems, Tokyo Institute of Technology
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