1. Effective Algorithms for Solving Trace Minimization Problem in Multivariate Statistics
- Author
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Kai Wang, Ya-qiong Wen, Jiao-fen Li, and Xue-lin Zhou
- Subjects
Multivariate statistics ,021103 operations research ,Trace (linear algebra) ,Article Subject ,Computer science ,General Mathematics ,0211 other engineering and technologies ,General Engineering ,010103 numerical & computational mathematics ,02 engineering and technology ,Engineering (General). Civil engineering (General) ,01 natural sciences ,Least squares ,symbols.namesake ,Rate of convergence ,Orthogonality ,symbols ,QA1-939 ,Orthonormal basis ,0101 mathematics ,TA1-2040 ,Majorization ,Newton's method ,Algorithm ,Mathematics - Abstract
This paper develops two novel and fast Riemannian second-order approaches for solving a class of matrix trace minimization problems with orthogonality constraints, which is widely applied in multivariate statistical analysis. The existing majorization method is guaranteed to converge but its convergence rate is at best linear. A hybrid Riemannian Newton-type algorithm with both global and quadratic convergence is proposed firstly. A Riemannian trust-region method based on the proposed Newton method is further provided. Some numerical tests and application to the least squares fitting of the DEDICOM model and the orthonormal INDSCAL model are given to demonstrate the efficiency of the proposed methods. Comparisons with some latest Riemannian gradient-type methods and some existing Riemannian second-order algorithms in the MATLAB toolbox Manopt are also presented.
- Published
- 2020
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