- On optimizing the sum of the Rayleigh quotient and the generalized Rayleigh quotient on the unit sphere Zhang Leihong(longzlhgmail.com) Abstract: Given symmetric matrices $B,D\in R^{n\times n}$ and a symmetric positive definite matrix $W\in R^{n\times n},$ maximizing the sum of the Rayleigh quotient $x^T Dx$ and the generalized Rayleigh quotient $x^T Bx/x^TWx$ on the unit sphere not only is of mathematical interest in its own right, but also finds applications in practice. In this paper, we first present a real world application arising from the sparse Fisher discriminant analysis. To tackle this problem, our first effort is to characterize the local and global maxima by investigating the optimality conditions. Our results reveal that fnding the global solution is closely related with a special extreme nonlinear eigenvalue problem, and in the special case $D = µW (µ > 0),$ the set of the global solutions is essentially an eigenspace corresponding to the largest eigenvalue of a specially-defined matrix. The characterization of the global solution not only sheds some lights on the maximization problem, but motives a starting point strategy to obtain the global aximizer for any monotonically convergent iteration. Our second part then realizes the Riemannian trust-region method of [Absil, Baker and Gallivan, Found. Comput. Math., 7, 303-330 (2007)] into a practical algorithm to solve this problem, which enjoys the nice convergence properties: global convergence and local superlinear convergence. Preliminary numerical tests are carried out and empirical evaluation of its performance is reported. Keywords: Rayleigh quotient, the generalized Rayleigh quotient, the nonlinear eigenvalue problem, trust-region method, linear discriminant analysis Category 1: Nonlinear Optimization (Constrained Nonlinear Optimization ) Category 2: Global Optimization (Other ) Category 3: Applications -- Science and Engineering (Other ) Citation: Computational Optimization and Applications, 2012, accepted. Download: [PDF]Entry Submitted: 03/12/2012Entry Accepted: 03/12/2012Entry Last Modified: 03/12/2012Modify/Update this entry Visitors Authors More about us Links Subscribe, Unsubscribe Digest Archive Search, Browse the Repository Submit Update Policies Coordinator's Board Classification Scheme Credits Give us feedback Optimization Journals, Sites, Societies Optimization Online is supported by the Mathematical Optmization Society.