Given symmetric matrices B,D ∈ R n×n and a symmetric positive definite matrix W ∈ R n×n , maximizingthe sum of the Rayleighquotientx ? Dx andthe gener- alized Rayleigh quotient x ? Bx x ? Wx 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 investi- gating the optimality conditions. Our results reveal that finding the global solution is closely related with a special extreme nonlinear eigenvalue problem, and in the spe- cial 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 characteri- zation of the global solution not only sheds some lights on the maximization problem, but motives a starting point strategy to obtain the global maximizer for any monoton- ically 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 prop- erties: global convergence and local superlinear convergence. Preliminary numerical tests are carried out and empirical evaluation of its performance is reported.

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