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Method of centers for minimizing generalized eigenvalues. (English) Zbl 0781.65051
Authors’ abstract: We consider the problem of minimizing the largest generalized eigenvalue of a pair of symmetric matrices, each of which depends affinely on the decision variables. Although this problem may appear specialized, it is in fact quite general, and includes for example all linear, quadratic and linear fractional programs. Many problems arising in control theory can be cast in this form. The problem is non- differentiable but quasi-convex, so methods such as the cutting-plane algorithm of J. E. Kelley Jr. [J. Soc. Indust. Appl. Math. 8, 703- 712 (1960; Zbl 0098.12104)] or the ellipsoid algorithm of N. Z. Shor [Minimization methods for non-differentiable functions (1985; Zbl 0561.90058)] and A. S. Nemirovskij and D. B. Yudin [Problem complexity and method efficiency in optimization (1979; Zbl 0501.90061)] are guaranteed to minimize it.
In this paper we describe relevant background material and a simple interior-point method that solves such problems more efficiently. The algorithm is a variation on the method of centers by P. Huard [Resolution of mathematical programming with nonlinear constraints by the method of centres (1967; Zbl 0157.497)], using a self-concordant barrier for matrix inequalities developed by Yu. Nesterov and A. Nemirovsky [An interior point method for generalized linear-fractional programming. Math. Programming, Ser. B (to appear)]. (Nesterov and Nemirovsky have also extended their potential reduction methods to handle the same problem.)
Since the problem is quasi-convex but not convex, devising a non- heuristic stopping criterion (i.e. one that guarantees given accuracy) is more difficult than in the convex case. We describe several non-heuristic stopping criteria that are based on the dual of a related convex problem and a new ellipsoidal approximation that is slightly sharper, in some cases, than a more general result due to Nesterov and Nemirovsky [loc. cit.]. The algorithm is demonstrated on an example: determining the quadratic Lyapunov function that optimizes a decay-rate estimate for a differential inclusion.

MSC:
65K05 Numerical mathematical programming methods
65F15 Numerical computation of eigenvalues and eigenvectors of matrices
90C25 Convex programming
Software:
QDES
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