Abstract
The sum of the largest eigenvalues of a symmetric matrix is a nonsmooth convex function of the matrix elements. Max characterizations for this sum are established, giving a concise characterization of the subdifferential in terms of a dual matrix. This leads to a very useful characterization of the generalized gradient of the following convex composite function: the sum of the largest eigenvalues of a smooth symmetric matrix-valued function of a set of real parameters. The dual matrix provides the information required to either verify first-order optimality conditions at a point or to generate a descent direction for the eigenvalue sum from that point, splitting a multiple eigenvalue if necessary. Connections with the classical literature on sums of eigenvalues and eigenvalue perturbation theory are discussed. Sums of the largest eigenvalues in the absolute value sense are also addressed.
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This paper is dedicated to Phil Wolfe on the occasion of his 65th birthday.
The work of this author was supported by the National Science Foundation under grants CCR-8802408 and CCR-9101640.
The work of this author was supported in part during a visit to Argonne National Laboratory by the Applied Mathematical Sciences subprogram of the Office of Energy Research of the U.S. Department of Energy under contract W-31-109-Eng-38, and in part during a visit to the Courant Institute by the U.S. Department of Energy under Contract DEFG0288ER25053.
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Overton, M.L., Womersley, R.S. Optimality conditions and duality theory for minimizing sums of the largest eigenvalues of symmetric matrices. Mathematical Programming 62, 321–357 (1993). https://doi.org/10.1007/BF01585173
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DOI: https://doi.org/10.1007/BF01585173