The mathematics of eigenvalue optimization |
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Authors: | AS Lewis |
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Institution: | (1) Department of Mathematics, Simon Fraser University, Burnaby, BC V5A 1S6, Canada. e-mail: aslewis@sfu.ca. Website: www.cecm.sfu.ca/∼aslewis. Research supported in part by NSERC., CA |
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Abstract: | Optimization problems involving the eigenvalues of symmetric and nonsymmetric matrices present a fascinating mathematical
challenge. Such problems arise often in theory and practice, particularly in engineering design, and are amenable to a rich
blend of classical mathematical techniques and contemporary optimization theory. This essay presents a personal choice of
some central mathematical ideas, outlined for the broad optimization community. I discuss the convex analysis of spectral
functions and invariant matrix norms, touching briefly on semidefinite representability, and then outlining two broader algebraic
viewpoints based on hyperbolic polynomials and Lie algebra. Analogous nonconvex notions lead into eigenvalue perturbation
theory. The last third of the article concerns stability, for polynomials, matrices, and associated dynamical systems, ending
with a section on robustness. The powerful and elegant language of nonsmooth analysis appears throughout, as a unifying narrative
thread.
Received: December 4, 2002 / Accepted: April 22, 2003
Published online: May 28, 2003
Key Words. eigenvalue optimization – convexity – nonsmooth analysis – duality – semidefinite program – subdifferential – Clarke regular
– chain rule – sensitivity – eigenvalue perturbation – partly smooth – spectral function – unitarily invariant norm – hyperbolic
polynomial – stability – robust control – pseudospectrum – H
∞
norm
Mathematics Subject Classification (2000): 90C30, 15A42, 65F15, 49K40 |
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Keywords: | |
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