First-Order Algorithms for Optimization Problems with a Maximum Eigenvalue/Singular Value Cost and or Constraints

  • Elijah Polak
Part of the Nonconvex Optimization and Its Applications book series (NOIA, volume 57)

Abstract

Optimization problems with maximum eigenvalue or singular eigenvalue cost or constraints occur in the design of linear feedback systems, signal processing, and polynomial interpolation on a sphere. Since the maximum eigenvalue of a positive definite matrix Q(x) is given by max‖y‖=1〈(y, Q(x)y〉, we see that such problems are, in fact, semi-infinite optimization problems. We will show that the quadratic structure of these problems can be exploited in constructing specialized first-order algorithms for their solution that do not require the discretization of the unit sphere or the use of outer approximations techniques.

Keywords

Accumulation Point Support Point Maximum Eigenvalue Unique Minimizer Outer Approximation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media Dordrecht 2001

Authors and Affiliations

  • Elijah Polak
    • 1
  1. 1.Department of Electrical Engineering and Computer SciencesUniversity of CaliforniaBerkeleyUSA

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