Journal of Global Optimization

, Volume 64, Issue 2, pp 325–348

Constrained trace-optimization of polynomials in freely noncommuting variables

Article

Abstract

The study of matrix inequalities in a dimension-free setting is in the realm of free real algebraic geometry. In this paper we investigate constrained trace and eigenvalue optimization of noncommutative polynomials. We present Lasserre’s relaxation scheme for trace optimization based on semidefinite programming (SDP) and demonstrate its convergence properties. Finite convergence of this relaxation scheme is governed by flatness, i.e., a rank-preserving property for associated dual SDPs. If flatness is observed, then optimizers can be extracted using the Gelfand–Naimark–Segal construction and the Artin–Wedderburn theory verifying exactness of the relaxation. To enforce flatness we employ a noncommutative version of the randomization technique championed by Nie. The implementation of these procedures in our computer algebra system NCSOStoolsis presented and several examples are given to illustrate our results.

Keywords

Noncommutative polynomial Optimization Sum of squares Semidefinite programming Moment problem Hankel matrix Flat extension Matlab toolbox  Real algebraic geometry Free positivity 

Mathematics Subject Classification

Primary 90C22 14P10 Secondary 13J30 47A57 

Copyright information

© Springer Science+Business Media New York (outside the USA) 2015

Authors and Affiliations

  1. 1.Department of MathematicsThe University of AucklandAucklandNew Zealand
  2. 2.Faculty of information studies in Novo mestoNovo mestoSlovenia

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