Estimating lowest eigenvalues of symmetric polynomial differential operators by semidefinite programming

Details Estimating lowest eigenvalues of symmetric polynomial differential operators by semidefinite programming Estimating lowest eigenvalues of symmetric polynomial differential operators by semidefinite programming

2009-06-11

arxiv.org/abs/0906.2214v3

Mathematics

Optimization and Control

2 figures, to appear in JMAA

Scientific paper

A method for computing global minima of real multivariate polynomials based on semidefinite programming was developed by N. Z. Shor, J. B. Lasserre and P. A. Parrilo. The aim of this article is to extend a variant of their method to noncommutative symmetric polynomials in variables $X$ and $Y$ satisfying $YX-XY=1$ and $X^\ast=X$, $Y^\ast=-Y$. Global minima of such polynomials are defined and showed to be equal to minima of the spectra of the corresponding differential operators. We also discuss how to exploit sparsity and symmetry. Several numerical experiments are included. The last section explains how our theory fits into the framework of noncommutative real algebraic geometry.

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