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Lower bounds on the global minimum of a polynomial

M. Ghasemi (), J. Lasserre and M. Marshall

Computational Optimization and Applications, 2014, vol. 57, issue 2, 387-402

Abstract: We extend the method of Ghasemi and Marshall (SIAM J. Optim. 22(2):460–473, 2012 ), to obtain a lower bound f gp,M for a multivariate polynomial $f(\mathbf{x}) \in\mathbb{R}[\mathbf {x}]$ of degree ≤2d in n variables x=(x 1 ,…,x n ) on the closed ball $\{ \mathbf{x} \in\mathbb{R}^{n} : \sum x_{i}^{2d} \le M\}$ , computable by geometric programming, for any real M. We compare this bound with the (global) lower bound f gp obtained by Ghasemi and Marshall, and also with the hierarchy of lower bounds, computable by semidefinite programming, obtained by Lasserre (SIAM J. Optim. 11(3):796–816, 2001 ). Our computations show that the bound f gp,M improves on the bound f gp and that the computation of f gp,M , like that of f gp , can be carried out quickly and easily for polynomials having of large number of variables and/or large degree, assuming a reasonable sparsity of coefficients, cases where the corresponding computation using semidefinite programming breaks down. Copyright Springer Science+Business Media New York 2014

Keywords: Positive polynomials; Sums of squares; Optimization; Geometric programming (search for similar items in EconPapers)
Date: 2014
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DOI: 10.1007/s10589-013-9596-x

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