Semidefinite programming relaxations for semialgebraic problems |
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Authors: | Pablo A. Parrilo |
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Affiliation: | (1) Automatic Control Laboratory, Swiss Federal Institute of Technology (ETH), CH-8092 Zurich, Switzerland, e-mail: parrilo@aut.ee.ethz.ch, CH |
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Abstract: | A hierarchy of convex relaxations for semialgebraic problems is introduced. For questions reducible to a finite number of polynomial equalities and inequalities, it is shown how to construct a complete family of polynomially sized semidefinite programming conditions that prove infeasibility. The main tools employed are a semidefinite programming formulation of the sum of squares decomposition for multivariate polynomials, and some results from real algebraic geometry. The techniques provide a constructive approach for finding bounded degree solutions to the Positivstellensatz, and are illustrated with examples from diverse application fields. Received: May 10, 2001 / Accepted May 2002 Published online: April 10, 2003 Key Words. semidefinite programming – convex optimization – sums of squares – polynomial equations – real algebraic geometry The majority of this research has been carried out while the author was with the Control & Dynamical Systems Department, California Institute of Technology, Pasadena, CA 91125, USA. |
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