Border Basis relaxation for polynomial optimization

A relaxation method based on border basis reduction which improves the efficiency of Lasserre's approach is proposed to compute the optimum of a polynomial function on a basic closed semi algebraic set. A new stopping criterion is given to detect when the relaxation sequence reaches the minimum, using a sparse flat extension criterion. We also provide a new algorithm to reconstruct a finite sum of weighted Dirac measures from a truncated sequence of moments, which can be applied to other sparse reconstruction problems. As an application, we obtain a new algorithm to compute zero-dimensional minimizer ideals and the minimizer points or zero-dimensional G-radical ideals. Experimentations show the impact of this new method on significant benchmarks.Comment: Accepted for publication in Journal of Symbolic Computatio

Exact relaxation for polynomial optimization on semi-algebraic sets

In this paper, we study the problem of computing by relaxation hierarchies the infimum of a real polynomial function f on a closed basic semialgebraic set and the points where this infimum is reached, if they exist. We show that when the infimum is reached, a relaxation hierarchy constructed from the Karush-Kuhn-Tucker ideal is always exact and that the vanishing ideal of the KKT minimizer points is generated by the kernel of the associated moment matrix in that degree, even if this ideal is not zero-dimensional. We also show that this relaxation allows to detect when there is no KKT minimizer. We prove that the exactness of the relaxation depends only on the real points which satisfy these constraints.This exploits representations of positive polynomials as elementsof the preordering modulo the KKT ideal, which only involves polynomials in the initial set of variables. Applications to global optimization, optimization on semialgebraic sets defined by regular sets of constraints, optimization on finite semialgebraic sets, real radical computation are given

On the construction of general cubature formula by flat extensions

International audienceWe describe a new method to compute general cubature formulae. The problem is initially transformed into the computation of truncated Hankel operators with flat extensions. We then analyse the algebraic properties associated to flat extensions and show how to recover the cubature points and weights from the truncated Hankel operator. We next present an algorithm to test the flat extension property and to additionally compute the decomposition. To generate cubature formulae with a minimal number of points, we propose a new relaxation hierarchy of convex optimization problems minimizing the nuclear norm of the Hankel operators. For a suitably high order of convex relaxation, the minimizer of the optimization problem corresponds to a cubature formula. Furthermore cubature formulae with a minimal number of points are associated to faces of the convex sets. We illustrate our method on some examples, and for each we obtain a new minimal cubature formula

Moments matrices, real algebraic geometry and polynomial optimization

Le but de cette thèse est de calculer l'optimum d'un polynôme sur un ensemble semi-algébrique et les points où cet optimum est atteint. Pour atteindre cet objectif, nous combinons des méthodes de base de bord avec la hiérarchie de relaxation convexe de Lasserre afin de réduire la taille des matrices de moments dans les problèmes de programmation semi-définie positive (SDP). Afin de vérifier si le minimum est atteint, nous apportons un nouveau critère pour vérifier l'extension plate de Curto Fialkow utilisant des bases orthogonales. En combinant ces nouveaux résultats, nous fournissons un nouvel algorithme qui calcule l'optimum et les points minimiseurs. Nous décrivons plusieurs expérimentations et des applications dans différents domaines qui prouvent la performance de l'algorithme. Au niveau théorique nous prouvons aussi la convergence finie d'une hiérarchie SDP construite à partir d'un idéal de Karush-Kuhn-Tucker et ses conséquences dans des cas particuliers. Nous étudions aussi le cas particulier où les minimiseurs ne sont pas des points de KKT en utilisant la variété de Fritz-John.The objective of this thesis is to compute the optimum of a polynomial on a closed basic semialgebraic set and the points where this optimum is reached. To achieve this goal we combine border basis method with Lasserre's hierarchy in order to reduce the size of the moment matrices in the SemiDefinite Programming (SDP) problems. In order to verify if the minimum is reached we describe a new criterion to verify the flat extension condition using border basis. Combining these new results we provide a new algorithm which computes the optimum and the minimizers points. We show several experimentations and some applications in different domains which prove the perfomance of the algorithm. Theorethically we also prove the finite convergence of a SDP hierarchie contructed from a Karush-Kuhn-Tucker ideal and its consequences in particular cases. We also solve the particular case where the minimizers are not KKT points using Fritz-John Variety

Matrices de moments, géométrie algébrique réelle et optimisation polynomiale

The objective of this thesis is to compute the optimum of a polynomial on a closed basic semialgebraic set and the points where this optimum is reached. To achieve this goal we combine border basis method with Lasserre's hierarchy in order to reduce the size of the moment matrices in the SemiDefinite Programming (SDP) problems. In order to verify if the minimum is reached we describe a new criterion to verify the flat extension condition using border basis. Combining these new results we provide a new algorithm which computes the optimum and the minimizers points. We show several experimentations and some applications in different domains which prove the perfomance of the algorithm. Theorethically we also prove the finite convergence of a SDP hierarchie contructed from a Karush-Kuhn-Tucker ideal and its consequences in particular cases. We also solve the particular case where the minimizers are not KKT points using Fritz-John Variety.Le but de cette thèse est de calculer l'optimum d'un polynôme sur un ensemble semi-algébrique et les points où cet optimum est atteint. Pour atteindre cet objectif, nous combinons des méthodes de base de bord avec la hiérarchie de relaxation convexe de Lasserre afin de réduire la taille des matrices de moments dans les problèmes de programmation semi-définie positive (SDP). Afin de vérifier si le minimum est atteint, nous apportons un nouveau critère pour vérifier l'extension plate de Curto Fialkow utilisant des bases orthogonales. En combinant ces nouveaux résultats, nous fournissons un nouvel algorithme qui calcule l'optimum et les points minimiseurs. Nous décrivons plusieurs expérimentations et des applications dans différents domaines qui prouvent la performance de l'algorithme. Au niveau théorique nous prouvons aussi la convergence finie d'une hiérarchie SDP construite à partir d'un idéal de Karush-Kuhn-Tucker et ses conséquences dans des cas particuliers. Nous étudions aussi le cas particulier où les minimiseurs ne sont pas des points de KKT en utilisant la variété de Fritz-John

UNCONSTRAINT GLOBAL POLYNOMIAL OPTIMIZATION VIA GRADIENT IDEAL

Abstract. In this paper, we describe a new method to compute the minimum of a real polynomial function and the ideal defining the points which minimize this polynomial func-tion, assuming that the minimizer ideal is zero-dimensional. Our method is a generalization of Lasserre relaxation method and stops in a finite number of steps. The proposed algo-rithm combines Border Basis, Moment Matrices and Semidefinite Programming. In the case where the minimum is reached at a finite number of points, it provides a border basis of the minimizer ideal. 1