Improved convergence rates for Lasserre-type hierarchies of upper bounds for box-constrained polynomial optimization

We consider the problem of minimizing a given multivariate polynomial f over the hypercube [-1,1]^n. An idea, introduced by Lasserre, is to find a probability distribution on the hypercube with polynomial density function h (of given degree r) that minimizes the expectation of f over the hypercube with respect to this probability distribution. It is known that, for the Lebesgue measure one may show an error bound in 1/sqrt{r} if h is a sum-of-squares density, and an error bound in 1/r if h is the density of a beta distribution. In this paper, we show another probability distribution that permits to show an error bound in 1/r^2 when selecting a density function h with a Schmuedgen-type sum-of-squares decomposition. The convergence rate analysis relies on the theory of polynomial kernels, and in particular on Jackson kernels. We also show that the resulting upper bounds may be computed as generalized eigenvalue problems, as is also the case for sum-of-squares densitie

Une caractérisation algorithmique de la P-matricité II: ajustements, raffinements et validation

International audienceThe paper "An algorithmic characterization of P-matricity" (SIAM Journal on Matrix Analysis and Applications, 34:3 (2013) 904–916, by the same authors as here) implicitly assumes that the iterates generated by the Newton-min algorithm for solving a linear complementarity problem of dimension n, which reads 0 ⩽ x ⊥ (M x + q) ⩾ 0, are uniquely determined by some index subsets of [[1, n]]. Even if this is satisfied for a subset of vectors q that is dense in R^n, this assumption is improper, in particular in the statements where the vector q is not subject to restrictions. The goal of the present contribution is to show that, despite this blunder, the main result of that paper is preserved. This one claims that a nondegenerate matrix M is a P-matrix if and only if the Newton-min algorithm does not cycle between two distinct points, whatever is q. The proof is not more complex, requiring only some adjustments, which are essential however.L'article "An algorithmic characterization of P-matricity" (SIAM Journal on Matrix Analysis and Applications, 34:3 (2013) 904–916, par les mêmes auteurs qu'ici) suppose implicitement que les itérés générés par l'algorithme de Newton-min pour résoudre le problème de complémentarité linéaire de dimension n, qui s'écrit 0 ⩽ x ⊥ (M x + q) ⩾ 0, sont déterminés de manière unique par des sous-ensembles d'indices de [[1, n]]. Même si cette hypothèse est vérifiée pour un sous-ensemble de vecteurs q qui est dense dans R^n, elle n'est pas appropriée, en particulier dans les énoncés où le vecteur q n'est pas soumis à des restrictions. Le but du la contribution présente est de montrer que, malgré cette bévue, le résultat principal de l'article est préservé. Celui-ci affirme qu'une matrice non dégénérée M est une P-matrice si, et seulement si, l'algorithme de Newton-min ne cycle pas entre deux points distincts, quel que soit q. La démonstration n'est pas plus complexe et ne requiert que quelques ajustements, qui sont cependant essentiels

Primal-Dual Active-Set Methods for Convex Quadratic Optimization with Applications

Primal-dual active-set (PDAS) methods are developed for solving quadratic optimization problems (QPs). Such problems arise in their own right in optimal control and statistics–two applications of interest considered in this dissertation–and as subproblems when solving nonlinear optimization problems. PDAS methods are promising as they possess the same favorable properties as other active-set methods, such as their ability to be warm-started and to obtain highly accurate solutions by explicitly identifying sets of constraints that are active at an optimal solution. However, unlike traditional active-set methods, PDAS methods have convergence guarantees despite making rapid changes in active-set estimates, making them well suited for solving large-scale problems.Two PDAS variants are proposed for efficiently solving generally-constrained convex QPs. Both variants ensure global convergence of the iterates by enforcing montonicity in a measure of progress. Besides identifying an estimate set estimate, a novel uncertain set is introduced into the framework in order to house indices of variables that have been identified as being susceptible to cycling. The introduction of the uncertainty set guarantees convergence of the algorithm, and with techniques proposed to keep the set from expanding quickly, the practical performance of the algorithm is shown to be very efficient. Another PDAS variant is proposed for solving certain convex QPs that commonly arise when discretizing optimal control problems. The proposed framework allows inexactness in the subproblem solutions, which can significantly reduce computational cost in large-scale settings. By controlling the level inexactness either by exploiting knowledge of an upper bound of a matrix inverse or by dynamic estimation of such a value, the method achieves convergence guarantees and is shown to outperform a method that employs exact solutions computed by direct factorization techniques.Finally, the application of PDAS techniques for applications in statistics, variants are proposed for solving isotonic regression (IR) and trend filtering (TR) problems. It is shown that PDAS can solve an IR problem with n data points with only O(n) arithmetic operations. Moreover, the method is shown to outperform the state-of-the-art method for solving IR problems, especially when warm-starting is considered. Enhancements to themethod are proposed for solving general TF problems, and numerical results are presented to show that PDAS methods are viable for a broad class of such problems