Optimization Online


Semidefinite approximations of projections and polynomial images of semialgebraic sets

Victor Magron(v.magron***at***imperial.ac.uk)
Didier Henrion(henrion***at***laas.fr)
Jean-Bernard Lasserre(lasserre***at***laas.fr)

Abstract: Given a compact semialgebraic set S of R^n and a polynomial map f from R^n to R^m, we consider the problem of approximating the image set F = f(S) in R^m. This includes in particular the projection of S on R^m for n greater than m. Assuming that F is included in a set B which is ``simple'' (e.g. a box or a ball), we provide two methods to compute certified outer approximations of F. Method 1 exploits the fact that F can be defined with an existential quantifier, while Method 2 computes approximations of the support of image measures. The two methods output a sequence of superlevel sets defined with a single polynomial that yield explicit outer approximations of F. Finding the coefficients of this polynomial boils down to computing an optimal solution of a convex semidefinite program. We provide guarantees of strong convergence to F in L^1 norm on B, when the degree of the polynomial approximation tends to infinity. Several examples of applications are provided, together with numerical experiments.

Keywords: Semialgebraic sets; semidefinite programming; moment relaxations; polynomial sum of squares.

Category 1: Linear, Cone and Semidefinite Programming (Semi-definite Programming )


Download: [PDF]

Entry Submitted: 10/17/2014
Entry Accepted: 10/17/2014
Entry Last Modified: 10/17/2014

Modify/Update this entry

  Visitors Authors More about us Links
  Subscribe, Unsubscribe
Digest Archive
Search, Browse the Repository


Coordinator's Board
Classification Scheme
Give us feedback
Optimization Journals, Sites, Societies
Mathematical Optimization Society