The Generalized Moment problem is an  infinite-dimensional LP which takes one line to state in compact form, and whose  list of potential important applications is almost endless. However, in full generality  the GMP  is not solvable. On the other hand, if its data consist of polynomials (or more generally semi-algebraic functions) and semi-algebraic sets then one is able to define a systematic numerical scheme, the Moment-SOS hierarchy, to approximate its optimal value as closely as desired. (One has to solve a sequence of semidefinite-relaxations of increasing size.) It was initially designed for solving global optimization problem and in this context finite convergence of the hierarchy is « generic » and global minimizers can be extracted.  

I will describe the Moment-SOS approach and shows  how it can be applied to help solve several problems outside optimization, e.g. in computational geometry, probability & statistics, control & optimal control, as well as analysis of certain non-linear PDEs.

Bio:

JB Lasserre graduated from "Ecole Nationale Superieure d'Informatique et Mathematiques Appliquees" 
(ENSIMAG) in Grenoble (France),  and got his PhD (1978) and "Doctorat d'Etat" (1984) degrees 
both from Paul Sabatier University in  Toulouse (France). He has been at LAAS-CNRS in Toulouse 
since1980, wherehe is currently Directeur de Recherche. he is also a member of IMT, 
the Institute of Mathematics of Toulouse. He was a one year visitor (1978-79 and 1985-86) at the 
Electrical Engineering Dept. of the University of California at Berkeley with a fellowship from 
INRIA and NSF. He has done several one-month visits to Stanford University, MIT,

and invited visits to several Math Institutes (like MSRI, Berkeley, the Fields Institute,  IMA at Minneapolis, IPAM at UCLA, Cinvestav-IPN, Mexico, the Tinbergen Institute in Amsterdam.