Campuses:

polynomial optimization

Tuesday, January 26, 2016 - 10:15am - 11:05am
Didier Henrion (Centre National de la Recherche Scientifique (CNRS))
Optimal experimental design consists of choosing measurements to maximize the information or, equivalently, minimize noise. For linear regression, a popular criterion is D-optimality, which seeks to maximize the determinant of the information matrix. Maximization is with respect to the weights of a discrete measure whose atoms (measurement basis vectors) are given a priori. The information matrix contains moments of degree two of this measure, and
its inverse is the error covariance matrix. The resulting determinant
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