Người báo cáo:
Mai Ngọc Hoàng Anh
Thời gian: 9h30 thứ Tư ngày 18/10/2023
Địa điểm: Phòng seminar tầng 5 nhà A6, Viện Toán học
Tóm tắt: We rely on the volume computation developed by Dabbene and Henrion to build up a probabilistic Moment-SOS hierarchy for classification. More precisely, we minimize the integral of an unknown polynomial on a given semialgebraic set , subject to a positivity certificate of on and the positivity of on a set of uniformly random samples in a subset . Under mild conditions, the sequence of values returned by this hierarchy converges to the volume of . We also prove that with probability near one, the sequence of polynomials returned by our SOS hierarchy converges to the indicator function when the sample size is sufficiently large. Consequently, with probability near one and a sufficiently large number of uniformly random samples in each class , for almost all points in , we can determine which class the point belongs to under mild conditions. This result is proved using Friedrichs' mollifiers, Weierstrass' theorem, Putinar's Positivstellensatz, and Korda's net. This is based on joint work with Jean-Bernard Lasserre, Victor Magron, and Srecko Durasinovic.