Thời gian: 9h, Thứ 4, ngày 27/5/2015

Địa điểm: Phòng số 4, nhà A14, Viện Toán học, 18 Hoàng Quốc Việt, Hà Nội

Tóm tắt: Several optimization schemes have been known for convex optimization problems. However, numerical algorithms for solving nonconvex optimization problems are still underdeveloped. A progress to go beyond convexity was made by considering the class of functions representable as differences of convex functions. In this paper, we introduce a generalized proximal point algorithm to minimize the difference of a nonconvex function and a convex function. We also study convergence results of this algorithm under the main assumption that the objective function satisfies the Kurdyka - Lojasiewicz inequality.

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