摘要: This talk is devoted to solving the linearly constrained convex optimization problems by the monotone plus skew-symmetric splitting on KKT operators. This approach generalizes the Hermitian and skew-Hermitian splitting method (HSS), an unconditionally convergent algorithm for non-Hermitian positive definite linear systems, to the nonlinear scenario. The convergence of the proposed algorithm is guaranteed under some mild assumptions. In addition, we adapt our proposed algorithm for distributed computing solving composite convex optimization problems. Numerical simulations on an image restoration problem and the sparse logistic regression problem demonstrate the compelling performance of the proposed algorithm.
报告人简介：Dr. Weiyang Ding is Research Assistant Professor at Department of Mathematics, Hong Kong Baptist University. Dr. Ding received his B.Sc. degree in Mathematics and Applied Mathematics in July 2011 and his Ph.D. degree in Computational Mathematics in June 2016 both from School of Mathematical Sciences, Fudan University. He was a Postdoctoral Fellow at the Hong Kong Polytechnic University and joined Hong Kong Baptist University in September 2017. His research focuses include numerical (multi)linear algebra, tensor optimization, tensor spectral theory, and applications in data science and scientific computing. Dr. Ding has published one monograph and twelve journal papers, two of which are ESI highly cited papers.