报告人: 孙鸿鹏副教授，中国人民大学数学科学研究院

报告题目： 预条件的ADMM算法在图像分割Potts模型中的应用(公派访学回国报告)

报告时间和地点：2020年11月4日 14:00--15:00，中国人民大学环境学院楼316

摘要: The Potts model has many applications. It is equivalent to some min-cut and max-flow models. Primal-dual algorithms have been used to solve these problems. Due to the special structure of the models, convergence proof is still a difficult problem. In this work, we developed two novel, preconditioned, and over-relaxed alternating direction methods of multipliers (ADMM) with convergence guarantee for these models. Using the proposed preconditioners or block preconditioners, we get accelerations with the over-relaxation variants of preconditioned ADMM. The preconditioned and over-relaxed Douglas-Rachford splitting methods are also considered for the Potts model. Our framework can handle both the two-labeling or multi-labeling problems with appropriate block preconditioners based on Eckstein-Bertsekas and Fortin-Glowinski splitting techniques. This is joint work with Prof. Yuan Jing and Prof. Tai Xuecheng.