报告人学术简介
  刘文丽，辽宁师范大学数学学院讲师，博士。目前研究方向是结构优化、非线性方程组等，在Numerical Algorithms，Journal of Computational and Applied Mathematics等期刊发表数篇论文。
报告内容
  In this paper, a partial Bregman alternating direction method of multipliers(ADMM) with a general relaxation factor $\beta\in(0,2)$ is proposed for structured nonconvex and nonsmooth optimization, where the objectivefunction is the sum of a nonsmooth convex function and a smooth nonconvex function without coupled variables. We add a Bregman distance to alleviate the difficulty of solving the nonsmooth subproblem. For the smooth subproblem, we directly perform a gradient descent step of the augmented Lagrangian function, which makes the computational cost of each iteration of our method very cheap. Under some mild conditions, theboundedness of the generated sequence, the global convergence and the iteration complexity are established. The numerical results verify the efficiency the proposed method.