报告人：白建超 副教授 （西北工业大学）

时间：2026年06月23日 09:00-

地点：数统学院LD718

摘要：In this tall, we focus on a linearly constrained composite minimization problem involving a possibly nonsmooth and nonconvex objective function. Unlike the traditional construction of the augmented Lagrangian function, we design a proximal-perturbed augmented Lagrangian to develop a new Bregman-type ADMM. Convergence an convergence rates of the proposed method are discussed under mild assumptions. Comparative experiments on testing the linear equation problem, graph-guided fused lasso problem and robust principal component analysis problem demonstrate the efficiency and flexibility of the proposed method.

邀请人：蒋杰

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