On the Q-linear Convergence of a Majorized Proximal ADMM for Convex Composite Programming and Its Applications
[TMCSC]
July 22, 2020 14:30-15:30
腾讯会议（线上）
SPEAKER
张立卫 （大连理工大学）
ABSTRACT
This work aims to study the convergence rate of a majorized alternating direction method of multiplier with indefinite proximal terms (iPADMM) for solving linearly constrained convex composite optimization problems. We establish the Q-linear rate convergence theorem for 2-block majorized iPADMM under mild conditions. Based on this result, the convergence rate analysis of symmetric Gaussian-Seidel based majorized ADMM, which is designed for solving multi-block composite convex optimization problems, are given. We apply the majorized iPADMM to solve three types of regularized logistic regression problems: constrained regression, fused lasso and overlapping group lasso. The efficiency of majorized iPADMM are demonstrated on both simulation experiments and real data sets. (This is a joint work with Ning Zhang, Jia Wu)
ORGANIZERS
黄南京（四川大学）
寇 辉（四川大学）
连 增（四川大学）
张德学（四川大学）
张伟年（四川大学）
SUPPORTED BY
国家天元数学西南中心
四川大学数学学院
VIDEO
- On the Q-linear Convergence of a Majorized Proximal ADMM for Convex Composite Programming and Its Applications
- 14:30 - 15:30, 2020-07-22 at 腾讯会议（线上）
- 张立卫（大连理工大学）