报告主题：A block symmetric Gauss-Seidel decomposition theorem for convex quadratic programming and its applications (块对称高斯-塞得剖析定理在凸二次妄想中的应用)

报告人：Kim-Chuan Toh 教授（新加坡国立大学数学系）

报告时间：2020年9月21日（周一） 14:00-16:00

参会方法：腾讯 聚会

聚会ID：302 840 008

聚会密码：200921

聚会链接：https://meeting.tencent.com/s/Xbk2rAnqeKOE

主理部分：8188cc威尼斯运筹与优化开放实验室-国际科研相助平台、上海市运筹学会、8188cc威尼斯理学院数学系

报告摘要：For a multi-block convex composite quadratic programming (CCQP) with an additional nonsmooth term in the first block, we present a block symmetric Gauss-Seidel (sGS) decomposition theorem, which states that each cycle of the block sGS method is equivalent to solving the CCQP with an additional proximal term constructed from the sGS decomposition of the quadratic term. As a basic building block, the sGS decomposition theorem has played a key role in various recently developed algorithms such as the inexact proximal ALM/ADMM for linearly constrained multi-block convex composite conic programming. We demonstrate how our sGS-based ADMM can be applied to solve doubly nonnegative semidefinite programming and Wasserstein barycenter problems.

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