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Academy of Mathematics and Systems Science, CAS
Colloquia & Seminars

Speaker:

谢家新 博士, 计算数学与科学工程计算研究所

Inviter:  
Title:
On inexact ADMM swith relative error criteria
Time & Venue:
2017.12.6 16:00-17:00 Z311
Abstract:
This talk presents some works on inexact alternating direction methods of multipliers (ADMMs) for solving two-block separable linearly constrained convex optimization problems. Where the two subproblems in the classic ADMM are allowed to be solved inexactly by certain relative error criteria, in the sense that only a few parameters are needed to control the inexactness. Moreover, in many practical computations, the numerical performance is often improved if a larger step-length is used. Hence we also consider to seek a larger step-length to update the Lagrangian multiplier to accelerate the numerical performance. Specifically, if we only allow one subproblem in the classic ADMM to be solved inexactly by a certain relative error criterion, then a larger step-length can be adopted. Global convergences of those proposed algorithms are established under the assumption that the solution set to the KKT system of the problem is not empty. Preliminary numerical experiments on solving the total variation (TV)-based image denoising problems are provided to demonstrate the effectiveness of the proposed methods and the advantage of taking a larger step-length.
 

 

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