Convergence Analysis of Alternating Direction Method of Multipliers for a Class of Separable Convex Programming | Zendy

Zehui Jia | Zendy; Ke Guo | Zendy; Xingju Cai | Zendy

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Convergence Analysis of Alternating Direction Method of Multipliers for a Class of Separable Convex Programming

Author(s) -

Zehui Jia,

Ke Guo,

Xingju Cai

Publication year - 2013

Publication title -

abstract and applied analysis

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.228

H-Index - 56

eISSN - 1687-0409

pISSN - 1085-3375

DOI - 10.1155/2013/680768

Subject(s) - mathematics , separable space , convergence (economics) , convex analysis , regular polygon , convex function , convex optimization , class (philosophy) , subderivative , proper convex function , mathematical optimization , linear programming , convex combination , mathematical analysis , computer science , geometry , artificial intelligence , economics , economic growth

The purpose of this paper is extending the convergence analysis of Han and Yuan (2012) for alternating direction method of multipliers (ADMM) from the strongly convex to a more general case. Under the assumption that the individual functions are composites of strongly convex functions and linear functions, we prove that the classical ADMM for separable convex programming with two blocks can be extended to the case with more than three blocks. The problems, although still very special, arise naturally from some important applications, for example, route-based traffic assignment problems

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