An Accelerated Linearized Alternating Direction Method of Multipliers

Abstract

We present a novel framework, namely AADMM, for acceleration of linearized alternating direction method of multipliers (ADMM). The basic idea of AADMM is to incorporate a multi-step acceleration scheme into linearized ADMM. We demonstrate that for solving a class of convex composite optimization with linear constraints, the rate of convergence of AADMM is better than that of linearized ADMM, in terms of their dependence on the Lipschitz constant of the smooth component. Moreover, AADMM is capable to deal with the situation when the feasible region is unbounded, as long as the corresponding saddle point problem has a solution. A backtracking algorithm is also proposed for practical performance.

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Document Details

Document Type
Technical Report
Publication Date
Feb 01, 2014
Accession Number
ADA595588

Entities

People

  • Eduardo Pasiliao Jr.
  • Guanghui Lan
  • Yumei Chen
  • Yuyuan Ouyang

Organizations

  • University of Florida

Tags

Communities of Interest

  • C4I
  • Sensors

DTIC Thesaurus Topics

  • Acquisition
  • Air Force Research Laboratories
  • Algorithms
  • Artificial Intelligence
  • Composite Materials
  • Compressed Sensing
  • Convergence
  • Convex Sets
  • Data Acquisition
  • Equations
  • Image Processing
  • Image Reconstruction
  • Mathematics
  • Notation
  • Observation
  • Optimization
  • Two Dimensional

Readers

  • Control Systems Engineering.
  • Operations Research