scholarly journals D-ADMM: A distributed algorithm for compressed sensing and other separable optimization problems

2012 ◽  
Author(s):  
Joao F. C. Mota ◽  
Joao M. F. Xavier ◽  
Pedro M. Q. Aguiar ◽  
Markus Puschel
Keyword(s):  
Compressed Sensing ◽  
Distributed Algorithm ◽  
Optimization Problems ◽  
Separable Optimization

scholarly journals D-ADMM: A Communication-Efficient Distributed Algorithm for Separable Optimization

2013 ◽  
Vol 61 (10) ◽  
pp. 2718-2723 ◽  
Author(s):  
Joao F. C. Mota ◽  
Joao M. F. Xavier ◽  
Pedro M. Q. Aguiar ◽  
Markus Puschel
Keyword(s):  
Distributed Algorithm ◽  
Separable Optimization

Element-by-Element Preconditioners for Large Partially Separable Optimization Problems

1997 ◽  
Vol 18 (6) ◽  
pp. 1767-1787 ◽  
Author(s):  
Michel J. Daydé ◽  
Jean-Yves L'Excellent ◽  
Nicholas I. M. Gould
Keyword(s):  
Optimization Problems ◽  
Separable Optimization

scholarly journals A Dynamical Alternating Direction Multiplier Method for Two-Block Optimization Problems

2021 ◽  
Author(s):  
Miantao Chao ◽  
Liqun Liu
Keyword(s):  
Dynamical System ◽  
Optimization Problems ◽  
Suitable Condition ◽  
Time Discretization ◽  
Alternating Direction Method ◽  
Lagrangian Function ◽  
Method Of Multipliers ◽  
Worst Case ◽  
Alternating Direction ◽  
Separable Optimization

Abstract In this paper, we propose a dynamic alternating direction method of multipliers for two-block separable optimization problems. The well-known classical ADMM can be obtained after the time discretization of the dynamical system. Under suitable condition, we prove that the trajectory asymptotically converges to a saddle point of the Lagrangian function of the problems. When the coefficient matrices in the constraint are identiy matrices, we prove the worst-case O(1/t) convergence rate in ergodic sense.

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