DocumentCode :
3766098
Title :
Dual subgradient methods using approximate multipliers
Author :
Víctor Valls;Douglas J. Leith
Author_Institution :
Trinity College Dublin, Ireland
fYear :
2015
Firstpage :
1016
Lastpage :
1021
Abstract :
We consider the subgradient method for the dual problem in convex optimisation with approximate multipliers, i.e., the subgradient used in the update of the dual variables is obtained using an approximation of the true Lagrange multipliers. This problem is interesting for optimisation problems where the exact Lagrange multipliers might not be readily accessible. For example, in distributed optimisation the exact Lagrange multipliers might not be available at the nodes due to communication delays or losses. We show that we can construct approximate primal solutions that can get arbitrarily close to the set of optima as step size α is reduced. Applications of the analysis include unsynchronised subgradient updates in the dual variable update and unsynchronised max-weight scheduling.
Keywords :
"Optimization","Convergence","Processor scheduling","Noise measurement","Delays","Convex functions","Indexes"
Publisher :
ieee
Conference_Titel :
Communication, Control, and Computing (Allerton), 2015 53rd Annual Allerton Conference on
Type :
conf
DOI :
10.1109/ALLERTON.2015.7447119
Filename :
7447119
Link To Document :
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