Distributed optimization has gained popularity in many areas because it can to cope with the increasing volume of data, and the corresponding demand for computational resources. A popular distributed solver is the alternating direction method of multipliers (ADMM). Fully decentralized (D) ADMM arises when node-to-node communications have to abide by the underlying network connectivity. DADMM's convergence however, slows down as the network diameter grows large. To deal with this challenge, the recently proposed hybrid (H) ADMM provides considerable performance boost over DADMM by exploiting local topology information. But HADMM only applies to unweighted graphs. The present contribution develops a weighted hybrid (WH) consensus-based ADMM approach that can deal with weighted graphs. The resultant scheme further improves the performance of HADMM through graph-aware weight tuning. Theoretical analysis offers convergence guarantees and establishes linear convergence rate, while numerical tests on various graphs demonstrate the WHADMM merits.
|Original language||English (US)|
|Title of host publication||Conference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018|
|Editors||Michael B. Matthews|
|Publisher||IEEE Computer Society|
|Number of pages||5|
|State||Published - Feb 19 2019|
|Event||52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018 - Pacific Grove, United States|
Duration: Oct 28 2018 → Oct 31 2018
|Name||Conference Record - Asilomar Conference on Signals, Systems and Computers|
|Conference||52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018|
|Period||10/28/18 → 10/31/18|
Bibliographical noteFunding Information:
This work is supported in part by NSF grants 10121, 12321, 12345.
© 2018 IEEE.
- decentralized optimization
- hybrid ADMM
- weighted ADMM