TY - GEN
T1 - Finite Rate Quantized Distributed optimization with Geometric Convergence
AU - Lee, Chang Shen
AU - Michelusi, Nicolo
AU - Scutari, Gesualdo
N1 - Funding Information:
The work of Scutari and part of the work of Lee have been supported by the USA National Science Foundation under Grants CIF 1719205 and CIF 1564044; and in part by the Army Research Office under Grant W911NF1810238.
Funding Information:
The work of Scutari and part of the work of Lee have been supported by the USA National Science Foundation under Grants CIF 1719205
Publisher Copyright:
© 2018 IEEE.
PY - 2019/2/19
Y1 - 2019/2/19
N2 - This paper studies distributed (strongly convex) optimization over multi-agent networks, subject to finite rate communications. We propose the first distributed algorithm achieving geometric convergence to the exact solution of the problem, matching thus the rate of the centralized gradient algorithm (although with different constants). The algorithm combines gradient tracking with a quantized perturbed consensus scheme. The impact on the convergence (rate) of design and network parameters, such as number of bits, algorithm step-size, and network connectivity, is also investigated. Finally, numerical results validate our theoretical findings. They demonstrate the existence of an interesting trade-off among solution accuracy, convergence time and communication cost, defined as the total number of bits transmitted on one link to achieve a target solution error.
AB - This paper studies distributed (strongly convex) optimization over multi-agent networks, subject to finite rate communications. We propose the first distributed algorithm achieving geometric convergence to the exact solution of the problem, matching thus the rate of the centralized gradient algorithm (although with different constants). The algorithm combines gradient tracking with a quantized perturbed consensus scheme. The impact on the convergence (rate) of design and network parameters, such as number of bits, algorithm step-size, and network connectivity, is also investigated. Finally, numerical results validate our theoretical findings. They demonstrate the existence of an interesting trade-off among solution accuracy, convergence time and communication cost, defined as the total number of bits transmitted on one link to achieve a target solution error.
UR - http://www.scopus.com/inward/record.url?scp=85062993533&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85062993533&partnerID=8YFLogxK
U2 - 10.1109/ACSSC.2018.8645345
DO - 10.1109/ACSSC.2018.8645345
M3 - Conference contribution
AN - SCOPUS:85062993533
T3 - Conference Record - Asilomar Conference on Signals, Systems and Computers
SP - 1876
EP - 1880
BT - Conference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018
A2 - Matthews, Michael B.
PB - IEEE Computer Society
T2 - 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018
Y2 - 28 October 2018 through 31 October 2018
ER -