IEICE TRANSACTIONS on Fundamentals
Consensus-Based Quantized Algorithm for Convex Optimization with Smooth Cost Functions
Summary :
This paper proposes a distributed algorithm over quantized communication networks for unconstrained optimization with smooth cost functions. We consider a multi-agent system whose local communication is represented by a fixed and connected graph. Each agent updates a state and an auxiliary variable for the estimates of the optimal solution and the average gradient of the entire cost function by a consensus-based optimization algorithm. The state and the auxiliary variable are sent to neighbor agents through a uniform quantizer. We show a convergence rate of the proposed algorithm with respect to the errors between the cost at the time-averaged state and the optimal cost. Numerical examples show that the estimated solution by the proposed quantized algorithm converges to the optimal solution.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E103-A No.2 pp.435-442
- Publication Date
- 2020/02/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.2019MAP0008
- Type of Manuscript
- Special Section PAPER (Special Section on Mathematical Systems Science and its Applications)
- Category
Authors
Naoki HAYASHI
Osaka University
Yuichi KAJIYAMA
Osaka University
Shigemasa TAKAI
Osaka University
Keyword
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Naoki HAYASHI, Yuichi KAJIYAMA, Shigemasa TAKAI, "Consensus-Based Quantized Algorithm for Convex Optimization with Smooth Cost Functions" in IEICE TRANSACTIONS on Fundamentals,
vol. E103-A, no. 2, pp. 435-442, February 2020, doi: 10.1587/transfun.2019MAP0008.
Abstract: This paper proposes a distributed algorithm over quantized communication networks for unconstrained optimization with smooth cost functions. We consider a multi-agent system whose local communication is represented by a fixed and connected graph. Each agent updates a state and an auxiliary variable for the estimates of the optimal solution and the average gradient of the entire cost function by a consensus-based optimization algorithm. The state and the auxiliary variable are sent to neighbor agents through a uniform quantizer. We show a convergence rate of the proposed algorithm with respect to the errors between the cost at the time-averaged state and the optimal cost. Numerical examples show that the estimated solution by the proposed quantized algorithm converges to the optimal solution.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2019MAP0008/_p
Copy
@ARTICLE{e103-a_2_435,
author={Naoki HAYASHI, Yuichi KAJIYAMA, Shigemasa TAKAI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Consensus-Based Quantized Algorithm for Convex Optimization with Smooth Cost Functions},
year={2020},
volume={E103-A},
number={2},
pages={435-442},
abstract={This paper proposes a distributed algorithm over quantized communication networks for unconstrained optimization with smooth cost functions. We consider a multi-agent system whose local communication is represented by a fixed and connected graph. Each agent updates a state and an auxiliary variable for the estimates of the optimal solution and the average gradient of the entire cost function by a consensus-based optimization algorithm. The state and the auxiliary variable are sent to neighbor agents through a uniform quantizer. We show a convergence rate of the proposed algorithm with respect to the errors between the cost at the time-averaged state and the optimal cost. Numerical examples show that the estimated solution by the proposed quantized algorithm converges to the optimal solution.},
keywords={},
doi={10.1587/transfun.2019MAP0008},
ISSN={1745-1337},
month={February},}
Copy
TY - JOUR
TI - Consensus-Based Quantized Algorithm for Convex Optimization with Smooth Cost Functions
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 435
EP - 442
AU - Naoki HAYASHI
AU - Yuichi KAJIYAMA
AU - Shigemasa TAKAI
PY - 2020
DO - 10.1587/transfun.2019MAP0008
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E103-A
IS - 2
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - February 2020
AB - This paper proposes a distributed algorithm over quantized communication networks for unconstrained optimization with smooth cost functions. We consider a multi-agent system whose local communication is represented by a fixed and connected graph. Each agent updates a state and an auxiliary variable for the estimates of the optimal solution and the average gradient of the entire cost function by a consensus-based optimization algorithm. The state and the auxiliary variable are sent to neighbor agents through a uniform quantizer. We show a convergence rate of the proposed algorithm with respect to the errors between the cost at the time-averaged state and the optimal cost. Numerical examples show that the estimated solution by the proposed quantized algorithm converges to the optimal solution.
ER -
English
