This paper proposes a consensus-based subgradient method under a common constraint set with switching undirected graphs. In the proposed method, each agent has a state and an auxiliary variable as the estimates of an optimal solution and accumulated information of past gradients of neighbor agents. We show that the states of all agents asymptotically converge to one of the optimal solutions of the convex optimization problem. The simulation results show that the proposed consensus-based algorithm with accumulated subgradient information achieves faster convergence than the standard subgradient algorithm.
Yuichi KAJIYAMA
Osaka University
Naoki HAYASHI
Osaka University
Shigemasa TAKAI
Osaka University
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Yuichi KAJIYAMA, Naoki HAYASHI, Shigemasa TAKAI, "Distributed Constrained Convex Optimization with Accumulated Subgradient Information over Undirected Switching Networks" in IEICE TRANSACTIONS on Fundamentals,
vol. E102-A, no. 2, pp. 343-350, February 2019, doi: 10.1587/transfun.E102.A.343.
Abstract: This paper proposes a consensus-based subgradient method under a common constraint set with switching undirected graphs. In the proposed method, each agent has a state and an auxiliary variable as the estimates of an optimal solution and accumulated information of past gradients of neighbor agents. We show that the states of all agents asymptotically converge to one of the optimal solutions of the convex optimization problem. The simulation results show that the proposed consensus-based algorithm with accumulated subgradient information achieves faster convergence than the standard subgradient algorithm.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E102.A.343/_p
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@ARTICLE{e102-a_2_343,
author={Yuichi KAJIYAMA, Naoki HAYASHI, Shigemasa TAKAI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Distributed Constrained Convex Optimization with Accumulated Subgradient Information over Undirected Switching Networks},
year={2019},
volume={E102-A},
number={2},
pages={343-350},
abstract={This paper proposes a consensus-based subgradient method under a common constraint set with switching undirected graphs. In the proposed method, each agent has a state and an auxiliary variable as the estimates of an optimal solution and accumulated information of past gradients of neighbor agents. We show that the states of all agents asymptotically converge to one of the optimal solutions of the convex optimization problem. The simulation results show that the proposed consensus-based algorithm with accumulated subgradient information achieves faster convergence than the standard subgradient algorithm.},
keywords={},
doi={10.1587/transfun.E102.A.343},
ISSN={1745-1337},
month={February},}
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TY - JOUR
TI - Distributed Constrained Convex Optimization with Accumulated Subgradient Information over Undirected Switching Networks
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 343
EP - 350
AU - Yuichi KAJIYAMA
AU - Naoki HAYASHI
AU - Shigemasa TAKAI
PY - 2019
DO - 10.1587/transfun.E102.A.343
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E102-A
IS - 2
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - February 2019
AB - This paper proposes a consensus-based subgradient method under a common constraint set with switching undirected graphs. In the proposed method, each agent has a state and an auxiliary variable as the estimates of an optimal solution and accumulated information of past gradients of neighbor agents. We show that the states of all agents asymptotically converge to one of the optimal solutions of the convex optimization problem. The simulation results show that the proposed consensus-based algorithm with accumulated subgradient information achieves faster convergence than the standard subgradient algorithm.
ER -