The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
본 논문에서는 무방향 그래프 전환을 통해 공통 제약 조건 하에서 합의 기반 하위 그래디언트 방법을 제안합니다. 제안된 방법에서 각 에이전트는 최적해의 추정치인 상태와 보조변수를 가지며, 이웃 에이전트의 과거 기울기에 대한 누적정보를 갖는다. 우리는 모든 에이전트의 상태가 볼록 최적화 문제의 최적 솔루션 중 하나로 점근적으로 수렴됨을 보여줍니다. 시뮬레이션 결과는 제안된 하위 경사 정보를 축적한 합의 기반 알고리즘이 표준 하위 경사 알고리즘보다 더 빠른 수렴을 달성함을 보여줍니다.
Yuichi KAJIYAMA
Osaka University
Naoki HAYASHI
Osaka University
Shigemasa TAKAI
Osaka University
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부
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
부
@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},}
부
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 -