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 고정되고 강하게 연결된 네트워크에 대한 제한된 최적화 문제에 대한 새로운 분산 근위 최소화 알고리즘을 제안합니다. 각 반복에서 각 에이전트는 제약 조건 집합에 따라 목적 함수의 근위 연산자를 평가하고 단방향 통신으로 인한 불균형을 보상하여 자체 상태를 업데이트합니다. 우리는 모든 에이전트의 상태가 점근적으로 최적의 솔루션 중 하나로 수렴됨을 보여줍니다. 제안된 방법의 타당성을 확인하기 위해 수치 결과가 표시됩니다.
Naoki HAYASHI
Osaka University
Masaaki NAGAHARA
The University of Kitakyushu
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부
Naoki HAYASHI, Masaaki NAGAHARA, "Distributed Proximal Minimization Algorithm for Constrained Convex Optimization over Strongly Connected Networks" in IEICE TRANSACTIONS on Fundamentals,
vol. E102-A, no. 2, pp. 351-358, February 2019, doi: 10.1587/transfun.E102.A.351.
Abstract: This paper proposes a novel distributed proximal minimization algorithm for constrained optimization problems over fixed strongly connected networks. At each iteration, each agent updates its own state by evaluating a proximal operator of its objective function under a constraint set and compensating the unbalancing due to unidirectional communications. We show that the states of all agents asymptotically converge to one of the optimal solutions. Numerical results are shown to confirm the validity of the proposed method.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E102.A.351/_p
부
@ARTICLE{e102-a_2_351,
author={Naoki HAYASHI, Masaaki NAGAHARA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Distributed Proximal Minimization Algorithm for Constrained Convex Optimization over Strongly Connected Networks},
year={2019},
volume={E102-A},
number={2},
pages={351-358},
abstract={This paper proposes a novel distributed proximal minimization algorithm for constrained optimization problems over fixed strongly connected networks. At each iteration, each agent updates its own state by evaluating a proximal operator of its objective function under a constraint set and compensating the unbalancing due to unidirectional communications. We show that the states of all agents asymptotically converge to one of the optimal solutions. Numerical results are shown to confirm the validity of the proposed method.},
keywords={},
doi={10.1587/transfun.E102.A.351},
ISSN={1745-1337},
month={February},}
부
TY - JOUR
TI - Distributed Proximal Minimization Algorithm for Constrained Convex Optimization over Strongly Connected Networks
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 351
EP - 358
AU - Naoki HAYASHI
AU - Masaaki NAGAHARA
PY - 2019
DO - 10.1587/transfun.E102.A.351
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 novel distributed proximal minimization algorithm for constrained optimization problems over fixed strongly connected networks. At each iteration, each agent updates its own state by evaluating a proximal operator of its objective function under a constraint set and compensating the unbalancing due to unidirectional communications. We show that the states of all agents asymptotically converge to one of the optimal solutions. Numerical results are shown to confirm the validity of the proposed method.
ER -