IEICE TRANSACTIONS on Fundamentals
Distributed Proximal Minimization Algorithm for Constrained Convex Optimization over Strongly Connected Networks
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E102-A No.2 pp.351-358
- Publication Date
- 2019/02/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E102.A.351
- Type of Manuscript
- Special Section PAPER (Special Section on Mathematical Systems Science and its Applications)
- Category
Authors
Naoki HAYASHI
Osaka University
Masaaki NAGAHARA
The University of Kitakyushu
Keyword
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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
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@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},}
Copy
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 -
English
