Distributed Dual Decomposition is a method in convex optimization that decomposes a global optimization problem into localized subproblems solved in parallel across distributed agents, using dual decomposition to coordinate solutions through consensus on shared constraints via dual variable updates.
101
Publications
5.3K
Citations
254
Authors
113
Institutions
Publications per year
2017–2026
24
Parents
Publications
101
1–5 of 101
Authors
254
- AJ
5
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- AR
5
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- AO
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- JC
4
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- EW
4
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1–5 of 254
Institutions
113
Lund University
Lund, Sweden
6 h-index
11 publications
1.3K citations
Massachusetts Institute of Technology
Cambridge, United States
6 h-index
10 publications
862 citations
Delft University of Technology
Delft, The Netherlands
5 h-index
15 publications
1.2K citations
State Key Laboratory of Industrial Control Technology
Hangzhou, China
5 h-index
17 publications
2K citations
University of Pennsylvania
Philadelphia, United States
5 h-index
6 publications
452 citations
KTH Royal Institute of Technology
Stockholm, Sweden
4 h-index
6 publications
264 citations
Shanghai Jiao Tong University
Shanghai, China
4 h-index
6 publications
336 citations
University of Salento
Lecce, Italy
4 h-index
7 publications
141 citations
1–8 of 113
Venues
- IEEE Transactions on Automatic Control
6 publications · 771 citations
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- IEEE Transactions on Smart Grid
6 publications · 407 citations
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- IEEE Transactions on Power Systems
5 publications · 445 citations
5
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- IEEE Transactions on Wireless Communications
4 publications · 160 citations
4
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- IEEE Transactions on Control of Network Systems
3 publications · 70 citations
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1–5