Convex Network Optimization
Convex Network Optimization is a network optimization framework in which the objective function and all constraint functions are convex, ensuring that any local minimum is a global minimum and enabling efficient solution via convex optimization techniques.
128
Publications
6.1K
Citations
337
Authors
165
Institutions
Publications per year
2017–2026
39
Parents
128
Push–Pull Gradient Methods for Distributed Optimization in Networks
Shi Pu, Wei Shi, Jinming Xu et al. · IEEE Transactions on Automatic Control · 2020 · 360 citations
1Exact Convex Relaxation of Optimal Power Flow in Radial Networks
Lingwen Gan, Na Li, Ufuk Topcu et al. · 2016 · 308 citations
1–5 of 128
337
7
h-index
Massachusetts Institute of Technology, United States
5 publications · 291 citations
5
h-index
4
h-index
4
h-index
3
h-index
1–5 of 337
165
Massachusetts Institute of Technology
Cambridge, United States
9 h-index
14 publications
891 citations
California Institute of Technology
Pasadena, United States
8 h-index
17 publications
1.5K citations
University of Pennsylvania
Philadelphia, United States
7 h-index
15 publications
1.2K citations
Princeton University
Princeton, United States
5 h-index
7 publications
620 citations
University of Washington
Seattle, United States
4 h-index
7 publications
119 citations

University of Toronto
Toronto, Canada
4 h-index
7 publications
351 citations
Arizona State University
Tempe, United States
4 h-index
10 publications
907 citations
University of California, Berkeley
Berkeley, United States
3 h-index
4 publications
102 citations
1–8 of 165
5 publications · 876 citations
5
h-index
5 publications · 409 citations
5
h-index
4 publications · 105 citations
4
h-index
Repository · 4 publications · 138 citations
4
h-index
5 publications · 147 citations
4
h-index
1–5