Name
Title | ||
|---|---|---|
A Low-Complexity Parallelizable Numerical Algorithm for Sparse Semidefinite Programming. |
Abstract | ||
|---|---|---|
In the past two decades, the semidefinite programming (SDP) technique has been proven to be extremely successful in the convexification of hard optimization problems appearing in graph theory, control theory, polynomial optimization theory, and many areas in engineering. In particular, major power optimization problems, such as optimal power flow, state estimation, and unit commitment, can be form... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TCNS.2017.2774008 | IEEE Transactions on Control of Network Systems |
Keywords | Field | DocType |
Optimization,Power systems,Convex functions,Algorithm design and analysis,Programming,Matrix decomposition,Convergence | Parallelizable manifold,Graph theory,Mathematical optimization,Algorithm design,Tree decomposition,Algorithm,Large margin nearest neighbor,Semidefinite embedding,Optimization problem,Mathematics,Semidefinite programming | Journal |
Volume | Issue | ISSN |
5 | 4 | 2325-5870 |
Citations | PageRank | References |
3 | 0.46 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ramtin Madani | 1 | 35 | 7.99 |
| Abdulrahman Kalbat | 2 | 3 | 0.46 |
| Javad Lavaei | 3 | 587 | 71.90 |