A dual decomposition algorithm for separable nonconvex optimization using the penalty function framework

We propose a dual decomposition method for solving separable nonconvex optimization problems that arise e.g. in distributed model predictive control over networks. We first derive a new sequential convex programming (SCP) scheme based on penalty function approach to handle nonconvexity. Then, we com...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:52nd IEEE Conference on Decision and Control s. 2372 - 2377
Hlavní autoři: Quoc Tran Dinh, Necoara, Ion, Diehl, Moritz
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.12.2013
Témata:
Convergence
Convex functions
Decentralized control
Optimization
Prediction algorithms
Programming
Vectors
ISBN:1467357146, 9781467357142
ISSN:0191-2216
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:We propose a dual decomposition method for solving separable nonconvex optimization problems that arise e.g. in distributed model predictive control over networks. We first derive a new sequential convex programming (SCP) scheme based on penalty function approach to handle nonconvexity. Then, we combine this SCP scheme with a dual decomposition algorithm to obtain a two-level decomposition algorithm. The global convergence of this algorithm is analyzed under standard assumptions. Some preliminary numerical results are also given to illustrate the theoretical results.
ISBN:1467357146
9781467357142
ISSN:0191-2216
DOI:10.1109/CDC.2013.6760235