A Dual Decomposition Algorithm for Separable Nonconvex Optimization Using the Penalty Function Framework

被引：0

作者：

Quoc Tran Dinh ^{[1
,2
,4
]}

Necoara, Ion ^{[3
]}

Diehl, Moritz ^{[1
,2
]}

机构：

[1] Katholieke Univ Leuven, Dept Elect Engn ESAT SCD, Leuven, Belgium

[2] Katholieke Univ Leuven, Optimizat Engn Ctr OPTEC, Leuven, Belgium

[3] Univ Politehn Bucuresti, Automat Control & Syst Engn Dept, Bucharest, Romania

[4] Ecole Polytech Fed Lausanne, Lab Informat & Inference Syst LIONS, Lausanne, Switzerland

来源：

2013 IEEE 52ND ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC) | 2013年

关键词：

MODEL-PREDICTIVE CONTROL;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：2372 / 2377

页数：6

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