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Jan.  2014

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2014  >  1(1): 61-67
Yanqiong Zhang, Youcheng Lou and Yiguang Hong, "An Approximate Gradient Algorithm for Constrained Distributed Convex Optimization," IEEE/CAA J. of Autom. Sinica, vol. 1, no. 1, pp. 61-67, 2014.
Citation: Yanqiong Zhang, Youcheng Lou and Yiguang Hong, "An Approximate Gradient Algorithm for Constrained Distributed Convex Optimization," IEEE/CAA J. of Autom. Sinica, vol. 1, no. 1, pp. 61-67, 2014.
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An Approximate Gradient Algorithm for Constrained Distributed Convex Optimization

  • 1. Key Laboratory of Systems and Control, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China;

  • 2. National Center for Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China;

  • 3. Key Laboratory of Systems and Control, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China

Funds:

This work was supported by National Natural Science Foundation of China (61174071).

    Abstract
  • In this paper, we propose an approximate gradient algorithm for the multi-agent convex optimization problem with constraints. The agents cooperatively compute the minimum of the sum of the local objective functions which are subject to a global inequality constraint and a global constraint set. Instead of each agent can get exact gradient, as discussed in the literature, we only use approximate gradient with some computation or measurement errors. The gradient accuracy conditions are presented to ensure the convergence of the approximate gradient algorithm. Finally, simulation results demonstrate good performance of the approximate algorithm.

     

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