A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 1 Issue 1
Jan.  2014

IEEE/CAA Journal of Automatica Sinica

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Article Contents
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.

An Approximate Gradient Algorithm for Constrained Distributed Convex Optimization

Funds:

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

  • 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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