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Distributed Cooperative Optimization

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Distributed Optimization-Based Control of Multi-Agent Networks in Complex Environments

Part of the book series: SpringerBriefs in Electrical and Computer Engineering ((BRIEFSCONTROL))

Abstract

This chapter studies a class of generic distributed convex optimization problems. In particular, each agent is associated with a private objective function and a private convex constraint set. Meanwhile, all the agents are subject to a pair of global inequality and equality constraints. The key feature of the problem is that all the component functions depend upon a global decision variable. The agents aim to agree upon two global quantities: (1) a global minimizer of the sum of all private objective functions, simultaneously enforcing all the given constraints; (2) the induced optimal value.

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Notes

  1. 1.

    Each agent i executes the update law of \(y^i(k)\) for \(k\ge 1\).

  2. 2.

    Each agent i executes the update law of \(y^i(k)\) for \(k\ge 1\).

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Authors and Affiliations

  1. Department of Electrical Engineering, Pennsylvania State University, University Park, PA, USA

    Minghui Zhu

  2. Department of Mechanical and Aerospace Engineering, University of California, San Diego, La Jolla, CA, USA

    Sonia Martínez

Authors
  1. Minghui Zhu
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  2. Sonia Martínez
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Correspondence to Minghui Zhu .

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Zhu, M., Martínez, S. (2015). Distributed Cooperative Optimization. In: Distributed Optimization-Based Control of Multi-Agent Networks in Complex Environments. SpringerBriefs in Electrical and Computer Engineering(). Springer, Cham. https://doi.org/10.1007/978-3-319-19072-3_2

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  • DOI: https://doi.org/10.1007/978-3-319-19072-3_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19071-6

  • Online ISBN: 978-3-319-19072-3

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