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Sökning: WFRF:(Keviczky T.)

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1.
  • Johansson, Björn, et al. (författare)
  • Subgradient methods and consensus algorithms for solving convex optimization problems
  • 2008
  • Ingår i: Decision and Control, 2008. CDC 2008. 47th IEEE Conference on. - : IEEE. - 9781424431236 ; , s. 4185-4190
  • Konferensbidrag (refereegranskat)abstract
    • In this paper we propose a subgradient method for solving coupled optimization problems in a distributed way given restrictions on the communication topology. The iterative procedure maintains local variables at each node and relies on local subgradient updates in combination with a consensus process. The local subgradient steps are applied simultaneously as opposed to the standard sequential or cyclic procedure. We study convergence properties of the proposed scheme using results from consensus theory and approximate subgradient methods. The framework is illustrated on an optimal distributed finite-time rendezvous problem.
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2.
  • Simonetto, A., et al. (författare)
  • Distributed solution for a Maximum Variance Unfolding Problem with sensor and robotic network applications
  • 2012
  • Ingår i: Communication, Control, and Computing (Allerton), 2012 50th Annual Allerton Conference on. - : IEEE. - 9781467345385 ; , s. 63-70
  • Konferensbidrag (refereegranskat)abstract
    • We focus on a particular non-convex networked optimization problem, known as the Maximum Variance Unfolding problem and its dual, the Fastest Mixing Markov Process problem. These problems are of relevance for sensor networks and robotic applications. We propose to solve both these problems with the same distributed primal-dual subgradient iterations whose convergence is proven even in the case of approximation errors in the calculation of the subgradients. Furthermore, we illustrate the use of the algorithm for sensor network applications, such as localization problems, and for mobile robotic networks applications, such as dispersion problems.
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  • Resultat 1-2 av 2
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