Abstract
This paper deals with the minimization of average packet delay modelled as multicommodity flow problems. We use an approach based on proximal techniques in convex programming. This new decomposition method relies on the proximal point algorithm which allows to split a multicommodity flow problem into several single flow problems with quadratic cost functions. When each regularized subproblem is solved, we project its solution on an appropriate subspace which represents the coupling constraints, i.e. the capacity bound for the sum of the flows. We present a numerical application of this method on a real data network.
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Chifflet, J., Mahey, P. & Reynier, V. Proximal decomposition for multicommodity flow problems with convex costs. Telecommunication Systems 3, 1–10 (1994). https://doi.org/10.1007/BF02110041
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DOI: https://doi.org/10.1007/BF02110041