@InProceedings{mixed_maxflow-11,
  IS = { zkontrolovano 11 Jan 2012 },
  UPDATE  = { 2011-08-01 },
  author =      {Shekhovtsov, Alexander and Hlav{\' a}{\v c}, V{\' a}clav},
  title =       {A Distributed Mincut/Maxflow Algorithm Combining 
                 Path Augmentation and Push-Relabel},
  year =        {2011},
  pages =       {1--16},
  booktitle =   {Proceedings of the 8th International Conference on Energy 
                 Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR)},
  publisher =   {Springer},
  address =     {Berlin, Germany},
  isbn =        {978-3-642-23093-6},
  volume =      {6819},
  series =      {Lecture Notes in Computer Science},
  book_pages =  {450},
  month =       {July},
  day =         {25--27},
  venue =       {Saint Petersburg, Russia},
  annote   = {We develop a novel distributed algorithm for the
   minimum cut problem. We primarily aim at solving large sparse
   problems. Assuming vertices of the graph are partitioned into
   several regions, the algorithm performs path augmentations inside
   the regions and updates of the push-relabel style between the
   regions. The interaction between regions is considered expensive
   (regions are loaded into the memory one-by-one or located on
   separate machines in a network). The algorithm works in sweeps --
   passes over all regions.  Let B be the set of vertices incident
   to inter-region edges of the graph. We present a sequential and
   parallel versions of the algorithm which terminate in at most
   2|B|^2+1 sweeps. The competing algorithm by Delong and Boykov
   uses push-relabel updates inside regions.  In the case of a fixed
   partition we prove that this algorithm has a tight O(n^2) bound
   on the number of sweeps, where n is the number of vertices.  We
   tested sequential versions of the algorithms on instances of
   maxflow problems in computer vision. Experimentally, the number of
   sweeps required by the new algorithm is much lower than for the
   Delong and Boykov's variant.  Large problems (up to 10^8 vertices
   and 6.10^8 edges) are solved using under 1GB of memory in
   about 10 sweeps.},
  project =     {FP7-ICT-247870 NIFTi, FP7-ICT-247525 HUMAVIPS},
  subseries =   {Image Processing, Computer Vision, 
                 Pattern Recognition, and Graphics},
  editor      = { Boykov, Y. and Kahl, F. and Lempitsky, V. and Schmidt, F.R. },
  keywords    = { mincut, maxflow, distributed, streaming, parallel, 
   large-scale, vision, push-relabel, augmenting path },
}