Abstract
Subgradient descent methods for minimization of dual linear relaxed labeling problem are analysed. They are guaranteed to converge to the quality of the optimal relaxed labeling, but do not obtain an optimal relaxed labeling itself. Moreover, no stop condition is known for these methods upto now. The stop condition is defined and experimentally compared with the commonly-used stop conditions. The stop condition is defined in a way that when fulfilled a relaxed labeling is simultaneously obtained with arbitrary non-zero difference from the optimal labeling.
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Schlesinger, M., Vodolazskiy, E., Lopatka, N. (2011). Stop Condition for Subgradient Minimization in Dual Relaxed (max,+) Problem. In: Boykov, Y., Kahl, F., Lempitsky, V., Schmidt, F.R. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2011. Lecture Notes in Computer Science, vol 6819. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23094-3_9
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DOI: https://doi.org/10.1007/978-3-642-23094-3_9
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