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Random Coordinate Descent Methods for Minimizing Decomposable Submodular Functions

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LFCS Seminar by Dr. Alina Ene (Warwick)

What
  • LFCS Seminar
When Sep 22, 2015
from 04:00 PM to 05:00 PM
Where IF 4.31
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Submodular function minimization is a fundamental optimization problem that arises in several applications in machine learning and computer vision. The problem is known to be solvable in polynomial time, but general purpose algorithms have high running times and are unsuitable for large-scale problems. Recent work have used convex optimization techniques to obtain very practical algorithms for minimizing functions that are sums of "simple" functions. In this talk, we give two algorithms based on random coordinate descent with faster linear convergence rates and cheaper iteration costs.
 
This talk is based on joint work with Huy Nguyen.
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