Minimizing approximately submodular functions

Halabi, Marwa El, Jegelka, Stefanie

arXiv.org Machine Learning 

The problem of minimizing a submodular function is well studied; several polynomial-time algorithms have been developed to solve it exactly or up to arbitrary accuracy. However, in many applications, the objective functions are not exactly submodular. In this paper, we show that a classical algorithm used for submodular minimization performs well even for a class of non-submodular functions, namely weakly DR-submodular functions. We provide the first approximation guarantee for non-submodular minimization. This broadly expands the range of applications of submodular minimization techniques.

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