On Coordinate Minimization of Convex Piecewise-Affine Functions

نویسنده

  • Tomás Werner
چکیده

A popular class of algorithms to optimize the dual LP relaxation of the discrete energy minimization problem (a.k.a. MAP inference in graphical models or valued constraint satisfaction) are convergent message-passing algorithms, such as max-sum diffusion, TRW-S, MPLP and SRMP. These algorithms are successful in practice, despite the fact that they are a version of coordinate minimization applied to a convex piecewise-affine function, which is not guaranteed to converge to a global minimizer. These algorithms converge only to a local minimizer, characterized by local consistency known from constraint programming. We generalize max-sum diffusion to a version of coordinate minimization applicable to an arbitrary convex piecewise-affine function, which converges to a local consistency condition. This condition can be seen as the sign relaxation of the global optimality condition.

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عنوان ژورنال:
  • CoRR

دوره abs/1709.04989  شماره 

صفحات  -

تاریخ انتشار 2017