A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf
.
Multi-dimensional Parametric Mincuts for Constrained MAP Inference
[article]
2013
arXiv
pre-print
In this paper, we propose novel algorithms for inferring the Maximum a Posteriori (MAP) solution of discrete pairwise random field models under multiple constraints. We show how this constrained discrete optimization problem can be formulated as a multi-dimensional parametric mincut problem via its Lagrangian dual, and prove that our algorithm isolates all constraint instances for which the problem can be solved exactly. These multiple solutions enable us to even deal with 'soft constraints'
arXiv:1307.7793v1
fatcat:dfb3er6i5jaetmk7ntjyeadkdu