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Continuous Relaxation of MAP Inference: A Nonconvex Perspective
2018
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
In this paper, we study a nonconvex continuous relaxation of MAP inference in discrete Markov random fields (MRFs). We show that for arbitrary MRFs, this relaxation is tight, and a discrete stationary point of it can be easily reached by a simple block coordinate descent algorithm. In addition, we study the resolution of this relaxation using popular gradient methods, and further propose a more effective solution using a multilinear decomposition framework based on the alternating direction
doi:10.1109/cvpr.2018.00580
dblp:conf/cvpr/Le-HuuP18
fatcat:s2bofb52kfbvdbhbdaxfqs26zm