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A Study of Lagrangean Decompositions and Dual Ascent Solvers for Graph Matching
[article]
2017
arXiv
pre-print
We study the quadratic assignment problem, in computer vision also known as graph matching. Two leading solvers for this problem optimize the Lagrange decomposition duals with sub-gradient and dual ascent (also known as message passing) updates. We explore s direction further and propose several additional Lagrangean relaxations of the graph matching problem along with corresponding algorithms, which are all based on a common dual ascent framework. Our extensive empirical evaluation gives
arXiv:1612.05476v2
fatcat:q7svgp37kvdtnechnefe7asil4