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arXiv · 1612.05476

A Study of Lagrangean Decompositions and Dual Ascent Solvers for Graph Matching

Paul Swoboda·Carsten Rother·Hassan Abu Alhaija·Dagmar Kainmueller·Bogdan Savchynskyy
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Abstract

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 several theoretical insights and suggests a new state-of-the-art any-time solver for the considered problem. Our improvement over state-of-the-art is particularly visible on a new dataset with large-scale sparse problem instances containing more than 500 graph nodes each.

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Paul Swoboda, Carsten Rother, Hassan Abu Alhaija, Dagmar Kainmueller, Bogdan Savchynskyy. 2016-12-16. A Study of Lagrangean Decompositions and Dual Ascent Solvers for Graph Matching. https://arxiv.org/abs/1612.05476

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