--- res: bibo_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 this 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 anytime 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.@eng bibo_authorlist: - foaf_Person: foaf_givenName: Paul foaf_name: Swoboda, Paul foaf_surname: Swoboda foaf_workInfoHomepage: http://www.librecat.org/personId=446560C6-F248-11E8-B48F-1D18A9856A87 - foaf_Person: foaf_givenName: Carsten foaf_name: Rother, Carsten foaf_surname: Rother - foaf_Person: foaf_givenName: Carsten foaf_name: Abu Alhaija, Carsten foaf_surname: Abu Alhaija - foaf_Person: foaf_givenName: Dagmar foaf_name: Kainmueller, Dagmar foaf_surname: Kainmueller - foaf_Person: foaf_givenName: Bogdan foaf_name: Savchynskyy, Bogdan foaf_surname: Savchynskyy bibo_doi: 10.1109/CVPR.2017.747 bibo_volume: 2017 dct_date: 2017^xs_gYear dct_identifier: - UT:000418371407018 dct_isPartOf: - http://id.crossref.org/issn/978-153860457-1 dct_language: eng dct_publisher: IEEE@ dct_title: A study of lagrangean decompositions and dual ascent solvers for graph matching@ ...