---
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@
...