TY - CONF AB - 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. AU - Swoboda, Paul AU - Rother, Carsten AU - Abu Alhaija, Carsten AU - Kainmueller, Dagmar AU - Savchynskyy, Bogdan ID - 916 SN - 978-153860457-1 TI - A study of lagrangean decompositions and dual ascent solvers for graph matching VL - 2017 ER -