de.mpg.escidoc.pubman.appbase.FacesBean
English
 
Help Guide Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Paper

Tighter Lifting-Free Convex Relaxations for Quadratic Matching Problems

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons214986

Bernard,  Florian
Computer Graphics, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45610

Theobalt,  Christian
Computer Graphics, MPI for Informatics, Max Planck Society;

Locator
There are no locators available
Fulltext (public)

arXiv:1711.10733.pdf
(Preprint), 3MB

Supplementary Material (public)
There is no public supplementary material available
Citation

Bernard, F., Theobalt, C., & Moeller, M. (2017). Tighter Lifting-Free Convex Relaxations for Quadratic Matching Problems. Retrieved from http://arxiv.org/abs/1711.10733.


Cite as: http://hdl.handle.net/21.11116/0000-0000-6143-7
Abstract
In this work we study convex relaxations of quadratic optimisation problems over permutation matrices. While existing semidefinite programming approaches can achieve remarkably tight relaxations, they have the strong disadvantage that they lift the original $n {\times} n$-dimensional variable to an $n^2 {\times} n^2$-dimensional variable, which limits their practical applicability. In contrast, here we present a lifting-free convex relaxation that is provably at least as tight as existing (lifting-free) convex relaxations. We demonstrate experimentally that our approach is superior to existing convex and non-convex methods for various problems, including image arrangement and multi-graph matching.