We introduce a recently published convex relaxation approach for the quadratic assignment problem to the
field of computer vision. Due to convexity, a favourable property of this approach is the absence of any tuning parameters and the computation of
high–quality combinatorial solutions by solving a mathematically simple optimization
problem. Furthermore, the relaxation step always computes a tight lower bound of the
objective function and thus can additionally be used as an efficient subroutine of an exact
search algorithm.
We report the results of both established benchmark experiments from combinatorial mathematics and random ground-truth experiments using computer-generated graphs.
For comparison, a recently published deterministic annealing approach is investigated as well. Both approaches show similarly good performance. In contrast to the convex ap-
proach, however, the annealing approach yields no problem relaxation, and four parame-
ters have to be tuned by hand for the annealing algorithm to become competitive.
Dieser Eintrag ist Teil der Universitätsbibliographie.
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