Dynamical softassign and adaptive parameter tuning for graph matching

arXiv:2208.08233v3 Announce Type: replace-cross
Abstract: This paper studies a unified framework for graph matching problems called the constrained gradient method. Popular algorithms within this framework include graduated assignment (GA), integer projected fixed-point method (IPFP), and doubly stochastic projected fixed-point method (DSPFP). These algorithms differ from the step size parameter and constrained operator. Our contributed adaptive step size parameter can guarantee the underlying algorithms' convergence and enhance their efficiency and accuracy. A preliminary analysis suggests that the optimal step size parameter …

abstract algorithms arxiv cs.lg fixed-point framework gradient graph math.co paper popular stochastic studies type