A Lagrangian relaxation network for graph matching

Author

Rangarajan, Anand ; Mjolsness, Eric

Author_Institution

Dept. of Comput. Sci., Yale Univ., New Haven, CT, USA

Volume

7

fYear

1994

fDate

27 Jun-2 Jul 1994

Firstpage

4629

Abstract

Presents a Lagrangian relaxation network for graph matching. The problem is find a permutation matrix M that minimizes a distance between the two graphs. The authors adopt a deterministic annealing approach which is similar to a Lagrangian decomposition approach in that the row and column constraints of the permutation matrix are satisfied separately and Lagrange multipliers are used to equate the two “solutions”. A fixpoint preserving transformation is applied to the graph matching constraint. A symmetry-breaking term is added in order to obtain a permutation matrix and is reversed via another fixpoint preserving transformation. The resulting network performs minimization with respect to the Lagrange parameters and maximization with respect to the match matrix variables. Simulation results are shown on 100 node random graphs and for a wide range of connectivities

Keywords

conjugate gradient methods; graph theory; minimisation; neural nets; relaxation theory; simulated annealing; Lagrangian decomposition approach; Lagrangian relaxation network; deterministic annealing approach; fixpoint preserving transformation; graph matching; minimization; permutation matrix; random graphs; Annealing; Computer science; Computer vision; Genetic mutations; Lagrangian functions; Matrix decomposition; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on

Conference_Location

Orlando, FL

Print_ISBN

0-7803-1901-X

Type

conf

DOI

10.1109/ICNN.1994.375022

Filename

375022