Title :
A Lagrangian relaxation network for graph matching
Author :
Rangarajan, Anand ; Mjolsness, Eric
Author_Institution :
Dept. of Comput. Sci., Yale Univ., New Haven, CT, USA
fDate :
27 Jun-2 Jul 1994
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;
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
DOI :
10.1109/ICNN.1994.375022
