Convergence Properties of the Softassign Quadratic Assignment Algorithm
نویسندگان
چکیده
The softassign quadratic assignment algorithm is a discrete-time, continuous-state, synchronous updating optimizing neural network. While its effectiveness has been shown in the traveling salesman problem, graph matching, and graph partitioning in thousands of simulations, its convergence properties have not been studied. Here, we construct discrete-time Lyapunov functions for the cases of exact and approximate doubly stochastic constraint satisfaction, which show convergence to a fixed point. The combination of good convergence properties and experimental success makes the softassign algorithm an excellent choice for neural quadratic assignment optimization.
منابع مشابه
A Convergence Proof for the Softassign Quadratic Assignment Algorithm
The softassign quadratic assignment algorithm has recently emerged as an effective strategy for a variety of optimization problems in pattern recognition and combinatorial optimization. While the effectiveness of the algorithm was demonstrated in thousands of simulations, there was no known proof of convergence. Here, we provide a proof of convergence for the most general form of the algorithm.
متن کاملSubmitted to Advances in Neural Information Processing Systems ( NIPS ) 8
The softassign quadratic assignment algorithm has recently emerged as an eeective strategy for a variety of optimization problems in pattern recognition and combina-torial optimization. While the eeectiveness of the algorithm was demonstrated in thousands of simulations, there was no known proof of convergence. Here, we provide a proof of convergence for the most general form of the algorithm.
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عنوان ژورنال:
- Neural computation
دوره 11 6 شماره
صفحات -
تاریخ انتشار 1999