Title :
Optimization by neural networks
Author :
Ramanujam, J. ; Sadayappan, P.
Author_Institution :
Dept. of Comput. & Inf. Sci., Ohio State Univ., Columbus, OH, USA
Abstract :
The ability to map and solve a number of interesting problems on neural networks motivates a proposal for using neural networks as a highly parallel model for general-purpose computing. The author review this proposal, showing how to map combinational optimization problems, including graph K-partitioning, vertex cover, maximum independent set, maximum clique, number partitioning, and maximum matching. They report that performance results are quite encouraging; the solutions for graph partitioning and task allocation problems are comparable to those obtained using heuristics and the running times are significantly lower than those required using simulated annealing.<>
Keywords :
combinatorial mathematics; neural nets; optimisation; parallel algorithms; combinational optimization; general-purpose computing; graph K-partitioning; highly parallel model; maximum clique; maximum independent set; maximum matching; neural networks; number partitioning; performance results; task allocation; vertex cover; Combinatorial mathematics; Neural networks; Optimization methods; Parallel algorithms;
Conference_Titel :
Neural Networks, 1988., IEEE International Conference on
Conference_Location :
San Diego, CA, USA
DOI :
10.1109/ICNN.1988.23944
