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A neural algorithm for MAX-2SAT: Performance analysis and circuit implementation
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DOI：10.1016/S0893-6080(96)00065-2.png)
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Abstract
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A neural algorithm for solving approximately the maximum 2-satisfiability problem is presented and its performance is analysed: the worst case relative error is 0.25 and the computation time is bounded by nm/4, where n is the number of variables and m the number of clauses of a problem instance. Simulation experiments show a very good average case performance. We design a uniform family of circuits of small size and depth to implement the algorithm and present an efficient realization on field programmable gate arrays. (C) 1997 Elsevier Science Ltd.
Keywords:
approximation
optimization
satisfiability
hopfield networks
programmable gate arrays
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