A Neural Algorithm for MAX-2SAT: Performance Analysis and Circuit Implementation

Abstract 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. © 1997 Elsevier Science Ltd. All Rights Reserved.

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