Pekergin, F.Morgül, Ö.Güzeliş, C.2016-02-082016-02-081999-061057-7122http://hdl.handle.net/11693/25155We use a saturated linear gradient dynamical network for finding an approximate solution to the maximum clique problem. We show that for almost all initial conditions, any solution of the network defined on a closed hypercube reaches one of the vertices of the hypercube, and any such vertex corresponds to a maximal clique. We examine the performance of the method on a set of random graphs and compare the results with those of some existing methods. The proposed model presents a simple continuous, yet powerful, solution in approximating maximum clique, which may outperform many relatively complex methods, e.g., Hopfield-type neural network based methods and conventional heuristics.EnglishCombinatorial optimizationGradient systemsMax cliqueNeural networksApproximation theoryComputational complexityGraph theoryLinear programmingProblem solvingQuadratic programmingMaximum clique problemSaturated linear gradient dynamical networkNeural networksArticle10.1109/81.768824