A saturated linear dynamical network for approximating maximum clique

Cataloged from PDF version of article.We 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

A saturated linear dynamical network for approximating maximum clique

We 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. © 1999 IEEE

Computing Quantiles in Markov Reward Models

Probabilistic model checking mainly concentrates on techniques for reasoning about the probabilities of certain path properties or expected values of certain random variables. For the quantitative system analysis, however, there is also another type of interesting performance measure, namely quantiles. A typical quantile query takes as input a lower probability bound p and a reachability property. The task is then to compute the minimal reward bound r such that with probability at least p the target set will be reached before the accumulated reward exceeds r. Quantiles are well-known from mathematical statistics, but to the best of our knowledge they have not been addressed by the model checking community so far. In this paper, we study the complexity of quantile queries for until properties in discrete-time finite-state Markov decision processes with non-negative rewards on states. We show that qualitative quantile queries can be evaluated in polynomial time and present an exponential algorithm for the evaluation of quantitative quantile queries. For the special case of Markov chains, we show that quantitative quantile queries can be evaluated in time polynomial in the size of the chain and the maximum reward.Comment: 17 pages, 1 figure; typo in example correcte

An analysis of maximum clique formulations and saturated linear dynamical network

Several formulations and methods used in solving an NP-hard discrete optimization problem, maximum clique, are considered in a dynamical system perspective proposing continuous methods to the problem. A compact form for a saturated linear dynamical network, recently developed for obtaining approximations to maximum clique, is given so its relation to the classical gradient projection method of constrained optimization becomes more visible. Using this form, gradient-like dynamical systems as continuous methods for finding the maximum clique are discussed. To show the one to one correspondence between the stable equilibria of the saturated linear dynamical network and the minima of objective function related to the optimization problem, La Salle's invariance principle has been extended to the systems with a discontinuous right-hand side. In order to show the efficiency of the continuous methods simulation results are given comparing saturated the linear dynamical network, the continuous Hopfield network, the cellular neural networks and relaxation labelling networks. It is concluded that the quadratic programming formulation of the maximum clique problem provides a framework suitable to be incorporated with the continuous relaxation of binary optimization variables and hence allowing the use of gradient-like continuous systems which have been observed to be quite efficient for minimizing quadratic costs. © Springer-Verlag 1999

Determining the bistability parameter ranges of artificially induced lac operon using the root locus method

This paper employs the root locus method to conduct a detailed investigation of the parameter regions that ensure bistability in a well-studied gene regulatory network namely, lac operon of Escherichia coli (E. coli). In contrast to previous works, the parametric bistability conditions observed in this study constitute a complete set of necessary and sufficient conditions. These conditions were derived by applying the root locus method to the polynomial equilibrium equation of the lac operon model to determine the parameter values yielding the multiple real roots necessary for bistability. The lac operon model used was defined as an ordinary differential equation system in a state equation form with a rational right hand side, and it was compatible with the Hill and Michaelis-Menten approaches of enzyme kinetics used to describe biochemical reactions that govern lactose metabolism. The developed root locus method can be used to study the steady-state behavior of any type of convergent biological system model based on mass action kinetics. This method provides a solution to the problem of analyzing gene regulatory networks under parameter uncertainties because the root locus method considers the model parameters as variable, rather than fixed. The obtained bistability ranges for the lac operon model parameters have the potential to elucidate the appearance of bistability for E. coli cells in in vivo experiments, and they could also be used to design robust hysteretic switches in synthetic biology. (C) 2015 Elsevier Ltd. All rights reserved

Solving maximum clique problem by cellular neural network

An approximate solution of an NP-hard graph theoretical problem, namely finding maximum clique, is presented using cellular neural networks. Like the existing energy descent optimising dynamics, the maximal cliques will be the stable states of cellular neural networks. To illustrate the performance of the method, the results will be compared with those of continuous Hopfield dynamics