Phase Transition between Synchronous and Asynchronous Updating Algorithms

Abstract We update a one-dimensional chain of Ising spins of length L with algorithms which are parameterized by the probability p for a certain site to get updated in one time step. The result of the update event itself is determined by the energy change due to the local change in the configuration. In this way we interpolate between the Metropolis algorithm at zero temperature when p is of the order of 1/L and L is large, and a synchronous deterministic updating procedure for p=1. As a function of p we observe a phase transition between the stationary states to which the algorithm drives the system. These are non-absorbing stationary states with antiferromagnetic domains for p>pc, and absorbing states with ferromagnetic domains for p≤pc. This means that above this transition the stationary states have lost any remnants of the ferromagnetic Ising interaction. A measurement of the critical exponents shows that this transition belongs to the universality class of parity conservation.

[1]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[2]  Florian Greil,et al.  Dynamics of critical Kauffman networks under asynchronous stochastic update. , 2005, Physical review letters.

[3]  José F. Fontanari,et al.  Information processing in synchronous neural networks , 1988 .

[4]  N. Menyhárd,et al.  PHASE TRANSITIONS AND CRITICAL BEHAVIOUR IN ONE-DIMENSIONAL NON-EQUILIBRIUM KINETIC ISING MODELS WITH BRANCHING ANNIHILATING RANDOM WALK OF KINKS , 1996, cond-mat/9607089.

[5]  H. Blok,et al.  Synchronous versus asynchronous updating in the ''game of Life'' , 1999 .

[6]  R. B. Potts Some generalized order-disorder transformations , 1952, Mathematical Proceedings of the Cambridge Philosophical Society.

[7]  I. Jensen Conservation laws and universality in branching annihilating random walks , 1993 .

[8]  H. K. Janssen Spontaneous Symmetry Breaking in Directed Percolation with Many Colors: Differentiation of Species in the Gribov Process , 1997 .

[9]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.

[10]  Book Review: Nonequilibrium Statistical Mechanics in One Dimension , 1999 .

[11]  Gerard T. Barkema,et al.  Monte Carlo Methods in Statistical Physics , 1999 .

[12]  Stefan Bornholdt,et al.  Stable and unstable attractors in Boolean networks. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[13]  G. Ódor Universality classes in nonequilibrium lattice systems , 2002, cond-mat/0205644.

[14]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[15]  D. Bollé,et al.  Two-cycles in spin-systems: sequential versus synchronous updating in multi-state Ising-type ferromagnets , 2004 .

[16]  Q. Ouyang,et al.  The yeast cell-cycle network is robustly designed. , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[17]  Cardy,et al.  Theory of Branching and Annihilating Random Walks. , 1996, Physical review letters.

[18]  B. Drossel,et al.  Number and length of attractors in a critical Kauffman model with connectivity one. , 2004, Physical review letters.

[19]  Stefan Bornholdt,et al.  Topology of biological networks and reliability of information processing , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[21]  Jensen Critical exponents for branching annihilating random walks with an even number of offspring. , 1994, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[22]  Critical behavior of an even-offspringed branching and annihilating random-walk cellular automaton with spatial disorder. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[23]  B A Huberman,et al.  Evolutionary games and computer simulations. , 1993, Proceedings of the National Academy of Sciences of the United States of America.

[24]  Universality class of two-offspring branching annihilating random walks , 1995, cond-mat/9509048.

[25]  R. Glauber Time‐Dependent Statistics of the Ising Model , 1963 .

[26]  Nancy A. Lynch,et al.  Efficiency of Synchronous Versus Asynchronous Distributed Systems , 1983, J. ACM.

[27]  D. Bollé,et al.  The Blume-Emery-Griffiths neural network with synchronous updating and variable dilution , 2005 .

[28]  R. O. Grondin,et al.  Synchronous and asynchronous systems of threshold elements , 1983, Biological Cybernetics.

[29]  Propagation and extinction in branching annihilating random walks. , 1994, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[30]  B. Samuelsson,et al.  Superpolynomial growth in the number of attractors in Kauffman networks. , 2003, Physical review letters.

[31]  H. Hinrichsen Non-equilibrium critical phenomena and phase transitions into absorbing states , 2000, cond-mat/0001070.