Computational Physics: Selected Methods Simple Exercises by Dietrich Stauffer (auth.), Professor Dr. Karl Heinz

By Dietrich Stauffer (auth.), Professor Dr. Karl Heinz Hoffmann, Professor Dr. Michael Schreiber (eds.)

"Computational Physics. chosen equipment, uncomplicated routines, severe functions" is an outline written through major researchers of a number of fields and advancements. "Selected tools" introduce the reader to present fields, together with molecular dynamics, hybrid Monte-Carlo algorithms, and neural networks. "Simple workouts" supply hands-on recommendation for potent software recommendations from a small variety of traces to demonstration courses with complex photos. "Serious functions" express how questions touching on, for instance, getting older, many-minima optimisation, or section transitions could be taken care of through applicable instruments. The resource code and demonstration photos are incorporated on a 3.5" MS-DOS diskette. The diskette: textual content folgt (Stand: 27.03.1996)

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Comi, Phys. Rev. F. Baillie, R Gupta, KA. S. Pawley, Phys. Rev. M. P. Landau, Phys. Rev. J. Blote, E. R. Heringa, J. Phys. A 28, 6289 (1995) [112] J. Adler, J. Phys. A 16, 3585 (1983) [113] A. Rosengren, J. Phys. S. Goldner and G. Ahlers, Phys. Rev. P. Gottlob and M. Hasenbusch, Physica A 201, 593 (1993) [116] P. Butera, M. J. Guttmann, Phys. Rev. de Abstract. Metastable systems such as spin glasses show a wealth of interesting relaxation phenomena. Stochastic optimization procedures such as simulated annealing help to solve a number of industrially important minimization problems.

An example is a system with deep wells in its free energy function, from which an escape can take a very long time. Such a system should reveal itself when, for instance, its thermal relaxation is studied. Stochastic optimization procedures on the other hand try to provide solutions to optimization problems which have many local minima in their objective function. For these optimization problems the usual steepest descent algorithms fail as they get easily caught in local minima. For such problems stochastic optimization procedures, and especially simulated annealing, have been used with growing success - not to determine the global minimum but to provide "good" solutions with values of the objective which are not too "far" apart from the desired global minimum.

Rev. A 25, 1699 (1982) Monte Carlo Simulations of Spin Systems 43 [80] A. Billoire, R Lacaze, A. Morel, S. Gupta, A. Irback, and B. Petersson, NucL Phys. A. Berg and T. Neuhaus, Phys. Lett. A. Berg and T. Neuhaus, Phys. Rev. Lett. 68, 9 (1992) [83] W. A. Berg, and M. Katoot, NucL Phys. B 382, 649 (1992) [84] K Rummukainen, NucL Phys. B 390, 621 (1993) [85] W. Janke and T. Sauer, Phys. Rev. E 49, 3475 (1994) [86] T. D. Thesis, Freie Universitat Berlin (1994) [87] W. Janke and S. Kappler, Nucl. Phys.

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