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Advances in Chemical Physics: Monte Carlo Methods in by David M. Ferguson, J. Ilja Siepmann, Donald G. Truhlar, Ilya

By David M. Ferguson, J. Ilja Siepmann, Donald G. Truhlar, Ilya Prigogine, Stuart A. Rice

In Monte Carlo equipment in Chemical Physics: An advent to the Monte Carlo approach for Particle Simulations J. Ilja Siepmann Random quantity turbines for Parallel functions Ashok Srinivasan, David M. Ceperley and Michael Mascagni among Classical and Quantum Monte Carlo equipment: "Variational" QMC Dario Bressanini and Peter J. Reynolds Monte Carlo Eigenvalue equipment in Quantum Mechanics and Statistical Mechanics M. P. Nightingale and C.J. Umrigar Adaptive Path-Integral Monte Carlo equipment for exact Computation of Molecular Thermodynamic houses Robert Q. Topper Monte Carlo Sampling for Classical Trajectory Simulations Gilles H. Peslherbe Haobin Wang and William L. Hase Monte Carlo ways to the Protein Folding challenge Jeffrey Skolnick and Andrzej Kolinski Entropy Sampling Monte Carlo for Polypeptides and Proteins Harold A. Scheraga and Minh-Hong Hao Macrostate Dissection of Thermodynamic Monte Carlo Integrals Bruce W. Church, Alex Ulitsky, and David Shalloway Simulated Annealing-Optimal Histogram equipment David M. Ferguson and David G. Garrett Monte Carlo tools for Polymeric structures Juan J. de Pablo and Fernando A. Escobedo Thermodynamic-Scaling tools in Monte Carlo and Their software to section Equilibria John Valleau Semigrand Canonical Monte Carlo Simulation: Integration alongside Coexistence traces David A. Kofke Monte Carlo tools for Simulating section Equilibria of advanced Fluids J. Ilja Siepmann Reactive Canonical Monte Carlo J. Karl Johnson New Monte Carlo Algorithms for Classical Spin platforms G. T. Barkema and M.E.J. NewmanContent:

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Additional info for Advances in Chemical Physics: Monte Carlo Methods in Chemical Physics, Volume 105

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Comp. 6, 477-497 (1989). 15. R. P. Brent, “Uniform Random Number Generators for Supercomputers,” in Proceedings Fifth Australian Supercomputer Conference, 5ASC Organizing Committee, 1992, pp. 95104. 16. M. Mascagni, “Parallel Linear Congruential Generators with Prime Moduli,” Parallel Computing (in press). 17. M. Mascagni, S. A. Cuccaro, and D. V. Pryor, “Techniques for Testing the Quality of Parallel Pseudorandom Number Generators,” in Proceedings of the Seventh SIAM Conference on Parallel Processing for Scientific Computing, SIAM, Philadelphia, Pennsylvania, 1995, pp.

Phys. 15,211-219 (1995). 29. M. Mascagni, “A Parallel Non-Linear Fibonacci Pseudorandom Number Generator,” Abstract, 45th SIAM Annual Meeting, 1997. 30. J. Eichenauer and J. Lehn, “A Nonlinear Congruential Pseudorandom Number Generator,” Stat. Hefte 37, 315-326 (1986). 31. H. Niederreiter, “Statistical Independence of Nonlinear Congruential Pseudorandom Numbers,” Montash. Math. 106, 149-159 (1988). 32. H. Niederreiter, “On a New Class of Pseudorandom Numbers for Simulation Methods,” J. Comput. Appl.

Blocking Test. In the blocking test we add random numbers from several streams as well as from within a stream. If the streams are independent, then the distribution of these sums will approach the normal distribution. We now give some test results for the LCG with (parameterized) prime addend and a modified version of the LFG. Both of these generators performed acceptably in the sequential tests with 10" random numbers. Preliminary results from other tests of PPRNG can also be found in the paper by Coddington [37].

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