Monte Carlo Methods and Applications

Transcription

1 de Gruyter Proceedings in Mathematics Monte Carlo Methods and Applications Proceedings of the 8th IMACS Seminar on Monte Carlo Methods, August 29 September 2, 2011, Borovets, Bulgaria Bearbeitet von Enrique Alba, Donka Angelova, Maria Angelova, Sergey Artemchuk, Emanouil Atanassov, Krassimir Atanassov, Lilija Atanassova, Vassia Atanassova, Fabian Bastin, Oskar Baumgartner, Aleksandr Burmistrov, Avishy Carmi, Panagiotis Chountas, Carlos Cotta, Dimitar Dimitrov, Ivan Dimov, Nina Dobrinkova, Rami El Haddad, Rana Fakhreddine, Antonio J. Fernández-Leiva, Stefka Fidanova, Lado Filipovic, Amadou Gning, Oleg Iliev, Sofiya Ivanovska, Dimitri Kanevsky, Mariya Korotchenko, Hans Kosina, Christian Lécot, Jan Mandel, Pencho Marinov, Mikhail Mayorov, Ilya N. Medvedev, Lyudmila Mihaylova, Tigran Nagapetyan, Mihail Nedjalkov, Florian Pausinger, Tania Pencheva, Nikolay Petrov, Ivan Popchev, Klaus Ritter, David Rodríguez Rueda, Olympia Roeva, Wolfgang Ch. Schmid, Philipp Schwaha, Siegfried Selberherr, Zlatan Stanojevic, Hidemaro Suwa, Synge Todo, Mario Ullrich, Paula A. Whitlock, Karl K. Sabelfeld 1. Auflage Buch. XIII, 246 S. Hardcover ISBN Format (B x L): 17 x 24 cm Gewicht: 558 g Weitere Fachgebiete > Technik > Technik Allgemein > Modellierung & Simulation schnell und portofrei erhältlich bei Die Online-Fachbuchhandlung beck-shop.de ist spezialisiert auf Fachbücher, insbesondere Recht, Steuern und Wirtschaft. Im Sortiment finden Sie alle Medien (Bücher, Zeitschriften, CDs, ebooks, etc.) aller Verlage. Ergänzt wird das Programm durch Services wie Neuerscheinungsdienst oder Zusammenstellungen von Büchern zu Sonderpreisen. Der Shop führt mehr als 8 Millionen Produkte.

2 Contents Preface v 1 Maria Angelova and Tania Pencheva Improvement of Multi-population Genetic Algorithms Convergence Time Introduction Short Overview of MpGA Modifications Parameter Identification of S. cerevisiae Fed-Batch Cultivation Using Different Kinds of MpGA Analysis and Conclusions Sergey Artemchuk and Paula A. Whitlock Parallelization and Optimization of 4D Binary Mixture Monte Carlo Simulations Using Open MPI and CUDA Introduction The Metropolis Monte Carlo Method Decomposition into Subdomains and the Virtual Topology Using OpenMPI Management of Hypersphere Coordinate Migration Between Domains Communication between the CPU and the GPU Pseudorandom Number Generation Results of Running the Modified Code Conclusions Emanouil Atanassov, Dimitar Dimitrov, and Sofiya Ivanovska Efficient Implementation of the Heston Model Using GPGPU Introduction Our GPGPU-Based Algorithm for Option Pricing Numerical Results Conclusions and Future Work

3 viii Contents 4 Lilija Atanassova and Krassimir Atanassov On a Game-Method for Modeling with Intuitionistic Fuzzy Estimations. Part Introduction Short Remarks on the Game-Method for Modeling from Crisp Point of View On the Game-Method for Modeling with Intuitionistic Fuzzy Estimations Main Results Conclusion Vassia Atanassova, Stefka Fidanova, Ivan Popchev, and Panagiotis Chountas Generalized Nets, ACO Algorithms, and Genetic Algorithms Introduction ACOandGA GN for Hybrid ACO-GA Algorithm Conclusion Fabian Bastin Bias Evaluation and Reduction for Sample-Path Optimization Introduction Problem Formulation Taylor-Based Bias Correction Impact on the Optimization Bias NumericalExperiments Conclusions Oskar Baumgartner, Zlatan Stanojević, and Hans Kosina Monte Carlo Simulation of Electron Transport in Quantum Cascade Lasers Introduction QCLTransportModel Pauli Master Equation Calculation of Basis States Monte Carlo Solver Results and Discussion Conclusion... 65

4 Contents ix 8 Avishy Carmi and Lyudmila Mihaylova Markov Chain Monte Carlo Particle Algorithms for Discrete-Time Nonlinear Filtering Introduction General Particle Filtering Framework High Dimensional Particle Schemes Sequential MCMC Filtering Efficient Sampling in High Dimensions Setting Proposal and Steering Distributions Illustrative Examples Conclusions Nina Dobrinkova, Stefka Fidanova, Krassimir Atanassov, and Jan Mandel Game-Method for Modeling and WRF-Fire Model Working Together Introduction Description of the Game-Method for Modeling General Description of the Coupled Atmosphere Fire Modeling and WRF-Fire Wind Simulation Approach Conclusion Stefka Fidanova, Pencho Marinov, and Enrique Alba Wireless Sensor Network Layout Introduction Wireless Sensor Network Layout Problem ACO for WSN Layout Problem Experimental Results Conclusion Lado Filipovic and Siegfried Selberherr A Two-Dimensional Lorentzian Distribution for an Atomic Force Microscopy Simulator Introduction Modeling Oxidation Kinetics Development of the Lorentzian Model Algorithm for the Gaussian Model Development of the Lorentzian Model Conclusion

5 x Contents 12 Rami El Haddad, Rana Fakhreddine, and Christian Lécot Stratified Monte Carlo Integration Introduction Numerical Integration Conclusion Oleg Iliev, Tigran Nagapetyan, and Klaus Ritter Monte Carlo Simulation of Asymmetric Flow Field Flow Fractionation Motivation AFFFF Mathematical Model and Numerical Algorithm MathematicalModel The MLMC Algorithm Numerical Results Dimitri Kanevsky and Avishy Carmi Convexization in Markov Chain Monte Carlo Introduction Auxiliary Functions Definition of Auxiliary Functions Optimization Process for Auxiliary Functions Auxiliary Functions for Convex Functions Objective Function Which Is the Sum of Convex and Concave Functions Stochastic Auxiliary Functions Stochastic Convex Learning (Summary) Auxiliary Stochastic Functions Metropolis Hastings Auxiliary Algorithm NumericalExperiments Conclusion Mariya Korotchenko and Aleksandr Burmistrov Value Simulation of the Interacting Pair Number for Solution of the Monodisperse Coagulation Equation Introduction Value Simulation for Integral Equations Value Simulation of the Time Interval Between Interactions VSIPN to Estimate the Monomer Concentration J H

6 Contents xi VSIPN to Estimate the Monomer and Dimer Concentration J H Results of the Numerical Experiments Conclusion Mikhail Mayorov and Paula A. Whitlock Parallelization of Algorithms for Solving a Three-Dimensional Sudoku Puzzle Introduction The Simulated Annealing Method Successful Algorithms for Solving the Three-Dimensional Puzzle UsingMPI An Embarrassingly Parallel Algorithm Distributed Simulated Annealing Using a Master/Worker Organization Results Conclusions Ilya N. Medvedev The Efficiency Study of Splitting and Branching in the Monte Carlo Method Introduction Randomized Branching Splitting Florian Pausinger and Wolfgang Ch. Schmid On the Asymptotics of a Lower Bound for the Diaphony of Generalized van der Corput Sequences Introduction and Main Result Definitions and Previous Results ProofofTheorem Nikolay Petrov, Lyudmila Mihaylova, Amadou Gning, and Donka Angelova Group Object Tracking with a Sequential Monte Carlo Method Based on a Parameterized Likelihood Function Motivation Group Object Tracking within the Sequential Monte Carlo Framework 172

7 xii Contents 19.3 Measurement Likelihood for Group Object Tracking Introduction of the Notion of the Visible Surface Parametrization of the Visible Surface Performance Evaluation Conclusions David Rodríguez Rueda, Carlos Cotta, and Antonio J. Fernández-Leiva The Template Design Problem: A Perspective with Metaheuristics Introduction The Template Design Problem Solving the TDP under Deterministic Demand Representation and Evaluation MetaheuristicApproaches Experimental Results Conclusions and Future Work Olympia Roeva A Comparison of Simulated Annealing and Genetic Algorithm Approaches for Cultivation Model Identification Introduction Genetic Algorithm Simulated Annealing E. coli MC4110 Fed-Batch Cultivation Process Model Numerical Results and Discussion Conclusion Philipp Schwaha, Mihail Nedjalkov, Siegfried Selberherr, and Ivan Dimov Monte Carlo Investigations of Electron Decoherence due to Phonons Introduction The Algorithms Algorithm A Algorithm B Algorithm C

8 Contents xiii 23 Hidemaro Suwa and Synge Todo Geometric Allocation Approach for the Transition Kernel of a Markov Chain Introduction Geometric Approach Reversible Kernel Irreversible Kernel BenchmarkTest Conclusion Mario Ullrich Exact Sampling for the Ising Model at All Temperatures Introduction TheIsingModel Exact Sampling The Random Cluster Model Exact Sampling for the Ising Model