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Monte Carlo Methods and Applications Proceedings of the 8th IMACS Seminar on Monte Carlo Methods, August 29 - September 2, 2011, Borovets, Bulgaria

:	Karl K. Sabelfeld, Ivan Dimov
:	Monte Carlo Methods and Applications Proceedings of the 8th IMACS Seminar on Monte Carlo Methods, August 29 - September 2, 2011, Borovets, Bulgaria
:	Walter de Gruyter GmbH& Co.KG
:	9783110293586
:	De Gruyter Proceedings in Mathematics
:	1
:	CHF 124.20
:

:	Wahrscheinlichkeitstheorie, Stochastik, Mathematische Statistik
:	English

:	246
:	Wasserzeichen
:	PC/MAC/eReader/Tablet
:	PDF

< >This is the proceedings of the '8^th IMACS Seminar on Monte Carlo Methods' held from August 29 to September 2, 2011 in Borovets, Bulgaria, and organized by the Institute of Information and Communication Technologies of the Bulgarian Academy of Sciences in cooperation with the International Association for Mathematics and Computers in Simulation (IMACS). Included are 24 papers which cover all topics presented in the sessions of the seminar: stochastic computation and complexity of high dimensional problems, sensitivity analysis, high-performance computations for Monte Carlo applications, stochastic metaheuristics for optimization problems, sequential Monte Carlo methods for large-scale problems, semiconductor devices and nanostructures.

< >Karl K. Sabelfeld, Institute of Computational Mathematics and Geophysics, Russian Acacemy of Sciences, Novosibirsk, Russia;Ivan Dimov, Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Sofia, Bulgaria.

	Preface	5
	1 Improvement of Multi-population Genetic Algorithms Convergence Time	15
	1.1 Introduction	15
	1.2 Short Overview of MpGA Modifications	16
	1.3 Parameter Identification of S. cerevisiae Fed-Batch Cultivation Using Different Kinds of MpGA	18
	1.4 Analysis and Conclusions	21
	2 Parallelization and Optimization of 4D Binary Mixture Monte Carlo Simulations Using Open MPI and CUDA	25
	2.1 Introduction	25
	2.2 The Metropolis Monte Carlo Method	26
	2.3 Decomposition into Subdomains and the Virtual Topology Using OpenMPI	27
	2.4 Management of Hypersphere Coordinate Migration Between Domains	28
	2.4.1 Communication between the CPU and the GPU	29
	2.5 Pseudorandom Number Generation	29
	2.6 Results of Running the Modified Code	29
	2.7 Conclusions	32
	3 Efficient Implementation of the Heston Model Using GPGPU	35
	3.1 Introduction	35
	3.2 Our GPGPU-Based Algorithm for Option Pricing	37
	3.3 Numerical Results	39
	3.4 Conclusions and Future Work	41
	4 On a Game-Method for Modeling with Intuitionistic Fuzzy Estimations. Part 2	43
	4.1 Introduction	43
	4.2 Short Remarks on the Game-Method for Modeling from Crisp Point of View	43
	4.3 On the Game-Method for Modeling with Intuitionistic Fuzzy Estimations	45
	4.4 Main Results	48
	4.5 Conclusion	50
	5 Generalized Nets, ACO Algorithms, and Genetic Algorithms	53
	5.1 Introduction	53
	5.2 ACO and GA	54
	5.3 GN for Hybrid ACO-GA Algorithm	56
	5.4 Conclusion	58
	6 Bias Evaluation and Reduction for Sample-Path Optimization	61
	6.1 Introduction	61
	6.2 Problem Formulation	63
	6.3 Taylor-Based Bias Correction	65
	6.4 Impact on the Optimization Bias	66
	6.5 Numerical Experiments	67
	6.6 Conclusions	69
	7 Monte Carlo Simulation of Electron Transport in Quantum Cascade Lasers	73
	7.1 Introduction	73
	7.2 QCL Transport Model	73
	7.2.1 Pauli Master Equation	74
	7.2.2 Calculation of Basis States	75
	7.2.3 Monte Carlo Solver	76
	7.3 Results and Discussion	78
	7.4 Conclusion	79
	8 Markov Chain Monte Carlo Particle Algorithms for Discrete-Time Nonlinear Filtering	83
	8.1 Introduction	83
	8.2 General Particle Filtering Framework	84
	8.3 High Dimensional Particle Schemes	85
	8.3.1 Sequential MCMC Filtering	85
	8.3.2 Efficient Sampling in High Dimensions	86
	8.3.3 Setting Proposal and Steering Distributions	87
	8.4 Illustrative Examples	87
	8.5 Conclusions	90
	9 Game-Method for Modeling and WRF-Fire Model Working Together	93
	9.1 Introduction	93
	9.2 Description of the Game-Method for Modeling	94
	9.3 General Description of the Coupled Atmosphere Fire Modeling and WRF-Fire	95
	9.4 Wind Simulation Approach	97
	9.5 Conclusion	98
	10 Wireless Sensor Network Layout	101
	10.1 Introduction	101
	10.2 Wireless Sensor Network Layout Problem	102
	10.3 ACO for WSN Layout Problem	104
	10.4 Experimental Results	106
	10.5 Conclusion	107
	11 A Two-Dimensional Lorentzian Distribution for an Atomic Force Microscopy Simulator	111
	11.1 Introduction	111
	11.2 Modeling Oxidation Kinetics	112
	11.3 Development of the Lorentzian Model	114
	11.3.1 Algorithm for the Gaussian Model	114
	11.3.2 Development of the Lorentzian Model	115
	11.4 Conclusion	117
	12 Stratified Monte Carlo Integration	119
	12.1 Introduction	119
	12.2 Numerical Integration	120
	12.3 Conclusion	126
	13 Monte Carlo Simulation of Asymmetric Flow Field Flow Fractionation	129
	13.1 Motivation	129
	13.2 AFFFF	130
	13.3 Mathematical Model and Numerical Algorithm	131
	13.3.1 Mathematical Model	131
	13.3.2 The MLMC Algorithm	132
	13.4 Numerical Results	133
	14 Convexization in Markov Chain Monte Carlo	139
	14.1 Introduction	139
	14.2 Auxiliary Functions	140
	14.2.1 Definition of Auxiliary Functions	140
	14.2.2 Optimization Process for Auxiliary Functions	140
	14.2.3 Auxiliary Functions for Convex Functions	142
	14.2.4 Objective Function Which Is the Sum of Convex and Concave Functions	142
	14.3 Stochastic Auxiliary Functions	143
	14.3.1 Stochastic Convex Learning (Summary)	143
	14.3.2 Auxiliary Stochastic Functions	144
	14.4 Metropolis-Hastings Auxiliary Algorithm	144
	14.5 Numerical Experiments	145
	14.6 Conclusion	146
	15 Value Simulation of the Interacting Pair Number for Solution of the Monodisperse Coagulation Equation	149
	15.1 Introduction	149
	15.2 Value Simulation for Integral Equations	151
	15.2.1 Value Simulation of the Time Interval Between Interactions	152
	15.2.2 VSIPN to Estimate the Monomer Concentration Jh1	153
	15.2.3 VSIPN to Estimate the Monomer and Dimer Concentration Jh12	154
	15.3 Results of the Numerical Experiments	155
	15.4 Conclusion	157
	16 Parallelization of Algorithms for Solving a Three-Dimensional Sudoku Puzzle	159
	16.1 Introduction	159
	16.2 The Simulated Annealing Method