Fast sequential Monte Carlo methods for counting and optimization /
Rubinstein, Reuven Y.
Fast sequential Monte Carlo methods for counting and optimization / Reuven Rubinstein, Faculty of Industrial Engineering and Management, Technion, Israel Institute of Technology, Haifa, Israel, Ad Ridder, Department of Econometrics and Operations Research, Vrije University, Amsterdam, Netherlands, Radislav Vaisman, Faculty of Industrial Engineering and Management, Technion, Israel Institute of Technology, Haifa, Israel. - 1 online resource. - Wiley series in probability and statistics . - Wiley series in probability and statistics. .
Includes bibliographical references and index.
Series; Copyright; Dedication; Chapter 1: Introduction to Monte Carlo Methods; Chapter 2: Cross-Entropy Method; 2.1 Introduction; 2.2 Estimation of Rare-Event Probabilities; 2.3 Cross-Entropy Method forOptimization; 2.4 Continuous Optimization; 2.5 Noisy Optimization; Chapter 3: Minimum Cross-Entropy Method; 3.1 Introduction; 3.2 Classic MinxEnt Method; 3.3 Rare Events and MinxEnt; 3.4 Indicator MinxEnt Method; 3.5 IME Method for Combinatorial Optimization; Chapter 4: Splitting Method for Counting and Optimization; 4.1 Background; 4.2 Quick Glance at the Splitting Method 4.3 Splitting Algorithm with Fixed Levels4.4 Adaptive Splitting Algorithm; 4.5 Sampling Uniformly on Discrete Regions; 4.6 Splitting Algorithm for Combinatorial Optimization; 4.7 Enhanced Splitting Method for Counting; 4.8 Application of Splitting to Reliability Models; 4.9 Numerical Results with the Splitting Algorithms; 4.10 Appendix: Gibbs Sampler; Chapter 5: Stochastic Enumeration Method; 5.1 Introduction; 5.2 OSLA Method and Its Extensions; 5.3 SE Method; 5.4 Applications of SE; 5.5 Numerical Results; Appendix A: Additional Topics; A.1 Combinatorial Problems; A.2 Information A.3 Efficiency of EstimatorsBibliography; Abbreviations and Acronyms; List of Symbols; Index; Series
This book presents the first comprehensive account of fast sequential Monte Carlo (SMC) methods for counting and optimization at an exceptionally accessible level. Written by authorities in the field, it places great emphasis on cross-entropy, minimum cross-entropy, splitting, and stochastic enumeration. The overall aim is to make SMC methods accessible to readers who want to apply and to accentuate the unifying and novel mathematical ideas behind SMC in their future studies or work.
9781118612316 (pdf) 1118612310 (pdf) 9781118612354 1118612353 9781118612378 111861237X 9781118612323 1118612329 9781306118422 1306118425
EB00063967 Recorded Books
CL0500000419 Safari Books Online
2013019187
016482698 Uk
Monte Carlo method.
Mathematical optimization.
MATHEMATICS--Numerical Analysis.
Mathematical optimization.
Monte Carlo method.
Sequentielle Monte-Carlo-Methode.
Optimierung.
Electronic books.
T57.64
518/.282
Fast sequential Monte Carlo methods for counting and optimization / Reuven Rubinstein, Faculty of Industrial Engineering and Management, Technion, Israel Institute of Technology, Haifa, Israel, Ad Ridder, Department of Econometrics and Operations Research, Vrije University, Amsterdam, Netherlands, Radislav Vaisman, Faculty of Industrial Engineering and Management, Technion, Israel Institute of Technology, Haifa, Israel. - 1 online resource. - Wiley series in probability and statistics . - Wiley series in probability and statistics. .
Includes bibliographical references and index.
Series; Copyright; Dedication; Chapter 1: Introduction to Monte Carlo Methods; Chapter 2: Cross-Entropy Method; 2.1 Introduction; 2.2 Estimation of Rare-Event Probabilities; 2.3 Cross-Entropy Method forOptimization; 2.4 Continuous Optimization; 2.5 Noisy Optimization; Chapter 3: Minimum Cross-Entropy Method; 3.1 Introduction; 3.2 Classic MinxEnt Method; 3.3 Rare Events and MinxEnt; 3.4 Indicator MinxEnt Method; 3.5 IME Method for Combinatorial Optimization; Chapter 4: Splitting Method for Counting and Optimization; 4.1 Background; 4.2 Quick Glance at the Splitting Method 4.3 Splitting Algorithm with Fixed Levels4.4 Adaptive Splitting Algorithm; 4.5 Sampling Uniformly on Discrete Regions; 4.6 Splitting Algorithm for Combinatorial Optimization; 4.7 Enhanced Splitting Method for Counting; 4.8 Application of Splitting to Reliability Models; 4.9 Numerical Results with the Splitting Algorithms; 4.10 Appendix: Gibbs Sampler; Chapter 5: Stochastic Enumeration Method; 5.1 Introduction; 5.2 OSLA Method and Its Extensions; 5.3 SE Method; 5.4 Applications of SE; 5.5 Numerical Results; Appendix A: Additional Topics; A.1 Combinatorial Problems; A.2 Information A.3 Efficiency of EstimatorsBibliography; Abbreviations and Acronyms; List of Symbols; Index; Series
This book presents the first comprehensive account of fast sequential Monte Carlo (SMC) methods for counting and optimization at an exceptionally accessible level. Written by authorities in the field, it places great emphasis on cross-entropy, minimum cross-entropy, splitting, and stochastic enumeration. The overall aim is to make SMC methods accessible to readers who want to apply and to accentuate the unifying and novel mathematical ideas behind SMC in their future studies or work.
9781118612316 (pdf) 1118612310 (pdf) 9781118612354 1118612353 9781118612378 111861237X 9781118612323 1118612329 9781306118422 1306118425
EB00063967 Recorded Books
CL0500000419 Safari Books Online
2013019187
016482698 Uk
Monte Carlo method.
Mathematical optimization.
MATHEMATICS--Numerical Analysis.
Mathematical optimization.
Monte Carlo method.
Sequentielle Monte-Carlo-Methode.
Optimierung.
Electronic books.
T57.64
518/.282