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Título : Simulation-Based Optimization : Parametric Optimization Techniques and Reinforcement Learning Tipo de documento: documento electrónico Autores: Abhijit Gosavi ; SpringerLink (Online service) Editorial: Boston, MA : Springer US Fecha de publicación: 2015 Otro editor: Imprint: Springer Colección: Operations Research/Computer Science Interfaces Series, ISSN 1387-666X num. 55 Número de páginas: XXVI, 508 p. 42 illus Il.: online resource ISBN/ISSN/DL: 978-1-4899-7491-4 Idioma : Inglés (eng) Palabras clave: Business Operations research Decision making Computer simulation Management science and Operation Research/Decision Theory Research, Science Simulation Modeling Clasificación: 658 Empresas. Organización de empresas Resumen: Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning introduces the evolving area of static and dynamic simulation-based optimization. Covered in detail are model-free optimization techniques – especially designed for those discrete-event, stochastic systems which can be simulated but whose analytical models are difficult to find in closed mathematical forms. Key features of this revised and improved Second Edition include: · Extensive coverage, via step-by-step recipes, of powerful new algorithms for static simulation optimization, including simultaneous perturbation, backtracking adaptive search, and nested partitions, in addition to traditional methods, such as response surfaces, Nelder-Mead search, and meta-heuristics (simulated annealing, tabu search, and genetic algorithms) · Detailed coverage of the Bellman equation framework for Markov Decision Processes (MDPs), along with dynamic programming (value and policy iteration) for discounted, average, and total reward performance metrics · An in-depth consideration of dynamic simulation optimization via temporal differences and Reinforcement Learning: Q-Learning, SARSA, and R-SMART algorithms, and policy search, via API, Q-P-Learning, actor-critics, and learning automata · A special examination of neural-network-based function approximation for Reinforcement Learning, semi-Markov decision processes (SMDPs), finite-horizon problems, two time scales, case studies for industrial tasks, computer codes (placed online), and convergence proofs, via Banach fixed point theory and Ordinary Differential Equations Themed around three areas in separate sets of chapters – Static Simulation Optimization, Reinforcement Learning, and Convergence Analysis – this book is written for researchers and students in the fields of engineering (industrial, systems, electrical, and computer), operations research, computer science, and applied mathematics Nota de contenido: Background -- Simulation basics -- Simulation optimization: an overview -- Response surfaces and neural nets -- Parametric optimization -- Dynamic programming -- Reinforcement learning -- Stochastic search for controls -- Convergence: background material -- Convergence: parametric optimization -- Convergence: control optimization -- Case studies En línea: http://dx.doi.org/10.1007/978-1-4899-7491-4 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=35385 Simulation-Based Optimization : Parametric Optimization Techniques and Reinforcement Learning [documento electrónico] / Abhijit Gosavi ; SpringerLink (Online service) . - Boston, MA : Springer US : Imprint: Springer, 2015 . - XXVI, 508 p. 42 illus : online resource. - (Operations Research/Computer Science Interfaces Series, ISSN 1387-666X; 55) .
ISBN : 978-1-4899-7491-4
Idioma : Inglés (eng)
Palabras clave: Business Operations research Decision making Computer simulation Management science and Operation Research/Decision Theory Research, Science Simulation Modeling Clasificación: 658 Empresas. Organización de empresas Resumen: Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning introduces the evolving area of static and dynamic simulation-based optimization. Covered in detail are model-free optimization techniques – especially designed for those discrete-event, stochastic systems which can be simulated but whose analytical models are difficult to find in closed mathematical forms. Key features of this revised and improved Second Edition include: · Extensive coverage, via step-by-step recipes, of powerful new algorithms for static simulation optimization, including simultaneous perturbation, backtracking adaptive search, and nested partitions, in addition to traditional methods, such as response surfaces, Nelder-Mead search, and meta-heuristics (simulated annealing, tabu search, and genetic algorithms) · Detailed coverage of the Bellman equation framework for Markov Decision Processes (MDPs), along with dynamic programming (value and policy iteration) for discounted, average, and total reward performance metrics · An in-depth consideration of dynamic simulation optimization via temporal differences and Reinforcement Learning: Q-Learning, SARSA, and R-SMART algorithms, and policy search, via API, Q-P-Learning, actor-critics, and learning automata · A special examination of neural-network-based function approximation for Reinforcement Learning, semi-Markov decision processes (SMDPs), finite-horizon problems, two time scales, case studies for industrial tasks, computer codes (placed online), and convergence proofs, via Banach fixed point theory and Ordinary Differential Equations Themed around three areas in separate sets of chapters – Static Simulation Optimization, Reinforcement Learning, and Convergence Analysis – this book is written for researchers and students in the fields of engineering (industrial, systems, electrical, and computer), operations research, computer science, and applied mathematics Nota de contenido: Background -- Simulation basics -- Simulation optimization: an overview -- Response surfaces and neural nets -- Parametric optimization -- Dynamic programming -- Reinforcement learning -- Stochastic search for controls -- Convergence: background material -- Convergence: parametric optimization -- Convergence: control optimization -- Case studies En línea: http://dx.doi.org/10.1007/978-1-4899-7491-4 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=35385 Ejemplares
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Título : Simulation and Inference for Stochastic Differential Equations : With R Examples Tipo de documento: documento electrónico Autores: Stefano M. Iacus ; SpringerLink (Online service) Editorial: New York, NY : Springer New York Fecha de publicación: 2008 Colección: Springer Series in Statistics, ISSN 0172-7397 num. 1 Número de páginas: XVIII, 286 p Il.: online resource ISBN/ISSN/DL: 978-0-387-75839-8 Idioma : Inglés (eng) Palabras clave: Statistics Computer simulation Mathematical analysis Analysis (Mathematics) Economics, Probabilities Econometrics and Computing/Statistics Programs Probability Theory Stochastic Processes Quantitative Finance Simulation Modeling Clasificación: 51 Matemáticas Resumen: This book is unique because of its focus on the practical implementation of the simulation and estimation methods presented. The book will be useful to practitioners and students with only a minimal mathematical background because of the many R programs, and to more mathematically-educated practitioners. Many of the methods presented in the book have not been used much in practice because the lack of an implementation in a unified framework. This book fills the gap. With the R code included in this book, a lot of useful methods become easy to use for practitioners and students. An R package called "sde" provides functions with easy interfaces ready to be used on empirical data from real life applications. Although it contains a wide range of results, the book has an introductory character and necessarily does not cover the whole spectrum of simulation and inference for general stochastic differential equations. The book is organized into four chapters. The first one introduces the subject and presents several classes of processes used in many fields of mathematics, computational biology, finance and the social sciences. The second chapter is devoted to simulation schemes and covers new methods not available in other publications. The third one focuses on parametric estimation techniques. In particular, it includes exact likelihood inference, approximated and pseudo-likelihood methods, estimating functions, generalized method of moments, and other techniques. The last chapter contains miscellaneous topics like nonparametric estimation, model identification and change point estimation. The reader who is not an expert in the R language will find a concise introduction to this environment focused on the subject of the book. A documentation page is available at the end of the book for each R function presented in the book. Stefano M. Iacus is associate professor of Probability and Mathematical Statistics at the University of Milan, Department of Economics, Business and Statistics. He has a PhD in Statistics at Padua University, Italy and in Mathematics at Université du Maine, France. He is a member of the R Core team for the development of the R statistical environment, Data Base manager for the Current Index to Statistics, and IMS Group Manager for the Institute of Mathematical Statistics. He has been associate editor of the Journal of Statistical Software Nota de contenido: Stochastic Processes and Stochastic Differential Equations -- Numerical Methods for SDE -- Parametric Estimation -- Miscellaneous Topics En línea: http://dx.doi.org/10.1007/978-0-387-75839-8 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34211 Simulation and Inference for Stochastic Differential Equations : With R Examples [documento electrónico] / Stefano M. Iacus ; SpringerLink (Online service) . - New York, NY : Springer New York, 2008 . - XVIII, 286 p : online resource. - (Springer Series in Statistics, ISSN 0172-7397; 1) .
ISBN : 978-0-387-75839-8
Idioma : Inglés (eng)
Palabras clave: Statistics Computer simulation Mathematical analysis Analysis (Mathematics) Economics, Probabilities Econometrics and Computing/Statistics Programs Probability Theory Stochastic Processes Quantitative Finance Simulation Modeling Clasificación: 51 Matemáticas Resumen: This book is unique because of its focus on the practical implementation of the simulation and estimation methods presented. The book will be useful to practitioners and students with only a minimal mathematical background because of the many R programs, and to more mathematically-educated practitioners. Many of the methods presented in the book have not been used much in practice because the lack of an implementation in a unified framework. This book fills the gap. With the R code included in this book, a lot of useful methods become easy to use for practitioners and students. An R package called "sde" provides functions with easy interfaces ready to be used on empirical data from real life applications. Although it contains a wide range of results, the book has an introductory character and necessarily does not cover the whole spectrum of simulation and inference for general stochastic differential equations. The book is organized into four chapters. The first one introduces the subject and presents several classes of processes used in many fields of mathematics, computational biology, finance and the social sciences. The second chapter is devoted to simulation schemes and covers new methods not available in other publications. The third one focuses on parametric estimation techniques. In particular, it includes exact likelihood inference, approximated and pseudo-likelihood methods, estimating functions, generalized method of moments, and other techniques. The last chapter contains miscellaneous topics like nonparametric estimation, model identification and change point estimation. The reader who is not an expert in the R language will find a concise introduction to this environment focused on the subject of the book. A documentation page is available at the end of the book for each R function presented in the book. Stefano M. Iacus is associate professor of Probability and Mathematical Statistics at the University of Milan, Department of Economics, Business and Statistics. He has a PhD in Statistics at Padua University, Italy and in Mathematics at Université du Maine, France. He is a member of the R Core team for the development of the R statistical environment, Data Base manager for the Current Index to Statistics, and IMS Group Manager for the Institute of Mathematical Statistics. He has been associate editor of the Journal of Statistical Software Nota de contenido: Stochastic Processes and Stochastic Differential Equations -- Numerical Methods for SDE -- Parametric Estimation -- Miscellaneous Topics En línea: http://dx.doi.org/10.1007/978-0-387-75839-8 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34211 Ejemplares
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Título : Foundations and Methods of Stochastic Simulation : A First Course Tipo de documento: documento electrónico Autores: Barry L. Nelson ; SpringerLink (Online service) Editorial: Boston, MA : Springer US Fecha de publicación: 2013 Otro editor: Imprint: Springer Colección: International Series in Operations Research & Management Science, ISSN 0884-8289 num. 187 Número de páginas: XIV, 276 p Il.: online resource ISBN/ISSN/DL: 978-1-4614-6160-9 Idioma : Inglés (eng) Palabras clave: Business Operations research Decision making Computer simulation Management science and Operation Research/Decision Theory Research, Science Simulation Modeling Clasificación: 658 Empresas. Organización de empresas Resumen: This graduate-level text covers modeling, programming and analysis of simulation experiments and provides a rigorous treatment of the foundations of simulation and why it works. It introduces object-oriented programming for simulation, covers both the probabilistic and statistical basis for simulation in a rigorous but accessible manner (providing all necessary background material), and provides a modern treatment of experiment design and analysis that goes beyond classical statistics. The book emphasizes essential foundations throughout, rather than providing a compendium of algorithms and theorems, and prepares the reader to use simulation in research as well as practice. The book is a rigorous but concise treatment, emphasizing lasting principles, but also providing specific training in modeling, programming and analysis. In addition to teaching readers how to do simulation, it also prepares them to use simulation in their research; no other book does this Nota de contenido: Why Do We Simulate? -- Simulation Programming: Quick Start -- Examples -- Simulation Programming with VBASim -- Two Views of Simulation -- Simulation Input -- Simulation Output -- Experiment Design and Analysis -- Simulation for Research -- VBASim En línea: http://dx.doi.org/10.1007/978-1-4614-6160-9 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=36440 Foundations and Methods of Stochastic Simulation : A First Course [documento electrónico] / Barry L. Nelson ; SpringerLink (Online service) . - Boston, MA : Springer US : Imprint: Springer, 2013 . - XIV, 276 p : online resource. - (International Series in Operations Research & Management Science, ISSN 0884-8289; 187) .
ISBN : 978-1-4614-6160-9
Idioma : Inglés (eng)
Palabras clave: Business Operations research Decision making Computer simulation Management science and Operation Research/Decision Theory Research, Science Simulation Modeling Clasificación: 658 Empresas. Organización de empresas Resumen: This graduate-level text covers modeling, programming and analysis of simulation experiments and provides a rigorous treatment of the foundations of simulation and why it works. It introduces object-oriented programming for simulation, covers both the probabilistic and statistical basis for simulation in a rigorous but accessible manner (providing all necessary background material), and provides a modern treatment of experiment design and analysis that goes beyond classical statistics. The book emphasizes essential foundations throughout, rather than providing a compendium of algorithms and theorems, and prepares the reader to use simulation in research as well as practice. The book is a rigorous but concise treatment, emphasizing lasting principles, but also providing specific training in modeling, programming and analysis. In addition to teaching readers how to do simulation, it also prepares them to use simulation in their research; no other book does this Nota de contenido: Why Do We Simulate? -- Simulation Programming: Quick Start -- Examples -- Simulation Programming with VBASim -- Two Views of Simulation -- Simulation Input -- Simulation Output -- Experiment Design and Analysis -- Simulation for Research -- VBASim En línea: http://dx.doi.org/10.1007/978-1-4614-6160-9 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=36440 Ejemplares
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Título : Handbook of Simulation Optimization Tipo de documento: documento electrónico Autores: SpringerLink (Online service) ; Michael C. Fu Editorial: New York, NY : Springer New York Fecha de publicación: 2015 Otro editor: Imprint: Springer Colección: International Series in Operations Research & Management Science, ISSN 0884-8289 num. 216 Número de páginas: XVI, 387 p. 18 illus., 9 illus. in color Il.: online resource ISBN/ISSN/DL: 978-1-4939-1384-8 Idioma : Inglés (eng) Palabras clave: Business Operations research Decision making Computer simulation Management science Economic theory and Operation Research/Decision Theory Simulation Modeling Research, Science Theory/Quantitative Economics/Mathematical Methods Clasificación: 658 Empresas. Organización de empresas Resumen: The Handbook of Simulation Optimization presents an overview of the state of the art of simulation optimization, providing a survey of the most well-established approaches for optimizing stochastic simulation models and a sampling of recent research advances in theory and methodology. Leading contributors cover such topics as discrete optimization via simulation, ranking and selection, efficient simulation budget allocation, random search methods, response surface methodology, stochastic gradient estimation, stochastic approximation, sample average approximation, stochastic constraints, variance reduction techniques, model-based stochastic search methods, and Markov decision processes. This single volume should serve as a reference for those already in the field and as a means for those new to the field for understanding and applying the main approaches. The intended audience includes researchers, practitioners, and graduate students in the business/engineering fields of operations research, management science, operations management, and stochastic control, as well as in economics/finance and computer science Nota de contenido: Overview of the Handbook -- Discrete Optimization via Simulation -- Ranking and Selection: Efficient Simulation Budget Allocation -- Response Surface Methodology -- Stochastic Gradient Estimation -- An Overview of Stochastic Approximation -- Stochastic Approximation Methods and Their Finite-time Convergence Properties -- A Guide to Sample Average Approximation -- Stochastic Constraints and Variance Reduction Techniques -- A Review of Random Search Methods -- Stochastic Adaptive Search Methods: Theory and Implementation -- Model-Based Stochastic Search Methods -- Solving Markov Decision Processes via Simulation En línea: http://dx.doi.org/10.1007/978-1-4939-1384-8 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=35396 Handbook of Simulation Optimization [documento electrónico] / SpringerLink (Online service) ; Michael C. Fu . - New York, NY : Springer New York : Imprint: Springer, 2015 . - XVI, 387 p. 18 illus., 9 illus. in color : online resource. - (International Series in Operations Research & Management Science, ISSN 0884-8289; 216) .
ISBN : 978-1-4939-1384-8
Idioma : Inglés (eng)
Palabras clave: Business Operations research Decision making Computer simulation Management science Economic theory and Operation Research/Decision Theory Simulation Modeling Research, Science Theory/Quantitative Economics/Mathematical Methods Clasificación: 658 Empresas. Organización de empresas Resumen: The Handbook of Simulation Optimization presents an overview of the state of the art of simulation optimization, providing a survey of the most well-established approaches for optimizing stochastic simulation models and a sampling of recent research advances in theory and methodology. Leading contributors cover such topics as discrete optimization via simulation, ranking and selection, efficient simulation budget allocation, random search methods, response surface methodology, stochastic gradient estimation, stochastic approximation, sample average approximation, stochastic constraints, variance reduction techniques, model-based stochastic search methods, and Markov decision processes. This single volume should serve as a reference for those already in the field and as a means for those new to the field for understanding and applying the main approaches. The intended audience includes researchers, practitioners, and graduate students in the business/engineering fields of operations research, management science, operations management, and stochastic control, as well as in economics/finance and computer science Nota de contenido: Overview of the Handbook -- Discrete Optimization via Simulation -- Ranking and Selection: Efficient Simulation Budget Allocation -- Response Surface Methodology -- Stochastic Gradient Estimation -- An Overview of Stochastic Approximation -- Stochastic Approximation Methods and Their Finite-time Convergence Properties -- A Guide to Sample Average Approximation -- Stochastic Constraints and Variance Reduction Techniques -- A Review of Random Search Methods -- Stochastic Adaptive Search Methods: Theory and Implementation -- Model-Based Stochastic Search Methods -- Solving Markov Decision Processes via Simulation En línea: http://dx.doi.org/10.1007/978-1-4939-1384-8 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=35396 Ejemplares
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Título : Hemodynamical Flows : Modeling, Analysis and Simulation Tipo de documento: documento electrónico Autores: Giovanni P. Galdi ; SpringerLink (Online service) ; Anne M. Robertson ; Rolf Rannacher ; Stefan Turek Editorial: Basel : Birkhäuser Basel Fecha de publicación: 2008 Colección: Oberwolfach Seminars num. 37 Número de páginas: XII, 501 p Il.: online resource ISBN/ISSN/DL: 978-3-7643-7806-6 Idioma : Inglés (eng) Palabras clave: Medicine Human physiology Computer simulation Applied mathematics Engineering Mathematical models Biomathematics Biomedicine Physiology Modeling and Industrial Mathematics Simulation Applications of Physiological, Cellular Medical Topics Computational Numerical Analysis Clasificación: 51 Matemáticas Nota de contenido: Review of Relevant Continuum Mechanics -- Hemorheology -- Mathematical Problems in Classical and Non-Newtonian Fluid Mechanics -- Methods for Numerical Flow Simulation -- Numerics of Fluid-Structure Interaction -- Numerical Techniques for Multiphase Flow with Liquid-Solid Interaction En línea: http://dx.doi.org/10.1007/978-3-7643-7806-6 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34385 Hemodynamical Flows : Modeling, Analysis and Simulation [documento electrónico] / Giovanni P. Galdi ; SpringerLink (Online service) ; Anne M. Robertson ; Rolf Rannacher ; Stefan Turek . - Basel : Birkhäuser Basel, 2008 . - XII, 501 p : online resource. - (Oberwolfach Seminars; 37) .
ISBN : 978-3-7643-7806-6
Idioma : Inglés (eng)
Palabras clave: Medicine Human physiology Computer simulation Applied mathematics Engineering Mathematical models Biomathematics Biomedicine Physiology Modeling and Industrial Mathematics Simulation Applications of Physiological, Cellular Medical Topics Computational Numerical Analysis Clasificación: 51 Matemáticas Nota de contenido: Review of Relevant Continuum Mechanics -- Hemorheology -- Mathematical Problems in Classical and Non-Newtonian Fluid Mechanics -- Methods for Numerical Flow Simulation -- Numerics of Fluid-Structure Interaction -- Numerical Techniques for Multiphase Flow with Liquid-Solid Interaction En línea: http://dx.doi.org/10.1007/978-3-7643-7806-6 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34385 Ejemplares
Signatura Medio Ubicación Sub-localización Sección Estado ningún ejemplar PermalinkModeling and Simulation in Engineering, Economics and Management / Raúl León ; SpringerLink (Online service) ; María Jesús Muñoz Torres ; Jose M. Moneva (2016)
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