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Handbook of Approximation Algorithms and Metaheuristics Methologies and Traditional Applications Volume 1

Handbook of Approximation Algorithms and Metaheuristics Methologies and Traditional Applications Volume 1

Handbook of Approximation Algorithms and Metaheuristics Second Edition reflects the tremendous growth in the field over the past two decades. Through contributions from leading experts this handbook provides a comprehensive introduction to the underlying theory and methodologies as well as the various applications of approximation algorithms and metaheuristics. Volume 1 of this two-volume set deals primarily with methodologies and traditional applications. It includes restriction relaxation local ratio approximation schemes randomization tabu search evolutionary computation local search neural networks and other metaheuristics. It also explores multi-objective optimization reoptimization sensitivity analysis and stability. Traditional applications covered include: bin packing multi-dimensional packing Steiner trees traveling salesperson scheduling and related problems. Volume 2 focuses on the contemporary and emerging applications of methodologies to problems in combinatorial optimization computational geometry and graphs problems as well as in large-scale and emerging application areas. It includes approximation algorithms and heuristics for clustering networks (sensor and wireless) communication bioinformatics search streams virtual communities and more. About the EditorTeofilo F. Gonzalez is a professor emeritus of computer science at the University of California Santa Barbara. He completed his Ph. D. in 1975 from the University of Minnesota. He taught at the University of Oklahoma the Pennsylvania State University and the University of Texas at Dallas before joining the UCSB computer science faculty in 1984. He spent sabbatical leaves at the Monterrey Institute of Technology and Higher Education and Utrecht University. He is known for his highly cited pioneering research in the hardness of approximation; for his sublinear and best possible approximation algorithm for k-tMM clustering; for introducing the open-shop scheduling problem as well as algorithms for its solution that have found applications in numerous research areas; as well as for his research on problems in the areas of job scheduling graph algorithms computational geometry message communication wire routing etc. | Handbook of Approximation Algorithms and Metaheuristics Methologies and Traditional Applications Volume 1

GBP 44.99
1

Statistical Machine Learning A Unified Framework

Statistical Machine Learning A Unified Framework

The recent rapid growth in the variety and complexity of new machine learning architectures requires the development of improved methods for designing analyzing evaluating and communicating machine learning technologies. Statistical Machine Learning: A Unified Framework provides students engineers and scientists with tools from mathematical statistics and nonlinear optimization theory to become experts in the field of machine learning. In particular the material in this text directly supports the mathematical analysis and design of old new and not-yet-invented nonlinear high-dimensional machine learning algorithms. Features: Unified empirical risk minimization framework supports rigorous mathematical analyses of widely used supervised unsupervised and reinforcement machine learning algorithms Matrix calculus methods for supporting machine learning analysis and design applications Explicit conditions for ensuring convergence of adaptive batch minibatch MCEM and MCMC learning algorithms that minimize both unimodal and multimodal objective functions Explicit conditions for characterizing asymptotic properties of M-estimators and model selection criteria such as AIC and BIC in the presence of possible model misspecification This advanced text is suitable for graduate students or highly motivated undergraduate students in statistics computer science electrical engineering and applied mathematics. The text is self-contained and only assumes knowledge of lower-division linear algebra and upper-division probability theory. Students professional engineers and multidisciplinary scientists possessing these minimal prerequisites will find this text challenging yet accessible. About the Author: Richard M. Golden (Ph. D. M. S. E. E. B. S. E. E. ) is Professor of Cognitive Science and Participating Faculty Member in Electrical Engineering at the University of Texas at Dallas. Dr. Golden has published articles and given talks at scientific conferences on a wide range of topics in the fields of both statistics and machine learning over the past three decades. His long-term research interests include identifying conditions for the convergence of deterministic and stochastic machine learning algorithms and investigating estimation and inference in the presence of possibly misspecified probability models. | Statistical Machine Learning A Unified Framework

GBP 99.99
1

Bayesian Applications in Pharmaceutical Development

Bayesian Applications in Pharmaceutical Development

The cost for bringing new medicine from discovery to market has nearly doubled in the last decade and has now reached $2. 6 billion. There is an urgent need to make drug development less time-consuming and less costly. Innovative trial designs/ analyses such as the Bayesian approach are essential to meet this need. This book will be the first to provide comprehensive coverage of Bayesian applications across the span of drug development from discovery to clinical trial to manufacturing with practical examples. This book will have a wide appeal to statisticians scientists and physicians working in drug development who are motivated to accelerate and streamline the drug development process as well as students who aspire to work in this field. The advantages of this book are: Provides motivating worked practical case examples with easy to grasp models technical details and computational codes to run the analyses Balances practical examples with best practices on trial simulation and reporting as well as regulatory perspectives Chapters written by authors who are individual contributors in their respective topics Dr. Mani Lakshminarayanan is a researcher and statistical consultant with more than 30 years of experience in the pharmaceutical industry. He has published over 50 articles technical reports and book chapters besides serving as a referee for several journals. He has a PhD in Statistics from Southern Methodist University Dallas Texas and is a Fellow of the American Statistical Association. Dr. Fanni Natanegara has over 15 years of pharmaceutical experience and is currently Principal Research Scientist and Group Leader for the Early Phase Neuroscience Statistics team at Eli Lilly and Company. She played a key role in the Advanced Analytics team to provide Bayesian education and statistical consultation at Eli Lilly. Dr. Natanegara is the chair of the cross industry-regulatory-academic DIA BSWG to ensure that Bayesian methods are appropriately utilized for design and analysis throughout the drug-development process. | Bayesian Applications in Pharmaceutical Development

GBP 44.99
1