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Author: Stephen J. Wright
Publisher: Cambridge University Press
ISBN: 1316518981
Category : Computers
Languages : en
Pages : 239
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Book Description
A concise text that presents and analyzes the fundamental techniques and methods in optimization that are useful in data science.
Author: Stephen J. Wright
Publisher: Cambridge University Press
ISBN: 1316518981
Category : Computers
Languages : en
Pages : 239
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Book Description
A concise text that presents and analyzes the fundamental techniques and methods in optimization that are useful in data science.
Author: Ali Emrouznejad
Publisher: Springer
ISBN: 3319302655
Category : Technology & Engineering
Languages : en
Pages : 487
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Book Description
The main objective of this book is to provide the necessary background to work with big data by introducing some novel optimization algorithms and codes capable of working in the big data setting as well as introducing some applications in big data optimization for both academics and practitioners interested, and to benefit society, industry, academia, and government. Presenting applications in a variety of industries, this book will be useful for the researchers aiming to analyses large scale data. Several optimization algorithms for big data including convergent parallel algorithms, limited memory bundle algorithm, diagonal bundle method, convergent parallel algorithms, network analytics, and many more have been explored in this book.
Author: Panos M. Pardalos
Publisher: Springer
ISBN: 3319991426
Category : Mathematics
Languages : en
Pages : 330
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Book Description
Computational and theoretical open problems in optimization, computational geometry, data science, logistics, statistics, supply chain modeling, and data analysis are examined in this book. Each contribution provides the fundamentals needed to fully comprehend the impact of individual problems. Current theoretical, algorithmic, and practical methods used to circumvent each problem are provided to stimulate a new effort towards innovative and efficient solutions. Aimed towards graduate students and researchers in mathematics, optimization, operations research, quantitative logistics, data analysis, and statistics, this book provides a broad comprehensive approach to understanding the significance of specific challenging or open problems within each discipline. The contributions contained in this book are based on lectures focused on “Challenges and Open Problems in Optimization and Data Science” presented at the Deucalion Summer Institute for Advanced Studies in Optimization, Mathematics, and Data Science in August 2016.
Author: Lawrence Hubert
Publisher: SIAM
ISBN: 9780898718553
Category : Science
Languages : en
Pages : 174
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Book Description
Combinatorial data analysis (CDA) refers to a wide class of methods for the study of relevant data sets in which the arrangement of a collection of objects is absolutely central. The focus of this monograph is on the identification of arrangements, which are then further restricted to where the combinatorial search is carried out by a recursive optimization process based on the general principles of dynamic programming (DP).
Author: Stephen J. Wright
Publisher:
ISBN: 9781009004282
Category : MATHEMATICS
Languages : en
Pages :
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Book Description
"Optimization formulations and algorithms have long played a central role in data analysis and machine learning. Maximum likelihood concepts date to Gauss and Laplace in the late 1700s; problems of this type drove developments in unconstrained optimization in the latter half of the 20th century. Mangasarian's papers in the 1960s on pattern separation using linear programming made an explicit connection between machine learning and optimization in the early days of the former subject. During the 1990s, optimization techniques (especially quadratic programming and duality) were key to the development of support vector machines and kernel learning. The period 1997-2010 saw many synergies emerge between regularized / sparse optimization, variable selection, and compressed sensing. In the current era of deep learning, two optimization techniques-stochastic gradient and automatic differentiation (a.k.a. back-propagation)-are essential"--
Author: Thomas, J. Joshua
Publisher: IGI Global
ISBN: 1799811948
Category : Computers
Languages : en
Pages : 355
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Book Description
Many approaches have sprouted from artificial intelligence (AI) and produced major breakthroughs in the computer science and engineering industries. Deep learning is a method that is transforming the world of data and analytics. Optimization of this new approach is still unclear, however, and there’s a need for research on the various applications and techniques of deep learning in the field of computing. Deep Learning Techniques and Optimization Strategies in Big Data Analytics is a collection of innovative research on the methods and applications of deep learning strategies in the fields of computer science and information systems. While highlighting topics including data integration, computational modeling, and scheduling systems, this book is ideally designed for engineers, IT specialists, data analysts, data scientists, engineers, researchers, academicians, and students seeking current research on deep learning methods and its application in the digital industry.
Author: Boris Goldengorin
Publisher: Springer
ISBN: 3319420569
Category : Mathematics
Languages : en
Pages : 507
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Book Description
This book focuses on recent research in modern optimization and its implications in control and data analysis. This book is a collection of papers from the conference “Optimization and Its Applications in Control and Data Science” dedicated to Professor Boris T. Polyak, which was held in Moscow, Russia on May 13-15, 2015. This book reflects developments in theory and applications rooted by Professor Polyak’s fundamental contributions to constrained and unconstrained optimization, differentiable and nonsmooth functions, control theory and approximation. Each paper focuses on techniques for solving complex optimization problems in different application areas and recent developments in optimization theory and methods. Open problems in optimization, game theory and control theory are included in this collection which will interest engineers and researchers working with efficient algorithms and software for solving optimization problems in market and data analysis. Theoreticians in operations research, applied mathematics, algorithm design, artificial intelligence, machine learning, and software engineering will find this book useful and graduate students will find the state-of-the-art research valuable.
Author: John Wright
Publisher: Cambridge University Press
ISBN: 1108805558
Category : Computers
Languages : en
Pages : 718
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Book Description
Connecting theory with practice, this systematic and rigorous introduction covers the fundamental principles, algorithms and applications of key mathematical models for high-dimensional data analysis. Comprehensive in its approach, it provides unified coverage of many different low-dimensional models and analytical techniques, including sparse and low-rank models, and both convex and non-convex formulations. Readers will learn how to develop efficient and scalable algorithms for solving real-world problems, supported by numerous examples and exercises throughout, and how to use the computational tools learnt in several application contexts. Applications presented include scientific imaging, communication, face recognition, 3D vision, and deep networks for classification. With code available online, this is an ideal textbook for senior and graduate students in computer science, data science, and electrical engineering, as well as for those taking courses on sparsity, low-dimensional structures, and high-dimensional data. Foreword by Emmanuel Candès.
Author: C. F. Jeff Wu
Publisher: John Wiley & Sons
ISBN: 1118211537
Category : Mathematics
Languages : en
Pages : 562
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Book Description
Praise for the First Edition: "If you . . . want an up-to-date, definitive reference written by authors who have contributed much to this field, then this book is an essential addition to your library." —Journal of the American Statistical Association Fully updated to reflect the major progress in the use of statistically designed experiments for product and process improvement, Experiments, Second Edition introduces some of the newest discoveries—and sheds further light on existing ones—on the design and analysis of experiments and their applications in system optimization, robustness, and treatment comparison. Maintaining the same easy-to-follow style as the previous edition while also including modern updates, this book continues to present a new and integrated system of experimental design and analysis that can be applied across various fields of research including engineering, medicine, and the physical sciences. The authors modernize accepted methodologies while refining many cutting-edge topics including robust parameter design, reliability improvement, analysis of non-normal data, analysis of experiments with complex aliasing, multilevel designs, minimum aberration designs, and orthogonal arrays. Along with a new chapter that focuses on regression analysis, the Second Edition features expanded and new coverage of additional topics, including: Expected mean squares and sample size determination One-way and two-way ANOVA with random effects Split-plot designs ANOVA treatment of factorial effects Response surface modeling for related factors Drawing on examples from their combined years of working with industrial clients, the authors present many cutting-edge topics in a single, easily accessible source. Extensive case studies, including goals, data, and experimental designs, are also included, and the book's data sets can be found on a related FTP site, along with additional supplemental material. Chapter summaries provide a succinct outline of discussed methods, and extensive appendices direct readers to resources for further study. Experiments, Second Edition is an excellent book for design of experiments courses at the upper-undergraduate and graduate levels. It is also a valuable resource for practicing engineers and statisticians.
Author: Thomas H. Davenport
Publisher: Pearson Education
ISBN: 0133039439
Category : Business & Economics
Languages : en
Pages : 287
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Book Description
"International Institute for Analytics"--Dust jacket.
