Optimization for Data Analysis

Optimization for Data Analysis
Title Optimization for Data Analysis PDF eBook
Author Stephen J. Wright
Publisher Cambridge University Press
Pages 239
Release 2022-04-21
Genre Computers
ISBN 1316518981

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A concise text that presents and analyzes the fundamental techniques and methods in optimization that are useful in data science.

Open Problems in Optimization and Data Analysis

Open Problems in Optimization and Data Analysis
Title Open Problems in Optimization and Data Analysis PDF eBook
Author Panos M. Pardalos
Publisher Springer
Pages 341
Release 2018-12-04
Genre Mathematics
ISBN 3319991426

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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.

Statistical Analysis and Optimization for VLSI: Timing and Power

Statistical Analysis and Optimization for VLSI: Timing and Power
Title Statistical Analysis and Optimization for VLSI: Timing and Power PDF eBook
Author Ashish Srivastava
Publisher Springer Science & Business Media
Pages 284
Release 2006-04-04
Genre Technology & Engineering
ISBN 0387265287

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Covers the statistical analysis and optimization issues arising due to increased process variations in current technologies. Comprises a valuable reference for statistical analysis and optimization techniques in current and future VLSI design for CAD-Tool developers and for researchers interested in starting work in this very active area of research. Written by author who lead much research in this area who provide novel ideas and approaches to handle the addressed issues

Big Data Optimization: Recent Developments and Challenges

Big Data Optimization: Recent Developments and Challenges
Title Big Data Optimization: Recent Developments and Challenges PDF eBook
Author Ali Emrouznejad
Publisher Springer
Pages 492
Release 2016-05-26
Genre Technology & Engineering
ISBN 3319302655

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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.

Encyclopedia of Business Analytics and Optimization

Encyclopedia of Business Analytics and Optimization
Title Encyclopedia of Business Analytics and Optimization PDF eBook
Author Wang, John
Publisher IGI Global
Pages 2862
Release 2014-02-28
Genre Business & Economics
ISBN 1466652039

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As the age of Big Data emerges, it becomes necessary to take the five dimensions of Big Data- volume, variety, velocity, volatility, and veracity- and focus these dimensions towards one critical emphasis - value. The Encyclopedia of Business Analytics and Optimization confronts the challenges of information retrieval in the age of Big Data by exploring recent advances in the areas of knowledge management, data visualization, interdisciplinary communication, and others. Through its critical approach and practical application, this book will be a must-have reference for any professional, leader, analyst, or manager interested in making the most of the knowledge resources at their disposal.

Combinatorial Data Analysis

Combinatorial Data Analysis
Title Combinatorial Data Analysis PDF eBook
Author Lawrence Hubert
Publisher SIAM
Pages 174
Release 2001-01-01
Genre Science
ISBN 9780898718553

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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).

High-Dimensional Data Analysis with Low-Dimensional Models

High-Dimensional Data Analysis with Low-Dimensional Models
Title High-Dimensional Data Analysis with Low-Dimensional Models PDF eBook
Author John Wright
Publisher Cambridge University Press
Pages 718
Release 2022-01-13
Genre Computers
ISBN 1108805558

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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.