Bayesian Optimization and Data Science, Francesco Archetti; Antonio Candelieri

Автор: Anjos
Название: Handbook on Semidefinite, Conic and Polynomial Optimization
ISBN: 1461407680 ISBN-13(EAN): 9781461407683
Издательство: Springer
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Цена: 163040.00 T
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Описание: Semidefinite and conic optimization is a major and thriving research area within the optimization community. Although semidefinite optimization has been studied (under different names) since at least the 1940s, its importance grew immensely during the 1990s after polynomial-time interior-point methods for linear optimization were extended to solve semidefinite optimization problems. Since the beginning of the 21st century, not only has research into semidefinite and conic optimization continued unabated, but also a fruitful interaction has developed with algebraic geometry through the close connections between semidefinite matrices and polynomial optimization. This has brought about important new results and led to an even higher level of research activity. This Handbook on Semidefinite, Conic and Polynomial Optimization provides the reader with a snapshot of the state-of-the-art in the growing and mutually enriching areas of semidefinite optimization, conic optimization, and polynomial optimization. It contains a compendium of the recent research activity that has taken place in these thrilling areas, and will appeal to doctoral students, young graduates, and experienced researchers alike. The Handbook’s thirty-one chapters are organized into four parts:Theory, covering significant theoretical developments as well as the interactions between conic optimization and polynomial optimization;Algorithms, documenting the directions of current algorithmic development;Software, providing an overview of the state-of-the-art;Applications, dealing with the application areas where semidefinite and conic optimization has made a significant impact in recent years.

Автор: Cichocki Andrzej, Lee Namgil, Oseledets Ivan
Название: Tensor Networks for Dimensionality Reduction and Large-Scale Optimization: Part 2 Applications and Future Perspectives
ISBN: 168083276X ISBN-13(EAN): 9781680832761
Издательство: Неизвестно
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Цена: 91040.00 T
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Описание: This monograph builds on Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 1 Low-Rank Tensor Decompositions by discussing tensor network models for super-compressed higher-order representation of data/parameters and cost functions, together with an outline of their applications in machine learning and data analytics. A particular emphasis is on elucidating, through graphical illustrations, that by virtue of the underlying low-rank tensor approximations and sophisticated contractions of core tensors, tensor networks have the ability to perform distributed computations on otherwise prohibitively large volume of data/parameters, thereby alleviating the curse of dimensionality. The usefulness of this concept is illustrated over a number of applied areas, including generalized regression and classification, generalized eigenvalue decomposition and in the optimization of deep neural networks. The monograph focuses on tensor train (TT) and Hierarchical Tucker (HT) decompositions and their extensions, and on demonstrating the ability of tensor networks to provide scalable solutions for a variety of otherwise intractable large-scale optimization problems. Tensor Networks for Dimensionality Reduction and Large-scale Optimization Parts 1 and 2 can be used as stand-alone texts, or together as a comprehensive review of the exciting field of low-rank tensor networks and tensor decompositions. See also: Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 1 Low-Rank Tensor Decompositions. ISBN 978-1-68083-222-8

Автор: Snyman, Jan A, Wilke, Daniel N
Название: Practical Mathematical Optimization
ISBN: 3319775855 ISBN-13(EAN): 9783319775852
Издательство: Springer
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Цена: 74530.00 T
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Описание: This book presents basic optimization principles and gradient-based algorithms to a general audience, in a brief and easy-to-read form. It enables professionals to apply optimization theory to engineering, physics, chemistry, or business economics.

Автор: Kaushik Kumar and J. Paulo Davim
Название: Optimization using evolutionary algorithms and metaheuristics
ISBN: 0367260441 ISBN-13(EAN): 9780367260446
Издательство: Taylor&Francis
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Цена: 168430.00 T
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Описание: This book covers developments and advances of algorithm based optimization techniques These techniques were only used for non-engineering problems. This book applies them to engineering problems.

Автор: Sebastian Bubeck.
Название: Convex Optimization: Algorithms and Complexity
ISBN: 1601988605 ISBN-13(EAN): 9781601988607
Издательство: Mare Nostrum (Eurospan)
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Цена: 84090.00 T
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Описание: Presents the main complexity theorems in convex optimization and their corresponding algorithms. The book begins with the fundamental theory of black-box optimization and proceeds to guide the reader through recent advances in structural optimization and stochastic optimization.

Автор: Boyd Stephen
Название: Introduction to Applied Linear Algebra
ISBN: 1316518965 ISBN-13(EAN): 9781316518960
Издательство: Cambridge Academ
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Цена: 45410.00 T
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Описание: A groundbreaking introductory textbook covering the linear algebra methods needed for data science and engineering applications. It combines straightforward explanations with numerous practical examples and exercises from data science, machine learning and artificial intelligence, signal and image processing, navigation, control, and finance.

Автор: Steven L. Brunton, J. Nathan Kutz
Название: Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control
ISBN: 1108422098 ISBN-13(EAN): 9781108422093
Издательство: Amazon Internet
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Цена: 0.00 T
Наличие на складе: Невозможна поставка.
Описание: Data-driven discovery is revolutionizing the modeling, prediction, and control of complex systems. Aimed at advanced undergraduate and beginning graduate students, this textbook provides an integrated viewpoint that shows how to apply emerging methods from data science, data mining, and machine learning to engineering and the physical sciences.

Автор: Tsan-Ming Choi; Jianjun Gao; James H. Lambert; Chi
Название: Optimization and Control for Systems in the Big-Data Era
ISBN: 3319535161 ISBN-13(EAN): 9783319535166
Издательство: Springer
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Цена: 139750.00 T
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Описание: This book focuses on optimal control and systems engineering in the big data era. Part I offers reviews on optimization and control theories, and Part II examines the optimization and control applications.

Автор: Emrouznejad
Название: Big Data Optimization: Recent Developments and Challenges
ISBN: 3319302639 ISBN-13(EAN): 9783319302638
Издательство: Springer
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Цена: 139310.00 T
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Описание: Themain objective of this book is to provide the necessary background to work withbig data by introducing some novel optimization algorithms and codes capable ofworking in the big data setting as well as introducing some applications in bigdata optimization for both academics and practitioners interested, and tobenefit society, industry, academia, and government. Presenting applications ina variety of industries, this book will be useful for the researchers aiming toanalyses large scale data. Several optimization algorithms for big dataincluding convergent parallel algorithms, limited memory bundle algorithm,diagonal bundle method, convergent parallel algorithms, network analytics, andmany more have been explored in this book.

Автор: Panos Pardalos; Mario Pavone; Giovanni Maria Farin
Название: Machine Learning, Optimization, and Big Data
ISBN: 3319279254 ISBN-13(EAN): 9783319279251
Издательство: Springer
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Цена: 52170.00 T
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Описание: This bookconstitutes revised selected papers from the First International Workshop onMachine Learning, Optimization, and Big Data, MOD 2015, held in Taormina, Sicily,Italy, in July 2015. The 32papers presented in this volume were carefully reviewed and selected from 73submissions.

Автор: Miroslav Fiedler; Josef Nedoma; Jaroslav Ramik; Ji
Название: Linear Optimization Problems with Inexact Data
ISBN: 1441940944 ISBN-13(EAN): 9781441940940
Издательство: Springer
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Цена: 88500.00 T
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Описание: Linear programming has attracted the interest of mathematicians since World War II when the first computers were constructed. This book presents a comprehensive treatment of linear optimization with inexact data, summarizing existing results and presenting new ones within a unifying framework.

Автор: Giuseppe Nicosia; Panos Pardalos; Renato Umeton; G
Название: Machine Learning, Optimization, and Data Science
ISBN: 3030375986 ISBN-13(EAN): 9783030375980
Издательство: Springer
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Цена: 91300.00 T
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Описание: This book constitutes the post-conference proceedings of the 5th International Conference on Machine Learning, Optimization, and Data Science, LOD 2019, held in Siena, Italy, in September 2019.