Convex Optimization: Algorithms and Complexity, Sebastian Bubeck.

Автор: Lin Zhouchen, Li Huan, Fang Cong
Название: Accelerated Optimization for Machine Learning: First-Order Algorithms
ISBN: 9811529094 ISBN-13(EAN): 9789811529092
Издательство: Springer
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Цена: 139750.00 T
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Описание: The acceleration of first-order optimization algorithms is crucial for the efficiency of machine learning.Written by leading experts in the field, this book provides a comprehensive introduction to, and state-of-the-art review of accelerated first-order optimization algorithms for machine learning.

Автор: Tor Lattimore, Csaba Szepesvari
Название: Bandit Algorithms
ISBN: 1108486827 ISBN-13(EAN): 9781108486828
Издательство: Cambridge Academ
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Цена: 46470.00 T
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Описание: Decision-making in the face of uncertainty is a challenge in machine learning, and the multi-armed bandit model is a common framework to address it. This comprehensive introduction is an excellent reference for established researchers and a resource for graduate students interested in exploring stochastic, adversarial and Bayesian frameworks.

Автор: Kochenderfer Mykel J., Wheeler Tim A.
Название: Algorithms for Optimization
ISBN: 0262039427 ISBN-13(EAN): 9780262039420
Издательство: MIT Press
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Цена: 95930.00 T
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Описание: A comprehensive introduction to optimization with a focus on practical algorithms for the design of engineering systems.

This book offers a comprehensive introduction to optimization with a focus on practical algorithms. The book approaches optimization from an engineering perspective, where the objective is to design a system that optimizes a set of metrics subject to constraints. Readers will learn about computational approaches for a range of challenges, including searching high-dimensional spaces, handling problems where there are multiple competing objectives, and accommodating uncertainty in the metrics. Figures, examples, and exercises convey the intuition behind the mathematical approaches. The text provides concrete implementations in the Julia programming language.

Topics covered include derivatives and their generalization to multiple dimensions; local descent and first- and second-order methods that inform local descent; stochastic methods, which introduce randomness into the optimization process; linear constrained optimization, when both the objective function and the constraints are linear; surrogate models, probabilistic surrogate models, and using probabilistic surrogate models to guide optimization; optimization under uncertainty; uncertainty propagation; expression optimization; and multidisciplinary design optimization. Appendixes offer an introduction to the Julia language, test functions for evaluating algorithm performance, and mathematical concepts used in the derivation and analysis of the optimization methods discussed in the text. The book can be used by advanced undergraduates and graduate students in mathematics, statistics, computer science, any engineering field, (including electrical engineering and aerospace engineering), and operations research, and as a reference for professionals.

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: 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: 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: Quaintance Jocelyn, Gallier Jean H
Название: Linear Algebra And Optimization With Applications To Machine Learning - Volume Ii: Fundamentals Of Optimization Theory With Applications To Machine Learning
ISBN: 9811216568 ISBN-13(EAN): 9789811216565
Издательство: World Scientific Publishing
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Цена: 190080.00 T
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Описание: Volume 2 applies the linear algebra concepts presented in Volume 1 to optimization problems which frequently occur throughout machine learning. This book blends theory with practice by not only carefully discussing the mathematical under pinnings of each optimization technique but by applying these techniques to linear programming, support vector machines (SVM), principal component analysis (PCA), and ridge regression. Volume 2 begins by discussing preliminary concepts of optimization theory such as metric spaces, derivatives, and the Lagrange multiplier technique for finding extrema of real valued functions. The focus then shifts to the special case of optimizing a linear function over a region determined by affine constraints, namely linear programming. Highlights include careful derivations and applications of the simplex algorithm, the dual-simplex algorithm, and the primal-dual algorithm. The theoretical heart of this book is the mathematically rigorous presentation of various nonlinear optimization methods, including but not limited to gradient decent, the Karush-Kuhn-Tucker (KKT) conditions, Lagrangian duality, alternating direction method of multipliers (ADMM), and the kernel method. These methods are carefully applied to hard margin SVM, soft margin SVM, kernel PCA, ridge regression, lasso regression, and elastic-net regression. Matlab programs implementing these methods are included.

Автор: Surjit Singh, Rajeev Mohan Sharma
Название: Handbook of Research on the IoT, Cloud Computing, and Wireless Network Optimization
ISBN: 1522573356 ISBN-13(EAN): 9781522573357
Издательство: Mare Nostrum (Eurospan)
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Цена: 413950.00 T
Наличие на складе: Невозможна поставка.
Описание: ICT technologies have contributed to the advances in wireless systems, which provide seamless connectivity for worldwide communication. The growth of interconnected devices and the need to store, manage, and process the data from them has led to increased research on the intersection of the internet of things and cloud computing. The Handbook of Research on the IoT, Cloud Computing, and Wireless Network Optimization is a pivotal reference source that provides the latest research findings and solutions for the design and augmentation of wireless systems and cloud computing. The content within this publication examines data mining, machine learning, and software engineering, and is designed for IT specialists, software engineers, researchers, academicians, industry professionals, and students.

Автор: Aggarwal Charu C.
Название: Linear Algebra and Optimization for Machine Learning: A Textbook
ISBN: 3030403432 ISBN-13(EAN): 9783030403430
Издательство: Springer
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Цена: 46570.00 T
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Описание: This textbook introduces linear algebra and optimization in the context of machine learning. The tight integration of linear algebra methods with examples from machine learning differentiates this book from generic volumes on linear algebra.

Автор: Mitsuo Gen; Runwei Cheng; Lin Lin
Название: Network Models and Optimization
ISBN: 1849967466 ISBN-13(EAN): 9781849967464
Издательство: Springer
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Цена: 174130.00 T
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Описание: Network models are critical tools in business, management, science and industry. This book presents an insightful, comprehensive, and up-to-date treatment of multiple objective genetic algorithms to network optimization problems in many disciplines.

Автор: Marwala Tshilidzi, Leke Collins Achepsah
Название: Handbook Of Machine Learning - Volume 2: Optimization And Decision Making
ISBN: 9811205663 ISBN-13(EAN): 9789811205668
Издательство: World Scientific Publishing
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Цена: 126720.00 T
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Описание:

Building on Handbook of Machine Learning - Volume 1: Foundation of Artificial Intelligence, this volume on Optimization and Decision Making covers a range of algorithms and their applications. Like the first volume, it provides a starting point for machine learning enthusiasts as a comprehensive guide on classical optimization methods. It also provides an in-depth overview on how artificial intelligence can be used to define, disprove or validate economic modeling and decision making concepts.

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.