Convex Optimization: Algorithms and Complexity, Sebastian Bubeck.
Автор: Kochenderfer Mykel J., Wheeler Tim A. Название: Algorithms for Optimization ISBN: 0262039427 ISBN-13(EAN): 9780262039420 Издательство: MIT Press Рейтинг: Цена: 14390.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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: Boyd Stephen Название: Introduction to Applied Linear Algebra ISBN: 1316518965 ISBN-13(EAN): 9781316518960 Издательство: Cambridge Academ Рейтинг: Цена: 6811.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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.
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.
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 Рейтинг: Цена: 26120.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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 Рейтинг: Цена: 19008.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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 Рейтинг: Цена: 7826.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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.
Название: Optimization for machine learning ISBN: 0262537761 ISBN-13(EAN): 9780262537766 Издательство: MIT Press Рейтинг: Цена: 13794.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: An up-to-date account of the interplay between optimization and machine learning, accessible to students and researchers in both communities.
The interplay between optimization and machine learning is one of the most important developments in modern computational science. Optimization formulations and methods are proving to be vital in designing algorithms to extract essential knowledge from huge volumes of data. Machine learning, however, is not simply a consumer of optimization technology but a rapidly evolving field that is itself generating new optimization ideas. This book captures the state of the art of the interaction between optimization and machine learning in a way that is accessible to researchers in both fields. Optimization approaches have enjoyed prominence in machine learning because of their wide applicability and attractive theoretical properties. The increasing complexity, size, and variety of today's machine learning models call for the reassessment of existing assumptions. This book starts the process of reassessment. It describes the resurgence in novel contexts of established frameworks such as first-order methods, stochastic approximations, convex relaxations, interior-point methods, and proximal methods. It also devotes attention to newer themes such as regularized optimization, robust optimization, gradient and subgradient methods, splitting techniques, and second-order methods. Many of these techniques draw inspiration from other fields, including operations research, theoretical computer science, and subfields of optimization. The book will enrich the ongoing cross-fertilization between the machine learning community and these other fields, and within the broader optimization community.
Автор: Carlos Alberto Ochoa Ortiz Zezzatti, Camelia Chira, Arturo Hernandez, Miguel Basurto Название: Logistics Management and Optimization through Hybrid Artificial Intelligence Systems ISBN: 146660297X ISBN-13(EAN): 9781466602977 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 28413.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Logistics Management and Optimization through Hybrid Artificial Intelligence Systems offers the latest research within the field of HAIS, surveying the broad topics and collecting case studies, future directions, and cutting edge analyses. Using biologically inspired algorithms such as ant colony optimization and particle swarm optimization, this text includes solutions and heuristics for practitioners and academics alike, offering a vital resource for staying abreast in this ever-burgeoning field.
This book provides the mathematical fundamentals of linear algebra to practicers in computer vision, machine learning, robotics, applied mathematics, and electrical engineering. By only assuming a knowledge of calculus, the authors develop, in a rigorous yet down to earth manner, the mathematical theory behind concepts such as: vectors spaces, bases, linear maps, duality, Hermitian spaces, the spectral theorems, SVD, and the primary decomposition theorem. At all times, pertinent real-world applications are provided. This book includes the mathematical explanations for the tools used which we believe that is adequate for computer scientists, engineers and mathematicians who really want to do serious research and make significant contributions in their respective fields.
Автор: J. Joshua Thomas, Pinar Karagoz, B. Bazeer Ahamed, Название: Deep Learning Techniques and Optimization Strategies in Big Data Analytics ISBN: 1799811921 ISBN-13(EAN): 9781799811923 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 35897.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: 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.
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.
ООО "Логосфера " Тел:+7(495) 980-12-10 www.logobook.ru
