Mathematical Optimization Theory and Operations Research, Michael Khachay; Yury Kochetov; Panos Pardalos
Автор: Strang Gilbert Название: Linear Algebra and Learning from Data ISBN: 0692196382 ISBN-13(EAN): 9780692196380 Издательство: Cambridge Academ Рейтинг: Цена: 9978.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Linear algebra and the foundations of deep learning, together at last! From Professor Gilbert Strang, acclaimed author of Introduction to Linear Algebra, comes Linear Algebra and Learning from Data, the first textbook that teaches linear algebra together with deep learning and neural nets. This readable yet rigorous textbook contains a complete course in the linear algebra and related mathematics that students need to know to get to grips with learning from data. Included are: the four fundamental subspaces, singular value decompositions, special matrices, large matrix computation techniques, compressed sensing, probability and statistics, optimization, the architecture of neural nets, stochastic gradient descent and backpropagation.
Автор: William J. Cook; L?szl? Lov?sz; Jens Vygen Название: Research Trends in Combinatorial Optimization ISBN: 364209547X ISBN-13(EAN): 9783642095474 Издательство: Springer Рейтинг: Цена: 20263.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Written by leading experts in combinatorial optimization, this book features in-depth surveys of current research areas in combinatorial optimization in the broad sense. These range from applied graph theory to mathematical programming.
Автор: Igor Bykadorov; Vitaly Strusevich; Tatiana Tchemis Название: Mathematical Optimization Theory and Operations Research ISBN: 3030333930 ISBN-13(EAN): 9783030333935 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book constitutes revised and selected papers from the 18th International Conference on Mathematical Optimization Theory and Operations Research, MOTOR 2019, held in Ekaterinburg, Russia, in July 2019.
The 40 full papers and 4 short papers presented in this volume were carefully reviewed and selected from a total of 170 submissions. The papers in the volume are organised according to the following topical headings: ?combinatorial optimization; game theory and mathematical economics; data mining and computational geometry; integer programming; mathematical programming; operations research; optimal control and applications.
Автор: Kochetov Название: Discrete Optimization and Operations Research ISBN: 3319449133 ISBN-13(EAN): 9783319449135 Издательство: Springer Рейтинг: Цена: 10342.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book constitutes the proceedings of the 9th International Conference on Discrete Optimization and Operations Research, DOOR 2016, held in Vladivostok, Russia, in September 2016. The 39 full papers presented in this volume were carefully reviewed and selected from 181 submissions.
Автор: Arora Rajesh Kumar Название: Optimization: Algorithms and Applications ISBN: 1498721125 ISBN-13(EAN): 9781498721127 Издательство: Taylor&Francis Рейтинг: Цена: 29093.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Choose the Correct Solution Method for Your Optimization Problem
Optimization: Algorithms and Applications presents a variety of solution techniques for optimization problems, emphasizing concepts rather than rigorous mathematical details and proofs.
The book covers both gradient and stochastic methods as solution techniques for unconstrained and constrained optimization problems. It discusses the conjugate gradient method, Broyden-Fletcher-Goldfarb-Shanno algorithm, Powell method, penalty function, augmented Lagrange multiplier method, sequential quadratic programming, method of feasible directions, genetic algorithms, particle swarm optimization (PSO), simulated annealing, ant colony optimization, and tabu search methods. The author shows how to solve non-convex multi-objective optimization problems using simple modifications of the basic PSO code. The book also introduces multidisciplinary design optimization (MDO) architectures--one of the first optimization books to do so--and develops software codes for the simplex method and affine-scaling interior point method for solving linear programming problems. In addition, it examines Gomory's cutting plane method, the branch-and-bound method, and Balas' algorithm for integer programming problems.
The author follows a step-by-step approach to developing the MATLAB(R) codes from the algorithms. He then applies the codes to solve both standard functions taken from the literature and real-world applications, including a complex trajectory design problem of a robot, a portfolio optimization problem, and a multi-objective shape optimization problem of a reentry body. This hands-on approach improves your understanding and confidence in handling different solution methods. The MATLAB codes are available on the book's CRC Press web page.
Автор: Snyman, Jan A, Wilke, Daniel N Название: Practical Mathematical Optimization ISBN: 3319775855 ISBN-13(EAN): 9783319775852 Издательство: Springer Рейтинг: Цена: 11179.00 р. Наличие на складе: Поставка под заказ.
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: Watrous, John (university Of Waterloo, Ontario) Название: The theory of quantum information ISBN: 1107180562 ISBN-13(EAN): 9781107180567 Издательство: Cambridge Academ Рейтинг: Цена: 11563.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Intended for graduate students and researchers, this book presents a formal development of the mathematical theory of quantum information. Largely self-contained, with clear proofs and a wide range of exercises, it will help the reader grasp the fundamental facts and techniques that form the mathematical foundations of the subject.
Discover the art and science of solving artificial intelligence problems with Python using optimization modeling. This book covers the practical creation and analysis of mathematical algebraic models such as linear continuous models, non-obviously linear continuous models,
and pure linear integer models. Rather than focus on theory, Practical Python AI Projects, the product of the author's decades of industry teaching and consulting, stresses the model creation aspect; contrasting alternate approaches and practical variations.
Each model is explained thoroughly and written to be executed. The source code from all examples in the book is available, written in Python using Google OR-Tools. It also includes a random problem generator, useful for industry application or study.
What You Will Learn
Build basic Python-based artificial intelligence (AI) applications Work with mathematical optimization methods and the Google OR-Tools (Optimization Tools) suiteCreate several types of projects using Python and Google OR-Tools
Who This Book Is For
Developers and students who already have prior experience in Python coding. Some prior mathematical experience or comfort level may be helpful as well.
Автор: Dingyu Xue Название: Solving Optimization Problems with MATLAB ISBN: 3110663643 ISBN-13(EAN): 9783110663648 Издательство: Walter de Gruyter Цена: 11148.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
This book focuses on solving optimization problems with MATLAB. Descriptions and solutions of nonlinear equations of any form are studied first. Focuses are made on the solutions of various types of optimization problems, including unconstrained and constrained optimizations, mixed integer, multiobjective and dynamic programming problems. Comparative studies and conclusions on intelligent global solvers are also provided.
Автор: Tor Lattimore, Csaba Szepesvari Название: Bandit Algorithms ISBN: 1108486827 ISBN-13(EAN): 9781108486828 Издательство: Cambridge Academ Рейтинг: Цена: 6970.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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.
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