Название: An Introduction to Optimization: With Applications to Machine Learning, 5th Edition
Автор: Edwin K.P. Chong, Wu-Sheng Lu, Stanislaw H. Zak
Издательство: Wiley
Год: 2024
Страниц: 675
Язык: английский
Формат: pdf (true)
Размер: 20.0 MB
Accessible introductory textbook on optimization theory and methods, with an emphasis on engineering design, featuring MATLAB exercises and worked examples. Fully updated to reflect modern developments in the field, the Fifth Edition of An Introduction to Optimization fills the need for an accessible, yet rigorous, introduction to optimization theory and methods, featuring innovative coverage and a straightforward approach. The book begins with a review of basic definitions and notations while also providing the related fundamental background of linear algebra, geometry, and calculus. With this foundation, the authors explore the essential topics of unconstrained optimization problems, linear programming problems, and nonlinear constrained optimization. In addition, the book includes an introduction to artificial neural networks, convex optimization, multi-objective optimization, and applications of optimization in Machine Learning. Numerous diagrams and figures found throughout the book complement the written presentation of key concepts, and each chapter is followed by MATLAB exercises and practice problems that reinforce the discussed theory and algorithms.
Автор: Edwin K.P. Chong, Wu-Sheng Lu, Stanislaw H. Zak
Издательство: Wiley
Год: 2024
Страниц: 675
Язык: английский
Формат: pdf (true)
Размер: 20.0 MB
Accessible introductory textbook on optimization theory and methods, with an emphasis on engineering design, featuring MATLAB exercises and worked examples. Fully updated to reflect modern developments in the field, the Fifth Edition of An Introduction to Optimization fills the need for an accessible, yet rigorous, introduction to optimization theory and methods, featuring innovative coverage and a straightforward approach. The book begins with a review of basic definitions and notations while also providing the related fundamental background of linear algebra, geometry, and calculus. With this foundation, the authors explore the essential topics of unconstrained optimization problems, linear programming problems, and nonlinear constrained optimization. In addition, the book includes an introduction to artificial neural networks, convex optimization, multi-objective optimization, and applications of optimization in Machine Learning. Numerous diagrams and figures found throughout the book complement the written presentation of key concepts, and each chapter is followed by MATLAB exercises and practice problems that reinforce the discussed theory and algorithms.