{"product_id":"9781119877639-an-introduction-to-optimization","title":"An Introduction to Optimization","description":"\u003cmeta content=\"text\/html; charset=utf-8\" http-equiv=\"Content-Type\"\u003e\u003cp\u003e\u003cspan\u003eWith Applications to Machine Learning\u003cbr\u003e\u003cb\u003eAn Introduction to Optimization\u003c\/b\u003e \u003cp\u003e\u003cb\u003eAccessible introductory textbook on optimization theory and methods, with an emphasis on engineering design, featuring MATLAB\u003csup\u003e®\u003c\/sup\u003e exercises and worked examples\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003eFully updated to reflect modern developments in the field, the Fifth Edition of \u003ci\u003eAn Introduction to Optimization\u003c\/i\u003e 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.  \u003c\/p\u003e\n\u003cp\u003eWith 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. \u003c\/p\u003e\n\u003cp\u003eNumerous diagrams and figures found throughout the book complement the written presentation of key concepts, and each chapter is followed by MATLAB\u003csup\u003e®\u003c\/sup\u003e exercises and practice problems that reinforce the discussed theory and algorithms. \u003c\/p\u003e\n\u003cp\u003eThe Fifth Edition features a new chapter on Lagrangian (nonlinear) duality, expanded coverage on matrix games, projected gradient algorithms, machine learning, and numerous new exercises at the end of each chapter.  \u003c\/p\u003e\n\u003cp\u003e\u003ci\u003eAn Introduction to Optimization\u003c\/i\u003e includes information on: \u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe mathematical definitions, notations, and relations from linear algebra, geometry, and calculus used in optimization\u003c\/li\u003e\n\u003cli\u003eOptimization algorithms, covering one-dimensional search, randomized search, and gradient, Newton, conjugate direction, and quasi-Newton methods\u003c\/li\u003e\n\u003cli\u003eLinear programming methods, covering the simplex algorithm, interior point methods, and duality \u003c\/li\u003e\n\u003cli\u003eNonlinear constrained optimization, covering theory and algorithms, convex optimization, and Lagrangian duality\u003c\/li\u003e\n\u003cli\u003eApplications of optimization in machine learning, including neural network training, classification, stochastic gradient descent, linear regression, logistic regression, support vector machines, and clustering.\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003e\u003ci\u003eAn Introduction to Optimization\u003c\/i\u003e is an ideal textbook for a one- or two-semester senior undergraduate or beginning graduate course in optimization theory and methods. The text is also of value for researchers and professionals in mathematics, operations research, electrical engineering, economics, statistics, and business.\u003c\/p\u003e\n\u003cbr\u003e\u003cbr\u003e\u003c\/span\u003e\u003c\/p\u003e","brand":"Rarewaves","offers":[{"title":"Default Title","offer_id":41053730144353,"sku":"9781119877639","price":137.75,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0092\/7504\/8033\/files\/orig_27716373.jpg?v=1722000812","url":"https:\/\/www.rarewaves.com\/products\/9781119877639-an-introduction-to-optimization","provider":"Rarewaves.com","version":"1.0","type":"link"}