Optimization theory : a concise introduction
By: Yong, Jiongmin.
Publisher: New Jersey World Scientific 2018Description: x, 223p.ISBN: 9789813237643.Subject(s): Mathematical optimization | Optimization theoryDDC classification: 515 | Y8o Summary: Mathematically, most of the interesting optimization problems can be formulated to optimize some objective function, subject to some equality and/or inequality constraints. This book introduces some classical and basic results of optimization theory, including nonlinear programming with Lagrange multiplier method, the Karush-Kuhn-Tucker method, Fritz John's method, problems with convex or quasi-convex constraints, and linear programming with geometric method and simplex method. A slim book such as this which touches on major aspects of optimization theory will be very much needed for most readers. We present nonlinear programming, convex programming, and linear programming in a self-contained manner. This book is for a one-semester course for upper level undergraduate students or first/second year graduate students. It should also be useful for researchers working on many interdisciplinary areas other than optimization.Item type | Current location | Collection | Call number | url | Status | Date due | Barcode | Item holds |
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Text Books | PK Kelkar Library, IIT Kanpur | TEXT | 515 Y8o (Browse shelf) | Available | A183584 |
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515 Si53c pt.2B A comprehensive course in analysis [set] | 515 Si53c pt.3 A comprehensive course in analysis [set] | 515 Si53c pt.4 A comprehensive course in analysis [set] | 515 Y8o Optimization theory | 515.1 R453m2 Mathematical methods for physics and engineering [2nd ed.] | 515.1 R453m2 Mathematical methods for physics and engineering [2nd ed.] | 515.1 R453m2 Mathematical methods for physics and engineering [2nd ed.] |
Mathematically, most of the interesting optimization problems can be formulated to optimize some objective function, subject to some equality and/or inequality constraints. This book introduces some classical and basic results of optimization theory, including nonlinear programming with Lagrange multiplier method, the Karush-Kuhn-Tucker method, Fritz John's method, problems with convex or quasi-convex constraints, and linear programming with geometric method and simplex method. A slim book such as this which touches on major aspects of optimization theory will be very much needed for most readers. We present nonlinear programming, convex programming, and linear programming in a self-contained manner. This book is for a one-semester course for upper level undergraduate students or first/second year graduate students. It should also be useful for researchers working on many interdisciplinary areas other than optimization.
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