Optimization theory : a concise introduction /
Yong, Jiongmin
Optimization theory : a concise introduction / Jiongmin Yong. - Singapore ; New Jersey : World Scientific, c2018 - x, 223 pages : illustrations ; 24 cm
Includes bibliographical references (page 217) and index.
Mathematical preparations -- Optimization problems and existence of optimal solutions -- Necessary and sufficient conditions of optimal solutions -- Problems with convexity and quasi-convexity conditions -- Linear programming.
"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"-- Provided by publisher.
9789813237643 (hardcover : alk. paper)
Mathematical optimization
Mathematical analysis
QA402.5 / Y8 2018
Optimization theory : a concise introduction / Jiongmin Yong. - Singapore ; New Jersey : World Scientific, c2018 - x, 223 pages : illustrations ; 24 cm
Includes bibliographical references (page 217) and index.
Mathematical preparations -- Optimization problems and existence of optimal solutions -- Necessary and sufficient conditions of optimal solutions -- Problems with convexity and quasi-convexity conditions -- Linear programming.
"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"-- Provided by publisher.
9789813237643 (hardcover : alk. paper)
Mathematical optimization
Mathematical analysis
QA402.5 / Y8 2018