Optimization theory : a concise introduction
Material type: TextPublication details: Singapore World Scientific 2020Description: x, 223 p. illustrationsISBN: 9780000988935Subject(s): Mathematical optimization | Mathematical analysisDDC classification: 519.6 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 library | Collection | Call number | Status | Date due | Barcode |
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BK | Stack | Stack | 519.6 YON/O (Browse shelf (Opens below)) | Available | 59497 |
Browsing Kannur University Central Library shelves, Shelving location: Stack, Collection: Stack Close shelf browser (Hides shelf browser)
519.6 GUP/O Optimization theory:techniques of operations research | 519.6 MIS/O Optimization- linear programming | 519.6 MOH/O Optimisation Techniques. | 519.6 YON/O Optimization theory : a concise introduction | 519.72 GRI/L Linear and nonlinear optimization | 519.72 HAD/L Linear programming | 519.72 HAD/L 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"--
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