000			01471 a2200205 4500
005			20180712150212.0
008			180702b xxu||||| |||| 00| 0 eng d
020			_a9789813237643
040			_cIIT Kanpur
041			_aeng
082			_a515 _bY8o
100			_aYong, Jiongmin
245			_aOptimization theory _ba concise introduction _cJiongmin Yong
260			_bWorld Scientific _c2018 _aNew Jersey
300			_ax, 223p
520			_aMathematically, 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.
650			_a Mathematical optimization
650			_aOptimization theory
942			_cBK
999			_c559141 _d559141