| 000 | 02403cam a2200325 i 4500 | ||
|---|---|---|---|
| 999 |
_c39737 _d39737 |
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| 001 | 20561141 | ||
| 005 | 20190424142545.0 | ||
| 008 | 180514s2018 njua b 001 0 eng c | ||
| 010 | _a 2018014044 | ||
| 020 | _a9789813237643 (hardcover : alk. paper) | ||
| 040 |
_aLBSOR/DLC _beng _cLBSOR _erda _dDLC |
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| 042 | _apcc | ||
| 050 | 0 | 0 |
_aQA402.5 _b.Y66 2018 |
| 082 | 0 | 0 | _a519.6 YON-J |
| 100 | 1 | _aYong, Jiongmin | |
| 245 | 1 | 0 |
_aOptimization theory : _ba concise introduction / _cJiongmin Yong |
| 260 |
_aSingapore _bWorld Scientific Publishing _c2018 |
||
| 300 | _a223 p. | ||
| 365 |
_aUSD _b78.00. |
||
| 500 | _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. | ||
| 504 | _aIncludes bibliographical references and index. | ||
| 505 | 0 | _aMathematical preparations -- Optimization problems and existence of optimal solutions -- Necessary and sufficient conditions of optimal solutions -- Problems with convexity and quasi-convexity conditions -- Linear programming. | |
| 520 | _a"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"-- | ||
| 650 | 0 | _aMathematical optimization. | |
| 650 | 0 | _aMathematical analysis. | |
| 906 |
_a7 _bcbc _corignew _d1 _eecip _f20 _gy-gencatlg |
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| 942 | _2ddc | ||
| 955 |
_aLBSOR _axn13 2018-09-06 1 copy rec'd., to CIP ver. _arl00 2018-09-12 to SMA |
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