Optimization
Linear and Nonlinear Programming 豆瓣
作者: David G. Luenberger Springer US 2009
This third edition of the classic textbook in Optimization has been fully revised and updated. It comprehensively covers modern theoretical insights in this crucial computing area, and will be required reading for analysts and operations researchers in a variety of fields. The book connects the purely analytical character of an optimization problem, and the behavior of algorithms used to solve it. Now, the third edition has been completely updated with recent Optimization Methods. The book also has a new co-author, Yinyu Ye of California's Stanford University, who has written lots of extra material including some on Interior Point Methods.
Introduction to Linear Optimization 豆瓣
作者: Dimitris Bertsimas / John N. Tsitsiklis Athena Scientific 1997 - 2
This book provides a unified, insightful, and modern treatment of linear optimization, that is, linear programming, network flow problems, and discrete optimization. It includes classical topics as well as the state of the art, in both theory and practice.
Dynamic Optimization 豆瓣
作者: Morton I. Kamien / Nancy L. Schwartz Elsevier Science 1991 - 10
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The long awaited second edition of Dynamic Optimization is now available. Clear exposition and numerous worked examples made the first edition the premier text on this subject. Now, the new edition is expanded and updated to include essential coverage of current developments on differential games, especially as they apply to important economic questions; new developments in comparative dynamics; and new material on optimal control with integral state equations. The second edition of Dynamic Optimization provides expert coverage on:- methods of calculus of variations - optimal control - continuous dynamic programming - stochastic optimal control -differential games. The authors also include appendices on static optimization and on differential games. Now in its new updated and expanded edition, Dynamic Optimization is, more than ever, the optimum choice for graduate and advanced undergraduate courses in economics, mathematical methods in economics and dynamic optimization, management science, mathematics and engineering. New features of Dynamic Optimization will show students:advances in how to do comparative dynamics; how to optimally switch from one state equation to another during the planning period; how to take into account the history of the system governing an optimization problem through the use of an integral state equation; and how to apply differential games to problems in economics and management sciences.
Numerical Optimization 豆瓣
作者: Jorge Nocedal / Stephen Wright Springer 2006 - 7
Optimization is an important tool used in decision science and for the analysis of physical systems used in engineering. One can trace its roots to the Calculus of Variations and the work of Euler and Lagrange. This natural and reasonable approach to mathematical programming covers numerical methods for finite-dimensional optimization problems. It begins with very simple ideas progressing through more complicated concepts, concentrating on methods for both unconstrained and constrained optimization.
Introduction to Linear & Nonlinear Programming 豆瓣
作者: David G. Luenberger Addison Wesley Publishing Company 1973 - 1
"Linear and Nonlinear Programming" is considered a classic textbook in Optimization. While it is a classic, it also reflects modern theoretical insights. These insights provide structure to what might otherwise be simply a collection of techniques and results, and this is valuable both as a means for learning existing material and for developing new results. One major insight of this type is the connection between the purely analytical character of an optimization problem, expressed perhaps by properties of the necessary conditions, and the behavior of algorithms used to solve a problem. This was a major theme of the first edition of this book and the second edition expands and further illustrates this relationship.</P>
"Linear and Nonlinear Programming" covers the central concepts of practical optimization techniques. It is designed for either self-study by professionals or classroom work at the undergraduate or graduate level for technical students. Like the field of optimization itself, which involves many classical disciplines, the book should be useful to system analysts, operations researchers, numerical analysts, management scientists, and other specialists from the host of disciplines from which practical optimization applications are drawn. </P>