Background
Luenberger, David Gilbert was born on September 16, 1937 in Los Angeles, California, United States. Son of Frederick Otto and Marion (Crumley) Luenberger.
(Investment Science, Second Edition, provides thorough and...)
Investment Science, Second Edition, provides thorough and highly accessible mathematical coverage of the fundamental topics of intermediate investments, including fixed-income securities, capital asset pricing theory, derivatives, and innovations in optimal portfolio growth and valuation of multi-period risky investments. Eminent scholar and teacher David G. Luenberger, known for his ability to make complex ideas simple, presents essential ideas of investments and their applications, offering students the most comprehensive treatment of the subject available.
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(Integrates the traditional approach to differential equat...)
Integrates the traditional approach to differential equations with the modern systems and control theoretic approach to dynamic systems, emphasizing theoretical principles and classic models in a wide variety of areas. Provides a particularly comprehensive theoretical development that includes chapters on positive dynamic systems and optimal control theory. Contains numerous problems.
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( This third edition of the classic textbook in Optimizat...)
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.
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( The original edition of this book was celebrated for it...)
The original edition of this book was celebrated for its coverage of the central concepts of practical optimization techniques. This updated edition expands and illuminates the connection between the purely analytical character of an optimization problem, expressed by properties of the necessary conditions, and the behavior of algorithms used to solve a problem. Incorporating modern theoretical insights, this classic text is even more useful.
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(Programming.)
Programming.
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(Engineers must make decisions regarding the distribution ...)
Engineers must make decisions regarding the distribution of expensive resources in a manner that will be economically beneficial. This problem can be realistically formulated and logically analyzed with optimization theory. This book shows engineers how to use optimization theory to solve complex problems. Unifies the large field of optimization with a few geometric principles. Covers functional analysis with a minimum of mathematics. Contains problems that relate to the applications in the book.
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(Fueled in part by some extraordinary theoretical developm...)
Fueled in part by some extraordinary theoretical developments in finance, an explosive growth of information and computing technology, and the global expansion of investment activity, investment theory currently commands a high level of intellectual attention. Recent developments in the field are being infused into university classrooms, financial service organizations, business ventures, and into the awareness of many individual investors. Modern investment theory using the language of mathematics is now an essential aspect of academic and practitioner training. Representing a breakthrough in the organization of finance topics, Investment Science will be an indispensable tool in teaching modern investment theory. It presents sound fundamentals and shows how real problems can be solved with modern, yet simple, methods. David Luenberger gives thorough yet highly accessible mathematical coverage of standard and recent topics of introductory investments: fixed-income securities, modern portfolio theory and capital asset pricing theory, derivatives (futures, options, and swaps), and innovations in optimal portfolio growth and valuation of multiperiod risky investments. Throughout the book, he uses mathematics to present essential ideas of investments and their applications in business practice. The creative use of binomial lattices to formulate and solve a wide variety of important finance problems is a special feature of the book. In moving from fixed-income securities to derivatives, Luenberger increases naturally the level of mathematical sophistication, but never goes beyond algebra, elementary statistics/probability, and calculus. He includes appendices on probability and calculus at the end of the book for student reference. Creative examples and end-of-chapter exercises are also included to provide additional applications of principles given in the text. Ideal for investment or investment management courses in finance, engineering economics, operations research, and management science departments, Investment Science has been successfully class-tested at Boston University, Stanford University, and the University of Strathclyde, Scotland, and used in several firms where knowledge of investment principles is essential. Executives, managers, financial analysts, and project engineers responsible for evaluation and structuring of investments will also find the book beneficial. The methods described are useful in almost every field, including high-technology, utilities, financial service organizations, and manufacturing companies.
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( This new edition covers the central concepts of practic...)
This new edition covers the central concepts of practical optimization techniques, with an emphasis on methods that are both state-of-the-art and popular. One major insight is the connection between the purely analytical character of an optimization problem and the behavior of algorithms used to solve a problem. This was a major theme of the first edition of this book and the fourth edition expands and further illustrates this relationship. As in the earlier editions, the material in this fourth edition is organized into three separate parts. Part I is a self-contained introduction to linear programming. The presentation in this part is fairly conventional, covering the main elements of the underlying theory of linear programming, many of the most effective numerical algorithms, and many of its important special applications. Part II, which is independent of Part I, covers the theory of unconstrained optimization, including both derivations of the appropriate optimality conditions and an introduction to basic algorithms. This part of the book explores the general properties of algorithms and defines various notions of convergence. Part III extends the concepts developed in the second part to constrained optimization problems. Except for a few isolated sections, this part is also independent of Part I. It is possible to go directly into Parts II and III omitting Part I, and, in fact, the book has been used in this way in many universities. New to this edition is a chapter devoted to Conic Linear Programming, a powerful generalization of Linear Programming. Indeed, many conic structures are possible and useful in a variety of applications. It must be recognized, however, that conic linear programming is an advanced topic, requiring special study. Another important topic is an accelerated steepest descent method that exhibits superior convergence properties, and for this reason, has become quite popular. The proof of the convergence property for both standard and accelerated steepest descent methods are presented in Chapter 8. As in previous editions, end-of-chapter exercises appear for all chapters. From the reviews of the Third Edition: “… this very well-written book is a classic textbook in Optimization. It should be present in the bookcase of each student, researcher, and specialist from the host of disciplines from which practical optimization applications are drawn.” (Jean-Jacques Strodiot, Zentralblatt MATH, Vol. 1207, 2011)
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Luenberger, David Gilbert was born on September 16, 1937 in Los Angeles, California, United States. Son of Frederick Otto and Marion (Crumley) Luenberger.
Biosystems Engineering.E., California Institute of Technology, 1959. Microsoft Security Essentials.E., Stanford University, 1961. Doctor of Philosophy in Electrical Engineering, Stanford University, 1963.
Assistant professor electrical engineering, Stanford (California) U., 1963-1967;
associate professor engineering-economics systems, Stanford (California) U., 1967-1971;
professor, Stanford (California) U., since 1971;
department chairman, Stanford (California) U., 1980-1991. Technical assistant director United States Office Science and Technology, Executive Office of President, Washington, 1971-1972. Visiting professor Massachusetts Institute of Technology, Cambridge, 1976.
Guest professor Technology University of Denmark, Lyngby, 1986.
(Investment Science, Second Edition, provides thorough and...)
(Fueled in part by some extraordinary theoretical developm...)
(Integrates the traditional approach to differential equat...)
( This new edition covers the central concepts of practic...)
( This new edition covers the central concepts of practic...)
(Engineers must make decisions regarding the distribution ...)
( The original edition of this book was celebrated for it...)
( This third edition of the classic textbook in Optimizat...)
(This is a balance of traditional topics with modern devel...)
(Programming.)
Fellow Institute of Electrical and Electronics Engineers. Member Econometric Society, Society for Advancement Economics Theory, Society for Promotion of Economics Theory, Institute Management Science, Society Economics Dynamics and Control (president 1987-1988), Math Programming Society, Palo Alto Camera Club, Sigma Xi, Tau Beta Pi.
Married Nancy Ann Iversen, January 7, 1962. Children: Susan Ann, Robert Alden, Jill Alison, Jenna Emmy.
