In 1984, N. Karmarkar published a seminal paper on algorithmic linear programming. During the subsequent decade, it stimulated a huge outpouring of new algorithmic results by researchers world-wide in many areas of mathematical programming and numerical computation. This book gives an overview of the resulting, dramatic reorganization that has occurred in one of these areas: algorithmic differentiable optimization and equation-solving, or, more simply, algorithmic differentiable programming. The book is aimed at readers familiar with advanced calculus, numerical analysis, in particular numerical linear algebra, the theory and algorithms of linear and nonlinear programming, and the fundamentals of computer science, in particular, computer programming and the basic models of computation and complexity theory. J.L. Nazareth is a Professor in the Department of Pure and Applied Mathematics at Washington State University. He is the author of two books previously published by Springer-Verlag, DLP and Extensions: An Optimization Model and Decision Support System (2001) and The Newton-Cauchy Framework: A Unified Approach to Unconstrained Nonlinear Minimization (1994)

In recent years portfolio optimization and construction methodologies have become an increasingly critical ingredient of asset and fund management, while at the same time portfolio risk assessment has become an essential ingredient in risk management, and this trend will only accelerate in the coming years. Unfortunately there is a large gap between the limited treatment of portfolio construction methods that are presented in most university courses with relatively little hands-on experience and limited computing tools, and the rich and varied aspects of portfolio construction that are used in practice in the finance industry. Current practice demands the use of modern methods of portfolio construction that go well beyond the classical Markowitz mean-variance optimality theory and require the use of powerful scalable numerical optimization methods. This book fills the gap between current university instruction and current industry practice by providing a comprehensive computationally-oriented treatment of modern portfolio optimization and construction methods. The computational aspect of the book is based on extensive use of S-Plus®, the S+NuOPT™ optimization module, the S-Plus Robust Library and the S+Bayes™ Library, along with about 100 S-Plus scripts and some CRSP® sample data sets of stock returns. A special time-limited version of the S-Plus software is available to purchasers of this book

In a context of global competition, the optimization of logistics systems is inescapable. Logistics Systems: Design and Optimization falls within this perspective and presents twelve chapters that well illustrate the variety and the complexity of logistics activities. Each chapter is written by recognized researchers who have been commissioned to survey a specific topic or emerging area of logistics. The first chapter, by Riopel, Langevin, and Campbell, develops a framework for the entire book. It classifies logistics decisions and highlights the relevant linkages to logistics decisions. The intricacy of these linkages demonstrates how thoroughly the decisions are interrelated and underscores the complexity of managing logistics activities. Each of the chapters focus on quantitative methods for the design and optimization of logistics systems.

This textbook thoroughly outlines combinatorial algorithms for generation, enumeration, and search. Topics include backtracking and heuristic search methods applied to various combinatorial structures, such as: · Combinations · Permutations · Graphs · Designs · Many classical areas are covered as well as new research topics not included in most existing texts, such as: · Group algorithms · Graph isomorphism · Hill-climbing · Heuristic search algorithms · This work serves as an exceptional textbook for a modern course in combinatorial algorithms, providing a unified and focused collection of recent topics of interest in the area. The authors, synthesizing material that can only be found scattered through many different sources, introduce the most important combinatorial algorithmic techniques - thus creating an accessible, comprehensive text that students of mathematics, electrical engineering, and computer science can understand without needing a prior course on combinatorics

To balance the competing goals of reduced infrastructure and enhanced information technology (IT) skills, the Army must manage all its workforces in the IT field. Using Fort Bragg as a test case, this report shows how a linear programming framework helps gauge the impact of decisions that change the IT workforce. With a determination of who can do what in the IT field, the authors conclude that with linear programming the effects of policy changes can be quantified and better understood, various policies can be studied, and the approach can be extended beyond the installation level

Introduction to Stochastic Search and Optimization provides comprehensive, current information on methods for real-world problem solving, including stochastic gradient and non-gradient techniques, as well as relatively recent innovations such as simulated annealing, genetic algorithms, and MCMC. It is written to be read and understood by graduate students, industrial practitioners, and experienced researchers in the field. Web links to software and data sets, and an extensive list of references of the book allows the reader to explore deeper into certain topic areas. I also found the index to be very comprehensive and carefully done. The appendices are as a refresher and summary of much of the prerequisite material. The book is somewhat unique in providing a balanced discussion of algorithms, including both their strengths and weaknesses. The book is among very few books that have integrated essential parts of statistical fields with optimization and decision making. The book's inclusion of a chapter on optimal experimental design is an example of such integration. The approaches discussed in the book could be used for financial decision making, forecasting, and quality improvement, among many other areas

The search for optimal solutions pervades our daily lives. From the scientific point of view, optimization procedures play an eminent role whenever exact solutions to a given problem are not at hand or a compromise has to be sought, e.g. to obtain a sufficiently accurate solution within a given amount of time. This book addresses stochastic optimization procedures in a broad manner, giving an overview of the most relevant optimization philosophies in the first part. The second part deals with benchmark problems in depth, by applying in sequence a selection of optimization procedures to them. While having primarily scientists and students from the physical and engineering sciences in mind, this book addresses the larger community of all those wishing to learn about stochastic optimization techniques and how to use them

Optimization using Mathematical Programming makes it possible to solve many economic, commercial and industrial problems. The development of powerful and easy to use software means that this tool is now available to a large audience. This book concentrates on the modeling process, which is then applied to solve 60 real problems grouped by subject into ten chapters. Besides classical industrial problems, such as transport and scheduling, there are less well known and more recent application areas such as telecommunications, personnel management and public services.

The book begins with an easy-to-read introduction to the concepts associated with the creation of optimization models for production planning. These concepts are then applied to well-known planning models, namely mrp and MRP II. From this foundation fairly sophisticated models for supply chain management are developed. Another unique feature is that models are developed with an eye toward implementation. In fact, there is a chapter that provides explicit examples of implementation of the basic models using a variety of popular, commercially available modeling languages. The new edition is updated and provides extensions

This book, a result of the author's teaching and research experience in various universities and institutes over the past ten years, can be used as a textbook for an optimization course for graduates and senior undergraduates. It systematically describes optimization theory and several powerful methods, including recent results. For most methods, the authors discuss an idea’s motivation, study the derivation, establish the global and local convergence, describe algorithmic steps, and discuss the numerical performance. The book deals with both theory and algorithms of optimization concurrently. It also contains an extensive bibliography. Finally, apart from its use for teaching, Optimization Theory and Methods will be very beneficial as a research reference

Building on the success of Real-Time Rendering, this completely revised and updated second edition addresses new developments in rendering realistic three-dimensional images in a fraction of a second. It discusses the latest graphics hardware capabilities, while also presenting theoretical underpinnings and algorithms of lasting value.

A Companion to American Foreign Relations is an authoritative volume of historiographical essays that survey the state of U.S. diplomatic history. The essays cover the entire range of the history of American foreign relations from the colonial period to the present. They discuss the major sources and analyze the most influential books and articles in the field. The contributors – eminent scholars and experts in their subject matter–delve deeply into the literature and integrate discussions of new methodological approaches with more traditional diplomatic history. Each essay concludes with prospects for future work in the field.For the student, scholar, and those interested in the history of American foreign relations, this is an invaluable reference work.

The Computational Intelligence and its Applications Series publishes academic work based either on one of the computational intelligence techniques or on applications of these techniques to a field of human endeavor. The books may be single author monographs, multiple author editions, conference proceedings, handbooks, or edited volumes by leading researchers with chapters contributed by researchers in the field. Computational Economics: A Perspective from Computational Intelligence is a part of this series. Computational Economics: A Perspective from Computational Intelligence provides models of various economic and financial issues while using computational intelligence as a foundation. The scope of this volume comprises finance, economics, management, organizational theory and public policies. It explains the ongoing and novel research in this field, and displays the power of these computational methods in coping with difficult problems with methods from traditional perspectives. By encouraging the discussion of different views, this book serves as an introductory and inspiring volume that helps to flourish studies in computational economics.