Dynamic Programming in Economics is an outgrowth of a course intended for students in the first year PhD program and for researchers in Macroeconomics Dynamics. It can be used by students and researchers in Mathematics as well as in Economics. The purpose of Dynamic Programming in Economics is twofold: (a) to provide a rigorous, but not too complicated, treatment of optimal growth models in infinite discrete time horizon, (b) to train the reader to the use of optimal growth models and hence to help him to go further in his research. We are convinced that there is a place for a book which stays somewhere between the "minimum tool kit" and specialized monographs leading to the frontiers of research on optimal growth.

Optimization models based on a nonlinear systems description often possess multiple local optima. The objective of Global Optimization (GO) is to find the best possible solution of multiextremal problems. This volume illustrates the applicability of GO modeling techniques and solution strategies to real-world problems. Coverage extends to a broad range of applications, from agroecosystem management to robot design. Proposed solutions encompass a range of practical and viable methods.

This book provides a complete overview of production systems and describes the best approaches to analyze their performance. Written by experts in the field, this work also presents numerous techniques that can be used to describe, model, and optimize the performance of various types of production lines. The book is intended for researchers, production managers, and graduate students in industrial, mechanical, and systems engineering.

This book provides a self-contained, comprehensive and up-to-date presentation of uncertainty theory. The purpose is to equip the readers with an axiomatic approach to deal with uncertainty. For this new edition the entire text has been totally rewritten. The chapters on chance theory and uncertainty theory are completely new. Mathematicians, researchers, engineers, designers, and students will find this work a stimulating and useful reference.

Optimization problems arising in practice involve random model parameters. For the computation of robust optimal solutions, i.e., optimal solutions being insenistive with respect to random parameter variations, appropriate deterministic substitute problems are needed. Based on the probability distribution of the random data, and using decision theoretical concepts, optimization problems under stochastic uncertainty are converted into appropriate deterministic substitute problems. Due to the occurring probabilities and expectations, approximative solution techniques must be applied. Several deterministic and stochastic approximation methods are provided: Taylor expansion methods, regression and response surface methods (RSM), probability inequalities, multiple linearization of survival/failure domains, discretization methods, convex approximation/deterministic descent directions/efficient points, stochastic approximation and gradient procedures, differentiation formulas for probabilities and expectations.

Substantially revised, reorganised and updated, the second edition now comprises eighteen chapters, carefully arranged in a straightforward and logical manner, with many new results and open problems.

As well as covering the theoretical aspects of the subject, with detailed proofs of many important results, the authors present a number of algorithms, and whole chapters are devoted to topics such as branchings, feedback arc and vertex sets, connectivity augmentations, sparse subdigraphs with prescribed connectivity, and also packing, covering and decompositions of digraphs. Throughout the book, there is a strong focus on applications which include quantum mechanics, bioinformatics, embedded computing, and the travelling salesman problem.

Detailed indices and topic-oriented chapters ease navigation, and more than 650 exercises, 170 figures and 150 open problems are included to help immerse the reader in all aspects of the subject.

Switched linear systems have enjoyed a particular growth in interest since the 1990s. The large amount of data and ideas thus generated have, until now, lacked a co-ordinating framework to focus them effectively on some of the fundamental issues such as the problems of robust stabilizing switching design, feedback stabilization and optimal switching.

This deficiency is resolved by this book which features: nucleus of constructive design approaches based on canonical decomposition and forming a sound basis for the systematic treatment of secondary results; theoretical exploration and logical association of several independent but pivotal concerns in control design as they pertain to switched linear systems: controllability and observability, feedback stabilization, optimization and periodic switching; a reliable foundation for further theoretical research as well as design guidance for real life engineering applications through the integration of novel ideas, fresh insights and rigorous results.

Multilevel decision theory arises to resolve the contradiction between increasing requirements towards the process of design, synthesis, control and management of complex systems and the limitation of the power of technical, control, computer and other executive devices, which have to perform actions and to satisfy requirements in real time. This theory rises suggestions how to replace the centralised management of the system by hierarchical co-ordination of sub-processes. All sub-processes have lower dimensions, which support easier management and decision making. But the sub-processes are interconnected and they influence each other. Multilevel systems theory supports two main methodological tools: decomposition and co-ordination. Both have been developed, and implemented in practical applications concerning design, control and management of complex systems. In general, it is always beneficial to find the best or optimal solution in processes of system design, control and management. The real tendency towards the best (optimal) decision requires to present all activities in the form of a definition and then the solution of an appropriate optimization problem. Every optimization process needs the mathematical definition and solution of a well stated optimization problem. These problems belong to two classes: static optimization and dynamic optimization. Static optimization problems are solved applying methods of mathematical programming: conditional and unconditional optimization. Dynamic optimization problems are solved by methods of variation calculus: Euler Lagrange method; maximum principle; dynamical programming.

One might expect that after their identification in the 19th century, all aspects of Giffen goods would have been studied by now. This appears not to be the case. This book contains the latest insights into the theory of Giffen goods. In the past, surprisingly few goods could be categorized as "Giffen." This may be because of a lack of understanding of the character of these goods. Therefore, the theories explained in this book may also produce a solid basis for further empirical research in the field. Experts throughout the world have contributed to this book, which predominantly pursues a mathematically rigorous approach. It may be used by researchers in the field of fundamental economics and in graduate-level courses in advanced microeconomics.

Industrial optimization lies on the crossroads between mathematics, computer science, engineering and management. This book presents these fields in interdependence as a conversation between theoretical aspects of mathematics and computer science and the mathematical field of optimization theory at a practical level. The 19 case studies that were conducted by the author in real enterprises in cooperation and co-authorship with some of the leading industrial enterprises, including RWE, Vattenfall, EDF, PetroChina, Vestolit, Sasol, and Hella, illustrate the results that may be reasonably expected from an optimization project in a commercial enterprise. The book is aimed at persons working in industrial facilities as managers or engineers; it is also suitable for university students and their professors as an illustration of how the academic material may be used in real life. It will not make its reader a mathematician but it will help its reader in improving his plant.

This book concentrates on providing technical tools to make the user of Multiple Criteria Decision Making (MCDM) methodologies independent of bulky optimization computations. These bulky computations have been a necessary, but limiting, characteristic of interactive MCDM methodologies and algorithms. The book removes these limitations of MCDM problems by reducing a problem's computational complexity. The result is a wider and more functional general framework for presenting, teaching, implementing and applying a wide range of MCDM methodologies.

This book is a comprehensive survey of the mathematical concepts and principles of industrial mathematics. Its purpose is to provide students and professionals with an understanding of the fundamental mathematical principles used in Industrial Mathematics/OR in modeling problems and application solutions. All the concepts presented in each chapter have undergone the learning scrutiny of the author and his students. The illustrative material throughout the book was refined for student comprehension as the manuscript developed through its iterations, and the chapter exercises are refined from the previous year's exercises.

Stochastic programming - the science that provides us with tools to design and control stochastic systems with the aid of mathematical programming techniques - lies at the intersection of statistics and mathematical programming. The book Stochastic Programming is a comprehensive introduction to the field and its basic mathematical tools. While the mathematics is of a high level, the developed models offer powerful applications, as revealed by the large number of examples presented. The material ranges form basic linear programming to algorithmic solutions of sophisticated systems problems and applications in water resources and power systems, shipbuilding, inventory control, etc. Audience: Students and researchers who need to solve practical and theoretical problems in operations research, mathematics, statistics, engineering, economics, insurance, finance, biology and environmental protection.

Semi-infinite programming (SIP) deals with optimization problems in which either the number of decision variables or the number of constraints is finite. This book presents the state of the art in SIP in a suggestive way, bringing the powerful SIP tools close to the potential users in different scientific and technological fields. The volume is divided into four parts. Part I reviews the first decade of SIP (1962-1972). Part II analyses convex and generalised SIP, conic linear programming, and disjunctive programming. New numerical methods for linear, convex, and continuously differentiable SIP problems are proposed in Part III. Finally, Part IV provides an overview of the applications of SIP to probability, statistics, experimental design, robotics, optimization under uncertainty, production games, and separation problems. Audience: This book is an indispensable reference and source for advanced students and researchers in applied mathematics and engineering.

Semidefinite programming has been described as linear programming for the year 2000. It is an exciting new branch of mathematical programming, due to important applications in control theory, combinatorial optimization and other fields. Moreover, the successful interior point algorithms for linear programming can be extended to semidefinite programming.
In this monograph the basic theory of interior point algorithms is explained. This includes the latest results on the properties of the central path as well as the analysis of the most important classes of algorithms. Several "classic" applications of semidefinite programming are also described in detail. These include the Lov sz theta function and the MAX-CUT approximation algorithm by Goemans and Williamson.
Audience: Researchers or graduate students in optimization or related fields, who wish to learn more about the theory and applications of semidefinite programming.

As the telecommunication industry introduces new sophisticated technologies, the nature of services and the volume of demands have changed. Indeed, a broad range of new services for users appear, combining voice, data, graphics, video, etc. This implies new planning issues. Fiber transmission systems that can carry large amounts of data on a few strands of wire were introduced. These systems have such a large bandwidth that the failure of even a single transmission link: in the network can create a severe service loss to customers. Therefore, a very high level of service reliability is becoming imperative for both system users and service providers. Since equipment failures and accidents cannot be avoided entirely, networks have to be designed so as to "survive" failures. This is done by judiciously installing spare capacity over the network so that all traffic interrupted by a failure may be diverted around that failure by way of this spare or reserve capacity. This of course translates into huge investments for network operators. Designing such survivable networks while minimizing spare capacity costs is, not surprisingly, a major concern of operating companies which gives rise to very difficult combinatorial problems. In order to make telecommunication networks survivable, one can essentially use two different strategies: protection or restoration. The protection approach preas signs spare capacity to protect each element of the network independently, while the restoration approach spreads the redundant capacity over the whole network and uses it as required in order to restore the disrupted traffic."

Motivated by practical problems in engineering and physics, drawing on a wide range of applied mathematical disciplines, this book is the first to provide, within a unified framework, a self-contained comprehensive mathematical theory of duality for general non-convex, non-smooth systems, with emphasis on methods and applications in engineering mechanics. Topics covered include the classical (minimax) mono-duality of convex static equilibria, the beautiful bi-duality in dynamical systems, the interesting tri-duality in non-convex problems and the complicated multi-duality in general canonical systems. A potentially powerful sequential canonical dual transformation method for solving fully nonlinear problems is developed heuristically and illustrated by use of many interesting examples as well as extensive applications in a wide variety of nonlinear systems, including differential equations, variational problems and inequalities, constrained global optimization, multi-well phase transitions, non-smooth post-bifurcation, large deformation mechanics, structural limit analysis, differential geometry and non-convex dynamical systems. With exceptionally coherent and lucid exposition, the work fills a big gap between the mathematical and engineering sciences. It shows how to use formal language and duality methods to model natural phenomena, to construct intrinsic frameworks in different fields and to provide ideas, concepts and powerful methods for solving non-convex, non-smooth problems arising naturally in engineering and science. Much of the book contains material that is new, both in its manner of presentation and in its research development. A self-contained appendix provides some necessary background from elementary functional analysis. Audience: The book will be a valuable resource for students and researchers in applied mathematics, physics, mechanics and engineering. The whole volume or selected chapters can also be recommended as a text for both senior undergraduate and graduate courses in applied mathematics, mechanics, general engineering science and other areas in which the notions of optimization and variational methods are employed.

In science, engineering and economics, decision problems are frequently modelled by optimizing the value of a (primary) objective function under stated feasibility constraints. In many cases of practical relevance, the optimization problem structure does not warrant the global optimality of local solutions; hence, it is natural to search for the globally best solution(s). Global Optimization in Action provides a comprehensive discussion of adaptive partition strategies to solve global optimization problems under very general structural requirements. A unified approach to numerous known algorithms makes possible straightforward generalizations and extensions, leading to efficient computer-based implementations. A considerable part of the book is devoted to applications, including some generic problems from numerical analysis, and several case studies in environmental systems analysis and management. The book is essentially self-contained and is based on the author's research, in cooperation (on applications) with a number of colleagues. Audience: Professors, students, researchers and other professionals in the fields of operations research, management science, industrial and applied mathematics, computer science, engineering, economics and the environmental sciences.

In t.lw fHll of !!)!)2, Professor Dr. M. Alt.ar, chairman of tIw newly established dppartnwnt or Managenwnt. wit.h Comput.er Science at thp Homanian -American Univprsity in Bucharest (a private univprsil.y), inl.roducod in t.he curriculum a course on DiffenHltial Equations and Optimal Cont.rol, asking lIS to teach such course. It was an inter8sting challengo, since for t.Iw first tim8 wo had to t8ach such mathemaLical course for st.udents with economic background and interosts. It was a natural idea to sl.m't by looking at pconomic models which were described by differpntial equations and for which problems in (\pcision making dir! ariso. Since many or such models were r!escribed in discret.e timp, wp eleculed to elpvolop in parallel t.he theory of differential equations anel thaI, of discrete-timo systpms aur! also control theory in continuous and discrete time. Tlw jll'eSPlu book is t.he result of our tpaehing px!wripnce wit.h this courge. It is an enlargud version of t.he actllal lectuf(~s where, depending on t.he background of tho St.lI(\('Ilts, not all proofs could be given in detail. We would like to express our grat.itude to tlw Board of the Romanian - American University, personally 1. 0 the Rector, Professor Dr. Ion Smedpscu, for support, encouragement and readinpss to accept advancnd ideas in tho curriculum. fhe authors express t.heir warmest thanks 1.0 Mrs. Monica Stan . Necula for tho oxcellent procC'ssing of t.he manuscript.

Algorithmic Principles of Mathematical Programming investigates the mathematical structures and principles underlying the design of efficient algorithms for optimization problems. Recent advances in algorithmic theory have shown that the traditionally separate areas of discrete optimization, linear programming, and nonlinear optimization are closely linked. This book offers a comprehensive introduction to the whole subject and leads the reader to the frontiers of current research. The prerequisites to use the book are very elementary. All the tools from numerical linear algebra and calculus are fully reviewed and developed. Rather than attempting to be encyclopedic, the book illustrates the important basic techniques with typical problems. The focus is on efficient algorithms with respect to practical usefulness. Algorithmic complexity theory is presented with the goal of helping the reader understand the concepts without having to become a theoretical specialist. Further theory is outlined and supplemented with pointers to the relevant literature.

Multiwavelength Optical Networks systematically studies the major research issues in WDM (Wavelength Division Multiplexing) optical networks, such as routing and wavelength assignment, QoS multicast routing, design of logical topologies, and placement of wavelength converters. The book consists of two parts. The first part studies the fundamental concepts and principles of WDM networks. The second part discusses advanced and research issues of WDM networks.
The authors of the book have many years of working experience in the areas of computer networks and network optimization. The book discusses many difficult issues of WDM networks in a very comprehensive way. For each problem, there is a background discussion, and then the mathematical formulation, followed by the solutions.

System Modeling and Optimization is an indispensable reference for anyone interested in the recent advances in these two disciplines. The book collects, for the first time, selected articles from the 21st and most recent IFIP TC 7 conference in Sophia Antipolis, France.

Applied mathematicians and computer scientists can attest to the ever-growing influence of these two subjects. The practical applications of system modeling and optimization can be seen in a number of fields: environmental science, transport and telecommunications, image analysis, free boundary problems, bioscience, and non-cylindrical evolution control, to name just a few.

New developments in each of these fields have contributed to a more complex understanding of both system modeling and optimization. Editors John Cagnol and Jean-Paul Zol sio, chairs of the conference, have assembled System Modeling and Optimization to present the most up-to-date developments to professionals and academics alike.

The problem of "Shortest Connectivity," which is discussed here, has a long and convoluted history. Many scientists from many fields as well as laymen have stepped on its stage. Usually, the problem is known as Steiner's Problem and it can be described more precisely in the following way: Given a finite set of points in a metric space, search for a network that connects these points with the shortest possible length. This shortest network must be a tree and is called a Steiner Minimal Tree (SMT). It may contain vertices different from the points which are to be connected. Such points are called Steiner points. Steiner's Problem seems disarmingly simple, but it is rich with possibilities and difficulties, even in the simplest case, the Euclidean plane. This is one of the reasons that an enormous volume of literature has been published, starting in 1 the seventeenth century and continuing until today. The difficulty is that we look for the shortest network overall. Minimum span ning networks have been well-studied and solved eompletely in the case where only the given points must be connected. The novelty of Steiner's Problem is that new points, the Steiner points, may be introduced so that an intercon necting network of all these points will be shorter. This also shows that it is impossible to solve the problem with combinatorial and geometric methods alone."

Continuous optimization is the study of problems in which we wish to opti mize (either maximize or minimize) a continuous function (usually of several variables) often subject to a collection of restrictions on these variables. It has its foundation in the development of calculus by Newton and Leibniz in the 17* DEGREES century. Nowadys, continuous optimization problems are widespread in the mathematical modelling of real world systems for a very broad range of applications. Solution methods for large multivariable constrained continuous optimiza tion problems using computers began with the work of Dantzig in the late 1940s on the simplex method for linear programming problems. Recent re search in continuous optimization has produced a variety of theoretical devel opments, solution methods and new areas of applications. It is impossible to give a full account of the current trends and modern applications of contin uous optimization. It is our intention to present a number of topics in order to show the spectrum of current research activities and the development of numerical methods and applications."

Computing has become essential for the modeling, analysis, and optimization of systems. This book is devoted to algorithms, computational analysis, and decision models. The chapters are organized in two parts: optimization models of decisions and models of pricing and equilibria.
Optimization is at the core of rational decision making. Even when the decision maker has more than one goal or there is significant uncertainty in the system, optimization provides a rational framework for efficient decisions. The Markowitz mean-variance formulation is a classical example. The first part of the book is on recent developments in optimization decision models for finance and economics. The first four chapters of this part focus directly on multi-stage problems in finance. Chapters 5-8 involve the use of worst-case robust analysis. Chapters 9-11 are devoted to portfolio optimization. The final four chapters are on transportation-inventory with stochastic demand; optimal investment with CRRA utility; hedging financial contracts; and, automatic differentiation for computational finance.
The uncertainty associated with prediction and modeling constantly requires the development of improved methods and models. Similarly, as systems strive towards equilibria, the characterization and computation of equilibria assists analysis and prediction. The second part of the book is devoted to recent research in computational tools and models of equilibria, prediction, and pricing. The first three chapters of this part consider hedging issues in finance. Chapters 19-22 consider prediction and modeling methodologies. Chapters 23-26 focus on auctions and equilibria. Volatility models are investigated in chapters 27-28. The final two chapters investigate risk assessment and product pricing.
Audience: Researchers working in computational issues related to economics, finance, and management science.