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| dynamic optimization economics lecture notes: Lectures on Stochastic Programming Alexander Shapiro, Darinka Dentcheva, Andrzej Ruszczy?ski, 2009-01-01 Optimization problems involving stochastic models occur in almost all areas of science and engineering, such as telecommunications, medicine, and finance. Their existence compels a need for rigorous ways of formulating, analyzing, and solving such problems. This book focuses on optimization problems involving uncertain parameters and covers the theoretical foundations and recent advances in areas where stochastic models are available. Readers will find coverage of the basic concepts of modeling these problems, including recourse actions and the nonanticipativity principle. The book also includes the theory of two-stage and multistage stochastic programming problems; the current state of the theory on chance (probabilistic) constraints, including the structure of the problems, optimality theory, and duality; and statistical inference in and risk-averse approaches to stochastic programming. |
| dynamic optimization economics lecture notes: Dynamic Stochastic Optimization Kurt Marti, Yuri Ermoliev, Georg Ch. Pflug, 2012-12-06 Uncertainties and changes are pervasive characteristics of modern systems involving interactions between humans, economics, nature and technology. These systems are often too complex to allow for precise evaluations and, as a result, the lack of proper management (control) may create significant risks. In order to develop robust strategies we need approaches which explic itly deal with uncertainties, risks and changing conditions. One rather general approach is to characterize (explicitly or implicitly) uncertainties by objec tive or subjective probabilities (measures of confidence or belief). This leads us to stochastic optimization problems which can rarely be solved by using the standard deterministic optimization and optimal control methods. In the stochastic optimization the accent is on problems with a large number of deci sion and random variables, and consequently the focus ofattention is directed to efficient solution procedures rather than to (analytical) closed-form solu tions. Objective and constraint functions of dynamic stochastic optimization problems have the form of multidimensional integrals of rather involved in that may have a nonsmooth and even discontinuous character - the tegrands typical situation for hit-or-miss type of decision making problems involving irreversibility ofdecisions or/and abrupt changes ofthe system. In general, the exact evaluation of such functions (as is assumed in the standard optimization and control theory) is practically impossible. Also, the problem does not often possess the separability properties that allow to derive the standard in control theory recursive (Bellman) equations. |
| dynamic optimization economics lecture notes: Elements of Dynamic Optimization Alpha C. Chiang, 1992 An in-depth exploration of dynamic optimization in Economics, written by the author of the best-selling FUNDAMENTAL METHODS OF MATHEMATICAL ECONOMICS. It can be used in sequence with FUNDAMENTAL METHODS, or independently, at the advanced undergraduate or a beginning graduate level. It is written at the same analytical level, with the same care, as the other Chiang text. |
| dynamic optimization economics lecture notes: Stochastic Two-Stage Programming Karl Frauendorfer, 2012-12-06 Stochastic Programming offers models and methods for decision problems wheresome of the data are uncertain. These models have features and structural properties which are preferably exploited by SP methods within the solution process. This work contributes to the methodology for two-stagemodels. In these models the objective function is given as an integral, whose integrand depends on a random vector, on its probability measure and on a decision. The main results of this work have been derived with the intention to ease these difficulties: After investigating duality relations for convex optimization problems with supply/demand and prices being treated as parameters, a stability criterion is stated and proves subdifferentiability of the value function. This criterion is employed for proving the existence of bilinear functions, which minorize/majorize the integrand. Additionally, these minorants/majorants support the integrand on generalized barycenters of simplicial faces of specially shaped polytopes and amount to an approach which is denoted barycentric approximation scheme. |
| dynamic optimization economics lecture notes: Recursive Methods in Economic Dynamics Nancy L. Stokey, 1989-10-10 This rigorous but brilliantly lucid book presents a self-contained treatment of modern economic dynamics. Stokey, Lucas, and Prescott develop the basic methods of recursive analysis and illustrate the many areas where they can usefully be applied. |
| dynamic optimization economics lecture notes: Lecture Notes on Resource and Environmental Economics Anthony C. Fisher, 2020-06-26 This book, based on lectures on natural and environmental resource economics, offers a nontechnical exposition of the modern theory of sustainability in the presence of resource scarcity. It applies an alternative take on environmental economics, focusing on the economics of the natural environment, including development, computation, and potential empirical importance of the concept of option value, as opposed to the standard treatment of the economics of pollution control. The approach throughout is primarily conceptual and theoretical, though empirical estimation and results are sometimes noted. Mathematics, ranging from elementary calculus to more formal dynamic optimization, is used, especially in the early chapters on the optimal management of exhaustible and renewable resources, but results are always given an economic interpretation. Diagrams and numerical examples are also used extensively. The first chapter introduces the classical economists as the first resource economists, in their discussion of the implications of a limited natural resource base (agricultural land) for the evolution of the wider economy. A later chapter returns to the same concerns, along with others stimulated by the energy and environmental “crises” of the 1970s and beyond. One section considers alternative measures of resource scarcity and empirical findings on their behavior over time. Another introduces the modern concept of sustainability with an intuitive development of the analytics. A chapter on the dynamics of environmental management motivates the concept of option value, shows how to compute it, then demonstrates its importance in an illustrative empirical example. The closing chapter, on climate change, first projects future changes and potential catastrophic impacts, then discusses the policy relevance of both option value and discounting for the very long run. This book is intended for resource and environmental economists and can be read by interested graduate and advanced undergraduate students in the field as well. |
| dynamic optimization economics lecture notes: Introduction to Stochastic Programming John R. Birge, François Louveaux, 2011-06-15 The aim of stochastic programming is to find optimal decisions in problems which involve uncertain data. This field is currently developing rapidly with contributions from many disciplines including operations research, mathematics, and probability. At the same time, it is now being applied in a wide variety of subjects ranging from agriculture to financial planning and from industrial engineering to computer networks. This textbook provides a first course in stochastic programming suitable for students with a basic knowledge of linear programming, elementary analysis, and probability. The authors aim to present a broad overview of the main themes and methods of the subject. Its prime goal is to help students develop an intuition on how to model uncertainty into mathematical problems, what uncertainty changes bring to the decision process, and what techniques help to manage uncertainty in solving the problems. In this extensively updated new edition there is more material on methods and examples including several new approaches for discrete variables, new results on risk measures in modeling and Monte Carlo sampling methods, a new chapter on relationships to other methods including approximate dynamic programming, robust optimization and online methods. The book is highly illustrated with chapter summaries and many examples and exercises. Students, researchers and practitioners in operations research and the optimization area will find it particularly of interest. Review of First Edition: The discussion on modeling issues, the large number of examples used to illustrate the material, and the breadth of the coverage make 'Introduction to Stochastic Programming' an ideal textbook for the area. (Interfaces, 1998) |
| dynamic optimization economics lecture notes: Optimization and Stability Theory for Economic Analysis Brian Beavis, Ian M. Dobbs, 1990 This book presents a coherent and systematic exposition of the mathematical theory of the problems of optimization and stability. Both of these are topics central to economic analysis since the latter is so much concerned with the optimizing behaviour of economic agents and the stability of the interaction processes to which this gives rise. The topics covered include convexity, mathematical programming, fixed point theorems, comparative static analysis and duality, the stability of dynamic systems, the calculus of variations and optimal control theory. The authors present a more detailed and wide-ranging discussion of these topics than is to be found in the few books which attempt a similar coverage. Although the text deals with fairly advanced material, the mathematical prerequisites are minimised by the inclusion of an integrated mathematical review designed to make the text self-contained and accessible to the reader with only an elementary knowledge of calculus and linear algebra. A novel feature of the book is that it provides the reader with an understanding and feel for the kinds of mathematical techniques most useful for dealing with particular economic problems. This is achieved through an extensive use of a broad range of economic examples (rather than the numerical/algebraic examples so often found). This is suitable for use in advanced undergraduate and postgraduate courses in economic analysis and should in addition prove a useful reference work for practising economists. |
| dynamic optimization economics lecture notes: Introduction to Stochastic Programming John Birge, François Louveaux, 2000-02-02 This rapidly developing field encompasses many disciplines including operations research, mathematics, and probability. Conversely, it is being applied in a wide variety of subjects ranging from agriculture to financial planning and from industrial engineering to computer networks. This textbook provides a first course in stochastic programming suitable for students with a basic knowledge of linear programming, elementary analysis, and probability. The authors present a broad overview of the main themes and methods of the subject, thus helping students develop an intuition for how to model uncertainty into mathematical problems, what uncertainty changes bring to the decision process, and what techniques help to manage uncertainty in solving the problems. The early chapters introduce some worked examples of stochastic programming, demonstrate how a stochastic model is formally built, develop the properties of stochastic programs and the basic solution techniques used to solve them. The book then goes on to cover approximation and sampling techniques and is rounded off by an in-depth case study. A well-paced and wide-ranging introduction to this subject. |
| dynamic optimization economics lecture notes: ICM Millennium Lectures on Games Leon A. Petrosjan, David W.K. Yeung, 2013-04-17 Since the first Congress in Zürich in 1897, the ICM has been an eagerly awaited event every four years. Many of these occasions are celebrated for historie developments and seminal contributions to mathematics. 2002 marks the year of the 24th ICM, the first of the new millennium. Also historie is the first ICM Satellite Conference devoted to game theory and applications. It is one of those rare occasions, in which masters of the field are able to meet under congenial surroundings to talk and share their gathered wisdom. As is usually the case in ICM meetings, participants of the ICM Satellite Conference on Game Theory and Applications (Qingdao, August 2(02) hailed from the four corners of the world. In addition to presentations of high qual ity research, the program also included twelve invited plenary sessions with distinguished speakers. This volume, which gathers together selected papers read at the conference, is divided into four sections: (I) Foundations, Concepts, and Structure. (II) Equilibrium Properties. (III) Applications to the Natural and Social Sciences. (IV) Computational Aspects of Games. |
| dynamic optimization economics lecture notes: Notes on Optimization P. P. Varaiya, 1972 |
| dynamic optimization economics lecture notes: Handbook of Markov Decision Processes Eugene A. Feinberg, Adam Shwartz, 2012-12-06 Eugene A. Feinberg Adam Shwartz This volume deals with the theory of Markov Decision Processes (MDPs) and their applications. Each chapter was written by a leading expert in the re spective area. The papers cover major research areas and methodologies, and discuss open questions and future research directions. The papers can be read independently, with the basic notation and concepts ofSection 1.2. Most chap ters should be accessible by graduate or advanced undergraduate students in fields of operations research, electrical engineering, and computer science. 1.1 AN OVERVIEW OF MARKOV DECISION PROCESSES The theory of Markov Decision Processes-also known under several other names including sequential stochastic optimization, discrete-time stochastic control, and stochastic dynamic programming-studiessequential optimization ofdiscrete time stochastic systems. The basic object is a discrete-time stochas tic system whose transition mechanism can be controlled over time. Each control policy defines the stochastic process and values of objective functions associated with this process. The goal is to select a good control policy. In real life, decisions that humans and computers make on all levels usually have two types ofimpacts: (i) they cost orsavetime, money, or other resources, or they bring revenues, as well as (ii) they have an impact on the future, by influencing the dynamics. In many situations, decisions with the largest immediate profit may not be good in view offuture events. MDPs model this paradigm and provide results on the structure and existence of good policies and on methods for their calculation. |
| dynamic optimization economics lecture notes: Integer Programming and Related Areas A Classified Bibliography 1976–1978 D. Hausmann, 2012-12-06 |
| dynamic optimization economics lecture notes: New Issues in the Theory of Investment Marcel Savioz, 2012-12-06 The investment good market, together with the consumer good market, the money market and the labour market, are indeed the most extensively studied markets. The exhaustive survey of investment theory by Eisner and Strotz, already quoted four hundred references in 1963, although this work advocating for adjustment costs, was in fact only carried out at the very beginning of modern investment theory! This chapter gives an introduction of the extensive field and is an attempt to present some key ideas of investment theory. 1) We show that modern investment theory is the integration of many traditional approaches. The content of the chapter is set as follows. Section 2 presents an illustrative model of investment theory. Section 3, using this model, describes the investment decision of the firm. Sections 4 to 10 each present a classical investment hypothesis within the framework of the model. Section 11 concludes. For convenience, the key to the symbols used is given in Table 1. 2. The Model of the Firm Investment theory was born with the claim of Keynes (1936) that besides the capital demand (demand for a stock of capital at a point in time), an investment demand (demand for the increment of the capital stock in a period 1) Recent surveys are: Abel (1988), Coen and Eisner (1987) Artus and Muet (1984). The book on investment theory by Nickell (1978) is outstanding. |
| dynamic optimization economics lecture notes: Algorithm Theory - SWAT 2000 Magnús M. Halldórsson, 2000-06-21 This book constitutes the refereed proceedings of the 7th Scandinavian Workshop on Algorithm Theory, SWAT 2000, held in Bergen, Norway, in July 2000. The 43 revised full papers presented together with 3 invited contributions were carefully reviewed and selected from a total of 105 submissions. The papers are organized in sections on data structures, dynamic partitions, graph algorithms, online algorithms, approximation algorithms, matchings, network design, computational geometry, strings and algorithm engineering, external memory algorithms, optimization, and distributed and fault-tolerant computing. |
| dynamic optimization economics lecture notes: Knapsack Problems Hans Kellerer, Ulrich Pferschy, David Pisinger, 2013-03-19 Thirteen years have passed since the seminal book on knapsack problems by Martello and Toth appeared. On this occasion a former colleague exclaimed back in 1990: How can you write 250 pages on the knapsack problem? Indeed, the definition of the knapsack problem is easily understood even by a non-expert who will not suspect the presence of challenging research topics in this area at the first glance. However, in the last decade a large number of research publications contributed new results for the knapsack problem in all areas of interest such as exact algorithms, heuristics and approximation schemes. Moreover, the extension of the knapsack problem to higher dimensions both in the number of constraints and in the num ber of knapsacks, as well as the modification of the problem structure concerning the available item set and the objective function, leads to a number of interesting variations of practical relevance which were the subject of intensive research during the last few years. Hence, two years ago the idea arose to produce a new monograph covering not only the most recent developments of the standard knapsack problem, but also giving a comprehensive treatment of the whole knapsack family including the siblings such as the subset sum problem and the bounded and unbounded knapsack problem, and also more distant relatives such as multidimensional, multiple, multiple-choice and quadratic knapsack problems in dedicated chapters. |
| dynamic optimization economics lecture notes: Economists' Mathematical Manual Knut Sydsaeter, Arne Strøm, Peter Berck, 2011-10-20 This volume presents mathematical formulas and theorems commonly used in economics. It offers the first grouping of this material for a specifically economist audience, and it includes formulas like Roy’s identity and Leibniz's rule. |
| dynamic optimization economics lecture notes: Algorithmic Methods for Railway Optimization Frank Geraets, Leo Kroon, Anita Schoebel, Dorothea Wagner, Christos Zaroliagiis, 2007-09-14 This state-of-the-art survey features papers that were selected after an open call following the International Dagstuhl Seminar on Algorithmic Methods for Railway Optimization. The second part of the volume constitutes the refereed proceedings of the 4th International Workshop on Algorithmic Methods and Models for Optimization of Railways. The 17 full papers presented here were carefully reviewed and selected from numerous submissions. |
| dynamic optimization economics lecture notes: The Knowledge Base for Fisheries Management Lorenzo Motos, Douglas Wilson, 2006-08-18 Fisheries are in a state of crisis throughout the world. While there has been some success, truly effective fisheries management seems beyond our grasp. The knowledge needed for proper management contains a broad array of facts and connections from statistical stock assessments, to the information that allows government agencies to track compliance with rules and beyond.This book describes the state-of-the-art knowledge about fishery systems. Seldom seen in a scientific publication regarding fisheries science, this book presents a multidisciplinary perspective of fisheries management. Leading fisheries scholars with backgrounds in biology, ecology, economics and sociology ask how management institutions can learn and put their lessons to use. The Knowledge Base for Fisheries Management offers a unique overview of the world of fisheries management and provides the background to draw conclusions of what is needed to improve management.Covering a wide range of regimes, case studies and professional perspectives, this publication will be an obliged reference to anyone involved on fisheries management, assessment, policy making or fisheries development all over the world.* The only book on the market that analyzes fisheries in a biological, sociological and economic way* Fills a gap, focusing not only on the production of knowledge for fisheries management but also on how it is used in all steps of the management system and the decision making processes * Focuses on the hot topic: scientific knowledge and society-science based policies * Documents disseminated research from many different management systems, both European and world wide |
| dynamic optimization economics lecture notes: Stochastic Systems Roger J.-B. Wets, 1976 |
| dynamic optimization economics lecture notes: Integer and Combinatorial Optimization Laurence A. Wolsey, George L. Nemhauser, 2014-08-28 Rave reviews for INTEGER AND COMBINATORIAL OPTIMIZATION This book provides an excellent introduction and survey of traditional fields of combinatorial optimization . . . It is indeed one of the best and most complete texts on combinatorial optimization . . . available. [And] with more than 700 entries, [it] has quite an exhaustive reference list.-Optima A unifying approach to optimization problems is to formulate them like linear programming problems, while restricting some or all of the variables to the integers. This book is an encyclopedic resource for such formulations, as well as for understanding the structure of and solving the resulting integer programming problems.-Computing Reviews [This book] can serve as a basis for various graduate courses on discrete optimization as well as a reference book for researchers and practitioners.-Mathematical Reviews This comprehensive and wide-ranging book will undoubtedly become a standard reference book for all those in the field of combinatorial optimization.-Bulletin of the London Mathematical Society This text should be required reading for anybody who intends to do research in this area or even just to keep abreast of developments.-Times Higher Education Supplement, London Also of interest . . . INTEGER PROGRAMMING Laurence A. Wolsey Comprehensive and self-contained, this intermediate-level guide to integer programming provides readers with clear, up-to-date explanations on why some problems are difficult to solve, how techniques can be reformulated to give better results, and how mixed integer programming systems can be used more effectively. 1998 (0-471-28366-5) 260 pp. |
| dynamic optimization economics lecture notes: Stochastic Programming András Prékopa, 2013-03-09 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. |
| dynamic optimization economics lecture notes: Structural Optimization, A. Borkowski, S. Jendo, W. Prager, M. Save, 1990-01-31 |
| dynamic optimization economics lecture notes: Approaches to the Theory of Optimization J. P. Ponstein, 2004-06-03 A concise account which finds the optimal solution to mathematical problems arising in economics, engineering, the social and mathematical sciences. |
| dynamic optimization economics lecture notes: Time-Varying Network Optimization Dan Sha, C. K. Wong, 2007-05-05 Network ?ow optimization problems may arise in a wide variety of important ?elds, such as transportation, telecommunication, computer networking, ?nancial planning, logistics and supply chain management, energy systems, etc. Signi?cant and elegant results have been achieved onthetheory,algorithms,andapplications,ofnetwork?owoptimization in the past few decades; See, for example, the seminal books written by Ahuja, Magnanti and Orlin (1993), Bazaraa, Jarvis and Sherali (1990), Bertsekas (1998), Ford and Fulkerson (1962), Gupta (1985), Iri (1969), Jensen and Barnes (1980), Lawler (1976), and Minieka (1978). Most network optimization problems that have been studied up to date are, however, static in nature, in the sense that it is assumed that it takes zero time to traverse any arc in a network and that all attributes of the network are constant without change at any time. Networks in the real world are, nevertheless, time-varying in essence, in which any ?ow must take a certain amount of time to traverse an arc and the network structure and parameters (such as arc and node capacities) may change over time. In such a problem, how to plan and control the transmission of ?ow becomes very important, since waiting at a node, or travelling along a particular arc with di?erent speed, may allow one to catch the best timing along his path, and therefore achieve his overall objective, such as a minimum overall cost or a minimum travel time from the origin to the destination. |
| dynamic optimization economics lecture notes: Integer Programming and Related Areas R.v. Randow, 2012-12-06 |
| dynamic optimization economics lecture notes: Government-wide Index to Federal Research & Development Reports , 1967-07 |
| dynamic optimization economics lecture notes: The Yield Curve and Financial Risk Premia Felix Geiger, 2011-08-17 The determinants of yield curve dynamics have been thoroughly discussed in finance models. However, little can be said about the macroeconomic factors behind the movements of short- and long-term interest rates as well as the risk compensation demanded by financial investors. By taking on a macro-finance perspective, the book’s approach explicitly acknowledges the close feedback between monetary policy, the macroeconomy and financial conditions. Both theoretical and empirical models are applied in order to get a profound understanding of the interlinkages between economic activity, the conduct of monetary policy and the underlying macroeconomic factors of bond price movements. Moreover, the book identifies a broad risk-taking channel of monetary transmission which allows a reassessment of the role of financial constraints; it enables policy makers to develop new guidelines for monetary policy and for financial supervision of how to cope with evolving financial imbalances. |
| dynamic optimization economics lecture notes: Handbooks in Operations Research and Management Science: Financial Engineering John R. Birge, Vadim Linetsky, 2007-11-16 The remarkable growth of financial markets over the past decades has been accompanied by an equally remarkable explosion in financial engineering, the interdisciplinary field focusing on applications of mathematical and statistical modeling and computational technology to problems in the financial services industry. The goals of financial engineering research are to develop empirically realistic stochastic models describing dynamics of financial risk variables, such as asset prices, foreign exchange rates, and interest rates, and to develop analytical, computational and statistical methods and tools to implement the models and employ them to design and evaluate financial products and processes to manage risk and to meet financial goals. This handbook describes the latest developments in this rapidly evolving field in the areas of modeling and pricing financial derivatives, building models of interest rates and credit risk, pricing and hedging in incomplete markets, risk management, and portfolio optimization. Leading researchers in each of these areas provide their perspective on the state of the art in terms of analysis, computation, and practical relevance. The authors describe essential results to date, fundamental methods and tools, as well as new views of the existing literature, opportunities, and challenges for future research. |
| dynamic optimization economics lecture notes: Multi-objective Optimization Gade Pandu Rangaiah, 2009 Following a brief introduction and general review on the development of multi-objective optimization applications in chemical engineering since 2000, the book gives a description of selected multi-objective techniques and then goes on to discuss chemical engineering applications. These applications are from diverse areas within chemical engineering, and are presented in detail. Several exercises are included at the end of many chapters. |
| dynamic optimization economics lecture notes: Multi-objective Optimization: Techniques And Applications In Chemical Engineering (Second Edition) Gade Pandu Rangaiah, 2016-12-22 Optimization is now essential in the design, planning and operation of chemical and related processes. Although process optimization for multiple objectives was studied in the 1970s and 1980s, it has attracted active research in the last 15 years, spurred by the new and effective techniques for multi-objective optimization (MOO). To capture this renewed interest, this monograph presents recent research in MOO techniques and applications in chemical engineering.Following a brief introduction and review of MOO applications in chemical engineering since 2000, the book presents selected MOO techniques and many chemical engineering applications in detail. In this second edition, several chapters from the first edition have been updated, one chapter is completely revised and three new chapters have been added. One of the new chapters describes three MS Excel programs useful for MOO of application problems. All the chapters will be of interest to researchers in MOO and/or chemical engineering. Several exercises are included at the end of many chapters, for use by both practicing engineers and students. |
| dynamic optimization economics lecture notes: Advances in Fisheries Economics Trond Bjorndal, Daniel Gordon, Ragnar Arnason, Ussif Rashid Sumaila, 2008-04-15 A true landmark publication, Advances in Fisheries Economics brings together many of the world’s leading fisheries economists to authoritatively cover the many issues facing the field of fisheries economics and management today. Compiled in honour of the work and achievements of Professor Gordon Munro of the University of British Columbia, Canada, this exceptional volume of research serves as both a valuable reference tool and fitting tribute to a man whose work has shaped the discipline. Divided into four sections, the text includes coverage of: • Property Rights and Fisheries Management • Capital Theory and Natural Resources • Game Theory and International Fisheries • Applied Fisheries Economics and Management The book is an important addition to the resources of all fisheries economists, managers, scientists and fish biologists. Libraries in universities and research establishments where these subjects are studied and taught should have copies on their shelves. About the Editors Dr. Trond Bjørndal is Professor of Economics, Centre for Fisheries Economics, Institute for Research in Economics and Business Administration, Bergen, Norway and Director, CEMARE, University of Portsmouth, England. Dr. Daniel V. Gordon is Professor of Economics, University of Calgary, Canada and Distinguished Research Fellow, Centre for Fisheries Economics, Institute for Research in Economics and Business Administration, Bergen, Norway Dr. Ragnar Arnason is Professor of Economics and the Chairman of the Institute of Economic Studies, University of Iceland. Dr. U. Rashid Sumaila is Director of the Fisheries Economics Research Unit, Fisheries Centre, University of British Columbia. |
| dynamic optimization economics lecture notes: Combinatorial Optimization Pierre Fouilhoux, Luis Eduardo Neves Gouveia, A. Ridha Mahjoub, Vangelis T. Paschos, 2014-07-21 This book constitutes the thoroughly refereed post-conference proceedings of the Third International Symposium on Combinatorial Optimization, ISCO 2014, held in Lisbon, Portugal, in March 2014. The 37 revised full papers presented together with 64 short papers were carefully reviewed and selected from 97 submissions. They present original research on all aspects of combinatorial optimization, such as algorithms and complexity; mathematical programming; operations research; stochastic optimization; graphs and combinatorics. |
| dynamic optimization economics lecture notes: Theory of Linear and Integer Programming Alexander Schrijver, 1998-06-11 Theory of Linear and Integer Programming Alexander Schrijver Centrum voor Wiskunde en Informatica, Amsterdam, The Netherlands This book describes the theory of linear and integer programming and surveys the algorithms for linear and integer programming problems, focusing on complexity analysis. It aims at complementing the more practically oriented books in this field. A special feature is the author's coverage of important recent developments in linear and integer programming. Applications to combinatorial optimization are given, and the author also includes extensive historical surveys and bibliographies. The book is intended for graduate students and researchers in operations research, mathematics and computer science. It will also be of interest to mathematical historians. Contents 1 Introduction and preliminaries; 2 Problems, algorithms, and complexity; 3 Linear algebra and complexity; 4 Theory of lattices and linear diophantine equations; 5 Algorithms for linear diophantine equations; 6 Diophantine approximation and basis reduction; 7 Fundamental concepts and results on polyhedra, linear inequalities, and linear programming; 8 The structure of polyhedra; 9 Polarity, and blocking and anti-blocking polyhedra; 10 Sizes and the theoretical complexity of linear inequalities and linear programming; 11 The simplex method; 12 Primal-dual, elimination, and relaxation methods; 13 Khachiyan's method for linear programming; 14 The ellipsoid method for polyhedra more generally; 15 Further polynomiality results in linear programming; 16 Introduction to integer linear programming; 17 Estimates in integer linear programming; 18 The complexity of integer linear programming; 19 Totally unimodular matrices: fundamental properties and examples; 20 Recognizing total unimodularity; 21 Further theory related to total unimodularity; 22 Integral polyhedra and total dual integrality; 23 Cutting planes; 24 Further methods in integer linear programming; Historical and further notes on integer linear programming; References; Notation index; Author index; Subject index |
| dynamic optimization economics lecture notes: Stochastic Optimization Methods Kurt Marti, 2015-02-21 This book examines optimization problems that in practice involve random model parameters. It details the computation of robust optimal solutions, i.e., optimal solutions that are insensitive with respect to random parameter variations, where 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 probabilities and expectations involved, the book also shows how to apply approximative solution techniques. 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 and differentiation formulas for probabilities and expectations. In the third edition, this book further develops stochastic optimization methods. In particular, it now shows how to apply stochastic optimization methods to the approximate solution of important concrete problems arising in engineering, economics and operations research. |
| dynamic optimization economics lecture notes: Stochastic Programming Willem K. Klein Haneveld, Maarten H. van der Vlerk, Ward Romeijnders, 2019-10-24 This book provides an essential introduction to Stochastic Programming, especially intended for graduate students. The book begins by exploring a linear programming problem with random parameters, representing a decision problem under uncertainty. Several models for this problem are presented, including the main ones used in Stochastic Programming: recourse models and chance constraint models. The book not only discusses the theoretical properties of these models and algorithms for solving them, but also explains the intrinsic differences between the models. In the book’s closing section, several case studies are presented, helping students apply the theory covered to practical problems. The book is based on lecture notes developed for an Econometrics and Operations Research course for master students at the University of Groningen, the Netherlands - the longest-standing Stochastic Programming course worldwide. |
| dynamic optimization economics lecture notes: Stochastic Programming Horand Gassmann, W. T. Ziemba, 2013 This book shows the breadth and depth of stochastic programming applications. All the papers presented here involve optimization over the scenarios that represent possible future outcomes of the uncertainty problems. The applications, which were presented at the 12th International Conference on Stochastic Programming held in Halifax, Nova Scotia in August 2010, span the rich field of uses of these models. The finance papers discuss such diverse problems as longevity risk management of individual investors, personal financial planning, intertemporal surplus management, asset management with benchmarks, dynamic portfolio management, fixed income immunization and racetrack betting. The production and logistics papers discuss natural gas infrastructure design, farming Atlantic salmon, prevention of nuclear smuggling and sawmill planning. The energy papers involve electricity production planning, hydroelectric reservoir operations and power generation planning for liquid natural gas plants. Finally, two telecommunication papers discuss mobile network design and frequency assignment problems. |
| dynamic optimization economics lecture notes: Optimal Decisions Under Uncertainty J.K. Sengupta, 2012-12-06 Understanding the stochastic enviornment is as much important to the manager as to the economist. From production and marketing to financial management, a manager has to assess various costs imposed by uncertainty. The economist analyzes the role of incomplete and too often imperfect information structures on the optimal decisions made by a firm. The need for understanding the role of uncertainty in quantitative decision models, both in economics and management science provide the basic motivation of this monograph. The stochastic environment is analyzed here in terms of the following specific models of optimization: linear and quadratic models, linear programming, control theory and dynamic programming. Uncertainty is introduced here through the para meters, the constraints, and the objective function and its impact evaluated. Specifically recent developments in applied research are emphasized, so that they can help the decision-maker arrive at a solution which has some desirable charac teristics like robustness, stability and cautiousness. Mathematical treatment is kept at a fairly elementary level and applied as pects are emphasized much more than theory. Moreover, an attempt is made to in corporate the economic theory of uncertainty into the stochastic theory of opera tions research. Methods of optimal decision rules illustrated he re are applicable in three broad areas: (a) applied economic models in resource allocation and economic planning, (b) operations research models involving portfolio analysis and stochastic linear programming and (c) systems science models in stochastic control and adaptive behavior. |
| dynamic optimization economics lecture notes: New Optimization Techniques in Engineering Godfrey C. Onwubolu, B. V. Babu, 2013-03-14 Presently, general-purpose optimization techniques such as Simulated Annealing, and Genetic Algorithms, have become standard optimization techniques. Concerted research efforts have been made recently in order to invent novel optimization techniques for solving real life problems, which have the attributes of memory update and population-based search solutions. The book describes a variety of these novel optimization techniques which in most cases outperform the standard optimization techniques in many application areas. New Optimization Techniques in Engineering reports applications and results of the novel optimization techniques considering a multitude of practical problems in the different engineering disciplines – presenting both the background of the subject area and the techniques for solving the problems. |
| dynamic optimization economics lecture notes: The Traveling Salesman Problem Weiqi Li, 2023-07-08 This book presents a new search paradigm for solving the Traveling Salesman Problem (TSP). The intrinsic difficulty of the TSP is associated with the combinatorial explosion of potential solutions in the solution space. The author introduces the idea of using the attractor concept in dynamical systems theory to reduce the search space for exhaustive search for the TSP. Numerous examples are used to describe how to use this new search algorithm to solve the TSP and its variants including: multi-objective TSP, dynamic TSP, and probabilistic TSP. This book is intended for readers in the field of optimization research and application. |
DYNAMIC Definition & Meaning - Merriam-Webster
The meaning of DYNAMIC is marked by usually continuous and productive activity or change. How to use dynamic in a sentence.
DYNAMIC | English meaning - Cambridge Dictionary
DYNAMIC definition: 1. having a lot of ideas and enthusiasm: 2. continuously changing or developing: 3. relating …
DYNAMIC Definition & Meaning | Dictionary.com
Dynamic definition: pertaining to or characterized by energy or effective action; vigorously active or forceful; energetic.. See examples of DYNAMIC used in a sentence.
DYNAMIC definition and meaning | Collins English Dic…
The dynamic of a system or process is the force that causes it to change or progress. The dynamic of the market demands constant change and adjustment. Politics has its own …
Dynamic - definition of dynamic by The Free Dictionary
dynamic - characterized by action or forcefulness or force of personality; "a dynamic market"; "a dynamic speaker"; "the dynamic president of the firm"
DYNAMIC Definition & Meaning - Merriam-Webster
The meaning of DYNAMIC is marked by usually continuous and productive activity or change. How to use dynamic in a sentence.
DYNAMIC | English meaning - Cambridge Dictionary
DYNAMIC definition: 1. having a lot of ideas and enthusiasm: 2. continuously changing or developing: 3. relating to…. Learn more.
DYNAMIC Definition & Meaning | Dictionary.com
Dynamic definition: pertaining to or characterized by energy or effective action; vigorously active or forceful; energetic.. See examples of DYNAMIC used in a sentence.
DYNAMIC definition and meaning | Collins English Dictionary
The dynamic of a system or process is the force that causes it to change or progress. The dynamic of the market demands constant change and adjustment. Politics has its own dynamic.
Dynamic - definition of dynamic by The Free Dictionary
dynamic - characterized by action or forcefulness or force of personality; "a dynamic market"; "a dynamic speaker"; "the dynamic president of the firm"
What does dynamic mean? - Definitions.net
Dynamic is a term often used to refer to something that is constantly changing or evolving. It may also refer to an interaction or system characterized by constant change, activity, or progress. In …
What Does Dynamic Mean? | The Word Counter
Apr 3, 2022 · Dictionary states that the word dynamic is an adjective that means energetic, forceful, or active. However, dynamic is used in a more specific way in the fields of physics and music. In …
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Dynamic Definition & Meaning | Britannica Dictionary
DYNAMIC meaning: 1 : always active or changing; 2 : having or showing a lot of energy
645 Synonyms & Antonyms for DYNAMIC - Thesaurus.com
Find 645 different ways to say DYNAMIC, along with antonyms, related words, and example sentences at Thesaurus.com.
