Advances in Sensitivity Analysis and Parametric Programming

Advances in Sensitivity Analysis and Parametric Programming PDF

Author: Tomas Gal

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 595

ISBN-13: 1461561035

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The standard view of Operations Research/Management Science (OR/MS) dichotomizes the field into deterministic and probabilistic (nondeterministic, stochastic) subfields. This division can be seen by reading the contents page of just about any OR/MS textbook. The mathematical models that help to define OR/MS are usually presented in terms of one subfield or the other. This separation comes about somewhat artificially: academic courses are conveniently subdivided with respect to prerequisites; an initial overview of OR/MS can be presented without requiring knowledge of probability and statistics; text books are conveniently divided into two related semester courses, with deterministic models coming first; academics tend to specialize in one subfield or the other; and practitioners also tend to be expert in a single subfield. But, no matter who is involved in an OR/MS modeling situation (deterministic or probabilistic - academic or practitioner), it is clear that a proper and correct treatment of any problem situation is accomplished only when the analysis cuts across this dichotomy.

Linear and Nonlinear Programming

Linear and Nonlinear Programming PDF

Author: David G. Luenberger

Publisher: Springer Science & Business Media

Published: 2008-07-07

Total Pages: 550

ISBN-13: 0387745025

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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.

Multi-parametric Optimization and Control

Multi-parametric Optimization and Control PDF

Author: Efstratios N. Pistikopoulos

Publisher: John Wiley & Sons

Published: 2020-11-10

Total Pages: 320

ISBN-13: 1119265193

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Recent developments in multi-parametric optimization and control Multi-Parametric Optimization and Control provides comprehensive coverage of recent methodological developments for optimal model-based control through parametric optimization. It also shares real-world research applications to support deeper understanding of the material. Researchers and practitioners can use the book as reference. It is also suitable as a primary or a supplementary textbook. Each chapter looks at the theories related to a topic along with a relevant case study. Topic complexity increases gradually as readers progress through the chapters. The first part of the book presents an overview of the state-of-the-art multi-parametric optimization theory and algorithms in multi-parametric programming. The second examines the connection between multi-parametric programming and model-predictive control—from the linear quadratic regulator over hybrid systems to periodic systems and robust control. The third part of the book addresses multi-parametric optimization in process systems engineering. A step-by-step procedure is introduced for embedding the programming within the system engineering, which leads the reader into the topic of the PAROC framework and software platform. PAROC is an integrated framework and platform for the optimization and advanced model-based control of process systems. Uses case studies to illustrate real-world applications for a better understanding of the concepts presented Covers the fundamentals of optimization and model predictive control Provides information on key topics, such as the basic sensitivity theorem, linear programming, quadratic programming, mixed-integer linear programming, optimal control of continuous systems, and multi-parametric optimal control An appendix summarizes the history of multi-parametric optimization algorithms. It also covers the use of the parametric optimization toolbox (POP), which is comprehensive software for efficiently solving multi-parametric programming problems.

Proportional Optimization and Fairness

Proportional Optimization and Fairness PDF

Author: Wieslaw Kubiak

Publisher: Springer Science & Business Media

Published: 2008-11-16

Total Pages: 300

ISBN-13: 0387877193

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Proportional Optimization and Fairness is a long-needed attempt to reconcile optimization with apportionment in just-in-time (JIT) sequences and find the common ground in solving problems ranging from sequencing mixed-model just-in-time assembly lines through just-in-time batch production, balancing workloads in event graphs to bandwidth allocation internet gateways and resource allocation in computer operating systems. The book argues that apportionment theory and optimization based on deviation functions provide natural benchmarks for a process, and then looks at the recent research and developments in the field. Individual chapters look at the theory of apportionment and just-in-time sequences; minimization of just-in-time sequence deviation; optimality of cyclic sequences and the oneness; bottleneck minimization; competition-free instances, Fraenkel’s Conjecture, and optimal admission sequences; response time variability; applications to the Liu-Layland Problem and pinwheel scheduling; temporal capacity constraints and supply chain balancing; fair queuing and stride scheduling; and smoothing and batching.

Handbook of Markov Decision Processes

Handbook of Markov Decision Processes PDF

Author: Eugene A. Feinberg

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 560

ISBN-13: 1461508053

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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 Portfolio Strategies: quantitative methods and empirical rules for incomplete information

Dynamic Portfolio Strategies: quantitative methods and empirical rules for incomplete information PDF

Author: Nikolai Dokuchaev

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 213

ISBN-13: 1461509211

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Dynamic Portfolio Strategies: Quantitative Methods and Empirical Rules for Incomplete Information investigates optimal investment problems for stochastic financial market models. It is addressed to academics and students who are interested in the mathematics of finance, stochastic processes, and optimal control, and also to practitioners in risk management and quantitative analysis who are interested in new strategies and methods of stochastic analysis. While there are many works devoted to the solution of optimal investment problems for various models, the focus of this book is on analytical strategies based on "technical analysis" which are model-free. The technical analysis of these strategies has a number of characteristics. Two of the more important characteristics are: (1) they require only historical data, and (2) typically they are more widely used by traders than analysis based on stochastic models. Hence it is the objective of this book to reduce the gap between model-free strategies and strategies that are "optimal" for stochastic models. We hope that researchers, students and practitioners will be interested in some of the new empirically based methods of "technical analysis" strategies suggested in this book and evaluated via stochastic market models.

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