Advertisement
| dynamic optimization: Dynamic Optimization Karl Hinderer, Ulrich Rieder, Michael Stieglitz, 2017-01-12 This book explores discrete-time dynamic optimization and provides a detailed introduction to both deterministic and stochastic models. Covering problems with finite and infinite horizon, as well as Markov renewal programs, Bayesian control models and partially observable processes, the book focuses on the precise modelling of applications in a variety of areas, including operations research, computer science, mathematics, statistics, engineering, economics and finance. Dynamic Optimization is a carefully presented textbook which starts with discrete-time deterministic dynamic optimization problems, providing readers with the tools for sequential decision-making, before proceeding to the more complicated stochastic models. The authors present complete and simple proofs and illustrate the main results with numerous examples and exercises (without solutions). With relevant material covered in four appendices, this book is completely self-contained. |
| dynamic optimization: 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: Anticipatory Optimization for Dynamic Decision Making Stephan Meisel, 2011-06-23 The availability of today’s online information systems rapidly increases the relevance of dynamic decision making within a large number of operational contexts. Whenever a sequence of interdependent decisions occurs, making a single decision raises the need for anticipation of its future impact on the entire decision process. Anticipatory support is needed for a broad variety of dynamic and stochastic decision problems from different operational contexts such as finance, energy management, manufacturing and transportation. Example problems include asset allocation, feed-in of electricity produced by wind power as well as scheduling and routing. All these problems entail a sequence of decisions contributing to an overall goal and taking place in the course of a certain period of time. Each of the decisions is derived by solution of an optimization problem. As a consequence a stochastic and dynamic decision problem resolves into a series of optimization problems to be formulated and solved by anticipation of the remaining decision process. However, actually solving a dynamic decision problem by means of approximate dynamic programming still is a major scientific challenge. Most of the work done so far is devoted to problems allowing for formulation of the underlying optimization problems as linear programs. Problem domains like scheduling and routing, where linear programming typically does not produce a significant benefit for problem solving, have not been considered so far. Therefore, the industry demand for dynamic scheduling and routing is still predominantly satisfied by purely heuristic approaches to anticipatory decision making. Although this may work well for certain dynamic decision problems, these approaches lack transferability of findings to other, related problems. This book has serves two major purposes: ‐ It provides a comprehensive and unique view of anticipatory optimization for dynamic decision making. Itfully integrates Markov decision processes, dynamic programming, data mining and optimization and introduces a new perspective on approximate dynamic programming. Moreover, the book identifies different degrees of anticipation, enabling an assessment of specific approaches to dynamic decision making. ‐ It shows for the first time how to successfully solve a dynamic vehicle routing problem by approximate dynamic programming. It elaborates on every building block required for this kind of approach to dynamic vehicle routing. Thereby the book has a pioneering character and is intended to provide a footing for the dynamic vehicle routing community. |
| dynamic optimization: Evolutionary Computation for Dynamic Optimization Problems Shengxiang Yang, Xin Yao, 2013-11-18 This book provides a compilation on the state-of-the-art and recent advances of evolutionary computation for dynamic optimization problems. The motivation for this book arises from the fact that many real-world optimization problems and engineering systems are subject to dynamic environments, where changes occur over time. Key issues for addressing dynamic optimization problems in evolutionary computation, including fundamentals, algorithm design, theoretical analysis, and real-world applications, are presented. Evolutionary Computation for Dynamic Optimization Problems is a valuable reference to scientists, researchers, professionals and students in the field of engineering and science, particularly in the areas of computational intelligence, nature- and bio-inspired computing, and evolutionary computation. |
| dynamic optimization: Evolutionary Optimization in Dynamic Environments Jürgen Branke, 2002 |
| dynamic optimization: Metaheuristics for Dynamic Optimization Enrique Alba, Amir Nakib, Patrick Siarry, 2014-09-20 This book is an updated effort in summarizing the trending topics and new hot research lines in solving dynamic problems using metaheuristics. An analysis of the present state in solving complex problems quickly draws a clear picture: problems that change in time, having noise and uncertainties in their definition are becoming very important. The tools to face these problems are still to be built, since existing techniques are either slow or inefficient in tracking the many global optima that those problems are presenting to the solver technique. Thus, this book is devoted to include several of the most important advances in solving dynamic problems. Metaheuristics are the more popular tools to this end, and then we can find in the book how to best use genetic algorithms, particle swarm, ant colonies, immune systems, variable neighborhood search, and many other bioinspired techniques. Also, neural network solutions are considered in this book. Both, theory and practice have been addressed in the chapters of the book. Mathematical background and methodological tools in solving this new class of problems and applications are included. From the applications point of view, not just academic benchmarks are dealt with, but also real world applications in logistics and bioinformatics are discussed here. The book then covers theory and practice, as well as discrete versus continuous dynamic optimization, in the aim of creating a fresh and comprehensive volume. This book is targeted to either beginners and experienced practitioners in dynamic optimization, since we took care of devising the chapters in a way that a wide audience could profit from its contents. We hope to offer a single source for up-to-date information in dynamic optimization, an inspiring and attractive new research domain that appeared in these last years and is here to stay. |
| dynamic optimization: Dynamic Optimization Arthur Earl Bryson, 1999 Dynamic Optimization takes an applied approach to its subject, offering many examples and solved problems that draw from aerospace, robotics, and mechanics. The abundance of thoroughly tested general algorithms and MATLAB codes provide the reader with the practice necessary to master the subject, while the realistic engineering problems and examples keep the material interesting and relevant. |
| dynamic optimization: Dynamic Optimization Arthur Earl Bryson, 1999 Dynamic Optimization takes an applied approach to its subject, offering many examples and solved problems that draw from aerospace, robotics, and mechanics. The abundance of thoroughly tested general algorithms and Matlab codes provide the reader with the practice necessary to master this inherently difficult subject, while the realistic engineering problems and examples keep the material interesting and relevant. FEATURES/BENEFITS Covers dynamic programming, relating it to the calculus of variations and optimal control, and neighboring optimum control (differential dynamic programming), a practical method for nonlinear feedback control. Includes a disk that contains 40 gradient and shooting codes, as well as codes that solve the time-varying Riccati equation (the DYNOPT Toolbox). These codes have been thoroughly tested on hundreds of problems. Contains many realistic examples and problems. Solutions to the examples and problems, as well as the codes that produce the figures, are included on the accompanying disk. Covers dynamic optimization with inequality constraints and singular arcs using inverse dynamic optimization (differential inclusion). |
| dynamic optimization: Encyclopedia of Optimization Christodoulos A. Floudas, Panos M. Pardalos, 2008-09-04 The goal of the Encyclopedia of Optimization is to introduce the reader to a complete set of topics that show the spectrum of research, the richness of ideas, and the breadth of applications that has come from this field. The second edition builds on the success of the former edition with more than 150 completely new entries, designed to ensure that the reference addresses recent areas where optimization theories and techniques have advanced. Particularly heavy attention resulted in health science and transportation, with entries such as Algorithms for Genomics, Optimization and Radiotherapy Treatment Design, and Crew Scheduling. |
| dynamic optimization: Principles of Dynamic Optimization Piernicola Bettiol, Richard B. Vinter, 2024-02-16 This monograph explores key principles in the modern theory of dynamic optimization, incorporating important advances in the field to provide a comprehensive, mathematically rigorous reference. Emphasis is placed on nonsmooth analytic techniques, and an in-depth treatment of necessary conditions, minimizer regularity, and global optimality conditions related to the Hamilton-Jacobi equation is given. New, streamlined proofs of fundamental theorems are incorporated throughout the text that eliminate earlier, cumbersome reductions and constructions. The first chapter offers an extended overview of dynamic optimization and its history that details the shortcomings of the elementary theory and demonstrates how a deeper analysis aims to overcome them. Aspects of dynamic programming well-matched to analytical techniques are considered in the final chapter, including characterization of extended-value functions associated with problems having endpoint and state constraints, inverse verification theorems, sensitivity relationships, and links to the maximum principle. This text will be a valuable resource for those seeking an understanding of dynamic optimization. The lucid exposition, insights into the field, and comprehensive coverage will benefit postgraduates, researchers, and professionals in system science, control engineering, optimization, and applied mathematics. |
| dynamic optimization: Applied Dynamic Programming for Optimization of Dynamical Systems Rush D. Robinett III, David G. Wilson, G. Richard Eisler, John E. Hurtado, 2005-01-01 Based on the results of over 10 years of research and development by the authors, this book presents a broad cross section of dynamic programming (DP) techniques applied to the optimization of dynamical systems. The main goal of the research effort was to develop a robust path planning/trajectory optimization tool that did not require an initial guess. The goal was partially met with a combination of DP and homotopy algorithms. DP algorithms are presented here with a theoretical development, and their successful application to variety of practical engineering problems is emphasized. |
| dynamic optimization: Dynamic Economics Gregory C. Chow, 1997 This work presents the optimization framework for dynamic economics and treats a number of topics in economics, including growth, macroeconomics, microeconomics, finance and dynamic games. The book also teaches by examples, using concepts to solve simple problems, moving on to general propositions. |
| dynamic optimization: Metaheuristics for Dynamic Optimization Enrique Alba, Amir Nakib, Patrick Siarry, 2012-08-11 This book is an updated effort in summarizing the trending topics and new hot research lines in solving dynamic problems using metaheuristics. An analysis of the present state in solving complex problems quickly draws a clear picture: problems that change in time, having noise and uncertainties in their definition are becoming very important. The tools to face these problems are still to be built, since existing techniques are either slow or inefficient in tracking the many global optima that those problems are presenting to the solver technique. Thus, this book is devoted to include several of the most important advances in solving dynamic problems. Metaheuristics are the more popular tools to this end, and then we can find in the book how to best use genetic algorithms, particle swarm, ant colonies, immune systems, variable neighborhood search, and many other bioinspired techniques. Also, neural network solutions are considered in this book. Both, theory and practice have been addressed in the chapters of the book. Mathematical background and methodological tools in solving this new class of problems and applications are included. From the applications point of view, not just academic benchmarks are dealt with, but also real world applications in logistics and bioinformatics are discussed here. The book then covers theory and practice, as well as discrete versus continuous dynamic optimization, in the aim of creating a fresh and comprehensive volume. This book is targeted to either beginners and experienced practitioners in dynamic optimization, since we took care of devising the chapters in a way that a wide audience could profit from its contents. We hope to offer a single source for up-to-date information in dynamic optimization, an inspiring and attractive new research domain that appeared in these last years and is here to stay. |
| dynamic optimization: Optimal Control Theory and Static Optimization in Economics Daniel Léonard, Ngo van Long, 1992-01-31 Optimal control theory is a technique being used increasingly by academic economists to study problems involving optimal decisions in a multi-period framework. This textbook is designed to make the difficult subject of optimal control theory easily accessible to economists while at the same time maintaining rigour. Economic intuitions are emphasized, and examples and problem sets covering a wide range of applications in economics are provided to assist in the learning process. Theorems are clearly stated and their proofs are carefully explained. The development of the text is gradual and fully integrated, beginning with simple formulations and progressing to advanced topics such as control parameters, jumps in state variables, and bounded state space. For greater economy and elegance, optimal control theory is introduced directly, without recourse to the calculus of variations. The connection with the latter and with dynamic programming is explained in a separate chapter. A second purpose of the book is to draw the parallel between optimal control theory and static optimization. Chapter 1 provides an extensive treatment of constrained and unconstrained maximization, with emphasis on economic insight and applications. Starting from basic concepts, it derives and explains important results, including the envelope theorem and the method of comparative statics. This chapter may be used for a course in static optimization. The book is largely self-contained. No previous knowledge of differential equations is required. |
| dynamic optimization: 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: Optimization in Economics and Finance Bruce D. Craven, Sardar M. N. Islam, 2005-10-24 Many optimization questions arise in economics and finance; an important example of this is the society's choice of the optimum state of the economy (the social choice problem). Optimization in Economics and Finance extends and improves the usual optimization techniques, in a form that may be adopted for modeling social choice problems. Problems discussed include: when is an optimum reached; when is it unique; relaxation of the conventional convex (or concave) assumptions on an economic model; associated mathematical concepts such as invex and quasimax; multiobjective optimal control models; and related computational methods and programs. These techniques are applied to economic growth models (including small stochastic perturbations), finance and financial investment models (and the interaction between financial and production variables), modeling sustainability over long time horizons, boundary (transversality) conditions, and models with several conflicting objectives. Although the applications are general and illustrative, the models in this book provide examples of possible models for a society's social choice for an allocation that maximizes welfare and utilization of resources. As well as using existing computer programs for optimization of models, a new computer program, named SCOM, is presented in this book for computing social choice models by optimal control. |
| dynamic optimization: Dynamic Economics Jerome Adda, Russell W. Cooper, 2003-08-29 An integrated approach to the empirical application of dynamic optimization programming models, for students and researchers. This book is an effective, concise text for students and researchers that combines the tools of dynamic programming with numerical techniques and simulation-based econometric methods. Doing so, it bridges the traditional gap between theoretical and empirical research and offers an integrated framework for studying applied problems in macroeconomics and microeconomics. In part I the authors first review the formal theory of dynamic optimization; they then present the numerical tools and econometric techniques necessary to evaluate the theoretical models. In language accessible to a reader with a limited background in econometrics, they explain most of the methods used in applied dynamic research today, from the estimation of probability in a coin flip to a complicated nonlinear stochastic structural model. These econometric techniques provide the final link between the dynamic programming problem and data. Part II is devoted to the application of dynamic programming to specific areas of applied economics, including the study of business cycles, consumption, and investment behavior. In each instance the authors present the specific optimization problem as a dynamic programming problem, characterize the optimal policy functions, estimate the parameters, and use models for policy evaluation. The original contribution of Dynamic Economics: Quantitative Methods and Applications lies in the integrated approach to the empirical application of dynamic optimization programming models. This integration shows that empirical applications actually complement the underlying theory of optimization, while dynamic programming problems provide needed structure for estimation and policy evaluation. |
| dynamic optimization: Extensions of Dynamic Programming for Combinatorial Optimization and Data Mining Hassan AbouEisha, Talha Amin, Igor Chikalov, Shahid Hussain, Mikhail Moshkov, 2019-01-25 Dynamic programming is an efficient technique for solving optimization problems. It is based on breaking the initial problem down into simpler ones and solving these sub-problems, beginning with the simplest ones. A conventional dynamic programming algorithm returns an optimal object from a given set of objects. This book develops extensions of dynamic programming, enabling us to (i) describe the set of objects under consideration; (ii) perform a multi-stage optimization of objects relative to different criteria; (iii) count the number of optimal objects; (iv) find the set of Pareto optimal points for bi-criteria optimization problems; and (v) to study relationships between two criteria. It considers various applications, including optimization of decision trees and decision rule systems as algorithms for problem solving, as ways for knowledge representation, and as classifiers; optimization of element partition trees for rectangular meshes, which are used in finite element methods for solving PDEs; and multi-stage optimization for such classic combinatorial optimization problems as matrix chain multiplication, binary search trees, global sequence alignment, and shortest paths. The results presented are useful for researchers in combinatorial optimization, data mining, knowledge discovery, machine learning, and finite element methods, especially those working in rough set theory, test theory, logical analysis of data, and PDE solvers. This book can be used as the basis for graduate courses. |
| dynamic optimization: Structural Optimization Franklin Y. Cheng, Kevin Z. Truman, 2017-12-21 Today’s biggest structural engineering challenge is to design better structures, and a key issue is the need to take an integrated approach which balances control of costs with the requirement for handling earthquakes and other dynamic forces. Structural optimization is based on rigorous mathematical formulation and requires computation algorithms for sizing structural elements and synthesizing systems. Now that the right software and enough computing power are readily available, professionals can now develop a suite of alternative designs and a select suitable one. A thoroughly-written and practical book on structural optimization is long overdue. This solid book comprehensively presents current optimization strategies, illustrated with sufficient examples of the design of elements and systems and presenting descriptions of the process and results. Emphasis is given to dynamic loading, in particular to seismic forces. Researchers and practising engineers will find this book an excellent reference, and advanced undergraduates or graduate students can use it as a resource for structural optimization design. |
| dynamic optimization: Approximate Dynamic Programming for Dynamic Vehicle Routing Marlin Wolf Ulmer, 2018-05-09 This book provides a straightforward overview for every researcher interested in stochastic dynamic vehicle routing problems (SDVRPs). The book is written for both the applied researcher looking for suitable solution approaches for particular problems as well as for the theoretical researcher looking for effective and efficient methods of stochastic dynamic optimization and approximate dynamic programming (ADP). To this end, the book contains two parts. In the first part, the general methodology required for modeling and approaching SDVRPs is presented. It presents adapted and new, general anticipatory methods of ADP tailored to the needs of dynamic vehicle routing. Since stochastic dynamic optimization is often complex and may not always be intuitive on first glance, the author accompanies the ADP-methodology with illustrative examples from the field of SDVRPs. The second part of this book then depicts the application of the theory to a specific SDVRP. The process starts from the real-world application. The author describes a SDVRP with stochastic customer requests often addressed in the literature, and then shows in detail how this problem can be modeled as a Markov decision process and presents several anticipatory solution approaches based on ADP. In an extensive computational study, he shows the advantages of the presented approaches compared to conventional heuristics. To allow deep insights in the functionality of ADP, he presents a comprehensive analysis of the ADP approaches. |
| dynamic optimization: Energy Optimization in Process Systems and Fuel Cells Stanislaw Sieniutycz, Jacek Jezowski, 2013-02-14 Energy Optimization in Process Systems and Fuel Cells, Second Edition covers the optimization and integration of energy systems, with a particular focus on fuel cell technology. With rising energy prices, imminent energy shortages, and increasing environmental impacts of energy production, energy optimization and systems integration is critically important. The book applies thermodynamics, kinetics and economics to study the effect of equipment size, environmental parameters, and economic factors on optimal power production and heat integration. Author Stanislaw Sieniutycz, highly recognized for his expertise and teaching, shows how costs can be substantially reduced, particularly in utilities common in the chemical industry. This second edition contains substantial revisions, with particular focus on the rapid progress in the field of fuel cells, related energy theory, and recent advances in the optimization and control of fuel cell systems. - New information on fuel cell theory, combined with the theory of flow energy systems, broadens the scope and usefulness of the book - Discusses engineering applications including power generation, resource upgrading, radiation conversion, and chemical transformation in static and dynamic systems - Contains practical applications of optimization methods that help solve the problems of power maximization and optimal use of energy and resources in chemical, mechanical, and environmental engineering |
| dynamic optimization: Speech Processing Li Deng, Douglas O'Shaughnessy, 2018-10-03 Based on years of instruction and field expertise, this volume offers the necessary tools to understand all scientific, computational, and technological aspects of speech processing. The book emphasizes mathematical abstraction, the dynamics of the speech process, and the engineering optimization practices that promote effective problem solving in this area of research and covers many years of the authors' personal research on speech processing. Speech Processing helps build valuable analytical skills to help meet future challenges in scientific and technological advances in the field and considers the complex transition from human speech processing to computer speech processing. |
| dynamic optimization: Stochastic Optimization Models In Finance (2006 Edition) William T Ziemba, Raymond G Vickson, 2006-09-11 A reprint of one of the classic volumes on portfolio theory and investment, this book has been used by the leading professors at universities such as Stanford, Berkeley, and Carnegie-Mellon. It contains five parts, each with a review of the literature and about 150 pages of computational and review exercises and further in-depth, challenging problems.Frequently referenced and highly usable, the material remains as fresh and relevant for a portfolio theory course as ever. |
| dynamic optimization: Elements of Dynamic Optimization Alpha C. Chiang, 1992 Designed to be used with Chiang's Fundamental Methods of Mathematical Economics, or independently at advanced undergraduate or graduate level, this text presents an in-depth exploration of dynamic optimization in economics. |
| dynamic optimization: Dynamic Optimization for Beginners Piermarco Cannarsa, Filippo Gazzola, 2021 |
| dynamic optimization: Elements of Numerical Mathematical Economics with Excel Giovanni Romeo, 2019-11-28 Elements of Numerical Mathematical Economics with Excel: Static and Dynamic Optimization shows readers how to apply static and dynamic optimization theory in an easy and practical manner, without requiring the mastery of specific programming languages that are often difficult and expensive to learn. Featuring user-friendly numerical discrete calculations developed within the Excel worksheets, the book includes key examples and economic applications solved step-by-step and then replicated in Excel. After introducing the fundamental tools of mathematical economics, the book explores the classical static optimization theory of linear and nonlinear programming, applying the core concepts of microeconomics and some portfolio theory. This provides a background for the more challenging worksheet applications of the dynamic optimization theory. The book also covers special complementary topics such as inventory modelling, data analysis for business and economics, and the essential elements of Monte Carlo analysis. Practical and accessible, Elements of Numerical Mathematical Economics with Excel: Static and Dynamic Optimization increases the computing power of economists worldwide. This book is accompanied by a companion website that includes Excel examples presented in the book, exercises, and other supplementary materials that will further assist in understanding this useful framework. - Explains how Excel provides a practical numerical approach to optimization theory and analytics - Increases access to the economic applications of this universally-available, relatively simple software program - Encourages readers to go to the core of theoretical continuous calculations and learn more about optimization processes |
| dynamic optimization: Pricing and Revenue Optimization Robert Phillips, 2005-08-05 This is the first comprehensive introduction to the concepts, theories, and applications of pricing and revenue optimization. From the initial success of yield management in the commercial airline industry down to more recent successes of markdown management and dynamic pricing, the application of mathematical analysis to optimize pricing has become increasingly important across many different industries. But, since pricing and revenue optimization has involved the use of sophisticated mathematical techniques, the topic has remained largely inaccessible to students and the typical manager. With methods proven in the MBA courses taught by the author at Columbia and Stanford Business Schools, this book presents the basic concepts of pricing and revenue optimization in a form accessible to MBA students, MS students, and advanced undergraduates. In addition, managers will find the practical approach to the issue of pricing and revenue optimization invaluable. Solutions to the end-of-chapter exercises are available to instructors who are using this book in their courses. For access to the solutions manual, please contact marketing@www.sup.org. |
| dynamic optimization: Advanced Mathematics for Economists : Static and Dynamic Optimization Peter J. Lambert, 1985 |
| dynamic optimization: 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: Evolutionary Computation in Dynamic and Uncertain Environments Shengxiang Yang, Yew-Soon Ong, Yaochu Jin, 2007-04-03 This book compiles recent advances of evolutionary algorithms in dynamic and uncertain environments within a unified framework. The book is motivated by the fact that some degree of uncertainty is inevitable in characterizing any realistic engineering systems. Discussion includes representative methods for addressing major sources of uncertainties in evolutionary computation, including handle of noisy fitness functions, use of approximate fitness functions, search for robust solutions, and tracking moving optimums. |
| dynamic optimization: Handbook of Formal Optimization Anand J. Kulkarni, Amir H. Gandomi, 2024-07-16 The formal optimization handbook is a comprehensive guide that covers a wide range of subjects. It includes a literature review, a mathematical formulation of optimization methods, flowcharts and pseudocodes, illustrations, problems and applications, results and critical discussions, and much more. The book covers a vast array of formal optimization fields, including mathematical and Bayesian optimization, neural networks and deep learning, genetic algorithms and their applications, hybrid optimization methods, combinatorial optimization, constraint handling in optimization methods, and swarm-based optimization. This handbook is an excellent reference for experts and non-specialists alike, as it provides stimulating material. The book also covers research trends, challenges, and prospective topics, making it a valuable resource for those looking to expand their knowledge in this field. |
| dynamic optimization: Combinatorial Data Analysis Lawrence Hubert, Phipps Arabie, Jacqueline Meulman, 2001-01-01 Combinatorial data analysis (CDA) refers to a wide class of methods for the study of relevant data sets in which the arrangement of a collection of objects is absolutely central. The focus of this monograph is on the identification of arrangements, which are then further restricted to where the combinatorial search is carried out by a recursive optimization process based on the general principles of dynamic programming (DP). |
| dynamic optimization: Optimization in Economic Theory Avinash K. Dixit, 1990 A new edition of a student text which provides a broad study of optimization methods. It builds on the base of simple economic theory, elementary linear algebra and calculus, and reinforces each new mathematical idea by relating it to its economic application. |
| dynamic optimization: Numerical Methods and Optimization Jean-Pierre Corriou, 2022-01-05 This text, covering a very large span of numerical methods and optimization, is primarily aimed at advanced undergraduate and graduate students. A background in calculus and linear algebra are the only mathematical requirements. The abundance of advanced methods and practical applications will be attractive to scientists and researchers working in different branches of engineering. The reader is progressively introduced to general numerical methods and optimization algorithms in each chapter. Examples accompany the various methods and guide the students to a better understanding of the applications. The user is often provided with the opportunity to verify their results with complex programming code. Each chapter ends with graduated exercises which furnish the student with new cases to study as well as ideas for exam/homework problems for the instructor. A set of programs made in MatlabTM is available on the author’s personal website and presents both numerical and optimization methods. |
| dynamic optimization: Dynamic Programming Multi-Objective Combinatorial Optimization Michal Mankowski, Mikhail Moshkov, 2021-02-08 This book introduces a fairly universal approach to the design and analysis of exact optimization algorithms for multi-objective combinatorial optimization problems. It proposes the circuits without repetitions representing the sets of feasible solutions along with the increasing and strictly increasing cost functions as a model for such problems. The book designs the algorithms for multi-stage and bi-criteria optimization and for counting the solutions in the framework of this model. As applications, this book studies eleven known combinatorial optimization problems: matrix chain multiplication, global sequence alignment, optimal paths in directed graphs, binary search trees, convex polygon triangulation, line breaking (text justification), one-dimensional clustering, optimal bitonic tour, segmented least squares, optimization of matchings in trees, and 0/1 knapsack problem. The results presented are useful for researchers in combinatorial optimization. This book is also useful as the basis for graduate courses. |
| dynamic optimization: Dynamic Optimization, Second Edition Morton I. Kamien, Nancy L. Schwartz, 2013-04-17 Since its initial publication, this text has defined courses in dynamic optimization taught to economics and management science students. The two-part treatment covers the calculus of variations and optimal control. 1998 edition. |
| dynamic optimization: Engineering Optimization Singiresu S. Rao, 1996-02-29 In Engineering Optimization, Professor Singiresu S. Rao provides an application-oriented presentation of the full array of classical and newly developed optimization techniques now being used by engineers in a wide range of industries. |
| dynamic optimization: Real-time PDE-constrained Optimization Lorenz T. Biegler, Omar Ghattas, Matthias Heinkenschloss, David Keyes, Bart van Bloemen Waanders, 2007-01-01 Many engineering and scientific problems in design, control, and parameter estimation can be formulated as optimization problems that are governed by partial differential equations (PDEs). The complexities of the PDEs--and the requirement for rapid solution--pose significant difficulties. A particularly challenging class of PDE-constrained optimization problems is characterized by the need for real-time solution, i.e., in time scales that are sufficiently rapid to support simulation-based decision making. Real-Time PDE-Constrained Optimization, the first book devoted to real-time optimization for systems governed by PDEs, focuses on new formulations, methods, and algorithms needed to facilitate real-time, PDE-constrained optimization. In addition to presenting state-of-the-art algorithms and formulations, the text illustrates these algorithms with a diverse set of applications that includes problems in the areas of aerodynamics, biology, fluid dynamics, medicine, chemical processes, homeland security, and structural dynamics. Audience: readers who have expertise in simulation and are interested in incorporating optimization into their simulations, who have expertise in numerical optimization and are interested in adapting optimization methods to the class of infinite-dimensional simulation problems, or who have worked in offline optimization contexts and are interested in moving to online optimization. |
| dynamic optimization: Frontier Applications of Nature Inspired Computation Mahdi Khosravy, Neeraj Gupta, Nilesh Patel, Tomonobu Senjyu, 2020-03-11 This book addresses the frontier advances in the theory and application of nature-inspired optimization techniques, including solving the quadratic assignment problem, prediction in nature-inspired dynamic optimization, the lion algorithm and its applications, optimizing the operation scheduling of microgrids, PID controllers for two-legged robots, optimizing crane operating times, planning electrical energy distribution systems, automatic design and evaluation of classification pipelines, and optimizing wind-energy power generation plants. The book also presents a variety of nature-inspired methods and illustrates methods of adapting these to said applications. Nature-inspired computation, developed by mimicking natural phenomena, makes a significant contribution toward the solution of non-convex optimization problems that normal mathematical optimizers fail to solve. As such, a wide range of nature-inspired computing approaches has been used in multidisciplinary engineering applications. Written by researchers and developers from a variety of fields, this book presents the latest findings, novel techniques and pioneering applications. |
| dynamic optimization: A Selected Annotated Bibliography on the Analysis of Water Resource Systems Water Resources Scientific Information Center, 1973 |
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 …
Best Dentist Near Me in Ashburn, VA 20147 | Dynamic Dental …
Dynamic Dental Wellness is a full spectrum dental office incorporating all aspects of modern technology. We offer the most comprehensive analysis and treatment options available for …
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.
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. …
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 …
Best Dentist Near Me in Ashburn, VA 20147 | Dynamic Dental …
Dynamic Dental Wellness is a full spectrum dental office incorporating all aspects of modern technology. We offer the most comprehensive analysis and treatment options available for …
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.
