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Multivariate Interpolation In Continuous State Binary Control Stochastic Dynamic Programming With Application To Plant Pathogen Control
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Book Synopsis Multivariate Interpolation in Continuous State Binary Control Stochastic Dynamic Programming with Application to Plant Pathogen Control by : Elizabeth Allen Eschenbach
Download or read book Multivariate Interpolation in Continuous State Binary Control Stochastic Dynamic Programming with Application to Plant Pathogen Control written by Elizabeth Allen Eschenbach and published by . This book was released on 1991 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Parallel Algorithms of Continuous State and Control Stochastic Dynamic Programming Applied to Multi-reservoir Management by : Elizabeth Allen Eschenbach
Download or read book Parallel Algorithms of Continuous State and Control Stochastic Dynamic Programming Applied to Multi-reservoir Management written by Elizabeth Allen Eschenbach and published by . This book was released on 1994 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Masters Theses in the Pure and Applied Sciences by : W. H. Shafer
Download or read book Masters Theses in the Pure and Applied Sciences written by W. H. Shafer and published by Springer Science & Business Media. This book was released on 1993 with total page 368 pages. Available in PDF, EPUB and Kindle. Book excerpt: Volume 36 reports (for thesis year 1991) a total of 11,024 thesis titles from 23 Canadian and 161 US universities. The organization of the volume, as in past years, consists of thesis titles arranged by discipline, and by university within each discipline. The titles are contributed by any and all a
Book Synopsis Optimization Over Time by : Peter Whittle
Download or read book Optimization Over Time written by Peter Whittle and published by . This book was released on 1982 with total page 338 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Controlled Markov Processes and Viscosity Solutions by : Wendell H. Fleming
Download or read book Controlled Markov Processes and Viscosity Solutions written by Wendell H. Fleming and published by Springer. This book was released on 2010-11-19 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is an introduction to optimal stochastic control for continuous time Markov processes and the theory of viscosity solutions. It covers dynamic programming for deterministic optimal control problems, as well as to the corresponding theory of viscosity solutions. New chapters in this second edition introduce the role of stochastic optimal control in portfolio optimization and in pricing derivatives in incomplete markets and two-controller, zero-sum differential games.
Book Synopsis Optimization, Control, and Applications of Stochastic Systems by : Daniel Hernández-Hernández
Download or read book Optimization, Control, and Applications of Stochastic Systems written by Daniel Hernández-Hernández and published by Birkhäuser. This book was released on 2012-08-14 with total page 309 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume provides a general overview of discrete- and continuous-time Markov control processes and stochastic games, along with a look at the range of applications of stochastic control and some of its recent theoretical developments. These topics include various aspects of dynamic programming, approximation algorithms, and infinite-dimensional linear programming. In all, the work comprises 18 carefully selected papers written by experts in their respective fields. Optimization, Control, and Applications of Stochastic Systems will be a valuable resource for all practitioners, researchers, and professionals in applied mathematics and operations research who work in the areas of stochastic control, mathematical finance, queueing theory, and inventory systems. It may also serve as a supplemental text for graduate courses in optimal control and dynamic games.
Author :Harold Joseph Kushner Publisher :Springer Science & Business Media ISBN 13 :9780387978345 Total Pages :439 pages Book Rating :4.9/5 (783 download)
Book Synopsis Numerical Methods for Stochastic Control Problems in Continuous Time by : Harold Joseph Kushner
Download or read book Numerical Methods for Stochastic Control Problems in Continuous Time written by Harold Joseph Kushner and published by Springer Science & Business Media. This book was released on 1992 with total page 439 pages. Available in PDF, EPUB and Kindle. Book excerpt: Stochastic control is a very active area of research and this monograph, written by two leading authorities in the field, has been updated to reflect the latest developments. It covers effective numerical methods for stochastic control problems in continuous time on two levels: that of practice (algorithms and applications) and that of mathematical development. It is broadly accessible for graduate students and researchers.
Book Synopsis Discrete-time Control Algorithms and Adaptive Intelligent Systems Designs by : Asma Azmi Al-Tamimi
Download or read book Discrete-time Control Algorithms and Adaptive Intelligent Systems Designs written by Asma Azmi Al-Tamimi and published by ProQuest. This book was released on 2007 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In this work, approximate dynamic programming (ADP) designs based on adaptive critic structures are developed to solve the discrete-time H2/Hinfinity optimal control problems in which the state and action spaces are continuous. This work considers linear discrete-time systems as well as nonlinear discrete-time systems that are affine in the input. This research resulted in forward-in-time reinforcement learning algorithms that converge to the solution of the Generalized Algebraic Riccati Equation (GARE) for linear systems. For the nonlinear case, a forward-in-time reinforcement learning algorithm is presented that converges to the solution of the associated Hamilton-Jacobi Bellman equation (HJB). The results in the linear case can be thought of as a way to solve the GARE of the well-known discrete-time Hinfinity optimal control problem forward in time. Four design algorithms are developed: Heuristic Dynamic programming (HDP), Dual Heuristic dynamic programming (DHP), Action dependent Heuristic Dynamic programming (ADHDP) and Action dependent Dual Heuristic dynamic programming (ADDHP). The significance of these algorithms is that for some of them, particularly the ADHDP algorithm, a priori knowledge of the plant model is not required to solve the dynamic programming problem. Another major outcome of this work is that we introduce a convergent policy iteration scheme based on the HDP algorithm that allows the use of neural networks to arbitrarily approximate for the value function of the discrete-time HJB equation. This online algorithm may be implemented in a way that requires only partial knowledge of the model of the nonlinear dynamical system. The dissertation includes detailed proofs of convergence for the proposed algorithms, HDP, DHP, ADHDP, ADDHP and the nonlinear HDP. Practical numerical examples are provided to show the effectiveness of the developed optimization algorithms. For nonlinear systems, a comparison with methods based on the State-Dependent Riccati Equation (SDRE) is also presented. In all the provided examples, parametric structures like neural networks have been used to find compact representations of the value function and optimal policies for the corresponding optimal control problems.
Book Synopsis Optimization Over Time by : Peter Whittle
Download or read book Optimization Over Time written by Peter Whittle and published by . This book was released on 1982 with total page 317 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Dynamic Programming and Optimal Control by : D. P. Bertsekas
Download or read book Dynamic Programming and Optimal Control written by D. P. Bertsekas and published by . This book was released on with total page 387 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Reinforcement Learning and Dynamic Programming Using Function Approximators by : Lucian Busoniu
Download or read book Reinforcement Learning and Dynamic Programming Using Function Approximators written by Lucian Busoniu and published by CRC Press. This book was released on 2017-07-28 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: From household appliances to applications in robotics, engineered systems involving complex dynamics can only be as effective as the algorithms that control them. While Dynamic Programming (DP) has provided researchers with a way to optimally solve decision and control problems involving complex dynamic systems, its practical value was limited by algorithms that lacked the capacity to scale up to realistic problems. However, in recent years, dramatic developments in Reinforcement Learning (RL), the model-free counterpart of DP, changed our understanding of what is possible. Those developments led to the creation of reliable methods that can be applied even when a mathematical model of the system is unavailable, allowing researchers to solve challenging control problems in engineering, as well as in a variety of other disciplines, including economics, medicine, and artificial intelligence. Reinforcement Learning and Dynamic Programming Using Function Approximators provides a comprehensive and unparalleled exploration of the field of RL and DP. With a focus on continuous-variable problems, this seminal text details essential developments that have substantially altered the field over the past decade. In its pages, pioneering experts provide a concise introduction to classical RL and DP, followed by an extensive presentation of the state-of-the-art and novel methods in RL and DP with approximation. Combining algorithm development with theoretical guarantees, they elaborate on their work with illustrative examples and insightful comparisons. Three individual chapters are dedicated to representative algorithms from each of the major classes of techniques: value iteration, policy iteration, and policy search. The features and performance of these algorithms are highlighted in extensive experimental studies on a range of control applications. The recent development of applications involving complex systems has led to a surge of interest in RL and DP methods and the subsequent need for a quality resource on the subject. For graduate students and others new to the field, this book offers a thorough introduction to both the basics and emerging methods. And for those researchers and practitioners working in the fields of optimal and adaptive control, machine learning, artificial intelligence, and operations research, this resource offers a combination of practical algorithms, theoretical analysis, and comprehensive examples that they will be able to adapt and apply to their own work. Access the authors' website at www.dcsc.tudelft.nl/rlbook/ for additional material, including computer code used in the studies and information concerning new developments.
Book Synopsis Reinforcement Learning and Stochastic Optimization by : Warren B. Powell
Download or read book Reinforcement Learning and Stochastic Optimization written by Warren B. Powell and published by John Wiley & Sons. This book was released on 2022-03-15 with total page 1090 pages. Available in PDF, EPUB and Kindle. Book excerpt: REINFORCEMENT LEARNING AND STOCHASTIC OPTIMIZATION Clearing the jungle of stochastic optimization Sequential decision problems, which consist of “decision, information, decision, information,” are ubiquitous, spanning virtually every human activity ranging from business applications, health (personal and public health, and medical decision making), energy, the sciences, all fields of engineering, finance, and e-commerce. The diversity of applications attracted the attention of at least 15 distinct fields of research, using eight distinct notational systems which produced a vast array of analytical tools. A byproduct is that powerful tools developed in one community may be unknown to other communities. Reinforcement Learning and Stochastic Optimization offers a single canonical framework that can model any sequential decision problem using five core components: state variables, decision variables, exogenous information variables, transition function, and objective function. This book highlights twelve types of uncertainty that might enter any model and pulls together the diverse set of methods for making decisions, known as policies, into four fundamental classes that span every method suggested in the academic literature or used in practice. Reinforcement Learning and Stochastic Optimization is the first book to provide a balanced treatment of the different methods for modeling and solving sequential decision problems, following the style used by most books on machine learning, optimization, and simulation. The presentation is designed for readers with a course in probability and statistics, and an interest in modeling and applications. Linear programming is occasionally used for specific problem classes. The book is designed for readers who are new to the field, as well as those with some background in optimization under uncertainty. Throughout this book, readers will find references to over 100 different applications, spanning pure learning problems, dynamic resource allocation problems, general state-dependent problems, and hybrid learning/resource allocation problems such as those that arose in the COVID pandemic. There are 370 exercises, organized into seven groups, ranging from review questions, modeling, computation, problem solving, theory, programming exercises and a “diary problem” that a reader chooses at the beginning of the book, and which is used as a basis for questions throughout the rest of the book.
Book Synopsis Optimal Control Theory by : Suresh P. Sethi
Download or read book Optimal Control Theory written by Suresh P. Sethi and published by Taylor & Francis US. This book was released on 2006 with total page 536 pages. Available in PDF, EPUB and Kindle. Book excerpt: Optimal control methods are used to determine optimal ways to control a dynamic system. The theoretical work in this field serves as a foundation for the book, which the authors have applied to business management problems developed from their research and classroom instruction. Sethi and Thompson have provided management science and economics communities with a thoroughly revised edition of their classic text on Optimal Control Theory. The new edition has been completely refined with careful attention to the text and graphic material presentation. Chapters cover a range of topics including finance, production and inventory problems, marketing problems, machine maintenance and replacement, problems of optimal consumption of natural resources, and applications of control theory to economics. The book contains new results that were not available when the first edition was published, as well as an expansion of the material on stochastic optimal control theory.
Book Synopsis Planning Algorithms by : Steven Michael LaValle
Download or read book Planning Algorithms written by Steven Michael LaValle and published by . This book was released on 2006 with total page 826 pages. Available in PDF, EPUB and Kindle. Book excerpt: Written for computer scientists and engineers with interests in artificial intelligence, robotics, or control theory, this is the only book on this topic that integrates literature from several fields into a coherent source for teaching and reference in applications including robotics, computational biology, computer graphics, manufacturing, aerospace applications, and medicine.
Book Synopsis Stochastic Decomposition by : Julia L. Higle
Download or read book Stochastic Decomposition written by Julia L. Higle and published by Springer Science & Business Media. This book was released on 2013-11-27 with total page 237 pages. Available in PDF, EPUB and Kindle. Book excerpt: Motivation Stochastic Linear Programming with recourse represents one of the more widely applicable models for incorporating uncertainty within in which the SLP optimization models. There are several arenas model is appropriate, and such models have found applications in air line yield management, capacity planning, electric power generation planning, financial planning, logistics, telecommunications network planning, and many more. In some of these applications, modelers represent uncertainty in terms of only a few seenarios and formulate a large scale linear program which is then solved using LP software. However, there are many applications, such as the telecommunications planning problem discussed in this book, where a handful of seenarios do not capture variability well enough to provide a reasonable model of the actual decision-making problem. Problems of this type easily exceed the capabilities of LP software by several orders of magnitude. Their solution requires the use of algorithmic methods that exploit the structure of the SLP model in a manner that will accommodate large scale applications.
Book Synopsis Decision Making Under Uncertainty by : Mykel J. Kochenderfer
Download or read book Decision Making Under Uncertainty written by Mykel J. Kochenderfer and published by MIT Press. This book was released on 2015-07-24 with total page 350 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to decision making under uncertainty from a computational perspective, covering both theory and applications ranging from speech recognition to airborne collision avoidance. Many important problems involve decision making under uncertainty—that is, choosing actions based on often imperfect observations, with unknown outcomes. Designers of automated decision support systems must take into account the various sources of uncertainty while balancing the multiple objectives of the system. This book provides an introduction to the challenges of decision making under uncertainty from a computational perspective. It presents both the theory behind decision making models and algorithms and a collection of example applications that range from speech recognition to aircraft collision avoidance. Focusing on two methods for designing decision agents, planning and reinforcement learning, the book covers probabilistic models, introducing Bayesian networks as a graphical model that captures probabilistic relationships between variables; utility theory as a framework for understanding optimal decision making under uncertainty; Markov decision processes as a method for modeling sequential problems; model uncertainty; state uncertainty; and cooperative decision making involving multiple interacting agents. A series of applications shows how the theoretical concepts can be applied to systems for attribute-based person search, speech applications, collision avoidance, and unmanned aircraft persistent surveillance. Decision Making Under Uncertainty unifies research from different communities using consistent notation, and is accessible to students and researchers across engineering disciplines who have some prior exposure to probability theory and calculus. It can be used as a text for advanced undergraduate and graduate students in fields including computer science, aerospace and electrical engineering, and management science. It will also be a valuable professional reference for researchers in a variety of disciplines.
Book Synopsis Multistage Stochastic Optimization by : Georg Ch. Pflug
Download or read book Multistage Stochastic Optimization written by Georg Ch. Pflug and published by Springer. This book was released on 2014-11-12 with total page 301 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multistage stochastic optimization problems appear in many ways in finance, insurance, energy production and trading, logistics and transportation, among other areas. They describe decision situations under uncertainty and with a longer planning horizon. This book contains a comprehensive treatment of today’s state of the art in multistage stochastic optimization. It covers the mathematical backgrounds of approximation theory as well as numerous practical algorithms and examples for the generation and handling of scenario trees. A special emphasis is put on estimation and bounding of the modeling error using novel distance concepts, on time consistency and the role of model ambiguity in the decision process. An extensive treatment of examples from electricity production, asset liability management and inventory control concludes the book.
