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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 Dynamic Programming and Stochastic Control by : Bertsekas
Download or read book Dynamic Programming and Stochastic Control written by Bertsekas and published by Academic Press. This book was released on 1976-11-26 with total page 415 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dynamic Programming and Stochastic Control
Book Synopsis Relative Optimization of Continuous-Time and Continuous-State Stochastic Systems by : Xi-Ren Cao
Download or read book Relative Optimization of Continuous-Time and Continuous-State Stochastic Systems written by Xi-Ren Cao and published by Springer Nature. This book was released on 2020-05-13 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph applies the relative optimization approach to time nonhomogeneous continuous-time and continuous-state dynamic systems. The approach is intuitively clear and does not require deep knowledge of the mathematics of partial differential equations. The topics covered have the following distinguishing features: long-run average with no under-selectivity, non-smooth value functions with no viscosity solutions, diffusion processes with degenerate points, multi-class optimization with state classification, and optimization with no dynamic programming. The book begins with an introduction to relative optimization, including a comparison with the traditional approach of dynamic programming. The text then studies the Markov process, focusing on infinite-horizon optimization problems, and moves on to discuss optimal control of diffusion processes with semi-smooth value functions and degenerate points, and optimization of multi-dimensional diffusion processes. The book concludes with a brief overview of performance derivative-based optimization. Among the more important novel considerations presented are: the extension of the Hamilton–Jacobi–Bellman optimality condition from smooth to semi-smooth value functions by derivation of explicit optimality conditions at semi-smooth points and application of this result to degenerate and reflected processes; proof of semi-smoothness of the value function at degenerate points; attention to the under-selectivity issue for the long-run average and bias optimality; discussion of state classification for time nonhomogeneous continuous processes and multi-class optimization; and development of the multi-dimensional Tanaka formula for semi-smooth functions and application of this formula to stochastic control of multi-dimensional systems with degenerate points. The book will be of interest to researchers and students in the field of stochastic control and performance optimization alike.
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 336 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 New Dynamic Programming Approaches to Stochastic Optimal Control Problems in Chemical Engineering [microform] by : Adrian Martell Thompson
Download or read book New Dynamic Programming Approaches to Stochastic Optimal Control Problems in Chemical Engineering [microform] written by Adrian Martell Thompson and published by Library and Archives Canada = Bibliothèque et Archives Canada. This book was released on 2005 with total page 460 pages. Available in PDF, EPUB and Kindle. Book excerpt: The second algorithm, a policy iteration (PI) variant employing Nystrom's discretization method, allows computation of continuous stochastic ROC policies without quadrature, function approximation, interpolation, or Monte Carlo methods. Lipschitz continuity assumptions allow reformulation of the original problem into an equivalent finite state problem solvable in a Luus-Jaakola global optimization framework. This enables exponential computation reductions relative to standard PI. Simulations, involving stochastic ROC of a nonlinear reactor, exhibited a 99.9% reduction in computation with identical accuracy. Additionally, the average performance of the policy obtained was 58.2% better than the certainty equivalence policy. The first, a Monte Carlo extension of iterative dynamic programming (IDP), reduces discretization requirements by restricting the control policy to the dominant portion of the state space. A proof of strong probabilistic convergence of IDP is derived, and is shown to extend to the new stochastic IDP (SIDP) algorithm. Simulations demonstrate that SIDP can provide significant COD mitigation in DAC applications, relative to the standard SDP approach. Specifically, a 96% computation reduction, 92% storage reduction and less than 2% accuracy loss were simultaneously achieved using SIDP. Optimal control of chemical processes in the presence of stochastic model uncertainty is addressed. Contributions are made in two areas of process control interest: dual adaptive control (DAC) and robust optimal control (ROC). These are synergistic in that DAC involves sequences of stochastic ROC problems. In chemical engineering, these problems typically have continuous state and control spaces, and are subject to a curse of dimensionality (COD) within the stochastic dynamic programming (SDP) framework. The main novelty presented here is the method by which this COD is mitigated. Existing methods to mitigate the COD include state space aggregation, function approximation (FA), or exploitation of problem structure, e.g. system linearity. The first two yield problems of reduced but still large complexity. The third is problem specific and does not generalize well to non-linear, non-convex or non-Gaussian structures. Here, two new algorithms are developed that mitigate the COD without these simplifications, with only minimal restrictions imposed on problem structure.
Book Synopsis Solving Multi-Dimensional Dynamic Programming Problems Using Stochastic Grids and Nearest-Neighbor Interpolation by : Jakob Almerud
Download or read book Solving Multi-Dimensional Dynamic Programming Problems Using Stochastic Grids and Nearest-Neighbor Interpolation written by Jakob Almerud and published by . This book was released on 2017 with total page 29 pages. Available in PDF, EPUB and Kindle. Book excerpt: We propose two modifications to the method of endogenous grid points that greatly decreases the computational time for life cycle models with many exogenous state variables. First, we use simulated stochastic grids on the exogenous state variables. Second, when we interpolate to find the continuation value of the model, we split the interpolation step into two: We use nearest-neighbor interpolation over the exogenous state variables, and multilinear interpolation over the endogenous state variables. We evaluate the numerical accuracy and computational efficiency of the algorithm by solving a standard consumption/savings life-cycle model with an arbitrary number of exogenous state variables. The model with eight exogenous state variables is solved in around eight minutes on a standard desktop computer. We then use a more realistic income process estimated by Guvenen et al (2015) to demonstrate the usefulness of the algorithm. We demonstrate that the consumption dynamics differ compared to agents facing a more traditional income process.
Book Synopsis Optimal Real-time Control of Stochastic, Multipurpose, Multireservoir Systems by : C. Russ Philbrick
Download or read book Optimal Real-time Control of Stochastic, Multipurpose, Multireservoir Systems written by C. Russ Philbrick and published by . This book was released on 1996 with total page 381 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis presents new systems analysis methods that are appropriate for complex, nonlinear systems that are driven by uncertain inputs. These methods extend the ability of discrete dynamic programming (DDP) to system models that include six or more state variables and a similar number of stochastic variables. This is accomplished by interpolation and quadrature methods that have high-order accuracy and that provide significant computational savings over traditional DDP interpolation and quadrature methods. These new methods significantly improve our ability to apply DDP to large-scale systems. Using these methods, DDP can solve a variety of systems analysis problems without resorting to the simplifying assumptions required by other stochastic optimization methods. This is demonstrated in the application of DDP to problems with as many as seven state variables. Of particular interest, this thesis applied DDP to the practical problem of conjunctively managing groundwater and surface water. Moreover, the applications also demonstrate that DDP can be a powerfill planning tool, such as when evaluating a range of capacity expansion alternatives.
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 State Increment Dynamic Programming Applied to Stochastic and Adaptive Optimization Problems by : Robert E. Heath II
Download or read book State Increment Dynamic Programming Applied to Stochastic and Adaptive Optimization Problems written by Robert E. Heath II and published by . This book was released on 1966 with total page 142 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dr. R.E. Larson has proposed an optimization technique, state increment dynamic programming, which reduces the fast digital computer memory requirement of conventional dynamic programming while retaining its many desirable features. The primary modification in state increment dynamic programming is that, instead of a fixed time of application of control as in conventional dynamic programming, the duration of application of control is selected as the time such that no state changes by more than one increment. This guarantees that the state resulting from the application of a control lies in a small neighborhood of the original state. Consequently, the entire region of state space is divided into smaller units called 'blocks.' The smaller amount of information contained in one of these blocks, as compared with the entire region of state space, accounts for the reduction in the fast memory requirement. State increment dynamic programming is applied to stochastic optimization problems in which the stochastic variable is a set of values each with a known discrete probability. The equations are presented in a form suitable for a digital computer solution.
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 Planning Algorithms by : Steven M. LaValle
Download or read book Planning Algorithms written by Steven M. LaValle and published by Cambridge University Press. This book was released on 2006-05-29 with total page 844 pages. Available in PDF, EPUB and Kindle. Book excerpt: Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. Written for computer scientists and engineers with interests in artificial intelligence, robotics, or control theory, this is the only book on this topic that tightly integrates a vast body of literature from several fields into a coherent source for teaching and reference in a wide variety of applications. Difficult mathematical material is explained through hundreds of examples and illustrations.
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 Patterns, Predictions, and Actions: Foundations of Machine Learning by : Moritz Hardt
Download or read book Patterns, Predictions, and Actions: Foundations of Machine Learning written by Moritz Hardt and published by Princeton University Press. This book was released on 2022-08-23 with total page 321 pages. Available in PDF, EPUB and Kindle. Book excerpt: An authoritative, up-to-date graduate textbook on machine learning that highlights its historical context and societal impacts Patterns, Predictions, and Actions introduces graduate students to the essentials of machine learning while offering invaluable perspective on its history and social implications. Beginning with the foundations of decision making, Moritz Hardt and Benjamin Recht explain how representation, optimization, and generalization are the constituents of supervised learning. They go on to provide self-contained discussions of causality, the practice of causal inference, sequential decision making, and reinforcement learning, equipping readers with the concepts and tools they need to assess the consequences that may arise from acting on statistical decisions. Provides a modern introduction to machine learning, showing how data patterns support predictions and consequential actions Pays special attention to societal impacts and fairness in decision making Traces the development of machine learning from its origins to today Features a novel chapter on machine learning benchmarks and datasets Invites readers from all backgrounds, requiring some experience with probability, calculus, and linear algebra An essential textbook for students and a guide for researchers
Book Synopsis Approximate Dynamic Programming by : Warren B. Powell
Download or read book Approximate Dynamic Programming written by Warren B. Powell and published by John Wiley & Sons. This book was released on 2007-10-05 with total page 487 pages. Available in PDF, EPUB and Kindle. Book excerpt: A complete and accessible introduction to the real-world applications of approximate dynamic programming With the growing levels of sophistication in modern-day operations, it is vital for practitioners to understand how to approach, model, and solve complex industrial problems. Approximate Dynamic Programming is a result of the author's decades of experience working in large industrial settings to develop practical and high-quality solutions to problems that involve making decisions in the presence of uncertainty. This groundbreaking book uniquely integrates four distinct disciplines—Markov design processes, mathematical programming, simulation, and statistics—to demonstrate how to successfully model and solve a wide range of real-life problems using the techniques of approximate dynamic programming (ADP). The reader is introduced to the three curses of dimensionality that impact complex problems and is also shown how the post-decision state variable allows for the use of classical algorithmic strategies from operations research to treat complex stochastic optimization problems. Designed as an introduction and assuming no prior training in dynamic programming of any form, Approximate Dynamic Programming contains dozens of algorithms that are intended to serve as a starting point in the design of practical solutions for real problems. The book provides detailed coverage of implementation challenges including: modeling complex sequential decision processes under uncertainty, identifying robust policies, designing and estimating value function approximations, choosing effective stepsize rules, and resolving convergence issues. With a focus on modeling and algorithms in conjunction with the language of mainstream operations research, artificial intelligence, and control theory, Approximate Dynamic Programming: Models complex, high-dimensional problems in a natural and practical way, which draws on years of industrial projects Introduces and emphasizes the power of estimating a value function around the post-decision state, allowing solution algorithms to be broken down into three fundamental steps: classical simulation, classical optimization, and classical statistics Presents a thorough discussion of recursive estimation, including fundamental theory and a number of issues that arise in the development of practical algorithms Offers a variety of methods for approximating dynamic programs that have appeared in previous literature, but that have never been presented in the coherent format of a book Motivated by examples from modern-day operations research, Approximate Dynamic Programming is an accessible introduction to dynamic modeling and is also a valuable guide for the development of high-quality solutions to problems that exist in operations research and engineering. The clear and precise presentation of the material makes this an appropriate text for advanced undergraduate and beginning graduate courses, while also serving as a reference for researchers and practitioners. A companion Web site is available for readers, which includes additional exercises, solutions to exercises, and data sets to reinforce the book's main concepts.
Book Synopsis Ecological Models and Data in R by : Benjamin M. Bolker
Download or read book Ecological Models and Data in R written by Benjamin M. Bolker and published by Princeton University Press. This book was released on 2008-07-21 with total page 408 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduction and background; Exploratory data analysis and graphics; Deterministic functions for ecological modeling; Probability and stochastic distributions for ecological modeling; Stochatsic simulation and power analysis; Likelihood and all that; Optimization and all that; Likelihood examples; Standar statistics revisited; Modeling variance; Dynamic models.
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