Optimization and probability in systems engineering - Search

ia/optimizationprob0000rauj.pdf

Optimization and probability in systems engineering [by] John G. Rau New York, Van Nostrand Reinhold Co, New York, New York State, 1970

xi, 403 p. 24 cm Includes bibliographies

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OPTIMIZATION AND PROBABILITY IN SYSTEMS ENGINEERING JOHN G.RAU VAN NOSTRAND REINHOLD COMPANY, 1970, 1970

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Stochastic processes, optimization, and control theory : applications in financial engineering, queueing networks, and manufacturing systems : a volume in honour of Suresh Sethi edited by Houmin Yan, G. George Yin and Qing Zhang Springer US, 1st Edition., 2010

This edited volume contains 16 research articles. It presents recent and pressing issues in stochastic processes, control theory, differential games, optimization, and their applications in finance, manufacturing, queueing networks, and climate control. One of the salient features is that the book is highly multi-disciplinary. The book is dedicated to Professor Suresh Sethi on the occasion of his 60th birthday, in view of his distinguished career.

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Modelling and optimization of distributed parameter systems : applications to engineering : selected proceedings of the IFIP WG7.2 on Modelling and Optimization of Distributed Parameter Systems with Applications to Engineering, June 1995 V. Barbu (auth.), Kazimierz Malanowski, Zbigniew Nahorski, Małgorzata Peszyńska (eds.) Springer Science Business Media, B.V, IFIP — The International Federation for Information Processing, 1, 1996

Preface. International program committee. Invited lectures. Properties of solutions to PDE. Control and optimization. Numerical modelling. Mechanical applications. Index of contributors. Keyword index.

English [en] · PDF · 11.6MB · 1996 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/scihub/zlib · Save

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Discrete-time Stochastic Systems : Estimation and Control T. Söderström PhD (auth.) Springer-Verlag London, Advanced Textbooks in Control and Signal Processing, Advanced Textbooks in Control and Signal Processing, 2, 2002

__Discrete-time Stochastic Systems__ gives a comprehensive introduction to the estimation and control of dynamic stochastic systems and provides complete derivations of key results such as the basic relations for Wiener filtering. The book covers both state-space methods and those based on the polynomial approach. Similarities and differences between these approaches are highlighted. Some non-linear aspects of stochastic systems (such as the bispectrum and extended Kalman filter) are also introduced and analysed. The books chief features are as follows: • inclusion of the polynomial approach provides alternative and simpler computational methods than simple reliance on state-space methods; • algorithms for analysis and design of stochastic systems allow for ease of implementation and experimentation by the reader; • the highlighting of spectral factorization gives appropriate emphasis to this key concept often overlooked in the literature; • explicit solutions of Wiener problems are handy schemes, well suited for computations compared with more commonly available but abstract formulations; • complex-valued models that are directly applicable to many problems in signal processing and communications. Changes in the second edition include: • additional information covering spectral factorisation and the innovations form; • the chapter on optimal estimation being completely rewritten to focus on __a posteriori__ estimates rather than maximum likelihood; • new material on fixed lag smoothing and algorithms for solving Riccati equations are improved and more up to date; • new presentation of polynomial control and new derivation of linear-quadratic-Gaussian control. __Discrete-time Stochastic Systems__ is primarily of benefit to students taking M.Sc. courses in stochastic estimation and control, electronic engineering and signal processing but may also be of assistance for self study and as a reference.

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Sensing, Modeling and Optimization of Cardiac Systems: A New Generation of Digital Twin for Heart Health Informatics (SpringerBriefs in Service Science) Hui Yang, Bing Yao Springer International Publishing, Springer Nature, Cham, 2023

This book reviews the development of physics-based modeling and sensor-based data fusion for optimizing medical decision making in connection with spatiotemporal cardiovascular disease processes. To improve cardiac care services and patients’ quality of life, it is very important to detect heart diseases early and optimize medical decision making. This book introduces recent research advances in machine learning, physics-based modeling, and simulation optimization to fully exploit medical data and promote the data-driven and simulation-guided diagnosis and treatment of heart disease. Specifically, it focuses on three major topics: computer modeling of cardiovascular systems, physiological signal processing for disease diagnostics and prognostics, and simulation optimization in medical decision making. It provides a comprehensive overview of recent advances in personalized cardiac modeling by integrating physics-based knowledge of the cardiovascular system with machine learning and multi-source medical data. It also discusses the state-of-the-art in electrocardiogram (ECG) signal processing for the identification of disease-altered cardiac dynamics. Lastly, it introduces readers to the early steps of optimal decision making based on the integration of sensor-based learning and simulation optimization in the context of cardiac surgeries. This book will be of interest to researchers and scholars in the fields of biomedical engineering, systems engineering and operations research, as well as professionals working in the medical sciences.

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nexusstc/Numerical Methods in Sensitivity Analysis and Shape Optimization/0441cd71a2dc0b02cdbf114a160af586.djvu

Numerical Methods in Sensitivity Analysis and Shape Optimization (Modeling and Simulation in Science, Engineering & Technology) Volker Stalmann, Emmanuel Laporte Birkhäuser Boston, Modeling and simulation in science, engineering, and technology, 1, 2002

Sensitivity analysis and optimal shape design are key issues in engineering that have been affected by advances in numerical tools currently available. This book/interactive CD-ROM presents basic optimization techniques that can be used to compute the sensitivity of a given design to local change or to improve its performance by local optimization of these data. The relevance and scope of these techniques have improved dramatically in recent years because of progress in discretization strategies, optimization algorithms, automatic differentiation, software availability, and the power of personal computers. Key features of this original, progressive, and comprehensive approach: * description of mathematical background and underlying tools * up-to-date review of grid construction and control, optimization algorithms, software differentiation and gradient calculations * practical solutions for implementation in many real-life problems * solution of illustrative examples and exercises * basic mathematical programming techniques used to solve constrained minimization problems are presented; these fairly self-contained chapters can serve as an introduction to the numerical solution of generic constrained optimization problems * companion CD-ROM contains source files and data; readers can test different solution strategies to determine their relevance and efficiency * CD-ROM also offers software building, updating computational grids, performing automatic code differentiation, and computing basic aeroelastic solutions. "Numerical Methods in Sensitivity Analysis and Shape Optimization" will be of interest to graduate students involved in mathematical modeling and simulation, as well as engineers and researchers in applied mathematics looking for an up-to-date introduction to optimization techniques, sensitivity analysis, and optimal design. The work is suitable as a textbook for graduate courses in any of the topics mentioned above, and as a reference text.

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ia/isbn_9781441905338.pdf

Performance models and risk management in communications systems$by Nalân Gülpınar ; Peter Harrison ; Berç Rüstem. Eds Nalân Gülpınar; Peter G. Harrison; Berc Rustem Springer New York : Imprint: Springer, Springer Nature, New York, 2011

This volume covers recent developments in the design, operation, and management of telecommunication and computer network systems in performance engineering and addresses issues of uncertainty, robustness, and risk. Uncertainty regarding loading and system parameters leads to challenging optimization and robustness issues. Stochastic modeling combined with optimization theory ensures the optimum end-to-end performance of telecommunication or computer network systems. In view of the diverse design options possible, supporting models have many adjustable parameters and choosing the best set for a particular performance objective is delicate and time-consuming. An optimization based approach determines the optimal possible allocation for these parameters. Researchers and graduate students working at the interface of telecommunications and operations research will benefit from this book. Due to the practical approach, this book will also serve as a reference tool for scientists and engineers in telecommunication and computer networks who depend upon optimization. Erscheinungsdatum: 10.11.2010

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Evolutionary Global Optimization, Manifolds And Applications (studies In Systems, Decision And Control) Hime Aguiar e Oliveira Junior (auth.) Springer International Publishing : Imprint : Springer, Studies in Systems, Decision and Control, Studies in Systems, Decision and Control 43, 1, 2016

This book presents powerful techniques for solving global optimization problems on manifolds by means of evolutionary algorithms, and shows in practice how these techniques can be applied to solve real-world problems. It describes recent findings and well-known key facts in general and differential topology, revisiting them all in the context of application to current optimization problems. Special emphasis is put on game theory problems. Here, these problems are reformulated as constrained global optimization tasks and solved with the help of Fuzzy ASA. In addition, more abstract examples, including minimizations of well-known functions, are also included. Although the Fuzzy ASA approach has been chosen as the main optimizing paradigm, the book suggests that other metaheuristic methods could be used as well. Some of them are introduced, together with their advantages and disadvantages. Readers should possess some knowledge of linear algebra, and of basic concepts of numerical analysis and probability theory. Many necessary definitions and fundamental results are provided, with the formal mathematical requirements limited to a minimum, while the focus is kept firmly on continuous problems. The book offers a valuable resource for students, researchers and practitioners. It is suitable for university courses on optimization and for self-study.

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[Topics in Safety, Risk, Reliability and Quality] Quality Improvement with Design of Experiments Volume 7 || Ivan N. Vuchkov, Lidia N. Boyadjieva (auth.) Springer Netherlands : Imprint : Springer, 10.1007/97, 2001

Residualplots 74 Normaland half-normal plots 77 2. 3. 10. TRANSFORMATIONS OF VARIABLES 80 2. 3. 11. WEIGHTED LEAST SQUARES 82 2. 4. Bibliography 84 Appendix A. 2. 1. Basic equation ofthe analysis ofvariance 84 Appendix A. 2. 2. Derivation of the simplified formulae (2. 1 0) and (2. 11) 85 Appendix A. 2. 3. Basic properties ofleast squares estimates 86 Appendix A. 2. 4. Sums ofsquares for tests for lack offit 88 Appendix A. 2. 5. Properties ofthe residuals 90 3. DESIGN OF REGRESSION EXPERIMENTS 96 3. 1. Introduction 96 3. 2. Variance-optimality of response surface designs 98 3. 3. Two Ievel full factorial designs 106 3. 3. 1. DEFINITIONS AND CONSTRUCTION 106 3. 3. 2. PROPERTIES OF TWO LEVEL FULL FACTORIAL DESIGNS 109 3. 3. 3. REGRESSION ANALYSIS OF DAT A OBT AlNED THROUGH TWO LEVEL FULL F ACTORIAL DESIGNS 113 Parameter estimation 113 Effects of factors and interactions 116 Statistical analysis of individual effects and test for lack of fit 118 3. 4. Two Ievel fractional factorial designs 123 3. 4. 1. CONSTRUCTION OF FRACTIONAL F ACTORIAL DESIGNS 123 3. 4. 2. FITTING EQUATIONS TO DATA OBTAlNED BY FRACTIONAL F ACTORIAL DESIGNS 130 3. 5. Bloclung 133 3. 6. Steepest ascent 135 3. 7. Second order designs 142 3. 7. 1. INTRODUCTION 142 3. 7. 2. COMPOSITE DESIGNS 144 Rotatable central composite designs 145 D-optimal composite designs 146 Hartley' s designs 146 3. 7. 3.

English [en] · PDF · 37.7MB · 2001 · 📘 Book (non-fiction) · 🚀/lgli/scihub/zlib · Save

lgli/M_Mathematics/MOc_Optimization and control/Dragan V., Morozan T., Stoica A.M. Mathematical methods in robust control of discrete-time linear stochastic systems (Springer, 2010)(ISBN 1441906290)(O)(349s)_MOc_.pdf

Mathematical methods in robust control of discrete-time linear stochastic systems Vasile Dragan, Toader Morozan, Adrian-Mihail Stoica (auth.) Springer-Verlag New York, 1, 2010

In this monograph the authors develop a theory for the robust control of discrete-time stochastic systems, subjected to both independent random perturbations and to Markov chains. Such systems are widely used to provide mathematical models for real processes in fields such as aerospace engineering, communications, manufacturing, finance and economy. The theory is a continuation of the authors’ work presented in their previous book entitled "Mathematical Methods in Robust Control of Linear Stochastic Systems" published by Springer in 2006. Key features: - Provides a common unifying framework for discrete-time stochastic systems corrupted with both independent random perturbations and with Markovian jumps which are usually treated separately in the control literature; - Covers preliminary material on probability theory, independent random variables, conditional expectation and Markov chains; - Proposes new numerical algorithms to solve coupled matrix algebraic Riccati equations; - Leads the reader in a natural way to the original results through a systematic presentation; - Presents new theoretical results with detailed numerical examples. The monograph is geared to researchers and graduate students in advanced control engineering, applied mathematics, mathematical systems theory and finance. It is also accessible to undergraduate students with a fundamental knowledge in the theory of stochastic systems.

English [en] · PDF · 4.2MB · 2010 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/scihub/zlib · Save

lgli/M_Mathematics/MOc_Optimization and control/Gavalec M., Ramik J., Zimmermann K. Decision making and optimization (LNEMS0677, Springer, 2015)(ISBN 9783319083223)(O)(231s)_MOc_.pdf

Decision Making and Optimization: Special Matrices and Their Applications in Economics and Management (Lecture Notes in Economics and Mathematical Systems, 677) Martin Gavalec, Jaroslav Ramík, Karel Zimmermann Springer International Publishing : Imprint : Springer, Lecture Notes in Economics and Mathematical Systems, 2015, 2014

The book is a benefit for graduate and postgraduate students in the areas of operations research, decision theory, optimization theory, linear algebra, interval analysis and fuzzy sets. The book will also be useful for the researchers in the respective areas. The first part of the book deals with decision making problems and procedures that have been established to combine opinions about alternatives related to different points of view. Procedures based on pairwise comparisons are thoroughly investigated. In the second part we investigate optimization problems where objective functions and constraints are characterized by extremal operators such as maximum, minimum or various triangular norms (t-norms). Matrices in max-min algebra are useful in applications such as automata theory, design of switching circuits, logic of binary relations, medical diagnosis, Markov chains, social choice, models of organizations, information systems, political systems and clustering. The input data in real problems are usually not exact and can be characterized by interval values. Erscheinungsdatum: 27.10.2014

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ia/stochasticprogra0000gamm.pdf

Stochastic Programming Methods and Technical Applications : Proceedings of the 3rd GAMM/IFIP-Workshop on “Stochastic Optimization: Numerical Methods and Technical Applications” Held at the Federal Armed Forces University Munich, Neubiberg/München, Germany, June 17–20, 1996 Kurt Marti; Peter Kall; GAMM/IFIP-Workshop on "Stochastic Optimization: Numerical Methods and Technical Applications" Springer Berlin Heidelberg, Springer Nature, Berlin, Heidelberg, 2012

Optimization problems arising in practice usually contain several random parameters. Hence, in order to obtain optimal solutions being robust with respect to random parameter variations, the mostly available statistical information about the random parameters should be considered already at the planning phase. The original problem with random parameters must be replaced by an appropriate deterministic substitute problem, and efficient numerical solution or approximation techniques have to be developed for those problems. This proceedings volume contains a selection of papers on modelling techniques, approximation methods, numerical solution procedures for stochastic optimization problems and applications to the reliability-based optimization of concrete technical or economic systems. Erscheinungsdatum: 18.03.1998

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nexusstc/Stochastic Programming Methods and Technical Applications: Proceedings of the 3rd GAMM/IFIP-Workshop on “Stochastic Optimization: Numerical Methods and Technical Applications” held at the Federal Armed Forces University Munich, Neubiberg/München, Germany, June 17–20, 1996/ce472a131adfb67a0713b18c53de9db8.pdf

Stochastic Programming Methods and Technical Applications : Proceedings of the 3rd GAMM/IFIP-Workshop on “Stochastic Optimization: Numerical Methods and Technical Applications” Held at the Federal Armed Forces University Munich, Neubiberg/München, Germany, June 17–20, 1996 P. Kall (auth.), Prof. Dr. Kurt Marti, Prof. Dr. Peter Kall (eds.) Springer-Verlag Berlin Heidelberg, Lecture Notes in Economics and Mathematical Systems, Lecture Notes in Economics and Mathematical Systems 458, 1, 1998

Optimization problems arising in practice usually contain several random parameters. Hence, in order to obtain optimal solutions being robust with respect to random parameter variations, the mostly available statistical information about the random parameters should be considered already at the planning phase. The original problem with random parameters must be replaced by an appropriate deterministic substitute problem, and efficient numerical solution or approximation techniques have to be developed for those problems. This proceedings volume contains a selection of papers on modelling techniques, approximation methods, numerical solution procedures for stochastic optimization problems and applications to the reliability-based optimization of concrete technical or economic systems. Erscheinungsdatum: 18.03.1998

English [en] · PDF · 17.9MB · 1998 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/scihub/zlib · Save

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lgli/M_Mathematics/MV_Probability/MVsa_Statistics and applications/Isermann R., Muenchhof M. Identification of Dynamic Systems (Springer, 2010)(ISBN 3540788786)(O)(732s)_MVsa_.pdf

Identification of Dynamic Systems: An Introduction with Applications (Advanced Textbooks in Control and Signal Processing) Rolf Isermann, Marco Münchhof (auth.) Springer-Verlag Berlin Heidelberg, Advanced Textbooks in Control and Signal Processing, 1, 2011

Precise dynamic models of processes are required for many applications, ranging from control engineering to the natural sciences and economics. Frequently, such precise models cannot be derived using theoretical considerations alone. Therefore, they must be determined experimentally. This book treats the determination of dynamic models based on measurements taken at the process, which is known as system identification or process identification. Both offline and online methods are presented, i.e. methods that post-process the measured data as well as methods that provide models during the measurement. The book is theory-oriented and application-oriented and most methods covered have been used successfully in practical applications for many different processes. Illustrative examples in this book with real measured data range from hydraulic and electric actuators, machine tools, industrial robots, pumps, vehicles to combustion engines. Real experimental data is also provided on the Springer webpage, allowing readers to gather their first experience with the methods presented in this book. Among others, the book covers the following subjects: determination of the nonparametric frequency response, (fast) Fourier transform, correlation analysis, parameter estimation with a focus on the method of Least Squares and modifications, identification of time-variant processes, identification in closed-loop, identification of continuous time processes, and subspace methods. Some methods for nonlinear system identification are also considered, such as the Extended Kalman filter and neural networks. The different methods are compared by using a real three-mass oscillator process, a model of a drive train. For many identification methods, hints for the practical implementation and application are provided. The book is intended to meet the needs of students and practicing engineers working in research and development, design and manufacturing.

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Optimization in Machine Learning and Applications Anand Jayant Kulkarni; Suresh Chandra Satapathy Springer Singapore : Imprint: Springer, Algorithms for Intelligent Systems, Algorithms for Intelligent Systems, 1, 2020

This book discusses one of the major applications of artificial intelligence: the use of machine learning to extract useful information from multimodal data. It discusses the optimization methods that help minimize the error in developing patterns and classifications, which further helps improve prediction and decision-making. The book also presents formulations of real-world machine learning problems, and discusses AI solution methodologies as standalone or hybrid approaches. Lastly, it proposes novel metaheuristic methods to solve complex machine learning problems. Featuring valuable insights, the book helps readers explore new avenues leading toward multidisciplinary research discussions. Erscheinungsdatum: 10.12.2019

English [en] · PDF · 5.8MB · 2020 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/scihub/zlib · Save

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IUTAM Symposium on Advances in Nonlinear Stochastic Mechanics : Proceedings of the IUTAM Symposium Held in Trondheim, Norway, 3–7 July 1995 A. A. Afaneh (auth.), A. Naess, S. Krenk (eds.) Springer Netherlands, Solid Mechanics and its Applications, Solid Mechanics and its Applications 47, 1, 1996

The IUTAM Symposium on Advances in Nonlinear Stochastic Mechanics, held in Trondheim July 3-7, 1995, was the eighth of a series of IUTAM sponsored symposia which focus on the application of stochastic methods in mechanics. The previous meetings took place in Coventry, UK (1972), Sout'hampton, UK (1976), FrankfurtjOder, Germany (1982), Stockholm, Sweden (1984), Innsbruckjlgls, Austria (1987), Turin, Italy (1991) and San Antonio, Texas (1993). The symposium provided an extraordinary opportunity for scholars to meet and discuss recent advances in stochastic mechanics. The participants represented a wide range of expertise, from pure theoreticians to people primarily oriented toward applications. A significant achievement of the symposium was the very extensive discussions taking place over the whole range from highly theoretical questions to practical engineering applications. Several presentations also clearly demonstrated the substantial progress that has been achieved in recent years in terms of developing and implement ing stochastic analysis techniques for mechanical engineering systems. This aspect was further underpinned by specially invited extended lectures on computational stochastic mechanics, engineering applications of stochastic mechanics, and nonlinear active control. The symposium also reflected the very active and high-quality research taking place in the field of stochastic stability. Ten presentations were given on this topic ofa total of47 papers. A main conclusion that can be drawn from the proceedings of this symposium is that stochastic mechanics as a subject has reached great depth and width in both methodology and applicability. Erscheinungsdatum: 20.09.2011

English [en] · PDF · 19.1MB · 1996 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

lgli/Modeling with Stochastic Programming (Alan J. King • Stein W. Wallace).pdf

Modeling with Stochastic Programming 2nd Edition Alan J. King , Stein W. Wallace Springer International Publishing : Imprint: Springer, Springer Series in Operations Research and Financial Engineering, 2, 2024

This is an updated version of what is still the only text to address basic questions about how to model uncertainty in mathematical programming, including how to reformulate a deterministic model so that it can be analyzed in a stochastic setting. This second edition has important extensions regarding how to represent random phenomena in the models (also called scenario generation) as well as a new chapter on multi-stage models. This text would be suitable as a stand-alone or supplement for a second course in OR/MS or in optimization-oriented engineering disciplines where the instructor wants to explain where models come from and what the fundamental modeling issues are. The book is easy-to-read, highly illustrated with lots of examples and discussions. It will be suitable for graduate students and researchers working in operations research, mathematics, engineering and related departments where there is interest in learning how to model uncertainty. Alan King is a Research Staff Member at IBM's Thomas J. Watson Research Center in New York. Stein W. Wallace is a Professor of Operational Research and head of Center for Shipping and Logistics at NHH Norwegian School of Economics, Bergen, Norway.

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Deep Learning Classifiers with Memristive Networks: Theory and Applications (Modeling and Optimization in Science and Technologies Book 14) Alex Pappachen James Springer International Publishing, Springer Nature, Cham, 2019

"This book introduces readers to the fundamentals of deep neural network architectures, with a special emphasis on memristor circuits and systems. At first, the book offers an overview of neuro-memristive systems, including memristor devices, models, and theory, as well as an introduction to deep learning neural networks such as multi-layer networks, convolution neural networks, hierarchical temporal memory, and long short term memories, and deep neuro-fuzzy networks. It then focuses on the design of these neural networks using memristor crossbar architectures in detail. The book integrates the theory with various applications of neuro-memristive circuits and systems. It provides an introductory tutorial on a range of issues in the design, evaluation techniques, and implementations of different deep neural network architectures with memristors." - Prové de l'editor

English [en] · EPUB · 36.1MB · 2019 · 📗 Book (unknown) · 🚀/upload/zlib · Save

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Stochastic Hydrology and its Use in Water Resources Systems Simulation and Optimization (NATO Science Series E:, 237) Vujica Yevjevich (auth.), J. B. Marco, R. Harboe, J. D. Salas (eds.) Springer Netherlands : Imprint : Springer, NATO ASI Series 237, 1, 1993

Stochastic hydrology is an essential base of water resources systems analysis, due to the inherent randomness of the input, and consequently of the results. These results have to be incorporated in a decision-making process regarding the planning and management of water systems. It is through this application that stochastic hydrology finds its true meaning, otherwise it becomes merely an academic exercise. A set of well known specialists from both stochastic hydrology and water resources systems present a synthesis of the actual knowledge currently used in real-world planning and management. The book is intended for both practitioners and researchers who are willing to apply advanced approaches for incorporating hydrological randomness and uncertainty into the simulation and optimization of water resources systems. (abstract) Stochastic hydrology is a basic tool for water resources systems analysis, due to inherent randomness of the hydrologic cycle. This book contains actual techniques in use for water resources planning and management, incorporating randomness into the decision making process. Optimization and simulation, the classical systems-analysis technologies, are revisited under up-to-date statistical hydrology findings backed by real world applications.

English [en] · PDF · 14.2MB · 1993 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

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EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation VI (Advances in Intelligent Systems and Computing Book 674) Alexandru-Adrian Tantar,Emilia Tantar,Michael Emmerich,Pierrick Legrand,Lenuta Alboaie,Henri Luchian (eds.) Springer International Publishing : Imprint: Springer, Advances in Intelligent Systems and Computing, Advances in Intelligent Systems and Computing 674, 1, 2018

This book comprises selected research papers from the 2015 edition of the EVOLVE conference, which was held on June 18–June 24, 2015 in Iași, Romania. It presents the latest research on Probability, Set Oriented Numerics, and Evolutionary Computation. The aim of the EVOLVE conference was to provide a bridge between probability, set oriented numerics and evolutionary computation and to bring together experts from these disciplines. The broad focus of the EVOLVE conference made it possible to discuss the connection between these related fields of study computational science. The selected papers published in the proceedings book were peer reviewed by an international committee of reviewers (at least three reviews per paper) and were revised and enhanced by the authors after the conference. The contributions are categorized into five major parts, which are: Multicriteria and Set-Oriented Optimization; Evolution in ICT Security; Computational Game Theory; Theory on Evolutionary Computation; Applications of Evolutionary Algorithms. The 2015 edition shows a major progress in the aim to bring disciplines together and the research on a number of topics that have been discussed in previous editions of the conference matured over time and methods have found their ways in applications. In this sense the book can be considered an important milestone in bridging and thereby advancing state-of-the-art computational methods.

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upload/newsarch_ebooks_2025_10/2021/05/25/3540050450_Large-Scale.pdf

Large-Scale PDE-Constrained Optimization Lorenz T. Biegler; Matthias Heinkenschloss; Omar Ghattas; Bart van Bloemen Waanders Springer Berlin Heidelberg : Imprint: Springer, Lecture Notes in Computational Science and Engineering, Lecture Notes in Computational Science and Engineering 30, 1, 2003

Optimal design, optimal control, and parameter estimation of systems governed by partial differential equations (PDEs) give rise to a class of problems known as PDE-constrained optimization. The size and complexity of the discretized PDEs often pose significant challenges for contemporary optimization methods. With the maturing of technology for PDE simulation, interest has now increased in PDE-based optimization. The chapters in this volume collectively assess the state of the art in PDE-constrained optimization, identify challenges to optimization presented by modern highly parallel PDE simulation codes, and discuss promising algorithmic and software approaches for addressing them. These contributions represent current research of two strong scientific computing communities, in optimization and PDE simulation. This volume merges perspectives in these two different areas and identifies interesting open questions for further research.

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ia/theorypracticeof0002liub.pdf

Theory and Practice of Uncertain Programming Studies in Fuzziness and Soft Computing Baoding Liu; Janusz Kacprzyk Springer Berlin Heidelberg, Springer Nature, Berlin, Heidelberg, 2008

Real-life decisions are usually made in the state of uncertainty such as randomness and fuzziness. How do we model optimization problems in uncertain environments? How do we solve these models? In order to answer these questions, this book provides a self-contained, comprehensive and up-to-date presentation of uncertain programming theory, including numerous modeling ideas, hybrid intelligent algorithms, and applications in system reliability design, project scheduling problem, vehicle routing problem, facility location problem, and machine scheduling problem. Researchers, practitioners and students in operations research, management science, information science, system science, and engineering will find this work a stimulating and useful reference.

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Mathematical Methods in Robust Control of Discrete-Time Linear Stochastic Systems Vasile Dragan, Toader Morozan, Adrian-Mihail Stoica (auth.) Springer-Verlag New York, 1, 2010

In this monograph the authors develop a theory for the robust control of discrete-time stochastic systems, subjected to both independent random perturbations and to Markov chains. Such systems are widely used to provide mathematical models for real processes in fields such as aerospace engineering, communications, manufacturing, finance and economy. The theory is a continuation of the authors’ work presented in their previous book entitled "Mathematical Methods in Robust Control of Linear Stochastic Systems" published by Springer in 2006. Key features: - Provides a common unifying framework for discrete-time stochastic systems corrupted with both independent random perturbations and with Markovian jumps which are usually treated separately in the control literature; - Covers preliminary material on probability theory, independent random variables, conditional expectation and Markov chains; - Proposes new numerical algorithms to solve coupled matrix algebraic Riccati equations; - Leads the reader in a natural way to the original results through a systematic presentation; - Presents new theoretical results with detailed numerical examples. The monograph is geared to researchers and graduate students in advanced control engineering, applied mathematics, mathematical systems theory and finance. It is also accessible to undergraduate students with a fundamental knowledge in the theory of stochastic systems.

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Reliability And Safety Of Complex Technical Systems And Processes: Modeling – Identification – Prediction - Optimization (springer Series In Reliability Engineering) Krzysztof Kołowrocki, Joanna Soszyńska-Budny (auth.) Springer-Verlag London Limited, Springer Series in Reliability Engineering, Springer Series in Reliability Engineering, 1, 2011

__Reliability and Safety of Complex Technical Systems and Processes__ offers a comprehensive approach to the analysis, identification, evaluation, prediction and optimization of complex technical systems operation, reliability and safety. Its main emphasis is on multistate systems with ageing components, changes to their structure, and their components reliability and safety parameters during the operation processes. __Reliability and Safety of Complex Technical Systems and Processes__ presents integrated models for the reliability, availability and safety of complex non-repairable and repairable multistate technical systems, with reference to their operation processes and their practical applications to real industrial systems. The authors consider variables in different operation states, reliability and safety structures, and the reliability and safety parameters of components, as well as suggesting a cost analysis for complex technical systems. Researchers and industry practitioners will find information on a wide range of complex technical systems in __Reliability and Safety of Complex Technical Systems and Processes.__ It may prove an easy-to-use guide to reliability and safety evaluations of real complex technical systems, both during their operation and at the design stages.

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Many Agent Games in Socio-economic Systems: Corruption, Inspection, Coalition Building, Network Growth, Security (Springer Series in Operations Research and Financial Engineering) Vassili N. Kolokoltsov, Oleg A. Malafeyev Springer International Publishing : Imprint: Springer, Springer Series in Operations Research and Financial Engineering, Springer Series in Operations Research and Financial Engineering, 1, 2019

There has been an increase in attention toward systems involving large numbers of small players, giving rise to the theory of mean field games, mean field type control and nonlinear Markov games. Exhibiting various real world problems involving major and minor agents, this book presents a systematic continuous-space approximation approach for __mean-field interacting agents__ models and __mean-field games__ models. After describing Markov-chain methodology and a modeling of mean-field interacting systems, the text presents various structural conditions on the chain to yield respective socio-economic models, focusing on migration models via binary interactions. The specific applications are wide-ranging – including inspection and corruption, cyber-security, counterterrorism, coalition building and network growth, minority games, and investment policies and optimal allocation – making this book relevant to a wide audience of applied mathematicians interested in operations research, computer science, national security, economics, and finance.

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Evolve - A Bridge Between Probability, Set Oriented Numerics, And Evolutionary Computation V (advances In Intelligent Systems And Computing) Alexandru-Adrian Tantar, Emilia Tantar, Jian-Qiao Sun, Wei Zhang, Qian Ding, Oliver Schütze, Michael Emmerich, Pierrick Legrand, Pierre Del Moral, Carlos A. Coello Coello (eds.) Springer International Publishing : Imprint : Springer, Advances in Intelligent Systems and Computing, Advances in Intelligent Systems and Computing 288, 1, 2014

This volume encloses research articles that were presented at the EVOLVE 2014 International Conference in Beijing, China, July 1–4, 2014. The book gathers contributions that emerged from the conference tracks, ranging from probability to set oriented numerics and evolutionary computation; all complemented by the bridging purpose of the conference, e.g. Complex Networks and Landscape Analysis, or by the more application oriented perspective. The novelty of the volume, when considering the EVOLVE series, comes from targeting also the practitioner’s view. This is supported by the Machine Learning Applied to Networks and Practical Aspects of Evolutionary Algorithms tracks, providing surveys on new application areas, as in the networking area and useful insights in the development of evolutionary techniques, from a practitioner’s perspective. Complementary to these directions, the conference tracks supporting the volume, follow on the individual advancements of the subareas constituting the scope of the conference, through the Computational Game Theory, Local Search and Optimization, Genetic Programming, Evolutionary Multi-objective optimization tracks.

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Performance models and risk management in communications systems$by Nalân Gülpınar ; Peter Harrison ; Berç Rüstem. Eds Tansu Alpcan (auth.), Nalân Gülpınar, Peter Harrison, Berç Rüstem (eds.) Springer-Verlag New York, Springer Optimization and Its Applications 46, 1, 2011

This volume covers recent developments in the design, operation, and management of telecommunication and computer network systems in performance engineering and addresses issues of uncertainty, robustness, and risk. Uncertainty regarding loading and system parameters leads to challenging optimization and robustness issues. Stochastic modeling combined with optimization theory ensures the optimum end-to-end performance of telecommunication or computer network systems. In view of the diverse design options possible, supporting models have many adjustable parameters and choosing the best set for a particular performance objective is delicate and time-consuming. An optimization based approach determines the optimal possible allocation for these parameters. Researchers and graduate students working at the interface of telecommunications and operations research will benefit from this book. Due to the practical approach, this book will also serve as a reference tool for scientists and engineers in telecommunication and computer networks who depend upon optimization. Erscheinungsdatum: 10.11.2010

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Switched Linear Systems: Control and Design (Communications and Control Engineering) Zhendong Sun PhD, Shuzhi Sam Ge PhD, DIC (auth.) Springer-Verlag London Ltd, Communications and Control Engineering, Communications and Control Engineering, 1, 2005

Switched linear systems have a long history in the control literature but-along with hybrid systems more generally-they have enjoyed a particular growth in interest since the 1990s. The large amount of data and ideas thus generated have, until now, lacked a co-ordinating framework to focus them effectively on some of the fundamental issues such as the problems of robust stabilizing switching design, feedback stabilization and optimal switching. This deficiency is resolved by __Switched Linear Systems__ which features: • nucleus of constructive design approaches based on canonical decomposition and forming a sound basis for the systematic treatment of secondary results; • theoretical exploration and logical association of several independent but pivotal concerns in control design as they pertain to switched linear systems: controllability and observability, feedback stabilization, optimization and periodic switching; • a reliable foundation for further theoretical research as well as design guidance for real life engineering applications through the integration of novel ideas, fresh insights and rigorous results. Primarily intended for researchers and engineers in the systems and control community, postgraduate students will also discover that this is perfect complementary reading especially for those studying intelligent, adaptive or robust control.

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Large-Scale PDE-Constrained Optimization Lorenz T. Biegler, Omar Ghattas, Matthias Heinkenschloss, Bart van Bloemen Waanders (auth.), Lorenz T. Biegler, Matthias Heinkenschloss, Omar Ghattas, Bart van Bloemen Waanders (eds.) Springer-Verlag Berlin Heidelberg, Lecture Notes in Computational Science and Engineering, Lecture Notes in Computational Science and Engineering 30, 1, 2003

Optimal design, optimal control, and parameter estimation of systems governed by partial differential equations (PDEs) give rise to a class of problems known as PDE-constrained optimization. The size and complexity of the discretized PDEs often pose significant challenges for contemporary optimization methods. With the maturing of technology for PDE simulation, interest has now increased in PDE-based optimization. The chapters in this volume collectively assess the state of the art in PDE-constrained optimization, identify challenges to optimization presented by modern highly parallel PDE simulation codes, and discuss promising algorithmic and software approaches for addressing them. These contributions represent current research of two strong scientific computing communities, in optimization and PDE simulation. This volume merges perspectives in these two different areas and identifies interesting open questions for further research.

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Computational Mathematics Driven by Industrial Problems : Lectures Given at the 1st Session of the Centro Internazionale Matematico Estivo (C.I.M.E.) Held in Martina Franca, Italy, June 21-27, 1999 Rainer E. Burkard (auth.), Rainer E. Burkard, Antony Jameson, Gilbert Strang, Peter Deuflhard, Jacques-Louis Lions, Vincenzo Capasso, Jacques Periaux, Heinz W. Engl (eds.) Springer-Verlag Berlin Heidelberg, Lecture Notes in Mathematics, Lecture Notes in Mathematics 1739, 1, 2000

These Lecture Notes By Very Authoritative Scientists Survey Recent Advances Of Mathematics Driven By Industrial Application Showing Not Only How Mathematics Is Applied To Industry But Also How Mathematics Has Drawn Benefit From Interaction With Real-word Problems. The Famous David Report Underlines That Innovative High Technology Depends Crucially For Its Development On Innovation In Mathematics. The Speakers Include Three Recent Presidents Of Ecmi, One Of Eccomas (in Europe) And The President Of Siam. Trees And Paths: Graph Optimisation Problems With Industrial Applications / R.e. Burkard -- Mathematical Models For Polymer Crystallization Processes / V. Capasso -- Differential Equations In Technology And Medicine: Computational Concepts, Adaptive Algorithms, And Virtual Labs / P. Deuflhard -- Inverse Problems And Their Regularization / H.w. Engl -- Aerodynamic Shape Optimization Techniques Based On Control Theory / A. Jameson, L. Martinelli -- Complexity In Industrial Problems. Some Remarks / J.-l. Lions -- Flow And Heat Transfer In Pressing Of Glass Products / K. Laevksy, B.j. Van Der Linden, R.m.m. Mattheij -- Drag Reduction By Active Control For Flow Past Cylinders / J.-w. He [and Others] -- Signal Processing For Everyone / G. Strang. R. Burkard ... [et Al.] ; Editors V. Capasso, H. Engel, J. Periaux. Includes Bibliographical References (p. 411-412).

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Analysis and Synthesis of Nonlinear Control Systems: A Convex Optimisation Approach (Studies in Systems, Decision and Control, 408) Miguel Bernal, Antonio Sala, Zsófia Lendek, Thierry Marie Guerra Springer International Publishing AG, Studies in Systems, Decision and Control, 408, 2022

This book presents a modern perspective on the modelling, analysis, and synthesis ideas behind convex-optimisation-based control of nonlinear systems: it embeds them in models with convex structures. Analysis and Synthesis of Nonlinear Control Systems begins with an introduction to the topic and a discussion of the problems to be solved. It then explores modelling via convex structures, including quasi-linear parameter-varying, Takagi–Sugeno models, and linear fractional transformation structures. The authors cover stability analysis, addressing Lyapunov functions and the stability of polynomial models, as well as the performance and robustness of the models. With detailed examples, simulations, and programming code, this book will be useful to instructors, researchers, and graduate students interested in nonlinear control systems.

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Robust Data Mining (SpringerBriefs in Optimization) Petros Xanthopoulos; Panos M Pardalos; Theodore B Trafalis; et al Springer New York : Imprint: Springer, SpringerBriefs in Optimization, Springer briefs in optimization, 2013

Data uncertainty is a concept closely related with most real life applications that involve data collection and interpretation. Examples can be found in data acquired with biomedical instruments or other experimental techniques. Integration of robust optimization in the existing data mining techniques aim to create new algorithms resilient to error and noise. This work encapsulates all the latest applications of robust optimization in data mining. This brief contains an overview of the rapidly growing field of robust data mining research field and presents the most well known machine learning algorithms, their robust counterpart formulations and algorithms for attacking these problems. This brief will appeal to theoreticians and data miners working in this field. 1. Introduction 2. Least Squares Problems 3. Principal Component Analysis 4. Linear Discriminant Analysis 5. Support Vector Machines 6. Conclusion

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Lyapunov Exponents: Proceedings of a Conference held in Oberwolfach, May 28 - June 2, 1990 (Lecture Notes in Mathematics, 1486) (English and French Edition) Ludwig Arnold, Hans Crauel, Jean-Pierre Eckmann, Conference on Lyapynov Exponents, Ludwig Arnold, Jean Pierre Eckmann, H Crauel, L. Arnold, H. Crauel, J.-P. Eckmann, eds, L Arnold, H Crauel, Jean Pierre Eckmann Springer-Verlag; Springer, 1991, 1991

Since the predecessor to this volume (LNM 1186, Eds. L. Arnold, V. Wihstutz)appeared in 1986, significant progress has been made in the theory and applications of Lyapunov exponents - one of the key concepts of dynamical systems - and in particular, pronounced shifts towards nonlinear and infinite-dimensional systems and engineering applications are observable. This volume opens with an introductory survey article (Arnold/Crauel) followed by 26 original (fully refereed) research papers, some of which have in part survey character. From the Contents: L. Arnold, H. Crauel: Random Dynamical Systems.- I.Ya. Goldscheid: Lyapunov exponents and asymptotic behaviour of the product of random matrices.- Y. Peres: Analytic dependence of Lyapunov exponents on transition probabilities.- O. Knill: The upper Lyapunov exponent of Sl (2, R) cocycles:Discontinuity and the problem of positivity.- Yu.D. Latushkin, A.M. Stepin: Linear skew-product flows and semigroups of weighted composition operators.- P. Baxendale: Invariant measures for nonlinear stochastic differential equations.- Y. Kifer: Large deviationsfor random expanding maps.- P. Thieullen: Generalisation du theoreme de Pesin pour l' -entropie.- S.T. Ariaratnam, W.-C. Xie: Lyapunov exponents in stochastic structural mechanics.- F. Colonius, W. Kliemann: Lyapunov exponents of control flows Examines all significant recent progress made in the theory and applications of a key component of dynamical systems, known as the Lyapunov exponents. Emphasis is placed on shifts towards nonlinear and infinite-dimensional systems and observable engineering applications.

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nexusstc/How to Solve It: Modern Heuristics/bace00516bc83ebdd16294c2a38d5fd4.pdf

How to solve it : modern heuristics Dr. Zbigniew Michalewicz, Dr. David B. Fogel (auth.) Springer-Verlag Berlin Heidelberg, 2nd ed. 2004, Berlin, Heidelberg, 2004

No pleasure lasts long unless there is variety in it. Publilius Syrus, Moral Sayings We've been very fortunate to receive fantastic feedback from our readers during the last four years, since the first edition of How to Solve It: Modern Heuristics was published in 1999. It's heartening to know that so many people appreciated the book and, even more importantly, were using the book to help them solve their problems. One professor, who published a review of the book, said that his students had given the best course reviews he'd seen in 15 years when using our text. There can be hardly any better praise, except to add that one of the book reviews published in a SIAM journal received the best review award as well. We greatly appreciate your kind words and personal comments that you sent, including the few cases where you found some typographical or other errors. Thank you all for this wonderful support. Erscheinungsdatum: 06.12.2010

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How to solve it : modern heuristics Dr. Zbigniew Michalewicz, Dr. David B. Fogel (auth.) Springer-Verlag Berlin Heidelberg, 2nd ed. 2004, Berlin, Heidelberg, 2004

No pleasure lasts long unless there is variety in it. Publilius Syrus, Moral Sayings We've been very fortunate to receive fantastic feedback from our readers during the last four years, since the first edition of How to Solve It: Modern Heuristics was published in 1999. It's heartening to know that so many people appreciated the book and, even more importantly, were using the book to help them solve their problems. One professor, who published a review of the book, said that his students had given the best course reviews he'd seen in 15 years when using our text. There can be hardly any better praise, except to add that one of the book reviews published in a SIAM journal received the best review award as well. We greatly appreciate your kind words and personal comments that you sent, including the few cases where you found some typographical or other errors. Thank you all for this wonderful support. Erscheinungsdatum: 06.12.2010

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Lyapunov Exponents: Proceedings of a Conference held in Oberwolfach, May 28 - June 2, 1990 (Lecture Notes in Mathematics, 1486) (English and French Edition) Ludwig Arnold; Hans Crauel; Jean-Pierre Eckmann Springer Berlin Heidelberg : Imprint : Springer, Springer Nature, Berlin, Heidelberg, 2006

Since the predecessor to this volume (LNM 1186, Eds. L. Arnold, V. Wihstutz)appeared in 1986, significant progress has been made in the theory and applications of Lyapunov exponents - one of the key concepts of dynamical systems - and in particular, pronounced shifts towards nonlinear and infinite-dimensional systems and engineering applications are observable. This volume opens with an introductory survey article (Arnold/Crauel) followed by 26 original (fully refereed) research papers, some of which have in part survey character. From the Contents: L. Arnold, H. Crauel: Random Dynamical Systems.- I. Ya. Goldscheid: Lyapunov exponents and asymptotic behaviour of the product of random matrices.- Y. Peres: Analytic dependence of Lyapunov exponents on transition probabilities.- O. Knill: The upper Lyapunov exponent of Sl (2, R) cocycles:Discontinuity and the problem of positivity.- Yu. D. Latushkin, A.M. Stepin: Linear skew-product flows and semigroups of weighted composition operators.- P. Baxendale: Invariant measures for nonlinear stochastic differential equations.- Y. Kifer: Large deviationsfor random expanding maps.- P. Thieullen: Generalisation du theoreme de Pesin pour l' -entropie.- S.T. Ariaratnam, W.-C. Xie: Lyapunov exponents in stochastic structural mechanics.- F. Colonius, W. Kliemann: Lyapunov exponents of control flows

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How to Solve It: Modern Heuristics || Dr. Zbigniew Michalewicz, Dr. David B. Fogel (auth.) Springer Nature (Textbooks & Major Reference Works) : Springer, 10.1007/97, 2004

No pleasure lasts long unless there is variety in it. Publilius Syrus, Moral Sayings We've been very fortunate to receive fantastic feedback from our readers during the last four years, since the first edition of How to Solve It: Modern Heuristics was published in 1999. It's heartening to know that so many people appreciated the book and, even more importantly, were using the book to help them solve their problems. One professor, who published a review of the book, said that his students had given the best course reviews he'd seen in 15 years when using our text. There can be hardly any better praise, except to add that one of the book reviews published in a SIAM journal received the best review award as well. We greatly appreciate your kind words and personal comments that you sent, including the few cases where you found some typographical or other errors. Thank you all for this wonderful support.

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[Advances in Mechanics and Mathematics] Markov Decision Processes With Their Applications Volume 14 || Professor Qiying Hu, Professor Wuyi Yue (auth.) Springer Science+Business Media, LLC, 10.1007/97, 2008

Markov decision processes (MDPs), also called stochastic dynamic programming, were first studied in the 1960s. MDPs can be used to model and solve dynamic decision-making problems that are multi-period and occur in stochastic circumstances. There are three basic branches in MDPs: discrete-time MDPs, continuous-time MDPs and semi-Markov decision processes. Starting from these three branches, many generalized MDPs models have been applied to various practical problems. These models include partially observable MDPs, adaptive MDPs, MDPs in stochastic environments, and MDPs with multiple objectives, constraints or imprecise parameters. Markov Decision Processes With Their Applications examines MDPs and their applications in the optimal control of discrete event systems (DESs), optimal replacement, and optimal allocations in sequential online auctions. The book presents four main topics that are used to study optimal control problems: *a new methodology for MDPs with discounted total reward criterion; *transformation of continuous-time MDPs and semi-Markov decision processes into a discrete-time MDPs model, thereby simplifying the application of MDPs; *MDPs in stochastic environments, which greatly extends the area where MDPs can be applied; *applications of MDPs in optimal control of discrete event systems, optimal replacement, and optimal allocation in sequential online auctions. This book is intended for researchers, mathematicians, advanced graduate students, and engineers who are interested in optimal control, operation research, communications, manufacturing, economics, and electronic commerce

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Deep Learning Classifiers with Memristive Networks: Theory and Applications (Modeling and Optimization in Science and Technologies Book 14) Alex Pappachen James Springer International Publishing, Modeling and Optimization in Science and Technologies, Modeling and Optimization in Science and Technologies 14, 1, 2020

"This book introduces readers to the fundamentals of deep neural network architectures, with a special emphasis on memristor circuits and systems. At first, the book offers an overview of neuro-memristive systems, including memristor devices, models, and theory, as well as an introduction to deep learning neural networks such as multi-layer networks, convolution neural networks, hierarchical temporal memory, and long short term memories, and deep neuro-fuzzy networks. It then focuses on the design of these neural networks using memristor crossbar architectures in detail. The book integrates the theory with various applications of neuro-memristive circuits and systems. It provides an introductory tutorial on a range of issues in the design, evaluation techniques, and implementations of different deep neural network architectures with memristors." - Prové de l'editor

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Markov Decision Processes with Their Applications (Advances in Mechanics and Mathematics Book 14) Professor Qiying Hu, Professor Wuyi Yue (auth.) Springer Science+Business Media, LLC, Advances in Mechanics and Mathematics, Advances in Mechanics and Mathematics 14, 1, 2008

Markov decision processes (MDPs), also called stochastic dynamic programming, were first studied in the 1960s. MDPs can be used to model and solve dynamic decision-making problems that are multi-period and occur in stochastic circumstances. There are three basic branches in MDPs: discrete-time MDPs, continuous-time MDPs and semi-Markov decision processes. Starting from these three branches, many generalized MDPs models have been applied to various practical problems. These models include partially observable MDPs, adaptive MDPs, MDPs in stochastic environments, and MDPs with multiple objectives, constraints or imprecise parameters. __Markov Decision Processes With Their Applications__ examines MDPs and their applications in the optimal control of discrete event systems (DESs), optimal replacement, and optimal allocations in sequential online auctions. The book presents four main topics that are used to study optimal control problems: \*a new methodology for MDPs with discounted total reward criterion; \*transformation of continuous-time MDPs and semi-Markov decision processes into a discrete-time MDPs model, thereby simplifying the application of MDPs; \*MDPs in stochastic environments, which greatly extends the area where MDPs can be applied; \*applications of MDPs in optimal control of discrete event systems, optimal replacement, and optimal allocation in sequential online auctions. This book is intended for researchers, mathematicians, advanced graduate students, and engineers who are interested in optimal control, operation research, communications, manufacturing, economics, and electronic commerce.

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lgli/M_Mathematics/MV_Probability/MVspa_Stochastic processes/Hu Q., Yue W. Markov decision processes with their applications (Springer, 2008)(ISBN 9780387369501)(305s)_MVspa_.pdf

Markov Decision Processes with Their Applications (Advances in Mechanics and Mathematics Book 14) Professor Qiying Hu, Professor Wuyi Yue (auth.) Springer Science+Business Media, LLC, Advances in Mechanics and Mathematics, Advances in Mechanics and Mathematics 14, 1, 2008

Markov decision processes (MDPs), also called stochastic dynamic programming, were first studied in the 1960s. MDPs can be used to model and solve dynamic decision-making problems that are multi-period and occur in stochastic circumstances. There are three basic branches in MDPs: discrete-time MDPs, continuous-time MDPs and semi-Markov decision processes. Starting from these three branches, many generalized MDPs models have been applied to various practical problems. These models include partially observable MDPs, adaptive MDPs, MDPs in stochastic environments, and MDPs with multiple objectives, constraints or imprecise parameters. __Markov Decision Processes With Their Applications__ examines MDPs and their applications in the optimal control of discrete event systems (DESs), optimal replacement, and optimal allocations in sequential online auctions. The book presents four main topics that are used to study optimal control problems: \*a new methodology for MDPs with discounted total reward criterion; \*transformation of continuous-time MDPs and semi-Markov decision processes into a discrete-time MDPs model, thereby simplifying the application of MDPs; \*MDPs in stochastic environments, which greatly extends the area where MDPs can be applied; \*applications of MDPs in optimal control of discrete event systems, optimal replacement, and optimal allocation in sequential online auctions. This book is intended for researchers, mathematicians, advanced graduate students, and engineers who are interested in optimal control, operation research, communications, manufacturing, economics, and electronic commerce.

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Harmony Search and Nature Inspired Optimization Algorithms: Theory and Applications, ICHSA 2018 (Advances in Intelligent Systems and Computing (741)) Neha Yadav, Anupam Yadav, Jagdish Chand Bansal, Kusum Deep, Joong Hoon Kim, (eds.) Springer Singapore : Imprint: Springer, Advances in Intelligent Systems and Computing, Advances in Intelligent Systems and Computing 741, 1, 2019

"The book covers different aspects of real-world applications of optimization algorithms. It provides insights from the Fourth International Conference on Harmony Search, Soft Computing and Applications held at BML Munjal University, Gurgaon, India on February 7-9, 2018. It consists of research articles on novel and newly proposed optimization algorithms; the theoretical study of nature-inspired optimization algorithms; numerically established results of nature-inspired optimization algorithms; and real-world applications of optimization algorithms and synthetic benchmarking of optimization algorithms." -- Springer

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Large-Scale PDE-Constrained Optimization Lorenz T. Biegler; Omar Ghattas; Matthias Heinkenschloss; Bart van Bloemen Waanders Springer Berlin Heidelberg, Springer Nature, Berlin, Heidelberg, 2012

Optimal design, optimal control, and parameter estimation of systems governed by partial differential equations (PDEs) give rise to a class of problems known as PDE-constrained optimization. The size and complexity of the discretized PDEs often pose significant challenges for contemporary optimization methods. With the maturing of technology for PDE simulation, interest has now increased in PDE-based optimization. The chapters in this volume collectively assess the state of the art in PDE-constrained optimization, identify challenges to optimization presented by modern highly parallel PDE simulation codes, and discuss promising algorithmic and software approaches for addressing them. These contributions represent current research of two strong scientific computing communities, in optimization and PDE simulation. This volume merges perspectives in these two different areas and identifies interesting open questions for further research.

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nexusstc/Theory and Practice of Uncertain Programming/69d70ef3a2553ae2fa4edd840405caea.pdf

Theory and Practice of Uncertain Programming Studies in Fuzziness and Soft Computing Baoding Liu (auth.) Springer-Verlag Berlin Heidelberg, Studies in Fuzziness and Soft Computing, Studies in Fuzziness and Soft Computing 239, 2, 2009

Real-life decisions are usually made in the state of uncertainty such as randomness and fuzziness. How do we model optimization problems in uncertain environments? How do we solve these models? In order to answer these questions, this book provides a self-contained, comprehensive and up-to-date presentation of uncertain programming theory, including numerous modeling ideas, hybrid intelligent algorithms, and applications in system reliability design, project scheduling problem, vehicle routing problem, facility location problem, and machine scheduling problem. Researchers, practitioners and students in operations research, management science, information science, system science, and engineering will find this work a stimulating and useful reference.

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upload/newsarch_ebooks_2025_10/2017/01/21/Theory And Practice Of Uncertain Programming.pdf

Theory and Practice of Uncertain Programming Studies in Fuzziness and Soft Computing Baoding Liu (auth.) Springer-Verlag Berlin Heidelberg, Studies in Fuzziness and Soft Computing, Studies in Fuzziness and Soft Computing 239, 2, 2009

Real-life decisions are usually made in the state of uncertainty such as randomness and fuzziness. How do we model optimization problems in uncertain environments? How do we solve these models? In order to answer these questions, this book provides a self-contained, comprehensive and up-to-date presentation of uncertain programming theory, including numerous modeling ideas, hybrid intelligent algorithms, and applications in system reliability design, project scheduling problem, vehicle routing problem, facility location problem, and machine scheduling problem. Researchers, practitioners and students in operations research, management science, information science, system science, and engineering will find this work a stimulating and useful reference.

English [en] · PDF · 3.2MB · 2009 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/scihub/upload/zlib · Save

nexusstc/How to Solve It: Modern Heuristics/bb0d249b6a951f823994b52366999482.djvu

How to solve it: modern heuristics with 7 tables Zbigniew Michalewicz, David B. Fogel Springer Berlin, Enlarged 2nd, PS, 2004

This book is the only source that provides comprehensive, current, and correct information on problem solving using modern heuristics. It covers classic methods of optimization, including dynamic programming, the simplex method, and gradient techniques, as well as recent innovations such as simulated annealing, tabu search, and evolutionary computation. Integrated into the discourse is a series of problems and puzzles to challenge the reader. The book is written in a lively, engaging style and is intended for students and practitioners alike. Anyone who reads and understands the material in the book will be armed with the most powerful problem solving tools currently known. This second edition contains two new chapters, one on coevolutionary systems and one on multicriterial decision-making. Also some new puzzles are added and various subchapters are revised.

English [en] · DJVU · 4.4MB · 2004 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

nexusstc/Max-Plus Linear Stochastic Systems and Perturbation Analysis/1b63ba73196c9d8e5d84c9f7ba52bf41.pdf

Max-Plus Linear Stochastic Systems and Perturbation Analysis (The International Series on Discrete Event Dynamic Systems (15)) Bernd F. Heidergott Springer US, The International Series on Discrete Event Dynamic Systems, October 12, 2006

During the last decade, the area of stochastic max-plus linear systems has witnessed a rapid development, which created a growing interest in this area. This book provides a thorough treatment of the theory of stochastic max-plus linear systems. Max-plus algebra is an algebraic approach to discrete event systems (DES), like queuing networks that are prone to synchronization. Perturbation analysis studies the sensitivity of the performance of DES with respect to changes in a particular system parameter. The first part of the book addresses modeling issues and stability theory for stochastic max-plus systems. The second part of the book treats perturbation analysis of max-plus systems: a calculus for differentiation of max-plus systems is developed. This calculus leads to numerical evaluations of performance indices of max-plus linear stochastic systems, such as the Lyapunov exponent or waiting times. This book will be of interest to researchers and professionals in the area of applied probability who are interested in numerical evaluation of stochastic max-plus linear discrete event systems

English [en] · PDF · 12.0MB · 2006 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

lgli/F_Finance/FC_Credit, Risk/Ermoliev Yu., et al. (eds.) Managing safety of heterogeneous systems. Decisions under uncertainties and risks (LNEMS0658, Springer, 2012)(ISBN 9783642228834)(O)(403s)_G_.pdf

Managing Safety of Heterogeneous Systems: Decisions under Uncertainties and Risks (Lecture Notes in Economics and Mathematical Systems, 658) Yuri Ermoliev, Marek Makowski, Kurt Marti (auth.), Yuri Ermoliev, Marek Makowski, Kurt Marti (eds.) Springer-Verlag Berlin Heidelberg, Lecture Notes in Economics and Mathematical Systems, Lecture Notes in Economics and Mathematical Systems 658, 1, 2012

Managing safety of diverse systems requires decision-making under uncertainties and risks. Such systems are typically characterized by spatio-temporal heterogeneities, inter-dependencies, externalities, endogenous risks, discontinuities, irreversibility, practically irreducible uncertainties, and rare events with catastrophic consequences. Traditional scientific approaches rely on data from real observations and experiments; yet no sufficient observations exist for new problems, and experiments are usually impossible. Therefore, science-based support for addressing such new class of problems needs to replace the traditional “deterministic predictions” analysis by new methods and tools for designing decisions that are robust against the involved uncertainties and risks. The new methods treat uncertainties explicitly by using “synthetic” information derived by integration of “hard” elements, including available data, results of possible experiments, and formal representations of scientific facts, with “soft” elements based on diverse representations of scenarios and opinions of public, stakeholders, and experts. The volume presents such effective new methods, and illustrates their applications in different problem areas, including engineering, economy, finance, agriculture, environment, and policy making.

English [en] · PDF · 3.3MB · 2012 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/scihub/zlib · Save

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Heuristics for Optimization and Learning (Studies in Computational Intelligence Book 906) Farouk Yalaoui; Lionel Amodeo; El-Ghazali Talbi; International Conference on Metaheuristics and Nature Inspired Computing Springer International Publishing : Imprint: Springer, Studies in Computational Intelligence, Studies in Computational Intelligence, 1, 2021

This book is a new contribution aiming to give some last research findings in the field of optimization and computing. This work is in the same field target than our two previous books published: “Recent Developments in Metaheuristics” and “Metaheuristics for Production Systems”, books in Springer Series in Operations Research/Computer Science Interfaces. The challenge with this work is to gather the main contribution in three fields, optimization technique for production decision, general development for optimization and computing method and wider spread applications. The number of researches dealing with decision maker tool and optimization method grows very quickly these last years and in a large number of fields. We may be able to read nice and worthy works from research developed in chemical, mechanical, computing, automotive and many other fields.

English [en] · PDF · 13.5MB · 2021 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/scihub/upload/zlib · Save