Search results for "Optimization Modelling Using R"
Items we need to order for you
Modeling, Simulation and Optimization of Complex Processes
This proceedings volume contains a selection of papers presented at the Fourth International Conference on High Performance Scientific Computing held at the Hanoi Institute of Mathematics, Vietnamese Academy of Science and Technology (VAST), March 2-6, 2009. The conference was organized by the Hanoi Institute of Mathematics, the Interdisciplinary Center for Scientific Computing (IWR), Heidelberg, and its Heidelberg Graduate School of Mathematical and Computational Methods for the Sciences, and Ho Chi Minh City University of Technology. The contributions cover the broad interdisciplinary spectrum of scientific computing and present recent advances in theory, development of methods, and applications in practice. Subjects covered are mathematical modelling, numerical simulation, methods for optimization and control, parallel computing, software development, applications of scientific computing in physics, mechanics, biology and medicine, engineering, hydrology problems, transport, communication networks, production scheduling, industrial and commercial problems.
R 5,794.88
Modeling and Simulation in Ecotoxicology with Applications in MATLAB and Simulink
Exploring roles critical to environmental toxicology, Modeling and Simulation in Ecotoxicology with Applications in MATLAB® and Simulink® covers the steps in modeling and simulation from problem conception to validation and simulation analysis. Using the MATLAB and Simulink programming languages, the book presents examples of mathematical functions and simulations, with special emphasis on how to develop mathematical models and run computer simulations of ecotoxicological processes.Designed for students and professionals with little or no experience in modeling, the book includes: General principles of modeling and simulation and an introduction to MATLAB and SimulinkStochastic modeling where variability and uncertainty are acknowledged by making parameters random variablesToxicological processes from the level of the individual organism, with worked examples of process models in either MATLAB or SimulinkToxicological processes at the level of populations, communities, and ecosystemsParameter estimation using least squares regression methodsThe design of simulation experiments similar to the experimental design applied to laboratory or field experimentsMethods of postsimulation analysis, including stability analysis and sensitivity analysisDifferent levels of model validation and how they are related to the modeling purposeThe book also provides three individual case studies. The first involves a model developed to assess the relative risk of mortality following exposure to insecticides in different avian species. The second explores the role of diving behavior on the inhalation and distribution of oil spill naphthalene in bottlenose dolphins. The final case study looks at the dynamics of mercury in Daphnia that are exposed to simulated thermal plumes from a hypothetical power plant cooling system. Presented in a rigorous yet accessible style, the methodology is versatile enough to be readily applicable not only to environmental toxicology but a range of other biological fields.
R 10,257.00
Mathematical Modelling in Real Life Problems
This book is intended to be a useful contribution for the modern teaching of applied mathematics, educating Industrial Mathematicians that will meet the growing demand for such experts. It covers many applications where mathematics play a fundamental role, from biology, telecommunications, medicine, physics, finance and industry. It is presented in such a way that can be useful in Modelation, Simulation and Optimization courses, targeting master and PhD students. Its content is based on many editions from the successful series of Modelling Weeks organized by the European Consortium of Mathematics in Industry (ECMI). Each chapter addresses a particular problem, and is written in a didactic way, providing the description of the problem, the particular way of approaching it and the proposed solution, along with the results obtained.
R 4,825.53
Stochastic Modeling and Optimization
This books covers the broad range of research in stochastic models and optimization. Applications presented include networks, financial engineering, production planning, and supply chain management. Each contribution is aimed at graduate students working in operations research, probability, and statistics.
R 1,585.16
Structural Optimization
Structural Optimization is intended to supplement the engineer’s box of analysis and design tools making optimization as commonplace as the finite element method in the engineering workplace. It begins with an introduction to structural optimization and the methods of nonlinear programming such as Lagrange multipliers, Kuhn-Tucker conditions, and calculus of variations. It then discusses solution methods for optimization problems such as the classic method of linear programming which leads to the method of sequential linear programming. It then proposes using sequential linear programming together with the incremental equations of structures as a general method for structural optimization. It is furthermore intended to give the engineer an overview of the field of structural optimization.
R 6,812.57
Machine Learning and Optimization Models for Optimization in Cloud
Machine Learning and Models for Optimization in Cloud’s main aim is to meet the user requirement with high quality of service, least time for computation and high reliability. With increase in services migrating over cloud providers, the load over the cloud increases resulting in fault and various security failure in the system results in decreasing reliability. To fulfill this requirement cloud system uses intelligent metaheuristic and prediction algorithm to provide resources to the user in an efficient manner to manage the performance of the system and plan for upcoming requests. Intelligent algorithm helps the system to predict and find a suitable resource for a cloud environment in real time with least computational complexity taking into mind the system performance in under loaded and over loaded condition.This book discusses the future improvements and possible intelligent optimization models using artificial intelligence, deep learning techniques and other hybrid models to improve the performance of cloud. Various methods to enhance the directivity of cloud services have been presented which would enable cloud to provide better services, performance and quality of service to user. It talks about the next generation intelligent optimization and fault model to improve security and reliability of cloud.Key Features· Comprehensive introduction to cloud architecture and its service models.· Vulnerability and issues in cloud SAAS, PAAS and IAAS· Fundamental issues related to optimizing the performance in Cloud Computing using meta-heuristic, AI and ML models· Detailed study of optimization techniques, and fault management techniques in multi layered cloud.· Methods to improve reliability and fault in cloud using nature inspired algorithms and artificial neural network.· Advanced study of algorithms using artificial intelligence for optimization in cloud· Method for power efficient virtual machine placement using neural network in cloud· Method for task scheduling using metaheuristic algorithms.· A study of machine learning and deep learning inspired resource allocation algorithm for cloud in fault aware environment.This book aims to create a research interest & motivation for graduates degree or post-graduates. It aims to present a study on optimization algorithms in cloud for researchers to provide them with a glimpse of future of cloud computing in the era of artificial intelligence.
R 2,945.95
Optimal Control Theory and Static Optimization in Economics
Optimal control theory is a technique being used increasingly by academic economists to study problems involving optimal decisions in a multi-period framework. This textbook is designed to make the difficult subject of optimal control theory easily accessible to economists while at the same time maintaining rigour. Economic intuitions are emphasized, and examples and problem sets covering a wide range of applications in economics are provided to assist in the learning process. Theorems are clearly stated and their proofs are carefully explained. The development of the text is gradual and fully integrated, beginning with simple formulations and progressing to advanced topics such as control parameters, jumps in state variables, and bounded state space. For greater economy and elegance, optimal control theory is introduced directly, without recourse to the calculus of variations. The connection with the latter and with dynamic programming is explained in a separate chapter. A second purpose of the book is to draw the parallel between optimal control theory and static optimization. Chapter 1 provides an extensive treatment of constrained and unconstrained maximization, with emphasis on economic insight and applications. Starting from basic concepts, it derives and explains important results, including the envelope theorem and the method of comparative statics. This chapter may be used for a course in static optimization. The book is largely self-contained. No previous knowledge of differential equations is required.
R 4,073.23
Linear Algebra And Optimization With Applications To Machine Learning - Volume Ii: Fundamentals Of Optimization Theory With Applications To Machine Learning
Volume 2 applies the linear algebra concepts presented in Volume 1 to optimization problems which frequently occur throughout machine learning. This book blends theory with practice by not only carefully discussing the mathematical under pinnings of each optimization technique but by applying these techniques to linear programming, support vector machines (SVM), principal component analysis (PCA), and ridge regression. Volume 2 begins by discussing preliminary concepts of optimization theory such as metric spaces, derivatives, and the Lagrange multiplier technique for finding extrema of real valued functions. The focus then shifts to the special case of optimizing a linear function over a region determined by affine constraints, namely linear programming. Highlights include careful derivations and applications of the simplex algorithm, the dual-simplex algorithm, and the primal-dual algorithm. The theoretical heart of this book is the mathematically rigorous presentation of various nonlinear optimization methods, including but not limited to gradient decent, the Karush-Kuhn-Tucker (KKT) conditions, Lagrangian duality, alternating direction method of multipliers (ADMM), and the kernel method. These methods are carefully applied to hard margin SVM, soft margin SVM, kernel PCA, ridge regression, lasso regression, and elastic-net regression. Matlab programs implementing these methods are included.
R 7,254.00
High-Performance Simulation-Based Optimization
This book presents the state of the art in designing high-performance algorithms that combine simulation and optimization in order to solve complex optimization problems in science and industry, problems that involve time-consuming simulations and expensive multi-objective function evaluations. As traditional optimization approaches are not applicable per se, combinations of computational intelligence, machine learning, and high-performance computing methods are popular solutions. But finding a suitable method is a challenging task, because numerous approaches have been proposed in this highly dynamic field of research. That’s where this book comes in: It covers both theory and practice, drawing on the real-world insights gained by the contributing authors, all of whom are leading researchers. Given its scope, if offers a comprehensive reference guide for researchers, practitioners, and advanced-level students interested in using computational intelligence and machine learning to solve expensive optimization problems.
R 4,041.81
Multi-Criteria Decision Modelling
This book examines Multi-Criteria Decision Modelling (MCDM) methodologies and facilitates diverse ways for strategic decision-making in a variety of practical applications. This book also provides a pragmatic foundation for solving real-life problems in different scenarios of emerging global markets.Multi-Criteria Decision Modelling: Applicational Techniques and Case Studies depicts the use of sensitivity analysis and modelling and includes case studies to understand and illustrate challenging concepts. It also offers step-by-step comprehensive methodologies for the utilization of MCDM to a variety of situations. The book deliberates ways for companies to use these methods to their advantage in order to achieve sustainability. Furthermore, it also presents an overview of the major streams of thought and provides a holistic view of the latest research and development trends in modelling and optimization.FEATURES Offers a stepwise comprehensive methodology for the application of MCDM to a variety of situations Presents an overview of the major streams of thought present in the MCDM technique Provides a holistic view of the latest research and development trends in the emerging markets in terms of modelling and optimization using MCDM for different industrial sectors Illuminates a practical foundation in order to provide a guide to address the problems of emerging markets Enlightens the ways for companies to use these methods to their advantage to be able to achieve sustainabilityThis book is a guide for those performing decision analysis for academic purposes as well as for researchers aspiring to expand their knowledge on MCDM problem solving.
R 2,788.65
Maintenance, Modeling and Optimization
Production costs are being reduced by automation, robotics, computer-integrated manufacturing, cost reduction studies and more. These new technologies are expensive to buy, repair, and maintain. Hence, the demand on maintenance is growing and its costs are escalating. This new environment is compelling industrial maintenance organizations to make the transition from fixing broken machines to higher-level business units for securing production capacity. On the academic front, research in the area of maintenance management and engineering is receiving tremendous interest from researchers. Many papers have appeared in the literature dealing with the modeling and solution of maintenance problems using operations research (OR) and management science (MS) techniques. This area represents an opportunity for making significant contributions by the OR and MS communities. Maintenance, Modeling, and Optimization provides in one volume the latest developments in the area of maintenance modeling. Prominent scholars have contributed chapters covering a wide range of topics. We hope that this initial contribution will serve as a useful informative introduction to this field that may permit additional developments and useful directions for more research in this fast-growing area. The book is divided into six parts and contains seventeen chapters. Each chapter has been subject to review by at least two experts in the area of maintenance modeling and optimization. The first chapter provides an introduction to major maintenance modeling areas illustrated with some basic models. Part II contains five chapters dealing with maintenance planning and scheduling. Part III deals with preventive maintenance in six chapters. Part IV focuses on condition-based maintenance and contains two chapters. Part V deals with integrated production and maintenance models and contains two chapters. Part VI addresses issues related to maintenance and new technologies, and also deals with Just-in-Time (JIT) and Maintenance.
R 6,034.20
Nonlinear Optimization Applications Using the GAMS Technology
Here is a collection of nonlinear optimization applications from the real world, expressed in the General Algebraic Modeling System (GAMS). The concepts are presented so that the reader can quickly modify and update them to represent real-world situations.
R 4,233.17
Mathematical Models of Crop Growth and Yield
Highlighting effective, analytical functions that have been found useful for the comparison of alternative management techniques to maximize water and nutrient resources, this reference describes the application of viable mathematical models in data analysis to increase crop growth and yields. Featuring solutions to various differential equations, the book covers the characteristics of the functions related to the phenomenological growth model. Including more than 1300 literature citations, display equations, tables, and figures and outlining an approach to mathematical crop modeling, Mathematical Models of Crop Growth and Yield will prove an invaluable resource.
R 3,836.08
Recent Advances in Manufacturing Modelling and Optimization
This book presents the selected proceedings of 2nd International Conference on Recent Advances in Manufacturing (RAM 2021). The book provides insights to current research trends and opportunities in modelling and optimization of manufacturing processes and systems. The topics covered include modelling analysis, computing and simulation, traditional and non-traditional optimization techniques, surface coating methods, additive manufacturing processes, CAD/CAM, robotics and automation, welding and joining processes, supply chain management and CAE and reverse engineering. This book will be a good reference for beginners, researchers and professionals interested in modelling and optimization related to manufacturing engineering and related fields.
R 7,326.00
Optimal Control Theory and Static Optimization in Economics
Optimal control theory is a technique being used increasingly by academic economists to study problems involving optimal decisions in a multi-period framework. This textbook is designed to make the difficult subject of optimal control theory easily accessible to economists while at the same time maintaining rigour. Economic intuitions are emphasized, and examples and problem sets covering a wide range of applications in economics are provided to assist in the learning process. Theorems are clearly stated and their proofs are carefully explained. The development of the text is gradual and fully integrated, beginning with simple formulations and progressing to advanced topics such as control parameters, jumps in state variables, and bounded state space. For greater economy and elegance, optimal control theory is introduced directly, without recourse to the calculus of variations. The connection with the latter and with dynamic programming is explained in a separate chapter. A second purpose of the book is to draw the parallel between optimal control theory and static optimization. Chapter 1 provides an extensive treatment of constrained and unconstrained maximization, with emphasis on economic insight and applications. Starting from basic concepts, it derives and explains important results, including the envelope theorem and the method of comparative statics. This chapter may be used for a course in static optimization. The book is largely self-contained. No previous knowledge of differential equations is required.
R 8,333.95
Classical And Modern Optimization
The quest for the optimal is ubiquitous in nature and human behavior. The field of mathematical optimization has a long history and remains active today, particularly in the development of machine learning.Classical and Modern Optimization presents a self-contained overview of classical and modern ideas and methods in approaching optimization problems. The approach is rich and flexible enough to address smooth and non-smooth, convex and non-convex, finite or infinite-dimensional, static or dynamic situations. The first chapters of the book are devoted to the classical toolbox: topology and functional analysis, differential calculus, convex analysis and necessary conditions for differentiable constrained optimization. The remaining chapters are dedicated to more specialized topics and applications.Valuable to a wide audience, including students in mathematics, engineers, data scientists or economists, Classical and Modern Optimization contains more than 200 exercises to assist with self-study or for anyone teaching a third- or fourth-year optimization class.
R 2,569.49
Progress in Hybrid RANS-LES Modelling
This book reports on the latest developments in computational fluid dynamics and turbulence modeling, with a special emphasis on hybrid RANS-LES methods and their industrial applications. It gathers the proceedings of the Sixth Symposium on Hybrid RANS-LES Methods, held on September 26-28 in Strasbourg, France. The different chapters covers a wealth of topics such as flow control, aero-acoustics, aero-elasticity and CFD-based multidisciplinary optimization. Further topics include wall-modelled Large Eddy Simulation (WMLES), embedded LES, Lattice-Bolzman methods, turbulence-resolving applications and comparisons between LES, hybrid RANS-LES and URANS methods. The book addresses academic researchers, graduate students, industrial engineers, as well as industrial R&D managers and consultants dealing with turbulence modelling, simulation and measurement, and with multidisciplinary applications of computational fluid dynamics.
R 8,790.85
Evolutionary Optimization and Game Strategies for Advanced Multi-Disciplinary Design
Many complex aeronautical design problems can be formulated with efficient multi-objective evolutionary optimization methods and game strategies.This book describes the role of advanced innovative evolution tools in the solution, or the set of solutions of single or multi disciplinary optimization. These tools use the concept of multi-population, asynchronous parallelization and hierarchical topology which allows different models including precise, intermediate and approximate models with each node belonging to the different hierarchical layer handled by a different Evolutionary Algorithm. The efficiency of evolutionary algorithms for both single and multi-objective optimization problems are significantly improved by the coupling of EAs with games and in particular by a new dynamic methodology named “Hybridized Nash-Pareto games”.Multi objective Optimization techniques and robust design problems taking into account uncertainties are introduced and explained in detail. Several applications dealing with civil aircraft and UAV, UCAV systems are implemented numerically and discussed. Applications of increasing optimization complexity are presented as well as two hands-on test cases problems. These examples focus on aeronautical applications and will be useful to the practitioner in the laboratory or in industrial design environments.The evolutionary methods coupled with games presented in this volume can be applied to other areas including surface and marine transport, structures, biomedical engineering, renewable energy and environmental problems.This book will be of interest to students, young scientists and engineers involved in the field of multi physics optimization.
R 3,736.66
Dynamic Econometrics For Empirical Macroeconomic Modelling
For Masters and PhD students in EconomicsIn this textbook, the duality between the equilibrium concept used in dynamic economic theory and the stationarity of economic variables is explained and used in the presentation of single equations models and system of equations such as VARs, recursive models and simultaneous equations models.The book also contains chapters on: exogeneity, in the context of estimation, policy analysis and forecasting; automatic (computer based) variable selection, and how it can aid in the specification of an empirical macroeconomic model; and finally, on a common framework for model-based economic forecasting.Supplementary materials and notes are available on the publisher's website.
R 3,590.00
Foundations of Generic Optimization
This is a comprehensive overview of the basics of fuzzy control, which also brings together some recent research results in soft computing, in particular fuzzy logic using genetic algorithms and neural networks. This book offers researchers not only a solid background but also a snapshot of the current state of the art in this field.
R 2,015.00
Computational Modeling, Optimization and Manufacturing Simulation of Advanced Engineering Materials
This volume presents recent research work focused in the development of adequate theoretical and numerical formulations to describe the behavior of advanced engineering materials. Particular emphasis is devoted to applications in the fields of biological tissues, phase changing and porous materials, polymers and to micro/nano scale modeling. Sensitivity analysis, gradient and non-gradient based optimization procedures are involved in many of the chapters, aiming at the solution of constitutive inverse problems and parameter identification. All these relevant topics are exposed by experienced international and inter institutional research teams resulting in a high level compilation. The book is a valuable research reference for scientists, senior undergraduate and graduate students, as well as for engineers acting in the area of computational material modeling.
R 5,986.22
Numerical Methods in Sensitivity Analysis and Shape Optimization
Sensitivity analysis and optimal shape design are key issues in engineering that have been affected by advances in numerical tools currently available. This book, and its supplementary online files, 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. 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.
R 1,542.57
Intelligent Optimization In Electric Machines, Renewable Energy Systems, And Microgrids: Metaheuristic Approaches To Modelling, Control, And Fault Diagnosis
This monograph is a research compendium on the modelling, control, fault diagnosis, and optimization of electric machines, renewable energy systems, and microgrids using advanced computational methods and metaheuristic algorithms, such as genetic algorithms (GA), particle swarm optimization (PSO) algorithms, artificial bee colony (ABC) algorithms, runner root (RR) algorithms, and cuckoo search (CS) algorithms.Intelligent Optimization in Electric Machines, Renewable Energy Systems, and Microgrids offers in-depth analysis of core components, including parameter estimation and energy-efficient control of induction machines, along with the modelling and maximum power point tracking (MPPT) of solar photovoltaic (PV) and wind energy systems. The book also covers modern techniques for fault location in transmission lines, the optimization of hybrid renewable energy systems, and the planning and control of microgrids.Designed for power systems engineers, researchers, academics, and students, this book offers the practical knowledge and advanced methodologies needed to address the most pressing challenges in a modern grid. It is an ideal textbook for graduate courses in electric power systems, renewable energy systems, and microgrids, providing both theoretical foundations and real-world applications.
R 4,323.00
Applied Statistics Using SPSS, STATISTICA, MATLAB and R
Intended for anyone needing to apply statistical analysis to a large variety of science and engineering problems, this book shows how to use SPSS, MATLAB, STATISTICA and R for data description, statistical inference, classification and regression, factor analysis, survival data and directional statistics. The 2nd edition includes the R language, a new section on bootstrap estimation methods and an improved treatment of tree classifiers, plus additional examples and exercises.
R 4,152.67
Quantum Machine Learning and Optimisation in Finance
Get a detailed introduction to quantum computing and quantum machine learning, with a focus on finance-related applicationsKey FeaturesFind out how quantum algorithms enhance financial modeling and decision-makingImprove your knowledge of the variety of quantum machine learning and optimisation algorithmsLook into practical near-term applications for tackling real-world financial challengesPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionAs quantum machine learning (QML) continues to evolve, many professionals struggle to apply its powerful algorithms to real-world problems using noisy intermediate-scale quantum (NISQ) hardware. This book bridges that gap by focusing on hands-on QML applications tailored to NISQ systems, moving beyond the traditional textbook approaches that explore standard algorithms like Shor's and Grover's, which lie beyond current NISQ capabilities.You’ll get to grips with major QML algorithms that have been widely studied for their transformative potential in finance and learn hybrid quantum-classical computational protocols, the most effective way to leverage quantum and classical computing systems together.The authors, Antoine Jacquier, a distinguished researcher in quantum computing and stochastic analysis, and Oleksiy Kondratyev, a Quant of the Year awardee with over 20 years in quantitative finance, offer a hardware-agnostic perspective. They present a balanced view of both analog and digital quantum computers, delving into the fundamental characteristics of the algorithms while highlighting the practical limitations of today’s quantum hardware.By the end of this quantum book, you’ll have a deeper understanding of the significance of quantum computing in finance and the skills needed to apply QML to solve complex challenges, driving innovation in your work.What you will learnFamiliarize yourself with analog and digital quantum computing principles and methodsExplore solutions to NP-hard combinatorial optimisation problems using quantum annealersBuild and train quantum neural networks for classification and market generationDiscover how to leverage quantum feature maps for enhanced data representationWork with variational algorithms to optimise quantum processesImplement symmetric encryption techniques on a quantum computerWho this book is forThis book is for academic researchers, STEM students, finance professionals in quantitative finance, and AI/ML experts. No prior knowledge of quantum mechanics is needed. Mathematical concepts are rigorously presented, but the emphasis is on understanding the fundamental properties of models and algorithms, making them accessible to a broader audience. With its deep coverage of QML applications for solving real-world financial challenges, this guide is an essential resource for anyone interested in finance and quantum computing.
R 1,465.00
Handbook of Sustainable Materials: Modelling, Characterization, and Optimization
Handbook of Sustainable Materials presents recent developments in sustainable materials and how these materials interact with the environment. It highlights the recent advancements involved in proper utilization of sustainable materials, including chemical and biological approaches. With chapters written by global experts, the book offers a guide and insights into sustainable materials from a variety of engineering disciplines. Each chapter provides in-depth technical information on the sustainable materials theory and explores synthesis strategies, green materials, and artificial intelligence. The book considers applications in sectors such as aerospace, automobile, and biomedical for rapid prototyping and customized production without negative environmental impacts. It features research outcomes and case studies of optimization and modeling techniques in practice.Features: Presents recent developments in sustainable materials from various engineering fields and industry applications. Emphasizes analytical strategies, computational, and simulation approaches develop innovative sustainable materials. Discusses an artificial intelligence approach, rapid prototyping, and customized production.This book is designed for researchers and professionals working with sustainable materials, clean manufacturing, and environmental impacts.
R 3,466.23
System Modelling and Optimization
From the preface: "These proceedings contain 73 contributed papers selected from among those presented at the 13th IFIP Conference System Modelling and Optimization, Tokyo, Japan, August 31 - September 4, 1987, as well as 4 papers for the plenary sessions of the Conference. The aim of the conference was to discuss recent advances in the mathematical representation of engineering, sociotechnical, socioeconomic systems as well as in the optimization of their performances."
R 1,912.11
Modeling and Optimization for Mobile Social Networks
This book investigates the modeling and optimization issues in mobile social networks (MSNs). Firstly, the architecture and applications of MSNs are examined. The existing works on MSNs are reviewed by specifying the critical challenges and research issues. Then, with the introduction of MSN-based social graph and information dissemination mechanisms, the analytical model for epidemic information dissemination with opportunistic Links in MSNs is discussed. In addition, optimal resource allocation is studied based on a heterogeneous architecture, which provides mobile social services with high capacity and low latency. Finally, this book summarize some open problems and future research directions in MSNs.Written for researchers and academics, this book is useful for anyone working on mobile networks, network architecture, or content delivery. It is also valuable for advanced-level students of computer science.
R 4,245.33
Power System Optimization Modeling in GAMS
This unique book describes how the General Algebraic Modeling System (GAMS) can be used to solve various power system operation and planning optimization problems. This book is the first of its kind to provide readers with a comprehensive reference that includes the solution codes for basic/advanced power system optimization problems in GAMS, a computationally efficient tool for analyzing optimization problems in power and energy systems. The book covers theoretical background as well as the application examples and test case studies. It is a suitable reference for dedicated and general audiences including power system professionals as well as researchers and developers from the energy sector and electrical power engineering community and will be helpful to undergraduate and graduate students.
R 6,594.00
Financial Modelling in Python
"Fletcher and Gardner have created a comprehensive resource that will be of interest not only to those working in the field of finance, but also to those using numerical methods in other fields such as engineering, physics, and actuarial mathematics. By showing how to combine the high-level elegance, accessibility, and flexibility of Python, with the low-level computational efficiency of C++, in the context of interesting financial modeling problems, they have provided an implementation template which will be useful to others seeking to jointly optimize the use of computational and human resources. They document all the necessary technical details required in order to make external numerical libraries available from within Python, and they contribute a useful library of their own, which will significantly reduce the start-up costs involved in building financial models. This book is a must read for all those with a need to apply numerical methods in the valuation of financial claims." –David Louton, Professor of Finance, Bryant University This book is directed at both industry practitioners and students interested in designing a pricing and risk management framework for financial derivatives using the Python programming language. It is a practical book complete with working, tested code that guides the reader through the process of building a flexible, extensible pricing framework in Python. The pricing frameworks' loosely coupled fundamental components have been designed to facilitate the quick development of new models. Concrete applications to real-world pricing problems are also provided. Topics are introduced gradually, each building on the last. They include basic mathematical algorithms, common algorithms from numerical analysis, trade, market and event data model representations, lattice and simulation based pricing, and model development. The mathematics presented is kept simple and to the point. The book also provides a host of information on practical technical topics such as C++/Python hybrid development (embedding and extending) and techniques for integrating Python based programs with Microsoft Excel.
R 4,722.00
Numerical Techniques for Stochastic Optimization
Rapid changes in today's environment emphasize the need for models and meth ods capable of dealing with the uncertainty inherent in virtually all systems re lated to economics, meteorology, demography, ecology, etc. Systems involving interactions between man, nature and technology are subject to disturbances which may be unlike anything which has been experienced in the past. In the technological revolution increases uncertainty-as each new stage particular, perturbs existing knowledge of structures, limitations and constraints. At the same time, many systems are often too complex to allow for precise measure ment of the parameters or the state of the system. Uncertainty, nonstationarity, disequilibrium are pervasivE' characteristics of most modern systems. In order to manage such situations (or to survive in such an environment) we must develop systems which can facilitate oar response to uncertainty and changing conditions. In our individual behavior we often follow guidelines that are conditioned by the need to be prepared for all (likely) eventualities: insur ance, wearing seat·belts, savings versus investments, annual medical check.ups, even keeping an umbrella at the office, etc. One can identify two major types of mechanisms: the short term adaptive adjustments (defensive driving, mar keting, inventory control, etc.) that are made after making some observations of the system's parameters, and the long term anticipative actions (engineer ing design, policy setting, allocation of resources, investment strategies, etc.).
R 1,604.71
Stochastic Optimization
Stochastic programming is the study of procedures for decision making under the presence of uncertainties and risks. Stochastic programming approaches have been successfully used in a number of areas such as energy and production planning, telecommunications, and transportation. Recently, the practical experience gained in stochastic programming has been expanded to a much larger spectrum of applications including financial modeling, risk management, and probabilistic risk analysis. Major topics in this volume include: (1) advances in theory and implementation of stochastic programming algorithms; (2) sensitivity analysis of stochastic systems; (3) stochastic programming applications and other related topics. Audience: Researchers and academies working in optimization, computer modeling, operations research and financial engineering. The book is appropriate as supplementary reading in courses on optimization and financial engineering.
R 6,024.78
Integrated Circuit Design: Power and Timing Modeling, Optimization and Simulation
Thisworkshopisthetenthinaseriesofinternationalworkshops. Thisyear ittakesplaceinG..ottingen,Germany,andisorganizedbytheUniversityof Hannover. G..ottingenhasonethemostfamousGermanuniversities,whereverywell knownscientistslikeLichtenberg,Hilbert,GaussandvonNeumannstudied, workedandtaught. ItalsohostsseveralresearchinstitutesoftheMax-Planck- Society. The?rstelectronictubecalculatorG1wasbuiltinG..ottingenin1952 byH. Billing. Additionally,G..ottingenwasselectedbecauseitisadjacenttothe worldexpositionEXPO2000inHannoverwhichgivesanoutlookintothe21st centurycoveringthemajortopicsofhumankind,natureandtechnology. WithrespecttotheseinspiringsurroundingsthetechnicalprogramofPAT- MOS2000includes10sessionsdedicatedtomostimportantsubjectsofpower andtimingmodeling,optimizationandsimulationatthedawnofthe21stc- tury. ThefourinvitedtalksaddresstheEuropeanresearchactivitiesinthewo- shop?elds,theevolvingneedsforminimalpowerconsumptionintheareaof wirelessandchipcardapplicationsanddesignmethodologiesofveryhighly- tegratedmultimediaprocessors. Theworkshopisaresultofthejointworkofalargenumberofindividuals, whocannotallbementionedhere.Inparticular,wewouldliketoacknowledge theoutstandingworkofthereviewers,whodidacompetentjobinatimely manner. Wealsohavetothankthemembersofthelocalorganizingcommittee fortheire?ortinenablingtheconferencetorunsmoothly. Finally,wegratefully acknowledgethesupportofallorganizationsandinstitutionssponsoringthe conference. September2000 PeterPirsch ErichBarke DimitriosSoudris Organization OrganizationCommitee GeneralCoChairs: PeterPirsch(UniversityofHannover,Germany) ErichBarke(UniversityofHannover,Germany) ProgramChair: DimitriosSoudris (DemocritusUniversityofThrace,Greece) FinanceChair: LarsHedrich(UniversityofHannover,Germany) PublicationChair: AchimFreimann (UniversityofHannover,Germany) Audio-VisualChair: J..orgAbke(UniversityofHannover,Germany) LocalArrangementsChair: CarstenReuter(UniversityofHannover,Germany) ProgramCommitee D. Auvergne(UniversityofMontpellier,France) J. Bormans(IMEC,Belgium) J. Figueras(UniversityofCatalunya,Spain) C. E. Goutis(UniversityofPatras,Greece) A. Guyot(INPGGrenoble,France) R. Hartenstein(UniversityofKaiserslautern,Germany) S. Jones(UniversityofLoughborough,UnitedKingdom) P. Larsson-Edefors(UniversityofLink..oping,Sweden) E.Macii(PolytechnicofTorino,Italy) V. Moshnyaga(UniversityofFukuoka,Japan) W. Nebel(UniversityofOldenburg,Germany) J. A. Nossek(TechnicalUniversityofMunc .. hen,Germany) A. Nunez(UniversityofLasPalmas,Spain) M. Papaefthymiou(UniversityofMichigan,UnitedStates) M. Pedram(UniversityofSouthernCalifornia,UnitedStates) H. P?eiderer(UniversityofUlm,Germany) C. Piguet(CSEM,Switzerland) R. Reis(UniversityofPortoAlegre,Brazil) M. Robert(UniversityofMontpellier,France) A. Rubio(UniversityofCatalunya,Spain) J. Sparso(TechnicalUniversityofDenmark,Denmark) A. Stempkowsky(AcademyofSciences,Russia) T. Stouraitis(UniversityofPatras,Greece) J. F. M. Theeuwen(Philips,TheNetherlands) A. -M. Trullemans-Anckaert(Unive
R 2,015.00
Data-Driven Evolutionary Optimization
Intended for researchers and practitioners alike, this book covers carefully selected yet broad topics in optimization, machine learning, and metaheuristics. Written by world-leading academic researchers who are extremely experienced in industrial applications, this self-contained book is the first of its kind that provides comprehensive background knowledge, particularly practical guidelines, and state-of-the-art techniques. New algorithms are carefully explained, further elaborated with pseudocode or flowcharts, and full working source code is made freely available. This is followed by a presentation of a variety of data-driven single- and multi-objective optimization algorithms that seamlessly integrate modern machine learning such as deep learning and transfer learning with evolutionary and swarm optimization algorithms. Applications of data-driven optimization ranging from aerodynamic design, optimization of industrial processes, to deep neural architecture search are included.
R 5,562.90
Clinical Trial Data Analysis Using R and SAS
Review of the First Edition"The goal of this book, as stated by the authors, is to fill the knowledge gap that exists between developed statistical methods and the applications of these methods. Overall, this book achieves the goal successfully and does a nice job. I would highly recommend it …The example-based approach is easy to follow and makes the book a very helpful desktop reference for many biostatistics methods."—Journal of Statistical SoftwareClinical Trial Data Analysis Using R and SAS, Second Edition provides a thorough presentation of biostatistical analyses of clinical trial data with step-by-step implementations using R and SAS. The book’s practical, detailed approach draws on the authors’ 30 years’ experience in biostatistical research and clinical development. The authors develop step-by-step analysis code using appropriate R packages and functions and SAS PROCS, which enables readers to gain an understanding of the analysis methods and R and SAS implementation so that they can use these two popular software packages to analyze their own clinical trial data.What’s New in the Second Edition Adds SAS programs along with the R programs for clinical trial data analysis. Updates all the statistical analysis with updated R packages. Includes correlated data analysis with multivariate analysis of variance. Applies R and SAS to clinical trial data from hypertension, duodenal ulcer, beta blockers, familial andenomatous polyposis, and breast cancer trials. Covers the biostatistical aspects of various clinical trials, including treatment comparisons, time-to-event endpoints, longitudinal clinical trials, and bioequivalence trials.
R 6,960.00
Natural Computing for Simulation-Based Optimization and Beyond
This SpringerBrief bridges the gap between the areas of simulation studies on the one hand, and optimization with natural computing on the other. Since natural computing methods have been applied with great success in several application areas, a review concerning potential benefits and pitfalls for simulation studies is merited. The brief presents such an overview and combines it with an introduction to natural computing and selected major approaches, as well as with a concise treatment of general simulation-based optimization. As such, it is the first review which covers both the methodological background and recent application cases. The brief is intended to serve two purposes: First, it can be used to gain more information concerning natural computing, its major dialects, and their usage for simulation studies. It also covers the areas of multi-objective optimization and neuroevolution. While the latter is only seldom mentioned in connection with simulation studies, it is a powerful potential technique. Second, the reader is provided with an overview of several areas of simulation-based optimization which range from logistic problems to engineering tasks. Additionally, the brief focuses on the usage of surrogate and meta-models. The brief presents recent application examples.
R 1,746.93
Mathematical Modelling with Differential Equations
Mathematical Modelling with Differential Equations aims to introduce various strategies for modelling systems using differential equations. Some of these methodologies are elementary and quite direct to comprehend and apply while others are complex in nature and require thoughtful, deep contemplation. Many topics discussed in the chapter do not appear in any of the standard textbooks and this provides users an opportunity to consider a more general set of interesting systems that can be modelled. For example, the book investigates the evolution of a "toy universe," discusses why "alternate futures" exists in classical physics, constructs approximate solutions to the famous Thomas—Fermi equation using only algebra and elementary calculus, and examines the importance of "truly nonlinear" and oscillating systems. Features Introduces, defines, and illustrates the concept of "dynamic consistency" as the foundation of modelling.Can be used as the basis of an upper-level undergraduate course on general procedures for mathematical modelling using differential equations.Discusses the issue of dimensional analysis and continually demonstrates its value for both the construction and analysis of mathematical modelling.
R 5,676.72
Modeling and Optimization in Green Logistics
This book presents recent work that analyzes general issues of green logistics and smart cities. The contributed chapters consider operating models with important ecological, economic, and social objectives.The content will be valuable for researchers and postgraduate students in computer science, information technology, industrial engineering, and applied mathematics.
R 6,551.19
Modeling and Optimization for Mobile Social Networks
This book investigates the modeling and optimization issues in mobile social networks (MSNs). Firstly, the architecture and applications of MSNs are examined. The existing works on MSNs are reviewed by specifying the critical challenges and research issues. Then, with the introduction of MSN-based social graph and information dissemination mechanisms, the analytical model for epidemic information dissemination with opportunistic Links in MSNs is discussed. In addition, optimal resource allocation is studied based on a heterogeneous architecture, which provides mobile social services with high capacity and low latency. Finally, this book summarize some open problems and future research directions in MSNs.Written for researchers and academics, this book is useful for anyone working on mobile networks, network architecture, or content delivery. It is also valuable for advanced-level students of computer science.
R 4,244.43
Progress in Hybrid RANS-LES Modelling
This book reports on the latest developments in computational fluid dynamics and turbulence modeling, with a special emphasis on hybrid RANS-LES methods and their industrial applications. It gathers the proceedings of the Sixth Symposium on Hybrid RANS-LES Methods, held on September 26-28 in Strasbourg, France. The different chapters covers a wealth of topics such as flow control, aero-acoustics, aero-elasticity and CFD-based multidisciplinary optimization. Further topics include wall-modelled Large Eddy Simulation (WMLES), embedded LES, Lattice-Bolzman methods, turbulence-resolving applications and comparisons between LES, hybrid RANS-LES and URANS methods. The book addresses academic researchers, graduate students, industrial engineers, as well as industrial R&D managers and consultants dealing with turbulence modelling, simulation and measurement, and with multidisciplinary applications of computational fluid dynamics.
R 9,400.62
Mathematical Models and Algorithms for Power System Optimization
Mathematical Models and Algorithms for Power System Optimization helps readers build a thorough understanding of new technologies and world-class practices developed by the State Grid Corporation of China, the organization responsible for the world’s largest power distribution network. This reference covers three areas: power operation planning, electric grid investment and operational planning and power system control. It introduces economic dispatching, generator maintenance scheduling, power flow, optimal load flow, reactive power planning, load frequency control and transient stability, using mathematic models including optimization, dynamic, differential and difference equations.
R 5,150.00
System Modeling and Optimization
System Modeling and Optimization is an indispensable reference for anyone interested in the recent advances in these two disciplines. The book collects, for the first time, selected articles from the 21st and most recent IFIP TC 7 conference in Sophia Antipolis, France. Applied mathematicians and computer scientists can attest to the ever-growing influence of these two subjects. The practical applications of system modeling and optimization can be seen in a number of fields: environmental science, transport and telecommunications, image analysis, free boundary problems, bioscience, and non-cylindrical evolution control, to name just a few.New developments in each of these fields have contributed to a more complex understanding of both system modeling and optimization. Editors John Cagnol and Jean-Paul Zolésio, chairs of the conference, have assembled System Modeling and Optimization to present the most up-to-date developments to professionals and academics alike.
R 3,041.00
Iterative Methods in Combinatorial Optimization
With the advent of approximation algorithms for NP-hard combinatorial optimization problems, several techniques from exact optimization such as the primal-dual method have proven their staying power and versatility. This book describes a simple and powerful method that is iterative in essence and similarly useful in a variety of settings for exact and approximate optimization. The authors highlight the commonality and uses of this method to prove a variety of classical polyhedral results on matchings, trees, matroids and flows. The presentation style is elementary enough to be accessible to anyone with exposure to basic linear algebra and graph theory, making the book suitable for introductory courses in combinatorial optimization at the upper undergraduate and beginning graduate levels. Discussions of advanced applications illustrate their potential for future application in research in approximation algorithms.
R 2,438.48
Optimization Theory: A Concise Introduction
Mathematically, most of the interesting optimization problems can be formulated to optimize some objective function, subject to some equality and/or inequality constraints. This book introduces some classical and basic results of optimization theory, including nonlinear programming with Lagrange multiplier method, the Karush-Kuhn-Tucker method, Fritz John's method, problems with convex or quasi-convex constraints, and linear programming with geometric method and simplex method.A slim book such as this which touches on major aspects of optimization theory will be very much needed for most readers. We present nonlinear programming, convex programming, and linear programming in a self-contained manner. This book is for a one-semester course for upper level undergraduate students or first/second year graduate students. It should also be useful for researchers working on many interdisciplinary areas other than optimization.
R 2,735.02
High-Performance Simulation-Based Optimization
This book presents the state of the art in designing high-performance algorithms that combine simulation and optimization in order to solve complex optimization problems in science and industry, problems that involve time-consuming simulations and expensive multi-objective function evaluations. As traditional optimization approaches are not applicable per se, combinations of computational intelligence, machine learning, and high-performance computing methods are popular solutions. But finding a suitable method is a challenging task, because numerous approaches have been proposed in this highly dynamic field of research. That’s where this book comes in: It covers both theory and practice, drawing on the real-world insights gained by the contributing authors, all of whom are leading researchers. Given its scope, if offers a comprehensive reference guide for researchers, practitioners, and advanced-level students interested in using computational intelligence and machine learning to solve expensive optimization problems.
R 5,038.02
Modern Numerical Nonlinear Optimization
This book includes a thorough theoretical and computational analysis of unconstrained and constrained optimization algorithms and combines and integrates the most recent techniques and advanced computational linear algebra methods. Nonlinear optimization methods and techniques have reached their maturity and an abundance of optimization algorithms are available for which both the convergence properties and the numerical performances are known. This clear, friendly, and rigorous exposition discusses the theory behind the nonlinear optimization algorithms for understanding their properties and their convergence, enabling the reader to prove the convergence of his/her own algorithms. It covers cases and computational performances of the most known modern nonlinear optimization algorithms that solve collections of unconstrained and constrained optimization test problems with different structures, complexities, as well as those with large-scale real applications. The book is addressed to all those interested in developing and using new advanced techniques for solving large-scale unconstrained or constrained complex optimization problems. Mathematical programming researchers, theoreticians and practitioners in operations research, practitioners in engineering and industry researchers, as well as graduate students in mathematics, Ph.D. and master in mathematical programming will find plenty of recent information and practical approaches for solving real large-scale optimization problems and applications.
R 6,228.00
