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Introduction to Nonlinear and Global Optimization
This self-contained text provides a solid introduction to global and nonlinear optimization, providing students of mathematics and interdisciplinary sciences with a strong foundation in applied optimization techniques. The book offers a unique hands-on and critical approach to applied optimization which includes the presentation of numerous algorithms, examples, and illustrations, designed to improve the reader’s intuition and develop the analytical skills needed to identify optimization problems, classify the structure of a model, and determine whether a solution fulfills optimality conditions.
R 2,266.29
Discrete Optimization with Interval Data
Operations research often solves deterministic optimization problems based on elegantand conciserepresentationswhereall parametersarepreciselyknown. In the face of uncertainty, probability theory is the traditional tool to be appealed for, and stochastic optimization is actually a signi?cant sub-area in operations research. However, the systematic use of prescribed probability distributions so as to cope with imperfect data is partially unsatisfactory. First, going from a deterministic to a stochastic formulation, a problem may becomeintractable. Agoodexampleiswhengoingfromdeterministictostoch- tic scheduling problems like PERT. From the inception of the PERT method in the 1950’s, it was acknowledged that data concerning activity duration times is generally not perfectly known and the study of stochastic PERT was launched quite early. Even if the power of today’s computers enables the stochastic PERT to be addressed to a large extent, still its solutions often require simplifying assumptions of some kind. Another di?culty is that stochastic optimization problems produce solutions in the average. For instance, the criterion to be maximized is more often than not expected utility. This is not always a meaningful strategy. In the case when the underlying process is not repeated a lot of times, let alone being one-shot, it is not clear if this criterion is realistic, in particular if probability distributions are subjective. Expected utility was proposed as a rational criterion from ?rst principles by Savage. In his view, the subjective probability distribution was - sically an artefact useful to implement a certain ordering of solutions.
R 3,027.31
Integrated Methods for Optimization
The first edition of Integrated Methods for Optimization was published in January 2007. Because the book covers a rapidly developing field, the time is right for a second edition. The book provides a unified treatment of optimization methods. It brings ideas from mathematical programming (MP), constraint programming (CP), and global optimization (GO)into a single volume. There is no reason these must be learned as separate fields, as they normally are, and there are three reasons they should be studied together. (1) There is much in common among them intellectually, and to a large degree they can be understood as special cases of a single underlying solution technology. (2) A growing literature reports how they can be profitably integrated to formulate and solve a wide range of problems. (3) Several software packages now incorporate techniques from two or more of these fields.The book provides a unique resource for graduate students and practitioners who want a well-rounded background in optimization methods within a single course of study. Engineering students are a particularly large potential audience, because engineering optimization problems often benefit from a combined approach—particularly where design, scheduling, or logistics are involved. The text is also of value to those studying operations research, because their educational programs rarely cover CP, and to those studying computer science and artificial intelligence (AI), because their curricula typically omit MP and GO. The text is also useful for practitioners in any of these areas who want to learn about another, because it provides a more concise and accessible treatment than other texts.The book can cover so wide a range of material because it focuses on ideas that arerelevant to the methods used in general-purpose optimization and constraint solvers. The book focuses on ideas behind the methods that have proved useful in general-purpose optimization and constraint solvers, as well as integrated solvers of the present and foreseeable future. The second edition updates results in this area and includes several major new topics:Background material in linear, nonlinear, and dynamic programming.Network flow theory, due to its importance in filtering algorithms.A chapter on generalized duality theory that more explicitly develops a unifying primal-dual algorithmic structure for optimization methods.An extensive survey of search methods from both MP and AI, using the primal-dual framework as an organizing principle.Coverage of several additional global constraints used in CP solvers.The book continues to focus on exact as opposed to heuristic methods. It is possible to bring heuristic methods into the unifying scheme described in the book, and the new edition will retain the brief discussion of how this might be done.
R 2,231.79
Modelling Seasonality
The realization among econometricians and applied economists that seasonal variation in many time series is often larger and less regular than has been supposed has recently led to an increased interest in seriously modelling seasonality. The relative size of seasonal variation also means that such modelling is of major economic interest. Important developments in modelling seasonality have occurred - the last ten years have seen improvements in the model-based procedures, the discovery of periodic models, seasonal integration and co-integration and in the development of economic theories of seasonality. This volume brings together some leading papers on the existing standard economic theory of seasonality as well as papers which apply newer statistical tools to the modelling of seasonal phenomena. In addition, it presents and discusses the X-11 method of seasonal adjustment and the introduction includes a description and assessment of recent developments.
R 5,607.00
Exploring Complex Survey Data Analysis Using R
Surveys are powerful tools for gathering information, uncovering insights, and facilitating decision-making. However, to ensure the accurate interpretation of results, they require specific analysis methods. In this book, readers embark on an in-depth journey into conducting complex survey analysis with the {srvyr} package and tidyverse family of functions from the R programming language. Intended for intermediate R users familiar with the basics of the tidyverse, this book gives readers a deeper understanding of applying appropriate survey analysis techniques using {srvyr}, {survey}, and other related packages. With practical walkthroughs featuring real-world datasets, such as the American National Election Studies and Residential Energy Consumption Survey, readers will develop the skills necessary to perform impactful survey analysis on survey data collected through a randomized sample design. Additionally, this book teaches readers how to interpret and communicate results of survey data effectively.Key Features:Uses the {srvyr} package and tidyverse family of packages.Grants a conceptual understanding of the statistical methods that the functions apply to.Includes practical walkthroughs using publicly available survey data.Provides the reader with the tools for interpreting, visualizing, and presenting results.
R 4,066.00
Parallel Scientific Computing and Optimization
Parallel Scientific Computing and Optimization introduces new developments in the construction, analysis, and implementation of parallel computing algorithms. This book presents 23 self-contained chapters, including survey chapters and surveys, written by distinguished researchers in the field of parallel computing. Each chapter is devoted to some aspects of the subject: parallel algorithms for matrix computations, parallel optimization, management of parallel programming models and data, with the largest focus on parallel scientific computing in industrial applications.This volume is intended for scientists and graduate students specializing in computer science and applied mathematics who are engaged in parallel scientific computing.
R 4,115.50
Applied General Equilibrium Modelling
Over the last decade or so, applied general equilibrium models have rapidly become a major tool for policy advice on issues regarding allocation and efficiency, most notably taxes and tariffs. This reflects the power of the general equilibrium approach to allocative questions and the capability of today's applied models to come up with realistic answers. However, it by no means implies that the theoretical, practical and empirical problems faced by researchers in applied modelling have all been solved in a satisfactory way. Rather, a promising field of research has been opened up, inviting theorists and practitioners to further explore and exploit its potential. The state of the art in applied general equilibrium modelling is reflected in this volume. The introductory Chapter (Part I) evaluates the use of economic modelling to address policy questions, and discusses the advantages and disadvantages of applied general equilibrium models. Three substantive issues are dealt with in Chapters 2-8: Tax Reform and Capital (Part II), Intertemporal Aspects and Expectations (Part III), and Taxes and the Labour Market (Part IV). While all parts contain results relevant for economic policy, it is clear that theory and applications for these areas are in different stages of development. We hope that this book will bring inspiration, insight and information to researchers, students and policy advisors.
R 1,540.07
Mathematical Modelling of Aerospace Dynamic Systems with Practical Applications
Mathematical Modelling of Aerospace Dynamic Systems with Practical Applications provides mathematical models for several aerospace dynamic systems: aircraft, rotorcraft, missiles, unmanned aerial vehicles (UAVs), mini air vehicles (MAVs), autonomous underwater vehicles (AUWVs), and satellite-coordinate systems.Presenting the use of mathematical models for analysis, prediction, and control of these systems, this book discusses numerous applications in aircraft/helicopter parameter estimation, guidance and navigation of these vehicles, underwater object search, aerial terrain mapping, and satellite orbit determination. It explains path planning with obstacle avoidance, object occlusion detection and tracking, and multisensory target tracking and sensor data fusion.This book is intended for senior undergraduate mechanical and aerospace engineering students taking courses in aerospace systems and dynamics, flight dynamics and control, and dynamical systems and estimation.Instructors will be able to utilize a Solutions Manual and Figure Slides for their course.
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Explanation of Constrained Optimization for Economists
In a constrained optimization problem, the decisionmaker wants to select the "optimal" choice - the one most valuable to him or her - that also meets all of the constraints imposed by the problem. Such problems are at the heart of modern economics, where the typical behavioral postulate is that a decisionmaker behaves "rationally"; that is, chooses optimally from a set of constrained choices. Most books on constrained optimization are technical and full of jargon that makes it hard for the inexperienced reader to gain a holistic understanding of the topic. Peter B. Morgan's Explanation of Constrained Optimization for Economists solves this problem by emphasizing explanations, both written and visual, of the manner in which many constrained optimization problems can be solved. Suitable as a textbook or a reference for advanced undergraduate and graduate students familiar with the basics of one-variable calculus and linear algebra, this book is an accessible, user-friendly guide to this key concept.
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Computational Intelligence-based Optimization Algorithms
Computational intelligence-based optimization methods, also known as metaheuristic optimization algorithms, are a popular topic in mathematical programming.These methods have bridged the gap between various approaches and created a new school of thought to solve real-world optimization problems. In this book, we have selected some of the most effective and renowned algorithms in the literature. These algorithms are not only practical but also provide thought-provoking theoretical ideas to help readers understand how they solve optimization problems. Each chapter includes a brief review of the algorithm’s background and the fields it has been used in.Additionally, Python code is provided for all algorithms at the end of each chapter, making this book a valuable resource for beginner and intermediate programmers looking to understand these algorithms.
R 8,572.00
Modelling Nonlinear Economic Time Series
This book contains an extensive up-to-date overview of nonlinear time series models and their application to modelling economic relationships. It considers nonlinear models in stationary and nonstationary frameworks, and both parametric and nonparametric models are discussed. The book contains examples of nonlinear models in economic theory and presents the most common nonlinear time series models. Importantly, it shows the reader how to apply these models inpractice. For this purpose, the building of various nonlinear models with its three stages of model building: specification, estimation and evaluation, is discussed in detail and is illustrated by several examples involving both economic and non-economic data. Since estimation of nonlinear time seriesmodels is carried out using numerical algorithms, the book contains a chapter on estimating parametric nonlinear models and another on estimating nonparametric ones. Forecasting is a major reason for building time series models, linear or nonlinear. The book contains a discussion on forecasting with nonlinear models, both parametric and nonparametric, and considers numerical techniques necessary for computing multi-period forecasts from them. The main focus of the book is on models of the conditional mean, but models of the conditional variance, mainly those of autoregressive conditional heteroskedasticity, receive attention as well. A separate chapter isdevoted to state space models. As a whole, the book is an indispensable tool for researchers interested in nonlinear time series and is also suitable for teaching courses in econometrics and time series analysis.
R 3,442.00
Genetic Optimization Techniques for Sizing and Management of Modern Power Systems
Genetic Optimization Techniques for Sizing and Management of Modern Power Systems explores the design and management of energy systems using a genetic algorithm as the primary optimization technique. Coverage ranges across topics related to resource estimation and energy systems simulation. Chapters address the integration of distributed generation, the management of electric vehicle charging, and microgrid dimensioning for resilience enhancement with detailed discussion and solutions using parallel genetic algorithms. The work is suitable for researchers and practitioners working in power systems optimization requiring information for systems planning purposes, seeking knowledge on mathematical models available for simulation and assessment, and relevant applications in energy policy.
R 4,414.00
Using Excel for Principles of Econometrics
Principles of Econometrics is an introductory book for undergraduate students in economics and finance, and can be used for MBA and first-year graduate students in many fields. The 4th Edition provides students with an understanding of why econometrics is necessary and a working knowledge of basic econometric tools. This text emphasizes motivation, understanding and implementation by introducing very simple economic models and asking economic questions that students can answer.
R 1,176.00
Interactive Web-Based Data Visualization with R, plotly, and shiny
The richly illustrated Interactive Web-Based Data Visualization with R, plotly, and shiny focuses on the process of programming interactive web graphics for multidimensional data analysis. It is written for the data analyst who wants to leverage the capabilities of interactive web graphics without having to learn web programming. Through many R code examples, you will learn how to tap the extensive functionality of these tools to enhance the presentation and exploration of data. By mastering these concepts and tools, you will impress your colleagues with your ability to quickly generate more informative, engaging, and reproducible interactive graphics using free and open source software that you can share over email, export to pdf, and more.Key Features: Convert static ggplot2 graphics to an interactive web-based form Link, animate, and arrange multiple plots in standalone HTML from R Embed, modify, and respond to plotly graphics in a shiny app Learn best practices for visualizing continuous, discrete, and multivariate data Learn numerous ways to visualize geo-spatial dataThis book makes heavy use of plotly for graphical rendering, but you will also learn about other R packages that support different phases of a data science workflow, such as tidyr, dplyr, and tidyverse. Along the way, you will gain insight into best practices for visualization of high-dimensional data, statistical graphics, and graphical perception. The printed book is complemented by an interactive website where readers can view movies demonstrating the examples and interact with graphics.
R 12,115.28
Singular-Perturbation Theory
This book presents an introduction to singular-perturbation problems, problems which depend on a parameter in such a way that solutions behave non-uniformly as the parameter tends toward some limiting value of interest. The author considers and solves a variety of problems, mostly for ordinary differential equations. He constructs (approximate) solutions for oscillation problems, using the methods of averaging and of multiple scales. For problems of the nonoscillatory type, where solutions exhibit 'fast dynamics' in a thin initial layer, he derives solutions using the O'Malley/Hoppensteadt method and the method of matched expansions. He obtains solutions for boundary-value problems, where solutions exhibit rapid variation in thin layers, using a multivariable method. After a suitable approximate solution is constructed, the author linearizes the problem about the proposed approximate solution, and, emphasizing the use of the Banach/Picard fixed-point theorem, presents a study of the linearization. This book will be useful to students at the graduate and senior undergraduate levels studying perturbation theory for differential equations, and to pure and applied mathematicians, engineers, and scientists who use differential equations in the modelling of natural phenomena.
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Construction Scheduling, Cost Optimization and Management
In an increasingly competitive environment, construction professionals must ensure that efficient schedules are kept and that costs are kept to a minimum. Presenting a new construction scheduling model - CONSCOM - this book aims to provide the solution to effective construction scheduling and cost optimization. It presents a general mathematical formulation for the scheduling of construction projects and using this formula repetitive and non-repetitive tasks, work continuity considerations, multiple-crew strategies, and the effects of varying job conditions on the performance of a crew can be modelled. The authors intend to show that for any given construction duration, the CONSCOM model automatically yields the optimum construction schedule for minimum construction cost. By varying the construction duration, readers are shown how to solve the cost-duration trade-off construction cost. The model provides features for repetitive projects such as highway construction and allows schedulers greater flexibility. It is particularly suitable for studying the effects of change on the construction cost. This book's approach to the construction scheduling problem provides a practical methodo
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Energy Optimization and Prediction in Office Buildings
This book explains how energy demand and energy consumption in new buildings can be predicted and how these aspects and the resulting CO2 emissions can be reduced. It is based upon the authors’ extensive research into the design and energy optimization of office buildings in Chile. The authors first introduce a calculation procedure that can be used for the optimization of energy parameters in office buildings, and to predict how a changing climate may affect energy demand. The prediction of energy demand, consumption and CO2 emissions is demonstrated by solving simple equations using the example of Chilean buildings, and the findings are subsequently applied to buildings around the globe.An optimization process based on Artificial Neural Networks is discussed in detail, which predicts heating and cooling energy demands, energy consumption and CO2 emissions. Taken together, these processes will show readers how to reduce energy demand, consumption and CO2 emissions associated with office buildings in the future. Readers will gain an advanced understanding of energy use in buildings and how it can be reduced.
R 1,815.25
Sine Cosine Algorithm for Optimization
This open access book serves as a compact source of information on sine cosine algorithm (SCA) and a foundation for developing and advancing SCA and its applications. SCA is an easy, user-friendly, and strong candidate in the field of metaheuristics algorithms. Despite being a relatively new metaheuristic algorithm, it has achieved widespread acceptance among researchers due to its easy implementation and robust optimization capabilities. Its effectiveness and advantages have been demonstrated in various applications ranging from machine learning, engineering design, and wireless sensor network to environmental modeling. The book provides a comprehensive account of the SCA, including details of the underlying ideas, the modified versions, various applications, and a working MATLAB code for the basic SCA.
R 1,392.00
Modeling, Optimization, and Control of Zinc Hydrometallurgical Purification Process
Modeling, Optimization and Control of Zinc Hydrometallurgical Purification Process provides a clear picture on how to develop a mathematical model for complex industrial processes, how to design the optimization strategy, and how to apply control methods in order to achieve desired production target. This book shares the authors’ recent ideas/methodologies/algorithms on the intelligent manufacturing of complex industry processes, e.g., how to develop a descriptive framework which could enable the digitalization and visualization of a process and how to develop the controller when the process model is not available.
R 5,885.00
Model Selection and Multimodel Inference
A unique and comprehensive text on the philosophy of model-based data analysis and strategy for the analysis of empirical data. The book introduces information theoretic approaches and focuses critical attention on a priori modeling and the selection of a good approximating model that best represents the inference supported by the data. It contains several new approaches to estimating model selection uncertainty and incorporating selection uncertainty into estimates of precision. An array of examples is given to illustrate various technical issues. The text has been written for biologists and statisticians using models for making inferences from empirical data.
R 5,497.16
System Reliability Assessment and Optimization
This book offers a comprehensive overview of recently developed methods for assessing and optimizing system reliability. It consists of two main parts, for treating assessment methods and optimization methods, respectively. The first part covers methods of multi-state system reliability modelling and evaluation, Markov processes, Monte Carlo simulation and uncertainty analysis. The methods considered range from piecewise-deterministic Markov processes to belief function analysis. The second part covers optimization methods of mathematical programming and evolutionary algorithms, and problems of multi-objective optimization and optimization under uncertainty. The methods of this part range from non-dominated sorting genetic algorithm to robust optimization. The book also includes the application of the assessment and optimization methods considered on real case studies, particularly with respect to the reliability assessment and optimization of renewable energy systems, and bridges the gap between theoretical method development and engineering practice.
R 4,233.00
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.
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R 1,889.00
Optimization Methods for Gas and Power Markets
As power and gas markets are becoming more and more mature and globally competitive, the importance of reaching maximum potential economic efficiency is fundamental in all the sectors of the value chain, from investments selection to asset optimization, trading and sales. Optimization techniques can be used in many different fields of the energy industry, in order to reduce production and financial costs, increase sales revenues and mitigate all kinds of risks potentially affecting the economic margin. For this reason the industry has now focused its attention on the general concept of optimization and to the different techniques (mainly mathematical techniques) to reach it.Optimization Methods for Gas and Power Markets presents both theoretical elements and practical examples for solving energy optimization issues in gas and power markets. Starting with the theoretical framework and the basic business and economics of power and gas optimization, it quickly moves on to review the mathematical optimization problems inherent to the industry, and their solutions – all supported with examples from the energy sector. Coverage ranges from very long-term (and capital intensive) optimization problems such as investment valuation/diversification to asset (gas and power) optimization/hedging problems, and pure trading decisions.This book first presents the readers with various examples of optimization problems arising in power and gas markets, then deals with general optimization problems and describes the mathematical tools useful for their solution. The remainder of the book is dedicated to presenting a number of key business cases which apply the proposed techniques to concrete market problems. Topics include static asset optimization, real option evaluation, dynamic optimization of structured products like swing, virtual storage or virtual power plant contracts and optimal trading in intra-day power markets. As the book progresses, so too does the level of mathematical complexity, providing readers with an appreciation of the growing sophistication of even common problems in current market practice.Optimization Methods for Gas and Power Markets provides a valuable quantitative guide to the technicalities of optimization methodologies in gas and power markets; it is essential reading for practitioners in the energy industry and financial sector who work in trading, quantitative analysis and energy risk modeling.
R 3,291.99
Computational Intelligence based Optimization of Manufacturing Process for Sustainable Materials
The text comprehensively discusses computational models including artificial neural networks, agent-based models, and decision field theory for reliability engineering. It will serve as an ideal reference text for graduate students and academic researchers in the fields of industrial engineering, manufacturing engineering, computer engineering, and materials science. Discusses the development of sustainable materials using metaheuristic approaches. Covers computational models such as agent-based models, ontology, and decision field theory for reliability engineering. Presents swarm intelligence methods such as ant colony optimization, particle swarm optimization, and grey wolf optimization for solving the manufacturing process. Include case studies for industrial optimizations. Explores the use of computational optimization for reliability and maintainability theory. The text covers swarm intelligence techniques including ant colony optimization, particle swarm optimization, cuckoo search, and genetic algorithms for solving complex industrial problems of the manufacturing industry as well as predicting reliability, maintainability, and availability of several industrial components.
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Topics in the Mathematical Modelling of Composite Materials
Andrej V. Cherkaev and Robert V. Kohn In the past twenty years we have witnessed a renaissance of theoretical work on the macroscopic behavior of microscopically heterogeneous mate rials. This activity brings together a number of related themes, including: ( 1) the use of weak convergence as a rigorous yet general language for the discussion of macroscopic behavior; (2) interest in new types of questions, particularly the "G-closure problem," motivated in large part by applications of optimal control theory to structural optimization; (3) the introduction of new methods for bounding effective moduli, including one based on "com pensated compactness"; and (4) the identification of deep links between the analysis of microstructures and the multidimensional calculus of variations. This work has implications for many physical problems involving optimal design, composite materials, and coherent phase transitions. As a result it has received attention and support from numerous scientific communities, including engineering, materials science, and physics as well as mathematics. There is by now an extensive literature in this area. But for various reasons certain fundamental papers were never properly published, circu lating instead as mimeographed notes or preprints. Other work appeared in poorly distributed conference proceedings volumes. Still other work was published in standard books or journals, but written in Russian or French. The net effect is a sort of "gap" in the literature, which has made the subject unnecessarily difficult for newcomers to penetrate.
R 6,006.11
Dynamic General Equilibrium Modelling for Forecasting and Policy
The "Contributions to Economic Analysis" series consists of a number of previously unpublished studies. The term economic analysis is used because it covers the activities of the theoretical economist and the research worker.
R 7,558.40
Using Statistics in Small-Scale Language Education Research
Assuming no familiarity with statistical methods, this text for language education research methods and statistics courses provides detailed guidance and instruction on principles of designing, conducting, interpreting, reading, and evaluating statistical research done in classroom settings or with a small number of participants. While three different types of statistics are addressed (descriptive, parametric, non-parametric) the emphasis is on non-parametric statistics because they are appropriate when the number of participants is small and the conditions for use of parametric statistics are not satisfied. The emphasis on non-parametric statistics is unique and complements the growing interest among second and foreign language educators in doing statistical research in classrooms. Designed to help students and other language education researchers to identify and use analyses that are appropriate for their studies, taking into account the number of participants and the shape of the data distribution, the text includes sample studies to illustrate the important points in each chapter and exercises to promote understanding of the concepts and the development of practical research skills. Mathematical operations are explained in detail, and step-by-step illustrations in the use of R (a very powerful, online, freeware program) to perform all calculations are provided.A Companion Website extends and enhances the text with PowerPoint presentations illustrating how to carry out calculations and use R; practice exercises with answer keys; data sets in Excel MS-DOS format; and quiz, midterm, and final problems with answer keys.
R 9,961.28
Stochastic Modelling of Big Data in Finance
Stochastic Modelling of Big Data in Finance provides a rigorous overview and exploration of stochastic modelling of big data in finance (BDF). The book describes various stochastic models, including multivariate models, to deal with big data in finance. This includes data in high-frequency and algorithmic trading, specifically in limit order books (LOB), and shows how those models can be applied to different datasets to describe the dynamics of LOB, and to figure out which model is the best with respect to a specific data set. The results of the book may be used to also solve acquisition, liquidation and market making problems, and other optimization problems in finance.FeaturesSelf-contained book suitable for graduate students and post-doctoral fellows in financial mathematics and data science, as well as for practitioners working in the financial industry who deal with big dataAll results are presented visually to aid in understanding of conceptsDr. Anatoliy Swishchuk is a Professor in Mathematical Finance at the Department of Mathematics and Statistics, University of Calgary, Calgary, AB, Canada. He got his B.Sc. and M.Sc. degrees from Kyiv State University, Kyiv, Ukraine. He earned two doctorate degrees in Mathematics and Physics (PhD and DSc) from the prestigious National Academy of Sciences of Ukraine (NASU), Kiev, Ukraine, and is a recipient of NASU award for young scientist with a gold medal for series of research publications in random evolutions and their applications.Dr. Swishchuk is a chair and organizer of finance and energy finance seminar ‘Lunch at the Lab’ at the Department of Mathematics and Statistics. Dr. Swishchuk is a Director of Mathematical and Computational Finance Laboratory at the University of Calgary. He was a steering committee member of the Professional Risk Managers International Association (PRMIA), Canada (2006-2015), and is a steering committee member of Global Association of Risk Professionals (GARP), Canada (since 2015).Dr. Swishchuk is a creator of mathematical finance program at the Department of Mathematics & Statistics. He is also a proponent for a new specialization “Financial and Energy Markets Data Modelling” in the Data Science and Analytics program. His research areas include financial mathematics, random evolutions and their applications, biomathematics, stochastic calculus, and he serves on editorial boards for four research journals. He is the author of more than 200 publications, including 15 books and more than 150 articles in peer-reviewed journals. In 2018 he received a Peak Scholar award.
R 5,165.00
Fuzzy Optimization Techniques in the Areas of Science and Management
This book helps to enhance the application of fuzzy logic optimization in the areas of science and engineering. It includes implementation and models and paradigms, such as path planning and routing design for different wireless networks, organization behavior strategies models, and so forth. It also: Explains inventory control management, uncertainties management, loss minimization, game optimization, data analysis and prediction, different decision-making system and management, and so forth Describes applicability of fuzzy optimization techniques in areas of science and management Resolves several issues based on uncertainty using member function Helps map different problems based on mathematical models Includes issues and problems based on linear and nonlinear optimizations Focuses on management science such as manpower management and inventory planningThis book is aimed at researchers and graduate students in signal processing, power systems, systems and industrial engineering, and computer networks.
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Recent Advances in Optimization
This volume contains the Proceedings of the Twelfth French-German-Spanish Conference on Optimization held at the University of Avignon in 2004. We refer to this conference by using the acronym FGS-2004. During the period September 20-24, 2004, about 180 scientists from around the world met at Avignon (France) to discuss recent developments in optimization and related fields. The main topics discussed during this meeting were the following: 1. smooth and nonsmooth continuous optimization problems, 2. numerical methods for mathematical programming, 3. optimal control and calculus of variations, 4. differential inclusions and set-valued analysis, 5. stochastic optimization, 6. multicriteria optimization, 7. game theory and equilibrium concepts, 8. optimization models in finance and mathematical economics, 9. optimization techniques for industrial applications. The Scientific Committee of the conference consisted of F. Bonnans (Rocqu- court, France), J.-B. Hiriart-Urruty (Toulouse, France), F. Jarre (Diisseldorf, Germany), M.A. Lopez (Alicante, Spain), J.E. Martinez-Legaz (Barcelona, Spain), H. Maurer (Miinster, Germany), S. Pickenhain (Cottbus, Germany), A.Seeger (Avignon, France), and M. Thera (Limoges, France). The conference FGS-2004 is the 12th of the series of French-German meetings which started in Oberwolfach in 1980 and was continued in Confolant (1981), Luminy (1984), Irsee (1986), Varetz (1988), Lambrecht (1991), Dijon (1994), Trier (1996), Namur (1998), Montpellier (2000), and Cottbus (2002).
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Nonlinear Multiobjective Optimization
Problems with multiple objectives and criteria are generally known as multiple criteria optimization or multiple criteria decision-making (MCDM) problems. So far, these types of problems have typically been modelled and solved by means of linear programming. However, many real-life phenomena are of a nonlinear nature, which is why we need tools for nonlinear programming capable of handling several conflicting or incommensurable objectives. In this case, methods of traditional single objective optimization and linear programming are not enough; we need new ways of thinking, new concepts, and new methods - nonlinear multiobjective optimization. Nonlinear Multiobjective Optimization provides an extensive, up-to-date, self-contained and consistent survey, review of the literature and of the state of the art on nonlinear (deterministic) multiobjective optimization, its methods, its theory and its background. The amount of literature on multiobjective optimization is immense. The treatment in this book is based on approximately 1500 publications in English printed mainly after the year 1980. Problems related to real-life applications often contain irregularities and nonsmoothnesses. The treatment of nondifferentiable multiobjective optimization in the literature is rather rare. For this reason, this book contains material about the possibilities, background, theory and methods of nondifferentiable multiobjective optimization as well. This book is intended for both researchers and students in the areas of (applied) mathematics, engineering, economics, operations research and management science; it is meant for both professionals and practitioners in many different fields of application. The intention has been to provide a consistent summary that may help in selecting an appropriate method for the problem to be solved. It is hoped the extensive bibliography will be of value to researchers.
R 10,315.64
Modelling and Optimization of Distributed Parameter Systems Applications to engineering
This volume contains a selection of papers presented at the conference on Modelling and Optimization of Distributed Parameter Systems with Applications to Engineering, held in Warsaw on July 17-21, 1995. This conference was a consecutive one in the series of conferences sponsored by the IFIP Working Group WG 7.2 "Computational Techniques in Distributed Systems", chaired by Irena Lasiecka. It was organized by the Systems Research Institute of the Polish Academy of Sciences and supported financially by the following institutions: -European Community on Computational Methods in Applied Sciences, -Fundacja Stefana Batorego, -International Mathematical Union, - Telekomunikacja Polska S.A. The following scientists took an active part in preparation of the scientific program of the conference, organizing or helping to organize special sessions: - E. Casas and I.Lasiecka (Optimization and Optimal ControQ, Z.Mr6z (Mechanical Applications), - M.Niezg6dka (Properties of Solutions to P.D.E.s), - L.Pandolfi (Hamilton and Riccati Equation Approaches to Optimization), - K.Sobczyk and J.Zabczyk (Stochastic Systems), - J.Sokolowski and J.-P.Zolesio (Shape Optimization), - J.Wa8niewski (Scientific Computation).In the conference participated 133 scientists from 22 countries. Ten invited plenary lectures and 103 contributed papers have been presented. This volume contains a part of the presented material. The core of it is constituted by papers devoted to control and optimization of distributed parameter systems. Other selection will be included in a special issue of the quarterly Control & Cybernetics to be published in 1996.
R 6,017.45
Stochastic Modeling and Optimization of Manufacturing Systems and Supply Chains
This volume originates from two workshops, both focusing on themes that are reflected in the title of the volume. The first workshop took place at Eindhoven University of Technology, April 24-26, 2001, on the occasion of the University granting a doctorate honoris causa to Profes sor John A. Buzacott. The second workshop was held on June 15, 2002 at Cornell University (preceding the annual INFORMSjMSOM Confer ence), honoring John's retirement and his lifetime contributions. Each of the two workshops consisted of about a dozen technical presentations. The objective of the volume, however, is not to simply publish the proceedings of the two workshops. Rather, our objective is to put to gether a select set of articles, each organized into a well-written chapter, focusing on a timely topic. Collected into a single volume, these chapters aim to serve as a useful reference for researchers and practitioners alike, and also as reading materials for graduate courses or seminars.
R 4,674.26
Foundations of Real Estate Financial Modelling
NAMED ONE OF THE BEST COMMERCIAL REAL ESTATE BOOKS BY THE MOTLEY FOOL"Staiger gives us the technical tools needed to build robust pro forma modeling around our real estate assets." –MillionAcresNow in its third edition, Foundations of Real Estate Financial Modelling equips a new generation of students and professionals with a resource MillionAcres guarantees they’ll "use throughout [their] commercial investing career[s]". Designed to provide increased scalable basis of pro forma modelling for real estate projects, this complete update and revision of the classic text offers a step-by-step introduction to building and understanding the models underlying investments in properties from single family rentals to large-scale development. Case studies drawn from the author’s storied investment career put models into real world context while problem sets at the end of each chapter provide hands-on practice for learners at any stage of their real estate careers. This edition employs the innovative financial metric P(Gain) to quantify the probability of a Return of Capital, ensuring readers’ ability to answer the most fundamental question of investing—What is the probability I’ll get my money back?The fully revised and enhanced third edition is organized in three functional units: (1) Real Estate Valuation Basics, Theory and Skills, (2) Real Estate Pro Forma Modelling, (3) Real Estate Pro Forma (Enhancements). Chapters cover: Interest Rates (Prime, Libor, Sofr) Amortization (Cash-Out Refinance modelling) ADC (Acquisition, Development, Construction) Module Rent Roll Module (including seasonality) Waterfall Hotel Consolidation Stochastic Modelling and Optimization Additional chapters are dedicated to risk quantification and include scenario, stochastic and Monte Carlo simulations, equity waterfalls, and integration of U.S. GAAP financial statements. A companion website provides the real estate pro forma models to readers as a reference for their own constructed models, www.pgainllc.com. An ideal companion in the classroom and the boardroom, this new edition of Foundations of Real Estate Financial Modelling will make even novices the "experts in the room on [their] chosen asset class" (MillionAcres).
R 10,257.00
Learning Automata and Stochastic Optimization
In the last decade there has been a steadily growing need for and interest in computational methods for solving stochastic optimization problems with or wihout constraints. Optimization techniques have been gaining greater acceptance in many industrial applications, and learning systems have made a significant impact on engineering problems in many areas, including modelling, control, optimization, pattern recognition, signal processing and diagnosis. Learning automata have an advantage over other methods in being applicable across a wide range of functions. Featuring new and efficient learning techniques for stochastic optimization, and with examples illustrating the practical application of these techniques, this volume will be of benefit to practicing control engineers and to graduate students taking courses in optimization, control theory or statistics.
R 1,543.29
R 815.00
R 1,916.66
Throughput Optimization in Robotic Cells
Throughput Optimization In Robotic Cells provides practitioners, researchers, and students with up-to-date algorithmic results on sequencing of robot moves and scheduling of parts in robotic cells. It brings together the structural results developed over the last 25 years for the various realistic models of robotic cells. This book is ideally suited for use in a graduate course or a research seminar on robotic cells.
R 4,676.02
Multi-Objective Optimization using Artificial Intelligence Techniques
This book focuses on the most well-regarded and recent nature-inspired algorithms capable of solving optimization problems with multiple objectives. Firstly, it provides preliminaries and essential definitions in multi-objective problems and different paradigms to solve them. It then presents an in-depth explanations of the theory, literature review, and applications of several widely-used algorithms, such as Multi-objective Particle Swarm Optimizer, Multi-Objective Genetic Algorithm and Multi-objective GreyWolf Optimizer Due to the simplicity of the techniques and flexibility, readers from any field of study can employ them for solving multi-objective optimization problem. The book provides the source codes for all the proposed algorithms on a dedicated webpage.
R 2,023.14
Fuzzy Optimization Techniques in the Areas of Science and Management
This book helps to enhance the application of fuzzy logic optimization in the areas of science and engineering. It includes implementation and models and paradigms, such as path planning and routing design for different wireless networks, organization behavior strategies models, and so forth. It also: Explains inventory control management, uncertainties management, loss minimization, game optimization, data analysis and prediction, different decision-making system and management, and so forth Describes applicability of fuzzy optimization techniques in areas of science and management Resolves several issues based on uncertainty using member function Helps map different problems based on mathematical models Includes issues and problems based on linear and nonlinear optimizations Focuses on management science such as manpower management and inventory planningThis book is aimed at researchers and graduate students in signal processing, power systems, systems and industrial engineering, and computer networks.
R 7,566.46
Practical Financial Optimization
Practical Financial Optimization is a comprehensive guide to optimization techniques in financial decision making. This book illuminates the relationship between theory and practice, providing the readers with solid foundational knowledge. Focuses on classical static mean-variance analysis and portfolio immunization, scenario-based models, multi-period dynamic portfolio optimization, and the relationships between classes of modelsAnalyizes real world applications and implications for financial engineersIncludes a list of models and a section on notations that includes a glossary of symbols and abbreviations
R 2,117.00
Next Generation Smart Grids: Modeling, Control and Optimization
This book is a collection of chapters describing the advanced and future aspects of smart grid technology. The book emphasizes technical issues, theoretical background and practical applications that drive postgraduates, researchers and practicing engineers with the right advanced skills, vision and knowledge who will further be capable of leading in teams involved in the modelling, control, design, and optimization of the future smart grids. This feature strengthens the benefits of the book for the readers who will gain an insightful understanding of future smart grid challenges including: (i) the formulation of decision-making models, (ii) the familiarization with efficient solution algorithms for such models and (iii) insights into these problems through the detailed analysis of numerous illustrative examples. Further the chapters in this book provide comprehensive coverage of modelling, control and optimization of smart grid which are quite different from most technical publications.
R 7,326.00
Advances in Structural Engineering—Optimization
This book is an up-to-date source for computation applications of optimization, prediction via artificial intelligence methods, and evaluation of metaheuristic algorithm with different structural applications. As the current interest of researcher, metaheuristic algorithms are a high interest topic area since advance and non-optimized problems via mathematical methods are challenged by the development of advance and modified algorithms. The artificial intelligence (AI) area is also important in predicting optimum results by skipping long iterative optimization processes. The machine learning used in generation of AI models also needs optimum results of metaheuristic-based approaches.This book is a great source to researcher, graduate students, and bachelor students who gain project about structural optimization. Differently from the academic use, the chapter that emphasizes different scopes and methods can take the interest and help engineer working in design and production of structural engineering projects.
R 6,507.40
