Mathematical theory of computation Books
Taylor & Francis Ltd Computational Framework for the Finite Element
Book SynopsisComputational Framework for the Finite Element Method in MATLAB and Python aims to provide a programming framework for coding linear FEM using matrix-based MATLAB language and Python scripting language. It describes FEM algorithm implementation in the most generic formulation so that it is possible to apply this algorithm to as many application problems as possible. Readers can follow the step-by-step process of developing algorithms with clear explanations of its underlying mathematics and how to put it into MATLAB and Python code. The content is focused on aspects of numerical methods and coding FEM rather than FEM mathematical analysis. However, basic mathematical formulations for numerical techniques which are needed to implement FEM are provided. Particular attention is paid to an efficient programming style using sparse matrices. Features Contains ready-to-use coding recipes allowing fast prototyping and
£43.69
CRC Press Handbook of Price Impact Modeling
Book SynopsisHandbook of Price Impact Modeling provides practitioners and students with a mathematical framework grounded in academic references to apply price impact models to quantitative trading and portfolio management. Automated trading is now the dominant form of trading across all frequencies. Furthermore, trading algorithm rise introduces new questions professionals must answer, for instance: How do stock prices react to a trading strategy? How to scale a portfolio considering its trading costs and liquidity risk? How to measure and improve trading algorithms while avoiding biases? Price impact models answer these novel questions at the forefront of quantitative finance. Hence, practitioners and students can use this Handbook as a comprehensive, modern view of systematic trading.For financial institutions, the Handbookâs framework aims to minimize the firmâs price impact, measure market liquiditTrade Review"Kevin Webster has written a remarkable textbook that studies these problems in a uniquely comprehensive manner. To wit, he covers theory, empirics, and implementation by bringing together insights developed in a number of different research communities, ranging from Industry Practitioners, Financial Economists, Econophysicists, to Applied Mathematicians. In doing so, Kevin develops the underlying theory in a very accessible manner. He also presents important practical applications beyond optimal trading (such as risk management), which showcase that a good grasp of the mechanics of price impact is an essential part of any modern financial engineer's toolkit."- Johannes Muhle-Karbe, Imperial College London.Full article: Handbook of Price Impact Modeling (tandfonline.com)Table of Contents1. Introduction to Modeling Price Impact. 2. Mathematical Models of Price Impact. 3. Applications of Price Impact Models. 4. Further Applications of Price Impact Models. 5. An Introduction to the Mathematics of Causal Inference. 6. Dealing with Biases when Fitting Price Impact Models. 7. Empirical Analysis of Price Impact Models.
£73.14
Taylor and Francis Deep Learning
Book Synopsis
£47.49
CRC Press Introduction to Python for Science and
Book SynopsisIntroduction to Python for Science and Engineering offers a quick and incisive introduction to the Python programming language for use in any science or engineering discipline. The approach is pedagogical and âœbottom up,â which means starting with examples and extracting more general principles from that experience. No prior programming experience is assumed.Readers will learn the basics of Python syntax, data structures, input and output, conditionals and loops, user-defined functions, plotting, animation, and visualization. They will also learn how to use Python for numerical analysis, including curve fitting, random numbers, linear algebra, solutions to nonlinear equations, numerical integration, solutions to differential equations, and fast Fourier transforms.Readers learn how to interact and program with Python using JupyterLab and Spyder, two simple and widely used integrated development environments.All the major Python libraries for science and e
£47.49
CRC Press Python
Book SynopsisPython's simplicity and versatility make it an ideal language for both beginners and experienced programmers. Its syntax facilitates a smooth learning curve, enabling individuals to concentrate on grasping programming concepts instead of wrestling with intricate syntax rules. The extensive standard library reinforces its practicality, offering pre-built modules and functions that reduce manual coding efforts. Python's versatility is evident in its applications, spanning web development, data analysis, Machine Learning and automation.The language's interactive nature supports real-time code experimentation, stepping up the learning process and enhancing understanding. Python's wealth of online resources further enriches the learning experience, fostering a community where individuals can develop their programming skills. Python: A Practical Learning Approach exemplifies Python's simplicity and versatility with numerous examples, ensuring a seamless learning journ
£42.74
CRC Press Python Programming for Mathematics
Book SynopsisPython Programming for Mathematics focuses on the practical use of the Python language in a range of different areas of mathematics. Through fifty-five exercises of increasing difficulty, the book provides an expansive overview of the power of using programming to solve complex mathematical problems.This book is intended for undergraduate and graduate students who already have learned the basics of Python programming and would like to learn how to apply that programming skill in mathematics.Features Innovative style that teaches programming skills via mathematical exercises. Ideal as a main textbook for Python for Mathematics courses, or as a supplementary resource for Numerical Analysis and Scientific Computing courses.
£42.74
Cambridge University Press Advanced Topics in Bisimulation and Coinduction 52 Cambridge Tracts in Theoretical Computer Science Series Number 52
Book SynopsisCoinduction is a method for specifying and reasoning about infinite data types and automata with infinite behaviour. In recent years, it has come to play an ever more important role in the theory of computing. It is studied in many disciplines, including process theory and concurrency, modal logic and automata theory. Typically, coinductive proofs demonstrate the equivalence of two objects by constructing a suitable bisimulation relation between them. This collection of surveys is aimed at both researchers and Master's students in computer science and mathematics and deals with various aspects of bisimulation and coinduction, with an emphasis on process theory. Seven chapters cover the following topics: history, algebra and coalgebra, algorithmics, logic, higher-order languages, enhancements of the bisimulation proof method, and probabilities. Exercises are also included to help the reader master new material.Table of ContentsPreface; List of contributors; 1. Origins of bisimulation and coinduction Davide Sangiorgi; 2. An introduction to (co)algebra and (co)induction Bart Jacobs and Jan Rutten; 3. The algorithmics of bisimilarity Luca Aceto, Anna Ingolfsdottir and Jiří Srba; 4. Bisimulation and logic Colin Stirling; 5. Howe's method for higher-order languages Andrew Pitts; 6. Enhancements of the bisimulation proof method Damien Pous and Davide Sangiorgi; 7. Probabilistic bisimulation Prakash Panangaden.
£104.50
Cambridge University Press Quantum Information Theory
Book SynopsisThis new edition of Wilde's popular book promises over 100 pages of new material, exercises and references. New attention is given to the derivation of the Choi-Kraus theorem for quantum channels, the CHSH game, quantum relative entropy, and sequential decoding. The text offers an ideal entry point into the topic for graduate students.Trade Review'For years, I have been hoping that somebody would write a book on quantum information theory that was clear, comprehensive, and up to date. This is that book. And the second edition is even better than the first.' Peter Shor, Massachusetts Institute of Technology'Mark M. Wilde's Quantum Information Theory is a natural expositor's labor of love. Accessible to anyone comfortable with linear algebra and elementary probability theory, Wilde's book brings the reader to the forefront of research in the quantum generalization of Shannon's information theory. What had been a gaping hole in the literature has been replaced by an airy edifice, scalable with the application of reasonable effort and complete with fine vistas of the landscape below. Wilde's book has a permanent place not just on my bookshelf but on my desk.' Patrick Hayden, Stanford University, CaliforniaReview of previous edition: '… [its] clear, thorough, and above all self-contained presentation will aid quantum information researchers in coming up to speed with the latest results in this area of the field. Meanwhile, the familiar setting and language will help classical information theorists who wish to become more acquainted with the quantum aspects of information processing … The presentation is well-structured, making it easy to jump to the desired topic and quickly determine on what that topic depends and how it is used going forward … Quantum Information Theory fills an important gap in the existing literature and will, I expect, help propagate the latest and greatest results in quantum Shannon theory to both quantum and classical researchers.' Joseph M. Renes, Quantum Information ProcessingReview of previous edition: '… a modern self-contained text … suitable for graduate-level courses leading up to research level.' Journal of Discrete Mathematical Sciences and CryptographyReview of previous edition: '… the book does a phenomenal job of introducing, developing and nurturing a mathematical sense of quantum information processing … In a nutshell, this is an essential reference for students and researchers who work in the area or are trying to understand what it is that quantum information theorists study. Wilde, as mentioned in his book, beautifully illustrates 'the ultimate capability of noisy physical systems, governed by the laws of quantum mechanics, to preserve information and correlations' through this book. I would strongly recommend it to anyone who plans to continue working in the field of quantum information.' Subhayan Roy Moulick, SIGCAT NewsTable of ContentsPreface to the second edition; Preface to the first edition; How to use this book; Part I. Introduction: 1. Concepts in quantum Shannon theory; 2. Classical Shannon theory; Part II. The Quantum Theory: 3. The noiseless quantum theory; 4. The noisy quantum theory; 5. The purified quantum theory; Part III. Unit Quantum Protocols: 6. Three unit quantum protocols; 7. Coherent protocols; 8. Unit resource capacity region; Part IV. Tools of Quantum Shannon Theory: 9. Distance measures; 10. Classical information and entropy; 11. Quantum information and entropy; 12. Quantum entropy inequalities and recoverability; 13. The information of quantum channels; 14. Classical typicality; 15. Quantum typicality; 16. The packing lemma; 17. The covering lemma; Part V. Noiseless Quantum Shannon Theory: 18. Schumacher compression; 19. Entanglement manipulation; Part VI. Noisy Quantum Shannon Theory: 20. Classical communication; 21. Entanglement-assisted classical communication; 22. Coherent communication with noisy resources; 23. Private classical communication; 24. Quantum communication; 25. Trading resources for communication; 26. Summary and outlook; Appendix A. Supplementary results; Appendix B. Unique linear extension of a quantum physical evolution; References; Index.
£60.79
Cambridge University Press Compressive Imaging Structure Sampling Learning
Book SynopsisAccurate, robust and fast image reconstruction is a critical task in many scientific, industrial and medical applications. Over the last decade, image reconstruction has been revolutionized by the rise of compressive imaging. It has fundamentally changed the way modern image reconstruction is performed. This in-depth treatment of the subject commences with a practical introduction to compressive imaging, supplemented with examples and downloadable code, intended for readers without extensive background in the subject. Next, it introduces core topics in compressive imaging including compressed sensing, wavelets and optimization in a concise yet rigorous way, before providing a detailed treatment of the mathematics of compressive imaging. The final part is devoted to recent trends in compressive imaging: deep learning and neural networks. With an eye to the next decade of imaging research, and using both empirical and mathematical insights, it examines the potential benefits and the piTable of Contents1. Introduction; Part I. The Essentials of Compressive Imaging: 2. Images, transforms and sampling; 3. A short guide to compressive imaging; 4. Techniques for enhancing performance; Part II. Compressed Sensing, Optimization and Wavelets: 5. An introduction to conventional compressed sensing; 6. The LASSO and its cousins; 7. Optimization for compressed sensing; 8. Analysis of optimization algorithms; 9. Wavelets; 10. A taste of wavelet approximation theory; Part III. Compressed Sensing with Local Structure: 11. From global to local; 12. Local structure and nonuniform recovery; 13. Local structure and uniform recovery; 14. Infinite-dimensional compressed sensing; Part IV. Compressed Sensing for Imaging: 15. Sampling strategies for compressive imaging; 16. Recovery guarantees for wavelet-based compressive imaging; 17. Total variation minimization; Part V. From Compressed Sensing to Deep Learning: 18. Neural networks and deep learning; 19. Deep learning for compressive imaging; 20. Accuracy and stability of deep learning for compressive imaging; 21. Stable and accurate neural networks for compressive imaging; 22. Epilogue; Appendices: A. Linear Algebra; B. Functional analysis; C. Probability; D. Convex analysis and convex optimization; E. Fourier transforms and series; F. Properties of Walsh functions and the Walsh transform; Notation; Abbreviations; References; Index.
£59.84
Cambridge University Press Mathematical Logic and Computation
Book SynopsisThis book presents mathematical logic from the syntactic point of view, with an emphasis on aspects that are fundamental to computer science. It is an excellent introduction for graduate students and advanced undergraduates interested in logic in mathematics, computer science, and philosophy, and an invaluable reference for professional logicians.Trade Review'Avigad provides a much needed introduction to mathematical logic that foregrounds the role of syntax and computability in our understanding of consistency and inconsistency. The result provides a jumping off point to any of the fields of modern logic, not only teaching the technical groundwork, but also providing a window into how to think like a logician.' Henry Towsner, University of Pennsylvania'This book by one of the most knowledgeable researchers in the field covers a remarkably broad selection of material without sacrificing depth. Its clear organization and unified approach - focused on a syntactic approach and on the role of computation - make it suitable for a wide range of introductory logic sequences at the upper-level undergraduate and graduate level, as well as a valuable resource for background material in more advanced logic courses.' Denis Hirschfeldt, University of Chicago'… an excellent addition to the literature, with plenty more than enough divergences and side-steps from the more well-trodden paths through the material to be consistently interesting … this is most certainly a book to make sure your library gets.' Peter Smith, Logic MattersTable of ContentsPreface; 1. Fundamentals; 2. Propositional Logic; 3. Semantics of Propositional Logic; 4. First-Order Logic; 5. Semantics of First-Order Logic; 6. Cut Elimination; 7. Properties of First-Order Logic; 8. Primitive Recursion; 9. Primitive Recursive Arithmetic; 10. First-Order Arithmetic; 11. Computability 12. Undecidability and Incompleteness; 13. Finite Types; 14. Arithmetic and Computation; 15. Second-Order Logic and Arithmetic; 16. Subsystems of Second-Order Arithmetic; 17. Foundations; Appendix; References; Notation; Index.
£56.99
Cambridge University Press Theoretical Computer Science for the Working
Book SynopsisUsing basic category theory, this Element describes all the central concepts and proves the main theorems of theoretical computer science. Category theory, which works with functions, processes, and structures, is uniquely qualified to present the fundamental results of theoretical computer science. In this Element, readers will meet some of the deepest ideas and theorems of modern computers and mathematics, such as Turing machines, unsolvable problems, the P=NP question, Kurt Gödel''s incompleteness theorem, intractable problems, cryptographic protocols, Alan Turing''s Halting problem, and much more. The concepts come alive with many examples and exercises.Table of Contents1. Introduction; 2. Aide-Mémoire for Category Theory; 3. Models of Computation; 4. Computability Theory; 5. Complexity Theory; 6. Diagonal Arguments; 7. Conclusion; References.
£17.00
Cambridge University Press Topological Data Analysis with Applications
Book SynopsisThe continued and dramatic rise in the size of data sets has meant that new methods are required to model and analyze them. This timely account introduces topological data analysis (TDA), a method for modeling data by geometric objects, namely graphs and their higher-dimensional versions: simplicial complexes. The authors outline the necessary background material on topology and data philosophy for newcomers, while more complex concepts are highlighted for advanced learners. The book covers all the main TDA techniques, including persistent homology, cohomology, and Mapper. The final section focuses on the diverse applications of TDA, examining a number of case studies drawn from monitoring the progression of infectious diseases to the study of motion capture data. Mathematicians moving into data science, as well as data scientists or computer scientists seeking to understand this new area, will appreciate this self-contained resource which explains the underlying technology and how it can be used.Table of ContentsPart I. Background: 1. Introduction; 2. Data; Part II. Theory: 3. Topology; 4. Shape of data; 5. Structures on spaces of barcodes; Part III. Practice: 6. Case studies; References; Index.
£37.99
John Wiley & Sons Inc Foundations of Computational Finance with MATLAB
Book SynopsisGraduate from Excel to MATLAB to keep up with the evolution of finance data Foundations of Computational Finance with MATLAB is an introductory text for both finance professionals looking to branch out from the spreadsheet, and for programmers who wish to learn more about finance. As financial data grows in volume and complexity, its very nature has changed to the extent that traditional financial calculators and spreadsheet programs are simply no longer enough. Today's analysts need more powerful data solutions with more customization and visualization capabilities, and MATLAB provides all of this and more in an easy-to-learn skillset. This book walks you through the basics, and then shows you how to stretch your new skills to create customized solutions. Part I demonstrates MATLAB's capabilities as they apply to traditional finance concepts, and PART II shows you how to create interactive and reusable code, link with external data Table of ContentsIntroduction xiii Why You Should Read This Book xiii The Intended Reader xiv Why MATLAB®? xiv How to Use This Book xvi Font Conventions xvi About the Author xvii MathWorks Information xviii References xviii Part I MATLAB Conventions and Basic Skills 1 Chapter 1 Working with MATLAB® Data 3 1.1 Introduction 3 1.2 Arrays 3 1.2.1 Numerical Arrays 4 1.2.2 Math Calculations with Scalars,Vectors, and Matrices 10 1.2.3 Statistical Calculations on Vectors and Matrices 16 1.2.4 Extracting Values from Numerical Vectors and Matrices 19 1.2.5 Counting Elements 26 1.2.6 Sorting Vectors and Matrices 28 1.2.7 Relational Expressions and Logical Arrays 31 1.2.8 Dealing with NaNs (Not a Number) 35 1.2.9 Dealing with Missing Data 39 1.3 Character Arrays 40 1.3.1 String Arrays 44 1.4 Flexible Data Structures 46 1.4.1 Cell Arrays 47 1.4.2 Structure (“struct”) Arrays 49 1.4.3 Tables 51 References 60 Further Reading 60 Chapter 2 Working with Dates and Times 61 2.1 Introduction 61 2.2 Finance Background: Why Dates and Times Matter 61 2.2.1 First Challenge: Day Count Conventions 62 2.2.2 Second Challenge: Date Formats 63 2.3 Dates and Times in MATLAB 64 2.3.1 Datetime Variables 64 2.3.2 Date Conversions 73 2.3.3 Date Generation Functions with Serial Number Outputs 79 2.3.4 Duration Arrays 83 2.3.5 Calendar Duration Variables 86 2.3.6 Date Calculations and Operations 89 2.3.7 Plotting Date Variables Introduction 94 References 95 Chapter 3 Basic Programming with MATLAB® 97 3.1 Introduction 97 3.1.1 Algorithms 101 97 3.1.2 Go DIY or Use Built-In Code? 98 3.2 MATLAB Scripts and Functions 99 3.2.1 Scripts 99 3.2.2 Developing Functions 106 3.2.3 If Statements 112 3.2.4 Modular Programming 115 3.2.5 User Message Formats 121 3.2.6 Testing and Debugging 124 References 127 Chapter 4 Working with Financial Data 129 4.1 Introduction 129 4.2 Accessing Financial Data 129 4.2.1 Closing Prices versus Adjusted Close Prices for Stocks 130 4.2.2 Data Download Examples 131 4.2.3 Importing Data Interactively 133 4.2.4 Automating Data Imports with a Script 138 4.2.5 Automating Data Imports with a Function 140 4.2.6 Importing Data Programmatically 147 4.3 Working with Spreadsheet Data 154 4.3.1 Importing Spreadsheet Data with Import Tool 154 4.3.2 Importing Spreadsheet Data Programmatically 154 4.4 Data Visualization 156 4.4.1 Built-In Plot Functions 156 4.4.2 Using the Plot Tools 158 4.4.3 Plotting with Commands 159 4.4.4 Other Plot Tools 162 4.4.5 Built-In Financial Charts 173 References 176 Part II Financial Calculations with MATLAB 177 Chapter 5 The Time Value of Money 179 5.1 Introduction 179 5.2 Finance Background 180 5.2.1 Future Value with Single Cash Flows 180 5.2.2 Future Value with Multiple Cash Flows 185 5.2.3 Present Value with Single Cash Flows 187 5.2.4 Present Value with Multiple Variable Cash Flows 188 5.3 MATLAB Time Value of Money Functions 189 5.3.1 Future Value of Fixed Periodic Payments 190 5.3.2 Future Value of Variable Payments 191 5.3.3 Present Value of Fixed Payments 193 5.3.4 Present Value of Variable Payments 194 5.4 Internal Rate of Return 197 5.5 Effective Interest Rates 198 5.6 Compound Annual Growth Rate 198 5.7 Continuous Interest 200 5.8 Loans 200 References 202 Chapter 6 Bonds 203 6.1 Introduction 203 6.2 Finance Background 204 6.2.1 Bond Classifications 204 6.2.2 Bond Terminology 205 6.3 MATLAB Bond Functions 206 6.3.1 US Treasury Bills 206 6.3.2 Bond Valuation Principles 208 6.3.3 Calculating Bond Prices 209 6.3.4 Calculating Bond Yields 212 6.3.5 Calculating a Bond’s Total Return 214 6.3.6 Pricing Discount Bonds 216 6.4 Bond Analytics 216 6.4.1 Interest Rate Risk 217 6.4.2 Measuring Rate Sensitivity 219 6.4.3 Yield Curves 227 6.5 Callable Bonds 229 References 231 Further Reading 231 Chapter 7 Dealing with Uncertainty and Risk 233 7.1 Introduction 233 7.2 Overview of Financial Risk 234 7.3 Data Insights 234 7.3.1 Visualizing Data 235 7.3.2 Basic Single Series Plots 237 7.3.3 Basic Multiple Series Plots 237 7.3.4 Adding Plot Customization 238 7.3.5 Histograms 239 7.3.6 Measures of Central Location 241 7.3.7 Measures of Data Dispersion 243 7.4 Data Relationships 249 7.4.1 Covariance and Correlation 251 7.4.2 Correlation Coefficients 252 7.5 Creating a Basic Simulation Model 253 7.6 Value at Risk (VaR) 258 References 261 Further Reading 262 Chapter 8 Equity Derivatives 263 8.1 Introduction 263 8.2 Options 264 8.2.1 Option Quotes 265 8.2.2 Market Mechanics 266 8.2.3 Factors in Option Valuation 267 8.3 Option Pricing Models 268 8.3.1 Arbitrage 269 8.3.2 Binomial Option Pricing 270 8.3.3 Black-Scholes 274 8.4 Options’ Uses 276 8.4.1 Hedging 277 8.4.2 Speculation and Leverage 277 8.4.3 Customizing Payoff Profiles 278 8.5 Appendix: Other Types of Derivatives 279 8.5.1 Commodity and Energy 279 8.5.2 Credit 279 8.5.3 Exotic Options 280 References 281 Further Reading 281 Chapter 9 Portfolios 283 9.1 Introduction 283 9.2 Finance Background 283 9.3 Portfolio Optimization 285 9.4 MATLAB Portfolio Object 286 9.4.1 Object-Oriented Programming (OOP) 286 9.4.2 A Basic Example 287 9.4.3 Using Data Stored in a Table Format 294 References 296 Chapter 10 Regression and Time Series 297 10.1 Introduction 297 10.2 Basic Regression 297 10.2.1 Understanding Least Squares 300 10.2.2 Model Notation 301 10.2.3 Fitting a Polynomial with polyfit and polyval 303 10.2.4 Linear Regression Methods 305 10.3 Working with Time Series 308 10.3.1 Step 1: Load the Data (Single Series) 308 10.3.2 Step 2: Create the FTS Object 309 10.3.3 Step 3: Using FTS Tools 311 References 314 Appendix 1 Sharing Your Work 315 A1.1 Introduction 315 A1.2 Publishing a Script 316 A1.2.1 Publishing with Code Sections 317 A1.2.2 futureValueCalc3 319 A1.2.3 Formatting Options 321 A1.2.4 Working with Live Scripts 322 A1.2.5 Editing and Control 325 References 326 Appendix 2 Reference for Included MATLAB® Functions 327 Index 335
£27.54
John Wiley & Sons Inc Modern Computational Finance
Book SynopsisArguably the strongest addition to numerical finance of the past decade, Algorithmic Adjoint Differentiation (AAD) is the technology implemented in modern financial software to produce thousands of accurate risk sensitivities, within seconds, on light hardware.AAD recently became a centerpiece of modern financial systems and a key skill for all quantitative analysts, developers, risk professionals or anyone involved with derivatives. It is increasingly taught in Masters and PhD programs in finance.Danske Bank''s wide scale implementation of AAD in its production and regulatory systems won the In-House System of the Year 2015 Risk award. The Modern Computational Finance books, written by three of the very people who designed Danske Bank''s systems, offer a unique insight into the modern implementation of financial models. The volumes combine financial modelling, mathematics and programming to resolve real life financial problems and produce effective derivatives Table of ContentsModern Computational Finance xi Preface by Leif Andersen xv Acknowledgments xix Introduction xxi About the Companion C++ Code xxv PART I Modern Parallel Programming 1 Introduction 3 CHAPTER 1 Effective C++ 17 CHAPTER 2 Modern C++ 25 2.1 Lambda expressions 25 2.2 Functional programming in C++ 28 2.3 Move semantics 34 2.4 Smart pointers 41 CHAPTER 3 Parallel C++ 47 3.1 Multi-threaded Hello World 49 3.2 Thread management 50 3.3 Data sharing 55 3.4 Thread local storage 56 3.5 False sharing 57 3.6 Race conditions and data races 62 3.7 Locks 64 3.8 Spinlocks 66 3.9 Deadlocks 67 3.10 RAII locks 68 3.11 Lock-free concurrent design 70 3.12 Introduction to concurrent data structures 72 3.13 Condition variables 74 3.14 Advanced synchronization 80 3.15 Lazy initialization 83 3.16 Atomic types 86 3.17 Task management 89 3.18 Thread pools 96 3.19 Using the thread pool 108 3.20 Debugging and optimizing parallel programs 113 PART II Parallel Simulation 123 Introduction 125 CHAPTER 4 Asset Pricing 127 4.1 Financial products 127 4.2 The Arbitrage Pricing Theory 140 4.3 Financial models 151 CHAPTER 5 Monte-Carlo 185 5.1 The Monte-Carlo algorithm 185 5.2 Simulation of dynamic models 192 5.3 Random numbers 200 5.4 Better random numbers 202 CHAPTER 6Serial Implementation 213 6.1 The template simulation algorithm 213 6.2 Random number generators 223 6.3 Concrete products 230 6.4 Concrete models 245 6.5 User interface 263 6.6 Results 268 CHAPTER 7 Parallel Implementation 271 7.1 Parallel code and skip ahead 271 7.2 Skip ahead with mrg32k3a 276 7.3 Skip ahead with Sobol 282 7.4 Results 283 PART III Constant Time Differentiation 285 Introduction 287 CHAPTER 8 Manual Adjoint Differentiation 295 8.1 Introduction to Adjoint Differentiation 295 8.2 Adjoint Differentiation by hand 308 8.3 Applications in machine learning and finance 315 CHAPTER 9 Algorithmic Adjoint Differentiation 321 9.1 Calculation graphs 322 9.2 Building and applying DAGs 328 9.3 Adjoint mathematics 340 9.4 Adjoint accumulation and DAG traversal 344 9.5 Working with tapes 349 CHAPTER 10 Effective AAD and Memory Management 357 10.1 The Node class 359 10.2 Memory management and the Tape class 362 10.3 The Number class 379 10.4 Basic instrumentation 398 CHAPTER 11 Discussion and Limitations 401 11.1 Inputs and outputs 401 11.2 Higher-order derivatives 402 11.3 Control flow 402 11.4 Memory 403 CHAPTER 12 Differentiation of the Simulation Library 407 12.1 Active code 407 12.2 Serial code 409 12.3 User interface 417 12.4 Serial results 424 12.5 Parallel code 426 12.6 Parallel results 433 CHAPTER 13 Check-Pointing and Calibration 439 13.1 Check-pointing 439 13.2 Explicit calibration 448 13.3 Implicit calibration 475 CHAPTER 14 Multiple Differentiation in Almost Constant Time 483 14.1 Multidimensional differentiation 483 14.2 Traditional Multidimensional AAD 484 14.3 Multidimensional adjoints 485 14.4 AAD library support 487 14.5 Instrumentation of simulation algorithms 494 14.6 Results 499 CHAPTER 15 Acceleration with Expression Templates 503 15.1 Expression nodes 504 15.2 Expression templates 507 15.3 Expression templated AAD code 524 Debugging AAD Instrumentation 541 Conclusion 547 References 549 Index 555
£67.50
John Wiley & Sons Inc Machine Learning in the AWS Cloud
Book SynopsisPut the power of AWS Cloud machine learning services to work in your business and commercial applications! Machine Learning in the AWS Cloud introduces readers to the machine learning (ML) capabilities of the Amazon Web Services ecosystem and provides practical examples to solve real-world regression and classification problems. While readers do not need prior ML experience, they are expected to have some knowledge of Python and a basic knowledge of Amazon Web Services. Part One introduces readers to fundamental machine learning concepts. You will learn about the types of ML systems, how they are used, and challenges you may face with ML solutions. Part Two focuses on machine learning services provided by Amazon Web Services. You'll be introduced to the basics of cloud computing and AWS offerings in the cloud-based machine learning space. Then you'll learn to use Amazon Machine Learning to solve a simpler class of machine learning problems, and Amazon SageMaker to solve more complexTable of ContentsIntroduction xxiii Part 1 Fundamentals of Machine Learning 1 Chapter 1 Introduction to Machine Learning 3 What is Machine Learning? 4 Tools Commonly Used by Data Scientists 4 Common Terminology 5 Real-World Applications of Machine Learning 7 Types of Machine Learning Systems 8 Supervised Learning 8 Unsupervised Learning 9 Semi-Supervised Learning 10 Reinforcement Learning 11 Batch Learning 11 Incremental Learning 12 Instance-based Learning 12 Model-based Learning 12 The Traditional Versus the Machine Learning Approach 13 A Rule-based Decision System 14 A Machine Learning–based System 17 Summary 25 Chapter 2 Data Collection and Preprocessing 27 Machine Learning Datasets 27 Scikit-learn Datasets 27 AWS Public Datasets 30 Kaggle.com Datasets 30 UCI Machine Learning Repository 30 Data Preprocessing Techniques 31 Obtaining an Overview of the Data 31 Handling Missing Values 42 Creating New Features 44 Transforming Numeric Features 46 One-Hot Encoding Categorical Features 47 Summary 50 Chapter 3 Data Visualization with Python 51 Introducing Matplotlib 51 Components of a Plot 54 Figure 55 Axes55 Axis 56 Axis Labels 56 Grids 57 Title 57 Common Plots 58 Histograms 58 Bar Chart 62 Grouped Bar Chart 63 Stacked Bar Chart 65 Stacked Percentage Bar Chart 67 Pie Charts 69 Box Plot 71 Scatter Plots 73 Summary 78 Chapter 4 Creating Machine Learning Models with Scikit-learn 79 Introducing Scikit-learn 79 Creating a Training and Test Dataset 80 K-Fold Cross Validation 84 Creating Machine Learning Models 86 Linear Regression 86 Support Vector Machines 92 Logistic Regression 101 Decision Trees 109 Summary 114 Chapter 5 Evaluating Machine Learning Models 115 Evaluating Regression Models 115 RMSE Metric 117 R2 Metric 119 Evaluating Classification Models 119 Binary Classification Models 119 Multi-Class Classification Models 126 Choosing Hyperparameter Values 131 Summary 132 Part 2 Machine Learning with Amazon Web Services 133 Chapter 6 Introduction to Amazon Web Services 135 What is Cloud Computing? 135 Cloud Service Models 136 Cloud Deployment Models 138 The AWS Ecosystem 139 Machine Learning Application Services 140 Machine Learning Platform Services 141 Support Services 142 Sign Up for an AWS Free-Tier Account 142 Step 1: Contact Information 143 Step 2: Payment Information 145 Step 3: Identity Verification 145 Step 4: Support Plan Selection 147 Step 5: Confirmation 148 Summary 148 Chapter 7 AWS Global Infrastructure 151 Regions and Availability Zones 151 Edge Locations 153 Accessing AWS 154 The AWS Management Console 156 Summary 160 Chapter 8 Identity and Access Management 161 Key Concepts 161 Root Account 161 User 162 Identity Federation 162 Group 163 Policy164 Role 164 Common Tasks 165 Creating a User 167 Modifying Permissions Associated with an Existing Group 172 Creating a Role 173 Securing the Root Account with MFA 176 Setting Up an IAM Password Rotation Policy 179 Summary 180 Chapter 9 Amazon S3 181 Key Concepts 181 Bucket 181 Object Key 182 Object Value 182 Version ID 182 Storage Class 182 Costs 183 Subresources 183 Object Metadata 184 Common Tasks 185 Creating a Bucket 185 Uploading an Object 189 Accessing an Object 191 Changing the Storage Class of an Object 195 Deleting an Object 196 Amazon S3 Bucket Versioning 197 Accessing Amazon S3 Using the AWS CLI 199 Summary 200 Chapter 10 Amazon Cognito 201 Key Concepts 201 Authentication 201 Authorization 201 Identity Provider 202 Client 202 OAuth 2.0 202 OpenID Connect 202 Amazon Cognito User Pool 202 Identity Pool 203 Amazon Cognito Federated Identities 203 Common Tasks 204 Creating a User Pool 204 Retrieving the App Client Secret 213 Creating an Identity Pool 214 User Pools or Identity Pools: Which One Should You Use? 218 Summary 219 Chapter 11 Amazon DynamoDB 221 Key Concepts 221 Tables 222 Global Tables 222 Items 222 Attributes 222 Primary Keys 222 Secondary Indexes 223 Queries 223 Scans 223 Read Consistency 224 Read/Write Capacity Modes 224 Common Tasks 225 Creating a Table 225 Adding Items to a Table 228 Creating an Index 231 Performing a Scan 233 Performing a Query 235 Summary 236 Chapter 12 AWS Lambda 237 Common Use Cases for Lambda 237 Key Concepts 238 Supported Languages 238 Lambda Functions 238 Programming Model 239 Execution Environment 243 Service Limitations 244 Pricing and Availability 244 Common Tasks 244 Creating a Simple Python Lambda Function Using the AWS Management Console 244 Testing a Lambda Function Using the AWS Management Console 250 Deleting an AWS Lambda Function Using the AWS Management Console 253 Summary 255 Chapter 13 Amazon Comprehend 257 Key Concepts 257 Natural Language Processing 257 Topic Modeling 259 Language Support 259 Pricing and Availability 259 Text Analysis Using the Amazon Comprehend Management Console 260 Interactive Text Analysis with the AWS CLI 262 Entity Detection with the AWS CLI 263 Key Phrase Detection with the AWS CLI 264 Sentiment Analysis with the AWS CLI 265 Using Amazon Comprehend with AWS Lambda 266 Summary 274 Chapter 14 Amazon Lex 275 Key Concepts 275 Bot 275 Client Application 276 Intent 276 Slot 276 Utterance 277 Programming Model 277 Pricing and Availability 278 Creating an Amazon Lex Bot 278 Creating Amazon DynamoDB Tables 278 Creating AWS Lambda Functions 285 Creating the Chatbot 304 Customizing the AccountOverview Intent 308 Customizing the ViewTransactionList Intent 312 Testing the Chatbot 314 Summary 315 Chapter 15 Amazon Machine Learning 317 Key Concepts 317 Datasources 318 ML Model 318 Regularization 319 Training Parameters 319 Descriptive Statistics 320 Pricing and Availability 321 Creating Datasources 321 Creating the Training Datasource 324 Creating the Test Datasource 330 Viewing Data Insights 332 Creating an ML Model 337 Making Batch Predictions 341 Creating a Real-Time Prediction Endpoint for Your Machine Learning Model 346 Making Predictions Using the AWS CLI 347 Using Real-Time Prediction Endpoints with Your Applications 349 Summary 350 Chapter 16 Amazon SageMaker 353 Key Concepts 353 Programming Model 354 Amazon SageMaker Notebook Instances 354 Training Jobs 354 Prediction Instances 355 Prediction Endpoint and Endpoint Configuration 355 Amazon SageMaker Batch Transform 355 Data Channels 355 Data Sources and Formats 356 Built-in Algorithms 356 Pricing and Availability 357 Creating an Amazon SageMaker Notebook Instance 357 Preparing Test and Training Data 362 Training a Scikit-learn Model on an Amazon SageMaker Notebook Instance 364 Training a Scikit-learn Model on a Dedicated Training Instance 368 Training a Model Using a Built-in Algorithm on a Dedicated Training Instance 379 Summary 384 Chapter 17 Using Google TensorFlow with Amazon SageMaker 387 Introduction to Google TensorFlow 387 Creating a Linear Regression Model with Google TensorFlow 390 Training and Deploying a DNN Classifier Using the TensorFlow Estimators API and Amazon SageMaker 408 Summary 419 Chapter 18 Amazon Rekognition 421 Key Concepts 421 Object Detection 421 Object Location 422 Scene Detection 422 Activity Detection 422 Facial Recognition 422 Face Collection 422 API Sets 422 Non-Storage and Storage-Based Operations 423 Model Versioning 423 Pricing and Availability 423 Analyzing Images Using the Amazon Rekognition Management Console 423 Interactive Image Analysis with the AWS CLI 428 Using Amazon Rekognition with AWS Lambda 433 Creating the Amazon DynamoDB Table 433 Creating the AWS Lambda Function 435 Summary 444 Appendix A Anaconda and Jupyter Notebook Setup 445 Installing the Anaconda Distribution 445 Creating a Conda Python Environment 447 Installing Python Packages 449 Installing Jupyter Notebook 451 Summary 454 Appendix B AWS Resources Needed to Use This Book 455 Creating an IAM User for Development 455 Creating S3 Buckets 458 Appendix C Installing and Configuring the AWS CLI 461 Mac OS Users 461 Installing the AWS CLI 461 Configuring the AWS CLI 462 Windows Users 464 Installing the AWS CLI4 64 Configuring the AWS CLI 465 Appendix D Introduction to NumPy and Pandas 467 NumPy 467 Creating NumPy Arrays 467 Modifying Arrays 471 Indexing and Slicing 474 Pandas 475 Creating Series and Dataframes 476 Getting Dataframe Information 478 Selecting Data 481 Index 485
£28.49
John Wiley & Sons Inc Programming the Finite Element Method
Book SynopsisMany students, engineers, scientists and researchers have benefited from the practical, programming-oriented style of the previous editions of Programming the Finite Element Method, learning how to develop computer programs to solve specific engineering problems using the finite element method.Table of ContentsPreface to Fifth Edition xv Acknowledgements xvii 1 Preliminaries: Computer Strategies 1 1.1 Introduction 1 1.2 Hardware 2 1.3 Memory Management 2 1.4 Vector Processors 3 1.5 Multi-core Processors 3 1.6 Co-processors 4 1.7 Parallel Processors 4 1.8 Applications Software 5 1.9 Array Features 9 1.10 Third-party Libraries 17 1.11 Visualisation 18 1.12 Conclusions 23 References 24 2 Spatial Discretisation by Finite Elements 25 2.1 Introduction 25 2.2 Rod Element 25 2.3 The Eigenvalue Equation 28 2.4 Beam Element 29 2.5 Beam with an Axial Force 31 2.6 Beam on an Elastic Foundation 32 2.7 General Remarks on the Discretisation Process 33 2.8 Alternative Derivation of Element Stiffness 33 2.9 Two-dimensional Elements: Plane Stress 35 2.10 Energy Approach and Plane Strain 38 2.11 Plane Element Mass Matrix 40 2.12 Axisymmetric Stress and Strain 40 2.13 Three-dimensional Stress and Strain 42 2.14 Plate Bending Element 44 2.15 Summary of Element Equations for Solids 47 2.16 Flow of Fluids: Navier–Stokes Equations 47 2.17 Simplified Flow Equations 50 2.18 Further Coupled Equations: Biot Consolidation 54 2.19 Conclusions 56 References 56 3 Programming Finite Element Computations 59 3.1 Introduction 59 3.2 Local Coordinates for Quadrilateral Elements 59 3.3 Local Coordinates for Triangular Elements 64 3.4 Multi-Element Assemblies 66 3.5 ‘Element-by-Element’ Techniques 68 3.6 Incorporation of Boundary Conditions 72 3.7 Programming using Building Blocks 75 3.8 Solution of Equilibrium Equations 95 3.9 Evaluation of Eigenvalues and Eigenvectors 96 3.10 Solution of First-Order Time-Dependent Problems 99 3.11 Solution of Coupled Navier–Stokes Problems 103 3.12 Solution of Coupled Transient Problems 104 3.13 Solution of Second-Order Time-Dependent Problems 106 4 Static Equilibrium of Structures 115 4.1 Introduction 115 4.2 Conclusions 157 4.3 Glossary of Variable Names 157 4.4 Exercises 159 References 168 5 Static Equilibrium of Linear Elastic Solids 169 5.1 Introduction 169 5.2 Glossary of Variable Names 221 5.3 Exercises 224 References 232 6 Material Non-linearity 233 6.1 Introduction 233 6.2 Stress–strain Behaviour 235 6.3 Stress Invariants 236 6.4 Failure Criteria 238 6.5 Generation of Body Loads 240 6.6 Viscoplasticity 240 6.7 Initial Stress 242 6.8 Corners on the Failure and Potential Surfaces 243 6.9 Elastoplastic Rate Integration 270 6.10 Tangent Stiffness Approaches 275 6.11 The Geotechnical Processes of Embanking and Excavation 289 6.12 Undrained Analysis 305 6.13 Glossary of Variable Names 322 6.14 Exercises 327 References 331 7 Steady State Flow 333 7.1 Introduction 333 7.2 Glossary of Variable Names 359 7.3 Exercises 361 References 367 8 Transient Problems: First Order (Uncoupled) 369 8.1 Introduction 369 8.2 Comparison of Programs 8.4, 8.5, 8.6 and 8.7 397 8.3 Glossary of Variable Names 416 8.4 Exercises 419 References 422 9 Coupled Problems 423 9.1 Introduction 423 9.2 Glossary of Variable Names 454 9.3 Exercises 459 References 460 10 Eigenvalue Problems 461 10.1 Introduction 461 10.2 Glossary of Variable Names 477 10.3 Exercises 480 References 482 11 Forced Vibrations 483 11.1 Introduction 483 11.2 Glossary of Variable Names 517 11.3 Exercises 521 References 522 12 Parallel Processing of Finite Element Analyses 523 12.1 Introduction 523 12.2 Differences between Parallel and Serial Programs 525 12.3 Graphics Processing Units 589 12.4 Cloud Computing 594 12.5 Conclusions 596 12.6 Glossary of Variable Names 597 References 602 Appendix A Equivalent Nodal Loads 605 Appendix B Shape Functions and Element Node Numbering 611 Appendix C Plastic Stress-Strain Matrices and Plastic Potential Derivatives 619 Appendix D main Library Subprograms 623 Appendix E geom Library Subroutines 635 Appendix F Parallel Library Subroutines 639 Appendix G External Subprograms 645 Author Index 649 Subject Index 653
£78.26
Palgrave MacMillan UK Your Digital Afterlives Computational Theories of Life After Death Palgrave Frontiers in Philosophy of Religion
Book SynopsisDigitalism is a philosophical strategy that uses new computational ways of thinking to develop naturalistic but meaningful ways of thinking about bodies, souls, universes, gods, and life after death. Your Digital Afterlives examines four recently developed and digitally inspired theories of life after death.Table of ContentsPreface Series Editors' Preface 1. Ghosts 2. Persistence 3. Anatomy 4. Uploading 5. Promotion 6. Digital Gods 7. Revision 8. Superhuman Bodies 9. Infinite Bodies 10. Nature References Index
£42.74
Taylor & Francis Ltd Advanced Problem Solving Using Maple
Book SynopsisAdvanced Problem Solving Using Maple: Applied Mathematics, Operations Research, Business Analytics, and Decision Analysis applies the mathematical modeling process by formulating, building, solving, analyzing, and criticizing mathematical models. Scenarios are developed within the scope of the problem-solving process.The text focuses on discrete dynamical systems, optimization techniques, single-variable unconstrained optimization and applied problems, and numerical search methods. Additional coverage includes multivariable unconstrained and constrained techniques. Linear algebra techniques to model and solve problems such as the Leontief model, and advanced regression techniques including nonlinear, logistics, and Poisson are covered. Game theory, the Nash equilibrium, and Nash arbitration are also included.Features: The text's case studies and student projects involve students with real-world problem soTable of ContentsIntroduction to Problem Solving and Maple. Discrete Dynamical Systems. Single Variable Unconstrained and Constrained Optimization. Multi- Variable Unconstrained and Constrained Optimization. Linear Systems. Advanced Model Fitting.
£80.74
Cambridge University Press Mathematical Aspects of Deep Learning
Book SynopsisIn recent years the development of new classification and regression algorithms based on deep learning has led to a revolution in the fields of artificial intelligence, machine learning, and data analysis. The development of a theoretical foundation to guarantee the success of these algorithms constitutes one of the most active and exciting research topics in applied mathematics. This book presents the current mathematical understanding of deep learning methods from the point of view of the leading experts in the field. It serves both as a starting point for researchers and graduate students in computer science, mathematics, and statistics trying to get into the field and as an invaluable reference for future research.Table of Contents1. The modern mathematics of deep learning Julius Berner, Philipp Grohs, Gitta Kutyniok and Philipp Petersen; 2. Generalization in deep learning Kenji Kawaguchi, Leslie Pack Kaelbling, and Yoshua Bengio; 3. Expressivity of deep neural networks Ingo Gühring, Mones Raslan and Gitta Kutyniok; 4. Optimization landscape of neural networks René Vidal, Zhihui Zhu and Benjamin D. Haeffele; 5. Explaining the decisions of convolutional and recurrent neural networks Wojciech Samek, Leila Arras, Ahmed Osman, Grégoire Montavon and Klaus-Robert Müller; 6. Stochastic feedforward neural networks: universal approximation Thomas Merkh and Guido Montúfar; 7. Deep learning as sparsity enforcing algorithms A. Aberdam and J. Sulam; 8. The scattering transform Joan Bruna; 9. Deep generative models and inverse problems Alexandros G. Dimakis; 10. A dynamical systems and optimal control approach to deep learning Weinan E, Jiequn Han and Qianxiao Li; 11. Bridging many-body quantum physics and deep learning via tensor networks Yoav Levine, Or Sharir, Nadav Cohen and Amnon Shashua.
£66.49
Cambridge University Press Session Types
Book Synopsis
£45.59
Springer Us Handbook of Cloud Computing
Book SynopsisHandbook of Cloud Computing includes contributions from world experts in the field of cloud computing from academia, research laboratories and private industry. The basic concepts of cloud computing and cloud computing applications are also introduced.Trade ReviewFrom the reviews:“Cloud computing affects all areas where computers and mobile clients are used, including industry, government, and science. This book offers a multitude of examples of this new way of handling data and computer resources--from basic research in the life sciences and physics to enterprise-level applications in industry. … handbook is highly relevant and provides the reader with good information about the fundamentals of all aspects of cloud computing. After reading the book, readers will understand how much is already being done in the cloud.” (Aake Edlund, ACM Computing Reviews, May, 2011) Table of ContentsTechnologies and Systems.- Cloud Computing Fundamentals.- Cloud Computing Technologies and Applications.- Key Enabling Technologies for Virtual Private Clouds.- The Role of Networks in Cloud Computing.- Data-Intensive Technologies for Cloud Computing.- Survey of Storage and Fault Tolerance Strategies Used in Cloud Computing.- Scheduling Service Oriented Workflows Inside Clouds Using an Adaptive Agent Based Approach.- The Role of Grid Computing Technologies in Cloud Computing.- Cloudweaver: Adaptive and Data-Driven Workload Manager for Generic Clouds.- Architectures.- Enterprise Knowledge Clouds: Architecture and Technologies.- Integration of High-Performance Computing into Cloud Computing Services.- Vertical Load Distribution for Cloud Computing via Multiple Implementation Options.- SwinDeW-C: A Peer-to-Peer Based Cloud Workflow System.- Services.- Cloud Types and Services.- Service Scalability Over the Cloud.- Scientific Services on the Cloud.- A Novel Market-Oriented Dynamic Collaborative Cloud Service Platform.- Applications.- Enterprise Knowledge Clouds: Applications and Solutions.- Open Science in the Cloud: Towards a Universal Platform for Scientific and Statistical Computing.- Multidimensional Environmental Data Resource Brokering on Computational Grids and Scientific Clouds.- HPC on Competitive Cloud Resources.- Scientific Data Management in the Cloud: A Survey of Technologies, Approaches and Challenges.- Feasibility Study and Experience on Using Cloud Infrastructure and Platform for Scientific Computing.- A Cloud Computing Based Patient Centric Medical Information System.- Cloud@Home: A New Enhanced Computing Paradigm.- Using Hybrid Grid/Cloud Computing Technologies for Environmental Data Elastic Storage, Processing, and Provisioning.
£224.99
Springer London Understanding Concurrent Systems
Book SynopsisCSP notation has been used extensively for teaching and applying concurrency theory, ever since the publication of the text Communicating Sequential Processes by C.A.R. A first point of reference for anyone wanting to use CSP or learn about its theory, the book also introduces other views of concurrency, using CSP to model and explain these.Trade ReviewFrom the reviews:“This book is divided into four parts … . Part I is designed for an audience of both undergraduate and graduate computer science students. … Part II is designed for people who are familiar with Part I and have fairly theoretical interests. … Part III is intended for people who … want to be able to use them in a better way, or who are specifically interested in timed systems. Part IV is designed for people who already understand CSP.” (Günther Bauer, Zentralblatt MATH, Vol. 1211, 2011)Table of ContentsPart I: A Foundation Course in CSP Building a Simple Sequential Process Understanding CSP Parallel Operators CSP Case Studies Hiding and Renaming Beyond Traces Further Operators Using FDR Part II: Theory Operational Semantics Denotational Semantics and Behavioural Models Finite Observation Models Infinite-behaviour Models The Algebra of CSP Part III: Using CSP Timed Systems 1: tock-CSP Timed Systems 2: Discrete Timed CSP More About FDR State Explosion and Parameterised Verification Part IV: Exploring Concurrency Shared-variable Programs Understanding Shared-variable Concurrency Priority and Mobility
£42.74
Springer Mathematical Logic for Computer Science
Book SynopsisPreface.- Introduction.- Propositional Logic: Formulas, Models, Tableaux.- Propositional Logic: Deductive Systems.- Propositional Logic: Resolution.- Propositional Logic: Binary Decision Diagrams.- Propositional Logic: SAT Solvers.- First-Order Logic: Formulas, Models, Tableaux.- First-Order Logic: Deductive Systems.- First-Order Logic: Terms and Normal Forms.- First-Order Logic: Resolution.- First-Order Logic: Logic Programming.- First-Order Logic: Undecidability and Model Theory.- Temporal Logic: Formulas, Models, Tableaux.- Temporal Logic: A Deductive System.- Verification of Sequential Programs.- Verification of Concurrent Programs.- Set Theory.- Index of Symbols.- Index of Names.- Subject Index.Trade ReviewAsst. Prof. Manoj Raut, Dhirubhai Ambani Institute of Information and Communication Technology, IndiaExcerpts from full review posted Jan 15 2013 to Computing Reviews [Review #: CR140831]I have used the second edition of this book for my class. I find this new third edition more interesting and more elaborately written; I like it very much, and applaud the author for his work.Table of ContentsPreface.- Introduction.- Propositional Logic: Formulas, Models, Tableaux.- Propositional Logic: Deductive Systems.- Propositional Logic: Resolution.- Propositional Logic: Binary Decision Diagrams.- Propositional Logic: SAT Solvers.- First-Order Logic: Formulas, Models, Tableaux.- First-Order Logic: Deductive Systems.- First-Order Logic: Terms and Normal Forms.- First-Order Logic: Resolution.- First-Order Logic: Logic Programming.- First-Order Logic: Undecidability and Model Theory.- Temporal Logic: Formulas, Models, Tableaux.- Temporal Logic: A Deductive System.- Verification of Sequential Programs.- Verification of Concurrent Programs.- Set Theory.- Index of Symbols.- Index of Names.- Subject Index.
£52.24
Springer London Ltd Mathematics for Computer Graphics
Book SynopsisJohn Vince explains a comprehensive range of mathematical techniques and problem-solving strategies associated with computer games, computer animation, special effects, virtual reality, CAD and other areas of computer graphics in this completely revised and expanded sixth edition. The first five chapters cover a general introduction, number sets, algebra, trigonometry and coordinate systems, which are employed in the following chapters on determinants, vectors, matrix algebra, complex numbers, geometric transforms, quaternion algebra, quaternions in space, interpolation, curves and patches, analytical geometry and barycentric coordinates. Following this, the reader is introduced to the relatively new subject of geometric algebra, followed by two chapters that introduce differential and integral calculus. Finally, there is a chapter on worked examples.Mathematics for Computer Graphics covers all of the key areas of the subject, including: NuTrade Review“These days nobody can imagine a world without computer graphics. … It is a challenge to explain this theory in an easy-to-follow way. But this book shows that it is possible. … It can be used both as a textbook for a computer graphics course and for self-study by practitioners and starting researchers alike.” (Agnieszka Lisowska, zbMATH 1500.68003, 2023)Table of ContentsPreface.- Introduction.- Numbers.- Algebra.- Trigonometry.- Coordinate Systems.- Determinants.- Vectors.- Matrix Algebra.- Complex Numbers.- Geometric Transforms.- Quaternion Algebra.- Quaternions in Space.- Interpolation.- Curves and Patches.- Analytic Geometry.- Barycentric Coordinates.- Geometric Algebra.- Calculus: Derivatives.- Calculus: Integration.- Worked Examples.- Appendix A.- Appendix B.- Index.
£49.49
Springer New York Reflexive Structures An Introduction to Computability Theory
Book Synopsis1 Functions and Predicates.- 1. Definitions.- 2. Numerical Functions.- 3. Finitary Rules.- 4. Closure Properties.- 5. Minimal Closure.- 6. More Elementary Functions and Predicates.- 2 Recursive Functions.- 1. Primitive Recursion.- 2. Functional Transformations.- 3. Recursive Specifications.- 4. Recursive Evaluation.- 5. Church's Thesis.- 3 Enumeration.- 1. Predicate Classes.- 2. Enumeration Properties.- 3. Induction.- 4. Nondeterministic Computability.- 4 Reflexive Structures.- 1. Interpreters.- 2. A Universal Interpreter.- 3. Two Constructions.- 4. The Recursion Theorem.- 5. Relational Structures.- 6. Uniform Structures.- 5 Hyperenumeration.- 1. Function Quantification.- 2. Nonfinitary Induction.- 3. Functional Induction.- 4. Ordinal Notations.- 5. Reflexive Systems.- 6. Hyperhyperenumeration.- References.Table of Contents1 Functions and Predicates.- §1. Definitions.- §2. Numerical Functions.- §3. Finitary Rules.- §4. Closure Properties.- §5. Minimal Closure.- §6. More Elementary Functions and Predicates.- 2 Recursive Functions.- §1. Primitive Recursion.- §2. Functional Transformations.- §3. Recursive Specifications.- §4. Recursive Evaluation.- §5. Church’s Thesis.- 3 Enumeration.- §1. Predicate Classes.- §2. Enumeration Properties.- §3. Induction.- §4. Nondeterministic Computability.- 4 Reflexive Structures.- §1. Interpreters.- §2. A Universal Interpreter.- §3. Two Constructions.- §4. The Recursion Theorem.- §5. Relational Structures.- §6. Uniform Structures.- 5 Hyperenumeration.- §1. Function Quantification.- §2. Nonfinitary Induction.- §3. Functional Induction.- §4. Ordinal Notations.- §5. Reflexive Systems.- §6. Hyperhyperenumeration.- References.
£42.74
Springer Us Symbolic Model Checking
Book Synopsis1 Introduction.- 1.1 Background.- 1.2 Scope of this work.- 2 Model Checking.- 2.1 Temporal logic.- 2.2 The temporal logic CTL.- 2.3 Fixed points.- 2.4 CTL model checking.- 3 Symbolic Model Checking.- 3.1 Boolean representations.- 3.2 Symbolic models.- 3.3 Binary Decision Diagrams.- 3.4 Examples.- 3.5 Graph width and OBDDs.- 4 The SMV System.- 4.1 An informal introduction.- 4.2 The input language.- 4.3 Formal semantics.- 5 A Distributed Cache Protocol.- 5.1 The Protocol.- 5.2 Verifying the protocol.- 5.3 Discussion.- 6 Mu-Calculus Model Checking.- 6.1 The Mu-Calculus.- 6.2 Symbolic models.- 6.3 Symbolic algorithm.- 6.4 Applications of the Mu-Calculus.- 6.5 Related research.- 7 Induction and Model Checking.- 7.1 The general framework.- 7.2 Induction and symbolic model checking.- 7.3 Example: The Gigamax protocol.- 7.4 Induction in other models.- 7.5 Related research.- 8 Equivalence Computations.- 8.1 State equivalence.- 8.2 Methods for functional composition.- 8.3 Experimental results.- 9 A Partial Order Approach.- 9.1 Unfolding.- 9.2 Truncated unfoldings.- 9.3 Application example.- 9.4 Deadlock and occurrence nets.- 9.5 Conclusion.- 10 Conclusion.- References.Table of ContentsForeword. Preface. 1. Introduction. 2. Model Checking. 3. Symbolic Model Checking. 4. The SMV System. 5. A Distributed Cache Protocol. 6. Mu-Calculus Model Checking. 7. Induction and Model Checking. 8. Equivalence Computations. 9. A Partial Order Apporach. 10. Conclusion. References. Index.
£42.74
Springer New York An Introduction to Modern Mathematical Computing
Book Synopsisand the building of the Three “M’s” Maple, Mathematica and Matlab. We intend to persuade that Maple and other like tools are worth knowing assuming only that one wishes to be a mathematician, a mathematics educator, a computer scientist, an engineer or scientist, or anyone else who wishes/needs to use mathematics better.Trade ReviewFrom the reviews:“This book is intended to teach the reader the usage of the computer algebra system Maple. … The book is readable and valuable to mathematics, science, and engineering undergraduates at the sophomore or above level. It could also be valuable to practitioners in those fields who want to learn Maple in situ. … Summing Up: Recommended. Lower-division undergraduates through graduate students; professionals.” (D. Z. Spicer, Choice, Vol. 49 (5), January, 2012)“This is a Maple-application book which illustrates some basic areas of mathematics by symbolic computation examples. … The presentation is clear with all necessary details and comments for ensuring a full understanding of the considered examples. The intended beneficiaries are undergraduate students, teachers giving courses to undergraduate students, as well as programmers interested in using Maple for several classes of mathematical problems.” (Octavian Pastravanu, Zentralblatt MATH, Vol. 1228, 2012)“In An Introduction to Modern Mathematical Computing with Maple, Borwein and Skerritt show that computers are an excellent companion for learning mathematics. … The theme of the book is that Maple can supplement mathematics learning and, what is more, can do much of the mathematics for the students. … The temptation is tremendous for students to skip the real work to have a true understanding of mathematics.” (David S. Mazel, The Mathematical Association of America, June, 2012)Table of Contents-Preface. -Conventions and Notation.-1. Number Theory (Introduction to Maple, Putting it together, Enough code, already. Show me some maths!, Problems and Exercises, Further Explorations). -2. Calculus(Revision and Introduction, Univariate Calculus, Multivariate Calculus, Exercises, Further Explorations). -3. Linear Algebra (Introduction and Review, Vector Spaces, Linear Transformations, Exercises, Further Explorations). -4. Visualisation and Geometry: a postscript (Useful Visualisation Tools, Geometry and Geometric Constructions). –A. Sample Quizzes (Number Theory, Calculus, Linear Algebra). –Index. –References
£56.35
Springer Us The Verilog Hardware Description Language
Table of ContentsVerilog — A Tutorial Introduction.- Logic Synthesis.- Behavioral Modeling.- Concurrent Processes.- Module Hierarchy.- Logic Level Modeling.- Cycle-Accurate Specification.- Advanced Timing.- User-Defined Primitives.- Switch Level Modeling.- Projects.
£56.24
Springer Us RealTime Database Systems Architecture And Techniques 593 The Springer International Series in Engineering and Computer Science
Book SynopsisIn recent years, tremendous research has been devoted to the design of database systems for real-time applications, called real-time database systems (RTDBS), where transactions are associated with deadlines on their completion times, and some of the data objects in the database are associated with temporal constraints on their validity.Table of ContentsList of Figures. List of Tables. Acknowledgments. Preface. Contributing Authors. I: Overview, Misconceptions and Issues. 1. Real-Time Database Systems: An Overview of System Characteristics and Issues; Tei-Wei Kuo, Kam-Yiu Lam. 2. Misconceptions About Real-Time Databases; J.A. Stankovic, et al. 3. Applications and System Characteristics; D. Locke. II: Real-Time Concurrency Control. 4. Conservative and Optimistic Protocols; Tei-Wei Kuo, Kam-Yiu Lam. 5. Semantics-Based Concurrency Control; Tei-Wei Kuo. 6. Real-Time Index Concurrency Control; J.R. Haritsa, S. Seshadri. III: Run-Time System Management. 7. Buffer Management in Real-Time Active Database Systems; A. Datta, S. Mukherjee. 8. Disk Scheduling; Ben Kao, R. Cheng. 9. System Failure and Recovery; R.M. Sivasankaran, et al. 10. Overload Management in RTDBs; J. Hansson, S.H. Son. 11. Secure Real-Time Transaction Processing; J.R. Haritsa, B. George. IV: Active Issues and Triggering. 12. System Framework of ARTDBs; J. Hansson, S.F. Andler. 13. Reactive Mechanisms; J. Mellin, et al. 14. Updates and View Maintenance; Ben Kao, et al. V: Distributed Real-Time Database Systems. 15. Distributed Concurrency Control; Ö. Ulusoy. 16. Data Replication and Availability; Ö. Ulusoy. 17. Real-Time Commit Processing; J.R. Haritsa, et al. 18. Mobile Distributed Real-Time Database Systems; Kam-Yiu Liam, Tei-Wei Kuo.VI: Prototypes and Future Directions. 19. Prototypes: Programmed Stock Trading; B. Adelberg, Ben Kao. 20. Future Directions; Tei-Wei Kuo, Kam-Yiu Lam. Index.
£197.99
APress MATLAB Deep Learning
Book SynopsisGet started with MATLAB for deep learning and AI with this in-depth primer. In this book, you start with machine learning fundamentals, then move on to neural networks, deep learning, and then convolutional neural networks. In a blend of fundamentals and applications, MATLAB Deep Learning employs MATLAB as the underlying programming language and tool for the examples and case studies in this book. With this book, you''ll be able to tackle some of today''s real world big data, smart bots, and other complex data problems. You''ll see how deep learning is a complex and more intelligent aspect of machine learning for modern smart data analysis and usage.What You''ll Learn Use MATLAB for deep learning Discover neural networks and multi-layer neural networks Work with convolution and pooling layers Build a MNIST example with these layers WhoTable of Contents1. Machine Learning2. Neural Network3. Training of Multi-Layer Neural Network4. Neural Network and Classification5. Deep Learning6. Convolutional Neural Network
£49.49
APress Architecture of Advanced Numerical Analysis
Book SynopsisThis unique open access book applies the functional OCaml programming language to numerical or computational weighted data science, engineering, and scientific applications. This book is based on the authors' first-hand experience building and maintaining Owl, an OCaml-based numerical computing library.You'll first learn the various components in a modern numerical computation library. Then, you will learn how these components are designed and built up and how to optimize their performance. After reading and using this book, you'll have the knowledge required to design and build real-world complex systems that effectively leverage the advantages of the OCaml functional programming language. What You Will LearnOptimize core operations based on N-dimensional arraysDesign and implement an industry-level algorithmic differentiation moduleImplement mathematical optimization, regression, and deep neural network functionalities based on algorithmic differentiationDesign and optimize a compTable of ContentsPrologueA Brief HistoryReductionism vs. HolismKey FeaturesContact MePART 1: NUMERICAL TECHNIQUES1. IntroductionWhat Is Scientific ComputingWhat is Functional ProgrammingWho Is This Book ForStructure of the BookInstallationOption 1: Install from OPAMOption 2: Pull from Docker HubOption 3: Pin the Dev-RepoOption 4: Compile from SourceCBLAS/LAPACKE DependencyInteracting with OwlUsing ToplevelUsing NotebookUsing Owl-JupyterSummary2. ConventionsPure vs. ImpureNdarray vs. ScalarInfix OperatorsOperator ExtensionModule StructuresNumber and PrecisionPolymorphic FunctionsModule ShortcutsType Casting3. VisualisationCreate PlotsSpecificationSubplotsMultiple LinesLegendDrawing PatternsLine PlotScatter PlotStairs PlotBox PlotStem PlotArea PlotHistogram & CDF PlotLog Plot3D PlotAdvanced Statistical PlotSummaryReferences4. Mathematical FunctionsBasic FunctionsBasic Unary Math FunctionsBasic Binary FunctionsExponential and Logarithmic FunctionsTrigonometric FunctionsOther Math FunctionsSpecial FunctionsAiry FunctionsBessel FunctionsElliptic FunctionsGamma FunctionsBeta FunctionsStruve FunctionsZeta FunctionsError FunctionsIntegral FunctionsFactorialsInterpolation and ExtrapolationIntegrationUtility FunctionsSummary5. Statistical FunctionsRandom VariablesDiscrete Random VariablesContinuous Random VariablesDescriptive StatisticsOrder StatisticsSpecial DistributionGamma DistributionBeta DistributionChi-Square DistributionStudent-t DistributionCauchy DistributionMultiple VariablesSamplingHypothesis TestsTheoryGaussian Distribution in Hypothesis TestingTwo-Sample InferencesGoodness-of-fit TestsNon-parametric StatisticsCovariance and CorrelationsAnalysis of VarianceSummary6. N-Dimensional ArraysNdarray TypesCreation FunctionsProperties FunctionsMap FunctionsFold FunctionsScan FunctionsComparison FunctionsVectorised FunctionsIteration FunctionsManipulation FunctionsSerialisationTensorsSummaryReferences7. Slicing and BroadcastingSlicingBasic SlicingFancy SlicingConventions in DefinitionExtended OperatorsAdvanced UsageBroadcastingWhat Is Broadcasting?Shape ConstraintsSupported OperationsSlicing in NumPy and JuliaInternal MechanismSummary8. Linear AlgebraVectors and MatricesCreating MatricesAccessing ElementsIterate, Map, Fold, and FilterMath OperationsGaussian EliminationLU FactorisationInverse and TransposeVector SpacesRank and BasisOrthogonalitySolving Ax = bMatrix SensitivityDeterminantsEigenvalues and EigenvectorsSolving Ax=λ xComplex MatricesSimilarity Transformation and DiagonalisationPositive Definite MatricesPositive DefinitenessSingular Value DecompositionInternal: CBLAS and LAPACKELow-level Interface to CBLAS & LAPACKESparse MatricesSummaryReferences9. Ordinary Differential EquationsWhat Is An ODEExact SolutionsLinear SystemsSolving An ODE NumericallyOwl-ODEExample: Linear Oscillator SystemSolver StructureSymplectic SolversFeatures and LimitsExamples of using Owl-ODEExplicit ODETwo Body ProblemLorenz AttractorDamped OscillationStiffnessSolve Non-Stiff ODEsSolve Stiff ODEsSummaryReferences10. Signal ProcessingDiscrete Fourier TransformFast Fourier TransformExamplesApplications of FFTFind period of sunspotsDecipher the ToneImage ProcessingFilteringExample: SmoothingGaussian FilterSignal ConvolutionFFT and Image ConvolutionSummaryReferences11. Algorithmic DifferentiationChain RuleDifferentiation MethodsHow Algorithmic Differentiation WorksForward ModeReverse ModeForward or Reverse?A Strawman AD EngineSimple Forward ImplementationSimple Reverse ImplementationUnified ImplementationsForward and Reverse Propagation APIExpressing ComputationExample: Forward ModeExample: Reverse ModeHigh-Level APIsDerivative and GradientJacobianHessian and LaplacianOther APIsInternal of Algorithmic DifferentiationGo Beyond Simple ImplementationExtend AD moduleLazy EvaluationSummaryReferences12. OptimisationIntroductionRoot FindingUnivariate Function OptimisationUse DerivativesGolden Section SearchMultivariate Function OptimisationNelder-Mead Simplex MethodGradient Descent MethodsConjugate Gradient MethodNewton and Quasi-Newton MethodsGlobal Optimisation and Constrained OptimisationSummaryReferences13. RegressionLinear RegressionProblem: Where to locate a new McDonald’s restaurant?Cost FunctionSolving Problem with Gradient DescentMultiple RegressionFeature NormalisationAnalytical SolutionNon-linear regressionsRegularisationOls, Ridge, Lasso, and Elastic_netLogistic RegressionSigmoid FunctionCost FunctionExampleMulti-class classificationSupport Vector MachineKernel and Non-linear BoundaryExampleModel error and selectionError MetricsModel SelectionSummaryReferences14. Deep Neural NetworksPerceptronYet Another RegressionModel RepresentationForward PropagationBack propagationFeed Forward NetworkLayersActivation FunctionsInitialisationTrainingTestNeural Network ModuleModule StructureNeuronsNeural GraphTraining ParametersConvolutional Neural NetworkRecurrent Neural NetworkLong Short Term Memory (LSTM)Generative Adversarial NetworkSummaryReferences15. Natural Language ProcessingIntroductionText CorpusStep-by-step OperationUse the Corpus ModuleVector Space ModelsBag of Words (BOW)Term Frequency–Inverse Document Frequency (TF-IDF)Latent Dirichlet Allocation (LDA)ModelsDirichlet DistributionGibbs SamplingTopic Modelling ExampleLatent Semantic Analysis (LSA)Search Relevant DocumentsEuclidean and Cosine SimilarityLinear SearchingSummaryReferences16. Dataframe for Tabular DataBasic ConceptsCreate FramesManipulate FramesQuery FramesIterate, Map, and FilterRead/Write CSV FilesInfer Type and SeparatorSummary17. Symbolic RepresentationIntroductionDesignCore abstractionEnginesONNX EngineExample 1: Basic operationsExample 2: Variable InitialisationExample 3: Neural networkLaTeX EngineOwl EngineSummary18. Probabilistic ProgrammingGenerative Model vs Discriminative ModelBayesian NetworksSampling TechniquesInferencePART 2: SYSTEM ARCHITECTURE19. Architecture OverviewIntroductionArchitecture OverviewCore ImplementationN-dimensional ArrayInterfaced LibrariesAdvanced FunctionalityComputation GraphAlgorithmic DifferentiationRegressionNeural NetworkParallel ComputingActor EngineGPU ComputingOpenMPCommunity-Driven R&DSummary20. Core OptimisationBackgroundNumerical LibrariesOptimisation of Numerical ComputationInterfacing to C CodeNdarray OperationsFrom OCaml to COptimisation TechniquesMap OperationsConvolution OperationsReduction OperationsRepeat OperationsSummaryReferences21. Automatic Empirical TuningWhat is Parameter TuningWhy Parameter Tuning in OwlHow to Tune OpenMP ParametersMake a DifferenceSummary22. Computation GraphIntroductionWhat is a Computation Graph?From Dynamic to StaticSignificance in ComputingExamplesExample 01: Basic CGraphExample 02: CGraph with ADExample 03: CGraph with DNNDesign RationaleOptimisation of CGraphOptimising memory with pebblesAllocation AlgorithmAs Intermediate RepresentationsSummary23. Scripting and Zoo SystemIntroductionShare Script with ZooTypical ScenarioCreate a ScriptShare via GistImport in Another ScriptSelect a Specific VersionCommand Line ToolMore ExamplesSystem DesignServicesType CheckingBackendDomain Specific LanguageService DiscoveryUse CaseSummaryReferences24. Compiler BackendsBase LibraryBackend: JavaScriptUse Native OCamlUse Facebook ReasonBackend: MirageOSMirageOS and UnikernelExample: Gradient DescentExample: Neural NetworkEvaluationSummary25. Distributed ComputingActor SystemDesignActor EnginesMap-Reduce EngineParameter Server EnginePeer-to-Peer EngineClassic Synchronise ParallelBulk Synchronous ParallelAsynchronous ParallelStale Synchronous ParallelProbabilistic Synchronise ParallelBasic idea: samplingCompatibilityBarrier Trade-off DimensionsConvergenceA Distributed Training ExampleStep ProgressAccuracySummaryReferences26. Testing FrameworkUnit TestExampleWhat Could Go WrongCorner CasesTest CoverageUse FunctorSummary27. Constants and Metric SystemWhat Is a Metric SystemFour Metric SystemsSI PrefixExample: Physics and Math constantsInternational System of UnitsTimeLengthAreaVolumeSpeedMassForceEnergyPowerPressureViscosityLuminanceRadioactivity28. Internal Utility ModulesDataset ModuleMNISTCIFAR-10Graph ModuleStack and Heap ModulesCount-Min SketchSummaryPART 3: CASE STUDIES29. Case - Image RecognitionBackgroundLeNetAlexNetVGGResNetSqueezeNetCapsule NetworkBuilding InceptionV3 NetworkInceptionV1 and InceptionV2FactorisationGrid Size ReductionInceptionV3 ArchitecturePreparing WeightsProcessing ImageRunning InferenceApplicationsSummaryReferences30. Case - Instance SegmentationIntroductionMask R-CNN NetworkBuilding Mask R-CNNFeature ExtractorProposal GenerationClassificationRun the CodeSummaryReferences31. Case - Neural Style TransferContent and StyleContent ReconstructionStyle RecreationCombining Content and StyleRunning NSTExtending NSTFast Style TransferBuilding FST NetworkRunning FSTSummaryReferences32. Case - Recommender SystemIntroductionArchitectureBuild Topic ModelsIndex Text CorpusRandom ProjectionOptimising Vector StorageOptimise Data StructureOptimise Index AlgorithmSearch ArticlesCode ImplementationMake It LiveSummaryReferences33. Case - Applications in FinanceIntroductionBond PricingBlack-Scholes ModelMathematical ModelOption PricingPortfolio OptimisationMathematical ModelEfficient FrontierMaximise Sharpe Ratio
£33.74
APress Options and Derivatives Programming in C23
Book SynopsisThis book is a hands-on guide for programmers who want to learn how C++ is used to develop solutions for options and derivatives trading in the financial industry. It explores the main algorithms and programming techniques used in implementing systems and solutions for trading options and derivatives. This updated edition will bring forward new advances in C++ software language and libraries, with a particular focus on the new C++23 standard. The book starts by covering C++ language features that are frequently used to write financial software for options and derivatives. These features include the STL (standard template library), generic templates, functional programming, and support for numerical code. Examples include additional support for lambda functions with simplified syntax, improvements in automatic type detection for templates, custom literals, modules, constant expressions, and improved initialization strategies for C++ objects. This book also provides how-to examples thaTable of Contents
£39.99
APress Basic Math for Game Development with Unity 3D
Book SynopsisThis book will teach you fundamental mathematical concepts using Unity-based custom examples, explaining the implementations and demonstrating how these concepts are applied in building modern video game functionality. You will learn the theoretical foundation of each concept, and then interact, examine, and modify the implementation to inspect the effects. Basic Math for Game Development with Unity 3D begins by explaining points in the 3D Cartesian Coordinate system. From there, you'll gain insight into vectors and details of dot and cross products, quaternions, rotation and decomposition of vectors. These basic mathematical foundations are illustrated through Unity-based example implementations. Associated with these concept presentations are separate examples of how the concepts are applied in creating typical video game functionality, such as collision support, motion simulations, autonomous behaviors, shadow approximations, and reflections off surfaces with arbitrary orientationTable of Contents
£42.49
O'Reilly Media Effective DevOps
Book SynopsisSome companies think that adopting devops means bringing in specialists or a host of new tools. With this practical guide, you'll learn why devops is a professional and cultural movement that calls for change from inside your organization.
£29.99
Taylor & Francis Inc ModelBased Machine Learning
Book SynopsisToday, machine learning is being applied to a growing variety of problems in a bewildering variety of domains. A fundamental challenge when using machine learning is connecting the abstract mathematics of a machine learning technique to a concrete, real world problem. This book tackles this challenge through model-based machine learning which focuses on understanding the assumptions encoded in a machine learning system and their corresponding impact on the behaviour of the system.The key ideas of model-based machine learning are introduced through a series of case studies involving real-world applications. Case studies play a central role because it is only in the context of applications that it makes sense to discuss modelling assumptions. Each chapter introduces one case study and works through step-by-step to solve it using a model-based approach. The aim is not just to explain machine learning methods, but also showcase how to create, debug, and evolve them to solvTable of ContentsIntroduction. How Can Machine Learning Solve my Problem? 1. A Murder Mystery 2. Assessing People’s Skills Interlude. The Machine Learning Life Cycle 3. Meeting Your Match 4. Uncluttering Your Inbox 5. Making Recommendations 6. Understanding Asthma 7. Harnessing the Crowd 8. How to Read a Model Afterword
£68.39
Taylor & Francis Inc Big Data Management and Processing
Book SynopsisFrom the Foreword:Big Data Management and Processing is [a] state-of-the-art book that deals with a wide range of topical themes in the field of Big Data. The book, which probes many issues related to this exciting and rapidly growing field, covers processing, management, analytics, and applications... [It] is a very valuable addition to the literature. It will serve as a source of up-to-date research in this continuously developing area. The book also provides an opportunity for researchers to explore the use of advanced computing technologies and their impact on enhancing our capabilities to conduct more sophisticated studies.---Sartaj Sahni, University of Florida, USABig Data Management and Processing covers the latest Big Data research results in processing, analytics, management and applications. Both fundamental insights and representative applications are provided. This book is a timely and valuable resource for students, researchers and seaTable of ContentsBig Data Management. Big Data Design, implementation, evaluation and services. Big Data as integration of technologies. Big Data analytics and visualization. Query processing and indexing. Elasticity for data management systems. Self-adaptive and energy-efficient mechanisms. Performance evaluation. Security, privacy, trust, data ownership and risk simulations. Processing. Techniques, algorithms and innovative methods of processing. Business and economic models. Adoption cases, frameworks and user evaluations. Data-intensive and scalable computing on hybrid infrastructures. MapReduce based computations. Many-Task Computing in the Cloud. Streaming and real-time processing. Big Data systems and applications for multidisciplinary applications.
£117.00
Nova Science Publishers Inc Monte Carlo Simulation: Methods, Assessment &
Book SynopsisChapter One presents a study on application of Monte Carlo simulation in reliability assessment of composite electric power systems. Chapter Two develops a PK/PD model to evaluate, by Monte Carlo simulation as a data maximisation strategy, the antiviral activity of two stavudine formulations: conventional stavudine and stavudine-gold nanoparticles (stavudine-AuNPs). In Chapter Three, the magnetic properties of the kagomé lattice is studied with RudermanKittelKasuyaYosida (RKKY) exchange interactions in a spin-7/2 and alternate mixed spin-5/2 and spin-2 Ising model on the Bethe lattice by using the Monte Carlo simulations.
£78.39
Nova Science Publishers Inc Horizons in Computer Science Research: Volume 15
Book Synopsis
£205.59
Elsevier Science Fundamentals of the Theory of Computation
Book Synopsis
£59.96
Society for Industrial & Applied Mathematics,U.S. First-Order Methods In Optimization
Book SynopsisThe primary goal of this book is to provide a self-contained, comprehensive study of the main ?rst-order methods that are frequently used in solving large-scale problems. First-order methods exploit information on values and gradients/subgradients (but not Hessians) of the functions composing the model under consideration. With the increase in the number of applications that can be modeled as large or even huge-scale optimization problems, there has been a revived interest in using simple methods that require low iteration cost as well as low memory storage.The author has gathered, reorganized, and synthesized (in a unified manner) many results that are currently scattered throughout the literature, many of which cannot be typically found in optimization books.First-Order Methods in Optimization offers comprehensive study of first-order methods with the theoretical foundations; provides plentiful examples and illustrations; emphasizes rates of convergence and complexity analysis of the main first-order methods used to solve large-scale problems; and covers both variables and functional decomposition methods.Table of Contents Preface; Chapter 1: Vector Spaces; Chapter 2: Extended Real-Value Functions; Chapter 3: Subgradients; Chapter 4: Conjugate Functions; Chapter 5: Smoothness and Strong Convexity; Chapter 6: The Proximal Operator; Chapter 7: Spectral Functions; Chapter 8: Primal and Dual Projected Subgradient Methods; Chapter 9: Mirror Descent; Chapter 10: The Proximal Gradient Method; Chapter 11: The Block Proximal Gradient Method; Chapter 12: Dual-Based Proximal Gradient Methods; Chapter 13: The Generalized Conditional Gradient Method; Chapter 14: Alternating Minimization; Chapter 15: ADMM; Appendix A: Strong Duality and Optimality Conditions; Appendix B: Tables; Appendix C: Symbols and Notation; Appendix D: Bibliographic Notes; Bibliography; Index.
£86.70
Society for Industrial & Applied Mathematics,U.S. Sparse Polynomial Approximation of
Book SynopsisOver seventy years ago, Richard Bellman coined the term "the curse of dimensionality" to describe phenomena and computational challenges that arise in high dimensions. These challenges, in tandem with the ubiquity of high-dimensional functions in real-world applications, have led to a lengthy, focused research effort on high-dimensional approximation—that is, the development of methods for approximating functions of many variables accurately and efficiently from data. This book provides an in-depth treatment of one of the latest installments in this long and ongoing story: sparse polynomial approximation methods. These methods have emerged as useful tools for various high-dimensional approximation tasks arising in a range of applications in computational science and engineering. It begins with a comprehensive overview of best s-term polynomial approximation theory for holomorphic, high-dimensional functions, as well as a detailed survey of applications to parametric differential equations. It then describes methods for computing sparse polynomial approximations, focusing on least squares and compressed sensing techniques.Sparse Polynomial Approximation of High-Dimensional Functions presents the first comprehensive and unified treatment of polynomial approximation techniques that can mitigate the curse of dimensionality in high-dimensional approximation, including least squares and compressed sensing. It develops main concepts in a mathematically rigorous manner, with full proofs given wherever possible, and it contains many numerical examples, each accompanied by downloadable code. The authors provide an extensive bibliography of over 350 relevant references, with an additional annotated bibliography available on the book's companion website (www.sparse-hd-book.com).This text is aimed at graduate students, postdoctoral fellows, and researchers in mathematics, computer science, and engineering who are interested in high-dimensional polynomial approximation techniques.
£71.40
ISTE Ltd and John Wiley & Sons Inc The Uncertain Digital Revolution
Book SynopsisDigital information and communication technologies can be seen as a threat to privacy, a step forward for freedom of expression and communication, a tool in the fight against terrorism or the source of a new economic wealth. Computerization has unexpectedly progressed beyond our imagination, from a tool of management and control into one of widespread communication and expression. This book revisits the major questions that have emerged with the progress of computerization over nearly half a century, by describing the context in which these issues were formulated. By taking a social and digital approach, the author explores controversial issues surrounding the development of this "digital revolution", including freedom and privacy of the individual, social control, surveillance, public security and the economic exploitation of personal data. From students, teachers and researchers engaged in data analysis, to institutional decision-makers and actors in policy or business, all members of today's digital society will take from this book a better understanding of the essential issues of the current "digital revolution".Table of ContentsIntroduction ix Chapter 1. Technological Surveillance Subjected to Restrictions 1 Chapter 2. Security Over Liberty 21 Chapter 3. A Network Promoting Participation and Exchange 41 Chapter 4. Privitization and Economic Exploitation of Personal Data 65 Chapter 5. Digitalization and Revolution 87 Bibliography 107 Index 117
£125.06
Springer London Ltd Semantics with Applications: An Appetizer
Book SynopsisSemantics will play an important role in the future development of software systems and domain-specific languages. This book provides a needed introductory presentation of the fundamental ideas behind these approaches, stresses their relationship by formulating and proving the relevant theorems, and illustrates the applications of semantics in computer science. Historically important application areas are presented together with some exciting potential applications. The text investigates the relationship between various methods and describes some of the main ideas used, illustrating these by means of interesting applications. The book provides a rigorous introduction to the main approaches to formal semantics of programming languages.Trade ReviewFrom the reviews: "This book title, with its explicit reference to applications, quickly grabbed my attention due to the theoretical nature of formal semantics. … In any case, this book certainly fits the bill for an undergraduate course on the topic. … It also includes plenty of solved examples and exercises for students to help them grasp the key ideas and techniques behind the different mathematical models that can be used to describe the computations performed by a computer program." (Fernando Berzal, Computing Reviews, January, 2008) "This book presents a rigorous introduction to the main three approaches: operational semantics, denotational semantics, and axiomatic semantics. This book investigates the relationship between the various methods, and describes some of the main ideas by using applications. … Several exercises are provided. … help the student to understand definitions, results, and techniques … ." (G. Ciobanu, ACM Computing Reviews, May, 2009)Table of ContentsOperational Semantics.- More on Operational Semantics.- Provably Correct Implementation.- Denotational Semantics.- More on Denotational Semantics.- Program Analysis.- More on Program Analysis.- Axiomatic Program Verification.- More on Axiomatic Program Verification.- Further Reading.
£26.99
ISTE Ltd and John Wiley & Sons Inc Formal Languages, Automata and Numeration Systems
Book SynopsisFormal Languages, Automaton and Numeration Systems presents readers with a review of research related to formal language theory, combinatorics on words or numeration systems, such as Words, DLT (Developments in Language Theory), ICALP, MFCS (Mathematical Foundation of Computer Science), Mons Theoretical Computer Science Days, Numeration, CANT (Combinatorics, Automata and Number Theory). Combinatorics on words deals with problems that can be stated in a non-commutative monoid, such as subword complexity of finite or infinite words, construction and properties of infinite words, unavoidable regularities or patterns. When considering some numeration systems, any integer can be represented as a finite word over an alphabet of digits. This simple observation leads to the study of the relationship between the arithmetical properties of the integers and the syntactical properties of the corresponding representations. One of the most profound results in this direction is given by the celebrated theorem by Cobham. Surprisingly, a recent extension of this result to complex numbers led to the famous Four Exponentials Conjecture. This is just one example of the fruitful relationship between formal language theory (including the theory of automata) and number theory.Trade Review"This nice book is devoted to a quickly growing field, at the frontier between theoretical computer science, combinatorics, and number theory." (Zentralblatt MATH, 2016)Table of ContentsFOREWORD ix INTRODUCTION xiii CHAPTER 1. WORDS AND SEQUENCES FROM SCRATCH 1 1.1. Mathematical background and notation 2 1.1.1. About asymptotics 4 1.1.2. Algebraic number theory 5 1.2. Structures, words and languages 11 1.2.1. Distance and topology 16 1.2.2. Formal series 24 1.2.3. Language, factor and frequency 28 1.2.4. Period and factor complexity 33 1.3. Examples of infinite words 36 1.3.1. About cellular automata 43 1.3.2. Links with symbolic dynamical systems 46 1.3.3. Shift and orbit closure 59 1.3.4. First encounter with β-expansions 62 1.3.5. Continued fractions 69 1.3.6. Direct product, block coding and exercises 70 1.4. Bibliographic notes and comments 77 CHAPTER 2. MORPHIC WORDS 85 2.1. Formal definitions 89 2.2. Parikh vectors and matrices associated with a morphism 96 2.2.1. The matrix associated with a morphism 98 2.2.2. The tribonacci word 99 2.3. Constant-length morphisms 107 2.3.1. Closure properties 117 2.3.2. Kernel of a sequence 119 2.3.3. Connections with cellular automata 120 2.4. Primitive morphisms 122 2.4.1. Asymptotic behavior 127 2.4.2. Frequencies and occurrences of factors 127 2.5. Arbitrary morphisms 133 2.5.1. Irreducible matrices 134 2.5.2. Cyclic structure of irreducible matrices 144 2.5.3. Proof of theorem 2.35 150 2.6. Factor complexity and Sturmian words 153 2.7. Exercises 159 2.8. Bibliographic notes and comments 163 CHAPTER 3. MORE MATERIAL ON INFINITE WORDS 173 3.1. Getting rid of erasing morphisms 174 3.2. Recurrence 185 3.3. More examples of infinite words 191 3.4. Factor Graphs and special factors 202 3.4.1. de Bruijn graphs 202 3.4.2. Rauzy graphs 206 3.5. From the Thue–Morse word to pattern avoidance 219 3.6. Other combinatorial complexity measures 228 3.6.1. Abelian complexity 228 3.6.2. k-Abelian complexity 237 3.6.3. k-Binomial complexity 245 3.6.4. Arithmetical complexity 249 3.6.5. Pattern complexity 251 3.7. Bibliographic notes and comments 252 BIBLIOGRAPHY 257 INDEX 295 SUMMARY OF VOLUME 2 303
£125.06
Springer London Ltd Understanding Concurrent Systems
Book SynopsisCSP notation has been used extensively for teaching and applying concurrency theory, ever since the publication of the text Communicating Sequential Processes by C.A.R. Hoare in 1985. Both a programming language and a specification language, the theory of CSP helps users to understand concurrent systems, and to decide whether a program meets its specification. As a member of the family of process algebras, the concepts of communication and interaction are presented in an algebraic style. An invaluable reference on the state of the art in CSP, Understanding Concurrent Systems also serves as a comprehensive introduction to the field, in addition to providing material for a number of more advanced courses. A first point of reference for anyone wanting to use CSP or learn about its theory, the book also introduces other views of concurrency, using CSP to model and explain these. The text is fully integrated with CSP-based tools such as FDR, and describes how to create new tools based on FDR. Most of the book relies on no theoretical background other than a basic knowledge of sets and sequences. Sophisticated mathematical arguments are avoided whenever possible. Topics and features: presents a comprehensive introduction to CSP; discusses the latest advances in CSP, covering topics of operational semantics, denotational models, finite observation models and infinite-behaviour models, and algebraic semantics; explores the practical application of CSP, including timed modelling, discrete modelling, parameterised verifications and the state explosion problem, and advanced topics in the use of FDR; examines the ability of CSP to describe and enable reasoning about parallel systems modelled in other paradigms; covers a broad variety of concurrent systems, including combinatorial, timed, priority-based, mobile, shared variable, statecharts, buffered and asynchronous systems; contains exercises and case studies to support the text; supplies further tools and information at the associated website: http://www.comlab.ox.ac.uk/ucs/. From undergraduate students of computer science in need of an introduction to the area, to researchers and practitioners desiring a more in-depth understanding of theory and practice of concurrent systems, this broad-ranging text/reference is essential reading for anyone interested in Hoare’s CSP.Trade ReviewFrom the reviews:“This book is divided into four parts … . Part I is designed for an audience of both undergraduate and graduate computer science students. … Part II is designed for people who are familiar with Part I and have fairly theoretical interests. … Part III is intended for people who … want to be able to use them in a better way, or who are specifically interested in timed systems. Part IV is designed for people who already understand CSP.” (Günther Bauer, Zentralblatt MATH, Vol. 1211, 2011)Table of ContentsPart I: A Foundation Course in CSP Building a Simple Sequential Process Understanding CSP Parallel Operators CSP Case Studies Hiding and Renaming Beyond Traces Further Operators Using FDR Part II: Theory Operational Semantics Denotational Semantics and Behavioural Models Finite Observation Models Infinite-behaviour Models The Algebra of CSP Part III: Using CSP Timed Systems 1: tock-CSP Timed Systems 2: Discrete Timed CSP More About FDR State Explosion and Parameterised Verification Part IV: Exploring Concurrency Shared-variable Programs Understanding Shared-variable Concurrency Priority and Mobility
£40.49
Springer London Ltd Computational Methods in Biometric Authentication: Statistical Methods for Performance Evaluation
Book SynopsisBiometrics, the science of using physical traits to identify individuals, is playing an increasing role in our security-conscious society and across the globe. Biometric authentication, or bioauthentication, systems are being used to secure everything from amusement parks to bank accounts to military installations. Yet developments in this field have not been matched by an equivalent improvement in the statistical methods for evaluating these systems. Compensating for this need, this unique text/reference provides a basic statistical methodology for practitioners and testers of bioauthentication devices, supplying a set of rigorous statistical methods for evaluating biometric authentication systems. This framework of methods can be extended and generalized for a wide range of applications and tests. This is the first single resource on statistical methods for estimation and comparison of the performance of biometric authentication systems. The book focuses on six common performance metrics: for each metric, statistical methods are derived for a single system that incorporates confidence intervals, hypothesis tests, sample size calculations, power calculations and prediction intervals. These methods are also extended to allow for the statistical comparison and evaluation of multiple systems for both independent and paired data. Topics and features: * Provides a statistical methodology for the most common biometric performance metrics: failure to enroll (FTE), failure to acquire (FTA), false non-match rate (FNMR), false match rate (FMR), and receiver operating characteristic (ROC) curves * Presents methods for the comparison of two or more biometric performance metrics * Introduces a new bootstrap methodology for FMR and ROC curve estimation * Supplies more than 120 examples, using publicly available biometric data where possible * Discusses the addition of prediction intervals to the bioauthentication statistical toolset * Describes sample-size and power calculations for FTE, FTA, FNMR and FMR Researchers, managers and decisions makers needing to compare biometric systems across a variety of metrics will find within this reference an invaluable set of statistical tools. Written for an upper-level undergraduate or master’s level audience with a quantitative background, readers are also expected to have an understanding of the topics in a typical undergraduate statistics course. Dr. Michael E. Schuckers is Associate Professor of Statistics at St. Lawrence University, Canton, NY, and a member of the Center for Identification Technology Research.Table of ContentsPart I: Introduction Introduction Statistical Background Part II: Primary Matching and Classification Measures False Non-Match Rate False Match Rate Receiver Operating Characteristic Curve and Equal Error Rate Part III: Biometric Specific Measures Failure to Enrol Failure to Acquire Part IV: Additional Topics and Appendices Additional Topics and Discussion Tables
£123.49
Springer London Ltd Geometric Algebra for Computer Graphics
Book SynopsisGeometric algebra (a Clifford Algebra) has been applied to different branches of physics for a long time but is now being adopted by the computer graphics community and is providing exciting new ways of solving 3D geometric problems. The author tackles this complex subject with inimitable style, and provides an accessible and very readable introduction. The book is filled with lots of clear examples and is very well illustrated. Introductory chapters look at algebraic axioms, vector algebra and geometric conventions and the book closes with a chapter on how the algebra is applied to computer graphics.Table of ContentsElementary Algebra.- Complex Algebra.- Vector Algebra.- Quaternion Algebra.- Geometric Conventions.- Geometric Algebra.- The Geometric Product.- Reflections and Rotations.- Geometric Algebra and Geometry.- Conformal Geometry.- Applications of Geometric Algebra.- Programming Tools for Geometric Algebra.- Conclusion.
£42.75
Springer London Ltd Applied Interval Analysis: With Examples in Parameter and State Estimation, Robust Control and Robotics
Book SynopsisAt the core of many engineering problems is the solution of sets of equa tions and inequalities, and the optimization of cost functions. Unfortunately, except in special cases, such as when a set of equations is linear in its un knowns or when a convex cost function has to be minimized under convex constraints, the results obtained by conventional numerical methods are only local and cannot be guaranteed. This means, for example, that the actual global minimum of a cost function may not be reached, or that some global minimizers of this cost function may escape detection. By contrast, interval analysis makes it possible to obtain guaranteed approximations of the set of all the actual solutions of the problem being considered. This, together with the lack of books presenting interval techniques in such a way that they could become part of any engineering numerical tool kit, motivated the writing of this book. The adventure started in 1991 with the preparation by Luc Jaulin of his PhD thesis, under Eric Walter's supervision. It continued with their joint supervision of Olivier Didrit's and Michel Kieffer's PhD theses. More than two years ago, when we presented our book project to Springer, we naively thought that redaction would be a simple matter, given what had already been achieved . . .Trade ReviewFrom the reviews:"Applied Interval Analysis is the right book at the right time to move computing with intervals into the mainstream of engineering, financial, and scientific computing."G. William Walster, Interval Technology Engineering Manager, Sun Microsystems and Member of the Editorial Board of Reliable ComputingTable of ContentsI. Introduction.- 1. Introduction.- 1.1 What Are the Key Concepts?.- 1.2 How Did the Story Start?.- 1.3 What About Complexity?.- 1.4 How is the Book Organized?.- II. Tools.- 2. Interval Analysis.- 2.1 Introduction.- 2.2 Operations on Sets.- 2.2.1 Purely set-theoretic operations.- 2.2.2 Extended operations.- 2.2.3 Properties of set operators.- 2.2.4 Wrappers.- 2.3 Interval Analysis.- 2.3.1 Intervals.- 2.3.2 Interval computation.- 2.3.3 Closed intervals.- 2.3.4 Interval vectors.- 2.3.5 Interval matrices.- 2.4 Inclusion Functions.- 2.4.1 Definitions.- 2.4.2 Natural inclusion functions.- 2.4.3 Centred inclusion functions.- 2.4.4 Mixed centred inclusion functions.- 2.4.5 Taylor inclusion functions.- 2.4.6 Comparison.- 2.5 Inclusion Tests.- 2.5.1 Interval Booleans.- 2.5.2 Tests.- 2.5.3 Inclusion tests for sets.- 2.6 Conclusions.- 3. Subpavings.- 3.1 Introduction.- 3.2 Set Topology.- 3.2.1 Distances between compact sets.- 3.2.2 Enclosure of compact sets between subpavings.- 3.3 Regular Subpavings.- 3.3.1 Pavings and subpavings.- 3.3.2 Representing a regular subpaving as a binary tree.- 3.3.3 Basic operations on regular subpavings.- 3.4 Implementation of Set Computation.- 3.4.1 Set inversion.- 3.4.2 Image evaluation.- 3.5 Conclusions.- 4. Contractors.- 4.1 Introduction.- 4.2 Basic Contractors.- 4.2.1 Finite subsolvers.- 4.2.2 Intervalization of finite subsolvers.- 4.2.3 Fixed-point methods.- 4.2.4 Forward—backward propagation.- 4.2.5 Linear programming approach.- 4.3 External Approximation.- 4.3.1 Principle.- 4.3.2 Preconditioning.- 4.3.3 Newton contractor.- 4.3.4 Parallel linearization.- 4.3.5 Using formal transformations.- 4.4 Collaboration Between Contractors.- 4.4.1 Principle.- 4.4.2 Contractors and inclusion functions.- 4.5 Contractors for Sets.- 4.5.1 Definitions.- 4.5.2 Sets defined by equality and inequality constraints.- 4.5.3 Improving contractors using local search.- 4.6 Conclusions.- 5. Solvers.- 5.1 Introduction.- 5.2 Solving Square Systems of Non-linear Equations.- 5.3 Characterizing Sets Defined by Inequalities.- 5.4 Interval Hull of a Set Defined by Inequalities.- 5.4.1 First approach.- 5.4.2 Second approach.- 5.5 Global Optimization.- 5.5.1 The Moore—Skelboe algorithm.- 5.5.2 Hansen’s algorithm.- 5.5.3 Using interval constraint propagation.- 5.6 Minimax Optimization.- 5.6.1 Unconstrained case.- 5.6.2 Constrained case.- 5.6.3 Dealing with quantifiers.- 5.7 Cost Contours.- 5.8 Conclusions.- III. Applications.- 6. Estimation.- 6.1 Introduction.- 6.2 Parameter Estimation Via Optimization.- 6.2.1 Least-square parameter estimation in compartmental modelling.- 6.2.2 Minimax parameter estimation.- 6.3 Parameter Bounding.- 6.3.1 Introduction.- 6.3.2 The values of the independent variables are known.- 6.3.3 Robustification against outliers.- 6.3.4 The values of the independent variables are uncertain.- 6.3.5 Computation of the interval hull of the posterior feasible set.- 6.4 State Bounding.- 6.4.1 Introduction.- 6.4.2 Bounding the initial state.- 6.4.3 Bounding all variables.- 6.4.4 Bounding by constraint propagation.- 6.5 Conclusions.- 7. Robust Control.- 7.1 Introduction.- 7.2 Stability of Deterministic Linear Systems.- 7.2.1 Characteristic polynomial.- 7.2.2 Routh criterion.- 7.2.3 Stability degree.- 7.3 Basic Tests for Robust Stability.- 7.3.1 Interval polynomials.- 7.3.2 Polytope polynomials.- 7.3.3 Image-set polynomials.- 7.3.4 Conclusion.- 7.4 Robust Stability Analysis.- 7.4.1 Stability domains.- 7.4.2 Stability degree.- 7.4.3 Value-set approach.- 7.4.4 Robust stability margins.- 7.4.5 Stability radius.- 7.5 Controller Design.- 7.6 Conclusions.- 8. Robotics.- 8.1 Introduction.- 8.2 Forward Kinematics Problem for Stewart—Gough Platforms.- 8.2.1 Stewart—Gough platforms.- 8.2.2 From the frame of the mobile plate to that of the base.- 8.2.3 Equations to be solved.- 8.2.4 Solution.- 8.3 Path Planning.- 8.3.1 Graph discretization of configuration space.- 8.3.2 Algorithms for finding a feasible path.- 8.3.3 Test case.- 8.4 Localization and Tracking of a Mobile Robot.- 8.4.1 Formulation of the static localization problem.- 8.4.2 Model of the measurement process.- 8.4.3 Set inversion.- 8.4.4 Dealing with outliers.- 8.4.5 Static localization example.- 8.4.6 Tracking.- 8.4.7 Example.- 8.5 Conclusions.- IV. Implementation.- 9. Automatic Differentiation.- 9.1 Introduction.- 9.2 Forward and Backward Differentiations.- 9.2.1 Forward differentiation.- 9.2.2 Backward differentiation.- 9.3 Differentiation of Algorithms.- 9.3.1 First assumption.- 9.3.2 Second assumption.- 9.3.3 Third assumption.- 9.4 Examples.- 9.4.1 Example 1.- 9.4.2 Example 2.- 9.5 Conclusions.- 10. Guaranteed Computation with Floating-point Numbers.- 10.1 Introduction.- 10.2 Floating-point Numbers and IEEE 754.- 10.2.1 Representation.- 10.2.2 Rounding.- 10.2.3 Special quantities.- 10.3 Intervals and IEEE 754.- 10.3.1 Machine intervals.- 10.3.2 Closed interval arithmetic.- 10.3.3 Handling elementary functions.- 10.3.4 Improvements.- 10.4 Interval Resources.- 10.5 Conclusions.- 11. Do It Yourself.- 11.1 Introduction.- 11.2 Notions of C++.- 11.2.1 Program structure.- 11.2.2 Standard types.- 11.2.3 Pointers.- 11.2.4 Passing parameters to a function.- 11.3 INTERVAL Class.- 11.3.1 Constructors and destructor.- 11.3.2 Other member functions.- 11.3.3 Mathematical functions.- 11.4 Intervals with PROFIL/BIAS.- 11.4.1 BIAS.- 11.4.2 PROFIL.- 11.4.3 Getting started.- 11.5 Exercises on Intervals.- 11.6 Interval Vectors.- 11.6.1 INTERVAL_VECTOR class.- 11.6.2 Constructors, assignment and function call operators.- 11.6.3 Friend functions.- 11.6.4 Utilities.- 11.7 Vectors with PROFIL/BIAS.- 11.8 Exercises on Interval Vectors.- 11.9 Interval Matrices.- 11.10 Matrices with PROFIL/BIAS.- 11.11 Exercises on Interval Matrices.- 11.12 Regular Subpavings with PROFIL/BIAS.- 11.12.1 NODE class.- 11.12.2 Set inversion with subpavings.- 11.12.3 Image evaluation with subpavings.- 11.12.4 System simulation and state estimation with subpavings.- 11.13 Error Handling.- 11.13.1 Using exit.- 11.13.2 Exception handling.- 11.13.3 Mathematical errors.- References.
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