Maths for computer scientists Books

Book SynopsisIn this book the authors aim to endow the reader with an operational, conceptual, and methodological understanding of the discrete mathematics that can be used to study, understand, and perform computing. They want the reader to understand the elements of computing, rather than just know them. The basic topics are presented in a way that encourages readers to develop their personal way of thinking about mathematics. Many topics are developed at several levels, in a single voice, with sample applications from within the world of computing. Extensive historical and cultural asides emphasize the human side of mathematics and mathematicians.By means of lessons and exercises on “doing” mathematics, the book prepares interested readers to develop new concepts and invent new techniques and technologies that will enhance all aspects of computing. The book will be of value to students, scientists, and engineers engaged in the design and use of computing systems, and to scholars and practitioners beyond these technical fields who want to learn and apply novel computational ideas.Trade Review“The text is written in an easy to read format which generously incorporates narratives from the history of mathematics as well as rigorous proofs of the concepts presented. The appendices and references to other texts provide the reader with numerous sources of supplementary information for those wishing to delve into a subject at a deeper level … . chapters are organized and clearly labeled to express which sections are appropriate for a beginning learner, an intermediate learner, or the specialist.” (Tom French, MAA Reviews, October 3, 2021)“Each chapter comes with several exercises from easy to difficult, the latter with complete solutions in the appendix. To accommodate the book to readers with different backgrounds and goals, the authors provide a guide which gives paths through the book for several courses. The exposition is always clear and motivating, no prerequisites are presumed, all terms and concepts are defined precisely, and there are many look-and-see proofs.” (Dieter Riebesehl, zbMATH 1465.68004, 2021)Table of ContentsIntroduction.- “Doing” Mathematics: A Toolkit for Mathematical Reasoning.- Sets and Their Algebras: The Stem Cells of Mathematics.- Numbers I: The Basics of Our Number System.- Arithmetic: Putting Numbers to Work.- Summations: Complex Operations from Simple Components.- The Vertigo of Infinity: Handling the Very Large and the Infinite.- Numbers II: Building the Integers and Building with the Integers.- Recurrences: Rendering Complex Structure Manageable.- Numbers III: Operational Representations and Their Consequences.- The Art of Counting: Combinatorics, Probability, and Statistics.- Graphs I: Representing Relationships Mathematically.- Graphs II: Graphs Within Computation and Communication.- Solutions to Exercises.- App. A, Pairing Functions.- App. B, A Deeper Look at the Fibonacci Numbers.- App. C, Two Recurrence-Defined Number Families.- App. D, Signed-Digit Numerals: Carry-Free Addition.- App. E, The Diverse Delights of de Bruijn Networks.- List of Symbols.- References.- Index.

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Book SynopsisTo succeed in data science you need some math proficiency. But not just any math. This common-sense guide provides a clear, plain English survey of the math you'll need in data science, including probability, statistics, hypothesis testing, linear algebra, machine learning, and calculus.

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Book SynopsisThe mathematical paradigms that underlie deep learning typically start out as hard-to-read academic papers, often leaving engineers in the dark about how their models actually function. Math and Architectures of Deep Learning bridges the gap between theory and practice, laying out the math of deep learning side by side with practical implementations in Python and PyTorch. Written by deep learning expert Krishnendu Chaudhury, you'll peer inside the “black box” to understand how your code is working, and learn to comprehend cutting-edge research you can turn into practical applications. about the technology It's important to understand how your deep learning models work, both so that you can maintain them efficiently and explain them to other stakeholders. Learning mathematical foundations and neural network architecture can be challenging, but the payoff is big. You'll be free from blind reliance on pre-packaged DL models and able to build, customize, and re-architect for your specific needs. And when things go wrong, you'll be glad you can quickly identify and fix problems. about the book Math and Architectures of Deep Learning sets out the foundations of DL in a way that's both useful and accessible to working practitioners. Each chapter explores a new fundamental DL concept or architectural pattern, explaining the underpinning mathematics and demonstrating how they work in practice with well-annotated Python code. You'll start with a primer of basic algebra, calculus, and statistics, working your way up to state-of-the-art DL paradigms taken from the latest research. By the time you're done, you'll have a combined theoretical insight and practical skills to identify and implement DL architecture for almost any real-world challenge. Trade Review'This is a book that will reward your patience and perseverance with a clear and detailed knowledge of deep learning mathematics and associated techniques.' Tony Holdroyd 'Most online machine learning courses teach you how to get stuff done, but they don't give you the underlying math. If you want to know, this is the book for you!' Wiebe de Jong 'A really interesting book for people that want to understand the underlying mathematical mechanism of deep learning.' Julien Pohie 'Gives a unique perspective about machine learning and mathematical approaches.' Krzysztof Kamyczek 'An awesome book to get the grasp of the important mathematical skills to understand the very basics of deep learning.' Nicole KoenigsteinTable of Contentstable of contents READ IN LIVEBOOK 1AN OVERVIEW OF MACHINE LEARNING AND DEEP LEARNING READ IN LIVEBOOK 2INTRODUCTION TO VECTORS, MATRICES AND TENSORS FROM MACHINE LEARNING AND DATA SCIENCE POINT OF VIEW READ IN LIVEBOOK 3INTRODUCTION TO VECTOR CALCULUS FROM MACHINE LEARNING POINT OF VIEW READ IN LIVEBOOK 4LINEAR ALGEBRAIC TOOLS IN MACHINE LEARNING AND DATA SCIENCE READ IN LIVEBOOK 5PROBABILITY DISTRIBUTIONS FOR MACHINE LEARNING AND DATA SCIENCE READ IN LIVEBOOK 6BAYESIAN TOOLS FOR MACHINE LEARNING AND DATA SCIENCE READ IN LIVEBOOK 7FUNCTION APPROXIMATION: HOW NEURAL NETWORKS MODEL THE WORLD READ IN LIVEBOOK 8TRAINING NEURAL NETWORKS: FORWARD AND BACKPROPAGATION READ IN LIVEBOOK 9LOSS, OPTIMIZATION AND REGULARIZATION READ IN LIVEBOOK 10ONE, TWO AND THREE DIMENSIONAL CONVOLUTION AND TRANSPOSED CONVOLUTION IN NEURAL NETWORKS 11 IMAGE ANALYSIS: 2D CONVOLUTION BASED NEURAL NETWORK ARCHITECTURES FOR OBJECT RECOGNITION AND DETECTION 12 VIDEO ANALYSIS: 3D CONVOLUTION BASED SPATIO TEMPORAL NEURAL NETWORK ARCHITECTURES READ IN LIVEBOOK APPENDIX A: APPENDIX A.1Dot Product and cosine of the angle between two vectors A.2Computing variance of Gaussian Distribution A.3Two Theorems in Statistic

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Book SynopsisNew insight in many scientific and engineering fields is unthinkable without the use of numerical simulations running efficiently on modern computers. The faster we get new results, the bigger and accurate are the problems that we can solve. It is the combination of mathematical ideas plus efficient programming that drives the progress in many disciplines. Future champions in the area thus will have to be qualified in their application domain, they will need a profound understanding of some mathematical ideas, and they need the skills to deliver fast code. The present textbook targets students which have programming skills already and do not shy away from mathematics, though they might be educated in computer science or an application domain. It introduces the basic concepts and ideas behind applied mathematics and parallel programming that we need to write numerical simulations for today’s multicore workstations. Our intention is not to dive into one particular application domain or to introduce a new programming language – we lay the generic foundations for future courses and projects in the area. The text is written in an accessible style which is easy to digest for students without years and years of mathematics education. It values clarity and intuition over formalism, and uses a simple N-body simulation setup to illustrate basic ideas that are of relevance in various different subdomains of scientific computing. Its primary goal is to make theoretical and paradigmatic ideas accessible to undergraduate students and to bring the fascination of the field across.Table of Contents1. The Pillars of Science.- 2. Moore Myths.- 3. Our Model Problem.- 4. Floating Point Numbers.- 5. A Simplistic Machine Model.- 6. Round-off Error Propagation.- 7. SIMD Vector Crunching.- 8. Arithmetic Stability of an Implementation.- 9. Vectorisation of the Model Problem.- 10. Conditioning and Well-posedness.- 11. Taylor Expansion.- 12. Ordinary Differential Equations.- 13. Accuracy and Appropriateness of Numerical Schemes.- 14. Writing Parallel Codes.- 15. Upscaling Methods.- 16. OpenMP Primer.- 17. Shared Memory Tasking.- 18. GPGPUs with OpenMP.- 19. Higher Order Methods.- 20. Adaptive Time Stepping.

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Book SynopsisThis book constitutes the refereed proceedings of the 18th European Workshop on Computer Performance Engineering, EPEW 2022, held in Santa Pola, Spain, in September 2022.The 14 papers presented in this volume together with one invited talk were carefully reviewed and selected from 14 submissions. The papers presented at the workshop reflect the diversity of modern performance engineering. The sessions covered a wide range of topics including robustness analysis, machine learning, edge and cloud computing, as well as more traditional topics on stochastic modelling, techniques and tools.Table of ContentsRobustness analysis.- Applications.- Stochastic modelling.- Machine learning.- Edge-cloud computing.- Modelling paradigms and tools.

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Book SynopsisIn this introductory textbook the author explains the key topics in cryptography. He takes a modern approach, where defining what is meant by "secure" is as important as creating something that achieves that goal, and security definitions are central to the discussion throughout.The author balances a largely non-rigorous style — many proofs are sketched only — with appropriate formality and depth. For example, he uses the terminology of groups and finite fields so that the reader can understand both the latest academic research and "real-world" documents such as application programming interface descriptions and cryptographic standards. The text employs colour to distinguish between public and private information, and all chapters include summaries and suggestions for further reading.This is a suitable textbook for advanced undergraduate and graduate students in computer science, mathematics and engineering, and for self-study by professionals in information security. While the appendix summarizes most of the basic algebra and notation required, it is assumed that the reader has a basic knowledge of discrete mathematics, probability, and elementary calculus.Trade Review“The goal of cryptography is to obfuscate data for unintended recipients. … The book is divided into four parts. … The book is very comprehensive, and very accessible for dedicated students.” (Klaus Galensa, Computing Reviews, computingreviews.com, October, 2016)“Cryptography made simple is a textbook that provides a broad coverage of topics that form an essential working knowledge for the contemporary cryptographer. It is particularly suited to introducing graduate and advanced undergraduate students in computer science to the concepts necessary for understanding academic cryptography and its impact on real-world practice, though it will also be useful for mathematicians or engineers wishing to gain a similar perspective on this material.” (Maura Beth Paterson, Mathematical Reviews, July, 2016)“This is a very thorough introduction to cryptography, aimed at lower-division undergraduates. It is an engineering textbook that uses modern mathematical terminology (such as groups and finite fields). … Bottom line: really for engineers, and a useful book if used carefully; the organization makes is easy to get overwhelmed by the background material before you get to the 'good stuff', and even the good stuff has an overwhelming amount of detail.” (Allen Stenger, MAA Reviews, maa.org, June, 2016)“This very thorough book by Smart (Univ. of Bristol, UK) is aimed at graduate students and advanced undergraduates in mathematics and computer science and intended to serve as a bridge to research papers in the field. … Summing Up: Recommended. Upper-division undergraduates through professionals/practitioners.” (C. Bauer, Choice, Vol. 53 (10), June, 2016)Table of ContentsModular Arithmetic, Groups, Finite Fields and Probability.- Elliptic Curves.- Historical Ciphers.- The Enigma Machine.- Information Theoretic Security.- Historical Stream Ciphers.- Modern Stream Ciphers.- Block Ciphers.- Symmetric Key Distribution.- Hash Functions and Message Authentication Codes.- Basic Public Key Encryption Algorithms.- Primality Testing and Factoring.- Discrete Logarithms.- Key Exchange and Signature Schemes.- Implementation Issues.- Obtaining Authentic Public Keys.- Attacks on Public Key Schemes.- Definitions of Security.- Complexity Theoretic Approaches.- Provable Security: With Random Oracles.- Hybrid Encryption.- Provable Security: Without Random Oracles.- Secret Sharing Schemes.- Commitments and Oblivious Transfer.- Zero-Knowledge Proofs.- Secure Multiparty Computation.

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Book SynopsisThis book provides the mathematical fundamentals of linear algebra to practicers in computer vision, machine learning, robotics, applied mathematics, and electrical engineering. By only assuming a knowledge of calculus, the authors develop, in a rigorous yet down to earth manner, the mathematical theory behind concepts such as: vectors spaces, bases, linear maps, duality, Hermitian spaces, the spectral theorems, SVD, and the primary decomposition theorem. At all times, pertinent real-world applications are provided. This book includes the mathematical explanations for the tools used which we believe that is adequate for computer scientists, engineers and mathematicians who really want to do serious research and make significant contributions in their respective fields.

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Book SynopsisThis new edition of Modern Fortran Explained provides a clear and thorough description of the latest version of Fortran, written by experts in the field with the intention that it remain the main reference work in the field.Table of Contents1: Whence Fortran? 2: Language elements 3: Expressions and assignments 4: Control constructs 5: Program units and procedures 6: Allocation of data 7: Array features 8: Specification statements 9: Intrinsic procedures and modules 10: Data transfer 11: Edit descriptors 12: Operations on external files 13: Further type parameter featur 14: Abstract interfaces and procedure pointers 15: Object-oriented programming 16: Submodules 17: Coarrays 18: Coarray teams 19: Floating-point exception handling 20: Basic interoperability with C 21: Interoperating with C using descriptors 22: Generic programming 23: Other Fortran 2023 enhancements A: Deprecated features B: Obsolescent and deleted features C: Significant examples D: Solutions to exercises

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Book SynopsisIntuitionistic logic is presented here as part of familiar classical logic which allows mechanical extraction of programs from proofs.Trade Review`This is the most welcome addition to the literature on intuitionistic logic, providing a substantial reference of value comparable to that of better established references for classical mathematical logic. The development of Mints' book is natural, elegant and accessible, with a minimum of fuss but no lack of attention to important detail. Overall, the book is an excellent addition to the literature.' Mathematical Reviews, 2002bTable of ContentsIntroduction. I: Intuitionistic Propositional Logic. 1. Preliminaries. 2. Natural Deduction for Propositional Logic. 3. Negative Translation: Glivenko's Theorem. 4. Program Interpretation of Intuitionistic Logic. 5. Computations with Deductions. 6. Coherence Theorem. 7. Kripke Models. 8. Gentzen-type Propositional System LJpm. 9. Topological Completeness. 10. Proof-Search. 11. System LJpm. 12. Interpolation Theorem. II: Intuitionistic Predicate Logic. 13. Natural Deduction System NJ. 14. Kripke Models for Predicate Logic. 15. Systems LJm, LJ. 16. Proof-Search in Predicate Logic. References. Index.

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Book SynopsisWritten for any motivated reader with a high-school knowledge of mathematics, and the discipline to follow logical arguments, this book presents the proofs for revolutionary impossibility theorems in an accessible way, with less jargon and notation, and more background, intuition, examples, explanations, and exercises.Trade Review'This unique and lovely book takes us on a grand tour of the limitations of science, mathematics, and of reason itself. To appreciate what is possible we must know the impossible, and such limitations define the boundary between the two. Gusfield offers well-explained gems illustrating various limitations, showing why they arise, giving their historical context, and in contrast to other similar books for a broad audience, presenting rigorous proofs requiring limited background.' Michael Sipser, MIT'There are impossible problems in many different fields (e.g., Physics, Mathematics). This book is an excellent exposition of these difference ways a problem can be impossible. Along the way, the reader will pick up the needed background which is interesting in itself.' William Gasarch, University of MarylandTable of ContentsPreface; 1. Yes you can prove a negative!; 2. Bell's impossibility theorem(s); 3. Enjoying Bell magic; 4. Arrow's (and friends') impossibility theorems; 5. Clustering and impossibility; 6. Gödel-ish impossibility; 7. Turing undecidability and incompleteness; 8. Chaitin's theorem: More devastating; 9. Gödel (for real, this time).

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Book SynopsisWritten for any motivated reader with a high-school knowledge of mathematics, and the discipline to follow logical arguments, this book presents the proofs for revolutionary impossibility theorems in an accessible way, with less jargon and notation, and more background, intuition, examples, explanations, and exercises.Trade Review'This unique and lovely book takes us on a grand tour of the limitations of science, mathematics, and of reason itself. To appreciate what is possible we must know the impossible, and such limitations define the boundary between the two. Gusfield offers well-explained gems illustrating various limitations, showing why they arise, giving their historical context, and in contrast to other similar books for a broad audience, presenting rigorous proofs requiring limited background.' Michael Sipser, MIT'There are impossible problems in many different fields (e.g., Physics, Mathematics). This book is an excellent exposition of these difference ways a problem can be impossible. Along the way, the reader will pick up the needed background which is interesting in itself.' William Gasarch, University of MarylandTable of ContentsPreface; 1. Yes you can prove a negative!; 2. Bell's impossibility theorem(s); 3. Enjoying Bell magic; 4. Arrow's (and friends') impossibility theorems; 5. Clustering and impossibility; 6. Gödel-ish impossibility; 7. Turing undecidability and incompleteness; 8. Chaitin's theorem: More devastating; 9. Gödel (for real, this time).

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Book SynopsisApplied Math for Security is one of the first math-based guides specifically geared for information security practitioners. Readers will learn how to use concepts from various fields of mathematics - like graph theory, computational geometry, and statistics - to create and implement ready-to-use security tools. The book is written in a lively, conversational style that engages readers from the get-go. Chapters are enriched with code examples written in Python, and feature hands-on 'proof of concept' projects that involve developing math-based applications to solve real-world problems. Readers are also able to apply the mathematical constructs that they learn to a variety of challenging scenarios, like determining the ideal location for fire stations, disrupting information flow in a social network, building facial recognition software, and designing custom tools for modern security work.Trade Review"A very practical book for security. . . . a real eye-opener."—William Gasarch, Professor, University of Maryland-Dept of Computer Science"A really nice introduction to graph theory and computational geometry for people who know a bit of Python and without a mathematical background."—Julien Voisin, Artificial Truth"The book was very easy to follow, I'd expect anyone with a technical or stats background to be able to dive right in given the step-by-step instructions and explanations provided by Daniel."—@WithSandra, tech YouTuber and security analyst"Whether you're an aspiring security professional, a social network analyst, or an innovator seeking to create cutting-edge security solutions, Math for Security will empower you to solve complex problems with precision and confidence. "—Midwest Book ReviewTable of ContentsAcknowledgments IntroductionPART I: ENVIRONMENT AND CONVENTIONSChapter 1: Setting up the EnvironmentChapter 2: Programming and Math ConventionsPART II: GRAPH THEORY AND COMPUTATIONAL GEOMETRYChapter 3: Securing Networks with Graph TheoryChapter 4: Building a Network Traffic Analysis Tool Chapter 5: Identifying Threats with Social Network AnalysisChapter 6: Analyzing Social Networks to Prevent Security IncidentsChapter 7: Using Geometry to Improve Security PracticesChapter 8: Tracking People in Physical Space with Digital InformationChapter 9: Computational Geometry for Safety Resource DistributionChapter 10: Computational Geometry for Facial RecognitionPART III: THE ART GALLERY PROBLEMChapter 11: Distributing Security Resources to Guard a SpaceChapter 12: The Minimum Viable Product Approach to Security Software DevelopmentChapter 13: Delivering Python ApplicationsNotesIndex

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Book SynopsisThis book provides an undergraduate introduction to analysing data for data science, computer science, and quantitative social science students. It uniquely combines a hands-on approach to data analysis – supported by numerous real data examples and reusable [R] code – with a rigorous treatment of probability and statistical principles. Where contemporary undergraduate textbooks in probability theory or statistics often miss applications and an introductory treatment of modern methods (bootstrapping, Bayes, etc.), and where applied data analysis books often miss a rigorous theoretical treatment, this book provides an accessible but thorough introduction into data analysis, using statistical methods combining the two viewpoints. The book further focuses on methods for dealing with large data-sets and streaming-data and hence provides a single-course introduction of statistical methods for data science.Trade Review“Having taught data analytics at the introductory graduate level, I welcome the authors’ textbook as an essential resource for training well-grounded entry-level data scientists. … A data scientist shall provide competent data science professional services to a client. … Training in both the theory and practice of data analytics is a requirement for such competence. The authors’ textbook definitely provides a valuable resource for such training.” (Harry J. Foxwell, Computing Reviews, July 7, 2022)Table of Contents1 A First Look at Data.- 2 Sampling Plans and Estimates.- 3 Probability Theory.- 4 Random Variables and Distributions.- 5 Estimation.- 6 Multiple Random Variables.- 7 Making Decisions in Uncertainty.- 8 Bayesian Statistics.

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Book SynopsisMany believe mathematics is only about calculations, formulas, numbers, and strange letters. But mathematics is much more than just crunching numbers or manipulating symbols. Mathematics is about discovering patterns, uncovering hidden structures, finding counterexamples, and thinking logically. Mathematics is a way of thinking. It is an activity that is both highly creative and challenging. This book offers an introduction to mathematical reasoning for beginning university or college students, providing a solid foundation for further study in mathematics, computer science, and related disciplines. Written in a manner that directly conveys the sense of excitement and discovery at the heart of doing science, its 25 short and visually appealing chapters cover the basics of set theory, logic, proof methods, combinatorics, graph theory, and much more. In the book you will, among other things, find answers to: What is a proof? What is a counterexample? What does it mean to say that something follows logically from a set of premises? What does it mean to abstract over something? How can knowledge and information be represented and used in calculations? What is the connection between Morse code and Fibonacci numbers? Why could it take billions of years to solve Hanoi's Tower? Logical Methods is especially appropriate for students encountering such concepts for the very first time. Designed to ease the transition to a university or college level study of mathematics or computer science, it also provides an accessible and fascinating gateway to logical thinking for students of all disciplines.Trade Review"The definitions are followed by examples to help explain their meaning, along with counterexamples ... . Therefore, very little basic knowledge is required for this introduction to logical methods ... which is written in an accessible style ... . contained in the book are several hundred small figures; arrow, Venn, and Hasse diagrams; and simplifies visual representations ... . The author has also elected to use color to draw the reader's attention ... ." “From personal teaching experience, knowledge of these mathematical areas is necessary for disparate fields of CS and informatics. These foundations are needed for many fields, from database theory to various domains of information systems applications. The book’s presentation of topics and incentives for problem-solving, along with its exercises, is very useful for university-level instructors and students. The compact chapters contain clear explanations, diagrams, and brief descriptions of interesting facts.” (Bálint Molnár, Computing Reviews, July 27, 2021)Table of ContentsPreface.- 0 The Art of Thinking Abstractly and Mathematically.- 1 Basic Set Theory.- 2 Propositional Logic.- 3 Semantics from Propositional Logic.- 4 Concepts in Propositional Logic.- 5 Proofs, Conjectures, and Counterexamples.- 6 Relations.- 7 Functions.- 8 A Little More Set Theory.- 9 Closures and Inductively Defined Sets.- 10 Recursively Defined Functions.- 11 Mathematical Induction.- 12 Structural Induction.- 13 First-Order Languages.- 14 Representation of Quantified Statements.- 15 Interpretation in Models.- 16 Reasoning About Models.- 17 Abstraction with Equivalences and Partitions.- 18 Combinatorics.- 19 A Little More Combinatorics.- 20 A Bit of Abstract Algebra.- 21 Graph Theory.- 22 Walks in Graphs.- 23 Formal Languages and Grammars.- 24 Natural Deduction.- The Road Ahead.- Index. Symbols.

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Book SynopsisVolume 2 applies the linear algebra concepts presented in Volume 1 to optimization problems which frequently occur throughout machine learning. This book blends theory with practice by not only carefully discussing the mathematical under pinnings of each optimization technique but by applying these techniques to linear programming, support vector machines (SVM), principal component analysis (PCA), and ridge regression. Volume 2 begins by discussing preliminary concepts of optimization theory such as metric spaces, derivatives, and the Lagrange multiplier technique for finding extrema of real valued functions. The focus then shifts to the special case of optimizing a linear function over a region determined by affine constraints, namely linear programming. Highlights include careful derivations and applications of the simplex algorithm, the dual-simplex algorithm, and the primal-dual algorithm. The theoretical heart of this book is the mathematically rigorous presentation of various nonlinear optimization methods, including but not limited to gradient decent, the Karush-Kuhn-Tucker (KKT) conditions, Lagrangian duality, alternating direction method of multipliers (ADMM), and the kernel method. These methods are carefully applied to hard margin SVM, soft margin SVM, kernel PCA, ridge regression, lasso regression, and elastic-net regression. Matlab programs implementing these methods are included.

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Book SynopsisGraphs are the natural way to understand connected data. This book explores the most important algorithms and techniques for graphs in data science, with practical examples and concrete advice on implementation and deployment. In Graph Algorithms for Data Science you will learn: Labeled-property graph modeling Constructing a graph from structured data such as CSV or SQL NLP techniques to construct a graph from unstructured data Cypher query language syntax to manipulate data and extract insights Social network analysis algorithms like PageRank and community detection How to translate graph structure to a ML model input with node embedding models Using graph features in node classification and link prediction workflows Graph Algorithms for Data Science is a hands-on guide to working with graph-based data in applications like machine learning, fraud detection, and business data analysis. It's filled with fascinating and fun projects, demonstrating the ins-and-outs of graphs. You'll gain practical skills by analyzing Twitter, building graphs with NLP techniques, and much more. You don't need any graph experience to start benefiting from this insightful guide. These powerful graph algorithms are explained in clear, jargon-free text and illustrations that makes them easy to apply to your own projects. about the technology Graphs reveal the relationships in your data. Tracking these interlinking connections reveals new insights and influences and lets you analyze each data point as part of a larger whole. This interconnected data is perfect for machine learning, as well as analyzing social networks, communities, and even product recommendations. about the book Graph Algorithms for Data Science teaches you how to construct graphs from both structured and unstructured data. You'll learn how the flexible Cypher query language can be used to easily manipulate graph structures, and extract amazing insights. The book explores common and useful graph algorithms like PageRank and community detection/clustering algorithms. Each new algorithm you learn is instantly put into action to complete a hands-on data project, including modeling a social network! Finally, you'll learn how to utilize graphs to upgrade your machine learning, including utilizing node embedding models and graph neural networks.Trade Review'The book covers topics in-depth but is easy to understand. Though delving into theory, it doesn't lose its focus of being a more practical guide. ' Carl Yu 'A good starting point to getting started with network analysis and how to extract the essential information you need easily.' Andrea Paciolla 'A great introduction to how to use graphs and data they can provide.' Marcin SękTable of Contentstable of contents detailed TOC READ IN LIVEBOOK 1GRAPHS AND NETWORK SCIENCE: AN INTRODUCTION READ IN LIVEBOOK 2REPRESENTING NETWORK STRUCTURE - DESIGN YOUR FIRST GRAPH MODEL READ IN LIVEBOOK 3YOUR FIRST STEPS WITH THE CYPHER QUERY LANGUAGE READ IN LIVEBOOK 4CYPHER AGGREGATIONS AND SOCIAL NETWORK ANALYSIS 5 INFERRING NETWORKS AND MONOPARTITE PROJECTIONS 6 CONSTRUCT A GRAPH USING NLP TECHNIQUES 7 NODE EMBEDDINGS AND CLASSIFICATION 8 IMPROVE DOCUMENT CLASSIFICATION WITH GRAPH NEURAL NETWORKS 9 PREDICT NEW CONNECTIONS 10 KNOWLEDGE GRAPH COMPLETION READ IN LIVEBOOK APPENDIX A: ADJACENCY MATRIX

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Book SynopsisThis graduate-level textbook is primarily aimed at graduate students of statistics, mathematics, science, and engineering who have had an undergraduate course in statistics, an upper division course in analysis, and some acquaintance with measure theoretic probability. It provides a rigorous presentation of the core of mathematical statistics.Part I of this book constitutes a one-semester course on basic parametric mathematical statistics. Part II deals with the large sample theory of statistics - parametric and nonparametric, and its contents may be covered in one semester as well. Part III provides brief accounts of a number of topics of current interest for practitioners and other disciplines whose work involves statistical methods.Trade Review“It deals with advanced statistical theory with a special focus on statistical inference and large sample theory, aiming to cover the material for a modern two-semester graduate course in mathematical statistics. … Overall, the book is very advanced and is recommended to graduate students with sound statistical backgrounds, as well as to teachers, researchers, and practitioners who wish to acquire more knowledge on mathematical statistics and large sample theory.” (Lefteris Angelis, Computing Reviews, March, 2017)“This is a very nice book suitable for a theoretical statistics course after having worked through something at the level of Casella & Berger, as well as some measure theory. … In addition to the exercises, which range from doable to interesting, there are several projects scattered throughout the text. The explanations are clear and crisp, and the presentation is interesting. … the book would be a worthy addition to your statistics library.” (Peter Rabinovitch, MAA Reviews, maa.org, March, 2017)Table of Contents1 Introduction.- 2 Decision Theory.- 3 Introduction to General Methods of Estimation.- 4 Sufficient Statistics, Exponential Families, and Estimation.- 5 Testing Hypotheses.- 6 Consistency and Asymptotic Distributions and Statistics.- 7 Large Sample Theory of Estimation in Parametric Models.- 8 Tests in Parametric and Nonparametric Models.- 9 The Nonparametric Bootstrap.- 10 Nonparametric Curve Estimation.- 11 Edgeworth Expansions and the Bootstrap.- 12 Frechet Means and Nonparametric Inference on Non-Euclidean Geometric Spaces.- 13 Multiple Testing and the False Discovery Rate.- 14 Markov Chain Monte Carlo (MCMC) Simulation and Bayes Theory.- 15 Miscellaneous Topics.- Appendices.- Solutions of Selected Exercises in Part 1.

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Book SynopsisThis book constitutes the refereed post-conference proceedings of the Special Event on the Analysis of Experimental Algorithms, SEA² 2019, held in Kalamata, Greece, in June 2019.The 35 revised full papers presented were carefully reviewed and selected from 45 submissions. The papers cover a wide range of topics in both computer science and operations research/mathematical programming. They focus on the role of experimentation and engineering techniques in the design and evaluation of algorithms, data structures, and computational optimization methods.

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Book SynopsisGraph algorithms is a well-established subject in mathematics and computer science. Beyond classical application fields, such as approximation, combinatorial optimization, graphics, and operations research, graph algorithms have recently attracted increased attention from computational molecular biology and computational chemistry. Centered around the fundamental issue of graph isomorphism, this text goes beyond classical graph problems of shortest paths, spanning trees, flows in networks, and matchings in bipartite graphs. Advanced algorithmic results and techniques of practical relevance are presented in a coherent and consolidated way. This book introduces graph algorithms on an intuitive basis followed by a detailed exposition in a literate programming style, with correctness proofs as well as worst-case analyses. Furthermore, full C++ implementations of all algorithms presented are given using the LEDA library of efficient data structures and algorithms.Table of Contents1. Introduction.- 2. Algorithmic Techniques.- 3. Tree Traversal.- 4. Tree Isomorphism.- 5. Graph Traversal.- 6. Clique, Independent Set, and Vertex Cover.- 7. Graph Isomorphism.

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Book SynopsisThis book constitutes the refereed proceedings of the First Workshop on Monte Carlo Search, MCS 2020, organized in conjunction with IJCAI 2020. The event was supposed to take place in Yokohama, Japan, in July 2020, but due to the Covid-19 pandemic was held virtually on January 7, 2021. The 9 full papers of the specialized project were carefully reviewed and selected from 15 submissions. The following topics are covered in the contributions: discrete mathematics in computer science, games, optimization, search algorithms, Monte Carlo methods, neural networks, reinforcement learning, machine learning.Table of ContentsThe αµ Search Algorithm for the Game of Bridge.- Stabilized Nested Rollout Policy Adaptation.- zoNNscan: A Boundary-Entropy Index for Zone Inspection of Neural Models.- Ordinal Monte Carlo Tree Search.- Monte Carlo Game Solver.- Generalized Nested Rollout Policy Adaptation.- Monte Carlo Inverse Folding.- Monte Carlo Graph Coloring.- Enhancing Playout Policy Adaptation for General Game Playing.

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Book SynopsisThis textbook tackles the problem of formulating AI systems by combining probabilistic modeling and deep learning. Moreover, it goes beyond typical predictive modeling and brings together supervised learning and unsupervised learning. The resulting paradigm, called deep generative modeling, utilizes the generative perspective on perceiving the surrounding world. It assumes that each phenomenon is driven by an underlying generative process that defines a joint distribution over random variables and their stochastic interactions, i.e., how events occur and in what order. The adjective "deep" comes from the fact that the distribution is parameterized using deep neural networks. There are two distinct traits of deep generative modeling. First, the application of deep neural networks allows rich and flexible parameterization of distributions. Second, the principled manner of modeling stochastic dependencies using probability theory ensures rigorous formulation and prevents potential flaws in reasoning. Moreover, probability theory provides a unified framework where the likelihood function plays a crucial role in quantifying uncertainty and defining objective functions. Deep Generative Modeling is designed to appeal to curious students, engineers, and researchers with a modest mathematical background in undergraduate calculus, linear algebra, probability theory, and the basics in machine learning, deep learning, and programming in Python and PyTorch (or other deep learning libraries). It will appeal to students and researchers from a variety of backgrounds, including computer science, engineering, data science, physics, and bioinformatics, who wish to become familiar with deep generative modeling. To engage the reader, the book introduces fundamental concepts with specific examples and code snippets. The full code accompanying the book is available on github. The ultimate aim of the book is to outline the most important techniques in deep generative modeling and, eventually, enable readers to formulate new models and implement them.Table of ContentsWhy Deep Generative Modeling?.- Autoregressive Models.- Flow-based Models.- Latent Variable Models.- Hybrid Modeling.- Energy-based Models.- Generative Adversarial Networks.- Deep Generative Modeling for Neural Compression.- Useful Facts from Algebra and Calculus.- Useful Facts from Probability Theory and Statistics.- Index.

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Book SynopsisThis book constitutes the proceedings of the 43rd International Conference on Application and Theory of Petri Nets and Concurrency, PETRI NETS 2022, which was held virtually in June 2021. The 19 full papers presented in this volume were carefully reviewed and selected from 35 submissions. The papers are categorized into the following topical sub-headings: application of concurrency to system design; timed models; tools; applications; synthesis; petri nets architecture; and process mining.

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Book SynopsisThis book constitutes the proceedings of the 19th International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research, CPAIOR 2022, which was held in Los Angeles, CA, USA, in June 2022.The 28 regular papers presented were carefully reviewed and selected from a total of 60 submissions. The conference program included a Master Class on the topic "Bridging the Gap between Machine Learning and Optimization”.Table of ContentsA Two-Phase Hybrid Approach for the Hybrid Flexible Flowshop with Transportation Times.- A SAT Encoding to compute Aperiodic Tiling Rhythmic Canons.- Transferring Information across Restarts in MIP.- Towards Copeland Optimization in Combinatorial Problems.- Coupling Different Integer Encodings for SAT.- Model-Based Algorithm Configuration with Adaptive Capping and Prior Distributions.- Shattering Inequalities for Learning Optimal Decision Trees.- Learning Pseudo-Backdoors for Mixed Integer Programs.- Leveraging Integer Linear Programming to Learn Optimal Fair Rule Lists.- Solving the Job Shop Scheduling Problem extended with AGVs – Classical and Quantum Approaches.- Stochastic Decision Diagrams.- Improving the robustness of EPS to solve the TSP.- Efficient operations between MDDs and constraints.- Deep Policy Dynamic Programming for Vehicle Routing Problems.- Learning a Propagation Complete Formula.- A FastMap-Based Algorithm for Block Modeling.- Packing by Scheduling: Using Constraint Programming to Solve a Complex 2D Cutting Stock Problem.- Dealing with the product constraint.- Multiple-choice knapsack constraint in graphical models.- A Learning Large Neighborhood Search for the Staff Rerostering Problem.- Practically Uniform Solution Sampling in Constraint Programming.- Training Thinner and Deeper Neural Networks: Jumpstart Regularization.- Hybrid Offline/Online Optimization for Energy Management via Reinforcement Learning.- Enumerated Types and Type Extensions for MiniZinc.- A parallel algorithm for generalized arc-consistent filtering for the Alldifferent constraint.- Analyzing the Reachability Problem in Choice Networks.- Model-based Approaches to Multi-Attribute Diverse Matching.

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Book SynopsisThis book constitutes the proceedings of the 24th International Workshop on Computer Algebra in Scientific Computing, CASC 2022, which took place in Gebze, Turkey, in August 2022. The 20 full papers included in this book were carefully reviewed and selected from 32 submissions. They focus on the theory of symbolic computation and its implementation in computer algebra systems as well as all other areas of scientific computing with regard to their benefit from or use of computer algebra methods and software. Table of ContentsSurvey on Generalizations of the Intermediate Value Theorem and Applications (Invited Talk).- On Truncated Series Involved in Exponential-Logarithmic Solutions of Truncated LODEs.- Subresultant Chains Using B´ezout Matrices.- Application of Symbolic-Numerical Modeling Tools for Analysis of Gyroscopic Stabilization of Gyrostat Equilibria.- Computer Science for Continuous Data: Vision, Theory, and Practice of a Computer (Algebra) ANALYSIS System.- Computational Aspects of Equivariant Hilbert Series of Canonical Rings for Algebraic Curves.- Symbolic-Numeric Algorithm for Calculations in Geometric Collective Model of Atomic Nuclei.- Analyses and Implementations of Chordality-Preserving Top-Down Algorithms for Triangular Decomposition.- Accelerated Subdivision for Clustering Roots of Polynomials Given by Evaluation Oracles.- On Equilibrium Positions in the Problem of the Motion of a System of Two Bodies in a Uniform Gravity Field.- An Interpolation Algorithm for Computing Dixon Resultants.- Distance Evaluation to the Set of Matrices with Multiple Eigenvalues.- On Boundary Conditions Parametrized by Analytic Functions.- Computing the Integer Hull of Convex Polyhedral Sets.- A Comparison of Algorithms for Proving Positivity of Linearly Recurrent Sequences.- Stability Analysis of Periodic Motion of the Swinging Atwood Machine.- New Heuristic to Choose a Cylindrical Algebraic Decomposition Variable Ordering Motivated by Complexity Analysis.- An Implementation of Parallel Number-Theoretic Transform Using Intel AVX-512 Instructions.- Locating the Closest Singularity in a Polynomial Homotopy.- A General Method of Finding New Symplectic Schemes for Hamiltonian Mechanics.- A Mechanical Method for Isolating Locally Optimal Points of Certain Radical Functions.

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Book SynopsisThis book constitutes the proceedings of the 30th International Symposium on Graph Drawing and Network Visualization, GD 2022, held in Tokyo, Japan, during September 13-16, 2022. The 25 full papers, 7 short papers, presented together with 2 invited talks, one report on graph drawing contest, and one obituary in these proceedings were carefully reviewed and selected from 70 submissions. The abstracts of 5 posters presented at the conference can be found in the back matter of the volume. The contributions were organized in topical sections as follows: properties of drawings of complete graphs; stress-based visualizations of graphs; planar and orthogonal drawings; drawings and properties of directed graphs; beyond planarity; dynamic graph visualization; linear layouts; and contact and visibility graph representations. Table of ContentsProperties of Drawings of Complete Graphs.- Stress-based Visualizations of Graphs.- Planar and Orthogonal Drawings.- Drawings and Properties of Directed Graphs.- Beyond Planarity.- Dynamic Graph Visualization.- Linear Layouts.- Contact and Visibility Graph Representations.

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Book SynopsisThis book constitutes the refereed proceedings of the Third Southern African Conference on Artificial Intelligence Research, SACAIR 2022, held in Stellenbosch, South Africa, in December 2022. The 26 papers presented were thoroughly reviewed and selected from the 73 submissions. They are organized on the topical sections on​ algorithmic, data driven and symbolic AI; socio-technical and human-centered AI; responsible and ethical AI. Table of ContentsAlgorithmic, Data Driven and Symbolic AI.- Socio-technical and human-centered AI.- Responsible and Ethical AI.

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Book SynopsisThis book constitutes the refereed proceedings of the 17th Ibero-American Conference on Artificial Intelligence, IBERAMIA 2022, held in Cartagena de Indias, Colombia, in November 2022. The 33 full and 4 short papers presented were carefully reviewed and selected from 67 submissions. The papers are organized in the following topical sections: applications of AI; ethics and smart city; green and sustainable AI; machine learning; natural language processing; robotics and computer vision; simulation and forecasting.Table of ContentsApplications of AI.- Ethics and Smart City.- Green and Sustainable AI.- Machine Learning.- Natural Language Processing.- Robotics and Computer Vision.- Simulation and Forecasting.

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Book SynopsisThis book constitutes the thoroughly refereed post-workshop proceedings of the 8th International Workshop on the Arithmetic of Finite Field, WAIFI 2022, held in Chengdu, China, in August – September 2022.The 19 revised full papers and 3 invited talks presented were carefully reviewed and selected from 25 submissions. The papers are organized in topical sections: structures in finite fields; efficient finite field arithmetic; coding theory; cryptography; sequences.Table of ContentsStructures in Finite Fields.- On a conjecture on irreducible polynomials over finite fields with restricted coefficients.- On two applications of polynomials xk – cx – d over finite fields and more.- Efficient Finite Field Arithmetic.- Polynomial Constructions of Chudnovsky-Type Algorithms for Multiplication in Finite Fields with Linear Bilinear Complexity.- Reduction-free Multiplication for Finite Fields and Polynomial Rings.- Finite Field Arithmetic in Large Characteristic for Classical and Post-Quantum Cryptography.- Fast enumeration of superspecial hyperelliptic curves of genus 4 with automorphism group V4.- Coding theory.- Two Classes of Constacyclic Codes with Variable Parameters.- Near MDS Codes with Dimension 4 and Their Application in Locally Recoverable Codes.- Optimal possibly nonlinear 3-PIR codes of small size.- PIR codes from combinatorial structures.- The Projective General Linear Group PGL(2, 5m) and Linear Codes of Length 5m + 1.- Private Information Retrieval Schemes Using Cyclic Codes.- Two Classes of Optimal Few-Weight Codes over Fq + uFq.- Explicit Non-Malleable Codes from Bipartite Graphs.- Cryptography.- Algebraic Relation of Three MinRank Algebraic Modelings.- Decomposition of Dillon's APN permutation with efficient hardware implementation.- New Versions of Miller-loop Secured against Side-Channel Attacks.- A Class of Power Mappings with Low Boomerang Uniformity.- New Classes of Bent Functions via the Switching Method.- Sequences.- Correlation measure of binary sequence families with trace representation.- Linear complexity of generalized cyclotomic sequences with period pnqm.- On the 2-adic complexity of cyclotomic binary sequences with period p2 and 2p2.

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Book SynopsisThis book constitutes the refereed proceedings of the 13th International Conference on Advances in Optimization and Applications, OPTIMA 2022, held in Petrovac, Montenegro, during September 26–30, 2022. The 13 full papers included in this book were carefully reviewed and selected from 26 submissions. They were organized in topical sections as follows: ​mathematical programming; global optimization; discrete and combinatorial optimization; optimization and data analysis; game theory and mathematical economics; and applications.Table of Contents​Mathematical Programming.- A Derivative-Free Nonlinear Least Squares Solver.- Gradient-Type Methods for Optimization Problems with Polyak- Lojasiewicz Condition: Early Stopping and Adaptivity to Inexactness Parameter.- Global Optimization.- An Improved Genetic Algorithm for the Resource-Constrained Project Scheduling Problem.- Nonlocal Optimization Methods for Nonlinear Controlled Systems with Terminal Constraints.- Discrete and Combinatorial Optimization.- Three-Bar Charts Packing Problem.- An 11/7 – Approximation Algorithm for Single Machine Scheduling Problem with Release and Delivery Times.- Optimization and Data Analysis.- Decentralized Strongly-Convex Optimization with Affine Constraints: Primal and Dual Approaches.- Game Theory and Mathematical Economics.- Analysis of the Model of Optimal Expansion of a Firm.- Comparative Analysis of the Efficiency of Financing the State Budget through Emissions, Taxes and Public Debt.- Applications.- Construction of Optimal Feedback for Zooplankton Diel Vertical Migration.- Synthesis of Trajectory Planning Algorithms Using Evolutionary Optimization Algorithms.- Application of Attention Technique for Digital Pre-Distortion.- Forecasting with Using Quasilinear Recurrence Equation.

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Book SynopsisThis book constitutes the conference proceedings of the 48th International Conference on Current Trends in Theory and Practice of Computer Science, SOFSEM 2023, held in Nový Smokovec, Slovakia, during January 15–18, 2023.The 22 full papers presented together with 2 best papers and 2 best students papers in this book were carefully reviewed and selected from 43 submissions.This workshop focuses on graphs problems and optimization; graph drawing and visualization; NP-hardness and fixed parameter tractability; communication and temporal graphs; complexity and learning; and robots and strings. Table of ContentsThe Complexity of Finding Tangles.- A spectral algorithm for finding maximum cliques in dense random intersection graphs.- Solving Cut-Problems in Quadratic Time for Graphs With Bounded Treewidth.- More Effort Towards Multiagent Knapsack.- Dominance Drawings for DAGs with Bounded Modular Width.- Morphing Planar Graph Drawings Through 3D.- Visualizing Multispecies Coalescent Trees: Drawing Gene Trees Inside Species Trees.- Parameterized Approaches to Orthogonal Compaction.- Hardness of bounding influence via graph modification.- On the Parameterized Complexity of $s$-club Cluster Deletion Problems.- Balanced Substructures in Bicolored Graphs.- On the Complexity of Scheduling Problems With a Fixed Number of Parallel Identical Machines.- On the 2-Layer Window Width Minimization Problem.- Sequentially Swapping Tokens: Further on Graph Classes.- On the Preservation of Properties when Changing Communication Models.- Introduction to Routing Problems with Mandatory Transitions .- Multi-Parameter Analysis of Finding Minors and Subgraphs in Edge-Periodic Temporal Graphs.- Lower Bounds for Monotone $q$-Multilinear Boolean Circuits.- A faster algorithm for determining the linear feasibility of systems of BTVPI constraints.- Quantum complexity for vector domination problem.- Learning through Imitation by using Formal Verification.- Delivery to Safety with Two Cooperating Robots.- Space-Efficient STR-IC-LCS Computation.- The k-center Problem for Classes of Cyclic Words.

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Book SynopsisThis volume constitutes short papers and DETECT 2022 workshop papers, presented during the 11th International Conference on Model and Data Engineering, MEDI 2022, held in Cairo, Egypt, in November 2022.The 11 short papers presented were selected from the total of 65 submissions. This volume also contains the 4 accepted papers from the DETECT 2022 workshop, held at MEDI 2022. The volume focuses on advances in data management and modelling, including topics such as data models, data processing, database theory, database systems technology, and advanced database applications.Table of ContentsImage processing and diagnosis.- Machine Learning and Optimization.- Machine Learning and Optimization.- Modelling.- Database systems.- Applications.- DETECT Workshop: modeling, verification and testing of dependable critical systems.

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Book SynopsisThis book constitutes selected and revised papers from the 22nd International Conference on Mathematical Modeling and Supercomputer Technologies, MMST 2022, held in Nizhny Novgorod, Russia, in November 2022. The 20 full papers and 5 short papers presented in the volume were thoroughly reviewed and selected from the 48 submissions. They are organized in topical secions on ​computational methods for mathematical models analysis; computation in optimization and optimal control; supercomputer simulation. Table of ContentsComputational methods for mathematical models analysis.- Computation in optimization and optimal control.- Supercomputer simulation.

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Book SynopsisThis book constitutes the proceedings of the 9th International Conference on Algorithms and Discrete Applied Mathematics, CALDAM 2023, which was held in Gandhinagar, India, during February 9-11, 2023.The 32 papers presented in this volume were carefully reviewed and selected from 67 submissions. The papers were organized in topical sections named: algorithms and optimization; computational geometry; game theory; graph coloring; graph connectivity; graph domination; graph matching; graph partition and graph covering.Table of ContentsStable Approximation Schemes.- A whirlwind tour of intersection graph enumeration.- Graph modification problems with forbidden minors.- Algorithms & Optimization Efficient reductions and algorithms for Subset Product.- Optimal length cutting plane refutations of integer programs.- Fault-Tolerant Dispersion Resource management in device-to-device communications.- Computational Geometry Algorithms for k-Dispersion for Points in Convex Position in the Plane.- Arbitrary oriented color spanning region for line segments.- Games with a Simple Rectilinear Obstacle in Plane.- Diverse Fair Allocations: Complexity and Algorithms.- Graph Coloring New bounds and constructions for neighbor-locating colorings of graphs.- D K 5-list coloring toroidal 6-regular triangulations in linear time.- On Locally Identifying Coloring of Graphs.- On Structural Parameterizations of Star Coloring.- Reddy Perfectness of G-generalized join of graphs.- Coloring of a superclass of 2K2-free graphs.- The Weak (2,2)-Labelling Problem for graphs with forbidden induced structures.- Graph Connectivity Short cycles dictate dichotomy status of the Steiner tree problem on Bisplit graphs.- Some insights on dynamic maintenance of Gomory-Hu tree in cactus graphs and general graphs.- Monitoring edge-geodetic sets in graphs.- Cyclability, Connectivity and Circumference.- Graph Domination On three domination-based identification problems in block graphs.- Graph modification problems with forbidden minors.- Computational Aspects of Double Dominating Sequences in Graph.- Relation between broadcast domination and multipacking numbers on chordal graphs.- Pushing Cops and Robber on Oriented Graphs.- Mind the Gap: Edge Facility Location Problems in Theory and Practice.- Complexity Results on Cosecure Domination in Graphs.- Kusum and Arti Pandey Graph Matching Latin Hexahedra and Related Combinatorial Structures.- Minimum Maximal Acyclic Matching in Proper Interval Graphs.- Graph Partition & Graph Covering Transitivity on subclasses of chordal graphs.- Maximum subgraph problem for 3-regular Knödel graphs and its wirelength.- Covering using Bounded Size Subgraphs.- Axiomatic characterization of the the toll walk function of some graph classes.- Structural Parameterization of Alliance Problems.

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Book SynopsisThe multi-volume set LNAI 13713 until 13718 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2022, which took place in Grenoble, France, in September 2022.The 236 full papers presented in these proceedings were carefully reviewed and selected from a total of 1060 submissions. In addition, the proceedings include 17 Demo Track contributions. The volumes are organized in topical sections as follows: Part I: Clustering and dimensionality reduction; anomaly detection; interpretability and explainability; ranking and recommender systems; transfer and multitask learning; Part II: Networks and graphs; knowledge graphs; social network analysis; graph neural networks; natural language processing and text mining; conversational systems; Part III: Deep learning; robust and adversarial machine learning; generative models; computer vision; meta-learning, neural architecture search; Part IV: Reinforcement learning; multi-agent reinforcement learning; bandits and online learning; active and semi-supervised learning; private and federated learning; . Part V: Supervised learning; probabilistic inference; optimal transport; optimization; quantum, hardware; sustainability; Part VI: Time series; financial machine learning; applications; applications: transportation; demo track. Table of ContentsSupervised learning.- Probabilistic inference.- Optimal transport.- Optimization.- Quantum, hardware.- Sustainability.

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Book SynopsisPython Essentials.- Number Theory.- Rational Arithmetic.- Matrix Algebra.- Polynomial Algebra.- Polynomial Applications.- Multivariate Rational Algebra.- Differentiation.- Integration.

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Book SynopsisAdapted from a modular undergraduate course on computational mathematics, Concise Computer Mathematics delivers an easily accessible, self-contained introduction to the basic notions of mathematics necessary for a computer science degree. The text reflects the need to quickly introduce students from a variety of educational backgrounds to a number of essential mathematical concepts. The material is divided into four units: discrete mathematics (sets, relations, functions), logic (Boolean types, truth tables, proofs), linear algebra (vectors, matrices and graphics), and special topics (graph theory, number theory, basic elements of calculus). The chapters contain a brief theoretical presentation of the topic, followed by a selection of problems (which are direct applications of the theory) and additional supplementary problems (which may require a bit more work). Each chapter ends with answers or worked solutions for all of the problems.Trade ReviewFrom the reviews:“The book is ideally suited as an adjunct to a course in computer mathematics or as a refresher for someone with some background in computer mathematics. … The book fulfills its purpose of providing a distilled treatment of the mathematics most commonly used in computer science. It is of most value to computer science students who need a place to find a succinct treatment of the topics covered.” (Marlin Thomas, Computing Reviews, April, 2014)“Each of the chapters opens with a short summary followed by a set of essential problems and then a set of supplementary problems. … it would be very useful for someone that needs a quick and effective review that includes problems.” (Charles Ashbacher, MAA Reviews, January, 2014)Table of ContentsSets and NumbersRelations and DatabasesFunctionsBoolean Algebra, Logic and QuantifiersNormal Forms, Proof and ArgumentVectors and Complex NumbersMatrices and ApplicationsMatrix Transformations for Computer GraphicsElements of Graph TheoryElements of Number Theory and CryptographyElements of CalculusElementary Numerical Methods

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Book SynopsisThis book presents the state-of-the-art in simulation on supercomputers. Leading researchers present results achieved on systems of the High Performance Computing Center Stuttgart (HLRS) for the year 2010. The reports cover all fields of computational science and engineering, ranging from CFD to computational physics and chemistry to computer science, with a special emphasis on industrially relevant applications. Presenting results for both vector systems and microprocessor-based systems, the book makes it possible to compare the performance levels and usability of various architectures. As HLRS operates the largest NEC SX-8 vector system in the world, this book gives an excellent insight into the potential of vector systems, covering the main methods in high performance computing. Its outstanding results in achieving the highest performance for production codes are of particular interest for both scientists and engineers. The book includes a wealth of color illustrations and tables.

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Book SynopsisThis book seeks to bridge the gap between statistics and computer science. It provides an overview of Monte Carlo methods, including Sequential Monte Carlo, Markov Chain Monte Carlo, Metropolis-Hastings, Gibbs Sampler, Cluster Sampling, Data Driven MCMC, Stochastic Gradient descent, Langevin Monte Carlo, Hamiltonian Monte Carlo, and energy landscape mapping. Due to its comprehensive nature, the book is suitable for developing and teaching graduate courses on Monte Carlo methods. To facilitate learning, each chapter includes several representative application examples from various fields. The book pursues two main goals: (1) It introduces researchers to applying Monte Carlo methods to broader problems in areas such as Computer Vision, Computer Graphics, Machine Learning, Robotics, Artificial Intelligence, etc.; and (2) it makes it easier for scientists and engineers working in these areas to employ Monte Carlo methods to enhance their research.Trade Review“This monograph ... is intended to be a textbook for graduate students in statistics, computer science and engineering. It covers a very broad range of topics ... . Each chapter is finished by a rather long list of relevant references. Thus, it can be used also as a reference book by researches in the fields of machine learning, pattern recognition ... . it can be a useful reference to many important Monte Carol methods.” (Jaromír Antoch, zbMATH 1483.65001, 2022)“True to its goal, the text offers a comprehensive overview on Monte Carlo methods. … this text is a quality reference for researchers interested in computer vision, computer graphics, machine learning, artificial intelligence and related fields.” (Grant Innerst, MAA Reviews, July 18, 2021)Table of Contents1 Introduction to Monte Carlo Methods.- 2 Sequential Monte Carlo.- 3 Markov Chain Monte Carlo - the Basics.- 4 Metropolis Methods and Variants.- 5 Gibbs Sampler and its Variants.- 6 Cluster Sampling Methods.- 7 Convergence Analysis of MCMC.- 8 Data Driven Markov Chain Monte Carlo.- 9 Hamiltonian and Langevin Monte Carlo.- 10 Learning with Stochastic Gradient.- 11 Mapping the Energy Landscape.

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Book SynopsisThis monograph represents a historic breakthrough in the field of linear programming (LP)since George Dantzig first discovered the simplex method in 1947. Being both thoughtful and informative, it focuses on reflecting and promoting the state of the art by highlighting new achievements in LP. This new edition is organized in two volumes. The first volume addresses foundations of LP, including the geometry of feasible region, the simplex method and its implementation, duality and the dual simplex method, the primal-dual simplex method, sensitivity analysis and parametric LP, the generalized simplex method, the decomposition method, the interior-point method and integer LP method. The second volume mainly introduces contributions of the author himself, such as efficient primal/dual pivot rules, primal/dual Phase-I methods, reduced/D-reduced simplex methods, the generalized reduced simplex method, primal/dual deficient-basis methods, primal/dual face methods, a new decomposition principle, etc.Many important improvements were made in this edition. The first volume includes new results, such as the mixed two-phase simplex algorithm, dual elimination, fresh pricing scheme for reduced cost, bilevel LP models and intercepting of optimal solution set. In particular, the chapter Integer LP Method was rewritten with great gains of the objective cutting for new ILP solvers {\it controlled-cutting/branch} methods, as well as with an attractive implementation of the controlled-branch method. In the second volume, the `simplex feasible-point algorithm' was rewritten, and removed from the chapter Pivotal Interior-Point Method to form an independent chapter with the new title `Simplex Interior-Point Method', as it represents a class of efficient interior-point algorithms transformed from traditional simplex algorithms. The title of the original chapter was then changed to `Facial Interior-Point Method', as the remaining algorithms represent another class of efficient interior-point algorithms transformed from normal interior-point algorithms. Without exploiting sparsity, the original primal/dual face methods were implemented using Cholesky factorization. In order to deal with sparse computation, two new chapters discussing LU factorization were added to the second volume. The most exciting improvement came from the rediscovery of the reduced simplex method. In the first edition, the derivation of its prototype was presented in a chapter with the same title, and then converted into the so-called `improved' version in another chapter. Fortunately, the author recently found a quite concise new derivation, so he can now introduce the distinctive fresh simplex method in a single chapter. It is exciting that the reduced simplex method can be expected to be the best LP solver ever.With a focus on computation, the current edition contains many novel ideas, theories and methods, supported by solid numerical results. Being clear and succinct, its content reveals in a fresh manner, from simple to profound. In particular, a larger number of examples were worked out to demonstrate algorithms. This book is a rare work in LP and an indispensable tool for undergraduate and graduate students, teachers, practitioners, and researchers in LP and related fields.Table of ContentsChapter 1. Introduction.- Chapter 2. Geometry of Feasible Region.- Chapter 3. Simplex Method.- Chapter 4. Implementation of Simplex Method.- Chapter 5. Duality Principle and Dual Simplex Method.- Chapter 6. Primal-Dual Simplex Method.- Chapter 7. Sensitivity Analysis and Parametric LP.- Chapter 8. Generalized Simplex Method.- Chapter 9. Decomposition Method.- Chapter 10. Interior-Point Method.- Chapter 11. Integer Linear Programming (ILP).- Chapter 12. Pivot Rule.- Chapter 13. Dual Pivot Rule.- Chapter 14. Simplex Phase-I Method.- Chapter 15. Dual Simplex Phase-l Method.- Chapter 16. Reduced Simplex Method.- Chapter 17. D-Reduced Simplex Method.- Chapter 18. Generalized Reduced Simplex Method.- Chapter 19. Deficient-Basis Method.- Chapter 20. Dual Decient-Basis Method.- Chapter 21. Face Method with Cholesky Factorization.- Chapter 22. Dual Face Method with Cholesky Factorization.- Chapter 23. Face Method with LU Factorization.- Chapter 24. Dual Face Method with LU Factorization.- Chapter 25. Simplex Interior-Point Method.- Chapter 26. Facial Interior-Point Method.- Chapter 27. Decomposition Principle.

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a huge range and FREE tracked UK delivery on ALL orders.

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Book SynopsisUsing a novel integration of mathematics and Python codes, this book illustrates the fundamental concepts that link probability, statistics, and machine learning, so that the reader can not only employ statistical and machine learning models using modern Python modules, but also understand their relative strengths and weaknesses. To clearly connect theoretical concepts to practical implementations, the author provides many worked-out examples along with "Programming Tips" that encourage the reader to write quality Python code. The entire text, including all the figures and numerical results, is reproducible using the Python codes provided, thus enabling readers to follow along by experimenting with the same code on their own computers. Modern Python modules like Pandas, Sympy, Scikit-learn, Statsmodels, Scipy, Xarray, Tensorflow, and Keras are used to implement and visualize important machine learning concepts like the bias/variance trade-off, cross-validation, interpretability, and regularization. Many abstract mathematical ideas, such as modes of convergence in probability, are explained and illustrated with concrete numerical examples. This book is suitable for anyone with undergraduate-level experience with probability, statistics, or machine learning and with rudimentary knowledge of Python programming.Table of ContentsIntroduction.- Part 1 Getting Started with Scientific Python.- Installation and Setup.- Numpy.- Matplotlib.- Ipython.- Jupyter Notebook.- Scipy.- Pandas.- Sympy.- Interfacing with Compiled Libraries.- Integrated Development Environments.- Quick Guide to Performance and Parallel Programming.- Other Resources.- Part 2 Probability.- Introduction.- Projection Methods.- Conditional Expectation as Projection.- Conditional Expectation and Mean Squared Error.- Worked Examples of Conditional Expectation and Mean Square Error Optimization.- Useful Distributions.- Information Entropy.- Moment Generating Functions.- Monte Carlo Sampling Methods.- Useful Inequalities.- Part 3 Statistics.- Python Modules for Statistics.- Types of Convergence.- Estimation Using Maximum Likelihood.- Hypothesis Testing and P-Values.- Confidence Intervals.- Linear Regression.- Maximum A-Posteriori.- Robust Statistics.- Bootstrapping.- Gauss Markov.- Nonparametric Methods.- Survival Analysis.- Part 4 Machine Learning.- Introduction.- Python Machine Learning Modules.- Theory of Learning.- Decision Trees.- Boosting Trees.- Logistic Regression.- Generalized Linear Models.- Regularization.- Support Vector Machines.- Dimensionality Reduction.- Clustering.- Ensemble Methods.- Deep Learning.- Notation.- References.- Index.

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Book SynopsisActive student engagement is key to this classroom-tested combinatorics text, boasting 1200+ carefully designed problems, ten mini-projects, section warm-up problems, and chapter opening problems. The author an award-winning teacher writes in a conversational style, keeping the reader in mind on every page. Students will stay motivated through glimpses into current research trends and open problems as well as the history and global origins of the subject. All essential topics are covered, including Ramsey theory, enumerative combinatorics including Stirling numbers, partitions of integers, the inclusion-exclusion principle, generating functions, introductory graph theory, and partially ordered sets. Some significant results are presented as sets of guided problems, leading readers to discover them on their own. More than 140 problems have complete solutions and over 250 have hints in the back, making this book ideal for self-study. Ideal for a one semester upper undergraduate course, prerequisites include the calculus sequence and familiarity with proofs.Trade Review'I would certainly accept this 'invitation.' The text covers essentially all of the basic combinatorial subjects in a both gentle and intense way. The extensive problems, examples, and 'projects,' especially the collaborative projects, exemplify current pedagogical research on effective teaching methods. I would expect it to remain as a reference on many shelves.' Bruce Rothschild, University of California, Los Angeles'Shahriari's voice as an experienced classroom teacher shines through in this brilliantly crafted student-friendly text. Each mini-project provides a guided exploration of an interesting topic in combinatorics. These, together with the plethora of interesting exercises, help the student to build problem-solving muscle and to experience the joy of mathematical discovery.' Jamie Pommersheim, Reed College'From well-chosen motivating problems in the introduction to deeper material near the book's conclusion, Shahriari invites students encountering combinatorics systematically for the first time to think, to build, and to play. His warm writing style and cross-cultural approach to core topics of the field are sure to engage readers from many backgrounds and levels of preparation.' Joshua Cooper, University of South Carolina'This book is a mathematically rigorous introductory textbook on combinatorics. It contains an excellent range of problems and exercises that will help students practice and learn the material. It also lists open questions in combinatorics so students can see that the field continues to develop. The really special feature of this book is a lovely collection of mini-projects that let students explore a variety of topics and deepen their understanding.' David Auckly, Kansas State University'I highly recommend this text. Among its most interesting, unusual, and valuable features, one finds a long list of collaborative mini-projects for students to work on in groups, together with other problems to work on individually; nice historical asides, including references to the work of non-Western mathematicians; and a very accessible conversational style. It fits well with discovery-style or problem-oriented courses on the subject.' William Monty McGovern, University of Washington'One of the major attractions of this textbook is the writing style - it is designed to be very readable, as though the author were having a conversation with the reader. The result is a text which feels engaging - a quality which is sure to be of great benefit to undergraduate students.' Audie Warren, zbMATHTable of ContentsPreface; Introduction; 1. Induction and Recurrence Relations; 2. The Pigeonhole Principle and Ramsey Theory; 3. Counting, Probability, Balls and Boxes; 4. Permutations and Combinations; 5. Binomial and Multinomial Coefficients; 6. Stirling Numbers; 7. Integer Partitions; 8. The Inclusion-Exclusion Principle; 9. Generating Functions; 10. Graph Theory; 11. Posets, Matchings, and Boolean Lattices; Appendices; Bibliography; Index.

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Book SynopsisProfessor Stephen A. Cook is a pioneer of the theory of computational complexity. His work on NP-completeness and the P vs. NP problem remains a central focus of this field. Cook won the 1982 Turing Award for "his advancement of our understanding of the complexity of computation in a significant and profound way." This volume includes a selection of seminal papers embodying the work that led to this award, exemplifying Cook's synthesis of ideas and techniques from logic and the theory of computation including NP-completeness, proof complexity, bounded arithmetic, and parallel and space-bounded computation. These papers are accompanied by contributed articles by leading researchers in these areas, which convey to a general reader the importance of Cook's ideas and their enduring impact on the research community. The book also contains biographical material, Cook's Turing Award lecture, and an interview. Together these provide a portrait of Cook as a recognized leader and innovator in mathematics and computer science, as well as a gentle mentor and colleague.

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Book SynopsisActa Numerica is an annual publication containing invited survey papers by leading researchers in numerical mathematics and scientific computing. The papers present overviews of recent developments in their area and provide state-of-the-art techniques and analysis.Table of Contents1. Low-rank tensor methods for partial differential equations Markus Bachmayr; 2. The virtual element method Lourenço Beirão da Veiga, Franco Brezzi, L. Donatella Marini and Alessandro Russo; 3. Floating-point arithmetic Sylvie Boldo, Claude-Pierre Jeannerod, Guillaume Melquiond and Jean-Michel Muller; 4. Compatible finite element methods for geophysical fluid dynamics Colin J. Cotter; 5. Control of port-Hamiltonian differential-algebraic systems and applications Volker Mehrmann and Benjamin Unger; 6. Overcoming the timescale barrier in molecular dynamics: transfer operators, variational principles and machine learning Christof Schütte, Stefan Klus and Carsten Hartmann; 7. Linear optimization over homogeneous matrix cones Levent Tunçel and Lieven Vandenberghe.

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