MIVAR: Transition from Productions to Bipartite Graphs MIVAR Nets and Practical Realization of Automated Constructor of Algorithms Handling More than Three Million Production Rules. The theoretical transition from the graphs of production systems to the bipartite graphs of the MIVAR nets is shown. Examples of the implementation of the MIVAR nets in the formalisms of matrixes and graphs are given. The linear computational complexity of algorithms for automated building of objects and rules of the MIVAR nets is theoretically proved. On the basis of the MIVAR nets the UDAV software complex is developed, handling more than 1.17 million objects and more than 3.5 million rules on ordinary computers. The results of experiments that confirm a linear computational complexity of the MIVAR method of information processing are given.

The presentation of a novel theory in orthogonal regression The literature about neural-based algorithms is often dedicated to principal component analysis (PCA) and considers minor component analysis (MCA) a mere consequence. Breaking the mold, Neural-Based Orthogonal Data Fitting is the first book to start with the MCA problem and arrive at important conclusions about the PCA problem. The book proposes several neural networks, all endowed with a complete theory that not only explains their behavior, but also compares them with the existing neural and traditional algorithms. EXIN neurons, which are of the authors' invention, are introduced, explained, and analyzed. Further, it studies the algorithms as a differential geometry problem, a dynamic problem, a stochastic problem, and a numerical problem. It demonstrates the novel aspects of its main theory, including its applications in computer vision and linear system identification. The book shows both the derivation of the TLS EXIN from the MCA EXIN and the original derivation, as well as: Shows TLS problems and gives a sketch of their history and applications Presents MCA EXIN and compares it with the other existing approaches Introduces the TLS EXIN neuron and the SCG and BFGS acceleration techniques and compares them with TLS GAO Outlines the GeTLS EXIN theory for generalizing and unifying the regression problems Establishes the GeMCA theory, starting with the identification of GeTLS EXIN as a generalization eigenvalue problem In dealing with mathematical and numerical aspects of EXIN neurons, the book is mainly theoretical. All the algorithms, however, have been used in analyzing real-time problems and show accurate solutions. Neural-Based Orthogonal Data Fitting is useful for statisticians, applied mathematics experts, and engineers.

Discover how algorithms shape and impact our digital world All data, big or small, starts with algorithms. Algorithms are mathematical equations that determine what we see—based on our likes, dislikes, queries, views, interests, relationships, and more—online. They are, in a sense, the electronic gatekeepers to our digital, as well as our physical, world. This book demystifies the subject of algorithms so you can understand how important they are business and scientific decision making. Algorithms for Dummies is a clear and concise primer for everyday people who are interested in algorithms and how they impact our digital lives. Based on the fact that we already live in a world where algorithms are behind most of the technology we use, this book offers eye-opening information on the pervasiveness and importance of this mathematical science—how it plays out in our everyday digestion of news and entertainment, as well as in its influence on our social interactions and consumerism. Readers even learn how to program an algorithm using Python! Become well-versed in the major areas comprising algorithms Examine the incredible history behind algorithms Get familiar with real-world applications of problem-solving procedures Experience hands-on development of an algorithm from start to finish with Python If you have a nagging curiosity about why an ad for that hammock you checked out on Amazon is appearing on your Facebook page, you'll find Algorithm for Dummies to be an enlightening introduction to this integral realm of math, science, and business.

This book provides methods to train process operators to solve challenging problems. The book is split into two parts. The first part consists of two parts; first developing a daily monitoring system and second providing a structured 5 step problem solving approach that combines cause and effect problem solving thinking with the formulation of theoretically correct hypotheses. The 5 step approach emphasizes the classical problem solving approach (defining the sequence of events) with the addition of the steps of formulating a theoretically correct working hypothesis, providing a means to test the hypothesis, and providing a foolproof means to eliminate the problem. The initial part of the book focuses on defining the problem that must be solved and obtaining the location, time and quantity based specifications of the problem. This part of the book also presents techniques to find and define problems at an early point before they progress to the critical level. The second part of the book deals with the utilization of fundamental chemical engineering skills to develop a technically correct working hypothesis that is the key to successful problem solving. The primary emphasis is on simple pragmatic calculation techniques that are theoretically correct. It is believed that any operator can perform these calculations if he is provided the correct prototype. Throughout the book, the theory behind each pragmatic calculation technique is explained in understandable terms prior to presenting the author's approach. These techniques have been developed by the author in 50+ years of industrial experience. The book includes many sample problems and examples of real world problem solving. Using these techniques, theoretically correct working hypotheses can be developed in an expedient fashion.

A major challenge in constraint programming is to develop efficient generic approaches to solve instances of the constraint satisfaction problem (CSP). With this aim in mind, this book provides an accessible synthesis of the author's research and work in this area, divided into four main topics: representation, inference, search, and learning. The results obtained and reproduced in this book have a wide applicability, regardless of the nature of the problem or the constraints involved, making it an extremely user-friendly resource for those involved in this field.

Beginning Algorithms A good understanding of algorithms, and the knowledge of when to apply them, is crucial to producing software that not only works correctly, but also performs efficiently. This is the only book to impart all this essential information-from the basics of algorithms, data structures, and performance characteristics to the specific algorithms used in development and programming tasks. Packed with detailed explanations and instructive examples, the book begins by offering you some fundamental data structures and then goes on to explain various sorting algorithms. You'll then learn efficient practices for storing and searching by way of hashing, trees, sets, and maps. The authors also share tips on optimization techniques and ways to avoid common performance pitfalls. In the end, you'll be prepared to build the algorithms and data structures most commonly encountered in day-to-day software development. What you will learn from this book The basics of algorithms, such as iteration and recursion Elementary data structures such as lists, stacks, and queues Basic and advanced sorting algorithms including insertion sort, quicksort, and shell sort Advanced data structures such as binary trees, ternary trees, and heaps Algorithms for string searching, string matching, hashing, and computational geometry How to use test-driven development techniques to ensure your code works as intended How to dramatically improve the performance of your code with hands-on techniques for profiling and optimization Who this book is for This book is for anyone who develops applications, or is just beginning to do so, and is looking to understand algorithms and data structures. An understanding of computer programming is beneficial. Wrox Beginning guides are crafted to make learning programming languages and technologies easier than you think, providing a structured, tutorial format that will guide you through all the techniques involved.

Beginning Algorithms A good understanding of algorithms, and the knowledge of when to apply them, is crucial to producing software that not only works correctly, but also performs efficiently. This is the only book to impart all this essential information-from the basics of algorithms, data structures, and performance characteristics to the specific algorithms used in development and programming tasks. Packed with detailed explanations and instructive examples, the book begins by offering you some fundamental data structures and then goes on to explain various sorting algorithms. You'll then learn efficient practices for storing and searching by way of hashing, trees, sets, and maps. The authors also share tips on optimization techniques and ways to avoid common performance pitfalls. In the end, you'll be prepared to build the algorithms and data structures most commonly encountered in day-to-day software development. What you will learn from this book The basics of algorithms, such as iteration and recursion Elementary data structures such as lists, stacks, and queues Basic and advanced sorting algorithms including insertion sort, quicksort, and shell sort Advanced data structures such as binary trees, ternary trees, and heaps Algorithms for string searching, string matching, hashing, and computational geometry How to use test-driven development techniques to ensure your code works as intended How to dramatically improve the performance of your code with hands-on techniques for profiling and optimization Who this book is for This book is for anyone who develops applications, or is just beginning to do so, and is looking to understand algorithms and data structures. An understanding of computer programming is beneficial. Wrox Beginning guides are crafted to make learning programming languages and technologies easier than you think, providing a structured, tutorial format that will guide you through all the techniques involved.

Video Tracking provides a comprehensive treatment of the fundamental aspects of algorithm and application development for the task of estimating, over time, the position of objects of interest seen through cameras. Starting from the general problem definition and a review of existing and emerging video tracking applications, the book discusses popular methods, such as those based on correlation and gradient-descent. Using practical examples, the reader is introduced to the advantages and limitations of deterministic approaches, and is then guided toward more advanced video tracking solutions, such as those based on the Bayes’ recursive framework and on Random Finite Sets. Key features: Discusses the design choices and implementation issues required to turn the underlying mathematical models into a real-world effective tracking systems. Provides block diagrams and simil-code implementation of the algorithms. Reviews methods to evaluate the performance of video trackers – this is identified as a major problem by end-users. The book aims to help researchers and practitioners develop techniques and solutions based on the potential of video tracking applications. The design methodologies discussed throughout the book provide guidelines for developers in the industry working on vision-based applications. The book may also serve as a reference for engineering and computer science graduate students involved in vision, robotics, human-computer interaction, smart environments and virtual reality programmes

Praise for the First Edition This book is refreshing to read since it takes an important topic… and presents it in a clear and concise manner by using examples that include visual presentations of the problem, solution methods, and results along with an explanation of the mathematical and procedural steps required to model the problem and work through to a solution.” —Journal of Classification Thoroughly updated and revised, Network and Discrete Location: Models, Algorithms, and Applications, Second Edition remains the go-to guide on facility location modeling. The book offers a unique introduction to methodological tools for solving location models and provides insight into when each approach is useful and what information can be obtained. The Second Edition focuses on real-world extensions of the basic models used in locating facilities, including production and distribution systems, location-inventory models, and defender-interdictor problems. A unique taxonomy of location problems and models is also presented. Featuring examples using the author’s own software—SITATION, MOD-DIST, and MENU-OKF—as well as Microsoft Office® Excel®, the book provides: • A theoretical and applied perspective on location models and algorithms • An intuitive presentation of the uses and limits of modeling techniques • An introduction to integrated location-inventory modeling and defender-interdictor models for the design of reliable facility location systems • A full range of exercises to equip readers with an understanding of the basic facility location model types Network and Discrete Location: Models, Algorithms, and Applications, Second Edition is an essential resource for practitioners in applied and discrete mathematics, operations research, industrial engineering, and quantitative geography. The book is also a useful textbook for upper-level undergraduate, graduate, and MBA courses.

Explores regular structures in graphs and contingency tables by spectral theory and statistical methods This book bridges the gap between graph theory and statistics by giving answers to the demanding questions which arise when statisticians are confronted with large weighted graphs or rectangular arrays. Classical and modern statistical methods applicable to biological, social, communication networks, or microarrays are presented together with the theoretical background and proofs. This book is suitable for a one-semester course for graduate students in data mining, multivariate statistics, or applied graph theory; but by skipping the proofs, the algorithms can also be used by specialists who just want to retrieve information from their data when analysing communication, social, or biological networks. Spectral Clustering and Biclustering: Provides a unified treatment for edge-weighted graphs and contingency tables via methods of multivariate statistical analysis (factoring, clustering, and biclustering). Uses spectral embedding and relaxation to estimate multiway cuts of edge-weighted graphs and bicuts of contingency tables. Goes beyond the expanders by describing the structure of dense graphs with a small spectral gap via the structural eigenvalues and eigen-subspaces of the normalized modularity matrix. Treats graphs like statistical data by combining methods of graph theory and statistics. Establishes a common outline structure for the contents of each algorithm, applicable to networks and microarrays, with unified notions and principles.

This book was written in response to the growing demand for a text that provides a unified treatment of linear and nonlinear complex valued adaptive filters, and methods for the processing of general complex signals (circular and noncircular). It brings together adaptive filtering algorithms for feedforward (transversal) and feedback architectures and the recent developments in the statistics of complex variable, under the powerful frameworks of CR (Wirtinger) calculus and augmented complex statistics. This offers a number of theoretical performance gains, which is illustrated on both stochastic gradient algorithms, such as the augmented complex least mean square (ACLMS), and those based on Kalman filters. This work is supported by a number of simulations using synthetic and real world data, including the noncircular and intermittent radar and wind signals.

This book covers the most essential techniques for designing and building dependable distributed systems. Instead of covering a broad range of research works for each dependability strategy, the book focuses only a selected few (usually the most seminal works, the most practical approaches, or the first publication of each approach) are included and explained in depth, usually with a comprehensive set of examples. The goal is to dissect each technique thoroughly so that readers who are not familiar with dependable distributed computing can actually grasp the technique after studying the book. The book contains eight chapters. The first chapter introduces the basic concepts and terminologies of dependable distributed computing, and also provide an overview of the primary means for achieving dependability. The second chapter describes in detail the checkpointing and logging mechanisms, which are the most commonly used means to achieve limited degree of fault tolerance. Such mechanisms also serve as the foundation for more sophisticated dependability solutions. Chapter three covers the works on recovery-oriented computing, which focus on the practical techniques that reduce the fault detection and recovery times for Internet-based applications. Chapter four outlines the replication techniques for data and service fault tolerance. This chapter also pays particular attention to optimistic replication and the CAP theorem. Chapter five explains a few seminal works on group communication systems. Chapter six introduces the distributed consensus problem and covers a number of Paxos family algorithms in depth. Chapter seven introduces the Byzantine generals problem and its latest solutions, including the seminal Practical Byzantine Fault Tolerance (PBFT) algorithm and a number of its derivatives. The final chapter covers the latest research results on application-aware Byzantine fault tolerance, which is an important step forward towards practical use of Byzantine fault tolerance techniques.

An accessible introduction to metaheuristics and optimization, featuring powerful and modern algorithms for application across engineering and the sciences From engineering and computer science to economics and management science, optimization is a core component for problem solving. Highlighting the latest developments that have evolved in recent years, Engineering Optimization: An Introduction with Metaheuristic Applications outlines popular metaheuristic algorithms and equips readers with the skills needed to apply these techniques to their own optimization problems. With insightful examples from various fields of study, the author highlights key concepts and techniques for the successful application of commonly-used metaheuristc algorithms, including simulated annealing, particle swarm optimization, harmony search, and genetic algorithms. The author introduces all major metaheuristic algorithms and their applications in optimization through a presentation that is organized into three succinct parts: Foundations of Optimization and Algorithms provides a brief introduction to the underlying nature of optimization and the common approaches to optimization problems, random number generation, the Monte Carlo method, and the Markov chain Monte Carlo method Metaheuristic Algorithms presents common metaheuristic algorithms in detail, including genetic algorithms, simulated annealing, ant algorithms, bee algorithms, particle swarm optimization, firefly algorithms, and harmony search Applications outlines a wide range of applications that use metaheuristic algorithms to solve challenging optimization problems with detailed implementation while also introducing various modifications used for multi-objective optimization Throughout the book, the author presents worked-out examples and real-world applications that illustrate the modern relevance of the topic. A detailed appendix features important and popular algorithms using MATLAB® and Octave software packages, and a related FTP site houses MATLAB code and programs for easy implementation of the discussed techniques. In addition, references to the current literature enable readers to investigate individual algorithms and methods in greater detail. Engineering Optimization: An Introduction with Metaheuristic Applications is an excellent book for courses on optimization and computer simulation at the upper-undergraduate and graduate levels. It is also a valuable reference for researchers and practitioners working in the fields of mathematics, engineering, computer science, operations research, and management science who use metaheuristic algorithms to solve problems in their everyday work.

This book represents the most comprehensive and up-to-date collection of information on the topic of computational molecular biology. Bringing the most recent research into the forefront of discussion, Algorithms in Computational Molecular Biology studies the most important and useful algorithms currently being used in the field, and provides related problems. It also succeeds where other titles have failed, in offering a wide range of information from the introductory fundamentals right up to the latest, most advanced levels of study.

Combinatorial problems based on graph partitioning enable us to mathematically represent and model many practical applications. Mission planning and the routing problems occurring in logistics perfectly illustrate two such examples. Nevertheless, these problems are not based on the same partitioning pattern: generally, patterns like cycles, paths, or trees are distinguished. Moreover, the practical applications are often not limited to theoretical problems like the Hamiltonian path problem, or K-node disjoint path problems. Indeed, they usually combine the graph partitioning problem with several restrictions related to the topology of nodes and arcs. The diversity of implied constraints in real-life applications is a practical limit to the resolution of such problems by approaches considering the partitioning problem independently from each additional restriction. This book focuses on constraint satisfaction problems related to tree partitioning problems enriched by several additional constraints that restrict the possible partitions topology. On the one hand, this title focuses on the structural properties of tree partitioning constraints. On the other hand, it is dedicated to the interactions between the tree partitioning problem and classical restrictions (such as precedence relations or incomparability relations between nodes) involved in practical applications. Precisely, Tree-based Graph Partitioning Constraint shows how to globally take into account several restrictions within one single tree partitioning constraint. Another interesting aspect of this book is related to the implementation of such a constraint. In the context of graph-based global constraints, the book illustrates how a fully dynamic management of data structures makes the runtime of filtering algorithms independent of the graph density.

## algorithms on the book crossing number problem of graphs в наличии / купить интернет-магазине

## Олег Варламов Mivar NETs and logical inference with the linear complexity ISBN: 9785990338845

MIVAR: Transition from Productions to Bipartite Graphs MIVAR Nets and Practical Realization of Automated Constructor of Algorithms Handling More than Three Million Production Rules. The theoretical transition from the graphs of production systems to the bipartite graphs of the MIVAR nets is shown. Examples of the implementation of the MIVAR nets in the formalisms of matrixes and graphs are given. The linear computational complexity of algorithms for automated building of objects and rules of the MIVAR nets is theoretically proved. On the basis of the MIVAR nets the UDAV software complex is developed, handling more than 1.17 million objects and more than 3.5 million rules on ordinary computers. The results of experiments that confirm a linear computational complexity of the MIVAR method of information processing are given.

## Cirrincione Giansalvo Neural-Based Orthogonal Data Fitting. The EXIN Neural Networks ISBN: 9780470638279

The presentation of a novel theory in orthogonal regression The literature about neural-based algorithms is often dedicated to principal component analysis (PCA) and considers minor component analysis (MCA) a mere consequence. Breaking the mold, Neural-Based Orthogonal Data Fitting is the first book to start with the MCA problem and arrive at important conclusions about the PCA problem. The book proposes several neural networks, all endowed with a complete theory that not only explains their behavior, but also compares them with the existing neural and traditional algorithms. EXIN neurons, which are of the authors' invention, are introduced, explained, and analyzed. Further, it studies the algorithms as a differential geometry problem, a dynamic problem, a stochastic problem, and a numerical problem. It demonstrates the novel aspects of its main theory, including its applications in computer vision and linear system identification. The book shows both the derivation of the TLS EXIN from the MCA EXIN and the original derivation, as well as: Shows TLS problems and gives a sketch of their history and applications Presents MCA EXIN and compares it with the other existing approaches Introduces the TLS EXIN neuron and the SCG and BFGS acceleration techniques and compares them with TLS GAO Outlines the GeTLS EXIN theory for generalizing and unifying the regression problems Establishes the GeMCA theory, starting with the identification of GeTLS EXIN as a generalization eigenvalue problem In dealing with mathematical and numerical aspects of EXIN neurons, the book is mainly theoretical. All the algorithms, however, have been used in analyzing real-time problems and show accurate solutions. Neural-Based Orthogonal Data Fitting is useful for statisticians, applied mathematics experts, and engineers.

## Luca Massaron Algorithms For Dummies ISBN: 9781119330523

Discover how algorithms shape and impact our digital world All data, big or small, starts with algorithms. Algorithms are mathematical equations that determine what we see—based on our likes, dislikes, queries, views, interests, relationships, and more—online. They are, in a sense, the electronic gatekeepers to our digital, as well as our physical, world. This book demystifies the subject of algorithms so you can understand how important they are business and scientific decision making. Algorithms for Dummies is a clear and concise primer for everyday people who are interested in algorithms and how they impact our digital lives. Based on the fact that we already live in a world where algorithms are behind most of the technology we use, this book offers eye-opening information on the pervasiveness and importance of this mathematical science—how it plays out in our everyday digestion of news and entertainment, as well as in its influence on our social interactions and consumerism. Readers even learn how to program an algorithm using Python! Become well-versed in the major areas comprising algorithms Examine the incredible history behind algorithms Get familiar with real-world applications of problem-solving procedures Experience hands-on development of an algorithm from start to finish with Python If you have a nagging curiosity about why an ad for that hammock you checked out on Amazon is appearing on your Facebook page, you'll find Algorithm for Dummies to be an enlightening introduction to this integral realm of math, science, and business.

## Joseph Bonem M. Problem Solving for Process Operators and Specialists ISBN: 9780470929452

This book provides methods to train process operators to solve challenging problems. The book is split into two parts. The first part consists of two parts; first developing a daily monitoring system and second providing a structured 5 step problem solving approach that combines cause and effect problem solving thinking with the formulation of theoretically correct hypotheses. The 5 step approach emphasizes the classical problem solving approach (defining the sequence of events) with the addition of the steps of formulating a theoretically correct working hypothesis, providing a means to test the hypothesis, and providing a foolproof means to eliminate the problem. The initial part of the book focuses on defining the problem that must be solved and obtaining the location, time and quantity based specifications of the problem. This part of the book also presents techniques to find and define problems at an early point before they progress to the critical level. The second part of the book deals with the utilization of fundamental chemical engineering skills to develop a technically correct working hypothesis that is the key to successful problem solving. The primary emphasis is on simple pragmatic calculation techniques that are theoretically correct. It is believed that any operator can perform these calculations if he is provided the correct prototype. Throughout the book, the theory behind each pragmatic calculation technique is explained in understandable terms prior to presenting the author's approach. These techniques have been developed by the author in 50+ years of industrial experience. The book includes many sample problems and examples of real world problem solving. Using these techniques, theoretically correct working hypotheses can be developed in an expedient fashion.

## Christophe Lecoutre Constraint Networks. Targeting Simplicity for Techniques and Algorithms ISBN: 9780470610381

A major challenge in constraint programming is to develop efficient generic approaches to solve instances of the constraint satisfaction problem (CSP). With this aim in mind, this book provides an accessible synthesis of the author's research and work in this area, divided into four main topics: representation, inference, search, and learning. The results obtained and reproduced in this book have a wide applicability, regardless of the nature of the problem or the constraints involved, making it an extremely user-friendly resource for those involved in this field.

## Simon Harris Beginning Algorithms ISBN: 9780471783831

Beginning Algorithms A good understanding of algorithms, and the knowledge of when to apply them, is crucial to producing software that not only works correctly, but also performs efficiently. This is the only book to impart all this essential information-from the basics of algorithms, data structures, and performance characteristics to the specific algorithms used in development and programming tasks. Packed with detailed explanations and instructive examples, the book begins by offering you some fundamental data structures and then goes on to explain various sorting algorithms. You'll then learn efficient practices for storing and searching by way of hashing, trees, sets, and maps. The authors also share tips on optimization techniques and ways to avoid common performance pitfalls. In the end, you'll be prepared to build the algorithms and data structures most commonly encountered in day-to-day software development. What you will learn from this book The basics of algorithms, such as iteration and recursion Elementary data structures such as lists, stacks, and queues Basic and advanced sorting algorithms including insertion sort, quicksort, and shell sort Advanced data structures such as binary trees, ternary trees, and heaps Algorithms for string searching, string matching, hashing, and computational geometry How to use test-driven development techniques to ensure your code works as intended How to dramatically improve the performance of your code with hands-on techniques for profiling and optimization Who this book is for This book is for anyone who develops applications, or is just beginning to do so, and is looking to understand algorithms and data structures. An understanding of computer programming is beneficial. Wrox Beginning guides are crafted to make learning programming languages and technologies easier than you think, providing a structured, tutorial format that will guide you through all the techniques involved.

## Simon Harris Beginning Algorithms ISBN: 9780470329726

Beginning Algorithms A good understanding of algorithms, and the knowledge of when to apply them, is crucial to producing software that not only works correctly, but also performs efficiently. This is the only book to impart all this essential information-from the basics of algorithms, data structures, and performance characteristics to the specific algorithms used in development and programming tasks. Packed with detailed explanations and instructive examples, the book begins by offering you some fundamental data structures and then goes on to explain various sorting algorithms. You'll then learn efficient practices for storing and searching by way of hashing, trees, sets, and maps. The authors also share tips on optimization techniques and ways to avoid common performance pitfalls. In the end, you'll be prepared to build the algorithms and data structures most commonly encountered in day-to-day software development. What you will learn from this book The basics of algorithms, such as iteration and recursion Elementary data structures such as lists, stacks, and queues Basic and advanced sorting algorithms including insertion sort, quicksort, and shell sort Advanced data structures such as binary trees, ternary trees, and heaps Algorithms for string searching, string matching, hashing, and computational geometry How to use test-driven development techniques to ensure your code works as intended How to dramatically improve the performance of your code with hands-on techniques for profiling and optimization Who this book is for This book is for anyone who develops applications, or is just beginning to do so, and is looking to understand algorithms and data structures. An understanding of computer programming is beneficial. Wrox Beginning guides are crafted to make learning programming languages and technologies easier than you think, providing a structured, tutorial format that will guide you through all the techniques involved.

## Maggio Emilio Video Tracking. Theory and Practice ISBN: 9780470974384

Video Tracking provides a comprehensive treatment of the fundamental aspects of algorithm and application development for the task of estimating, over time, the position of objects of interest seen through cameras. Starting from the general problem definition and a review of existing and emerging video tracking applications, the book discusses popular methods, such as those based on correlation and gradient-descent. Using practical examples, the reader is introduced to the advantages and limitations of deterministic approaches, and is then guided toward more advanced video tracking solutions, such as those based on the Bayes’ recursive framework and on Random Finite Sets. Key features: Discusses the design choices and implementation issues required to turn the underlying mathematical models into a real-world effective tracking systems. Provides block diagrams and simil-code implementation of the algorithms. Reviews methods to evaluate the performance of video trackers – this is identified as a major problem by end-users. The book aims to help researchers and practitioners develop techniques and solutions based on the potential of video tracking applications. The design methodologies discussed throughout the book provide guidelines for developers in the industry working on vision-based applications. The book may also serve as a reference for engineering and computer science graduate students involved in vision, robotics, human-computer interaction, smart environments and virtual reality programmes

## Mark Daskin S. Network and Discrete Location. Models, Algorithms, and Applications ISBN: 9781118537039

Praise for the First Edition This book is refreshing to read since it takes an important topic… and presents it in a clear and concise manner by using examples that include visual presentations of the problem, solution methods, and results along with an explanation of the mathematical and procedural steps required to model the problem and work through to a solution.” —Journal of Classification Thoroughly updated and revised, Network and Discrete Location: Models, Algorithms, and Applications, Second Edition remains the go-to guide on facility location modeling. The book offers a unique introduction to methodological tools for solving location models and provides insight into when each approach is useful and what information can be obtained. The Second Edition focuses on real-world extensions of the basic models used in locating facilities, including production and distribution systems, location-inventory models, and defender-interdictor problems. A unique taxonomy of location problems and models is also presented. Featuring examples using the author’s own software—SITATION, MOD-DIST, and MENU-OKF—as well as Microsoft Office® Excel®, the book provides: • A theoretical and applied perspective on location models and algorithms • An intuitive presentation of the uses and limits of modeling techniques • An introduction to integrated location-inventory modeling and defender-interdictor models for the design of reliable facility location systems • A full range of exercises to equip readers with an understanding of the basic facility location model types Network and Discrete Location: Models, Algorithms, and Applications, Second Edition is an essential resource for practitioners in applied and discrete mathematics, operations research, industrial engineering, and quantitative geography. The book is also a useful textbook for upper-level undergraduate, graduate, and MBA courses.

## Marianna Bolla Spectral Clustering and Biclustering. Learning Large Graphs and Contingency Tables ISBN: 9781118650707

Explores regular structures in graphs and contingency tables by spectral theory and statistical methods This book bridges the gap between graph theory and statistics by giving answers to the demanding questions which arise when statisticians are confronted with large weighted graphs or rectangular arrays. Classical and modern statistical methods applicable to biological, social, communication networks, or microarrays are presented together with the theoretical background and proofs. This book is suitable for a one-semester course for graduate students in data mining, multivariate statistics, or applied graph theory; but by skipping the proofs, the algorithms can also be used by specialists who just want to retrieve information from their data when analysing communication, social, or biological networks. Spectral Clustering and Biclustering: Provides a unified treatment for edge-weighted graphs and contingency tables via methods of multivariate statistical analysis (factoring, clustering, and biclustering). Uses spectral embedding and relaxation to estimate multiway cuts of edge-weighted graphs and bicuts of contingency tables. Goes beyond the expanders by describing the structure of dense graphs with a small spectral gap via the structural eigenvalues and eigen-subspaces of the normalized modularity matrix. Treats graphs like statistical data by combining methods of graph theory and statistics. Establishes a common outline structure for the contents of each algorithm, applicable to networks and microarrays, with unified notions and principles.

## Goh Vanessa SuLee Complex Valued Nonlinear Adaptive Filters. Noncircularity, Widely Linear and Neural Models ISBN: 9780470742631

This book was written in response to the growing demand for a text that provides a unified treatment of linear and nonlinear complex valued adaptive filters, and methods for the processing of general complex signals (circular and noncircular). It brings together adaptive filtering algorithms for feedforward (transversal) and feedback architectures and the recent developments in the statistics of complex variable, under the powerful frameworks of CR (Wirtinger) calculus and augmented complex statistics. This offers a number of theoretical performance gains, which is illustrated on both stochastic gradient algorithms, such as the augmented complex least mean square (ACLMS), and those based on Kalman filters. This work is supported by a number of simulations using synthetic and real world data, including the noncircular and intermittent radar and wind signals.

## Wenbing Zhao Building Dependable Distributed Systems ISBN: 9781118912706

This book covers the most essential techniques for designing and building dependable distributed systems. Instead of covering a broad range of research works for each dependability strategy, the book focuses only a selected few (usually the most seminal works, the most practical approaches, or the first publication of each approach) are included and explained in depth, usually with a comprehensive set of examples. The goal is to dissect each technique thoroughly so that readers who are not familiar with dependable distributed computing can actually grasp the technique after studying the book. The book contains eight chapters. The first chapter introduces the basic concepts and terminologies of dependable distributed computing, and also provide an overview of the primary means for achieving dependability. The second chapter describes in detail the checkpointing and logging mechanisms, which are the most commonly used means to achieve limited degree of fault tolerance. Such mechanisms also serve as the foundation for more sophisticated dependability solutions. Chapter three covers the works on recovery-oriented computing, which focus on the practical techniques that reduce the fault detection and recovery times for Internet-based applications. Chapter four outlines the replication techniques for data and service fault tolerance. This chapter also pays particular attention to optimistic replication and the CAP theorem. Chapter five explains a few seminal works on group communication systems. Chapter six introduces the distributed consensus problem and covers a number of Paxos family algorithms in depth. Chapter seven introduces the Byzantine generals problem and its latest solutions, including the seminal Practical Byzantine Fault Tolerance (PBFT) algorithm and a number of its derivatives. The final chapter covers the latest research results on application-aware Byzantine fault tolerance, which is an important step forward towards practical use of Byzantine fault tolerance techniques.

## Xin-She Yang Engineering Optimization. An Introduction with Metaheuristic Applications ISBN: 9780470640418

An accessible introduction to metaheuristics and optimization, featuring powerful and modern algorithms for application across engineering and the sciences From engineering and computer science to economics and management science, optimization is a core component for problem solving. Highlighting the latest developments that have evolved in recent years, Engineering Optimization: An Introduction with Metaheuristic Applications outlines popular metaheuristic algorithms and equips readers with the skills needed to apply these techniques to their own optimization problems. With insightful examples from various fields of study, the author highlights key concepts and techniques for the successful application of commonly-used metaheuristc algorithms, including simulated annealing, particle swarm optimization, harmony search, and genetic algorithms. The author introduces all major metaheuristic algorithms and their applications in optimization through a presentation that is organized into three succinct parts: Foundations of Optimization and Algorithms provides a brief introduction to the underlying nature of optimization and the common approaches to optimization problems, random number generation, the Monte Carlo method, and the Markov chain Monte Carlo method Metaheuristic Algorithms presents common metaheuristic algorithms in detail, including genetic algorithms, simulated annealing, ant algorithms, bee algorithms, particle swarm optimization, firefly algorithms, and harmony search Applications outlines a wide range of applications that use metaheuristic algorithms to solve challenging optimization problems with detailed implementation while also introducing various modifications used for multi-objective optimization Throughout the book, the author presents worked-out examples and real-world applications that illustrate the modern relevance of the topic. A detailed appendix features important and popular algorithms using MATLAB® and Octave software packages, and a related FTP site houses MATLAB code and programs for easy implementation of the discussed techniques. In addition, references to the current literature enable readers to investigate individual algorithms and methods in greater detail. Engineering Optimization: An Introduction with Metaheuristic Applications is an excellent book for courses on optimization and computer simulation at the upper-undergraduate and graduate levels. It is also a valuable reference for researchers and practitioners working in the fields of mathematics, engineering, computer science, operations research, and management science who use metaheuristic algorithms to solve problems in their everyday work.

## Elloumi Mourad Algorithms in Computational Molecular Biology. Techniques, Approaches and Applications ISBN: 9780470892091

This book represents the most comprehensive and up-to-date collection of information on the topic of computational molecular biology. Bringing the most recent research into the forefront of discussion, Algorithms in Computational Molecular Biology studies the most important and useful algorithms currently being used in the field, and provides related problems. It also succeeds where other titles have failed, in offering a wide range of information from the introductory fundamentals right up to the latest, most advanced levels of study.

## Xavier Lorca Tree-based Graph Partitioning Constraint ISBN: 9781118604472

Combinatorial problems based on graph partitioning enable us to mathematically represent and model many practical applications. Mission planning and the routing problems occurring in logistics perfectly illustrate two such examples. Nevertheless, these problems are not based on the same partitioning pattern: generally, patterns like cycles, paths, or trees are distinguished. Moreover, the practical applications are often not limited to theoretical problems like the Hamiltonian path problem, or K-node disjoint path problems. Indeed, they usually combine the graph partitioning problem with several restrictions related to the topology of nodes and arcs. The diversity of implied constraints in real-life applications is a practical limit to the resolution of such problems by approaches considering the partitioning problem independently from each additional restriction. This book focuses on constraint satisfaction problems related to tree partitioning problems enriched by several additional constraints that restrict the possible partitions topology. On the one hand, this title focuses on the structural properties of tree partitioning constraints. On the other hand, it is dedicated to the interactions between the tree partitioning problem and classical restrictions (such as precedence relations or incomparability relations between nodes) involved in practical applications. Precisely, Tree-based Graph Partitioning Constraint shows how to globally take into account several restrictions within one single tree partitioning constraint. Another interesting aspect of this book is related to the implementation of such a constraint. In the context of graph-based global constraints, the book illustrates how a fully dynamic management of data structures makes the runtime of filtering algorithms independent of the graph density.