The revised edition of this book offers an extended overview of quantum walks and explains their role in building quantum algorithms, in particular search algorithms. Updated throughout, the book focuses on core topics including Grover´s algorithm and the most important quantum walk models, such as the coined, continuous-time, and Szedgedy´s quantum walk models. There is a new chapter describing the staggered quantum walk model. The chapter on spatial search algorithms has been rewritten to offer a more comprehensive approach and a new chapter describing the element distinctness algorithm has been added. There is a new appendix on graph theory highlighting the importance of graph theory to quantum walks. As before, the reader will benefit from the pedagogical elements of the book, which include exercises and references to deepen the reader´s understanding, and guidelines for the use of computer programs to simulate the evolution of quantum walks. Review of the first edition: ´´The book is nicely written, the concepts are introduced naturally, and many meaningful connections between them are highlighted. The author proposes a series of exercises that help the reader get some working experience with the presented concepts, facilitating a better understanding. Each chapter ends with a discussion of further references, pointing the reader to major results on the topics presented in the respective chapter.´´ - Florin Manea, zbMATH.
With significant growth of bio-molecular sequence data in the last decade the need for algorithms to extract patterns and meaningful information from such data has been felt strongly. Alignment of sequences, in order to determine regions of common descent, has also been an important area of research as it helps scientist discover the evolution of species. Another problem that researchers are putting in a lot of effort into, is document summary. As the lower bound for computation is being met for various algorithms, to further expedite the computing on large data sets, parallelization has become imperative. New multiprocessor architectures like the Cell Broadband Engine have the potential to do extensive calculations and act as mini-supercomputers. Other applications for these include onboard aircraft fault diagnosis and prognosis. We take a peek into some existing algorithms for these problems as well as propose novel algorithms along with their implementations to address these problems in the field of bioinformatics.
This textbook explains the concepts and techniques required to write programs that can handle large amounts of data efficiently. Project-oriented and classroom-tested, the book presents a number of important algorithms supported by examples that bring meaning to the problems faced by computer programmers. The idea of computational complexity is also introduced, demonstrating what can and cannot be computed efficiently so that the programmer can make informed judgements about the algorithms they use. Features: includes both introductory and advanced data structures and algorithms topics, with suggested chapter sequences for those respective courses provided in the preface; provides learning goals, review questions and programming exercises in each chapter, as well as numerous illustrative examples; offers downloadable programs and supplementary files at an associated website, with instructor materials available from the author; presents a primer on Python for those from a different language background.
This book is an introduction to relevant aspects of the foraging literature for algorithmic design, and an overview of key families of optimization algorithms that stem from a foraging metaphor. The authors first offer perspectives on foraging and foraging-inspired algorithms for optimization, they then explain the techniques inspired by the behaviors of vertebrates, invertebrates, and non-neuronal organisms, and they then discuss algorithms based on formal models of foraging, how to evolve a foraging strategy, and likely future developments. No prior knowledge of natural computing is assumed. This book will be of particular interest to graduate students, academics and practitioners in computer science, informatics, data science, management science, and other application domains.
Over the past decades, a large number of algorithms and methods have been developed in bioinformatics to process and analyze biological data generated in the research of molecular biology. Complex interactions often exist in biological systems and accurate analysis of these interactions is crucial for the understanding of these systems. This book, therefore, provides a new perspective to integrating the interactions in a biological system into the algorithms and methods developed to analyze it, which is supplemental to the more traditional algorithms and methods that most researchers in bioinformatics currently use. Specifically, parameters are introduced into the models for a few biological systems and parameterized methods are developed to study and solve four problems with fundamental importance in bioinformatics. The development and analysis of these methods should help shed some light on this newly emerged and exciting area, and should be especially useful to researchers and professionals in Bioinformatics, Computational Biology and Computer Science, or anyone else who may be considering utilizing parameterized methods for the study and analysis of complex biological systems
Continuing his exploration of the organization of complexity and the science of design, this new edition of Herbert Simon´s classic work on artificial intelligence adds a chapter that sorts out the current themes and tools--chaos, adaptive systems, genetic algorithms--for analyzing complexity and complex systems.
This book provides comprehensive coverage of the field of outlier analysis from a computer science point of view. It integrates methods from data mining, machine learning, and statistics within the computational framework and therefore appeals to multiple communities. The chapters of this book can be organized into three categories: Basic algorithms: Chapters 1 through 7 discuss the fundamental algorithms for outlier analysis, including probabilistic and statistical methods, linear methods, proximity-based methods, high-dimensional (subspace) methods, ensemble methods, and supervised methods. Domain-specific methods: Chapters 8 through 12 discuss outlier detection algorithms for various domains of data, such as text, categorical data, time-series data, discrete sequence data, spatial data, and network data. Applications: Chapter 13 is devoted to various applications of outlier analysis. Some guidance is also provided for the practitioner. The second edition of this book is more detailed and is written to appeal to both researchers and practitioners. Significant new material has been added on topics such as kernel methods, one-class support-vector machines, matrix factorization, neural networks, outlier ensembles, time-series methods, and subspace methods. It is written as a textbook and can be used for classroom teaching.
This book is a comprehensive introduction to the methods and algorithms of modern data analytics. It provides a sound mathematical basis, discusses advantages and drawbacks of different approaches, and enables the reader to design and implement data analytics solutions for real-world applications. This book has been used for more than ten years in the Data Mining course at the Technical University of Munich. Much of the content is based on the results of industrial research and development projects at Siemens.
Introducing the IBM SPSS Modeler, this book guides readers through data mining processes and presents relevant statistical methods. There is a special focus on step-by-step tutorials and well-documented examples that help demystify complex mathematical algorithms and computer programs. The variety of exercises and solutions as well as an accompanying website with data sets and SPSS Modeler streams are particularly valuable. While intended for students, the simplicity of the Modeler makes the book useful for anyone wishing to learn about basic and more advanced data mining, and put this knowledge into practice.
A decision procedure is an algorithm that, given a decision problem, terminates with a correct yes/no answer. Here, the authors focus on theories that are expressive enough to model real problems, but are still decidable. Specifically, the book concentrates on decision procedures for first-order theories that are commonly used in automated verification and reasoning, theorem-proving, compiler optimization and operations research. The techniques described in the book draw from fields such as graph theory and logic, and are routinely used in industry. The authors introduce the basic terminology of satisfiability modulo theories and then, in separate chapters, study decision procedures for each of the following theories: propositional logic; equalities and uninterpreted functions; linear arithmetic; bit vectors; arrays; pointer logic; and quantified formulas.