Download or read book Models of Computation written by Roberto Bruni and published by Springer. This book was released on 2017-04-03 with total page 406 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents in their basic form the most important models of computation, their basic programming paradigms, and their mathematical descriptions, both concrete and abstract. Each model is accompanied by relevant formal techniques for reasoning on it and for proving some properties. After preliminary chapters that introduce the notions of structure and meaning, semantic methods, inference rules, and logic programming, the authors arrange their chapters into parts on IMP, a simple imperative language; HOFL, a higher-order functional language; concurrent, nondeterministic and interactive models; and probabilistic/stochastic models. The authors have class-tested the book content over many years, and it will be valuable for graduate and advanced undergraduate students of theoretical computer science and distributed systems, and for researchers in this domain. Each chapter of the book concludes with a list of exercises addressing the key techniques introduced, solutions to selected exercises are offered at the end of the book.
Download or read book Models of Computation written by and published by . This book was released on 2002-01-01 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Models of Computation written by Maribel Fernandez and published by Springer Science & Business Media. This book was released on 2009-04-14 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt: A Concise Introduction to Computation Models and Computability Theory provides an introduction to the essential concepts in computability, using several models of computation, from the standard Turing Machines and Recursive Functions, to the modern computation models inspired by quantum physics. An in-depth analysis of the basic concepts underlying each model of computation is provided. Divided into two parts, the first highlights the traditional computation models used in the first studies on computability: - Automata and Turing Machines; - Recursive functions and the Lambda-Calculus; - Logic-based computation models. and the second part covers object-oriented and interaction-based models. There is also a chapter on concurrency, and a final chapter on emergent computation models inspired by quantum mechanics. At the end of each chapter there is a discussion on the use of computation models in the design of programming languages.
Download or read book Unconventional Models of Computation written by Cristian Calude and published by Springer. This book was released on 2003-06-30 with total page 339 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the Third International Conference on Unconventional Models of Computation, UMC 2002, held in Kobe, Japan in October 2002.The 18 revised full papers presented together with eight invited full papers were carefully reviewed and selected from 36 submissions. All major areas of unconventinal computing models are covered, especially quantum computing, DNA computing, membrane computing, cellular computing, and possibilities to break Turing's barrier. The authors address theoretical aspects, practical implementations, as well as philosophical reflections.
Download or read book Models of Computation and Formal Languages written by R. Gregory Taylor and published by Oxford University Press on Demand. This book was released on 1998 with total page 667 pages. Available in PDF, EPUB and Kindle. Book excerpt: Models of Computation and Formal Languages presents a comprehensive and rigorous treatment of the theory of computability. The text takes a novel approach focusing on computational models and is the first book of its kind to feature companion software. Deus Ex Machina, developed by Nicolae Savoiu, comprises software simulations of the various computational models considered and incorporates numerous examples in a user-friendly format. Part I of the text introduces several universal models including Turing machines, Markov algorithms, and register machines. Complexity theory is integrated gradually, starting in Chapter 1. The vector machine model of parallel computation is covered thoroughly both in text and software. Part II develops the Chomsky hierarchy of formal languages and provides both a grammar-theoretic and an automata-theoretic characterization of each language family. Applications to programming languages round out an in-depth theoretical discussion, making this an ideal text for students approaching this subject for the first time. Ancillary sections of several chapters relate classical computability theory to the philosophy of mind, cognitive science, and theoretical linguistics. Ideal for Theory of Computability and Theory of Algorithms courses at the advanced undergraduate or beginning graduate level, Models of Computation and Formal Languages is one of the only texts that... - - Features accompanying software available on the World Wide Web at http: //home.manhattan.edu/ gregory.taylor/thcomp/ Adopts an integrated approach to complexity theory - Offers a solutions manual containing full solutions to several hundred exercises. Most of these solutions are available to students on the World Wide Web at http: //home.manhattan.edu/ gregory.taylor/thcomp - Features examples relating the theory of computation to the probable programming experience of an undergraduate computer science major
Download or read book Theory and Applications of Models of Computation written by T-H. Hubert Chan and published by Springer. This book was released on 2013-04-15 with total page 368 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 10th International Conference on Theory and Applications of Models of Computation, TAMC 2013, held in Hong Kong, China, in May 2013. The 31 revised full papers presented were carefully reviewed and selected from 70 submissions. Bringing together a wide range of researchers with interests in computational theory and applications, the papers address the three main themes of the conference which were computability, complexity, and algorithms and present current research in these fields with aspects to theoretical computer science, algorithmic mathematics, and applications to the physical sciences.
Download or read book Unconventional Models of Computation written by Christian Calude and published by Springer Science & Business Media. This book was released on 1998-02-01 with total page 442 pages. Available in PDF, EPUB and Kindle. Book excerpt: Covering recent research into unconventional methods of computing for disciplines in computer science, mathematics, biology, physics and philosophy, the subjects include: nonconventional computational methods, DNA computation, quantum computation, and beyong Turing computability; new methods of discrete computation; theoretical and conceptual new computational paradigms; practical knowledge on new computing technologies.
Download or read book Models for Parallel and Distributed Computation written by R. Correa and published by Springer Science & Business Media. This book was released on 2013-06-29 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: Parallel and distributed computation has been gaining a great lot of attention in the last decades. During this period, the advances attained in computing and communication technologies, and the reduction in the costs of those technolo gies, played a central role in the rapid growth of the interest in the use of parallel and distributed computation in a number of areas of engineering and sciences. Many actual applications have been successfully implemented in various plat forms varying from pure shared-memory to totally distributed models, passing through hybrid approaches such as distributed-shared memory architectures. Parallel and distributed computation differs from dassical sequential compu tation in some of the following major aspects: the number of processing units, independent local dock for each unit, the number of memory units, and the programming model. For representing this diversity, and depending on what level we are looking at the problem, researchers have proposed some models to abstract the main characteristics or parameters (physical components or logical mechanisms) of parallel computers. The problem of establishing a suitable model is to find a reasonable trade-off among simplicity, power of expression and universality. Then, be able to study and analyze more precisely the behavior of parallel applications.
Download or read book Models of Computation for Big Data written by Rajendra Akerkar and published by Springer. This book was released on 2018-12-04 with total page 110 pages. Available in PDF, EPUB and Kindle. Book excerpt: The big data tsunami changes the perspective of industrial and academic research in how they address both foundational questions and practical applications. This calls for a paradigm shift in algorithms and the underlying mathematical techniques. There is a need to understand foundational strengths and address the state of the art challenges in big data that could lead to practical impact. The main goal of this book is to introduce algorithmic techniques for dealing with big data sets. Traditional algorithms work successfully when the input data fits well within memory. In many recent application situations, however, the size of the input data is too large to fit within memory. Models of Computation for Big Data, covers mathematical models for developing such algorithms, which has its roots in the study of big data that occur often in various applications. Most techniques discussed come from research in the last decade. The book will be structured as a sequence of algorithmic ideas, theoretical underpinning, and practical use of that algorithmic idea. Intended for both graduate students and advanced undergraduate students, there are no formal prerequisites, but the reader should be familiar with the fundamentals of algorithm design and analysis, discrete mathematics, probability and have general mathematical maturity.
Download or read book Programming Models for Parallel Computing written by Pavan Balaji and published by MIT Press. This book was released on 2015-11-06 with total page 488 pages. Available in PDF, EPUB and Kindle. Book excerpt: An overview of the most prominent contemporary parallel processing programming models, written in a unique tutorial style. With the coming of the parallel computing era, computer scientists have turned their attention to designing programming models that are suited for high-performance parallel computing and supercomputing systems. Programming parallel systems is complicated by the fact that multiple processing units are simultaneously computing and moving data. This book offers an overview of some of the most prominent parallel programming models used in high-performance computing and supercomputing systems today. The chapters describe the programming models in a unique tutorial style rather than using the formal approach taken in the research literature. The aim is to cover a wide range of parallel programming models, enabling the reader to understand what each has to offer. The book begins with a description of the Message Passing Interface (MPI), the most common parallel programming model for distributed memory computing. It goes on to cover one-sided communication models, ranging from low-level runtime libraries (GASNet, OpenSHMEM) to high-level programming models (UPC, GA, Chapel); task-oriented programming models (Charm++, ADLB, Scioto, Swift, CnC) that allow users to describe their computation and data units as tasks so that the runtime system can manage computation and data movement as necessary; and parallel programming models intended for on-node parallelism in the context of multicore architecture or attached accelerators (OpenMP, Cilk Plus, TBB, CUDA, OpenCL). The book will be a valuable resource for graduate students, researchers, and any scientist who works with data sets and large computations. Contributors Timothy Armstrong, Michael G. Burke, Ralph Butler, Bradford L. Chamberlain, Sunita Chandrasekaran, Barbara Chapman, Jeff Daily, James Dinan, Deepak Eachempati, Ian T. Foster, William D. Gropp, Paul Hargrove, Wen-mei Hwu, Nikhil Jain, Laxmikant Kale, David Kirk, Kath Knobe, Ariram Krishnamoorthy, Jeffery A. Kuehn, Alexey Kukanov, Charles E. Leiserson, Jonathan Lifflander, Ewing Lusk, Tim Mattson, Bruce Palmer, Steven C. Pieper, Stephen W. Poole, Arch D. Robison, Frank Schlimbach, Rajeev Thakur, Abhinav Vishnu, Justin M. Wozniak, Michael Wilde, Kathy Yelick, Yili Zheng
Download or read book Handbook of Computability and Complexity in Analysis written by Vasco Brattka and published by Springer Nature. This book was released on 2021-06-04 with total page 427 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computable analysis is the modern theory of computability and complexity in analysis that arose out of Turing's seminal work in the 1930s. This was motivated by questions such as: which real numbers and real number functions are computable, and which mathematical tasks in analysis can be solved by algorithmic means? Nowadays this theory has many different facets that embrace topics from computability theory, algorithmic randomness, computational complexity, dynamical systems, fractals, and analog computers, up to logic, descriptive set theory, constructivism, and reverse mathematics. In recent decades computable analysis has invaded many branches of analysis, and researchers have studied computability and complexity questions arising from real and complex analysis, functional analysis, and the theory of differential equations, up to (geometric) measure theory and topology. This handbook represents the first coherent cross-section through most active research topics on the more theoretical side of the field. It contains 11 chapters grouped into parts on computability in analysis; complexity, dynamics, and randomness; and constructivity, logic, and descriptive complexity. All chapters are written by leading experts working at the cutting edge of the respective topic. Researchers and graduate students in the areas of theoretical computer science and mathematical logic will find systematic introductions into many branches of computable analysis, and a wealth of information and references that will help them to navigate the modern research literature in this field.
Download or read book Parallel Computation written by Selim G. Akl and published by Upper Saddle River, N.J. : Prentice Hall. This book was released on 1997 with total page 632 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mathematics of Computing -- Parallelism.
Download or read book Formal Languages and Computation written by Alexander Meduna and published by CRC Press. This book was released on 2014-02-11 with total page 318 pages. Available in PDF, EPUB and Kindle. Book excerpt: Formal Languages and Computation: Models and Their Applications gives a clear, comprehensive introduction to formal language theory and its applications in computer science. It covers all rudimental topics concerning formal languages and their models, especially grammars and automata, and sketches the basic ideas underlying the theory of computation, including computability, decidability, and computational complexity. Emphasizing the relationship between theory and application, the book describes many real-world applications, including computer science engineering techniques for language processing and their implementation. Covers the theory of formal languages and their models, including all essential concepts and properties Explains how language models underlie language processors Pays a special attention to programming language analyzers, such as scanners and parsers, based on four language models—regular expressions, finite automata, context-free grammars, and pushdown automata Discusses the mathematical notion of a Turing machine as a universally accepted formalization of the intuitive notion of a procedure Reviews the general theory of computation, particularly computability and decidability Considers problem-deciding algorithms in terms of their computational complexity measured according to time and space requirements Points out that some problems are decidable in principle, but they are, in fact, intractable problems for absurdly high computational requirements of the algorithms that decide them In short, this book represents a theoretically oriented treatment of formal languages and their models with a focus on their applications. It introduces all formalisms concerning them with enough rigors to make all results quite clear and valid. Every complicated mathematical passage is preceded by its intuitive explanation so that even the most complex parts of the book are easy to grasp. After studying this book, both student and professional should be able to understand the fundamental theory of formal languages and computation, write language processors, and confidently follow most advanced books on the subject.
Download or read book Theory of Computation written by George Tourlakis and published by John Wiley & Sons. This book was released on 2014-08-21 with total page 410 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn the skills and acquire the intuition to assess the theoretical limitations of computer programming Offering an accessible approach to the topic, Theory of Computation focuses on the metatheory of computing and the theoretical boundaries between what various computational models can do and not do—from the most general model, the URM (Unbounded Register Machines), to the finite automaton. A wealth of programming-like examples and easy-to-follow explanations build the general theory gradually, which guides readers through the modeling and mathematical analysis of computational phenomena and provides insights on what makes things tick and also what restrains the ability of computational processes. Recognizing the importance of acquired practical experience, the book begins with the metatheory of general purpose computer programs, using URMs as a straightforward, technology-independent model of modern high-level programming languages while also exploring the restrictions of the URM language. Once readers gain an understanding of computability theory—including the primitive recursive functions—the author presents automata and languages, covering the regular and context-free languages as well as the machines that recognize these languages. Several advanced topics such as reducibilities, the recursion theorem, complexity theory, and Cook's theorem are also discussed. Features of the book include: A review of basic discrete mathematics, covering logic and induction while omitting specialized combinatorial topics A thorough development of the modeling and mathematical analysis of computational phenomena, providing a solid foundation of un-computability The connection between un-computability and un-provability: Gödel's first incompleteness theorem The book provides numerous examples of specific URMs as well as other programming languages including Loop Programs, FA (Deterministic Finite Automata), NFA (Nondeterministic Finite Automata), and PDA (Pushdown Automata). Exercises at the end of each chapter allow readers to test their comprehension of the presented material, and an extensive bibliography suggests resources for further study. Assuming only a basic understanding of general computer programming and discrete mathematics, Theory of Computation serves as a valuable book for courses on theory of computation at the upper-undergraduate level. The book also serves as an excellent resource for programmers and computing professionals wishing to understand the theoretical limitations of their craft.
Download or read book An Introduction to Computational Learning Theory written by Michael J. Kearns and published by MIT Press. This book was released on 1994-08-15 with total page 230 pages. Available in PDF, EPUB and Kindle. Book excerpt: Emphasizing issues of computational efficiency, Michael Kearns and Umesh Vazirani introduce a number of central topics in computational learning theory for researchers and students in artificial intelligence, neural networks, theoretical computer science, and statistics. Emphasizing issues of computational efficiency, Michael Kearns and Umesh Vazirani introduce a number of central topics in computational learning theory for researchers and students in artificial intelligence, neural networks, theoretical computer science, and statistics. Computational learning theory is a new and rapidly expanding area of research that examines formal models of induction with the goals of discovering the common methods underlying efficient learning algorithms and identifying the computational impediments to learning. Each topic in the book has been chosen to elucidate a general principle, which is explored in a precise formal setting. Intuition has been emphasized in the presentation to make the material accessible to the nontheoretician while still providing precise arguments for the specialist. This balance is the result of new proofs of established theorems, and new presentations of the standard proofs. The topics covered include the motivation, definitions, and fundamental results, both positive and negative, for the widely studied L. G. Valiant model of Probably Approximately Correct Learning; Occam's Razor, which formalizes a relationship between learning and data compression; the Vapnik-Chervonenkis dimension; the equivalence of weak and strong learning; efficient learning in the presence of noise by the method of statistical queries; relationships between learning and cryptography, and the resulting computational limitations on efficient learning; reducibility between learning problems; and algorithms for learning finite automata from active experimentation.
Download or read book Mathematics and Computation written by Avi Wigderson and published by Princeton University Press. This book was released on 2019-10-29 with total page 434 pages. Available in PDF, EPUB and Kindle. Book excerpt: From the winner of the Turing Award and the Abel Prize, an introduction to computational complexity theory, its connections and interactions with mathematics, and its central role in the natural and social sciences, technology, and philosophy Mathematics and Computation provides a broad, conceptual overview of computational complexity theory—the mathematical study of efficient computation. With important practical applications to computer science and industry, computational complexity theory has evolved into a highly interdisciplinary field, with strong links to most mathematical areas and to a growing number of scientific endeavors. Avi Wigderson takes a sweeping survey of complexity theory, emphasizing the field’s insights and challenges. He explains the ideas and motivations leading to key models, notions, and results. In particular, he looks at algorithms and complexity, computations and proofs, randomness and interaction, quantum and arithmetic computation, and cryptography and learning, all as parts of a cohesive whole with numerous cross-influences. Wigderson illustrates the immense breadth of the field, its beauty and richness, and its diverse and growing interactions with other areas of mathematics. He ends with a comprehensive look at the theory of computation, its methodology and aspirations, and the unique and fundamental ways in which it has shaped and will further shape science, technology, and society. For further reading, an extensive bibliography is provided for all topics covered. Mathematics and Computation is useful for undergraduate and graduate students in mathematics, computer science, and related fields, as well as researchers and teachers in these fields. Many parts require little background, and serve as an invitation to newcomers seeking an introduction to the theory of computation. Comprehensive coverage of computational complexity theory, and beyond High-level, intuitive exposition, which brings conceptual clarity to this central and dynamic scientific discipline Historical accounts of the evolution and motivations of central concepts and models A broad view of the theory of computation's influence on science, technology, and society Extensive bibliography
Download or read book Introduction to the Theory of Computation written by Michael Sipser and published by Thomson/Course Technology. This book was released on 2006 with total page 437 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Intended as an upper-level undergraduate or introductory graduate text in computer science theory," this book lucidly covers the key concepts and theorems of the theory of computation. The presentation is remarkably clear; for example, the "proof idea," which offers the reader an intuitive feel for how the proof was constructed, accompanies many of the theorems and a proof. Introduction to the Theory of Computation covers the usual topics for this type of text plus it features a solid section on complexity theory--including an entire chapter on space complexity. The final chapter introduces more advanced topics, such as the discussion of complexity classes associated with probabilistic algorithms.
