Mathematical theory of computation Books
Cambridge University Press Planning Algorithms
Book SynopsisWritten for computer scientists and engineers with interests in artificial intelligence, robotics, or control theory, this is the only book on this topic that integrates literature from several fields into a coherent source for teaching and reference in applications including robotics, computational biology, computer graphics, manufacturing, aerospace applications and medicine.Trade Review'This is a terrific book, a milestone in the robotics literature.' Matt Mason, Director of The Carnegie Mellon Robotics Institute'Motion planning is an important field of research with applications in such diverse terrains as robotics, molecular modeling, virtual environments, and games. Over the past two decades a huge number of techniques have been developed, all with their merits and shortcomings. The book by Steve LaValle gives an excellent overview of the current state of the art in the field. It should lie on the desk of everybody that is involved in motion planning research or the use of motion planning in applications.' Mark Overmars, Utrecht University'A great book at the junction where Robotics, Artificial Intelligence, and Control are crossing their paths. For many problems you will find in-depth discussion and algorithms; for virtually all others in the field, an intriguing introduction to make you at ease and entice you to further probing the matter.' Antonio Bicchi, della Università di Pisa'Since the early 90s, Latombe's book has been the authoritative source for students and researchers working on motion planning problems in robotics. During the succeeding decade and half, the motion planning field moved forward with significant developments. LaValle's book picks up the field where Latombe's book left it, describing in detail major developments such as probabilistic roadmaps, manipulation, and coverage planning. Moreover, the book describes a fundamental generalization of configuration spaces to information spaces. The chapters on information spaces appear here for the first time, making them accessible to students and researchers who wish to tackle progressively more challenging real-world motion planning problems in robotics.' Elon Rimon, Technion'Planning Algorithms is a daring title. It aims at being ecumenical gathering students and their professors scattered in various departments of Engineering and calling them to share the same mathematical foundations. The story starts with motion planning algorithms. Steve LaValle's deep extensive understanding and his effective expertise in that area are shared in this book. They allow the author to go further and to generalize the famous configuration space of the piano mover problem into the information space. This is the core of the title ambition. All the seminal material born with Robotics, Artificial Intelligence and Control, and developed for more than thirty years in a sparse way, are there uniquely unified. The book is not a catalogue of methods. It is the coherent view of a single researcher. The style is nice making the reading fluent: there is a good balance between informal introduction of concepts and the necessary technical developments. Students, researchers and engineers exploring routes in Artificial Intelligence and Robotics, in Graphics and CAD/CAM, and even Molecular Biology now, will find here amazing computational foundations for their topics.' Jean-Paul Laumond, LAAS-CNRS' … this book really is monumental and well-written piece of work, and although few will have cause to read more than a fraction of its content, at its price it deserves to find its way onto the bookshelves of many of us, as well as being recommended to our students.' ScienceDirectTable of ContentsPart I. Introductory Material: 1. Introduction; 2. Discrete planning; Part II. Motion Planning: 3. Geometric representations and transformations; 4. The configuration space; 5. Sampling-based motion planning; 6. Combinatorial motion planning; 7. Extensions of basic motion planning; 8. Feedback motion planning; Part III. Decision-Theoretic Planning: 9. Basic decision theory; 10. Sequential decision theory; 11. Information spaces; 12. Planning under sensing uncertainty; Part IV. Planning Under Differential Constraints: 13. Differential models; 14. Sampling-based planning under differential constraints; 15. System theory and analytical techniques.
£89.29
Cambridge University Press Computational Thermodynamics The CALPHAD Method
Book SynopsisA hands-on 2007 introductory guide to CALPHAD, the reader can directly apply the methods in the book to their own research. Several case studies put the methods into a practical context. Suitable for advanced materials design and engineering courses and to those using thermodynamic data in their research or simulations.Trade Review"Lukas (U. Stuttgart emeritus) and co-authors Sundman (Paul Sabatier U.) and independent scientist Fries provide the first introductory guide to this method of computation that combines data from thermodynamics, phase diagrams, and atomistic properties such as magnetism into a unified and consistent model. They introduce the science and art of computational thermodynamics and the past and present of the Calphad technique, the scientific basis of the technique (including thermodynamics, crystallography, equilibrium calculations and optimization methods), first principles and thermodynamic properties, experimental data needed for optimization, models for the Gibbs energy element, assessment methodology, optimization tools, and thermodynamic databases. They also offer a series of case studies, including a complete assessment of the Cu-Mg system and a complete binary system (Ca-Ng) and provide a list of websites along with comprehensive references." --Book NewsTable of ContentsPreface; 1. Introduction; 2. Basis; 3. First principles and thermodynamic properties; 4. Experimental data used for the optimisation; 5. Models for the Gibbs energy; 6. Assessment methodology; 7. Optimisation tools; 8. Creating thermodynamic databases; 9. Case studies; Bibliography; Index.
£107.35
Cambridge University Press A Distributed PiCalculus
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£64.59
Cambridge University Press Concentration of Measure for the Analysis of Randomized Algorithms
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£117.80
Cambridge University Press Computational Complexity A Conceptual Perspective
Book SynopsisA conceptual introduction to the study of the intrinsic complexity of computational tasks. It will serve advanced undergraduate and graduate students, either as a textbook or for self-study. It provides explanations of the various sub-areas of complexity theory such as hardness amplification, pseudorandomness, and probabilistic proof systems.Trade Review"This interesting book... is refreshing to read his [Goldreichs'] opinions... The very strong focus on conceptual issues makes the book indispensible as a reference volume for research libraries." M. Bona, University of Florida, CHOICE"This book provides very well developed material that should interest advanced students either studying or doing new work on computational complexity. It would also be a valuable text for professionals challenged with solving "hard" computing problems of intending to exploit these types of problems when designing of new types computing systems." Brian A. Lawler, Software Engineering Notes"The book offers a conceptual perspective on several sub-areas of complexity theory and is intended to be used as a textbook for students and educators as well as for experts who seek an overview of of several sub-areas." Gerhard Lischke, Mathematical ReviewsTable of Contents1. Introduction and preliminaries; 2. P, NP and NP-completeness; 3. Variations on P and NP; 4. More resources, more power?; 5. Space complexity; 6. Randomness and counting; 7. The bright side of hardness; 8. Pseudorandom generators; 9. Probabilistic proof systems; 10. Relaxing the requirements; Epilogue; Appendix A. Glossary of complexity classes; Appendix B. On the quest for lower bounds; Appendix C. On the foundations of modern cryptography; Appendix D. Probabilistic preliminaries and advanced topics in randomization; Appendix E. Explicit constructions; Appendix F. Some omitted proofs; Appendix G. Some computational problems.
£67.45
Cambridge University Press Finite Markov Chains and Algorithmic Applications 52 London Mathematical Society Student Texts Series Number 52
Book SynopsisBased on a lecture course given at Chalmers University of Technology, this 2002 book is ideal for advanced undergraduate or beginning graduate students. The author first develops the necessary background in probability theory and Markov chains before applying it to study a range of randomized algorithms with important applications in optimization and other problems in computing. Amongst the algorithms covered are the Markov chain Monte Carlo method, simulated annealing, and the recent Propp-Wilson algorithm. This book will appeal not only to mathematicians, but also to students of statistics and computer science. The subject matter is introduced in a clear and concise fashion and the numerous exercises included will help students to deepen their understanding.Trade Review'Has climbing up onto the MCMC juggernaut seemed to require just too much effort? This delightful little monograph provides an effortless way in. The chapters are bite-sized with helpful, do-able exercises (by virtue of strategically placed hints) that complement the text.' Publication of the International Statistical Institute'… a very nice introduction to the modern theory of Markov chain simulation algorithms.' R. E. Maiboroda, Zentralblatt MATH' … extremely elegant. I am sure that students will find great pleasure in using the book - and that teachers will have the same pleasure in using it to prepare a course on the subject.' Mathematics of Computation'This elegant little book is a beautiful introduction to the theory of simulation algorithms, using (discrete) Markov chains (on finite state spaces) … highly recommended to anyone interested in the theory of Markov chain simulation algorithms.' Nieuw Archief voor WiskundeTable of Contents1. Basics of probability theory; 2. Markov chains; 3. Computer simulation of Markov chains; 4. Irreducible and aperiodic Markov chains; 5. Stationary distributions; 6. Reversible Markov chains; 7. Markov chain Monte Carlo; 8. Fast convergence of MCMC algorithms; 9. Approximate counting; 10. Propp-Wilson algorithm; 11. Sandwiching; 12. Propp-Wilson with read once randomness; 13. Simulated annealing; 14. Further reading.
£37.37
Cambridge University Press Analytic Combinatorics
Book SynopsisThe definitive treatment of analytic combinatorics. This self-contained text covers the mathematics underlying the analysis of discrete structures, with thorough treatment of a large number of applications. Exercises, examples, appendices and notes aid understanding: ideal for individual self-study or for advanced undergraduate or graduate courses.Trade Review'… thorough and self-contained … presentation of … topics is very well organised … provides an ample amount of examples and illustrations, as well as a comprehensive bibliography. It is valuable both as a reference work for researchers working in the field and as an accessible introduction suitable for students at an advanced graduate level.' EMS NewsletterTable of ContentsPreface; An invitation to analytic combinatorics; Part A. Symbolic Methods: 1. Combinatorial structures and ordinary generating functions; 2. Labelled structures and exponential generating functions; 3. Combinatorial parameters and multivariate generating functions; Part B. Complex Asymptotics: 4. Complex analysis, rational and meromorphic asymptotics; 5. Applications of rational and meromorphic asymptotics; 6. Singularity analysis of generating functions; 7. Applications of singularity analysis; 8. Saddle-Point asymptotics; Part C. Random Structures: 9. Multivariate asymptotics and limit laws; Part D. Appendices: Appendix A. Auxiliary elementary notions; Appendix B. Basic complex analysis; Appendix C. Concepts of probability theory; Bibliography; Index.
£76.94
Cambridge University Press 200 Problems on Languages Automata and Computation
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£29.99
Cambridge University Press Formal Methods Informally
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£80.50
Cambridge University Press Primal Heuristics in Integer Programming
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£67.49
Cambridge University Press Syntax and Semantics of Petri Nets
£99.00
Cambridge University Press Advanced Topics in Bisimulation and Coinduction 52 Cambridge Tracts in Theoretical Computer Science Series Number 52
Book SynopsisCoinduction is a method for specifying and reasoning about infinite data types and automata with infinite behaviour. In recent years, it has come to play an ever more important role in the theory of computing. It is studied in many disciplines, including process theory and concurrency, modal logic and automata theory. Typically, coinductive proofs demonstrate the equivalence of two objects by constructing a suitable bisimulation relation between them. This collection of surveys is aimed at both researchers and Master's students in computer science and mathematics and deals with various aspects of bisimulation and coinduction, with an emphasis on process theory. Seven chapters cover the following topics: history, algebra and coalgebra, algorithmics, logic, higher-order languages, enhancements of the bisimulation proof method, and probabilities. Exercises are also included to help the reader master new material.Table of ContentsPreface; List of contributors; 1. Origins of bisimulation and coinduction Davide Sangiorgi; 2. An introduction to (co)algebra and (co)induction Bart Jacobs and Jan Rutten; 3. The algorithmics of bisimilarity Luca Aceto, Anna Ingolfsdottir and Jiří Srba; 4. Bisimulation and logic Colin Stirling; 5. Howe's method for higher-order languages Andrew Pitts; 6. Enhancements of the bisimulation proof method Damien Pous and Davide Sangiorgi; 7. Probabilistic bisimulation Prakash Panangaden.
£104.50
Cambridge University Press Mathematical Logic and Computation
Book SynopsisThis book presents mathematical logic from the syntactic point of view, with an emphasis on aspects that are fundamental to computer science. It is an excellent introduction for graduate students and advanced undergraduates interested in logic in mathematics, computer science, and philosophy, and an invaluable reference for professional logicians.Trade Review'Avigad provides a much needed introduction to mathematical logic that foregrounds the role of syntax and computability in our understanding of consistency and inconsistency. The result provides a jumping off point to any of the fields of modern logic, not only teaching the technical groundwork, but also providing a window into how to think like a logician.' Henry Towsner, University of Pennsylvania'This book by one of the most knowledgeable researchers in the field covers a remarkably broad selection of material without sacrificing depth. Its clear organization and unified approach - focused on a syntactic approach and on the role of computation - make it suitable for a wide range of introductory logic sequences at the upper-level undergraduate and graduate level, as well as a valuable resource for background material in more advanced logic courses.' Denis Hirschfeldt, University of Chicago'… an excellent addition to the literature, with plenty more than enough divergences and side-steps from the more well-trodden paths through the material to be consistently interesting … this is most certainly a book to make sure your library gets.' Peter Smith, Logic MattersTable of ContentsPreface; 1. Fundamentals; 2. Propositional Logic; 3. Semantics of Propositional Logic; 4. First-Order Logic; 5. Semantics of First-Order Logic; 6. Cut Elimination; 7. Properties of First-Order Logic; 8. Primitive Recursion; 9. Primitive Recursive Arithmetic; 10. First-Order Arithmetic; 11. Computability 12. Undecidability and Incompleteness; 13. Finite Types; 14. Arithmetic and Computation; 15. Second-Order Logic and Arithmetic; 16. Subsystems of Second-Order Arithmetic; 17. Foundations; Appendix; References; Notation; Index.
£56.99
Cambridge University Press Topological Data Analysis with Applications
Book SynopsisThe continued and dramatic rise in the size of data sets has meant that new methods are required to model and analyze them. This timely account introduces topological data analysis (TDA), a method for modeling data by geometric objects, namely graphs and their higher-dimensional versions: simplicial complexes. The authors outline the necessary background material on topology and data philosophy for newcomers, while more complex concepts are highlighted for advanced learners. The book covers all the main TDA techniques, including persistent homology, cohomology, and Mapper. The final section focuses on the diverse applications of TDA, examining a number of case studies drawn from monitoring the progression of infectious diseases to the study of motion capture data. Mathematicians moving into data science, as well as data scientists or computer scientists seeking to understand this new area, will appreciate this self-contained resource which explains the underlying technology and how it can be used.Table of ContentsPart I. Background: 1. Introduction; 2. Data; Part II. Theory: 3. Topology; 4. Shape of data; 5. Structures on spaces of barcodes; Part III. Practice: 6. Case studies; References; Index.
£37.99
Cambridge University Press Session Types
Book Synopsis
£45.59
Pearson Education Introduction to Computing and Algorithms
Book Synopsis
£149.98
Apress Raising Young Coders
Book Synopsis1. Why Learning Tech is Important for Kids.- 2. Early Learners.- 3. Getting Started with Block Coding.- 4. Youth Coding Projects.- 5. Five Women in Tech Role Models
£18.99
