Tractability (Hardcover)

Lucas Bordeaux

ISBN 10: 1107025192 / ISBN 13: 9781107025196

Editore: Cambridge University Press, Cambridge, 2014

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Hardcover. Classical computer science textbooks tell us that some problems are 'hard'. Yet many areas, from machine learning and computer vision to theorem proving and software verification, have defined their own set of tools for effectively solving complex problems. Tractability provides an overview of these different techniques, and of the fundamental concepts and properties used to tame intractability. This book will help you understand what to do when facing a hard computational problem. Can the problem be modelled by convex, or submodular functions? Will the instances arising in practice be of low treewidth, or exhibit another specific graph structure that makes them easy? Is it acceptable to use scalable, but approximate algorithms? A wide range of approaches is presented through self-contained chapters written by authoritative researchers on each topic. As a reference on a core problem in computer science, this book will appeal to theoreticians and practitioners alike. Many computational problems cannot be solved efficiently according to classical computer science textbooks. Tractability gives a cross-disciplinary overview of the advanced techniques that have been developed in order to solve these 'hard' computational problems, and a unified presentation of the properties that can be exploited to make them tractable. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Codice articolo 9781107025196

Dati bibliografici

Titolo: Tractability (Hardcover)

Casa editrice: Cambridge University Press, Cambridge

Data di pubblicazione: 2014

Legatura: Hardcover

Condizione: new

Descrizione articolo

Riassunto:

An overview of the techniques developed to circumvent computational intractability, a key challenge in many areas of computer science.

Informazioni sugli autori:

Lucas Bordeaux is a Senior Research Software Development Engineer at Microsoft Research, Cambridge, where he works on the design and applications of algorithms to solve hard inference problems.

Youssef Hamadi is a Senior Researcher at Microsoft Research, Cambridge. His work involves the practical resolution of large-scale real life problems set at the intersection of Optimization and Artificial Intelligence. His current research considers the design of complex systems based on multiple formalisms fed by different information channels which plan ahead and perform smart decisions. His current focus is on Autonomous Search, Parallel Search, and Propositional Satisfiability, with applications to Environmental Intelligence, Business Intelligence, and Software Verification.

Pushmeet Kohli is a Research Scientist in the Machine Learning and Perception group at Microsoft Research, Cambridge. His research interests span the fields of Computer Vision, Machine Learning, Discrete Optimization, Game Theory, and Human-Computer Interaction with the overall aim of 'teaching' computers to understand the behaviour and intent of human users, and to correctly interpret (or 'See') objects and scenes depicted in colour/depth images or videos. In the context of tractability and optimization, Pushmeet has worked on developing adaptive combinatorial and message passing-based optimization algorithms that exploit the structure of problems to achieve improved performance.