Complexity of Infinite-Domain Constraint Satisfaction

Manuel Bodirsky

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English

Cambridge University Press
10 June 2021

Mathematical logic; Combinatorics & graph theory; Mathematical theory of computation; Artificial intelligence

Series: Lecture Notes in Logic

Constraint Satisfaction Problems (CSPs) are natural computational problems that appear in many areas of theoretical computer science. Exploring which CSPs are solvable in polynomial time and which are NP-hard reveals a surprising link with central questions in universal algebra. This monograph presents a self-contained introduction to the universal-algebraic approach to complexity classification, treating both finite and infinite-domain CSPs. It includes the required background from logic and combinatorics, particularly model theory and Ramsey theory, and explains the recently discovered link between Ramsey theory and topological dynamics and its implications for CSPs. The book will be of interest to graduate students and researchers in theoretical computer science and to mathematicians in logic, combinatorics, and dynamics who wish to learn about the applications of their work in complexity theory.

By: Manuel Bodirsky
Imprint: Cambridge University Press
Country of Publication: United Kingdom
Dimensions: Height: 235mm, Width: 158mm, Spine: 34mm
Weight: 950g
ISBN: 9781107042841
ISBN 10: 1107042844
Series: Lecture Notes in Logic
Pages: 300
Publication Date: 10 June 2021
Audience: Professional and scholarly , Undergraduate
Format: Hardback
Publisher's Status: Active

Manuel Bodirsky is Professor at the Institute for Algebra in the Faculty of Mathematics at TU Dresden.

Reviews for Complexity of Infinite-Domain Constraint Satisfaction

'... this book is essential reading for anyone with the vaguest interest in computational complexity, as well as for those curious about potential applications of model theory and universal algebra. It brings together decades of intense research by different research communities in a uniform format.' Victor Lagerkvist, MathSciNet