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Many fundamental combinatorial problems, arising in such diverse fields as artificial intelligence, logic, graph theory, and linear algebra, can be formulated as Boolean constraint satisfaction problems (CSP). This book is devoted to the study of the complexity of such problems. The authors' goal is to develop a framework for classifying the complexity of Boolean CSP in a uniform way. In doing so, they bring out common themes underlying many concepts and results in both algorithms and complexity theory. The results and techniques presented here show that Boolean CSP provide an excellent framework for discovering and formally validating "global" inferences about the nature of computation.
Book Synopsis Complexity Classifications of Boolean Constraint Satisfaction Problems by : Nadia Creignou
Download or read book Complexity Classifications of Boolean Constraint Satisfaction Problems written by Nadia Creignou and published by SIAM. This book was released on 2001-01-01 with total page 112 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many fundamental combinatorial problems, arising in such diverse fields as artificial intelligence, logic, graph theory, and linear algebra, can be formulated as Boolean constraint satisfaction problems (CSP). This book is devoted to the study of the complexity of such problems. The authors' goal is to develop a framework for classifying the complexity of Boolean CSP in a uniform way. In doing so, they bring out common themes underlying many concepts and results in both algorithms and complexity theory. The results and techniques presented here show that Boolean CSP provide an excellent framework for discovering and formally validating "global" inferences about the nature of computation.
Book Synopsis Complexity Results for Boolean Constraint Satisfaction Problems by : Michael Bauland
Download or read book Complexity Results for Boolean Constraint Satisfaction Problems written by Michael Bauland and published by Cuvillier Verlag. This book was released on 2007 with total page 103 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Nowadays constraint satisfaction problems (CSPs) are ubiquitous in many different areas of computer science, from artificial intelligence and database systems to circuit design, network optimization, and theory of programming languages. Consequently, it is important to analyze and pinpoint the computational complexity of certain algorithmic tasks related to constraint satisfaction. The complexity-theoretic results of these tasks may have a direct impact on, for instance, the design and processing of database query languages, or strategies in data-mining, or the design and implementation of planners. This state-of-the-art survey contains the papers that were invited by the organizers after conclusion of an International Dagstuhl-Seminar on Complexity of Constraints, held in Dagstuhl Castle, Germany, in October 2006. A number of speakers were solicited to write surveys presenting the state of the art in their area of expertise. These contributions were peer-reviewed by experts in the field and revised before they were collated to the 9 papers of this volume. In addition, the volume contains a reprint of a survey by Kolaitis and Vardi on the logical approach to constraint satisfaction that first appeared in 'Finite Model Theory and its Applications', published by Springer in 2007.
Book Synopsis Complexity of Constraints by : Nadia Creignou
Download or read book Complexity of Constraints written by Nadia Creignou and published by Springer Science & Business Media. This book was released on 2008-12-18 with total page 326 pages. Available in PDF, EPUB and Kindle. Book excerpt: Nowadays constraint satisfaction problems (CSPs) are ubiquitous in many different areas of computer science, from artificial intelligence and database systems to circuit design, network optimization, and theory of programming languages. Consequently, it is important to analyze and pinpoint the computational complexity of certain algorithmic tasks related to constraint satisfaction. The complexity-theoretic results of these tasks may have a direct impact on, for instance, the design and processing of database query languages, or strategies in data-mining, or the design and implementation of planners. This state-of-the-art survey contains the papers that were invited by the organizers after conclusion of an International Dagstuhl-Seminar on Complexity of Constraints, held in Dagstuhl Castle, Germany, in October 2006. A number of speakers were solicited to write surveys presenting the state of the art in their area of expertise. These contributions were peer-reviewed by experts in the field and revised before they were collated to the 9 papers of this volume. In addition, the volume contains a reprint of a survey by Kolaitis and Vardi on the logical approach to constraint satisfaction that first appeared in 'Finite Model Theory and its Applications', published by Springer in 2007.
This dissertation furthers a systematic study of the complexity classification of counting problems. A central goal of this study is to prove complexity classification theorems which state that every problem in some large class is either polynomial-time computable (tractable) or #P-hard. Such classification results are important as they tend to give a unified explanation for the tractability of certain counting problems and a reasonable basis for the conjecture that the remaining problems are inherently intractable. In this dissertation, we focus on the framework of Holant problems on Boolean variables, as well as other frameworks that are expressible as Holant problems, such as counting constraint satisfaction problems and counting Eulerian orientation problems. First, we prove a complexity dichotomy for Holant problems on the Boolean domain with arbitrary sets of real-valued constraint functions. It is proved that for every set F of real-valued constraint functions, Holant(F) is either tractable or #P-hard. The classification has an explicit criterion. This is a culmination of much research on this decade-long study, and it uses many previous results and techniques. On the other hand, to achieve the present result, many new tools were developed, and a novel connection with quantum information theory was built. In particular, two functions exhibiting intriguing and extraordinary closure properties are related to Bell states in quantum information theory. Dealing with these functions plays an important role in the proof. Then, we consider the complexity of Holant problems with respect to planar graphs, where physicists had discovered some remarkable algorithms, such as the FKT algorithm for counting planar perfecting matchings in polynomial time. For a basic case of Holant problems, called six-vertex models, we discover a new tractable class over planar graphs beyond the reach of the FKT algorithm. After carving out this new planar tractable class which had not been discovered for six-vertex models in the past six decades, we prove that everything else is #P-hard, even for the planar case. This leads to a complete complexity classification for planar six-vertex models. This result is the first substantive advance towards a planar Holant classification with asymmetric constraints. We hope this work can help us better understand a fundamental question in theoretical computer science: What does it mean for a computational counting problem to be easy or to be hard?
Book Synopsis Complexity Classification of Counting Problems on Boolean Variables by : Shuai Shao
Download or read book Complexity Classification of Counting Problems on Boolean Variables written by Shuai Shao and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation furthers a systematic study of the complexity classification of counting problems. A central goal of this study is to prove complexity classification theorems which state that every problem in some large class is either polynomial-time computable (tractable) or #P-hard. Such classification results are important as they tend to give a unified explanation for the tractability of certain counting problems and a reasonable basis for the conjecture that the remaining problems are inherently intractable. In this dissertation, we focus on the framework of Holant problems on Boolean variables, as well as other frameworks that are expressible as Holant problems, such as counting constraint satisfaction problems and counting Eulerian orientation problems. First, we prove a complexity dichotomy for Holant problems on the Boolean domain with arbitrary sets of real-valued constraint functions. It is proved that for every set F of real-valued constraint functions, Holant(F) is either tractable or #P-hard. The classification has an explicit criterion. This is a culmination of much research on this decade-long study, and it uses many previous results and techniques. On the other hand, to achieve the present result, many new tools were developed, and a novel connection with quantum information theory was built. In particular, two functions exhibiting intriguing and extraordinary closure properties are related to Bell states in quantum information theory. Dealing with these functions plays an important role in the proof. Then, we consider the complexity of Holant problems with respect to planar graphs, where physicists had discovered some remarkable algorithms, such as the FKT algorithm for counting planar perfecting matchings in polynomial time. For a basic case of Holant problems, called six-vertex models, we discover a new tractable class over planar graphs beyond the reach of the FKT algorithm. After carving out this new planar tractable class which had not been discovered for six-vertex models in the past six decades, we prove that everything else is #P-hard, even for the planar case. This leads to a complete complexity classification for planar six-vertex models. This result is the first substantive advance towards a planar Holant classification with asymmetric constraints. We hope this work can help us better understand a fundamental question in theoretical computer science: What does it mean for a computational counting problem to be easy or to be hard?
Introduces the universal-algebraic approach to classifying the computational complexity of constraint satisfaction problems.
Book Synopsis Complexity of Infinite-Domain Constraint Satisfaction by : Manuel Bodirsky
Download or read book Complexity of Infinite-Domain Constraint Satisfaction written by Manuel Bodirsky and published by Cambridge University Press. This book was released on 2021-06-10 with total page 537 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduces the universal-algebraic approach to classifying the computational complexity of constraint satisfaction problems.
The topic of this book is the following optimisation problem: given a set of discrete variables and a set of functions, each depending on a subset of the variables, minimise the sum of the functions over all variables. This fundamental research problem has been studied within several different contexts of discrete mathematics, computer science and artificial intelligence under different names: Min-Sum problems, MAP inference in Markov random fields (MRFs) and conditional random fields (CRFs), Gibbs energy minimisation, valued constraint satisfaction problems (VCSPs), and, for two-state variables, pseudo-Boolean optimisation. In this book the author presents general techniques for analysing the structure of such functions and the computational complexity of the minimisation problem, and he gives a comprehensive list of tractable cases. Moreover, he demonstrates that the so-called algebraic approach to VCSPs can be used not only for the search for tractable VCSPs, but also for other questions such as finding the boundaries to the applicability of certain algorithmic techniques. The book is suitable for researchers interested in methods and results from the area of constraint programming and discrete optimisation.
Book Synopsis The Complexity of Valued Constraint Satisfaction Problems by : Stanislav Živný
Download or read book The Complexity of Valued Constraint Satisfaction Problems written by Stanislav Živný and published by Springer Science & Business Media. This book was released on 2012-10-19 with total page 176 pages. Available in PDF, EPUB and Kindle. Book excerpt: The topic of this book is the following optimisation problem: given a set of discrete variables and a set of functions, each depending on a subset of the variables, minimise the sum of the functions over all variables. This fundamental research problem has been studied within several different contexts of discrete mathematics, computer science and artificial intelligence under different names: Min-Sum problems, MAP inference in Markov random fields (MRFs) and conditional random fields (CRFs), Gibbs energy minimisation, valued constraint satisfaction problems (VCSPs), and, for two-state variables, pseudo-Boolean optimisation. In this book the author presents general techniques for analysing the structure of such functions and the computational complexity of the minimisation problem, and he gives a comprehensive list of tractable cases. Moreover, he demonstrates that the so-called algebraic approach to VCSPs can be used not only for the search for tractable VCSPs, but also for other questions such as finding the boundaries to the applicability of certain algorithmic techniques. The book is suitable for researchers interested in methods and results from the area of constraint programming and discrete optimisation.
In this thesis we study the worst-case complexity ofconstraint satisfaction problems and some of its variants. We use methods from universal algebra: in particular, algebras of total functions and partial functions that are respectively known as clones and strong partial clones. The constraint satisfactionproblem parameterized by a set of relations ? (CSP(?)) is the following problem: given a set of variables restricted by a set of constraints based on the relations ?, is there an assignment to thevariables that satisfies all constraints? We refer to the set ? as aconstraint language. The inverse CSPproblem over ? (Inv-CSP(?)) asks the opposite: given a relation R, does there exist a CSP(?) instance with R as its set of models? When ? is a Boolean language, then we use the term SAT(?) instead of CSP(?) and Inv-SAT(?) instead of Inv-CSP(?). Fine-grained complexity is an approach in which we zoom inside a complexity class and classify theproblems in it based on their worst-case time complexities. We start by investigating the fine-grained complexity of NP-complete CSP(?) problems. An NP-complete CSP(?) problem is said to be easier than an NP-complete CSP(?) problem if the worst-case time complexity of CSP(?) is not higher thanthe worst-case time complexity of CSP(?). We first analyze the NP-complete SAT problems that are easier than monotone 1-in-3-SAT (which can be represented by SAT(R) for a certain relation R), and find out that there exists a continuum of such problems. For this, we use the connection between constraint languages and strong partial clones and exploit the fact that CSP(?) is easier than CSP(?) when the strong partial clone corresponding to ? contains the strong partial clone of ?. An NP-complete CSP(?) problem is said to be the easiest with respect to a variable domain D if it is easier than any other NP-complete CSP(?) problem of that domain. We show that for every finite domain there exists an easiest NP-complete problem for the ultraconservative CSP(?) problems. An ultraconservative CSP(?) is a special class of CSP problems where the constraint language containsall unary relations. We additionally show that no NP-complete CSP(?) problem can be solved insub-exponential time (i.e. in2^o(n) time where n is the number of variables) given that theexponentialtime hypothesisis true. Moving to classical complexity, we show that for any Boolean constraint language ?, Inv-SAT(?) is either in P or it is coNP-complete. This is a generalization of an earlier dichotomy result, which was only known to be true for ultraconservative constraint languages. We show that Inv-SAT(?) is coNP-complete if and only if the clone corresponding to ? contains essentially unary functions only. For arbitrary finite domains our results are not conclusive, but we manage to prove that theinversek-coloring problem is coNP-complete for each k>2. We exploit weak bases to prove many of theseresults. A weak base of a clone C is a constraint language that corresponds to the largest strong partia clone that contains C. It is known that for many decision problems X(?) that are parameterized bya constraint language ?(such as Inv-SAT), there are strong connections between the complexity of X(?) and weak bases. This fact can be exploited to achieve general complexity results. The Boolean domain is well-suited for this approach since we have a fairly good understanding of Boolean weak bases. In the final result of this thesis, we investigate the relationships between the weak bases in the Boolean domain based on their strong partial clones and completely classify them according to the setinclusion. To avoid a tedious case analysis, we introduce a technique that allows us to discard a largenumber of cases from further investigation.
Book Synopsis Applications of Partial Polymorphisms in (Fine-Grained) Complexity of Constraint Satisfaction Problems by : Biman Roy
Download or read book Applications of Partial Polymorphisms in (Fine-Grained) Complexity of Constraint Satisfaction Problems written by Biman Roy and published by Linköping University Electronic Press. This book was released on 2020-03-23 with total page 57 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this thesis we study the worst-case complexity ofconstraint satisfaction problems and some of its variants. We use methods from universal algebra: in particular, algebras of total functions and partial functions that are respectively known as clones and strong partial clones. The constraint satisfactionproblem parameterized by a set of relations ? (CSP(?)) is the following problem: given a set of variables restricted by a set of constraints based on the relations ?, is there an assignment to thevariables that satisfies all constraints? We refer to the set ? as aconstraint language. The inverse CSPproblem over ? (Inv-CSP(?)) asks the opposite: given a relation R, does there exist a CSP(?) instance with R as its set of models? When ? is a Boolean language, then we use the term SAT(?) instead of CSP(?) and Inv-SAT(?) instead of Inv-CSP(?). Fine-grained complexity is an approach in which we zoom inside a complexity class and classify theproblems in it based on their worst-case time complexities. We start by investigating the fine-grained complexity of NP-complete CSP(?) problems. An NP-complete CSP(?) problem is said to be easier than an NP-complete CSP(?) problem if the worst-case time complexity of CSP(?) is not higher thanthe worst-case time complexity of CSP(?). We first analyze the NP-complete SAT problems that are easier than monotone 1-in-3-SAT (which can be represented by SAT(R) for a certain relation R), and find out that there exists a continuum of such problems. For this, we use the connection between constraint languages and strong partial clones and exploit the fact that CSP(?) is easier than CSP(?) when the strong partial clone corresponding to ? contains the strong partial clone of ?. An NP-complete CSP(?) problem is said to be the easiest with respect to a variable domain D if it is easier than any other NP-complete CSP(?) problem of that domain. We show that for every finite domain there exists an easiest NP-complete problem for the ultraconservative CSP(?) problems. An ultraconservative CSP(?) is a special class of CSP problems where the constraint language containsall unary relations. We additionally show that no NP-complete CSP(?) problem can be solved insub-exponential time (i.e. in2^o(n) time where n is the number of variables) given that theexponentialtime hypothesisis true. Moving to classical complexity, we show that for any Boolean constraint language ?, Inv-SAT(?) is either in P or it is coNP-complete. This is a generalization of an earlier dichotomy result, which was only known to be true for ultraconservative constraint languages. We show that Inv-SAT(?) is coNP-complete if and only if the clone corresponding to ? contains essentially unary functions only. For arbitrary finite domains our results are not conclusive, but we manage to prove that theinversek-coloring problem is coNP-complete for each k>2. We exploit weak bases to prove many of theseresults. A weak base of a clone C is a constraint language that corresponds to the largest strong partia clone that contains C. It is known that for many decision problems X(?) that are parameterized bya constraint language ?(such as Inv-SAT), there are strong connections between the complexity of X(?) and weak bases. This fact can be exploited to achieve general complexity results. The Boolean domain is well-suited for this approach since we have a fairly good understanding of Boolean weak bases. In the final result of this thesis, we investigate the relationships between the weak bases in the Boolean domain based on their strong partial clones and completely classify them according to the setinclusion. To avoid a tedious case analysis, we introduce a technique that allows us to discard a largenumber of cases from further investigation.
Complexity theory aims to understand and classify computational problems, especially decision problems, according to their inherent complexity. This book uses new techniques to expand the theory for use with counting problems. The authors present dichotomy classifications for broad classes of counting problems in the realm of P and NP. Classifications are proved for partition functions of spin systems, graph homomorphisms, constraint satisfaction problems, and Holant problems. The book assumes minimal prior knowledge of computational complexity theory, developing proof techniques as needed and gradually increasing the generality and abstraction of the theory. This volume presents the theory on the Boolean domain, and includes a thorough presentation of holographic algorithms, culminating in classifications of computational problems studied in exactly solvable models from statistical mechanics
Book Synopsis Complexity Dichotomies for Counting Problems by : Jin-yi Cai
Download or read book Complexity Dichotomies for Counting Problems written by Jin-yi Cai and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Complexity theory aims to understand and classify computational problems, especially decision problems, according to their inherent complexity. This book uses new techniques to expand the theory for use with counting problems. The authors present dichotomy classifications for broad classes of counting problems in the realm of P and NP. Classifications are proved for partition functions of spin systems, graph homomorphisms, constraint satisfaction problems, and Holant problems. The book assumes minimal prior knowledge of computational complexity theory, developing proof techniques as needed and gradually increasing the generality and abstraction of the theory. This volume presents the theory on the Boolean domain, and includes a thorough presentation of holographic algorithms, culminating in classifications of computational problems studied in exactly solvable models from statistical mechanics
Book Synopsis Complexity Dichotomies for Counting Problems by : Jin-Yi Cai
Download or read book Complexity Dichotomies for Counting Problems written by Jin-Yi Cai and published by Cambridge University Press. This book was released on 2017-11-16 with total page 473 pages. Available in PDF, EPUB and Kindle. Book excerpt: Volume 1. Boolean domain
Abstract: "In this paper we study the approximability of boolean constraint satisfaction problems. A problem in this class consists of some collection of 'constraints' (i.e., functions f: [0,1][superscript k] -> [0,1]); an instance of a problem is a set of constraints applied to specified subsets of n boolean variables. Schaefer earlier studied the question of whether one could find in polynomial time a setting of the variables satisfying all constraints; he showed that every such problem is either in P or is NP-complete. We consider optimization variants of these problems in which one either tries to maximize the number of satisfied constraints (as in MAX 3SAT or MAX CUT) or tries to find an assignment satisfying all constraints which maximizes the number of variables set to 1 (as in MAX CUT or MAX CLIQUE). We completely classify the approximability of all such problems. In the first case, we show that any such optimization problem is either in P or is MAX SNP-hard. In the second case, we show that such problems fall precisely into one of five classes: solvable in polynomial-time, approximable to within constant factors in polynomial time (but no better), approximable to within polynomial factors in polynomial time (but no better), not approximable to within any factor but decidable in polynomial time, and not decidable in polynomial time (unless P = NP). This result proves formally for this class of problems two results which to this point have only been empirical observations; namely, that NP-hard problems in MAX SNP alwyas turn out to be MAX SNP-hard, and that there seem to be no natural maximization problems approximable to within polylogarithmic factors but no better."
Book Synopsis A Complete Classification of the Approximability of Maximization Problems Derived from Boolean Constraint Satisfaction by : International Business Machines Corporation. Research Division
Download or read book A Complete Classification of the Approximability of Maximization Problems Derived from Boolean Constraint Satisfaction written by International Business Machines Corporation. Research Division and published by . This book was released on 1997 with total page 27 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: "In this paper we study the approximability of boolean constraint satisfaction problems. A problem in this class consists of some collection of 'constraints' (i.e., functions f: [0,1][superscript k] -> [0,1]); an instance of a problem is a set of constraints applied to specified subsets of n boolean variables. Schaefer earlier studied the question of whether one could find in polynomial time a setting of the variables satisfying all constraints; he showed that every such problem is either in P or is NP-complete. We consider optimization variants of these problems in which one either tries to maximize the number of satisfied constraints (as in MAX 3SAT or MAX CUT) or tries to find an assignment satisfying all constraints which maximizes the number of variables set to 1 (as in MAX CUT or MAX CLIQUE). We completely classify the approximability of all such problems. In the first case, we show that any such optimization problem is either in P or is MAX SNP-hard. In the second case, we show that such problems fall precisely into one of five classes: solvable in polynomial-time, approximable to within constant factors in polynomial time (but no better), approximable to within polynomial factors in polynomial time (but no better), not approximable to within any factor but decidable in polynomial time, and not decidable in polynomial time (unless P = NP). This result proves formally for this class of problems two results which to this point have only been empirical observations; namely, that NP-hard problems in MAX SNP alwyas turn out to be MAX SNP-hard, and that there seem to be no natural maximization problems approximable to within polylogarithmic factors but no better."
