IST Austria Thesis

An instance of the Constraint Satisfaction Problem (CSP) is given by a finite set of variables, a finite domain of labels, and a set of constraints, each constraint acting on a subset of the variables. The goal is to find an assignment of labels to its variables that satisfies all constraints (or decide whether one exists). If we allow more general “soft” constraints, which come with (possibly infinite) costs of particular assignments, we obtain instances from a richer class called Valued Constraint Satisfaction Problem (VCSP). There the goal is to find an assignment with minimum total cost. In this thesis, we focus (assuming that P 6 = NP) on classifying computational com- plexity of CSPs and VCSPs under certain restricting conditions. Two results are the core content of the work. In one of them, we consider VCSPs parametrized by a constraint language, that is the set of “soft” constraints allowed to form the instances, and finish the complexity classification modulo (missing pieces of) complexity classification for analogously parametrized CSP. The other result is a generalization of Edmonds’ perfect matching algorithm. This generalization contributes to complexity classfications in two ways. First, it gives a new (largest known) polynomial-time solvable class of Boolean CSPs in which every variable may appear in at most two constraints and second, it settles full classification of Boolean CSPs with planar drawing (again parametrized by a constraint language)

Complexity Theory

Computational Complexity Theory is the mathematical study of the intrinsic power and limitations of computational resources like time, space, or randomness. The current workshop focused on recent developments in various sub-areas including arithmetic complexity, Boolean complexity, communication complexity, cryptography, probabilistic proof systems, pseudorandomness, and quantum computation. Many of the developments are related to diverse mathematical fields such as algebraic geometry, combinatorial number theory, probability theory, representation theory, and the theory of error-correcting codes

Constraint Network Satisfaction for Finite Relation Algebras

Network satisfaction problems (NSPs) for finite relation algebras are computational decision problems, studied intensively since the 1990s. The major open research challenge in this field is to understand which of these problems are solvable by polynomial-time algorithms. Since there are known examples of undecidable NSPs of finite relation algebras it is advisable to restrict the scope of such a classification attempt to well-behaved subclasses of relation algebras. The class of relation algebras with a normal representation is such a well-behaved subclass. Many well-known examples of relation algebras, such as the Point Algebra, RCC5, and Allen’s Interval Algebra admit a normal representation. The great advantage of finite relation algebras with normal representations is that their NSP is essentially the same as a constraint satisfaction problem (CSP). For a relational structure B the problem CSP(B) is the computational problem to decide whether a given finite relational structure C has a homomorphism to B. The study of CSPs has a long and rich history, culminating for the time being in the celebrated proofs of the Feder-Vardi dichotomy conjecture. Bulatov and Zhuk independently proved that for every finite structure B the problem CSP(B) is in P or NP-complete. Both proofs rely on the universal-algebraic approach, a powerful theory that connects algebraic properties of structures B with complexity results for the decision problems CSP(B). Our contributions to the field are divided into three parts. Firstly, we provide two algebraic criteria for NP-hardness of NSPs. Our second result is a complete classification of the complexity of NSPs for symmetric relation algebras with a flexible atom; these problems are in P or NP-complete. Our result is obtained via a decidable condition on the relation algebra which implies polynomial-time tractability of the NSP. As a third contribution we prove that for a large class of NSPs, non-hardness implies that the problems can even be solved by Datalog programs, unless P = NP. This result can be used to strengthen the dichotomy result for NSPs of symmetric relation algebras with a flexible atom: every such problem can be solved by a Datalog program or is NP-complete. Our proof relies equally on known results and new observations in the algebraic analysis of finite structures. The CSPs that emerge from NSPs are typically of the form CSP(B) for an infinite structure B and therefore do not fall into the scope of the dichotomy result for finite structures. In this thesis we study NSPs of finite relation algebras with normal representations by the universal algebraic methods which were developed for the study of finite and infinite-domain CSPs. We additionally make use of model theory and a Ramsey-type result of Nešetril and Rödl. Our contributions to the field are divided into three parts. Firstly, we provide two algebraic criteria for NP-hardness of NSPs. Our second result is a complete classification of the complexity of NSPs for symmetric relation algebras with a flexible atom; these problems are in P or NP-complete. Our result is obtained via a decidable condition on the relation algebra which implies polynomial-time tractability of the NSP. As a third contribution we prove that for a large class of NSPs the containment in P implies that the problems can even be solved by Datalog programs, unless P = NP. As a third contribution we prove that for a large class of NSPs, non-hardness implies that the problems can even be solved by Datalog programs, unless P = NP. This result can be used to strengthen the dichotomy result for NSPs of symmetric relation algebras with a flexible atom: every such problem can be solved by a Datalog program or is NP-complete. Our proof relies equally on known results and new observations in the algebraic analysis of finite structures