Descriptive complexity theory establishes a connection between the computational complexity of algorithmic problems (the computational resources required to solve the problems) and their descriptive complexity (the language resources required to describe the problems). This groundbreaking book approaches descriptive complexity from the angle of modern structural graph theory, specifically graph minor theory. It develops a 'definable structure theory' concerned with the logical definability of graph theoretic concepts such as tree decompositions and embeddings. The first part starts with an introduction to the background, from logic, complexity, and graph theory, and develops the theory up to first applications in descriptive complexity theory and graph isomorphism testing. It may serve as the basis for a graduate-level course. The second part is more advanced and mainly devoted to the proof of a single, previously unpublished theorem: properties of graphs with excluded minors are decidable in polynomial time if, and only if, they are definable in fixed-point logic with counting.

Descriptive Complexity, Canonisation, and Definable Graph Structure Theory

We give a logical characterization of the polynomial-time properties of graphs embeddable in some surface. For every surface S, a property P of graphs embeddable in S is decidable in polynomial time if and only if it is definable in fixed-point logic with counting. It is a consequence of this result that for every surface S there is a k such that a simple combinatorial algorithm, namely “the k-dimensional Weisfeiler-Lehman algorithm”, decides isomorphism of graphs embeddable in S in polynomial time.We also present (without proof) generalizations of these results to arbitrary classes of graphs with excluded minors.

Fixed-point definability and polynomial time on graphs with excluded minors

Complex dynamics of iterated entire holomorphic functions is an active and exciting area of research. This manuscript collects known background in this field and describes several of the most active research areas within the dynamics of entire functions.

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Dynamics of Entire Functions

We give a new, simplified and detailed account of the correspondence
between levels of the Sherali-Adams relaxation of graph isomorphism
and levels of pebble-game equivalence with counting (higher-dimensional Weisfeiler-Lehman colour refinement). The correspondence between basic colour refinement and fractional isomorphism, due to Ramana, Scheinerman and Ullman, is re-interpreted as the base level of Sherali-Adams and generalised to higher levels in this sense by Atserias and Maneva, who prove that the two resulting hierarchies interleave.

In carrying this analysis further, we here give (a) a precise characterisation of the level-k Sherali-Adams relaxation in terms of a modified counting pebble game; (b) a variant of the Sherali-Adams levels that precisely match the k-pebble counting game; (c) a proof that the interleaving between these two hierarchies is strict.

We also investigate the variation based on boolean arithmetic instead
of real/rational arithmetic and obtain analogous correspondences and
separations for plain k-pebble equivalence (without counting). Our
results are driven by considerably simplified accounts of the
underlying combinatorics and linear algebra.

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Pebble Games and Linear Equations

In 1982, Neil Immerman proposed an extension of fixed-point logic by means of counting quantifiers (which we denote FPC) as a logic that might express all polynomial-time properties of unordered graphs. It was eventually proved (by Cai, Furer and Immerman) that there are polynomial-time graph properties that are not expressible in FPC. Nonetheless, FPC is a powerful and natural fragment of the complexity class PTime. In this article, I justify this claim by reviewing three recent positive results that demonstrate the expressive power and robustness of this logic.

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The nature and power of fixed-point logic with counting

We introduce extensions of first-order logic (FO) and fixed-point logic (FP) with operators that compute the rank of a definable matrix. These operators are generalizations of the counting operations in FP+C (i.e. fixed-point logic with counting) that allow us to count the dimension of a definable vector space, rather than just count the cardinality of a definable set. The logics we define have data complexity contained in polynomial time and all known examples of polynomial time queries that are not definable in FP+C are definable in FP+rk, the extension of FP with rank operators. For each prime number p and each positive integer n, we have rank operators rk_p for determining the rank of a matrix over the finite field GF_p defined by a formula over n-tuples. We compare the expressive power of the logics obtained by varying the values p and n can take. In particular, we show that increasing the arity of the operators yields an infinite hierarchy of expressive power. The rank operators are surprisingly expressive, even in the absence of fixed-point operators. We show that FO+rk_p can define deterministic and symmetric transitive closure. This allows us to show that, on ordered structures, FO+rk_p captures the complexity class MOD_pL, for all prime values of p.

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Logics with Rank Operators

We present a logspace algorithm for computing a canonical labeling, in fact, a canonical interval representation, for interval graphs. To achieve this, we compute canonical interval representations of interval hypergraphs. This approach also yields a canonical labeling of convex graphs. As a consequence, the isomorphism and automorphism problems for these graph classes are solvable in logspace. For proper interval graphs we also design logspace algorithms computing their canonical representations by proper and by unit interval systems.

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Interval Graphs: Canonical Representations in Logspace

Descriptive complexity theory is the area of theoretical computer science that is concerned with the abstract characterization of computations by means of mathematical logics. It shares the approach of complexity theory to classify problems on the basis of the resources that are needed to solve them. Instead of computational resources, however, descriptive complexity theory considers the expressiveness necessary for a logic to define classes of problems. Its primary goal is to provide alternative characterizations of computational complexity that give us a way to reason about the strengths and limitations of computational procedures without reference to the underlying machine model. This thesis is making contributions to three strands of descriptive complexity theory. First, we consider an incongruence between machine computations, which receive ordered strings as their input, and logics, whose abstract view on input structures omits the ordering. We show that a combinatorial, singly exponential-time graph canonization algorithm of Corneil and Goldberg (1984) can be extended to edge-colored directed graphs. The algorithm’s input invariance allows us to implement it in the logic Choiceless Polynomial Time with Counting (CPT+C) if we restrict our attention to logarithmic-sized fragments of such graphs. This means that on structures whose relations are of arity at most 2, the polynomial-time (PTIME) properties of logarithmic-sized fragments are precisely characterized by CPT+C. The second contribution investigates the descriptive complexity of PTIME computations on restricted classes of graphs. We present a novel canonical form for the class of interval graphs which is definable in fixed-point logic with counting (FP+C), resulting in FP+C capturing PTIME on this graph class. The methods developed for this result may serve as the foundation for a systematic study of the interrelation of logics and complexity classes on the basis of modular decompositions of graphs. Then we introduce the notion of non-capturing reductions that lead us to a wide variety of graph classes on which computational problems are equally hard to characterize as on the class of all graphs. We finally mold our methods into a canonical labeling algorithm for interval graphs which is computable in logarithmic space (LOGSPACE) and we draw the conclusion that interval graph isomorphism is complete for LOGSPACE. In this way, our methods developed for the logical domain make a contribution to classical complexity theory. In the final part of this thesis, we take aim at the open question whether there exists a logic which generally captures polynomial-time computations. We introduce a variety of rank logics with the ability to compute the ranks of matrices over (finite) prime fields. We argue that this introduction of linear algebra makes for a robust notion of a logic and that it increases the expressiveness of FP+C and many other logics considered in the context of capturing PTIME. By adapting a construction of Hella (1996), we establish that rank logics strictly gain in expressiveness when we increase the number of variables that index the matrices we consider. Rank computations are the first natural operation for which this property is shown in the presence of recursion. Our particular choice of rank computations is justified by showing that most classic problems in linear algebra can either be defined by rank logics or are already definable in FP+C. Then we establish a direct connection to standard complexity theory by showing that in the presence of orders, a variety of complexity classes between LOGSPACE and PTIME can be characterized by suitably defined rank logics. Our exposition provides evidence that rank logics are a natural object to study and establishes the most expressive of our rank logics as a viable candidate for capturing PTIME, suggesting that rank logics need to be better understood if progress is to be made towards a logic for polynomial time.

The structure of graphs and new logics for the characterization of Polynomial Time

We prove a characterization of all polynomialtime computable queries on the class of interval graphs by sentences of fixed-point logic with counting. More precisely, it is shown that on the class of unordered interval graphs, any query is polynomial-time computable if and only if it is definable in fixed-point logic with counting. This result is one of the first establishing the capturing of polynomial time on a graph class which is defined by forbidden induced subgraphs. For this, we define a canonical form of interval graphs using a type of modular decomposition, which is different from the method of tree decomposition that is used in most known capturing results for other graph classes, specifically those defined by forbidden minors. The method might also be of independent interest for its conceptual simplicity. Furthermore, it is shown that fixed-point logic with counting is not expressive enough to capture polynomial time on the classes of chordal graphs or incomparability graphs.

Capturing Polynomial Time on Interval Graphs

We prove a characterization of all polynomial-time computable queries on the class of interval graphs by sentences of fixed-point logic with counting. More precisely, it is shown that on the class of unordered interval graphs, any query is polynomial-time computable if and only if it is definable in fixed-point logic with counting. This result is one of the first establishing the capturing of polynomial time on a graph class which is defined by forbidden induced subgraphs. For this, we define a canonical form of interval graphs using a type of modular decomposition, which is different from the method of tree decomposition that is used in most known capturing results for other graph classes, specifically those defined by forbidden minors. The method might also be of independent interest for its conceptual simplicity. Furthermore, it is shown that fixed-point logic with counting is not expressive enough to capture polynomial time on the classes of chordal graphs or incomparability graphs.

Bastian Laubner

Papers

Logics with Rank Operators

Interval Graphs: Canonical Representations in Logspace

The structure of graphs and new logics for the characterization of Polynomial Time

Capturing Polynomial Time on Interval Graphs

Capturing Polynomial Time on Interval Graphs