Jakub Gajarský

Kernelization Using Structural Parameters on Sparse Graph Classes

Meta-theorems for polynomial (linear) kernels have been the subject of intensive research in parameterized complexity. Heretofore, there were meta-theorems for linear kernels on graphs of bounded genus, H-minor-free graphs, and H-topological-minor-free graphs. To the best of our knowledge, there are no known meta-theorems for kernels for any of the larger sparse graph classes: graphs of bounded expansion, locally bounded expansion, and nowhere dense graphs. In this paper we prove meta-theorems for these three graph classes. More specifically, we show that graph problems that have finite integer index (FII) admit linear kernels on hereditary graphs of bounded expansion when parameterized by the size of a modulator to constant-treedepth graphs. For hereditary graph classes of locally bounded expansion, our result yields a quadratic kernel and for hereditary nowhere dense graphs, a polynomial kernel. While our parameter may seem rather strong, a linear kernel result on graphs of bounded expansion with a weaker parameter would for some problems violate known lower bounds. Moreover, we use a relaxed notion of FII which allows us to prove linear kernels for problems such as Longest Path/Cycle and Exact s,t-Path which do not have FII in general graphs.

Kernelization using structural parameters on sparse graph classes

We prove that graph problems with finite integer index have linear kernels on graphs of bounded expansion when parameterized by the size of a modulator to constant-treedepth graphs. For nowhere dense graph classes, our result yields almost-linear kernels. We also argue that such a linear kernelization result with a weaker parameter would fail to include some of the problems covered by our framework. We only require the problems to have FII on graphs of constant treedepth. This allows to prove linear kernels also for problems such as Longest-Path/Cycle, Exact- s , t -Path, Treewidth, and Pathwidth, which do not have FII on general graphs. Meta-theorems for linear kernels have been the subject of intensive research.We follow the line toward even larger graph classes using stronger parametrization.FII problems have linear kernels on graphs of bounded expansion, parameterized by the size of a treedepth-modulator.For nowhere dense classes, this yields almost-linear kernels.FII is required only on graphs of bounded treedepth.

A New Perspective on FO Model Checking of Dense Graph Classes

We study the FO model checking problem of dense graph classes, namely those which are FO-interpretable in some sparse graph classes. Note that if an input dense graph is given together with the corresponding FO interpretation in a sparse graph, one can easily solve the model checking problem using the existing algorithms for sparse graph classes. However, if the assumed interpretation is not given, then the situation is markedly harder.In this paper we give a structural characterization of graph classes which are FO interpretable in graph classes of bounded degree. This characterization allows us to efficiently compute such an interpretation for an input graph. As a consequence, we obtain an FPT algorithm for FO model checking of graph classes FO interpretable in graph classes of bounded degree. The approach we use to obtain these results may also be of independent interest.

First order limits of sparse graphs: Plane trees and path-width

Nesetřil and Ossona de Mendez introduced the notion of first order convergence as an attempt to unify the notions of convergence for sparse and dense graphs. It is known that there exist first order convergent sequences of graphs with no limit modeling (an analytic representation of the limit). On the positive side, every first order convergent sequence of trees or graphs with no long path (graphs with bounded tree-depth) has a limit modeling. We strengthen these results by showing that every first order convergent sequence of plane trees (trees with embeddings in the plane) and every first order convergent sequence of graphs with bounded path-width has a limit modeling.

Parameterized Algorithms for Parity Games

Determining the winner of a Parity Game is a major problem in computational complexity with a number of applications in verification. In a parameterized complexity setting, the problem has often been considered with parameters such as (directed versions of) treewidth or clique-width, by applying dynamic programming techniques.

Faster Existential FO Model Checking on Posets

We prove that the model checking problem for the existential fragment of first order (FO) logic on partially ordered sets is fixed-parameter tractable (FPT) with respect to the formula and the width of a poset (the maximum size of an antichain). While there is a long line of research into FO model checking on graphs, the study of this problem on posets has been initiated just recently by Bova, Ganian and Szeider (LICS 2014), who proved that the existential fragment of FO has an FPT algorithm for a poset of fixed width. We improve upon their result in two ways: (1) the runtime of our algorithm is \(O(f(|\phi |,w)\cdot n^2)\) on \(n\)-element posets of width \(w\), compared to \(O(g(|\phi |)\cdot n^{h(w)})\) of Bova et al., and (2) our proofs are simpler and easier to follow. We complement this result by showing that, under a certain complexity-theoretical assumption, the existential FO model checking problem does not have a polynomial kernel.

Parameterized shifted combinatorial optimization

Abstract Shifted combinatorial optimization is a new nonlinear optimization framework broadly extending standard combinatorial optimization, involving the choice of several feasible solutions simultaneously. This framework captures well studied and diverse problems, from sharing and partitioning to so-called vulnerability problems. In particular, every standard combinatorial optimization problem has its shifted counterpart, typically harder. Already with explicitly given input set SCO may be NP -hard. Here we initiate a study of the parameterized complexity of this framework. First we show that SCO over an explicitly given set parameterized by its cardinality may be in XP , FPT or P , depending on the objective function. Second, we study SCO over sets definable in MSO logic (which includes, e.g., the well known MSO-partitioning problems). Our main results are that SCO over MSO definable sets is in XP parameterized by the MSO formula and treewidth (or clique-width) of the input graph, and W [1] -hard even under further severe restrictions.

Parameterized Shifted Combinatorial Optimization

Shifted combinatorial optimization is a new nonlinear optimization framework which is a broad extension of standard combinatorial optimization, involving the choice of several feasible solutions at a time. This framework captures well studied and diverse problems ranging from so-called vulnerability problems to sharing and partitioning problems. In particular, every standard combinatorial optimization problem has its shifted counterpart, which is typically much harder. Already with explicitly given input set the shifted problem may be NP-hard. In this article we initiate a study of the parameterized complexity of this framework. First we show that shifting over an explicitly given set with its cardinality as the parameter may be in XP, FPT or P, depending on the objective function. Second, we study the shifted problem over sets definable in MSO logic (which includes, e.g., the well known MSO partitioning problems). Our main results here are that shifted combinatorial optimization over MSO definable sets is in XP with respect to the MSO formula and the treewidth (or more generally clique-width) of the input graph, and is W[1]-hard even under further severe restrictions.

Parameterized Extension Complexity of Independent Set and Related Problems

Let

Finite Integer Index of Pathwidth and Treewidth

G