Abstract. For a fixed graph [Formula: see text], the [Formula: see text]-Recoloring problem asks whether, given two homomorphisms from a graph [Formula: see text] to [Formula: see text], one homomorphism can be transformed into the other by changing the image of a single vertex in each step and maintaining a homomorphism to [Formula: see text] throughout. The most general algorithmic result for [Formula: see text]-Recoloring so far was proposed by Wrochna in 2014, who introduced a topological approach to obtain a polynomial-time algorithm for any undirected loopless square-free graph [Formula: see text]. We show that the topological approach can be used to recover essentially all previous algorithmic results for [Formula: see text]-Recoloring and that it is applicable also in the more general setting of digraph homomorphisms. In particular, we show that [Formula: see text]-Recoloring admits a polynomial-time algorithm if (i) [Formula: see text] is a loopless digraph that does not contain a 4-cycle of algebraic girth 0 and (ii) [Formula: see text] is a reflexive digraph that contains no triangle of algebraic girth 1 and no 4-cycle of algebraic girth 0. In both cases, we obtain a polynomial-time algorithm for finding shortest transformations.

For a fixed graph H, the H-Recoloring problem asks whether, given two homomorphisms from a graph G to H, one homomorphism can be transformed into the other by changing the image of a single vertex in each step and maintaining a homomorphism to H throughout. The most general algorithmic result for H-Recoloring so far has been proposed by Wrochna in 2014, who introduced a topological approach to obtain a polynomial-time algorithm for any undirected loopless square-free graph H. We show that the topological approach can be used to recover essentially all previous algorithmic results for H-Recoloring and that it is applicable also in the more general setting of digraph homomorphisms. In particular, we show that H-Recoloring admits a polynomial-time algorithm i) if H is a loopless digraph that does not contain a 4-cycle of algebraic girth 0 and ii) if H is a reflexive digraph that contains no triangle of algebraic girth 1 and no 4-cycle of algebraic girth 0.

Abstract Meta-heuristics are powerful tools for solving optimization problems whose structural properties are unknown or cannot be exploited algorithmically. We propose such a meta-heuristic for a large class of optimization problems over discrete domains based on the particle swarm optimization (PSO) paradigm. We provide a comprehensive formal analysis of the performance of this algorithm on certain “easy” reference problems in a black-box setting, namely the sorting problem and the problem OneMax. In our analysis we use a Markov model of the proposed algorithm to obtain upper and lower bounds on its expected optimization time. Our bounds are essentially tight with respect to the Markov model. We show that for a suitable choice of algorithm parameters the expected optimization time is comparable to that of known algorithms and, furthermore, for other parameter regimes, the algorithm behaves less greedy and more explorative, which can be desirable in practice in order to escape local optima. Our analysis provides a precise insight on the tradeoff between optimization time and exploration. To obtain our results we introduce the notion of indistinguishability of states of a Markov chain and provide bounds on the solution of a recurrence equation with non-constant coefficients by integration.

Suppose we are given a bipartite graph that admits a perfect matching and an adversary may delete any edge from the graph with the intention of destroying all perfect matchings. We consider the task of adding a minimum-cost edge-set to the graph such that the adversary never wins. We show that this problem is equivalent to covering a digraph with nontrivial strongly connected components at minimal cost. We provide efficient exact and approximation algorithms for this task. In particular, for the unit-cost problem, we give a $\log_2 n$-factor approximation algorithm and a polynomial-time algorithm for chordal-bipartite graphs. Furthermore, we give a fixed parameter algorithm for the problem parameterized by the treewidth of the input graph. For general nonnegative weights we give tight upper and lower approximation bounds relative to the directed Steiner forest problem. Additionally, we prove a dichotomy theorem characterizing minor-closed graph classes which allow for a polynomial-time algorithm. To obtain our results, we exploit a close relation to the classical strong connectivity augmentation problem as well as directed Steiner problems.

Non-deterministic constraint logic (NCL) is a simple model of computation based on orientations of a constraint graph with edge weights and vertex demands. NCL captures PSPACE and has been a useful tool for proving algorithmic hardness of many puzzles, games, and reconfiguration problems. In particular, its usefulness stems from the fact that it remains PSPACE-complete even under severe restrictions of the weights (e.g., only edge-weights one and two are needed) and the structure of the constraint graph (e.g., planar AND/OR graphs of bounded bandwidth). While such restrictions on the structure of constraint graphs do not seem to limit the expressiveness of NCL, the building blocks of the constraint graphs cannot be limited without losing expressiveness: We consider as parameters the number of weight-one edges and the number of weight-two edges of a constraint graph, as well as the number of AND or OR vertices of an AND/OR constraint graph. We show that NCL is fixed-parameter tractable (FPT) for any of these parameters. In particular, for NCL parameterized by the number of weight-one edges or the number of AND vertices, we obtain a linear kernel. It follows that, in a sense, NCL as introduced by Hearn and Demaine is defined in the most economical way for the purpose of capturing PSPACE.

A k-coloring of a graph maps each vertex of the graph to a color in {1, 2, …, k}, such that no two adjacent vertices receive the same color. Given a k-coloring of a graph, a Kempe change produces a new k-coloring by swapping the colors in a bicolored connected component. We investigate the complexity of finding the smallest number of Kempe changes needed to transform a given k-coloring into another given k-coloring. We show that this problem admits a polynomial-time dynamic programming algorithm on path graphs, which turns out to be highly non-trivial. Furthermore, the problem is NP-hard even on star graphs and we show that on such graphs it admits a constant-factor approximation algorithm and is fixed-parameter tractable when parameterized by the number k of colors. The hardness result as well as the algorithmic results are based on the notion of a canonical transformation.

We study the perfect matching reconfiguration problem: Given two perfect matchings of a graph, is there a sequence of flip operations that transforms one into the other? Here, a flip operation exchanges the edges in an alternating cycle of length four. We are interested in the complexity of this decision problem from the viewpoint of graph classes. We first prove that the problem is PSPACE-complete even for split graphs and for bipartite graphs of bounded bandwidth with maximum degree five. We then investigate polynomial-time solvable cases. Specifically, we prove that the problem is solvable in polynomial time for strongly orderable graphs (that include interval graphs and strongly chordal graphs), for outerplanar graphs, and for cographs (also known as P_4-free graphs). Furthermore, for each yes-instance from these graph classes, we show that a linear number of flip operations is sufficient and we can exhibit a corresponding sequence of flip operations in polynomial time.

We present the analysis of a discrete particle swarm optimization (PSO) algorithm that works on a significantly large class of discrete optimization problems. Assuming a black-box setting, we prove upper and lower bounds on the expected number of function evaluations required by the proposed algorithm to solve the sorting problem and the problem of maximizing the number of ones in a bitstring, i.e., the function OneMax. We show that depending on the probability of moving towards the attractor, the expected optimization time may be polynomial or exponential. The cornerstone of our analysis are Theta-bounds on the expected time it takes until the PSO returns to the attractor. We obtain these bounds by solving linear recurrence equations with constant and non-constant coefficients. We also introduce a useful indistinguishability property of states of a Markov chain in order to obtain lower bounds on the expected optimization time of our proposed PSO algorithm.

In this paper, we present a strategy and an FPGA implementation of a Connect6 player submitted to the FPT 2011 Design Competition. Connect6 is a two-player strategy board game. The winner of the game is the player who first gets six pieces of his color in a connected horizontal, vertical or diagonal line. We assign a strategic value to each potential move depending on the current board configuration. Our approach uses a minimal amount of situation dependent game logic in order to take full advantage of the available compute resources and parallelism. The FPGA implementation of this strategy always wins against the software opponent provided for the competition. Additionally, our implementation wins on average against different software AIs from [1], as long as no sophisticated game-tree search is performed by the software.