19 research outputs found
A Linear Kernel for Planar Total Dominating Set
A total dominating set of a graph is a subset such
that every vertex in is adjacent to some vertex in . Finding a total
dominating set of minimum size is NP-hard on planar graphs and W[2]-complete on
general graphs when parameterized by the solution size. By the meta-theorem of
Bodlaender et al. [J. ACM, 2016], there exists a linear kernel for Total
Dominating Set on graphs of bounded genus. Nevertheless, it is not clear how
such a kernel can be effectively constructed, and how to obtain explicit
reduction rules with reasonably small constants. Following the approach of
Alber et al. [J. ACM, 2004], we provide an explicit kernel for Total Dominating
Set on planar graphs with at most vertices, where is the size of the
solution. This result complements several known constructive linear kernels on
planar graphs for other domination problems such as Dominating Set, Edge
Dominating Set, Efficient Dominating Set, Connected Dominating Set, or Red-Blue
Dominating Set.Comment: 33 pages, 13 figure
Explicit linear kernels via dynamic programming
Several algorithmic meta-theorems on kernelization have appeared in the last
years, starting with the result of Bodlaender et al. [FOCS 2009] on graphs of
bounded genus, then generalized by Fomin et al. [SODA 2010] to graphs excluding
a fixed minor, and by Kim et al. [ICALP 2013] to graphs excluding a fixed
topological minor. Typically, these results guarantee the existence of linear
or polynomial kernels on sparse graph classes for problems satisfying some
generic conditions but, mainly due to their generality, it is not clear how to
derive from them constructive kernels with explicit constants. In this paper we
make a step toward a fully constructive meta-kernelization theory on sparse
graphs. Our approach is based on a more explicit protrusion replacement
machinery that, instead of expressibility in CMSO logic, uses dynamic
programming, which allows us to find an explicit upper bound on the size of the
derived kernels. We demonstrate the usefulness of our techniques by providing
the first explicit linear kernels for -Dominating Set and -Scattered Set
on apex-minor-free graphs, and for Planar-\mathcal{F}-Deletion on graphs
excluding a fixed (topological) minor in the case where all the graphs in
\mathcal{F} are connected.Comment: 32 page
On improving matchings in trees, via bounded-length augmentations
Due to a classical result of Berge, it is known that a matching of any graph can be turned into a maximum matching by repeatedly augmenting alternating paths whose ends are not covered. In a recent work, Nisse, Salch and Weber considered the influence, on this process, of augmenting paths with length at most k only. Given a graph G, an initial matching M ⊆ E(G) and an odd integer k, the problem is to find a longest sequence of augmenting paths of length at most k that can be augmented sequentially from M. They proved that, when only paths of length at most k = 3 can be augmented, computing such a longest sequence can be done in polynomial time for any graph, while the same problem for any k ≥ 5 is NP-hard. Although the latter result remains true for bipartite graphs, the status of the complexity of the same problem for trees is not known. This work is dedicated to the complexity of this problem for trees. On the positive side, we first show that it can be solved in polynomial time for more classes of trees, namely bounded-degree trees (via a dynamic programming approach), caterpillars and trees where the nodes with degree at least 3 are sufficiently far apart. On the negative side, we show that, when only paths of length exactly k can be augmented, the problem becomes NP-hard already for k = 3, in the class of planar bipartite graphs with maximum degree 3 and arbitrary large girth. We also show that the latter problem is NP-hard in trees when k is part of the input
On improving matchings in trees, via bounded-length augmentations
International audienceDue to a classical result of Berge, it is known that a matching of any graph can be turned into a maximum matching by repeatedly augmenting alternating paths whose ends are not covered. In a recent work, Nisse, Salch and Weber considered the influence, on this process, of augmenting paths with length at most k only. Given a graph G, an initial matching M ⊆ E(G) and an odd integer k, the problem is to find a longest sequence of augmenting paths of length at most k that can be augmented sequentially from M. They proved that, when only paths of length at most k = 3 can be augmented, computing such a longest sequence can be done in polynomial time for any graph, while the same problem for any k ≥ 5 is NP-hard. Although the latter result remains true for bipartite graphs, the status of the complexity of the same problem for trees is not known. This work is dedicated to the complexity of this problem for trees. On the positive side, we first show that it can be solved in polynomial time for more classes of trees, namely bounded-degree trees (via a dynamic programming approach), caterpillars and trees where the nodes with degree at least 3 are sufficiently far apart. On the negative side, we show that, when only paths of length exactly k can be augmented, the problem becomes NP-hard already for k = 3, in the class of planar bipartite graphs with maximum degree 3 and arbitrary large girth. We also show that the latter problem is NP-hard in trees when k is part of the input
Recovery of disrupted airline operations using k-Maximum Matching in graphs
International audienceBy Berge's theorem, finding a maximum matching in a graph relies on the use of augmenting paths. When no further constraint is added, Edmonds' algorithm allows to compute a maximum matching in polynomial time by sequentially augmenting such paths. Motivated by applications in the scheduling of airline operations, we consider a similar problem where only paths of bounded length can be augmented. Precisely, let k ≥ 1 be an odd integer, a graph G and a matching M of G. What is the maximum size of a matching that can be obtained from M by using only augmenting paths of length at most k? We first prove that this problem can be solved in polynomial time for k ≤ 3 in any graph and that it is NP-complete for any fixed k ≥ 5 in the class of planar bipartite graphs of degree at most 3 and arbitrarily large girth. We then prove that this problem is in P, for any k, in several subclasses of trees such as caterpillars or trees with all vertices of degree at least 3 " far appart ". Moreover, this problem can be solved in time O(n) in the class of n-node trees when k and the maximum degree are fixed parameters. Finally, we consider a more constrained problem where only paths of length exactly k can be augmented. We prove that this latter problem becomes NP-complete for any fixed k ≥ 3 and in trees when k is part of the input
A linear kernel for planar red-blue dominating set
In the Red-Blue Dominating Set problem, we are given a bipartite graph G = (V B ∪ V R , E) and an integer k, and asked whether G has a subset D ⊆ V B of at most k 'blue' vertices such that each 'red' vertex from V R is adjacent to a vertex in D. We provide the first explicit linear kernel for this problem on planar graphs
(Méta)-noyaux constructifs et linéaires dans les graphes peu denses
In the fields of Algorithmic and Complexity, a large area of research is based on the assumption that P ≠NP(Polynomial time and Non deterministic Polynomial time), which means that there are problems for which a solution can be verified but not constructed in polynomial time. Many natural problems are not in P, which means, that they have no efficient algorithm. In order to tackle such problems, many different branches of Algorithmic have been developed. One of them is called Parametric Complexity. It consists in developing exact algorithms whose complexity is measured as a function of the size of the instance and of a parameter. Such a parameter allows a more precise analysis of the complexity. In this context, an algorithm will be considered to be efficient if it is fixed parameter tractable (fpt), that is, if it has a complexity which is exponential in the parameter and polynomial in the size of the instance. Problems that can be solved by such an algorithm form the FPT class.Kernelisation is a technical that produces fpt algorithms, among others. It can be viewed as a preprocessing of the instance, with a guarantee on the compression of the data. More formally, a kernelisation is a polynomial reduction from a problem to itself, with the additional constraint that the size of the kernel, the reduced instance, is bounded by a function of the parameter. In order to obtain an fpt algorithm, it is sufficient to solve the problem in the reduced instance, by brute-force for example (which has exponential complexity, in the parameter). Hence, the existence of a kernelisiation implies the existence of an fpt algorithm. It holds that the converse is true also. Nevertheless, the existence of an efficient fpt algorithm does not imply a small kernel, meaning a kernel with a linear or polynomial size. Under certain hypotheses, it can be proved that some problems can not have a kernel (that is, are not in FPT) and that some problems in FPT do not have a polynomial kernel.One of the main results in the field of Kernelisation is the construction of a linear kernel for the Dominating Set problem on planar graphs, by Alber, Fellows and Niedermeier.To begin with, the region decomposition method proposed by Alber, Fellows and Niedermeier has been reused many times to develop kernels for variants of Dominating Set on planar graphs. Nevertheless, this method had quite a few inaccuracies, which has invalidated the proofs. In the first part of our thesis, we present a more thorough version of this method and we illustrate it with two examples: Red Blue Dominating Set and Total Dominating Set.Next, the method has been generalised to larger classes of graphs (bounded genus, minor-free, topological-minor-free), and to larger families of problems. These meta-results prove the existence of a linear or polynomial kernel for all problems verifying some generic conditions, on a class of sparse graphs. As a price of generality, the proofs do not provide constructive algorithms and the bound on the size of the kernel is not explicit. In the second part of our thesis, we make a first step to constructive meta-results. We propose a framework to build linear kernels based on principles of dynamic programming and a meta-result of Bodlaender, Fomin, Lokshtanov, Penninkx, Saurabh and Thilikos.En algorithmique et en complexité, la plus grande part de la recherche se base sur l’hypothèse que P ≠NP (Polynomial time et Non deterministic Polynomial time), c'est-à -dire qu'il existe des problèmes dont la solution peut être vérifiée mais non construite en temps polynomial. Si cette hypothèse est admise, de nombreux problèmes naturels ne sont pas dans P (c'est-à -dire, n'admettent pas d'algorithme efficace), ce qui a conduit au développement de nombreuses branches de l'algorithmique. L'une d'elles est la complexité paramétrée. Elle propose des algorithmes exacts, dont l'analyse est faite en fonction de la taille de l'instance et d'un paramètre. Ce paramètre permet une granularité plus fine dans l'analyse de la complexité.Un algorithme sera alors considéré comme efficace s'il est à paramètre fixé, c'est-à -dire, lorsque sa complexité est exponentielle en fonction du paramètre et polynomiale en fonction de la taille de l'instance. Ces algorithmes résolvent les problèmes de la classe FPT (Fixed Parameter Tractable).L'extraction de noyaux est une technique qui permet, entre autre, d’élaborer des algorithmes à paramètre fixé. Elle peut être vue comme un pré-calcul de l'instance, avec une garantie sur la compression des données. Plus formellement, une extraction de noyau est une réduction polynomiale depuis un problème vers lui même, avec la contrainte supplémentaire que la taille du noyau (l'instance réduite) est bornée en fonction du paramètre. Pour obtenir l’algorithme à paramètre fixé, il suffit de résoudre le problème dans le noyau, par exemple par une recherche exhaustive (de complexité exponentielle, en fonction du paramètre). L’existence d'un noyau implique donc l'existence d'un algorithme à paramètre fixé, la réciproque est également vraie. Cependant, l’existence d'un algorithme à paramètre fixé efficace ne garantit pas un petit noyau, c'est a dire un noyau dont la taille est linéaire ou polynomiale. Sous certaines hypothèses, il existe des problèmes n’admettant pas de noyau (c'est-à -dire hors de FPT) et il existe des problèmes de FPT n’admettant pas de noyaux polynomiaux.Un résultat majeur dans le domaine des noyaux est la construction d'un noyau linéaire pour le problème Domination dans les graphes planaires, par Alber, Fellows et Niedermeier.Tout d'abord, la méthode de décomposition en régions proposée par Alber, Fellows et Niedermeier, a permis de construire de nombreux noyaux pour des variantes de Domination dans les graphes planaires. Cependant cette méthode comportait un certain nombre d’imprécisions, ce qui rendait les preuves invalides. Dans la première partie de notre thèse, nous présentons cette méthode sous une forme plus rigoureuse et nous l’illustrons par deux problèmes : Domination Rouge Bleue et Domination Totale.Ensuite, la méthode a été généralisée, d'une part, sur des classes de graphes plus larges (de genre borné, sans-mineur, sans-mineur-topologique), d'autre part, pour une plus grande variété de problèmes. Ces méta-résultats prouvent l’existence de noyaux linéaires ou polynomiaux pour tout problème vérifiant certaines conditions génériques, sur une classe de graphes peu denses. Cependant, pour atteindre une telle généralité, il a fallu sacrifier la constructivité des preuves : les preuves ne fournissent pas d'algorithme d'extraction constructif et la borne sur le noyau n'est pas explicite. Dans la seconde partie de notre thèse nous effectuons un premier pas vers des méta-résultats constructifs ; nous proposons un cadre général pour construire des noyaux linéaires en nous inspirant des principes de la programmation dynamique et d'un méta-résultat de Bodlaender, Fomin, Lokshtanov, Penninkx, Saurabh et Thilikos
(Méta)-noyaux constructifs et linéaires dans les graphes peu denses
In the fields of Algorithmic and Complexity, a large area of research is based on the assumption that P ≠NP (the classesPolynomial time and Non deterministic Polynomial time are different), which means that there are problems for which a solution can be verified but not constructed in polynomial time. Many natural problems are not in P, which means that they have no efficient algorithm. In order to tackle such problems, many different branches of Algorithmics have been developed. One of them is called Parametric Complexity. It consists of developing exact algorithms whose complexity is measured as a function of the size of the instance and of a parameter. Such a parameter allows a more precise analysis of the complexity. In this context, an algorithm will be considered to be efficient if it is fixed parameter tractable (fpt), that is, if it has a complexity which exponentially depends on the parameter and polynomially depends on the size of the instance. Problems that can be solved by such an algorithm form the FPT class.Kernelisation is one technique that produces fpt algorithms, among others. It can be viewed as a preprocessing of the instance, with a guarantee on the compression of the data. More formally, a kernelisation is a polynomial reduction from a problem to itself, with the additional constraint that the size of the kernel, the reduced instance, is bounded by a function of the parameter. In order to obtain an fpt algorithm, it is sufficient to solve the problem in the reduced instance, by brute-force for example (which has exponential complexity, in the parameter). Hence, the existence of a kernelisiation implies the existence of an fpt algorithm. It holds that the converse is true also. Nevertheless, the existence of an efficient fpt algorithm does not imply a small kernel, meaning a kernel with a linear or polynomial size. Under certain hypotheses, it can be proved that some problems can not have a kernel (that is, are not in FPT) and that some problems in FPT do not have a polynomial kernel.One of the main results in the field of Kernelisation is the construction of a linear kernel for the Dominating Set problem on planar graphs, by Alber, Fellows and Niedermeier.To begin with, the region decomposition method proposed by Alber, Fellows and Niedermeier has been reused many times to develop kernels for variants of Dominating Set on planar graphs. Nevertheless, this method had quite a few inaccuracies, which has invalidated the proofs. In the first part of our thesis, we present a more thorough version of this method and we illustrate it with two examples : Red Blue Dominating Set and Total Dominating Set.Next, the method has been generalised to larger classes of graphs (bounded genus, minor-free, topological-minor-free), and to larger families of problems. These meta-results prove the existence of a linear or polynomial kernel for all problems verifying some generic conditions, on a class of sparse graphs. As a price of generality, the proofs do not provide constructive algorithms and the bound on the size of the kernel is not explicit. In the second part of our thesis, we make a first step to constructive meta-results. We propose a framework to build linear kernels based on principles of dynamic programming and a meta-result of Bodlaender, Fomin, Lokshtanov, Penninkx, Saurabh and Thilikos.En algorithmique et en complexité, la plus grande part de la recherche se base sur l’hypothèse que P ≠NP (les classes, appelées en anglais Polynomial time et Non deterministic Polynomial time, sont différentes), c’est-à -dire qu’il existe des problèmes dont la solution peut être vérifiée mais non construite en temps polynomial. Si cette hypothèse est admise, de nombreux problèmes naturels ne sont pas dans P (c’est-à -dire, n’admettent pas d’algorithme efficace), ce qui a conduit au développement de nombreuses branches de l’algorithmique. L’une d’elles est la complexité paramétrée. Elle propose des algorithmes exacts, dont l’analyse est faite en fonction de la taille de l’instance et d’un paramètre. Ce paramètre permet une granularité plus fine dans l’analyse de la complexité. Un algorithme sera alors considéré comme efficace s’il est à paramètre fixé, c’est-à -dire, lorsque sa complexité est exponentielle en fonction du paramètre et polynomiale en fonction de la taille de l’instance. Ces algorithmes résolvent les problèmes de la classe FPT (en anglais, Fixed Parameter Tractable). L’extraction de noyaux est une technique qui permet, entre autre, d’élaborer des algorithmes à paramètre fixé. Elle peut être vue comme un pré-calcul de l’instance, avec une garantie sur la compression des données. Plus formellement, une extraction de noyau est une réduction polynomiale depuis un problème vers lui même, avec la contrainte supplémentaire que la taille du noyau (l’instance réduite) est bornée en fonction du paramètre. Pour obtenir l’algorithme à paramètre fixé, il suffit de résoudre le problème dans le noyau, par exemple par une recherche exhaustive (de complexité exponentielle, en fonction du paramètre). L’existence d’un noyau implique donc l’existence d’un algorithme à paramètre fixé, la réciproque est également vraie. Cependant, l’existence d’un algorithme à paramètre fixé efficace ne garantit pas un petit noyau, c’est a dire un noyau dont la taille est linéaire ou polynomiale. Sous certaines hypothèses, il existe des problèmes n’admettant pas de noyau (c’est-à -dire hors de FPT) et il existe des problèmes de FPT n’admettant pas de noyaux polynomiaux.Un résultat majeur dans le domaine des noyaux est la construction d’un noyau linéaire pour le problème Domination dans les graphes planaires, par Alber, Fellows et Niedermeier.Tout d’abord, la méthode de décomposition en régions proposée par Alber, Fellows et Niedermeier, a permis de construire de nombreux noyaux pour des variantes de Domination dans les graphes planaires. Cependant cette méthode comportait un certain nombre d’imprécisions, ce qui rendait les preuves invalides. Dans la première partie de notre thèse, nous présentons cette méthode sous une forme plus rigoureuse et nous l’illustrons par deux problèmes : Domination Rouge Bleu et Domination Totale.Ensuite, la méthode a été généralisée, d’une part, sur des classes de graphes plus larges (de genre borné, sans-mineur, sans-mineur-topologique), d’autre part, pour une plus grande variété de problèmes. Ces méta-résultats prouvent l’existence de noyaux linéaires ou polynomiaux pour tout problème vérifiant certaines conditions génériques, sur une classe de graphes peu denses. Cependant, pour atteindre une telle généralité, il a fallu sacrifier la constructivité des preuves : les preuves ne fournissent pas d’algorithme d’extraction constructif et la borne sur le noyau n’est pas explicite. Dans la seconde partie de notre thèse nous effectuons un premier pas vers des méta-résultats constructifs ; nous proposons un cadre général pour construire des noyaux linéaires en nous inspirant des principes de la programmation dynamique et d’un méta-résultat de Bodlaender, Fomin, Lokshtanov, Penninkx, Saurabh et Thilikos