Stable (théorie des graphes) | EPFL Graph Search

thumb|280px|L'ensemble des sommets en bleu dans ce graphe est un stable maximal du graphe. En théorie des graphes, un stable – appelé aussi ensemble indépendant ou independent set en anglais – est un ensemble de sommets deux à deux non adjacents. La taille d'un stable est égale au nombre de sommets qu'il contient. Propriétés combinatoires La taille maximum d'un stable d'un graphe, noté I(G), est un invariant du graphe. Il peut être relié à d'autres invariants, par exemple à la taille de l'ensemble dominant maximum, noté dom(G). On appelle carré d'un graphe G le graphe G''' utilisant les mêmes sommets et ayant une arête entre deux sommets u et v si et seulement s'il existe un chemin de longueur au plus 2 entre u et v dans G. Alors I(G') est inférieure ou égale à dom(G)''. Problème algorithmique La recherche d'un stable de taille maximum dans un graphe est un problème classique de la théorie de la complexité. Il est NP-complet et difficile à approximer. N

Strengths and Limitations of Linear Programming Relaxations

Abbas Bazzi

Many of the currently best-known approximation algorithms for NP-hard optimization problems are based on Linear Programming (LP) and Semi-definite Programming (SDP) relaxations. Given its power, this class of algorithms seems to contain the most favourable candidates for outperforming the current state-of-the-art approximation guarantees for NP-hard problems, for which there still exists a gap between the inapproximability results and the approximation guarantees that we know how to achieve in polynomial time. In this thesis, we address both the power and the limitations of these relaxations, as well as the connection between the shortcomings of these relaxations and the inapproximability of the underlying problem. In the first part, we study the limitations of LP relaxations of well-known graph problems such as the Vertex Cover problem and the Independent Set problem. We prove that any small LP relaxation for the aforementioned problems, cannot have an integrality gap strictly better than

2

and

\omega(1)

, respectively. Furthermore, our lower bound for the Independent Set problem also holds for any SDP relaxation. Prior to our work, it was only known that such LP relaxations cannot have an integrality gap better than

1.5

for the Vertex Cover Problem, and better than

2

for the Independent Set problem. In the second part, we study the so-called knapsack cover inequalities that are used in the current best relaxations for numerous combinatorial optimization problems of covering type. In spite of their widespread use, these inequalities yield LP relaxations of exponential size, over which it is not known how to optimize exactly in polynomial time. We address this issue and obtain LP relaxations of quasi-polynomial size that are at least as strong as that given by the knapsack cover inequalities. In the last part, we show a close connection between structural hardness for k-partite graphs and tight inapproximability results for scheduling problems with precedence constraints. This connection is inspired by a family of integrality gap instances of a certain LP relaxation. Assuming the hardness of an optimization problem on k-partite graphs, we obtain a hardness of

2-\varepsilon

for the problem of minimizing the makespan for scheduling with preemption on identical parallel machines, and a super constant inapproximability for the problem of scheduling on related parallel machines. Prior to this result, it was only known that the first problem does not admit a PTAS, and the second problem is NP-hard to approximate within a factor strictly better than 2, assuming the Unique Games Conjecture.

EPFL2017

Random walks and forbidden minors III: poly(d epsilon(-1))-time partition oracles for minor-free graph classes

Akash Kumar

Consider the family of bounded degree graphs in any minor-closed family (such as planar graphs). Let d be the degree bound and n be the number of vertices of such a graph. Graphs in these classes have hyperfinite decompositions, where, one removes a small fraction of edges of the graph controlled by a proximity parameter to get connected components of size independent of n. An important tool for sublinear algorithms and property testing for such classes is the partition oracle, introduced by the seminal work of Hassidim-Kelner-Nguyen-Onak (FOCS 2009). A partition oracle is a local procedure that gives consistent access to a hyperfinite decomposition, without any preprocessing. Given a query vertex v, the partition oracle outputs the component containing v in time independent of n. All the answers are consistent with a single hyperfinite decomposition. The partition oracle of Hassidim et al. runs in time exponential in the proximity parameter per query. They pose the open problem of whether partition oracles which run in time polynomial in reciprocal of proximity parameter can be built. Levi-Ron (ICALP 2013) give a refinement of the previous approach, to get a partition oracle that runs in quasipolynomial time per query. In this paper, we resolve this open problem and give polynomial time partition oracles (in reciprocal of proximity parameter) for bounded degree graphs in any minor-closed family. Unlike the previous line of work based on combinatorial methods, we employ techniques from spectral graph theory. We build on a recent spectral graph theoretical toolkit for minor-closed graph families, introduced by the authors to develop efficient property testers. A consequence of our result is an efficient property tester for any monotone and additive with running time property of minor-closed families (such as bipartite planar graphs). Our result also gives query efficient algorithms for additive approximations for problems such as maximum matching, minimum vertex cover, maximum independent set, and minimum dominating set for these graph families.

IEEE COMPUTER SOC2022

No Small Linear Program Approximates Vertex Cover within a Factor 2 - e

Abbas Bazzi, Ola Nils Anders Svensson

The vertex cover problem is one of the most important and intensively studied combinatorial optimization problems. Khot and Regevproved that the problem is NP-hard to approximate within a factor2 - ∈, assuming the Unique Games Conjecture (UGC). This is tight because the problem has an easy 2-approximation algorithm. Without resorting to the UGC, the best in approximability result for the problem is due to Dinur and Safra: vertex cover is NP-hard to approximate within a factor 1.3606. We prove the following unconditional result about linear programming (LP)relaxations of the problem: every LP relaxation that approximates vertex cover within a factor of 2-∈ has super-polynomially many inequalities. As a direct consequence of our methods, we also establish that LP relaxations (as well as SDP relaxations) that approximate the independent set problem within any constant factor have super-polynomially many inequalities. © 2015 IEEE.

IEEE Computer Society2015