We introduce a new notion of resilience for constraint satisfaction problems, with the goal of more precisely determining the boundary between NP-hardness and the existence of efficient algorithms for resilient instances. In particular, we study @math -resiliently @math -colorable graphs, which are those @math -colorable graphs that remain @math -colorable even after the addition of any @math new edges. We prove lower bounds on the NP-hardness of coloring resiliently colorable graphs, and provide an algorithm that colors sufficiently resilient graphs. We also analyze the corresponding notion of resilience for @math -SAT. This notion of resilience suggests an array of open questions for graph coloring and other combinatorial problems.
As our main results are on graph colorability, we review the relevant past work. A graph @math is @math -colorable if there is an assignment of @math distinct colors to the vertices of @math so that no edge is monochromatic. Determining whether @math is @math -colorable is a classic an NP-hard problem @cite_14 . Many attempts to simplify the problem, such as assuming planarity or bounded degree, still result in NP-hardness @cite_26 . A large body of work surrounds positive and negative results for explicit families of graphs. The list of families that are polynomial-time colorable includes triangle-free planar graphs, perfect graphs and almost-perfect graphs, bounded tree- and clique-width graphs, quadtrees, and various families of graphs defined by the lack of an induced subgraph @cite_21 @cite_6 @cite_19 @cite_27 @cite_10 .
@cite_14 @cite_26 @cite_21 @cite_6 @cite_19 @cite_27 @cite_10
1969999211 2104254886 2025904765 1799262171
We give a new proof showing that it is NP-hard to color a 3-colorable graph using just 4 colors. This result is already known , [S. Khanna, N. Linial, and S. Safra, Combinatorica, 20 (2000), pp. 393--415], but our proof is novel because it does not rely on the PCP theorem, while the known one does. This highlights a qualitative difference between the known hardness result for coloring 3-colorable graphs and the factor @math hardness for approximating the chromatic number of general graphs, as the latter result is known to imply (some form of) PCP theorem [M. Bellare, O. Goldreich, and M. Sudan, SIAM J. Comput., 27 (1998), pp. 805--915]. Another aspect in which our proof is novel is in its use of the PCP theorem to show that 4-coloring of 3-colorable graphs remains NP-hard even on bounded-degree graphs (this hardness result does not seem to follow from the earlier reduction of Khanna, Linial, and Safra). We point out that such graphs can always be colored using O(1) colors by a simple greedy algorithm, while the best known algorithm for coloring (general) 3-colorable graphs requires @math colors. Our proof technique also shows that there is an @math such that it is NP-hard to legally 4-color even a @math fraction of the edges of a 3-colorable graph. The paper considers the computational hardness of approximating the chromatic number of a graph. The authors first give a simple proof that approximating the chromatic number of a graph to within a constant power (of the value itself) in NP-hard. They then consider the hardness of coloring a 3-colorable graph with as few as possible colors. They show that determining whether a graph is 3-colorable or any legal coloring of it requires at least 5 colors is NP-hard. Therefore, coloring a 3-colorable graph with 4 colors is NP-hard. > Graph coloring problems, in which one would like to color the vertices of a given graph with a small number of colors so that no two adjacent vertices receive the same color, arise in many applications, including various scheduling and partitioning problems. In this paper the complexity and performance of algorithms which construct such colorings are investigated. For a graph G , let k( G ) denote the minimum possible number of colors required to color G and, for any graph coloring algorithm A , let A ( G ) denote the number of colors used by A when applied to G . Since the graph coloring problem is known to be “NP-complete,” it is considered unlikely that any efficient algorithm can guarantee A ( G ) = k( G ) for all input graphs. In this paper it is proved that even coming close to khgr;( G ) with a fast algorithm is hard. Specifically, it is shown that if for some constant r d there exists a polynomial-time algorithm A which guarantees A ( G ) ≤ r ·k( G ) + d , then there also exists a polynomial-time algorithm A which guarantees A ( G ) = k( G ). Given two graphs @math and @math , the Subgraph Isomorphism problem asks if @math is isomorphic to a subgraph of @math . While NP-hard in general, algorithms exist for various parameterized versions of the problem: for example, the problem can be solved (1) in time @math using the color-coding technique of Alon, Yuster, and Zwick; (2) in time @math using Courcelle's Theorem; (3) in time @math using a result on first-order model checking by Frick and Grohe; or (4) in time @math for connected @math using the algorithm of Matou s ek and Thomas. Already this small sample of results shows that the way an algorithm can depend on the parameters is highly nontrivial and subtle. We develop a framework involving 10 relevant parameters for each of @math and @math (such as treewidth, pathwidth, genus, maximum degree, number of vertices, number of components, etc.), and ask if an algorithm with running time [ f_1(p_1,p_2,..., p_ ) n^ f_2(p_ +1 ,..., p_k) ] exist, where each of @math is one of the 10 parameters depending only on @math or @math . We show that all the questions arising in this framework are answered by a set of 11 maximal positive results (algorithms) and a set of 17 maximal negative results (hardness proofs); some of these results already appear in the literature, while others are new in this paper. On the algorithmic side, our study reveals for example that an unexpected combination of bounded degree, genus, and feedback vertex set number of @math gives rise to a highly nontrivial algorithm for Subgraph Isomorphism. On the hardness side, we present W[1]-hardness proofs under extremely restricted conditions, such as when @math is a bounded-degree tree of constant pathwidth and @math is a planar graph of bounded pathwidth.