3,452 research outputs found

    Improved Hardness of Approximating Chromatic Number

    Full text link
    We prove that for sufficiently large K, it is NP-hard to color K-colorable graphs with less than 2^{K^{1/3}} colors. This improves the previous result of K versus K^{O(log K)} in Khot [14]

    From Gap-ETH to FPT-Inapproximability: Clique, Dominating Set, and More

    Full text link
    We consider questions that arise from the intersection between the areas of polynomial-time approximation algorithms, subexponential-time algorithms, and fixed-parameter tractable algorithms. The questions, which have been asked several times (e.g., [Marx08, FGMS12, DF13]), are whether there is a non-trivial FPT-approximation algorithm for the Maximum Clique (Clique) and Minimum Dominating Set (DomSet) problems parameterized by the size of the optimal solution. In particular, letting OPT\text{OPT} be the optimum and NN be the size of the input, is there an algorithm that runs in t(OPT)poly(N)t(\text{OPT})\text{poly}(N) time and outputs a solution of size f(OPT)f(\text{OPT}), for any functions tt and ff that are independent of NN (for Clique, we want f(OPT)=ω(1)f(\text{OPT})=\omega(1))? In this paper, we show that both Clique and DomSet admit no non-trivial FPT-approximation algorithm, i.e., there is no o(OPT)o(\text{OPT})-FPT-approximation algorithm for Clique and no f(OPT)f(\text{OPT})-FPT-approximation algorithm for DomSet, for any function ff (e.g., this holds even if ff is the Ackermann function). In fact, our results imply something even stronger: The best way to solve Clique and DomSet, even approximately, is to essentially enumerate all possibilities. Our results hold under the Gap Exponential Time Hypothesis (Gap-ETH) [Dinur16, MR16], which states that no 2o(n)2^{o(n)}-time algorithm can distinguish between a satisfiable 3SAT formula and one which is not even (1ϵ)(1 - \epsilon)-satisfiable for some constant ϵ>0\epsilon > 0. Besides Clique and DomSet, we also rule out non-trivial FPT-approximation for Maximum Balanced Biclique, Maximum Subgraphs with Hereditary Properties, and Maximum Induced Matching in bipartite graphs. Additionally, we rule out ko(1)k^{o(1)}-FPT-approximation algorithm for Densest kk-Subgraph although this ratio does not yet match the trivial O(k)O(k)-approximation algorithm.Comment: 43 pages. To appear in FOCS'1

    O(log2k/loglogk)O(\log^2k/\log\log{k})-Approximation Algorithm for Directed Steiner Tree: A Tight Quasi-Polynomial-Time Algorithm

    Get PDF
    In the Directed Steiner Tree (DST) problem we are given an nn-vertex directed edge-weighted graph, a root rr, and a collection of kk terminal nodes. Our goal is to find a minimum-cost arborescence that contains a directed path from rr to every terminal. We present an O(log2k/loglogk)O(\log^2 k/\log\log{k})-approximation algorithm for DST that runs in quasi-polynomial-time. By adjusting the parameters in the hardness result of Halperin and Krauthgamer, we show the matching lower bound of Ω(log2k/loglogk)\Omega(\log^2{k}/\log\log{k}) for the class of quasi-polynomial-time algorithms. This is the first improvement on the DST problem since the classical quasi-polynomial-time O(log3k)O(\log^3 k) approximation algorithm by Charikar et al. (The paper erroneously claims an O(log2k)O(\log^2k) approximation due to a mistake in prior work.) Our approach is based on two main ingredients. First, we derive an approximation preserving reduction to the Label-Consistent Subtree (LCST) problem. The LCST instance has quasi-polynomial size and logarithmic height. We remark that, in contrast, Zelikovsky's heigh-reduction theorem used in all prior work on DST achieves a reduction to a tree instance of the related Group Steiner Tree (GST) problem of similar height, however losing a logarithmic factor in the approximation ratio. Our second ingredient is an LP-rounding algorithm to approximately solve LCST instances, which is inspired by the framework developed by Rothvo{\ss}. We consider a Sherali-Adams lifting of a proper LP relaxation of LCST. Our rounding algorithm proceeds level by level from the root to the leaves, rounding and conditioning each time on a proper subset of label variables. A small enough (namely, polylogarithmic) number of Sherali-Adams lifting levels is sufficient to condition up to the leaves
    corecore