We prove several tight results on the fine-grained complexity of approximating the diameter of a graph. First, we prove that, for any \varepsilon>0, assuming the Strong Exponential Time Hypothesis (SETH), there are no near-linear time 2-\varepsilon-approximation algorithms for the Diameter of a sparse directed graph, even in unweighted graphs. This result shows that a simple near-linear time 2-approximation algorithm for Diameter is optimal under SETH, answering a question from a survey of Rubinstein and Vassilevska-Williams (SIGACT '19) for the case of directed graphs. In the same survey, Rubinstein and Vassilevska-Williams also asked if it is possible to show that there are no 2-\varepsilon approximation algorithms for Diameter in a directed graph in O(n^{1.499}) time. We show that, assuming a hypothesis called NSETH, one cannot use a deterministic SETH-based reduction to rule out the existence of such algorithms. Furthermore, assuming another hypothesis called Non-Uniform NSETH (NUNSETH), one cannot use a even a randomized SETH-based reduction to rule out the existence of such algorithms. Extending the techniques in these two results, we characterize whether a 2-\varepsilon approximation algorithm running in time O(n^{1+\delta}) for the Diameter of a sparse directed unweighted graph can be ruled out by a deterministic SETH-based reduction for every \delta\in(0,1) and essentially every \varepsilon\in(0,1), assuming NSETH. We make the same characterization for randomized SETH-based reductions, assuming NUNSETH. This settles the SETH-hardness of approximating the diameter of sparse directed unweighted graphs for deterministic reductions, up to NSETH , and for randomized reductions, up to NUNSETH. We prove additional hardness and non-reducibility results for undirected graphs.
We prove several tight results on the fine-grained complexity of approximating the diameter of a graph. First, we prove that, for any \varepsilon>0, assuming the Strong Exponential Time Hypothesis (SETH), there are no near-linear time 2-\varepsilon-approximation algorithms for the Diameter of a sparse directed graph, even in unweighted graphs. This result shows that a simple near-linear time 2-approximation algorithm for Diameter is optimal under SETH, answering a question from a survey of Rubinstein and Vassilevska-Williams (SIGACT '19) for the case of directed graphs. In the same survey, Rubinstein and Vassilevska-Williams also asked if it is possible to show that there are no 2-\varepsilon approximation algorithms for Diameter in a directed graph in O(n^{1.499}) time. We show that, assuming a hypothesis called NSETH, one cannot use a deterministic SETH-based reduction to rule out the existence of such algorithms. Extending the techniques in these two results, we characterize whether a 2-\varepsilon approximation algorithm running in time O(n^{1+\delta}) for the Diameter of a sparse directed unweighted graph can be ruled out by a deterministic SETH-based reduction for every \delta\in(0,1) and essentially every \varepsilon\in(0,1), assuming NSETH. This settles the SETH-hardness of approximating the diameter of sparse directed unweighted graphs for deterministic reductions, up to NSETH . We make the same characterization for randomized SETH-based reductions, assuming another hypothesis called NUNSETH. We prove additional hardness and non-reducibility results for undirected graphs.
D
Furthermore, assuming another hypothesis called Non-Uniform NSETH (NUNSETH), one cannot use a even a randomized SETH-based reduction to rule out the existence of such algorithms.
D
We make the same characterization for randomized SETH-based reductions, assuming NUNSETH.
R
, and for randomized reductions, up to
. We make the same characterization for randomized SETH-based reductions, assuming another hypothesis called