72 research outputs found
The complexity of the L(p,q)-labeling problem for bipartite planar graphs of small degree
AbstractGiven a simple graph G, by an L(p,q)-labeling of G we mean a function c that assigns nonnegative integers to its vertices in such a way that if two vertices u, v are adjacent then |c(u)−c(v)|≥p, and if they are at distance 2 then |c(u)−c(v)|≥q. The L(p,q)-labeling problem can be defined as follows: given a graph G and integer t, determine whether there exists an L(p,q)-labeling c of G such that c(V)⊆{0,1,…,t}. In the paper we show that the problem is NP-complete even when restricted to bipartite planar graphs of small maximum degree and for relatively small values of t. More precisely, we prove that: (1)if p<3q then the problem is NP-complete for bipartite planar graphs of maximum degree Δ≤3 and t=p+max{2q,p};(2)if p=3q then the problem is NP-complete for bipartite planar graphs of maximum degree Δ≤4 and t=6q;(3)if p>3q then the problem is NP-complete for bipartite planar graphs of maximum degree Δ≤4 and t=p+5q.In particular, these results imply that the L(2,1)-labeling problem in planar graphs is NP-complete for t=4, and that the L(p,q)-labeling problem in graphs of maximum degree Δ≤4 is NP-complete for all values of p and q, thus answering two well-known open questions
Fine-Grained Complexity Analysis of Two Classic TSP Variants
We analyze two classic variants of the Traveling Salesman Problem using the
toolkit of fine-grained complexity. Our first set of results is motivated by
the Bitonic TSP problem: given a set of points in the plane, compute a
shortest tour consisting of two monotone chains. It is a classic
dynamic-programming exercise to solve this problem in time. While the
near-quadratic dependency of similar dynamic programs for Longest Common
Subsequence and Discrete Frechet Distance has recently been proven to be
essentially optimal under the Strong Exponential Time Hypothesis, we show that
bitonic tours can be found in subquadratic time. More precisely, we present an
algorithm that solves bitonic TSP in time and its bottleneck
version in time. Our second set of results concerns the popular
-OPT heuristic for TSP in the graph setting. More precisely, we study the
-OPT decision problem, which asks whether a given tour can be improved by a
-OPT move that replaces edges in the tour by new edges. A simple
algorithm solves -OPT in time for fixed . For 2-OPT, this is
easily seen to be optimal. For we prove that an algorithm with a runtime
of the form exists if and only if All-Pairs
Shortest Paths in weighted digraphs has such an algorithm. The results for
may suggest that the actual time complexity of -OPT is
. We show that this is not the case, by presenting an algorithm
that finds the best -move in time for
fixed . This implies that 4-OPT can be solved in time,
matching the best-known algorithm for 3-OPT. Finally, we show how to beat the
quadratic barrier for in two important settings, namely for points in the
plane and when we want to solve 2-OPT repeatedly.Comment: Extended abstract appears in the Proceedings of the 43rd
International Colloquium on Automata, Languages, and Programming (ICALP 2016
The traveling salesman problem on cubic and subcubic graphs
We study the traveling salesman problem (TSP) on the metric completion of cubic and subcubic graphs, which is known to be NP-hard. The problem is of interest because of its relation to the famous 4/3-conjecture for metric TSP, which says that the integrality gap, i.e., the worst case ratio between the optimal value of a TSP instance and that of its linear programming relaxation (the subtour elimination relaxation), is 4/3. We present the first algorithm for cubic graphs with approximation ratio 4/3. The proof uses polyhedral techniques in a surprising way, which is of independent interest. In fact we prove constructively that for any cubic graph on TeX vertices a tour of length TeX exists, which also implies the 4/3-conjecture, as an upper bound, for this class of graph-TSP. Recently, Mömke and Svensson presented an algorithm that gives a 1.461-approximation for graph-TSP on general graphs and as a side result a 4/3-approximation algorithm for this problem on subcubic graphs, also settling the 4/3-conjecture for this class of graph-TSP. The algorithm by Mömke and Svensson is initially randomized but the authors remark that derandomization is trivial. We will present a different way to derandomize their algorithm which leads to a faster running time. All of the latter also works for multigraphs
Approximating the Regular Graphic TSP in near linear time
We present a randomized approximation algorithm for computing traveling
salesperson tours in undirected regular graphs. Given an -vertex,
-regular graph, the algorithm computes a tour of length at most
, with high probability, in time. This improves upon a recent result by Vishnoi (\cite{Vishnoi12}, FOCS
2012) for the same problem, in terms of both approximation factor, and running
time. The key ingredient of our algorithm is a technique that uses
edge-coloring algorithms to sample a cycle cover with cycles with
high probability, in near linear time.
Additionally, we also give a deterministic
factor approximation algorithm
running in time .Comment: 12 page
Dynamic Approximate All-Pairs Shortest Paths: Breaking the O(mn) Barrier and Derandomization
We study dynamic -approximation algorithms for the all-pairs
shortest paths problem in unweighted undirected -node -edge graphs under
edge deletions. The fastest algorithm for this problem is a randomized
algorithm with a total update time of and constant
query time by Roditty and Zwick [FOCS 2004]. The fastest deterministic
algorithm is from a 1981 paper by Even and Shiloach [JACM 1981]; it has a total
update time of and constant query time. We improve these results as
follows: (1) We present an algorithm with a total update time of and constant query time that has an additive error of
in addition to the multiplicative error. This beats the previous
time when . Note that the additive
error is unavoidable since, even in the static case, an -time
(a so-called truly subcubic) combinatorial algorithm with
multiplicative error cannot have an additive error less than ,
unless we make a major breakthrough for Boolean matrix multiplication [Dor et
al. FOCS 1996] and many other long-standing problems [Vassilevska Williams and
Williams FOCS 2010]. The algorithm can also be turned into a
-approximation algorithm (without an additive error) with the
same time guarantees, improving the recent -approximation
algorithm with running
time of Bernstein and Roditty [SODA 2011] in terms of both approximation and
time guarantees. (2) We present a deterministic algorithm with a total update
time of and a query time of . The
algorithm has a multiplicative error of and gives the first
improved deterministic algorithm since 1981. It also answers an open question
raised by Bernstein [STOC 2013].Comment: A preliminary version was presented at the 2013 IEEE 54th Annual
Symposium on Foundations of Computer Science (FOCS 2013
Deterministic Fully Dynamic SSSP and More
We present the first non-trivial fully dynamic algorithm maintaining exact
single-source distances in unweighted graphs. This resolves an open problem
stated by Sankowski [COCOON 2005] and van den Brand and Nanongkai [FOCS 2019].
Previous fully dynamic single-source distances data structures were all
approximate, but so far, non-trivial dynamic algorithms for the exact setting
could only be ruled out for polynomially weighted graphs (Abboud and
Vassilevska Williams, [FOCS 2014]). The exact unweighted case remained the main
case for which neither a subquadratic dynamic algorithm nor a quadratic lower
bound was known.
Our dynamic algorithm works on directed graphs, is deterministic, and can
report a single-source shortest paths tree in subquadratic time as well. Thus
we also obtain the first deterministic fully dynamic data structure for
reachability (transitive closure) with subquadratic update and query time. This
answers an open problem of van den Brand, Nanongkai, and Saranurak [FOCS 2019].
Finally, using the same framework we obtain the first fully dynamic data
structure maintaining all-pairs -approximate distances within
non-trivial sub- worst-case update time while supporting optimal-time
approximate shortest path reporting at the same time. This data structure is
also deterministic and therefore implies the first known non-trivial
deterministic worst-case bound for recomputing the transitive closure of a
digraph.Comment: Extended abstract to appear in FOCS 202
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