11,952 research outputs found
Approximation bounds on maximum edge 2-coloring of dense graphs
For a graph and integer , an edge -coloring of is an
assignment of colors to edges of , such that edges incident on a vertex span
at most distinct colors. The maximum edge -coloring problem seeks to
maximize the number of colors in an edge -coloring of a graph . The
problem has been studied in combinatorics in the context of {\em anti-Ramsey}
numbers. Algorithmically, the problem is NP-Hard for and assuming the
unique games conjecture, it cannot be approximated in polynomial time to a
factor less than . The case , is particularly relevant in practice,
and has been well studied from the view point of approximation algorithms. A
-factor algorithm is known for general graphs, and recently a -factor
approximation bound was shown for graphs with perfect matching. The algorithm
(which we refer to as the matching based algorithm) is as follows: "Find a
maximum matching of . Give distinct colors to the edges of . Let
be the connected components that results when M is
removed from G. To all edges of give the th color."
In this paper, we first show that the approximation guarantee of the matching
based algorithm is for graphs with perfect matching
and minimum degree . For , this is better than the approximation guarantee proved in {AAAP}. For triangle free graphs
with perfect matching, we prove that the approximation factor is , which is better than for .Comment: 11pages, 3 figure
Sublinear Time and Space Algorithms for Correlation Clustering via Sparse-Dense Decompositions
We present a new approach for solving (minimum disagreement) correlation
clustering that results in sublinear algorithms with highly efficient time and
space complexity for this problem. In particular, we obtain the following
algorithms for -vertex -labeled graphs :
-- A sublinear-time algorithm that with high probability returns a constant
approximation clustering of in time assuming access to the
adjacency list of the -labeled edges of (this is almost quadratically
faster than even reading the input once). Previously, no sublinear-time
algorithm was known for this problem with any multiplicative approximation
guarantee.
-- A semi-streaming algorithm that with high probability returns a constant
approximation clustering of in space and a single pass over
the edges of the graph (this memory is almost quadratically smaller than
input size). Previously, no single-pass algorithm with space was known
for this problem with any approximation guarantee.
The main ingredient of our approach is a novel connection to sparse-dense
graph decompositions that are used extensively in the graph coloring
literature. To our knowledge, this connection is the first application of these
decompositions beyond graph coloring, and in particular for the correlation
clustering problem, and can be of independent interest
Evanescent-wave coupled right angled buried waveguide: Applications in carbon nanotube mode-locking
In this paper we present a simple but powerful subgraph sampling primitive
that is applicable in a variety of computational models including dynamic graph
streams (where the input graph is defined by a sequence of edge/hyperedge
insertions and deletions) and distributed systems such as MapReduce. In the
case of dynamic graph streams, we use this primitive to prove the following
results:
-- Matching: First, there exists an space algorithm that
returns an exact maximum matching on the assumption the cardinality is at most
. The best previous algorithm used space where is the
number of vertices in the graph and we prove our result is optimal up to
logarithmic factors. Our algorithm has update time. Second,
there exists an space algorithm that returns an
-approximation for matchings of arbitrary size. (Assadi et al. (2015)
showed that this was optimal and independently and concurrently established the
same upper bound.) We generalize both results for weighted matching. Third,
there exists an space algorithm that returns a constant
approximation in graphs with bounded arboricity.
-- Vertex Cover and Hitting Set: There exists an space
algorithm that solves the minimum hitting set problem where is the
cardinality of the input sets and is an upper bound on the size of the
minimum hitting set. We prove this is optimal up to logarithmic factors. Our
algorithm has update time. The case corresponds to minimum
vertex cover.
Finally, we consider a larger family of parameterized problems (including
-matching, disjoint paths, vertex coloring among others) for which our
subgraph sampling primitive yields fast, small-space dynamic graph stream
algorithms. We then show lower bounds for natural problems outside this family
Parameterized (Approximate) Defective Coloring
In Defective Coloring we are given a graph G=(V,E) and two integers chi_d,Delta^* and are asked if we can partition V into chi_d color classes, so that each class induces a graph of maximum degree Delta^*. We investigate the complexity of this generalization of Coloring with respect to several well-studied graph parameters, and show that the problem is W-hard parameterized by treewidth, pathwidth, tree-depth, or feedback vertex set, if chi_d=2. As expected, this hardness can be extended to larger values of chi_d for most of these parameters, with one surprising exception: we show that the problem is FPT parameterized by feedback vertex set for any chi_d != 2, and hence 2-coloring is the only hard case for this parameter. In addition to the above, we give an ETH-based lower bound for treewidth and pathwidth, showing that no algorithm can solve the
problem in n^{o(pw)}, essentially matching the complexity of an algorithm obtained with standard techniques.
We complement these results by considering the problem\u27s approximability and show that, with respect to Delta^*, the problem admits an algorithm which for any epsilon>0 runs in time (tw/epsilon)^{O(tw)} and returns a solution with exactly the desired number of colors that approximates the optimal Delta^* within (1+epsilon). We also give a (tw)^{O(tw)} algorithm which achieves the desired Delta^* exactly while 2-approximating the minimum value of chi_d. We show that this is close to optimal, by establishing that no FPT algorithm can (under standard assumptions) achieve a better than 3/2-approximation to chi_d, even when an extra constant additive error is also allowed
Algorithms for the minimum sum coloring problem: a review
The Minimum Sum Coloring Problem (MSCP) is a variant of the well-known vertex
coloring problem which has a number of AI related applications. Due to its
theoretical and practical relevance, MSCP attracts increasing attention. The
only existing review on the problem dates back to 2004 and mainly covers the
history of MSCP and theoretical developments on specific graphs. In recent
years, the field has witnessed significant progresses on approximation
algorithms and practical solution algorithms. The purpose of this review is to
provide a comprehensive inspection of the most recent and representative MSCP
algorithms. To be informative, we identify the general framework followed by
practical solution algorithms and the key ingredients that make them
successful. By classifying the main search strategies and putting forward the
critical elements of the reviewed methods, we wish to encourage future
development of more powerful methods and motivate new applications
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