64 research outputs found
Finding the Minimum-Weight k-Path
Given a weighted -vertex graph with integer edge-weights taken from a
range , we show that the minimum-weight simple path visiting
vertices can be found in time \tilde{O}(2^k \poly(k) M n^\omega) = O^*(2^k
M). If the weights are reals in , we provide a
-approximation which has a running time of \tilde{O}(2^k
\poly(k) n^\omega(\log\log M + 1/\varepsilon)). For the more general problem
of -tree, in which we wish to find a minimum-weight copy of a -node tree
in a given weighted graph , under the same restrictions on edge weights
respectively, we give an exact solution of running time \tilde{O}(2^k \poly(k)
M n^3) and a -approximate solution of running time
\tilde{O}(2^k \poly(k) n^3(\log\log M + 1/\varepsilon)). All of the above
algorithms are randomized with a polynomially-small error probability.Comment: To appear at WADS 201
Fast Witness Extraction Using a Decision Oracle
The gist of many (NP-)hard combinatorial problems is to decide whether a
universe of elements contains a witness consisting of elements that
match some prescribed pattern. For some of these problems there are known
advanced algebra-based FPT algorithms which solve the decision problem but do
not return the witness. We investigate techniques for turning such a
YES/NO-decision oracle into an algorithm for extracting a single witness, with
an objective to obtain practical scalability for large values of . By
relying on techniques from combinatorial group testing, we demonstrate that a
witness may be extracted with queries to either a deterministic or
a randomized set inclusion oracle with one-sided probability of error.
Furthermore, we demonstrate through implementation and experiments that the
algebra-based FPT algorithms are practical, in particular in the setting of the
-path problem. Also discussed are engineering issues such as optimizing
finite field arithmetic.Comment: Journal version, 16 pages. Extended abstract presented at ESA'1
On -Simple -Path
An -simple -path is a {path} in the graph of length that passes
through each vertex at most times. The -SIMPLE -PATH problem, given a
graph as input, asks whether there exists an -simple -path in . We
first show that this problem is NP-Complete. We then show that there is a graph
that contains an -simple -path and no simple path of length greater
than . So this, in a sense, motivates this problem especially
when one's goal is to find a short path that visits many vertices in the graph
while bounding the number of visits at each vertex.
We then give a randomized algorithm that runs in time that solves the -SIMPLE -PATH on a graph with
vertices with one-sided error. We also show that a randomized algorithm
with running time with gives a
randomized algorithm with running time \poly(n)\cdot 2^{cn} for the
Hamiltonian path problem in a directed graph - an outstanding open problem. So
in a sense our algorithm is optimal up to an factor
On Fully Dynamic Graph Sparsifiers
We initiate the study of dynamic algorithms for graph sparsification problems and obtain fully dynamic algorithms, allowing both edge insertions and edge deletions, that take polylogarithmic time after each update in the graph. Our three main results are as follows. First, we give a fully dynamic algorithm for maintaining a -spectral sparsifier with amortized update time . Second, we give a fully dynamic algorithm for maintaining a -cut sparsifier with \emph{worst-case} update time . Both sparsifiers have size . Third, we apply our dynamic sparsifier algorithm to obtain a fully dynamic algorithm for maintaining a -approximation to the value of the maximum flow in an unweighted, undirected, bipartite graph with amortized update time
Monomial Testing and Applications
In this paper, we devise two algorithms for the problem of testing
-monomials of degree in any multivariate polynomial represented by a
circuit, regardless of the primality of . One is an time
randomized algorithm. The other is an time deterministic
algorithm for the same -monomial testing problem but requiring the
polynomials to be represented by tree-like circuits. Several applications of
-monomial testing are also given, including a deterministic
upper bound for the -set -packing problem.Comment: 17 pages, 4 figures, submitted FAW-AAIM 2013. arXiv admin note:
substantial text overlap with arXiv:1302.5898; and text overlap with
arXiv:1007.2675, arXiv:1007.2678, arXiv:1007.2673 by other author
Mixing Color Coding-Related Techniques
Narrow sieves, representative sets and divide-and-color are three
breakthrough color coding-related techniques, which led to the design of
extremely fast parameterized algorithms. We present a novel family of
strategies for applying mixtures of them. This includes: (a) a mix of
representative sets and narrow sieves; (b) a faster computation of
representative sets under certain separateness conditions, mixed with
divide-and-color and a new technique, "balanced cutting"; (c) two mixtures of
representative sets, iterative compression and a new technique, "unbalanced
cutting". We demonstrate our strategies by obtaining, among other results,
significantly faster algorithms for -Internal Out-Branching and Weighted
3-Set -Packing, and a framework for speeding-up the previous best
deterministic algorithms for -Path, -Tree, -Dimensional -Matching,
Graph Motif and Partial Cover
Large Scale Spectral Clustering Using Approximate Commute Time Embedding
Spectral clustering is a novel clustering method which can detect complex
shapes of data clusters. However, it requires the eigen decomposition of the
graph Laplacian matrix, which is proportion to and thus is not
suitable for large scale systems. Recently, many methods have been proposed to
accelerate the computational time of spectral clustering. These approximate
methods usually involve sampling techniques by which a lot information of the
original data may be lost. In this work, we propose a fast and accurate
spectral clustering approach using an approximate commute time embedding, which
is similar to the spectral embedding. The method does not require using any
sampling technique and computing any eigenvector at all. Instead it uses random
projection and a linear time solver to find the approximate embedding. The
experiments in several synthetic and real datasets show that the proposed
approach has better clustering quality and is faster than the state-of-the-art
approximate spectral clustering methods
Finding and counting vertex-colored subtrees
The problems studied in this article originate from the Graph Motif problem
introduced by Lacroix et al. in the context of biological networks. The problem
is to decide if a vertex-colored graph has a connected subgraph whose colors
equal a given multiset of colors . It is a graph pattern-matching problem
variant, where the structure of the occurrence of the pattern is not of
interest but the only requirement is the connectedness. Using an algebraic
framework recently introduced by Koutis et al., we obtain new FPT algorithms
for Graph Motif and variants, with improved running times. We also obtain
results on the counting versions of this problem, proving that the counting
problem is FPT if M is a set, but becomes W[1]-hard if M is a multiset with two
colors. Finally, we present an experimental evaluation of this approach on real
datasets, showing that its performance compares favorably with existing
software.Comment: Conference version in International Symposium on Mathematical
Foundations of Computer Science (MFCS), Brno : Czech Republic (2010) Journal
Version in Algorithmic
Approximating Multilinear Monomial Coefficients and Maximum Multilinear Monomials in Multivariate Polynomials
This paper is our third step towards developing a theory of testing monomials
in multivariate polynomials and concentrates on two problems: (1) How to
compute the coefficients of multilinear monomials; and (2) how to find a
maximum multilinear monomial when the input is a polynomial. We
first prove that the first problem is \#P-hard and then devise a
upper bound for this problem for any polynomial represented by an arithmetic
circuit of size . Later, this upper bound is improved to for
polynomials. We then design fully polynomial-time randomized
approximation schemes for this problem for polynomials. On the
negative side, we prove that, even for polynomials with terms of
degree , the first problem cannot be approximated at all for any
approximation factor , nor {\em "weakly approximated"} in a much relaxed
setting, unless P=NP. For the second problem, we first give a polynomial time
-approximation algorithm for polynomials with terms of
degrees no more a constant . On the inapproximability side, we
give a lower bound, for any on the
approximation factor for polynomials. When terms in these
polynomials are constrained to degrees , we prove a lower
bound, assuming ; and a higher lower bound, assuming the
Unique Games Conjecture
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