21 research outputs found
A New Regularity Lemma and Faster Approximation Algorithms for Low Threshold Rank Graphs
Kolla and Tulsiani [KT07,Kolla11} and Arora, Barak and Steurer [ABS10]
introduced the technique of subspace enumeration, which gives approximation
algorithms for graph problems such as unique games and small set expansion; the
running time of such algorithms is exponential in the threshold-rank of the
graph.
Guruswami and Sinop [GS11,GS12], and Barak, Raghavendra, and Steurer [BRS11]
developed an alternative approach to the design of approximation algorithms for
graphs of bounded threshold-rank, based on semidefinite programming relaxations
in the Lassere hierarchy and on novel rounding techniques. These algorithms are
faster than the ones based on subspace enumeration and work on a broad class of
problems.
In this paper we develop a third approach to the design of such algorithms.
We show, constructively, that graphs of bounded threshold-rank satisfy a weak
Szemeredi regularity lemma analogous to the one proved by Frieze and Kannan
[FK99] for dense graphs. The existence of efficient approximation algorithms is
then a consequence of the regularity lemma, as shown by Frieze and Kannan.
Applying our method to the Max Cut problem, we devise an algorithm that is
faster than all previous algorithms, and is easier to describe and analyze
Combining spectral sequencing and parallel simulated annealing for the MinLA problem
In this paper we present and analyze new sequential and parallel
heuristics to approximate the Minimum Linear Arrangement problem
(MinLA). The heuristics consist in obtaining a first global solution
using Spectral Sequencing and improving it locally through Simulated
Annealing. In order to accelerate the annealing process, we present a
special neighborhood distribution that tends to favor moves with high
probability to be accepted. We show how to make use of this
neighborhood to parallelize the Metropolis stage on distributed memory
machines by mapping partitions of the input graph to processors and
performing moves concurrently. The paper reports the results obtained
with this new heuristic when applied to a set of large graphs,
including graphs arising from finite elements methods and graphs
arising from VLSI applications. Compared to other heuristics, the
measurements obtained show that the new heuristic improves the
solution quality, decreases the running time and offers an excellent
speedup when ran on a commodity network made of nine personal
computers.Postprint (published version
Partitioning problems in dense hypergraphs
AbstractWe study the general partitioning problem and the discrepancy problem in dense hypergraphs. Using the regularity lemma (Szemerédi, Problemes Combinatories et Theorie des Graphes (1978), pp. 399–402) and its algorithmic version proved in Czygrinow and Rödl (SIAM J. Comput., to appear), we give polynomial-time approximation schemes for the general partitioning problem and for the discrepancy problem
Streaming Lower Bounds for Approximating MAX-CUT
We consider the problem of estimating the value of max cut in a graph in the
streaming model of computation. At one extreme, there is a trivial
-approximation for this problem that uses only space, namely,
count the number of edges and output half of this value as the estimate for max
cut value. On the other extreme, if one allows space, then a
near-optimal solution to the max cut value can be obtained by storing an
-size sparsifier that essentially preserves the max cut. An
intriguing question is if poly-logarithmic space suffices to obtain a
non-trivial approximation to the max-cut value (that is, beating the factor
). It was recently shown that the problem of estimating the size of a
maximum matching in a graph admits a non-trivial approximation in
poly-logarithmic space.
Our main result is that any streaming algorithm that breaks the
-approximation barrier requires space even if the
edges of the input graph are presented in random order. Our result is obtained
by exhibiting a distribution over graphs which are either bipartite or
-far from being bipartite, and establishing that
space is necessary to differentiate between these
two cases. Thus as a direct corollary we obtain that
space is also necessary to test if a graph is bipartite or -far
from being bipartite.
We also show that for any , any streaming algorithm that
obtains a -approximation to the max cut value when edges arrive
in adversarial order requires space, implying that
space is necessary to obtain an arbitrarily good approximation to
the max cut value
Graph removal lemmas
The graph removal lemma states that any graph on n vertices with o(n^{v(H)})
copies of a fixed graph H may be made H-free by removing o(n^2) edges. Despite
its innocent appearance, this lemma and its extensions have several important
consequences in number theory, discrete geometry, graph theory and computer
science. In this survey we discuss these lemmas, focusing in particular on
recent improvements to their quantitative aspects.Comment: 35 page
On the Complexity of Newman's Community Finding Approach for Biological and Social Networks
Given a graph of interactions, a module (also called a community or cluster)
is a subset of nodes whose fitness is a function of the statistical
significance of the pairwise interactions of nodes in the module. The topic of
this paper is a model-based community finding approach, commonly referred to as
modularity clustering, that was originally proposed by Newman and has
subsequently been extremely popular in practice. Various heuristic methods are
currently employed for finding the optimal solution. However, the exact
computational complexity of this approach is still largely unknown.
To this end, we initiate a systematic study of the computational complexity
of modularity clustering. Due to the specific quadratic nature of the
modularity function, it is necessary to study its value on sparse graphs and
dense graphs separately. Our main results include a (1+\eps)-inapproximability
for dense graphs and a logarithmic approximation for sparse graphs. We make use
of several combinatorial properties of modularity to get these results. These
are the first non-trivial approximability results beyond the previously known
NP-hardness results.Comment: Journal of Computer and System Sciences, 201
The algebraic structure of the densification and the sparsification tasks for CSPs
The tractability of certain CSPs for dense or sparse instances is known from
the 90s. Recently, the densification and the sparsification of CSPs were
formulated as computational tasks and the systematical study of their
computational complexity was initiated.
We approach this problem by introducing the densification operator, i.e. the
closure operator that, given an instance of a CSP, outputs all constraints that
are satisfied by all of its solutions. According to the Galois theory of
closure operators, any such operator is related to a certain implicational
system (or, a functional dependency) . We are specifically interested
in those classes of fixed-template CSPs, parameterized by constraint languages
, for which the size of an implicational system is a
polynomial in the number of variables . We show that in the Boolean case,
is of polynomial size if and only if is of bounded width. For
such languages, can be computed in log-space or in a logarithmic time
with a polynomial number of processors. Given an implicational system ,
the densification task is equivalent to the computation of the closure of input
constraints. The sparsification task is equivalent to the computation of the
minimal key. This leads to -algorithm for
the sparsification task where is the number of non-redundant
sparsifications of an original CSP.
Finally, we give a complete classification of constraint languages over the
Boolean domain for which the densification problem is tractable
Limits of Ordered Graphs and their Applications
The emerging theory of graph limits exhibits an analytic perspective on
graphs, showing that many important concepts and tools in graph theory and its
applications can be described more naturally (and sometimes proved more easily)
in analytic language. We extend the theory of graph limits to the ordered
setting, presenting a limit object for dense vertex-ordered graphs, which we
call an \emph{orderon}. As a special case, this yields limit objects for
matrices whose rows and columns are ordered, and for dynamic graphs that expand
(via vertex insertions) over time. Along the way, we devise an ordered
locality-preserving variant of the cut distance between ordered graphs, showing
that two graphs are close with respect to this distance if and only if they are
similar in terms of their ordered subgraph frequencies. We show that the space
of orderons is compact with respect to this distance notion, which is key to a
successful analysis of combinatorial objects through their limits.
We derive several applications of the ordered limit theory in extremal
combinatorics, sampling, and property testing in ordered graphs. In particular,
we prove a new ordered analogue of the well-known result by Alon and Stav
[RS\&A'08] on the furthest graph from a hereditary property; this is the first
known result of this type in the ordered setting. Unlike the unordered regime,
here the random graph model with an ordering over the vertices is
\emph{not} always asymptotically the furthest from the property for some .
However, using our ordered limit theory, we show that random graphs generated
by a stochastic block model, where the blocks are consecutive in the vertex
ordering, are (approximately) the furthest. Additionally, we describe an
alternative analytic proof of the ordered graph removal lemma [Alon et al.,
FOCS'17].Comment: Added a new application: An Alon-Stav type result on the furthest
ordered graph from a hereditary property; Fixed and extended proof sketch of
the removal lemma applicatio