349 research outputs found
On local search and LP and SDP relaxations for k-Set Packing
Set packing is a fundamental problem that generalises some well-known
combinatorial optimization problems and knows a lot of applications. It is
equivalent to hypergraph matching and it is strongly related to the maximum
independent set problem. In this thesis we study the k-set packing problem
where given a universe U and a collection C of subsets over U, each of
cardinality k, one needs to find the maximum collection of mutually disjoint
subsets. Local search techniques have proved to be successful in the search for
approximation algorithms, both for the unweighted and the weighted version of
the problem where every subset in C is associated with a weight and the
objective is to maximise the sum of the weights. We make a survey of these
approaches and give some background and intuition behind them. In particular,
we simplify the algebraic proof of the main lemma for the currently best
weighted approximation algorithm of Berman ([Ber00]) into a proof that reveals
more intuition on what is really happening behind the math. The main result is
a new bound of k/3 + 1 + epsilon on the integrality gap for a polynomially
sized LP relaxation for k-set packing by Chan and Lau ([CL10]) and the natural
SDP relaxation [NOTE: see page iii]. We provide detailed proofs of lemmas
needed to prove this new bound and treat some background on related topics like
semidefinite programming and the Lovasz Theta function. Finally we have an
extended discussion in which we suggest some possibilities for future research.
We discuss how the current results from the weighted approximation algorithms
and the LP and SDP relaxations might be improved, the strong relation between
set packing and the independent set problem and the difference between the
weighted and the unweighted version of the problem.Comment: There is a mistake in the following line of Theorem 17: "As an
induced subgraph of H with more edges than vertices constitutes an improving
set". Therefore, the proofs of Theorem 17, and hence Theorems 19, 23 and 24,
are false. It is still open whether these theorems are tru
Finding Linear Arrangements of Hypergraphs with Bounded Cutwidth in Linear Time
Cutwidth is a fundamental graph layout parameter. It generalises to hypergraphs in a natural way and has been studied in a wide range of contexts. For graphs it is known that for a fixed constant k there is a linear time algorithm that for any given G, decides whether G has cutwidth at most k and, in the case of a positive answer, outputs a corresponding linear arrangement. We show that such an algorithm also exists for hypergraphs
Dynamic Kernels for Hitting Sets and Set Packing
Computing small kernels for the hitting set problem is a well-studied computational problem where we are given a hypergraph with n vertices and m hyperedges, each of size d for some small constant d, and a parameter k. The task is to compute a new hypergraph, called a kernel, whose size is polynomial with respect to the parameter k and which has a size-k hitting set if, and only if, the original hypergraph has one. State-of-the-art algorithms compute kernels of size k^d (which is a polynomial kernel size as d is a constant), and they do so in time m? 2^d poly(d) for a small polynomial poly(d) (which is a linear runtime as d is again a constant).
We generalize this task to the dynamic setting where hyperedges may continuously be added or deleted and one constantly has to keep track of a size-k^d hitting set kernel in memory (including moments when no size-k hitting set exists). This paper presents a deterministic solution with worst-case time 3^d poly(d) for updating the kernel upon hyperedge inserts and time 5^d poly(d) for updates upon deletions. These bounds nearly match the time 2^d poly(d) needed by the best static algorithm per hyperedge. Let us stress that for constant d our algorithm maintains a dynamic hitting set kernel with constant, deterministic, worst-case update time that is independent of n, m, and the parameter k. As a consequence, we also get a deterministic dynamic algorithm for keeping track of size-k hitting sets in d-hypergraphs with update times O(1) and query times O(c^k) where c = d - 1 + O(1/d) equals the best base known for the static setting
Dagstuhl Reports : Volume 1, Issue 2, February 2011
Online Privacy: Towards Informational Self-Determination on the Internet (Dagstuhl Perspectives Workshop 11061) : Simone Fischer-HĂŒbner, Chris Hoofnagle, Kai Rannenberg, Michael Waidner, Ioannis Krontiris and Michael Marhöfer Self-Repairing Programs (Dagstuhl Seminar 11062) : Mauro PezzĂ©, Martin C. Rinard, Westley Weimer and Andreas Zeller Theory and Applications of Graph Searching Problems (Dagstuhl Seminar 11071) : Fedor V. Fomin, Pierre Fraigniaud, Stephan Kreutzer and Dimitrios M. Thilikos Combinatorial and Algorithmic Aspects of Sequence Processing (Dagstuhl Seminar 11081) : Maxime Crochemore, Lila Kari, Mehryar Mohri and Dirk Nowotka Packing and Scheduling Algorithms for Information and Communication Services (Dagstuhl Seminar 11091) Klaus Jansen, Claire Mathieu, Hadas Shachnai and Neal E. Youn
Finding secluded places of special interest in graphs.
Finding a vertex subset in a graph that satisfies a certain property is one of the most-studied topics
in algorithmic graph theory. The focus herein is often on minimizing or maximizing the size
of the solution, that is, the size of the desired vertex set. In several applications, however, we also
want to limit the âexposureâ of the solution to the rest of the graph. This is the case, for example,
when the solution represents persons that ought to deal with sensitive information or a segregated
community. In this work, we thus explore the (parameterized) complexity of finding such secluded
vertex subsets for a wide variety of properties that they shall fulfill. More precisely, we study the
constraint that the (open or closed) neighborhood of the solution shall be bounded by a parameter
and the influence of this constraint on the complexity of minimizing separators, feedback vertex
sets, F-free vertex deletion sets, dominating sets, and the maximization of independent sets
Finding secluded places of special interest in graphs
Finding a vertex subset in a graph that satisfies a certain property is one of the most-studied topics in algorithmic graph theory. The focus herein is often on minimizing or maximizing the size of the solution, that is, the size of the desired vertex set. In several applications, however, we also want to limit the âexposureâ of the solution to the rest of the graph. This is the case, for example, when the solution represents persons that ought to deal with sensitive information or a segregated community. In this work, we thus explore the (parameterized) complexity of finding such secluded vertex subsets for a wide variety of properties that they shall fulfill. More precisely, we study the constraint that the (open or closed) neighborhood of the solution shall be bounded by a parameter and the influence of this constraint on the complexity of minimizing separators, feedback vertex sets, F-free vertex deletion sets, dominating sets, and the maximization of independent sets
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Combinatorial Optimization
Combinatorial Optimization is an active research area that developed from the rich interaction among many mathematical areas, including combinatorics, graph theory, geometry, optimization, probability, theoretical computer science, and many others. It combines algorithmic and complexity analysis with a mature mathematical foundation and it yields both basic research and applications in manifold areas such as, for example, communications, economics, traffic, network design, VLSI, scheduling, production, computational biology, to name just a few. Through strong inner ties to other mathematical fields it has been contributing to and benefiting from areas such as, for example, discrete and convex geometry, convex and nonlinear optimization, algebraic and topological methods, geometry of numbers, matroids and combinatorics, and mathematical programming. Moreover, with respect to applications and algorithmic complexity, Combinatorial Optimization is an essential link between mathematics, computer science and modern applications in data science, economics, and industry
Streaming Approximation Resistance of Every Ordering CSP
An ordering constraint satisfaction problem (OCSP) is given by a positive
integer and a constraint predicate mapping permutations on
to . Given an instance of OCSP on
variables and constraints, the goal is to find an ordering of the
variables that maximizes the number of constraints that are satisfied, where a
constraint specifies a sequence of distinct variables and the constraint is
satisfied by an ordering on the variables if the ordering induced on the
variables in the constraint satisfies . OCSPs capture natural problems
including "Maximum acyclic subgraph (MAS)" and "Betweenness".
In this work we consider the task of approximating the maximum number of
satisfiable constraints in the (single-pass) streaming setting, where an
instance is presented as a stream of constraints. We show that for every ,
OCSP is approximation-resistant to -space streaming algorithms.
This space bound is tight up to polylogarithmic factors. In the case of MAS our
result shows that for every , MAS is not
-approximable in space. The previous best
inapproximability result only ruled out a -approximation in
space.
Our results build on recent works of Chou, Golovnev, Sudan, Velingker, and
Velusamy who show tight, linear-space inapproximability results for a broad
class of (non-ordering) constraint satisfaction problems over arbitrary
(finite) alphabets. We design a family of appropriate CSPs (one for every )
from any given OCSP, and apply their work to this family of CSPs. We show that
the hard instances from this earlier work have a particular "small-set
expansion" property. By exploiting this combinatorial property, in combination
with the hardness results of the resulting families of CSPs, we give optimal
inapproximability results for all OCSPs.Comment: 23 pages, 1 figure. Replaces earlier version with lower
bound, using new bounds from arXiv:2106.13078. To appear in APPROX'2
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