802 research outputs found
Parameterized Study of the Test Cover Problem
We carry out a systematic study of a natural covering problem, used for
identification across several areas, in the realm of parameterized complexity.
In the {\sc Test Cover} problem we are given a set of items
together with a collection, , of distinct subsets of these items called
tests. We assume that is a test cover, i.e., for each pair of items
there is a test in containing exactly one of these items. The
objective is to find a minimum size subcollection of , which is still a
test cover. The generic parameterized version of {\sc Test Cover} is denoted by
-{\sc Test Cover}. Here, we are given and a
positive integer parameter as input and the objective is to decide whether
there is a test cover of size at most . We study four
parameterizations for {\sc Test Cover} and obtain the following:
(a) -{\sc Test Cover}, and -{\sc Test Cover} are fixed-parameter
tractable (FPT).
(b) -{\sc Test Cover} and -{\sc Test Cover} are
W[1]-hard. Thus, it is unlikely that these problems are FPT
(Non-)existence of Polynomial Kernels for the Test Cover Problem
The input of the Test Cover problem consists of a set of vertices, and a
collection of distinct subsets of , called
tests. A test separates a pair of vertices if A subcollection is a test cover if each
pair of distinct vertices is separated by a test in . The
objective is to find a test cover of minimum cardinality, if one exists. This
problem is NP-hard.
We consider two parameterizations the Test Cover problem with parameter :
(a) decide whether there is a test cover with at most tests, (b) decide
whether there is a test cover with at most tests. Both
parameterizations are known to be fixed-parameter tractable. We prove that none
have a polynomial size kernel unless . Our proofs use
the cross-composition method recently introduced by Bodlaender et al. (2011)
and parametric duality introduced by Chen et al. (2005). The result for the
parameterization (a) was an open problem (private communications with Henning
Fernau and Jiong Guo, Jan.-Feb. 2012). We also show that the parameterization
(a) admits a polynomial size kernel if the size of each test is upper-bounded
by a constant
Algorithms for the workflow satisfiability problem engineered for counting constraints
The workflow satisfiability problem (WSP) asks whether there exists an
assignment of authorized users to the steps in a workflow specification that
satisfies the constraints in the specification. The problem is NP-hard in
general, but several subclasses of the problem are known to be fixed-parameter
tractable (FPT) when parameterized by the number of steps in the specification.
In this paper, we consider the WSP with user-independent counting constraints,
a large class of constraints for which the WSP is known to be FPT. We describe
an efficient implementation of an FPT algorithm for solving this subclass of
the WSP and an experimental evaluation of this algorithm. The algorithm
iteratively generates all equivalence classes of possible partial solutions
until, whenever possible, it finds a complete solution to the problem. We also
provide a reduction from a WSP instance to a pseudo-Boolean SAT instance. We
apply this reduction to the instances used in our experiments and solve the
resulting PB SAT problems using SAT4J, a PB SAT solver. We compare the
performance of our algorithm with that of SAT4J and discuss which of the two
approaches would be more effective in practice
Parameterized and Approximation Algorithms for the Load Coloring Problem
Let be two positive integers and let be a graph. The
-Load Coloring Problem (denoted -LCP) asks whether there is a
-coloring such that for every ,
there are at least edges with both endvertices colored . Gutin and Jones
(IPL 2014) studied this problem with . They showed -LCP to be fixed
parameter tractable (FPT) with parameter by obtaining a kernel with at most
vertices. In this paper, we extend the study to any fixed by giving
both a linear-vertex and a linear-edge kernel. In the particular case of ,
we obtain a kernel with less than vertices and less than edges. These
results imply that for any fixed , -LCP is FPT and that the
optimization version of -LCP (where is to be maximized) has an
approximation algorithm with a constant ratio for any fixed
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