78 research outputs found
On Matrices, Automata, and Double Counting
Matrix models are ubiquitous for constraint problems. Many such problems have a matrix of variables M, with the same constraint defined by a finite-state automaton A on each row of M and a global cardinality constraint gcc on each column of M. We give two methods for deriving, by double counting,
necessary conditions on the cardinality variables of the gcc constraints from the automaton A. The first method yields linear necessary conditions and simple arithmetic constraints. The second method introduces the cardinality automaton, which abstracts the overall behaviour of all the row automata and can be encoded by a set of linear constraints. We evaluate the impact of our methods on a large set of nurse rostering problem instances
Conjunctions of Among Constraints
Many existing global constraints can be encoded as a conjunction of among
constraints. An among constraint holds if the number of the variables in its
scope whose value belongs to a prespecified set, which we call its range, is
within some given bounds. It is known that domain filtering algorithms can
benefit from reasoning about the interaction of among constraints so that
values can be filtered out taking into consideration several among constraints
simultaneously. The present pa- per embarks into a systematic investigation on
the circumstances under which it is possible to obtain efficient and complete
domain filtering algorithms for conjunctions of among constraints. We start by
observing that restrictions on both the scope and the range of the among
constraints are necessary to obtain meaningful results. Then, we derive a
domain flow-based filtering algorithm and present several applications. In
particular, it is shown that the algorithm unifies and generalizes several
previous existing results.Comment: 15 pages plus appendi
Optimal General Matchings
Given a graph and for each vertex a subset of the
set , where denotes the degree of vertex
in the graph , a -factor of is any set such that
for each vertex , where denotes the number of
edges of incident to . The general factor problem asks the existence of
a -factor in a given graph. A set is said to have a {\em gap of
length} if there exists a natural number such that and . Without any restrictions the
general factor problem is NP-complete. However, if no set contains a gap
of length greater than , then the problem can be solved in polynomial time
and Cornuejols \cite{Cor} presented an algorithm for finding a -factor, if
it exists. In this paper we consider a weighted version of the general factor
problem, in which each edge has a nonnegative weight and we are interested in
finding a -factor of maximum (or minimum) weight. In particular, this
version comprises the minimum/maximum cardinality variant of the general factor
problem, where we want to find a -factor having a minimum/maximum number of
edges.
We present an algorithm for the maximum/minimum weight -factor for the
case when no set contains a gap of length greater than . This also
yields the first polynomial time algorithm for the maximum/minimum cardinality
-factor for this case
Constraint satisfaction parameterized by solution size
In the constraint satisfaction problem (CSP) corresponding to a constraint
language (i.e., a set of relations) , the goal is to find an assignment
of values to variables so that a given set of constraints specified by
relations from is satisfied. The complexity of this problem has
received substantial amount of attention in the past decade. In this paper we
study the fixed-parameter tractability of constraint satisfaction problems
parameterized by the size of the solution in the following sense: one of the
possible values, say 0, is "free," and the number of variables allowed to take
other, "expensive," values is restricted. A size constraint requires that
exactly variables take nonzero values. We also study a more refined version
of this restriction: a global cardinality constraint prescribes how many
variables have to be assigned each particular value. We study the parameterized
complexity of these types of CSPs where the parameter is the required number
of nonzero variables. As special cases, we can obtain natural and
well-studied parameterized problems such as Independent Set, Vertex Cover,
d-Hitting Set, Biclique, etc.
In the case of constraint languages closed under substitution of constants,
we give a complete characterization of the fixed-parameter tractable cases of
CSPs with size constraints, and we show that all the remaining problems are
W[1]-hard. For CSPs with cardinality constraints, we obtain a similar
classification, but for some of the problems we are only able to show that they
are Biclique-hard. The exact parameterized complexity of the Biclique problem
is a notorious open problem, although it is believed to be W[1]-hard.Comment: To appear in SICOMP. Conference version in ICALP 201
Improvement of the Embarrassingly Parallel Search for Data Centers
International audienceWe propose an adaptation of the Embarrassingly Parallel Search (EPS) method for data centers. EPS is a simple but efficient method for parallel solving of CSPs. EPS decomposes the problem in many distinct subproblems which are then solved independently by workers. EPS performed well on multi-cores machines (40), but some issues arise when using more cores in a datacenter. Here, we identify the decomposition as the cause of the degradation and propose a parallel decomposition to address this issue. Thanks to it, EPS gives almost linear speedup and outperforms work stealing by orders of magnitude using the Gecode solver
On the speed of constraint propagation and the time complexity of arc consistency testing
Establishing arc consistency on two relational structures is one of the most
popular heuristics for the constraint satisfaction problem. We aim at
determining the time complexity of arc consistency testing. The input
structures and can be supposed to be connected colored graphs, as the
general problem reduces to this particular case. We first observe the upper
bound , which implies the bound in terms of
the number of edges and the bound in terms of the number of
vertices. We then show that both bounds are tight up to a constant factor as
long as an arc consistency algorithm is based on constraint propagation (like
any algorithm currently known).
Our argument for the lower bounds is based on examples of slow constraint
propagation. We measure the speed of constraint propagation observed on a pair
by the size of a proof, in a natural combinatorial proof system, that
Spoiler wins the existential 2-pebble game on . The proof size is bounded
from below by the game length , and a crucial ingredient of our
analysis is the existence of with . We find one
such example among old benchmark instances for the arc consistency problem and
also suggest a new, different construction.Comment: 19 pages, 5 figure
Revisiting the tree Constraint
International audienceThis paper revisits the tree constraint introduced in [2] which partitions the nodes of a n-nodes, m-arcs directed graph into a set of node-disjoint anti-arborescences for which only certain nodes can be tree roots. We introduce a new filtering algorithm that enforces generalized arc-consistency in O(n + m) time while the original filtering algorithm reaches O(nm) time. This result allows to tackle larger scale problems involving graph partitioning
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