417 research outputs found
Testing of random matrices
Let be a positive integer and be an
\linebreak \noindent sized matrix of independent random variables
having joint uniform distribution \hbox{Pr} {x_{ij} = k \hbox{for} 1 \leq k
\leq n} = \frac{1}{n} \quad (1 \leq i, j \leq n) \koz. A realization
of is called \textit{good}, if its each row and
each column contains a permutation of the numbers . We present and
analyse four typical algorithms which decide whether a given realization is
good
On the eigenvalues of distance powers of circuits
Taking the d-th distance power of a graph, one adds edges between all pairs
of vertices of that graph whose distance is at most d. It is shown that only
the numbers -3, -2, -1, 0, 1, 2d can be integer eigenvalues of a circuit
distance power. Moreover, their respective multiplicities are determined and
explicit constructions for corresponding eigenspace bases containing only
vectors with entries -1, 0, 1 are given.Comment: 14 page
An Efficient Local Search for Partial Latin Square Extension Problem
A partial Latin square (PLS) is a partial assignment of n symbols to an nxn
grid such that, in each row and in each column, each symbol appears at most
once. The partial Latin square extension problem is an NP-hard problem that
asks for a largest extension of a given PLS. In this paper we propose an
efficient local search for this problem. We focus on the local search such that
the neighborhood is defined by (p,q)-swap, i.e., removing exactly p symbols and
then assigning symbols to at most q empty cells. For p in {1,2,3}, our
neighborhood search algorithm finds an improved solution or concludes that no
such solution exists in O(n^{p+1}) time. We also propose a novel swap
operation, Trellis-swap, which is a generalization of (1,q)-swap and
(2,q)-swap. Our Trellis-neighborhood search algorithm takes O(n^{3.5}) time to
do the same thing. Using these neighborhood search algorithms, we design a
prototype iterated local search algorithm and show its effectiveness in
comparison with state-of-the-art optimization solvers such as IBM ILOG CPLEX
and LocalSolver.Comment: 17 pages, 2 figure
Phase Transition and Network Structure in Realistic SAT Problems
A fundamental question in Computer Science is understanding when a specific
class of problems go from being computationally easy to hard. Because of its
generality and applications, the problem of Boolean Satisfiability (aka SAT) is
often used as a vehicle for investigating this question. A signal result from
these studies is that the hardness of SAT problems exhibits a dramatic
easy-to-hard phase transition with respect to the problem constrainedness. Past
studies have however focused mostly on SAT instances generated using uniform
random distributions, where all constraints are independently generated, and
the problem variables are all considered of equal importance. These assumptions
are unfortunately not satisfied by most real problems. Our project aims for a
deeper understanding of hardness of SAT problems that arise in practice. We
study two key questions: (i) How does easy-to-hard transition change with more
realistic distributions that capture neighborhood sensitivity and
rich-get-richer aspects of real problems and (ii) Can these changes be
explained in terms of the network properties (such as node centrality and
small-worldness) of the clausal networks of the SAT problems. Our results,
based on extensive empirical studies and network analyses, provide important
structural and computational insights into realistic SAT problems. Our
extensive empirical studies show that SAT instances from realistic
distributions do exhibit phase transition, but the transition occurs sooner (at
lower values of constrainedness) than the instances from uniform random
distribution. We show that this behavior can be explained in terms of their
clausal network properties such as eigenvector centrality and small-worldness
(measured indirectly in terms of the clustering coefficients and average node
distance)
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