144,006 research outputs found
A Review of integrity constraint maintenance and view updating techniques
Two interrelated problems may arise when updating a database. On one
hand, when an update is applied to the database, integrity constraints
may become violated. In such case, the integrity constraint maintenance
approach tries to obtain additional updates to keep integrity
constraints satisfied. On the other hand, when updates of derived or
view facts are requested, a view updating mechanism must be applied to
translate the update request into correct updates of the underlying base
facts.
This survey reviews the research performed on integrity constraint
maintenance and view updating. It is proposed a general framework to
classify and to compare methods that tackle integrity constraint
maintenance and/or view updating. Then, we analyze some of these methods
in more detail to identify their actual contribution and the main
limitations they may present.Postprint (published version
Updating constraint preconditioners for KKT systems in quadratic programming via low-rank corrections
This work focuses on the iterative solution of sequences of KKT linear
systems arising in interior point methods applied to large convex quadratic
programming problems. This task is the computational core of the interior point
procedure and an efficient preconditioning strategy is crucial for the
efficiency of the overall method. Constraint preconditioners are very effective
in this context; nevertheless, their computation may be very expensive for
large-scale problems, and resorting to approximations of them may be
convenient. Here we propose a procedure for building inexact constraint
preconditioners by updating a "seed" constraint preconditioner computed for a
KKT matrix at a previous interior point iteration. These updates are obtained
through low-rank corrections of the Schur complement of the (1,1) block of the
seed preconditioner. The updated preconditioners are analyzed both
theoretically and computationally. The results obtained show that our updating
procedure, coupled with an adaptive strategy for determining whether to
reinitialize or update the preconditioner, can enhance the performance of
interior point methods on large problems.Comment: 22 page
Fast Fourier Transform Simulation Techniques for Coulomb Gases
An improved approach to updating the electric field in simulations of Coulomb
gases using the local lattice technique introduced by Maggs and Rossetto, is
described and tested. Using the Fast Fourier Transform (FFT) an independent
configuration of electric fields subject to Gauss' law constraint can be
generated in a single update step. This FFT based method is shown to outperform
previous approaches to updating the electric field in the simulation of a basic
test problem in electrostatics of strongly correlated systems.Comment: 5 pages, 3 figure
A role of constraint in self-organization
In this paper we introduce a neural network model of self-organization. This
model uses a variation of Hebb rule for updating its synaptic weights, and
surely converges to the equilibrium status. The key point of the convergence is
the update rule that constrains the total synaptic weight and this seems to
make the model stable. We investigate the role of the constraint and show that
it is the constraint that makes the model stable. For analyzing this setting,
we propose a simple probabilistic game that models the neural network and the
self-organization process. Then, we investigate the characteristics of this
game, namely, the probability that the game becomes stable and the number of
the steps it takes.Comment: To appear in the Proc. RANDOM'98, Oct. 199
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Revisiting lagrange relaxation (LR) for processing large-scale mixed integer programming (MIP) problems
Lagrangean Relaxation has been successfully applied to process many well known
instances of NP-hard Mixed Integer Programming problems. In this paper we present
a Lagrangean Relaxation based generic solver for processing Mixed Integer
Programming problems. We choose the constraints, which are relaxed using a
constraint classification scheme. The tactical issue of updating the Lagrange
multiplier is addressed through sub-gradient optimisation; alternative rules for
updating their values are investigated. The Lagrangean relaxation provides a lower
bound to the original problem and the upper bound is calculated using a heuristic
technique. The bounds obtained by the Lagrangean Relaxation based generic solver
were used to warm-start the Branch and Bound algorithm; the performance of the
generic solver and the effect of the alternative control settings are reported for a wide
class of benchmark models. Finally, we present an alternative technique to calculate
the upper bound, using a genetic algorithm that benefits from the mathematical
structure of the constraints. The performance of the genetic algorithm is also
presented
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