Algorithms of choosing the pre-conditioner matrix for quadratic programming with linear bound constraints

Authors:
Nicolae Popoviciu;Mioara Boncut
Affiliations:
Hyperion University of Bucharest, Faculty of Mathematics-Informatics, Bucharest, Romania;Lucian Blaga University of Sibiu, Faculty of Sciences, Romania
Venue:
NN'08 Proceedings of the 9th WSEAS International Conference on Neural Networks
Year:
2008

Citing 2
Cited 1

Linear programming and network flows (2nd ed.)

Linear programming and network flows (2nd ed.)
Optimal preconditioners of a given sparsity pattern

BIT

Quadratic optimization models and algorithms: one algorithm for linear bound constraints based on neural networks

NN'08 Proceedings of the 9th WSEAS International Conference on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

The paper begins with several quadratic programming models (QP). For QP model with linear bound constraints a preconditioning technique [2] is necessary, in a neural network frame. This technique reduces the susceptibility of the system to errors. Two algorithms of preconditioning are presented: the first one is based on one matrix and the second is based on two matrixes. Both algorithms are used in three numerical applications. Each application ends by a test of correctitude of computations.