Abstract
As mentioned in §1.2, it is usually necessary to precondition an interval linear system to obtain meaningful bounds on the solution set. This is clear for the interval Gauss-Seidel method, since it reduces to the classical Gauss-Seidel method when the coefficients of the system of equations are thin, and since the classical Gauss-Seidel method in general does not converge without preconditioning. However, preconditioning is also necessary and useful for the Krawczyk method and interval Gaussian elimination. For example, if interval Gaussian elimination, i.e. Algorithm 1 is applied to the system \(AX = B\) of (1.19) on page 20, the result is
which gives only \({x_2} \in \frac{{\left[ { - 6,6} \right]}}{{\left[ { - 4,4} \right]}} = {\kern 1pt} and{\kern 1pt} {x_1} \in \), whereas, as is seen in Gigure 1.1, the smallest possible interval enclosure of the solution set is \( \sum {(A,B) = {{\left( {\left[ { - 4,4} \right],\left[ { - 4,4} \right]} \right)}^T}} \). Similarly, applying the interval Gauss-Seidel method results in \( \overline {{x_1}} \leftarrow \frac{{\left[ { - 2,2} \right] - \left[ { - 2,1} \right]\left[ { - 10,10} \right]}}{{\left[ {2,4} \right]}} = \left[ { - 11,11} \right] \supset \left[ { - 10,10} \right]{\kern 1pt} and{\kern 1pt} \overline {{x_2}} \leftarrow \frac{{\left[ { - 2,2} \right] - \left[ { - 1,2} \right]\left[ { - 10,10} \right]}}{{\left[ {2,4} \right]}} = \left[ { - 11,11} \right] \supset \left[ { - 10,10} \right]\). Finally, the Krawczyk method only makes sense in the context of presconditioners.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 1996 Springer Science+Business Media Dordrecht
About this chapter
Cite this chapter
Kearfott, R.B. (1996). On Preconditioning. In: Rigorous Global Search: Continuous Problems. Nonconvex Optimization and Its Applications, vol 13. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-2495-0_3
Download citation
DOI: https://doi.org/10.1007/978-1-4757-2495-0_3
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4419-4762-8
Online ISBN: 978-1-4757-2495-0
eBook Packages: Springer Book Archive