ACM Home Page
Please provide us with feedback. Feedback
On stopping criteria in verified nonlinear systems or optimization algorithms
Full text PdfPdf (141 KB)
Source ACM Transactions on Mathematical Software (TOMS) archive
Volume 26 ,  Issue 3  (September 2000) table of contents
Pages: 373 - 389  
Year of Publication: 2000
ISSN:0098-3500
Authors
R. B. Kearfott  Univ. of Louisiana at Lafayette, Lafayette
G. W. Walster  Sun Microsystems, Inc., Mountain View, CA
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): n/a,   Downloads (12 Months): n/a,   Citation Count: 0
Additional Information:

abstract   references   index terms   review   collaborative colleagues  

Tools and Actions: Request Permissions Request Permissions    Review this Article  
DOI Bookmark: Use this link to bookmark this Article: http://doi.acm.org/10.1145/358407.358418
What is a DOI?

ABSTRACT

Traditionally, iterative methods for nonlinear systems use heuristic domain and range stopping criteria to determine when accuracy tolerances have been met. However, such heuristics can cause stopping at points far from actual solutions, and can be unreliable due to the effects of round-off error or inaccuracies in data. In verified computations, rigorous determination of when a set of bounds has met a tolerance can be done analogously to the traditional approximate setting. Nonetheless, the range tolerance possibly cannot be met. If the criteria are used to determine when to stop subdivision of n-dimensional bounds into subregions, then failure of a range tolerance results in excessive, unnecessary subdivision, and could make the algorithm impractical. On the other hand, interval techniques can detect when inaccuracies or round-off will not permit residual bounds to be narrowed. These techniques can be incorporated into range thickness stopping criteria that complement the range stopping criteria. In this note, the issue is first introduced and illustrated with a simple example. The thickness stopping criterion is then formally introduced and analyzed. Third, inclusion of the criterion within a general verified global optimization algorithm is studied. An industrial example is presented. Finally, consequences and implications are discussed.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
ALEFELD,G.AND HERZBERGER, J. 1983. Introduction to Interval Computation. Academic Press, Inc., New York, NY.
 
2
CORLISS, G. F. 1998. Globsol entry page. http://www.mscs.mu.edu/~globsol/
 
3
HANSEN, E. R. 1992. Global Optimization Using Interval Analysis. Marcel Dekker, Inc., New York, NY.
4
 
5
KEARFOTT, R. B. 1996b. Rigorous Global Search: Continuous Problems. Kluwer Academic, Dordrecht, Netherlands.
6
 
7
 
8
NEUMAIER, A. 1990. Interval Methods for Systems of Equations. Cambridge University Press, New York, NY.
 
9
RATZ,D.AND CSENDES, T. 1995. On the selection of subdivision directions in interval branch-and-bound methods for global optimization. J. Global Optim. 7, 183-207.
 
10
ROCCO,C.M.,MILLER,A.J.,AND KEARFOTT, R. B. 1999. Uncertainty analysis of indirect-grouping maintenance strategy using an interval arithmetic approach. Preprint. Universidad Central de Venezuela, Miam, FL. Available via: POBA INTERNATIONAL No. 100, P.O. Box 02-5255, Universidad Central de Venezuela, Miami, FL 33102.


REVIEW

"Donald G. M. Anderson : Reviewer"

The basic tool in the verified solution of constrained or unconstrained optimization or root-finding problems is the interval Newton method. Analogues of conventional termination criteria can be problematic, potentially leading to inadequate a  more...

Collaborative Colleagues:
R. B. Kearfott: colleagues
G. W. Walster: colleagues