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Empirical sensitivity analysis for computational procedures

Published: 19 October 2005 Publication History

Abstract

Sensitivity analysis in computer science aims to improve stability in computer applications by considering uncertainty due to small perturbations in parameters. Mathematical and computational methods of sensitivity analysis are discussed. Advantages and disadvantages of both methods are addressed. A tool is developed to compute computational sensitivities. This tool was validated using three simple, well understood problems. The tool was then applied to a dynamic power grid system and an agent-based criminal computation. In the case of the power grid system, computational sensitivity analysis agrees with mathematical analysis in reporting stability and no excessive sensitivity. In the case of the criminal computation, the code is found to be unstable.

References

[1]
Campolongo F., and R. Braddock, The use of graph theory in sensitivity analysis of model output: A second order screening method, Reliability Engineering and System Safety, 64, 1--12, 1999.
[2]
Saltelli A., Tarantola S., and K. Chan, A quantitative, model independent method for global sensitivity analysis of model output, Technometrics, 41, 39--56, 1999.
[3]
Sastry S. Isukapalli and Panos G. Georgopoulos, Computational Methods for Sensitivity and Uncertainty Analysis for Environmental and Biological Models, EPA/600/R-01-068, Dec. 2001.
[4]
Sauer P.W. and Pai M.A, Power system steady-state stability and the load-flow Jacobean, IEEE Transactions on power systems, Vol. 5, No. 4, 1990.
[5]
Sauer P.W. and Pai M.A, Power system dynamics and stability, Prentice Hall, 1998.
[6]
Ian A. Haskins and Magnus Akke, Analysis of the Nordel Power Grid Distribution of January 1, 1997 Using Trajectory Sensitivities, IEEE Trans. on Power Systems, Vol. 14, No. 3, Aug. 1999.
[7]
H. Christopher Frey, Amirhossein Mokhtari and Tanwir Danish, Evaluation of Selected Sensitivity Analysis Methods Based Upon Applications to Two Food Safety Process Risk Models, Office of Risk Assessment and Cost-Benefit Analysis, U.S. Department of Agriculture, September 2003.
[8]
Ian A. Haskins, Nonlinear Dynamic Model Evaluation from Disturbance Measurements, IEEE Transactions of Power Systems, Vol. 16, No. 4, November 2001.
[9]
http://www.ubmail.ubalt.edu/~harsham/ref/RefSim.htm, Modeling & Simulation Resources, University of Baltimore, v. 3 September 2004.
[10]
http://sensitivity-analysis.jrc.cec.eu.int; Overview of Sensitivity, Institute for Systems Informatics and Safety, v. 6 April 2005.
[11]
Atherton R.W., Shainker R.B. and Ducot E.R. On the Statistical Sensitivity Analysis of Models for Chemical Kinetics AIChE Journal, 21(3), 441--447, 1971.
[12]
Andres T., Sampling methods and sensitivity analysis for large parameter sets, Journal of Statistics Computation and Simulation, 57, 77--110, 1997.
[13]
Arsham H., Sensitivity and optimization of computer simulation models, Modeling and Simulation, Instrument Society of America, 19, 1835--1842, 1988.
[14]
http://www-unix.mcs.anl.gov/autodiff/AD_Tools/adolc.anl, ADOL-C: A Package for the Automatic Differentiation of Algorithms Written in C/C++, Argonne National Laboratories, v. 6 April 2005.
[15]
http://www-fp.mcs.anl.gov/adic/, Automatic Differentiation of C, Argonne National Laboratories, v. 6 April 2005.
[16]
http://www.mcs.anl.gov/adifor, Automatic Differentiation of Fortran, Argonne National Laboratories, v. 6 April 2005.
[17]
L.M. Liebrock and R.K.R Mudhiganti, Preliminary Development of Computational Sensitivity Analysis, Technical Report, http://www.cs.nmt.edu/~liebrock/papers/TRDevelopmentComputationalSensitivityAnalysis.pdf, 2004.

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  1. Empirical sensitivity analysis for computational procedures

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    Peter Turner

    This paper is a low-level conference presentation tutorial on a computational approach to estimating condition numbers for, or analyzing the sensitivity of, a computational process. The basis of the approach is the standard one of recomputing the results for nearby data, though the definition of "nearby" is perhaps too broad to detect major instabilities. Computational problems are subdivided into three categories. The sector of interest is a subclass of a small change in inputs leading to a large variation in outputs, characterized by the fact that the "change is unacceptable; it does not correctly model reality and indicates an error." The interpretation is that the program needs to be revisited. It is important to note that this type of numerical instability may be inherent in the model or in the algorithm used, not necessarily in its implementation. The principal achievement described in the paper is an automated sensitivity analyzer that runs in conjunction with an Excel spreadsheet to generate variations on input data, and therefore allows the user to analyze the behavior. Unfortunately, it does not appear from the description that it is possible to select particular inputs for analysis, which appears to make it difficult to analyze sensitivity to a particular input, for example. Online Computing Reviews Service

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    cover image ACM Conferences
    TAPIA '05: Proceedings of the 2005 conference on Diversity in computing
    October 2005
    76 pages
    ISBN:1595932577
    DOI:10.1145/1095242
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 19 October 2005

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    Author Tags

    1. computational sensitivity analysis
    2. stability analysis

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    TAPIA05: Richard Tapia Celebration of Diversity in Computing Conference
    October 19 - 22, 2005
    New Mexico, Albuquerque, USA

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    • (2022)The Prediction of Peritoneal Carcinomatosis in Patients with Colorectal Cancer Using Machine LearningHealthcare10.3390/healthcare1008142510:8(1425)Online publication date: 29-Jul-2022
    • (2013)Optimization methodology based on neural networks and self-adaptive differential evolution algorithm applied to an aerobic fermentation processApplied Soft Computing10.1016/j.asoc.2012.08.00413:1(222-238)Online publication date: 1-Jan-2013
    • (2008)Developing fuzzy classifiers to predict the chance of occurrence of adult psychosesKnowledge-Based Systems10.1016/j.knosys.2008.03.00621:6(479-497)Online publication date: 1-Aug-2008
    • (2007)Integration of well posedness analysis in software engineeringProceedings of the 2007 ACM symposium on Applied computing10.1145/1244002.1244318(1479-1483)Online publication date: 11-Mar-2007

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