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Computational experience in sensitivity analysis for nonlinear programming

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Abstract

A method for sensitivity analysis in nonlinear programming has recently been developed using the sequential unconstrained minimization technique. It is applied here to perform sensitivity analyses on four example problems to demonstrate the computational feasibility and characteristics of the approach. The sensitivity analysis is conducted along the minimizing trajectory for each problem. The convergence characteristics of the first partial derivatives of the variables and objective function with respect to the parameters in the sensitivity analysis are illustrated.

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References

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Authors and Affiliations

  1. Coast Guard Headquarters, Washington, D.C., USA

    Robert L. Armacost

  2. The George Washington University, Washington, D.C., USA

    Anthony V. Fiacco

Authors
  1. Robert L. Armacost
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  2. Anthony V. Fiacco
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Additional information

Supported by contract N00014-67-A-0214, Task 0001, Project NR 347 020, Office of Naval Research.

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Armacost, R.L., Fiacco, A.V. Computational experience in sensitivity analysis for nonlinear programming. Mathematical Programming 6, 301–326 (1974). https://doi.org/10.1007/BF01580247

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  • DOI: https://doi.org/10.1007/BF01580247

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