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Stochastic and graphical comparisons of the convergence property for multiple runs of non-linear programming algorithms

Author

Listed:
  • Dewi Rahardja
  • Yan D. Zhao
  • Yongming Qu

Abstract

Non-Linear Programming (NLP) problems are often encountered in real world applications. For such problems numerous algorithms have been proposed and the convergence properties of single run of these algorithms have typically been assessed. In this article, we focus on computationally unintensive NLP algorithms with random starting values. Such algorithms enable multiple runs for a particular application and the optimum from the multiple runs is taken as the final solution. In such a scenario, the convergence property of multiple runs of an algorithm is of more interest than that of a single run. Therefore, we propose a stochastic and graphical method to assess the convergence property for multiple runs of NLP algorithms. We plot the mean best objective function values found in multiple runs versus the number of runs for several algorithms to examine how each algorithm converges and to compare among algorithms.

Suggested Citation

  • Dewi Rahardja & Yan D. Zhao & Yongming Qu, 2007. "Stochastic and graphical comparisons of the convergence property for multiple runs of non-linear programming algorithms," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 2(2), pages 159-165.
    • Handle: RePEc:ids:ijisen:v:2:y:2007:i:2:p:159-165
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