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A Filter Algorithm and Other NLP Solvers: Performance Comparative Analysis

In: Recent Advances in Optimization

Author

Listed:
  • António Sanches Antunes

    (University of Minho)

  • M. Teresa T. Monteiro

    (University of Minho)

Abstract

Summary A new algorithm based on filter SQP with line search to solve nonlinear constrained optimization problems is presented. The filter replaces the merit function avoiding the penalty parameter estimation. This new concept works like an oracle estimating the trial approximation of the iterative SQP algorithm. A collection of AMPL test problems is solved by this new code as well as NPSOL and LOQO solvers. A comparative analysis is made - the filter SQP with line search presents good performance.

Suggested Citation

  • António Sanches Antunes & M. Teresa T. Monteiro, 2006. "A Filter Algorithm and Other NLP Solvers: Performance Comparative Analysis," Lecture Notes in Economics and Mathematical Systems, in: Alberto Seeger (ed.), Recent Advances in Optimization, pages 425-434, Springer.
    • Handle: RePEc:spr:lnechp:978-3-540-28258-7_25
    DOI: 10.1007/3-540-28258-0_25
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