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.
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Antunes, A.S., Monteiro, M.T.T. (2006). A Filter Algorithm and Other NLP Solvers: Performance Comparative Analysis. In: Seeger, A. (eds) Recent Advances in Optimization. Lecture Notes in Economics and Mathematical Systems, vol 563. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-28258-0_25
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DOI: https://doi.org/10.1007/3-540-28258-0_25
Publisher Name: Springer, Berlin, Heidelberg
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