A primal-dual interior-point algorithm for nonlinear least squares constrained problems
This paper extends prior work by the authors on solving nonlinear least squares unconstrained problems using a factorized quasi-Newton technique. With this aim we use a primal-dual interior-point algorithm for nonconvex nonlinear program- ming. The factorized quasi-Newton technique is now applied to...
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| Formato: | article |
| Idioma: | eng |
| Publicado em: |
2005
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| Texto completo: | http://hdl.handle.net/1822/5415 |
| País: | Portugal |
| Oai: | oai:repositorium.sdum.uminho.pt:1822/5415 |
| Resumo: | This paper extends prior work by the authors on solving nonlinear least squares unconstrained problems using a factorized quasi-Newton technique. With this aim we use a primal-dual interior-point algorithm for nonconvex nonlinear program- ming. The factorized quasi-Newton technique is now applied to the Hessian of the Lagrangian function for the transformed problem which is based on a logarithmic barrier formulation. We emphasize the importance of establishing and maintain- ing symmetric quasi-definiteness of the reduced KKT system. The algorithm then tries to choose a step size that reduces a merit function, and to select a penalty parameter that ensures descent directions along the iterative process. Computa- tional results are included for a variety of least squares constrained problems and preliminary numerical testing indicates that the algorithm is robust and efficient in practice. |
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