Wednesday, 15:45 - 16:10 h, Room: H 0107


Sandra Santos
An adaptive spectral approximation-based algorithm for nonlinear least-squares problems

Coauthors: Marcia A. Gomes-Ruggiero, Douglas S. Goncalves


In this work we propose an adaptive algorithm for solving nonlinear least-squares problems, based on scalar spectral matrices employed in the approximation of the residual Hessians. Besides regularizing the Gauss-Newton step and providing an automatic updating for the so-called Levenberg- Marquardt parameter, the spectral approximation has a quasi-Newton flavour, including second-order information along the generated directions, obtained from the already computed first-order derivatives. A nonmonotone line search strategy is employed to ensure global convergence, and local convergence analysis is provided as well. Comparative numerical experiments with the routines LMDER and NL2SOL put the approach into perspective, indicating its effectiveness in two collections of problems from the literature.


Talk 2 of the invited session Wed.3.H 0107
"Line-search strategies" [...]
Cluster 16
"Nonlinear programming" [...]


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