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A structured diagonal Hessian approximation method with evaluation complexity analysis for nonlinear least squares
| Content Provider | Semantic Scholar |
|---|---|
| Author | Mohammad, Hassan Santos, Sandra Augusta |
| Copyright Year | 2018 |
| Abstract | This work proposes a Jacobian-free strategy for addressing large-scale nonlinear leastsquares problems, in which structured secant conditions are used to define a diagonal approximation for the Hessian matrix. Proper safeguards are devised to ensure descent directions along the generated sequence. Worst-case evaluation analysis is provided within the framework of a non-monotone line search. Numerical experiments contextualize the proposed strategy, by addressing structured problems from the literature, also solved by related and recently presented conjugate-gradient and multivariate spectral gradient strategies. The comparative computational results show a favorable performance of the proposed approach. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.optimization-online.org/DB_FILE/2018/06/6656.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Analysis of algorithms Approximation Computation Conjugate gradient method Descent direction Experiment Hessian Immunostimulating conjugate (antigen) Jacobian matrix and determinant Line search Non-linear least squares Nonlinear system Numerical method Secant method monotone |
| Content Type | Text |
| Resource Type | Article |
