This algorithm provides a numerical solution to the problem of unconstrained local minimization/maximization. This is more efficient than the Gauss-Newton-like algorithm when starting from points very far from the final minimum/maximum. A new convergence test is implemented (RDM) in addition to the usual stopping criterion : stopping rule is when the gradients are small enough in the parameters metric (GH^-1G).
Viviane Philipps, Cecile Proust-Lima, Boris Hejblum, Melanie Prague, Daniel Commenges, Amadou Diakite
descr <- packageDescription("marqLevAlg")
| Package: | marqLevAlg |
| Type: | Package |
| Version: | descr$Version |
| Date: | descr$Date |
| License: | GPL (>= 2.0) |
| LazyLoad: | yes |
This algorithm provides a numerical solution to the problem of optimizing a function. This is more efficient than the Gauss-Newton-like algorithm when starting from points very far from the final maximum. A new convergence test is implemented (RDM) in addition to the usual stopping criterion : stopping rule is when the gradients are small enough in the parameters metric (GH-1G).
marqLevAlg Algorithm
Philipps V. Hejblum B.P. Prague M. Commenge D. Proust-Lima C. Robust and Efficient Optimization Using a Marquardt-Levenberg Algorithm with R Package marqLevAlg. The R Journal (2021).
Donald W. marquardt An algorithm for Least-Squares Estimation of Nonlinear Parameters. Journal of the Society for Industrial and Applied Mathematics, Vol. 11, No. 2. (Jun, 1963), pp. 431-441.
Convergence criteria : Relative distance to Maximum
Commenges D. Jacqmin-Gadda H. Proust C. Guedj J. A Newton-like algorithm for likelihood maximization : the robust-variance scoring algorithm arxiv:math/0610402v2 (2006)