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General-purpose optimization based on Nelder–Mead, quasi-Newton, conjugate-gradient and simulated annealing algorithms.
The following parameters are available for fine tuning of the algorithm selected:
The resulting analysis is the following:(blue points are representing lowest values, red ones are highest values)

- Belisle, C. J. P. (1992) Convergence theorems for a class of simulated annealing algorithms on Rd. J Applied Probability, 29, 885–895.
- Byrd, R. H., Lu, P., Nocedal, J. and Zhu, C. (1995) A limited memory algorithm for bound constrained optimization. SIAM J. Scientific Computing, 16, 1190–1208.
- Fletcher, R. and Reeves, C. M. (1964) Function minimization by conjugate gradients. Computer Journal 7, 148–154.
- Nash, J. C. (1990) Compact Numerical Methods for Computers. Linear Algebra and Function Minimisation. Adam Hilger.
- Nelder, J. A. and Mead, R. (1965) A simplex algorithm for function minimization. Computer Journal 7, 308–313.
- Nocedal, J. and Wright, S. J. (1999) Numerical Optimization. Springer.

This Optim algorithms are wrapped from the [R] optim function.

- copy in “
**Promethee_Install_Path**/plugins/doe directory”, - restart Promethee GUI.