General-purpose optimization

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

  BFGS.jar ConjugateGradient.jar LBFGSB.jar NelderMead.jar SimulatedAnnealing.jar

  in “Promethee_Install_Path/plugins/doe directory”,
• restart Promethee GUI.