user:algorithm:optim [Promethee]

General-purpose optimization

Description

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:
BFGS
LBFGSG
Nelder-Mead
Conjugate gradient
Simulated annealing
The resulting analysis is the following:(blue points are representing lowest values, red ones are highest values)

Source

References

  • 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.

Implementation

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

Installation

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