package gsl

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gsl
- Gsl
- Gsl_blas
- Gsl_blas_flat
  - Complex
- Gsl_blas_gen
  - Complex
- Gsl_bspline
- Gsl_cdf
- Gsl_cheb
- Gsl_combi
- Gsl_complex
- Gsl_const
  - CGS
  - CGSM
  - MKS
  - MKSA
  - NUM
- Gsl_deriv
- Gsl_eigen
- Gsl_error
- Gsl_fft
  - Complex
  - Halfcomplex
  - Real
- Gsl_fit
- Gsl_fun
- Gsl_histo
- Gsl_ieee
- Gsl_integration
- Gsl_interp
- Gsl_linalg
- Gsl_math
- Gsl_matrix
  - Single
- Gsl_matrix_complex
  - Single
- Gsl_matrix_complex_flat
- Gsl_matrix_flat
- Gsl_min
- Gsl_misc
- Gsl_monte
- Gsl_multifit
- Gsl_multifit_nlin
- Gsl_multimin
  - Deriv
  - NoDeriv
- Gsl_multiroot
  - Deriv
  - NoDeriv
- Gsl_odeiv
- Gsl_permut
- Gsl_poly
- Gsl_qrng
- Gsl_randist
- Gsl_rng
- Gsl_root
  - Bracket
  - Polish
- Gsl_sf
- Gsl_siman
- Gsl_sort
- Gsl_stats
- Gsl_sum
  - Trunc
- Gsl_vectmat
- Gsl_vector
  - Single
- Gsl_vector_complex
  - Single
- Gsl_vector_complex_flat
- Gsl_vector_flat
- Gsl_version
- Gsl_wavelet

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Simulated Annealing

NB: This module is not interfaced to GSL, it is implemented in OCaml. It is quite simple in fact, so rather than using it you may want to copy the code and tweak the algorithm in your own program.

type params = {

iters_fixed_T : int;
(*
The number of iterations at each temperature
*)
step_size : float;
(*
The maximum step size in the random walk
*)
k : float;
(*
parameter of the Boltzmann distribution
*)
t_initial : float;
(*
initial temperature
*)
mu_t : float;
(*
cooling factor
*)
t_min : float;
(*
minimum temperature
*)

}

val solve : 
  Gsl_rng.t ->
  'a ->
  energ_func:('a -> float) ->
  step_func:(Gsl_rng.t -> 'a -> float -> 'a) ->
  ?print_func:('a -> unit) ->
  params ->
  'a