Simulated Annealing¶ ↑

Library¶ ↑

Module and classes¶ ↑

• GSL

  • Siman

    (Module)

    • Params (Class)

    • Efunc (Class)

    • Step (Class)

    • Metric (Class)

    • Print (Class)

Siman Module¶ ↑

Singleton method¶ ↑

• GSL::Siman.solve(rng, x0_p, efunc, stepper, metric, printer, params)

  This performs a simulated annealing search through a given space. The space is specified by providing the functions efunc and metric. The simulated annealing steps are generated using the random number generator rng and the function stepper. The starting configuration of the system should be given by a Vector object x0_p.

  The parameter params controls the run by providing the temperature schedule and other tunable parameters to the algorithm.

  On exit the best result achieved during the search is placed in x0_p. If the annealing process has been successful this should be a good approximation to the optimal point in the space.

  If the function printer is not nil, a debugging log will be printed to stdout with the following columns:

  number_of_iterations   temperature    x    x-(x0_p)   efunc(x)

  and the output of printer itself. If printer is nil then no information is printed.

Example¶ ↑

#!/usr/bin/env ruby
require("gsl")
include GSL::Siman

N_TRIES = 200
ITERS_FIXED_T = 10
STEP_SIZE = 10
K = 1.0
T_INITIAL = 0.002
MU_T = 1.005
T_MIN = 2.0e-6

params = Siman::Params.alloc(N_TRIES, ITERS_FIXED_T, STEP_SIZE, K, T_INITIAL,
                           MU_T, T_MIN)

efunc = Efunc.alloc { |vx|
  x = vx[0]
  s = (x - 1.0)
  Math::exp(-s*s)*Math::sin(8*x)
}

metric = Metric.alloc { |vx, vy|
  (x[0] - y[0]).abs
}

step = Step.alloc { |rng, vx, step_size|
  r = rng.uniform
  old_x = vx[0]
  a =  r * 2 * step_size - step_size + old_x
  vx[0] = a
}

simanprint = Print.alloc { |vx|
  printf("%12g", vx[0])
}

x = Vector.alloc([15.5])
Rng.env_setup()
rng = Rng.alloc()

#Siman::solve(rng, x, efunc, step, metric, simanprint, params)
Siman::solve(rng, x, efunc, step, metric, nil, params)
p x

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