File: siman.rdoc [rb-gsl-1.16.0.4 Documentation]

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# 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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