#### Previous topic

scipy.optimize.lsq_linear

#### Next topic

scipy.optimize.brute

# scipy.optimize.basinhopping

`scipy.optimize.``basinhopping`(func, x0, niter=100, T=1.0, stepsize=0.5, minimizer_kwargs=None, take_step=None, accept_test=None, callback=None, interval=50, disp=False, niter_success=None, seed=None)

  Find the global minimum of a function using the basin-hopping algorithm

  Parameters:

  func : callable `f(x, *args)`

  > Function to be optimized. `args` can be passed as an optional item in the dict `minimizer_kwargs`

  x0 : ndarray

  > Initial guess.

  niter : integer, optional

  > The number of basin hopping iterations

  T : float, optional

  > The “temperature” parameter for the accept or reject criterion. Higher “temperatures” mean that larger jumps in function value will be accepted. For best results `T` should be comparable to the separation (in function value) between local minima.

  stepsize : float, optional

  > initial step size for use in the random displacement.

  minimizer_kwargs : dict, optional

  > Extra keyword arguments to be passed to the minimizer `scipy.optimize.minimize()` Some important options could be:

  > > method : str

  > >   The minimization method (e.g. `"L-BFGS-B"`)

  > > args : tuple

  > >   Extra arguments passed to the objective function (`func`) and its derivatives (Jacobian, Hessian).

  take_step : callable `take_step(x)`, optional

  > Replace the default step taking routine with this routine. The default step taking routine is a random displacement of the coordinates, but other step taking algorithms may be better for some systems. `take_step` can optionally have the attribute `take_step.stepsize`. If this attribute exists, then `basinhopping` will adjust `take_step.stepsize` in order to try to optimize the global minimum search.

  accept_test : callable, `accept_test(f_new=f_new, x_new=x_new, f_old=fold, x_old=x_old)`, optional

  > Define a test which will be used to judge whether or not to accept the step. This will be used in addition to the Metropolis test based on “temperature” `T`. The acceptable return values are True, False, or `"force accept"`. If any of the tests return False then the step is rejected. If the latter, then this will override any other tests in order to accept the step. This can be used, for example, to forcefully escape from a local minimum that `basinhopping` is trapped in.

  callback : callable, `callback(x, f, accept)`, optional

  > A callback function which will be called for all minima found. `x` and `f` are the coordinates and function value of the trial minimum, and `accept` is whether or not that minimum was accepted. This can be used, for example, to save the lowest N minima found. Also, `callback` can be used to specify a user defined stop criterion by optionally returning True to stop the `basinhopping` routine.

  interval : integer, optional

  > interval for how often to update the `stepsize`

  disp : bool, optional

  > Set to True to print status messages

  niter_success : integer, optional

  > Stop the run if the global minimum candidate remains the same for this number of iterations.

  seed : int or np.random.RandomState, optional

  > If seed is not specified the np.RandomState singleton is used. If seed is an int, a new np.random.RandomState instance is used, seeded with seed. If seed is already a np.random.RandomState instance, then that np.random.RandomState instance is used. Specify seed for repeatable minimizations. The random numbers generated with this seed only affect the default Metropolis accept_test and the default take_step. If you supply your own take_step and accept_test, and these functions use random number generation, then those functions are responsible for the state of their random number generator.

  Returns:

  res : OptimizeResult

  > The optimization result represented as a `OptimizeResult` object. Important attributes are: `x` the solution array, `fun` the value of the function at the solution, and `message` which describes the cause of the termination. The `OptimzeResult` object returned by the selected minimizer at the lowest minimum is also contained within this object and can be accessed through the `lowest_optimization_result` attribute. See `OptimizeResult` for a description of other attributes.

  See also

  `minimize`
    The local minimization function called once for each basinhopping step. `minimizer_kwargs` is passed to this routine.

  Notes

  Basin-hopping is a stochastic algorithm which attempts to find the global minimum of a smooth scalar function of one or more variables [R158] [R159] [R160] [R161]. The algorithm in its current form was described by David Wales and Jonathan Doye [R159] http://www-wales.ch.cam.ac.uk/.

  The algorithm is iterative with each cycle composed of the following features

  1. random perturbation of the coordinates
  2. local minimization
  3. accept or reject the new coordinates based on the minimized function value

  The acceptance test used here is the Metropolis criterion of standard Monte Carlo algorithms, although there are many other possibilities [R160].

  This global minimization method has been shown to be extremely efficient for a wide variety of problems in physics and chemistry. It is particularly useful when the function has many minima separated by large barriers. See the Cambridge Cluster Database http://www-wales.ch.cam.ac.uk/CCD.html for databases of molecular systems that have been optimized primarily using basin-hopping. This database includes minimization problems exceeding 300 degrees of freedom.

  See the free software program GMIN (http://www-wales.ch.cam.ac.uk/GMIN) for a Fortran implementation of basin-hopping. This implementation has many different variations of the procedure described above, including more advanced step taking algorithms and alternate acceptance criterion.

  For stochastic global optimization there is no way to determine if the true global minimum has actually been found. Instead, as a consistency check, the algorithm can be run from a number of different random starting points to ensure the lowest minimum found in each example has converged to the global minimum. For this reason `basinhopping` will by default simply run for the number of iterations `niter` and return the lowest minimum found. It is left to the user to ensure that this is in fact the global minimum.

  Choosing `stepsize`: This is a crucial parameter in `basinhopping` and depends on the problem being solved. Ideally it should be comparable to the typical separation between local minima of the function being optimized. `basinhopping` will, by default, adjust `stepsize` to find an optimal value, but this may take many iterations. You will get quicker results if you set a sensible value for `stepsize`.

  Choosing `T`: The parameter `T` is the temperature used in the metropolis criterion. Basinhopping steps are accepted with probability `1` if `func(xnew) < func(xold)`, or otherwise with probability:

      exp( -(func(xnew) - func(xold)) / T )

  So, for best results, `T` should to be comparable to the typical difference in function values between local minima.

  New in version 0.12.0.

  References

  [R158] | (1, 2) Wales, David J. 2003, Energy Landscapes, Cambridge University Press, Cambridge, UK.

  [R159] | (1, 2, 3) Wales, D J, and Doye J P K, Global Optimization by Basin-Hopping and the Lowest Energy Structures of Lennard-Jones Clusters Containing up to 110 Atoms. Journal of Physical Chemistry A, 1997, 101, 5111.

  [R160] | (1, 2, 3) Li, Z. and Scheraga, H. A., Monte Carlo-minimization approach to the multiple-minima problem in protein folding, Proc. Natl. Acad. Sci. USA, 1987, 84, 6611.

  [R161] | (1, 2) Wales, D. J. and Scheraga, H. A., Global optimization of clusters, crystals, and biomolecules, Science, 1999, 285, 1368.

  Examples

  The following example is a one-dimensional minimization problem, with many local minima superimposed on a parabola.

      >>> from scipy.optimize import basinhopping
      >>> func = lambda x: np.cos(14.5 * x - 0.3) + (x + 0.2) * x
      >>> x0=[1.]

  Basinhopping, internally, uses a local minimization algorithm. We will use the parameter `minimizer_kwargs` to tell basinhopping which algorithm to use and how to set up that minimizer. This parameter will be passed to `scipy.optimize.minimize()`.

      >>> minimizer_kwargs = {"method": "BFGS"}
      >>> ret = basinhopping(func, x0, minimizer_kwargs=minimizer_kwargs,
      ...                    niter=200)
      >>> print("global minimum: x = %.4f, f(x0) = %.4f" % (ret.x, ret.fun))
      global minimum: x = -0.1951, f(x0) = -1.0009

  Next consider a two-dimensional minimization problem. Also, this time we will use gradient information to significantly speed up the search.

      >>> def func2d(x):
      ...     f = np.cos(14.5 * x[0] - 0.3) + (x[1] + 0.2) * x[1] + (x[0] +
      ...                                                            0.2) * x[0]
      ...     df = np.zeros(2)
      ...     df[0] = -14.5 * np.sin(14.5 * x[0] - 0.3) + 2. * x[0] + 0.2
      ...     df[1] = 2. * x[1] + 0.2
      ...     return f, df

  We’ll also use a different local minimization algorithm. Also we must tell the minimizer that our function returns both energy and gradient (jacobian)

      >>> minimizer_kwargs = {"method":"L-BFGS-B", "jac":True}
      >>> x0 = [1.0, 1.0]
      >>> ret = basinhopping(func2d, x0, minimizer_kwargs=minimizer_kwargs,
      ...                    niter=200)
      >>> print("global minimum: x = [%.4f, %.4f], f(x0) = %.4f" % (ret.x[0],
      ...                                                           ret.x[1],
      ...                                                           ret.fun))
      global minimum: x = [-0.1951, -0.1000], f(x0) = -1.0109

  Here is an example using a custom step taking routine. Imagine you want the first coordinate to take larger steps then the rest of the coordinates. This can be implemented like so:

      >>> class MyTakeStep(object):
      ...    def __init__(self, stepsize=0.5):
      ...        self.stepsize = stepsize
      ...    def __call__(self, x):
      ...        s = self.stepsize
      ...        x[0] += np.random.uniform(-2.*s, 2.*s)
      ...        x[1:] += np.random.uniform(-s, s, x[1:].shape)
      ...        return x

  Since `MyTakeStep.stepsize` exists basinhopping will adjust the magnitude of `stepsize` to optimize the search. We’ll use the same 2-D function as before

      >>> mytakestep = MyTakeStep()
      >>> ret = basinhopping(func2d, x0, minimizer_kwargs=minimizer_kwargs,
      ...                    niter=200, take_step=mytakestep)
      >>> print("global minimum: x = [%.4f, %.4f], f(x0) = %.4f" % (ret.x[0],
      ...                                                           ret.x[1],
      ...                                                           ret.fun))
      global minimum: x = [-0.1951, -0.1000], f(x0) = -1.0109

  Now let’s do an example using a custom callback function which prints the value of every minimum found

      >>> def print_fun(x, f, accepted):
      ...         print("at minimum %.4f accepted %d" % (f, int(accepted)))

  We’ll run it for only 10 basinhopping steps this time.

      >>> np.random.seed(1)
      >>> ret = basinhopping(func2d, x0, minimizer_kwargs=minimizer_kwargs,
      ...                    niter=10, callback=print_fun)
      at minimum 0.4159 accepted 1
      at minimum -0.9073 accepted 1
      at minimum -0.1021 accepted 1
      at minimum -0.1021 accepted 1
      at minimum 0.9102 accepted 1
      at minimum 0.9102 accepted 1
      at minimum 2.2945 accepted 0
      at minimum -0.1021 accepted 1
      at minimum -1.0109 accepted 1
      at minimum -1.0109 accepted 1

  The minimum at -1.0109 is actually the global minimum, found already on the 8th iteration.

  Now let’s implement bounds on the problem using a custom `accept_test`:

      >>> class MyBounds(object):
      ...     def __init__(self, xmax=[1.1,1.1], xmin=[-1.1,-1.1] ):
      ...         self.xmax = np.array(xmax)
      ...         self.xmin = np.array(xmin)
      ...     def __call__(self, **kwargs):
      ...         x = kwargs["x_new"]
      ...         tmax = bool(np.all(x <= self.xmax))
      ...         tmin = bool(np.all(x >= self.xmin))
      ...         return tmax and tmin

      >>> mybounds = MyBounds()
      >>> ret = basinhopping(func2d, x0, minimizer_kwargs=minimizer_kwargs,
      ...                    niter=10, accept_test=mybounds)