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)[source]¶ Find the global minimum of a function using the basinhopping algorithm
Basinhopping is a twophase method that combines a global stepping algorithm with local minimization at each step. Designed to mimic the natural process of energy minimization of clusters of atoms, it works well for similar problems with “funnellike, but rugged” energy landscapes [R193].
As the steptaking, step acceptance, and minimization methods are all customizable, this function can also be used to implement other twophase methods.
Parameters: func : callable
f(x, *args)
Function to be optimized.
args
can be passed as an optional item in the dictminimizer_kwargs
x0 : array_like
Initial guess.
niter : integer, optional
The number of basinhopping 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
Maximum step size for use in the random displacement.
minimizer_kwargs : dict, optional
Extra keyword arguments to be passed to the local minimizer
scipy.optimize.minimize()
Some important options could be: method : str
The minimization method (e.g.
"LBFGSB"
) args : tuple
Extra arguments passed to the objective function (
func
) and its derivatives (Jacobian, Hessian).
take_step : callable
take_step(x)
, optionalReplace the default steptaking routine with this routine. The default steptaking routine is a random displacement of the coordinates, but other steptaking algorithms may be better for some systems.
take_step
can optionally have the attributetake_step.stepsize
. If this attribute exists, thenbasinhopping
will adjusttake_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)
, optionalDefine 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 thatbasinhopping
is trapped in.callback : callable,
callback(x, f, accept)
, optionalA callback function which will be called for all minima found.
x
andf
are the coordinates and function value of the trial minimum, andaccept
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 thebasinhopping
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, andmessage
which describes the cause of the termination. TheOptimizeResult
object returned by the selected minimizer at the lowest minimum is also contained within this object and can be accessed through thelowest_optimization_result
attribute. SeeOptimizeResult
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
Basinhopping is a stochastic algorithm which attempts to find the global minimum of a smooth scalar function of one or more variables [R189] [R190] [R191] [R192]. The algorithm in its current form was described by David Wales and Jonathan Doye [R190] http://wwwwales.ch.cam.ac.uk/.
The algorithm is iterative with each cycle composed of the following features
 random perturbation of the coordinates
 local minimization
 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 [R191].
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://wwwwales.ch.cam.ac.uk/CCD.html for databases of molecular systems that have been optimized primarily using basinhopping. This database includes minimization problems exceeding 300 degrees of freedom.
See the free software program GMIN (http://wwwwales.ch.cam.ac.uk/GMIN) for a Fortran implementation of basinhopping. 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 iterationsniter
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 inbasinhopping
and depends on the problem being solved. The step is chosen uniformly in the region from x0stepsize to x0+stepsize, in each dimension. Ideally it should be comparable to the typical separation (in argument values) between local minima of the function being optimized.basinhopping
will, by default, adjuststepsize
to find an optimal value, but this may take many iterations. You will get quicker results if you set a sensible initial value forstepsize
.Choosing
T
: The parameterT
is the “temperature” used in the Metropolis criterion. Basinhopping steps are always accepted iffunc(xnew) < func(xold)
. Otherwise, they are accepted 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. (The height of “walls” between local minima is irrelevant.)If
T
is 0, the algorithm becomes Monotonic BasinHopping, in which all steps that increase energy are rejected.New in version 0.12.0.
References
[R189] (1, 2) Wales, David J. 2003, Energy Landscapes, Cambridge University Press, Cambridge, UK. [R190] (1, 2, 3) Wales, D J, and Doye J P K, Global Optimization by BasinHopping and the Lowest Energy Structures of LennardJones Clusters Containing up to 110 Atoms. Journal of Physical Chemistry A, 1997, 101, 5111. [R191] (1, 2, 3) Li, Z. and Scheraga, H. A., Monte Carlominimization approach to the multipleminima problem in protein folding, Proc. Natl. Acad. Sci. USA, 1987, 84, 6611. [R192] (1, 2) Wales, D. J. and Scheraga, H. A., Global optimization of clusters, crystals, and biomolecules, Science, 1999, 285, 1368. [R193] (1, 2) Olson, B., Hashmi, I., Molloy, K., and Shehu1, A., Basin Hopping as a General and Versatile Optimization Framework for the Characterization of Biological Macromolecules, Advances in Artificial Intelligence, Volume 2012 (2012), Article ID 674832, DOI:10.1155/2012/674832 Examples
The following example is a onedimensional 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 toscipy.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 twodimensional 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":"LBFGSB", "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 steptaking routine. Imagine you want the first coordinate to take larger steps than 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 ofstepsize
to optimize the search. We’ll use the same 2D 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)