Commit 7412dcc3 authored by Matevz, Sraml (sraml)'s avatar Matevz, Sraml (sraml)

documentation Line search and Quasi Newton minimizer added

parent ad3a7d38
Pipeline #12185 passed with stage
in 4 minutes and 47 seconds
...@@ -24,50 +24,48 @@ from nifty import LineEnergy ...@@ -24,50 +24,48 @@ from nifty import LineEnergy
class LineSearch(Loggable, object): class LineSearch(Loggable, object):
"""Class for finding a step size.
Initialize the line search procedure which can be used by a specific line
search method. Its finds the step size in a specific direction in the
minimization process.
Attributes
----------
line_energy : LineEnergy Object
LineEnergy object from which we can extract energy at a specific point.
f_k_minus_1 : Field
Value of the field at the k-1 iteration of the line search procedure.
prefered_initial_step_size : float
Initial guess for the step length.
""" """
Class for finding a step size.
"""
__metaclass__ = abc.ABCMeta __metaclass__ = abc.ABCMeta
def __init__(self): def __init__(self):
"""
Parameters
----------
f : callable f(x, *args)
Objective function.
fprime : callable f'(x, *args)
Objective functions gradient.
f_args : tuple (optional)
Additional arguments passed to objective function and its
derivation.
"""
self.line_energy = None self.line_energy = None
self.f_k_minus_1 = None self.f_k_minus_1 = None
self.prefered_initial_step_size = None self.prefered_initial_step_size = None
def _set_line_energy(self, energy, pk, f_k_minus_1=None): def _set_line_energy(self, energy, pk, f_k_minus_1=None):
""" """Set the coordinates for a new line search.
Set the coordinates for a new line search.
Parameters Parameters
---------- ----------
xk : ndarray, d2o, field energy : Energy object
Starting point. Energy object from which we can calculate the
energy, gradient and curvature at a specific point.
pk : ndarray, d2o, field pk : Field
Unit vector in search direction. Unit vector in search direction.
f_k : float (optional) f_k_minus_1 : float *optional*
Function value f(x_k). Value of the field at the k-1 iteration of the line search
procedure.
fprime_k : ndarray, d2o, field (optional)
Function value fprime(xk).
""" """
self.line_energy = LineEnergy(position=0., self.line_energy = LineEnergy(position=0.,
energy=energy, energy=energy,
......
...@@ -26,6 +26,54 @@ from .line_searching import LineSearchStrongWolfe ...@@ -26,6 +26,54 @@ from .line_searching import LineSearchStrongWolfe
class QuasiNewtonMinimizer(Loggable, object): class QuasiNewtonMinimizer(Loggable, object):
"""A Class used by other minimization methods to find local minimum.
Quasi-Newton methods are used to find local minima or maxima of a function
by approximating the Jacobian or Hessian matrix at every iteration. The
class performs general steps(gets the gradient, descend direction, step
size and checks the conergence) which can be used then by a specific
minimization method.
Parameters
----------
line_searcher : callable
Function which finds the step size into the descent direction. (default:
LineSearchStrongWolfe())
callback : function, *optional*
Function f(energy, iteration_number) specified by the user to print
iteration number and energy value at every iteration step. It accepts
a function(energy) and integer(iteration_number). (default: None)
convergence_tolerance : scalar
Tolerance specifying convergence. (default: 1E-4)
convergence_level : integer
Number of times the tolerance should be undershot before
exiting. (default: 3)
iteration_limit : integer *optional*
Maximum number of iterations performed. (default: None)
Attributes
----------
convergence_tolerance : float
Tolerance specifying convergence.
convergence_level : float
Number of times the tolerance should be undershot before
exiting.
iteration_limit : integer
Maximum number of iterations performed.
line_searcher : callable
Function which finds the step size into the descent direction
callback : function
Function f(energy, iteration_number) specified by the user to print
iteration number and energy value at every iteration step. It accepts
a function(energy) and integer(iteration_number).
Raises
------
StopIteration
Raised if
*callback function does not match the specified form.
"""
__metaclass__ = abc.ABCMeta __metaclass__ = abc.ABCMeta
def __init__(self, line_searcher=LineSearchStrongWolfe(), callback=None, def __init__(self, line_searcher=LineSearchStrongWolfe(), callback=None,
...@@ -43,33 +91,34 @@ class QuasiNewtonMinimizer(Loggable, object): ...@@ -43,33 +91,34 @@ class QuasiNewtonMinimizer(Loggable, object):
self.callback = callback self.callback = callback
def __call__(self, energy): def __call__(self, energy):
""" """Runs the minimization on the provided Energy class.
Runs the steepest descent minimization.
Accepts the NIFTY Energy class which describes our system and it runs
Parameters the minimization to find the minimum/maximum of the system.
----------
x0 : field Parameters
Starting guess for the minimization. ----------
alpha : scalar, *optional* energy : Energy object
Starting step width to be multiplied with normalized gradient Energy object provided by the user from which we can calculate the
(default: 1). energy, gradient and curvature at a specific point.
tol : scalar, *optional*
Tolerance specifying convergence; measured by maximal change in Returns
`x` (default: 1E-4). -------
clevel : integer, *optional* x : field
Number of times the tolerance should be undershot before Latest `energy` of the minimization.
exiting (default: 8). convergence : integer
self.iteration_limit : integer, *optional* Latest convergence level indicating whether the minimization
Maximum number of iterations performed (default: 100,000). has converged or not.
Returns Note
------- ----
x : field It stops the minimization if:
Latest `x` of the minimization. *callback function does not match the specified form.
convergence : integer *a perfectly flat point is reached.
Latest convergence level indicating whether the minimization *according to line-search the minimum is found.
has converged or not. *target convergence level is reached.
*iteration limit is reached.
""" """
convergence = 0 convergence = 0
......
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