Commit d5ac9eca authored by Matevz, Sraml (sraml)'s avatar Matevz, Sraml (sraml)

documentation steepest descent

parent 24d0873e
Pipeline #12342 passed with stage
in 4 minutes and 55 seconds
......@@ -28,7 +28,7 @@ class ConjugateGradient(Loggable, object):
It is an iterative method for solving a linear system of equations:
Ax = b
SUGESTED LITERATURE:
SUGGESTED LITERATURE:
Thomas V. Mikosch et al., "Numerical Optimization", Second Edition,
2006, Springer-Verlag New York
......@@ -46,7 +46,7 @@ class ConjugateGradient(Loggable, object):
conjugated directions. (default: None)
preconditioner : function *optional*
The user can provide a function which transforms the variables of the
system to make the convarge more favorable.(default: None)
system to make the converge more favorable.(default: None)
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
......@@ -65,7 +65,7 @@ class ConjugateGradient(Loggable, object):
conjugated directions.
preconditioner : function
The user can provide a function which transforms the variables of the
system to make the convarge more favorable.
system to make the converge more favorable.
callback : function
Function f(energy, iteration_number) specified by the user to print
iteration number and energy value at every iteration step. It accepts
......
......@@ -29,7 +29,10 @@ class RelaxedNewton(QuasiNewtonMinimizer):
---------
line_searcher : LineSearch,
An implementation of a line-search algorithm.
callback :
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
an Energy object(energy) and integer(iteration_number). (default: None)
convergence_tolerance : float,
Specifies the required accuracy for convergence. (default : 10e-4)
convergence_level : integer
......
......@@ -20,6 +20,22 @@ from .quasi_newton_minimizer import QuasiNewtonMinimizer
class SteepestDescent(QuasiNewtonMinimizer):
"""Implementation of the steepest descent minimization scheme.
It uses the gradient of the minimized function to get to the minimum.
Parameters
----------
energy : Energy object
The energy object providing implementations of the to be minimized
function and gradient.
Returns
-------
descend_direction : Field
Returns the descent direction.
"""
def _get_descend_direction(self, energy):
descend_direction = energy.gradient
norm = descend_direction.norm()
......
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