def __init__(self,eggs,spam=None,a=(0.2,0.5,1,2),c=(1E-4,0.9),note=False):
"""
Initializes the steepest_descent and sets the attributes (except
for `x`).
Parameters
----------
eggs : function
Given the current `x` it returns the tuple of energy and gradient.
spam : function, *optional*
Callback function which is given the current `x` and iteration
counter each iteration (default: None).
a : {4-tuple}, *optional*
Numbers obeying 0 < a1 ~ a2 < 1 ~ a3 < a4 that modify the step
widths (default: (0.2,0.5,1,2)).
c : {2-tuple}, *optional*
Numbers obeying 0 < c1 < c2 < 1 that specify the Wolfe-conditions
(default: (1E-4,0.9)).
note : bool, *optional*
Indicates whether notes are printed or not (default: False).
"""
self.eggs = eggs ## returns energy and gradient
self.spam = spam ## serves as callback given x and iteration number
self.a = a ## 0 < a1 ~ a2 < 1 ~ a3 < a4
self.c = c ## 0 < c1 < c2 < 1
self.note = notification(default=bool(note))
self._alpha = None ## last alpha
def __init__(self,
eggs,
spam=None,
a=(0.2, 0.5, 1, 2),
c=(1E-4, 0.9),
note=False):
"""
Initializes the steepest_descent and sets the attributes (except
for `x`).
Parameters
----------
eggs : function
Given the current `x` it returns the tuple of energy and gradient.
spam : function, *optional*
Callback function which is given the current `x` and iteration
counter each iteration (default: None).
a : {4-tuple}, *optional*
Numbers obeying 0 < a1 ~ a2 < 1 ~ a3 < a4 that modify the step
widths (default: (0.2,0.5,1,2)).
c : {2-tuple}, *optional*
Numbers obeying 0 < c1 < c2 < 1 that specify the Wolfe-conditions
(default: (1E-4,0.9)).
note : bool, *optional*
Indicates whether notes are printed or not (default: False).
"""
self.eggs = eggs ## returns energy and gradient
self.spam = spam ## serves as callback given x and iteration number
self.a = a ## 0 < a1 ~ a2 < 1 ~ a3 < a4
self.c = c ## 0 < c1 < c2 < 1
self.note = notification(default=bool(note))
self._alpha = None ## last alpha
def __init__(self, A, b, W=None, spam=None, reset=None, note=False):
"""
Initializes the conjugate_gradient and sets the attributes (except
for `x`).
Parameters
----------
A : {operator, function}
Operator `A` applicable to a field.
b : field
Resulting field of the operation `A(x)`.
W : {operator, function}, *optional*
Operator `W` that is a preconditioner on `A` and is applicable to a
field (default: None).
spam : function, *optional*
Callback function which is given the current `x` and iteration
counter each iteration (default: None).
reset : integer, *optional*
Number of iterations after which to restart; i.e., forget previous
conjugated directions (default: sqrt(b.dim())).
note : bool, *optional*
Indicates whether notes are printed or not (default: False).
"""
if (hasattr(A, "__call__")):
self.A = A ## applies A
else:
raise AttributeError(
about._errors.cstring("ERROR: invalid input."))
self.b = b
if (W is None) or (hasattr(W, "__call__")):
self.W = W ## applies W ~ A_inverse
else:
raise AttributeError(
about._errors.cstring("ERROR: invalid input."))
self.spam = spam ## serves as callback given x and iteration number
if (reset is None): ## 2 < reset ~ sqrt(dim)
self.reset = max(2, int(np.sqrt(b.domain.dim(split=False))))
else:
self.reset = max(2, int(reset))
self.note = notification(default=bool(note))
def __init__(self,A,b,W=None,spam=None,reset=None,note=False):
"""
Initializes the conjugate_gradient and sets the attributes (except
for `x`).
Parameters
----------
A : {operator, function}
Operator `A` applicable to a field.
b : field
Resulting field of the operation `A(x)`.
W : {operator, function}, *optional*
Operator `W` that is a preconditioner on `A` and is applicable to a
field (default: None).
spam : function, *optional*
Callback function which is given the current `x` and iteration
counter each iteration (default: None).
reset : integer, *optional*
Number of iterations after which to restart; i.e., forget previous
conjugated directions (default: sqrt(b.dim())).
note : bool, *optional*
Indicates whether notes are printed or not (default: False).
"""
if(hasattr(A,"__call__")):
self.A = A ## applies A
else:
raise AttributeError(about._errors.cstring("ERROR: invalid input."))
self.b = b
if(W is None)or(hasattr(W,"__call__")):
self.W = W ## applies W ~ A_inverse
else:
raise AttributeError(about._errors.cstring("ERROR: invalid input."))
self.spam = spam ## serves as callback given x and iteration number
if(reset is None): ## 2 < reset ~ sqrt(dim)
self.reset = max(2,int(np.sqrt(b.domain.dim(split=False))))
else:
self.reset = max(2,int(reset))
self.note = notification(default=bool(note))
