def __init__(self, model, noise_amplitude=1.0, **kwargs):
"""
A noisy nonlinear problem in which only first derivatives can be
evaluated. For help on individual methods, see `AmplModel`.
"""
AmplModel.__init__(self, model, **kwargs)
self.noise_amplitude = noise_amplitude
def __init__(self, model, noise_amplitude=1.0, **kwargs):
"""
A noisy nonlinear problem in which only first derivatives can be
evaluated. For help on individual methods, see `AmplModel`.
"""
AmplModel.__init__(self, model, **kwargs)
self.noise_amplitude = noise_amplitude
def cost(self):
c = AmplModel.cost(self)
for k in c.keys():
c[k] += self.noise_amplitude * _random()
return c
def consPos(self, x):
c = AmplModel.consPos(self, x)
noise = _random_array(self.m)
return c + self.noise_amplitude * noise
def grad(self, x):
g = AmplModel.grad(self, x)
noise = _random_array(self.n)
return g + self.noise_amplitude * noise
def sgrad(self, x):
sg = AmplModel.sgrad(self, x)
for k in sg.keys():
sg[k] += self.noise_amplitude * _random()
return sg
def A(self, *args):
A = AmplModel.A(self, *args)
noise = _random_array(A.nnz)
(val, irow, jcol) = A.find()
A.addAt(self.noise_amplitude * noise, irow, jcol)
return A
def sgrad(self, x):
sg = AmplModel.sgrad(self, x)
for k in sg.keys():
sg[k] += self.noise_amplitude * _random()
return sg
def irow(self, i):
row = AmplModel.irow(self, i)
for k in row.keys():
row[k] += self.noise_amplitude * _random()
return row
def sigrad(self, i, x):
sgi = AmplModel.sigrad(self, i, x)
for k in sgi.keys():
sgi[k] += self.noise_amplitude * _random()
return sgi
def igrad(self, i, x):
gi = AmplModel.igrad(self, i, x)
noise = _random_array(self.n)
return gi + self.noise_amplitude * noise
def sigrad(self, i, x):
sgi = AmplModel.sigrad(self, i, x)
for k in sgi.keys():
sgi[k] += self.noise_amplitude * _random()
return sgi
def icons(self, i, x):
ci = AmplModel.icons(self, i, x)
noise = _random()
return ci + self.noise_amplitude * noise
def consPos(self, x):
c = AmplModel.consPos(self, x)
noise = _random_array(self.m)
return c + self.noise_amplitude * noise
def cost(self):
c = AmplModel.cost(self)
for k in c.keys():
c[k] += self.noise_amplitude * _random()
return c
def icons(self, i, x):
ci = AmplModel.icons(self, i, x)
noise = _random()
return ci + self.noise_amplitude * noise
def jac(self, x, *args):
J = AmplModel.jac(self, x, *args)
noise = _random_array(J.nnz)
(val, irow, jcol) = J.find()
J.addAt(self.noise_amplitude * noise, irow, jcol)
return J
def igrad(self, i, x):
gi = AmplModel.igrad(self, i, x)
noise = _random_array(self.n)
return gi + self.noise_amplitude * noise
def obj(self, x):
f = AmplModel.obj(self, x)
noise = _random()
return f + self.noise_amplitude * noise
def irow(self, i):
row = AmplModel.irow(self, i)
for k in row.keys():
row[k] += self.noise_amplitude * _random()
return row
def obj(self, x):
f = AmplModel.obj(self, x)
noise = _random()
return f + self.noise_amplitude * noise
def jac(self, x, *args):
J = AmplModel.jac(self, x, *args)
noise = _random_array(J.nnz)
(val, irow, jcol) = J.find()
J.addAt(self.noise_amplitude * noise, irow, jcol)
return J
def grad(self, x):
g = AmplModel.grad(self, x)
noise = _random_array(self.n)
return g + self.noise_amplitude * noise
