def objFunc(self, x):
if isinstance(self.C, ndarray):
return norm(dot(self.C, x) - self.d, inf)
else:
# TODO: omit code clone in FD ooFun.py, function _D
t1 = self.C_as_csc
t2 = csr_matrix(x)
if t2.shape[0] != t1.shape[1]:
assert t2.shape[1] == t1.shape[1], 'incorrect shape in OpenOpt SLE, inform developers about the bug'
t2 = t2.T
rr = t1._mul_sparse_matrix(t2)
return norm(rr.toarray().flatten() - self.d, inf)
def objFunc(self, x):
if isinstance(self.C, ndarray):
return norm(dot(self.C, x) - self.d, inf)
else:
# TODO: omit code clone in FD ooFun.py, function _D
t1 = self.C_as_csc
t2 = scipy.sparse.csr_matrix(x)
if t2.shape[0] != t1.shape[1]:
if t2.shape[1] == t1.shape[1]:
t2 = t2.T
else:
raise FuncDesignerException('incorrect shape in FuncDesigner function _D(), inform developers about the bug')
rr = t1._mul_sparse_matrix(t2)
return norm(rr.toarray().flatten() - self.d, inf)
def objFunc(self, x):
if isinstance(self.C, ndarray):
return norm(dot(self.C, x) - self.d, inf)
else:
# TODO: omit code clone in FD ooFun.py, function _D
t1 = self.C_as_csc
t2 = scipy.sparse.csr_matrix(x)
if t2.shape[0] != t1.shape[1]:
if t2.shape[1] == t1.shape[1]:
t2 = t2.T
else:
raise FuncDesignerException(
'incorrect shape in FuncDesigner function _D(), inform developers about the bug'
)
rr = t1._mul_sparse_matrix(t2)
return norm(rr.toarray().flatten() - self.d, inf)
def objFunc(self, x):
r = norm(dot(self.C, x) - self.d, 1)
if not self.damp is None:
r += self.damp * norm(x-self.X, 1)
#if any(isfinite(self.f)): r += dot(self.f, x)
return r
def objFunc(self, x):
r = norm(dot(self.C, x) - self.d, inf)
# if not self.damp is None:
# r += self.damp * norm(x-self.X)**2 / 2.0
# if any(isfinite(self.f)): r += dot(self.f, x)
return r
def objFunc(self, x):
r = norm(dot(self.C, x) - self.d, 1)
if not self.damp is None:
r += self.damp * norm(x - self.X, 1)
#if any(isfinite(self.f)): r += dot(self.f, x)
return r
def objFuncMultiple2Single(self, fv):
return norm(atleast_1d(asfarray(fv)), inf)
def objFunc(self, x):
r = norm(self.matMultVec(self.C, x) - self.d) ** 2 / 2.0
if self.damp is not None:
r += self.damp * norm(x-self.X)**2 / 2.0
if self.f is not None: r += dot(self.f, x)
return r
def objFunc(self, x):
return norm(dot(self.M, x[x.size/2:]) +self.q - x[:x.size/2], inf)
def sum_of_all_active_constraints_gradient(self):
if not hasattr(self, '_sum_of_all_active_constraints_gradient'):
p = self.p
contol = p.contol
x = self.x
direction = self.all_lin_gradient()
if p.solver.__name__ == 'ralg':
new = 1
elif p.solver.__name__ == 'gsubg':
new = 0
else:
p.err('unhandled case in Point._getDirection')
if p.userProvided.c:
th = 0.0
#th = contol / 2.0
C = p.c(x)
Ind = C>th
ind = where(Ind)[0]
activeC = asarray(C[Ind])# asarray and Ind for PyPy compatibility
if len(ind) > 0:
tmp = p.dc(x, ind)
if new:
if tmp.ndim == 1 or min(tmp.shape) == 1:
if hasattr(tmp, 'toarray'):
tmp = tmp.toarray()#.flatten()
if activeC.size == prod(tmp.shape):
activeC = activeC.reshape(tmp.shape)
tmp *= (activeC-th*(1.0-1e-15))/norm(tmp)
else:
if hasattr(tmp, 'toarray'):
tmp = tmp.toarray()
tmp *= ((activeC - th*(1.0-1e-15))/sqrt((tmp**2).sum(1))).reshape(-1, 1)
if tmp.ndim > 1:
tmp = tmp.sum(0)
direction += (tmp.A if type(tmp) != ndarray else tmp).flatten()
if p.userProvided.h:
#th = 0.0
th = contol / 2.0
H = p.h(x)
Ind1 = H>th
ind1 = where(Ind1)[0]
H1 = asarray(H[Ind1])# asarray and Ind1 for PyPy compatibility
if len(ind1) > 0:
tmp = p.dh(x, ind1)
if new:
if tmp.ndim == 1 or min(tmp.shape) == 1:
if hasattr(tmp, 'toarray'):
tmp = tmp.toarray()#.flatten()
if H1.size == prod(tmp.shape):
H1 = H1.reshape(tmp.shape)
tmp *= (H1-th*(1.0-1e-15))/norm(tmp)
else:
if hasattr(tmp, 'toarray'):
tmp = tmp.toarray()
tmp *= ((H1 - th*(1.0-1e-15))/sqrt((tmp**2).sum(1))).reshape(-1, 1)
if tmp.ndim > 1:
tmp = tmp.sum(0)
direction += (tmp.A if isspmatrix(tmp) or hasattr(tmp, 'toarray') else tmp).flatten()
ind2 = where(H<-th)[0]
H2 = H[ind2]
if len(ind2) > 0:
tmp = p.dh(x, ind2)
if new:
if tmp.ndim == 1 or min(tmp.shape) == 1:
if hasattr(tmp, 'toarray'):
tmp = tmp.toarray()#.flatten()
if H2.size == prod(tmp.shape):
H2 = H2.reshape(tmp.shape)
tmp *= (-H2-th*(1.0-1e-15))/norm(tmp)
else:
if hasattr(tmp, 'toarray'):
tmp = tmp.toarray()
tmp *= ((-H2 - th*(1.0-1e-15))/sqrt((tmp**2).sum(1))).reshape(-1, 1)
if tmp.ndim > 1:
tmp = tmp.sum(0)
direction -= (tmp.A if type(tmp) != ndarray else tmp).flatten()
self._sum_of_all_active_constraints_gradient = direction
return Copy(self._sum_of_all_active_constraints_gradient)
def objFunc(self, x):
return norm(dot(self.M, x[x.size / 2:]) + self.q - x[:x.size / 2], inf)
def objFunc(self, x):
r = norm(self.matMultVec(self.C, x) - self.d)**2 / 2.0
if self.damp is not None:
r += self.damp * norm(x - self.X)**2 / 2.0
if self.f is not None: r += dot(self.f, x)
return r
def objFuncMultiple2Single(self, fv):
return norm(atleast_1d(asfarray(fv)), inf)
def objFunc(self, x):
r = norm(dot(self.C, x) - self.d, inf)
# if not self.damp is None:
# r += self.damp * norm(x-self.X)**2 / 2.0
# if any(isfinite(self.f)): r += dot(self.f, x)
return r
