def dc(self, ind=None):
if not self.p.userProvided.c: return empty_arr.copy().reshape(0, self.p.n)
if ind is None:
if not hasattr(self, '_dc'): self._dc = self.p.dc(self.x)
return Copy(self._dc)
else:
if hasattr(self, '_dc'): return Copy(self._dc[ind])
else: return Copy(self.p.dc(self.x, ind))
def sum_of_all_active_constraints(self):
if not hasattr(self, '_sum_of_all_active_constraints'):
p = self.p
if p.solver.__name__ == 'ralg':
tol = p.contol / 2.0
else:
tol = 0.0
# elif p.solver.__name__ == 'gsubg':
# tol = 0.0
# else:
# p.err('unimplemented case in Point.py')
c, h= self.c(), self.h()
all_lin = self.all_lin()
self._sum_of_all_active_constraints = (c[c>0] - 0).sum() + (h[h>tol] - tol).sum() - (h[h<-tol] + tol).sum() + all_lin
return Copy(self._sum_of_all_active_constraints)
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 df(self):
if not hasattr(self, '_df'): self._df = self.p.df(self.x)
return Copy(self._df)