def sign_for_intf(self, interface):
"""Test sign for a given interface.
"""
mat = interface.const_to_matrix([[1,2,3,4],[3,4,5,6]])
self.assertEquals(intf.sign(mat), Sign.POSITIVE)
self.assertEquals(intf.sign(-mat), Sign.NEGATIVE)
self.assertEquals(intf.sign(0*mat), Sign.ZERO)
mat = interface.const_to_matrix([[-1,2,3,4],[3,4,5,6]])
self.assertEquals(intf.sign(mat), Sign.UNKNOWN)
def sign_for_intf(self, interface):
"""Test sign for a given interface.
"""
mat = interface.const_to_matrix([[1, 2, 3, 4], [3, 4, 5, 6]])
self.assertEquals(intf.sign(mat), Sign.POSITIVE)
self.assertEquals(intf.sign(-mat), Sign.NEGATIVE)
self.assertEquals(intf.sign(0 * mat), Sign.ZERO)
mat = interface.const_to_matrix([[-1, 2, 3, 4], [3, 4, 5, 6]])
self.assertEquals(intf.sign(mat), Sign.UNKNOWN)
def sign_for_intf(self, interface):
"""Test sign for a given interface.
"""
mat = interface.const_to_matrix([[1, 2, 3, 4], [3, 4, 5, 6]])
self.assertEqual(intf.sign(mat), (True, False)) # Positive.
self.assertEqual(intf.sign(-mat), (False, True)) # Negative.
self.assertEqual(intf.sign(0*mat), (True, True)) # Zero.
mat = interface.const_to_matrix([[-1, 2, 3, 4], [3, 4, 5, 6]])
self.assertEqual(intf.sign(mat), (False, False)) # Unknown.
def sign_for_intf(self, interface):
"""Test sign for a given interface.
"""
mat = interface.const_to_matrix([[1, 2, 3, 4], [3, 4, 5, 6]])
self.assertEqual(intf.sign(mat), (True, False)) # Positive.
self.assertEqual(intf.sign(-mat), (False, True)) # Negative.
self.assertEqual(intf.sign(0*mat), (True, True)) # Zero.
mat = interface.const_to_matrix([[-1, 2, 3, 4], [3, 4, 5, 6]])
self.assertEqual(intf.sign(mat), (False, False)) # Unknown.
def _validate_value(self, val):
"""Check that the value satisfies the leaf's symbolic attributes.
Parameters
----------
val : numeric type
The value assigned.
Returns
-------
numeric type
The value converted to the proper matrix type.
"""
if val is not None:
# Convert val to the proper matrix type.
val = intf.DEFAULT_INTF.const_to_matrix(val)
size = intf.size(val)
if size != self.size:
raise ValueError("Invalid dimensions (%s, %s) for %s value." %
(size[0], size[1], self.__class__.__name__))
# All signs are valid if sign is unknown.
# Otherwise value sign must match declared sign.
pos_val, neg_val = intf.sign(val)
if self.is_positive() and not pos_val or \
self.is_negative() and not neg_val:
raise ValueError("Invalid sign for %s value." %
self.__class__.__name__)
# Round to correct sign.
elif self.is_positive():
val = np.maximum(val, 0)
elif self.is_negative():
val = np.minimum(val, 0)
return val
def _validate_value(self, val):
"""Check that the value satisfies the leaf's symbolic attributes.
Parameters
----------
val : numeric type
The value assigned.
Returns
-------
numeric type
The value converted to the proper matrix type.
"""
if val is not None:
# Convert val to the proper matrix type.
val = intf.DEFAULT_INTF.const_to_matrix(val)
size = intf.size(val)
if size != self.size:
raise ValueError(
"Invalid dimensions (%s, %s) for %s value." %
(size[0], size[1], self.__class__.__name__)
)
# All signs are valid if sign is unknown.
# Otherwise value sign must match declared sign.
pos_val, neg_val = intf.sign(val)
if self.is_positive() and not pos_val or \
self.is_negative() and not neg_val:
raise ValueError(
"Invalid sign for %s value." % self.__class__.__name__
)
return val
def _validate_value(self, val):
"""Check that the value satisfies the parameter's symbolic attributes.
Parameters
----------
val : numeric type
The value assigned.
Returns
-------
numeric type
The value converted to the proper matrix type.
"""
# Convert val to the proper matrix type.
val = intf.DEFAULT_INTERFACE.const_to_matrix(val)
size = intf.size(val)
if size != self.size:
raise ValueError("Invalid dimensions (%s, %s) for %s value." %
(size[0], size[1], self.__class__.__name__))
# All signs are valid if sign is unknown.
# Otherwise value sign must match declared sign.
sign = intf.sign(val)
if self.is_positive() and not sign.is_positive() or \
self.is_negative() and not sign.is_negative():
raise ValueError("Invalid sign for %s value." %
self.__class__.__name__)
return val
def _compute_attr(self):
"""Compute the attributes of the constant related to complex/real, sign.
"""
# Set DCP attributes.
is_real, is_imag = intf.is_complex(self.value)
if self.is_complex():
is_nonneg = is_nonpos = False
else:
is_nonneg, is_nonpos = intf.sign(self.value)
self._imag = (is_imag and not is_real)
self._nonpos = is_nonpos
self._nonneg = is_nonneg
def presolve(objective, constr_map):
"""Eliminates unnecessary constraints and short circuits the solver
if possible.
Parameters
----------
objective : LinOp
The canonicalized objective.
constr_map : dict
A map of constraint type to a list of constraints.
Returns
-------
bool
Is the problem infeasible?
"""
# Remove redundant constraints.
for key, constraints in constr_map.items():
ids = set()
uniq_constr = []
for c in constraints:
if c.constr_id not in ids:
uniq_constr.append(c)
ids.add(c.constr_id)
constr_map[key] = uniq_constr
# If there are no constraints, the problem is unbounded
# if any of the coefficients are non-zero.
# If all the coefficients are zero then return the constant term
# and set all variables to 0.
if not any(constr_map.values()):
str(objective) # TODO
# Remove constraints with no variables or parameters.
for key in [s.EQ, s.LEQ]:
new_constraints = []
for constr in constr_map[key]:
vars_ = lu.get_expr_vars(constr.expr)
if len(vars_) == 0 and not lu.get_expr_params(constr.expr):
V, I, J, coeff = canonInterface.get_problem_matrix(
[constr])
is_pos, is_neg = intf.sign(coeff)
# For equality constraint, coeff must be zero.
# For inequality (i.e. <= 0) constraint,
# coeff must be negative.
if key == s.EQ and not (is_pos and is_neg) or \
key == s.LEQ and not is_neg:
return s.INFEASIBLE
else:
new_constraints.append(constr)
constr_map[key] = new_constraints
return None
def presolve(objective, constr_map, check_params=False):
"""Eliminates unnecessary constraints and short circuits the solver
if possible.
Parameters
----------
objective : LinOp
The canonicalized objective.
constr_map : dict
A map of constraint type to a list of constraints.
check_params : bool, optional
Should constraints with parameters be evaluated?
Returns
-------
bool
Is the problem infeasible?
"""
# Remove redundant constraints.
for key, constraints in constr_map.items():
uniq_constr = unique(constraints,
key=lambda c: c.constr_id)
constr_map[key] = list(uniq_constr)
# If there are no constraints, the problem is unbounded
# if any of the coefficients are non-zero.
# If all the coefficients are zero then return the constant term
# and set all variables to 0.
if not any(constr_map.values()):
str(objective) # TODO
# Remove constraints with no variables or parameters.
for key in [s.EQ, s.LEQ]:
new_constraints = []
for constr in constr_map[key]:
vars_ = lu.get_expr_vars(constr.expr)
if len(vars_) == 0 and not lu.get_expr_params(constr.expr):
coeff = op2mat.get_constant_coeff(constr.expr)
sign = intf.sign(coeff)
# For equality constraint, coeff must be zero.
# For inequality (i.e. <= 0) constraint,
# coeff must be negative.
if key is s.EQ and not sign.is_zero() or \
key is s.LEQ and not sign.is_negative():
return s.INFEASIBLE
else:
new_constraints.append(constr)
constr_map[key] = new_constraints
return None
def presolve(objective, constr_map, check_params=False):
"""Eliminates unnecessary constraints and short circuits the solver
if possible.
Parameters
----------
objective : LinOp
The canonicalized objective.
constr_map : dict
A map of constraint type to a list of constraints.
check_params : bool, optional
Should constraints with parameters be evaluated?
Returns
-------
bool
Is the problem infeasible?
"""
# Remove redundant constraints.
for key, constraints in constr_map.items():
uniq_constr = unique(constraints, key=lambda c: c.constr_id)
constr_map[key] = list(uniq_constr)
# If there are no constraints, the problem is unbounded
# if any of the coefficients are non-zero.
# If all the coefficients are zero then return the constant term
# and set all variables to 0.
if not any(constr_map.values()):
str(objective) # TODO
# Remove constraints with no variables or parameters.
for key in [s.EQ, s.LEQ]:
new_constraints = []
for constr in constr_map[key]:
vars_ = lu.get_expr_vars(constr.expr)
if len(vars_) == 0 and not lu.get_expr_params(constr.expr):
coeff = op2mat.get_constant_coeff(constr.expr)
sign = intf.sign(coeff)
# For equality constraint, coeff must be zero.
# For inequality (i.e. <= 0) constraint,
# coeff must be negative.
if key is s.EQ and not sign.is_zero() or \
key is s.LEQ and not sign.is_negative():
return s.INFEASIBLE
else:
new_constraints.append(constr)
constr_map[key] = new_constraints
return None
def __init__(self, value):
# TODO HACK.
# A fix for c.T*x where c is a 1D array.
self.is_1D_array = False
# Keep sparse matrices sparse.
if intf.is_sparse(value):
self._value = intf.DEFAULT_SPARSE_INTF.const_to_matrix(value)
self._sparse = True
else:
if isinstance(value, np.ndarray) and len(value.shape) == 1:
self.is_1D_array = True
self._value = intf.DEFAULT_INTF.const_to_matrix(value)
self._sparse = False
# Set DCP attributes.
self._size = intf.size(self.value)
self._is_pos, self._is_neg = intf.sign(self.value)
super(Constant, self).__init__()
def __init__(self, value):
# TODO HACK.
# A fix for c.T*x where c is a 1D array.
self.is_1D_array = False
# Keep sparse matrices sparse.
if intf.is_sparse(value):
self._value = intf.DEFAULT_SPARSE_INTF.const_to_matrix(value)
self._sparse = True
else:
if isinstance(value, np.ndarray) and len(value.shape) == 1:
self.is_1D_array = True
self._value = intf.DEFAULT_INTF.const_to_matrix(value)
self._sparse = False
# Set DCP attributes.
self._size = intf.size(self.value)
self._is_pos, self._is_neg = intf.sign(self.value)
super(Constant, self).__init__()
def init_dcp_attr(self):
shape = u.Shape(*intf.size(self.value))
sign = intf.sign(self.value)
self._dcp_attr = u.DCPAttr(sign, u.Curvature.CONSTANT, shape)
def init_dcp_attr(self):
shape = u.Shape(*intf.size(self.value))
sign = intf.sign(self.value)
self._dcp_attr = u.DCPAttr(sign, u.Curvature.CONSTANT, shape)