def irow(self, i):
"""
Evaluate sparse gradient of the linear part of the
i-th constraint. Useful to obtain constraint rows
when problem is a linear programming problem.
"""
try:
sri_dict = _amplpy.eval_row(i)
except:
raise RunTimeError, "Failed to fetch sparse row"
return None
sri = sv.SparseVector(self.n, sri_dict)
return sri
def irow(self, i):
"""
Evaluate sparse gradient of the linear part of the
i-th constraint. Useful to obtain constraint rows
when problem is a linear programming problem.
"""
try:
sri_dict = self.model.eval_row(i)
except:
raise RuntimeError, "Failed to fetch sparse row"
return None
sri = sv.SparseVector(self.n, sri_dict)
if self.scale_con is not None: sri *= self.scale_con[i]
return sri
def sigrad(self, i, x):
"""
Evaluate sparse gradient of i-th constraint at x.
Returns a sparse vector representing the sparse gradient
in coordinate format.
"""
try:
sci_dict = _amplpy.eval_sgi(i, x)
except:
raise RunTimeError, "Failed to fetch sparse constraint gradient"
return None
self.Jeval += 1
sci = sv.SparseVector(self.n, sci_dict)
return sci
def sigrad(self, i, x):
"""
Evaluate sparse gradient of i-th constraint at x.
Returns a sparse vector representing the sparse gradient
in coordinate format.
"""
try:
sci_dict = self.model.eval_sgi(i, x)
except:
raise RuntimeError, "Failed to fetch sparse constraint gradient"
return None
self.Jeval += 1
sci = sv.SparseVector(self.n, sci_dict)
if self.scale_con is not None: sci *= self.scale_con[i]
return sci
def cost(self):
"""
Evaluate sparse cost vector.
Useful when problem is a linear program.
Return a sparse vector. This method changes the sign of the cost vector
if the problem is a maximization problem.
"""
try:
sc_dict = _amplpy.eval_cost()
except:
raise RunTimeError, "Failed to fetch sparse cost vector"
return None
sc = sv.SparseVector(self.n, sc_dict)
if not self.minimize: sc *= -1
return sc
def sgrad(self, x):
"""
Evaluate sparse objective gradient at x.
Returns a sparse vector. This method changes the sign of the objective
gradient if the problem is a maximization problem.
"""
try:
sg_dict = _amplpy.eval_sgrad(x)
except:
raise RunTimeError, "Failed to fetch sparse gradient of objective"
return None
self.geval += 1
sg = sv.SparseVector(self.n, sg_dict)
if not self.minimize: sg *= -1
return sg
