def add_model_soc_constr(
self,
model: ScipModel,
variables: List,
rows: Iterator,
A: dok_matrix,
b: ndarray,
) -> Tuple:
"""Adds SOC constraint to the model using the data from mat and vec.
Return tuple contains (QConstr, list of Constr, and list of variables).
"""
from pyscipopt.scip import quicksum
# Assume first expression (i.e. t) is nonzero.
expr_list = {i: [] for i in rows}
for (i, j), c in A.items():
v = variables[j]
try:
expr_list[i].append((c, v))
except Exception:
pass
# Make a variable and equality constraint for each term.
soc_vars = []
for i in rows:
lb = 0 if len(soc_vars) == 0 else None
var = model.addVar(
obj=0,
name="soc_t_%d" % i,
vtype=VariableTypes.CONTINUOUS,
lb=lb,
ub=None,
)
soc_vars.append(var)
lin_expr_list = [
b[i] - quicksum(coeff * var for coeff, var in expr_list[i])
for i in rows
]
new_lin_constrs = [
model.addCons(soc_vars[i] == lin_expr_list[i])
for i, _ in enumerate(lin_expr_list)
]
# Interesting because only <=?
t_term = soc_vars[0] * soc_vars[0]
x_term = quicksum([var * var for var in soc_vars[1:]])
constraint = model.addCons(x_term <= t_term)
return (
constraint,
new_lin_constrs,
soc_vars,
)
def _create_variables(self, model: ScipModel, data: Dict[str, Any],
c: ndarray) -> List:
"""Create a list of variables."""
variables = []
for n, obj in enumerate(c):
var_type = get_variable_type(n=n, data=data)
variables.append(
model.addVar(
obj=obj,
name="x_%d" % n,
vtype=var_type,
lb=None if var_type != VariableTypes.BINARY else 0,
ub=None if var_type != VariableTypes.BINARY else 1,
))
return variables