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Model.addGenConstrIndicator()
addGenConstrIndicator ( binvar, binval, lhs, sense=None, rhs=None, name="" )
Add a new general constraint of type GRB.GENCONSTR_INDICATOR to a model.
An INDICATOR constraint
states that if the binary
indicator variable 
is equal to 
, then the linear constraint
should hold.
On the other hand, if 
, the linear constraint may be violated.
The sense of the linear constraint can also be specified to be 
or 
.
Note that the indicator variable of a constraint will be forced
to be binary, independent of how it was created.
You can also add an INDICATOR constraint using a special overloaded syntax. See the examples below for details.
Arguments:
binvar (Var): The binary indicator variable.
binval (Boolean): The value for the binary indicator variable that would force the linear constraint to be satisfied.
lhs (float, Var, LinExpr, or TempConstr): Left-hand side expression for the linear constraint triggered by the indicator. Can be a constant, a Var, or a LinExpr. Alternatively, a temporary constraint object can be used to define the linear constraint that is triggered by the indicator. The temporary constraint object is created using an overloaded comparison operator. See TempConstr for more information. In this case, the “sense” and “rhs” parameters must stay at their default values None.
sense (char): Sense for the linear constraint. Options are GRB.LESS_EQUAL, GRB.EQUAL, or GRB.GREATER_EQUAL.
rhs (float): Right-hand side value for the linear constraint.
name (string, optional): Name for the new general constraint.
Note that name will be stored as an ASCII string. Thus, a name
like 'A
B' will produce an error, because
'' can not be represented as an ASCII character. Note
also that names that contain spaces are strongly discouraged,
because they can't be written to LP format files.
Example usage:
# x7 = 1 -> x1 + 2 x3 + x4 = 1 model.addGenConstrIndicator(x7, True, x1 + 2*x2 + x4, GRB.EQUAL, 1.0) # alternative form model.addGenConstrIndicator(x7, True, x1 + 2*x2 + x4 == 1.0) # overloaded form model.addConstr((x7 == 1) >> (x1 + 2*x2 + x4 == 1.0))
