SetIndicators Method

Add indicator constraints to this model. The provided arrays must all have length at least count and for any i in 0.. count-1 an indicator constraint is constructed from indicatorVariable[i], indicatorValue[i] and row[i]. If indicatorValue[i] is true then the constraints states that if indicatorVariable[i] is 1 (one), then row[i] must be Satisfied (and if indicator is 0 (zero) then row[i] is ignored). Otherwise, row[i] must be satisfied if indicator[i] is 0 (zero) and is ignored otherwise. All referenced variables and rows must exist.

Specifies that a set of rows in the matrix will be treated as indicator constraints during a tree search. An indicator constraint is made of a condition and a constraint. The condition is of the type "bin = value", where bin is a binary variable and value is either 0 or 1. The constraint is any matrix row (may be linear, quadratic or general nonlinear). During tree search, a row configured as an indicator constraint is enforced only when condition holds, that is only if the indicator variable bin has the specified value. Note that every row may only get assigned a single indicator variable and term. If a row needs to be activated by multiple different terms, the row needs to be duplicated so that each term can be assigned to a distinct row. If the indicator variable should be changed, the old term needs to be deleted first (by calling delIndicators or by calling this function with a comps argument of 0) before assigning a new one.

Add indicator constraints to this model. The function iterates through data and calls the provided functions for each element found. The return values of the functions are used to construct an indicator constraint. For a fixed t, if IndicatorValue(t) returns true then the created constraint states that if IndicatorVariable(t) is 1 (one), then Row(t) must be satisfied and is ignored otherwise. Otherwise, Row(t) must be satisfied if IndicatorVariable(t) is 0 (zero) and is ignored otherwise. All referenced variables and rows must exist.

Add indicator constraints to this model. The function iterates through data and calls the provided functions for each element found. The return values of the functions are used to construct an indicator constraint. For a fixed t, if IndicatorValue(t) returns true then the created constraint states that if IndicatorVariable(t) is 1 (one), then Row(t) must be satisfied and is ignored otherwise. Otherwise, Row(t) must be satisfied if IndicatorVariable(t) is 0 (zero) and is ignored otherwise. All referenced variables and rows must exist.

Add indicator constraints to this model. The function iterates through [0, ... ,count[ and calls the provided functions for each in this interval. The return values of the functions are used to construct an indicator constraint. For a fixed t, if IndicatorValue(t) returns true then the created constraint states that if IndicatorVariable(t) is 1 (one), then Row(t) must be satisfied and is ignored otherwise. Otherwise, Row(t) must be satisfied if IndicatorVariable(t) is 0 (zero) and is ignored otherwise. All referenced variables and rows must exist.

Add indicator constraints to this model. The function iterates through data and calls the provided functions for each element found. The return values of the functions are used to construct an indicator constraint. For a fixed t, if IndicatorValue(t) returns true then the created constraint states that if IndicatorVariable(t) is 1 (one), then Row(t) must be satisfied and is ignored otherwise. Otherwise, Row(t) must be satisfied if IndicatorVariable(t) is 0 (zero) and is ignored otherwise. All referenced variables and rows must exist.