Unfortunately, indicator constraints are only supported for

**linear constraints**. The

**Cplex**docs say:

- The constraint must be linear; a quadratic constraint is not allowed to have an indicator constraint.
- A lazy constraint cannot have an indicator constraint.
- A user-defined cut cannot have an indicator constraint.

**Gurobi**mentions:

An indicator constraint \(y=f \rightarrow a^Tx \le b\) states that if the binary variable \(y\) has the value \(f\in \{0,1\}\) in a given solution then thelinear constraint\(a^Tb \le b\) has to be satisfied.

**Xpress**and

**SCIP**say similar things: indicator constraints are only for linear constraints.

Somehow, I expected that I could use a (convex) quadratic constraint with an indicator. As none of the solvers has this implemented, I suspect there is a good reason why this is not possible.

#### Big-M and SOS1

So, now we cannot use indicators, we need to use more traditional implementation techniques. The most obvious is to use a big-\(M\) formulation: \[ d_{i,k} \ge \sum_j \left( \bar{x}_{k,j} - p_{i,j} \right)^2 - M(1-x_{i,k})\] The disadvantage of this formulation is that we need to establish a good value for \(M\) and that this value cannot be too large. Large values of \(M\) tend to confuse MIP solvers.

If you have no good (and small) values for \(M\), we can use SOS1 sets. I.e. we write \[\begin{align} &d_{i,k} \ge \sum_j \left( \bar{x}_{k,j} - p_{i,j} \right)^2 - s_{i,k} \\ & s_{i,k}\ge 0\\ & \{s_{i,k},x_{i,k}\} \in SOS1\end{align}\] This says: either \(s_{i,k}\) or \(x_{i,k}\) can be non-zero, but not both. To be precise: both can be zero. Most MIP solvers support SOS1 sets.

Finally, as stated in the comments, an alternative formulation would be \[\begin{align} & d'_{i,k} \ge \sum_j \left( \bar{x}_{k,j} - p_{i,j} \right)^2 \\ & x_{i,k}=1 \Rightarrow d_{i,k} \ge d'_{i,k} \end{align}\] This comes at the expense of extra variables and constraints. Basically we added one indirection. With this formulation, we have a linear indicator constraint and a separate convex quadratic inequality. I assume the presolver will not presolve this away.

Finally, as stated in the comments, an alternative formulation would be \[\begin{align} & d'_{i,k} \ge \sum_j \left( \bar{x}_{k,j} - p_{i,j} \right)^2 \\ & x_{i,k}=1 \Rightarrow d_{i,k} \ge d'_{i,k} \end{align}\] This comes at the expense of extra variables and constraints. Basically we added one indirection. With this formulation, we have a linear indicator constraint and a separate convex quadratic inequality. I assume the presolver will not presolve this away.

#### Error Messages

In my opinion well-formulated error messages are very important. As a user, we are already confused that the system is not doing what we want, so a confusing error message is just piling on. Let's see what we see when using a quadratic indicator constraint:

- GAMS has poor support for indicators in general, but is doing really poorly here. It does not give an error message, but just gives a wrong solution. This is really bad (I reported this, so it will be fixed).
- AMPL gives an error message that seems to come from outer space.

CPLEX 12.9.0.0: logical constraint _slogcon[1] is not an indicator constraint.Let's blame the slogcons (???). Obviously, I have no constraint (or any other symbol) called slogcon. - Cplex's interactive optimizer is doing a better job:

CPLEX Error 1605: Line 5: Illegal quadratic term in a constraint. - Cplex's OPL has a difficult time understanding the (convex) quadratic indicator constraint. It says:

*** FATAL[ENGINE_002]: Exception from IBM ILOG CPLEX: CPLEX Error 5002: 'q1' is not convex. Obviously the constraint is convex, so there is a problem in how OPL generates the constraint (i.e. this is a bug). The error message just does not make sense. The message is also bad: I don't have a `q1` in the model.

A better message would be something like:

Equation `e2`: quadratic terms in an indicator constraint are not supported. Please reformulate this constraint.

Basically you want an error message to describe the problem in a way that is understandable (even when the user is slightly panicked). And secondly, it is a good idea to tell the user what to do now. And, never mention the slogcons.

Of course, it would be even better if solvers would support quadratic indicator constraints. In design there is this concept of orthogonality: it is best to have as few exceptions as possible.