# Constraints without any variables

Sometimes when working with complex models, it might happen that some constraints are trivially feasible or infeasible, since they evaluate to a fixed number independent of all decision variables. This can cause problems for YALMIP in some cases.

Consider the following model, where the numbers **k** and **m** are constants in your code

```
sdpvar x y
Constraints = [x >= 1, y >= k*y + m]
optimize(F,obj)
```

This is a perfectly valid model. Now, let us assume that the magic numbers **k** and **m** all of a sudden happen to have the values 1 and 0. This would correspond to the following case

```
k = 1;
m = 1;
sdpvar x y
Constraints = [x >= 1, y >= k*y + m]
optimize(F,obj)
```

When concatenating the constraints, MATLAB will first tell YALMIP to evaluate all the expressions. When doing so, YALMIP changes the expressions to **x - 1>=0** and **y-ky-m\geq 0**. The second expression will simply lead to the expression **0>=0**, i.e., standard MATLAB code without any YALMIP context, which evaluates to **logical(1)**. Hence, the model will be

```
Constraints = [x-1 >= 0, logical(1)]
optimize(F,obj)
```

When encountering such a situation, YALMIP will issue a warning to make the user aware of the situation. An even worse case can occur if, e.g., **k=1** and **m = 1**. We then have the model

```
Constraints = [x-1 >= 0, -1 >= 0]
optimize(F,obj)
```

This will issue an error in YALMIP since the model by construction is infeasible.