Hi, I have a question related to nonlinear optimization.

I am trying to impose a nonlinear constraint that involves a matrix inversion. The matrix contains a variable of the optimization model. When I try to do this via @NLconstraint, I get an error. An example is as follows. May I know is there another way to properly impose these types of constraints, or should I use another optimizer? Many thanks!

The example here can be written as a linear form just for simplicity. What I was trying to do is related to a constraint that looks like A(x)B(x)^{-1}x = b, where A(x) and B(x) are two conformable matrices that contain the variable x.

Do you happen to know if there is another way to use vector-valued expressions, if it is not possible to be done in JuMP?