In Sequential Quadratic Programming we use an active set of the inequality constraints and handle them as equality constraints in the quadratic subproblem.
SQP is said to be able to deal with infeasible points in the design space, which violate the inequality constraints.
Whenever the algorithm takes you to a point which violates a constraint then that constrain is added to the active set for the next iteration.
But if the number of constraints in the current active set is more than the dimensionality of the design space, the quadratic subproblem is not solvable.
When implementing SQP, how can we deal with such over-constrained infeasible points in the design space?