# Overconstraining in SQP

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?

• Take an example: You are in ${\mathbb R}^2$ and you have three inequalities that all meet in one point and that point is the optimum. Then you can say that at that point all three constraints are "active", but in practice the way we think about this is that they are redundant, and that only two of them are active (namely, the two that delineate the feasible region) whereas the third can be removed from the active set without any harm. Jul 2 at 2:59
• Oh, I see what you mean. The active set strategy only ever keeps at most $d-1$ constraints active so that you have at least one direction to move in. If you end up with a move that would violate a constraint, you have to cycle that constraint into the active set and evict another constraint. Jul 2 at 15:37