What is the meaning of "local optimality" in NLP mentioned in Constraint Qualification?

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Most statements of Constraint Qualification I have found in the literature mention a locally "locally optimal solution" of the problem: $$ \begin{cases} \min f(x) \\ \text{s.t.}\\ g_i(x)\leq 0 \end{cases}$$

It is stated that when a C.Q. holds at a local optimum, then there exist Lagrange multipliers that satisfy KKT conditions.

But, I cannot get my head around this notion of local optimality. Does it mean locally optimal for the unconstrained problem? Does not local optimality imply the satisfaction of the KKT conditions?

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It means locally optimal for the constrained problem.

If a constraint qualification does not hold, along with the required continuous differentiability of f(x) and g(x), a locally optimal solution need not satisfy the KKT conditions.