The Karush-Kuhn-Tucker (KKT) approach is used for constrained optimization and attempts to create and solve a function called the generalized Lagrangian. The process involves: 1) Describing the feasible set, $\mathbb{S}$, in terms of equations and inequalities; 2) Introducing the variables $\lambda$ and $\alpha$ to formulate the generalized Lagrangian; and 3) Optimizing $\min_{x} \max_{\lambda} \max_{\alpha,\alpha\ge0} \mathop{\mathcal{L}}(x, \lambda, \alpha)$. This approach can also be used as a parameter norm penalty by which to regularize the cost function.