KKT Conditions
Karush-Kuhn-Tucker conditions are first-order necessary mathematical conditions for a solution in nonlinear programming to be optimal under constraints.
Think of It Like This
Like a set of strict physical laws that guarantee a ball rolling down a bumpy hill has genuinely reached the absolute lowest valley.
They generalize the method of Lagrange multipliers to accommodate inequality constraints. For a solution to be optimal, it must satisfy stationarity, primal and dual feasibility, and complementary slackness. KKT conditions form the theoretical backbone for training algorithms like Support Vector Machines (SVMs).