What is the role of Karush-Kuhn-Tucker (KKT) conditions in SVM?

KKT conditions define the optimality criteria for SVM’s constrained optimization problem.

SVM optimization is a constrained convex problem. KKT conditions combine stationarity, primal feasibility, dual feasibility, and complementary slackness. They ensure that only support vectors (with non-zero Lagrange multipliers) lie on or inside the margin, and all other points satisfy constraints strictly.