Why does SVM use convex optimization instead of gradient descent?

SVM uses convex optimization because it guarantees a global optimum solution.

Unlike neural networks, SVM’s objective is convex, meaning there are no local minima. This allows deterministic solvers like quadratic programming or SMO to find optimal hyperplanes efficiently without iterative gradient-based uncertainty.