What is the role of gradient in SVM optimization?

Gradients are used in optimization formulations but SVM is typically solved via quadratic programming.

SVM optimization is convex but not always solved using standard gradient descent. However, sub-gradients of hinge loss are used in linear SVM variants like SGDClassifier. Traditional SVM uses SMO or QP solvers instead.