Why is SVM considered a convex optimization problem?

SVM is convex because its objective function and constraints form a convex set.

The primal SVM optimization minimizes a quadratic function with linear constraints. Quadratic programming with positive semi-definite matrices ensures convexity, meaning no local minima exist and solutions are globally optimal.