The SVM derivation is centered on convex optimization. By definition, convex optimization requires a convex objective function and convex or linear constraints. The task is to minimize this function.
My question is: When the SVM problem is converted from primal to dual, it becomes a maximization problem (in the dual form). Since we are no longer minimizing a convex function, does this still qualify to be called convex optimization? I know this will be a silly question to an expert in this field – but I have to put on a brave face to ask it!