How does SVM achieve maximum margin separation?

SVM finds a hyperplane that maximizes the distance between itself and the nearest data points of each class.

SVM formulates an optimization problem where it maximizes the margin subject to correct classification constraints. The margin is defined as 2/||w||, so maximizing margin is equivalent to minimizing ||w|| while ensuring data points are correctly classified. This leads to a convex optimization problem ensuring a global optimum.