It might be a very basic question.$\\$I am considering the SVM optimization problem here.$\\$In a training set where the data is linearly separable, and we are using a hard margin (no slack allowed), the support vectors lie along the supporting hyperplanes but is the inverse true that all points lying along the supporting hyperplanes are support vectors? 
Support Vectors in SVM
machine learning;svm