prove that for this entire family of steepest descent algorithms you converge the SVM given in that normal okay so for example that one norm this is exactly a form of boosting a to prove this a long long time ago in s PhD that in fact does maximize the l1 margin the same proof works for all norms okay so some examples coordinate descent which is steepest ascent with respect to l1 norm you're going to maximize l1 margin it's not quite adaboost it's adaboost with some damping step size so add a boost with a dampened step size that maximizes