In Convex optimization, We are given a convex function f () with convex constraints hi (x) and g; (x). Normally in convex functions we saw that in order to find the minimum value we need to take derivative wit the variables and set it to 0. However, if we have constraints we can not simply do that. min f (x) s.t: hi (x) = 0; i = 1, . ..,P gi (x) 0 then it means yl- (ELITE; + b) = 1 which means the points are on the margin B. Case that 1 y,- (wal- + b) > 0 then it means that a; = U which means y;- (tvfmg + l3) > 1 [points after the margin}