\math norm. We consider separation in the original space of the data (i.e., there are no kernel transformations). For any of these cases, we focus on the following problem: having the optimal solution for a given training data set, one is given a new training example. The purpose is to use the information about the solution of the problem without the additional example in order to speed up the new optimization problem. We also consider the case of re-optimization after removing an example from the data set. We report results obtained for some standard benchmark problems.