By the reasoning of the previous paragraph, we know that j † = j * at the minimum of (37).
hence we can minimize (37) by minimizing We compute the gradient: As before, the derivative of the constraint C f = 0 is one, and we minimize (38) under the constraint by solving Substituting into the constraint gives us Substituting this back into (40) yields: the maximizing solution is peaked precision, which is the update rule for YS-P. We now show that YS-R also decreases K in each iteration.