is a function of
. You want to choose
(the vector to move in) to minimize
. The negative gradient direction is going to be the direction to move (this is shown in the chapter) and you have to rescale that so the step size is 0.5.
Quote:
Originally Posted by rpistu
I don’t quite understand the Problem 3.17b. What the meaning of minimize E1 over all possible (∆u, ∆v). Instead, I think it should minimize E(u+∆u,v+∆v), starting from the point (u,v)=(0,0). Is the optimal column vector [∆u,∆v]T is corresponding to the vt in the gradient descent algorithm (here, as the problem said, it is ∆E(u,v)), the norm (∆u,∆v)=0.5 corresponding to the step size ɧ, and (u,v) corresponding to the weight vector w? Then, what the meaning of compute the optimal (∆u, ∆v)?
