About the Problem 3.17b
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)?

Re: About the Problem 3.17b
Yes, E1 is a function of ∆u, ∆v, but it is also a function of u, v. Then, what is the u, v in this function? Still use (0, 0) as part (a) said? Also, what is the ininital value of ∆u, ∆v? In the textbook, it sets w to w(0) at step 0.
Further, does the norm (∆u,∆v)=0.5 means that for each iteration we should ensure that the values of ∆u,∆v meet this resuirements? Another point is that in textbook, we need specify the step size ɧ. However, we could not see any information about the step size. I don't quite understand the description of the question (Problem 3.17b), so I have so many questions. Could you probably clarify it for me? Quote:

Re: About the Problem 3.17b
Yes, in this problem you can use (u,v)=(0,0) from part (a).
(∆u,∆v)=0.5 means that the step size . In the chapter we considered two step sizes. First where the step size was fixed at . Second where the step size is proportional to the norm of the gradient. Here, for part (b) the step size is fixed at 0.5. Quote:

Re: About the Problem 3.17b
I have almost understund the problem. But still have a question that what the meaning of the resulting of E(u+∆u,v+∆v) in Part (b), (ei), and (eii). Is it a number or a formula?
Also what the difference of the two parts of (e). One is to minimize E2, the other is to minimize E(u+∆u,v+∆v). So, what the difference of those two? Quote:

Re: About the Problem 3.17b

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