In gradient descent we studied a similar problem: find the direction to move to minimize the error the most for a given step size. This direction was the negative gradient of
You can use that fact to solve part (b), because the gradient approximation is exact for linear functions.
Part (a) defined a function
. If you set (u,v)=(0,0),
becomes a function of
. You want to minimize this function under the constraint that
.
If you choose to use the gradient hint, the gradient of
is related to the coefficients
defined in part (a).
Quote:
Originally Posted by mileschen
Could you possibly redescribe the Problem 3.17b for me? I don't quite understand the requirements of this question. What's the relation between it and the gradient descent algorithm for logistic regression of the textbook?
