But the derivative of E is zero at the minimum so we can solve for wopt.
Matrix inversion is an expensive operation.
Also, if the input dimension, n, is very large then H is huge and may not even b possible to compute. If we are not able to compute the inverse Hessian or if we don't want to spend the time, then we can use gradient descent. Now, suppose we want to determine the optimal weight, wopt. 1 and m 2 are inversely related to the size of the curvature along each axis. If the axes are not aligned with coordinate axes, the we need a full matrix of learning rates.