1. Why is it important to keep a symmetric positive definite iteration matrix Bk while seeking a minimum of a smooth function φ(x)? Does Newton's method automatically guarantee this?
2. Define descent direction and line search, and explain their relationship.
3. What is a gradient descent method? State two advantages and two disadvantages that it has over Newton's method for unconstrained optimization