Newton's Method: Optimization Techniques

This lecture covers optimization techniques such as gradient descent, line search, Armijo condition, Wolfe conditions, and Newton's method. It explains the importance of step size selection, descent directions, and the use of quasi-Newton methods like DFP and BFGS. The instructor demonstrates how to solve optimization problems efficiently by iteratively updating approximations of the Hessian matrix.