This lecture introduces Newton's method for solving necessary optimality conditions in optimization problems. It explains how the method uses gradients and Jacobians to iteratively converge towards stationary points, illustrating its application with numerical examples. The lecture also discusses the limitations of Newton's method, such as convergence issues and the presence of saddle points, and proposes adaptations for global convergence.