Descent methods and line search: Second Wolfe condition

This lecture delves into the second Wolfe condition, which aims to avoid steps that are too short along a descent direction. By requiring a sufficient increase in the directional derivative, this condition provides flexibility to the algorithm, allowing for different step sizes based on the parameter beta_2. Through illustrations and examples, the instructor explains how the second Wolfe condition characterizes the progress of the algorithm and influences the acceptance or rejection of steps, ultimately impacting the optimization process.