Lecture

Descent methods and line search: Validity of the Wolfe conditions

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Description

This lecture explains the two Wolfe conditions that characterize valid steps in a descent direction. The first condition requires a sufficient decrease in the objective function, while the second condition imposes a sufficient progress of the iteration. The compatibility of these conditions is guaranteed by Theorem 11.9, ensuring the existence of steps that satisfy both conditions. By choosing appropriate parameters, steps can be found that meet the Wolfe conditions, facilitating the optimization process.

In MOOC

Optimization: principles and algorithms - Unconstrained nonlinear optimization

What you'll learn Formulation: you will learn from simple examples how to formulate, transform and characterize an optimization problem. Objective function: you will review the mathematical properties of the objective function that are important in optimization. Optimality conditions: you will learn sufficient and necessary conditions for an optimal solution. Solving equations, Newton: this is a reminder about Newton's method to solve nonlinear equations. Newton's local method: you will see how to interpret

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