Introduction to unconstrained nonlinear optimization, Newton’s algorithms and descent methods.
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 and adapt Newton's method in the context of optimization.
Descent methods: you will learn the family of descent methods, and its connection with Newton's method.
