Optimization: principles and algorithms - Unconstrained nonlinear optimization
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6 Weeks
$
49
Brief Introduction
Introduction to unconstrained nonlinear optimization, Newton’s algorithms and descent methods.
Description
Introduction to unconstrained nonlinear optimization, Newton’s algorithms and descent methods.
Knowledge
- The course is structured into 6 sections:
- 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.
Optimization: principles and algorithms - Unconstrained nonlinear optimization
- 0.0
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Introduction to unconstrained nonlinear optimization, Newton’s algorithms and descent methods. Introduction to unconstrained nonlinear optimization, Newton’s algorithms and descent methods. The course is structured into 6 sections:...
