Optimization: principles and algorithms - Unconstrained nonlinear optimization
Introduction to unconstrained nonlinear optimization, Newton’s algorithms and descent methods.
Monthly Subscription Starting at AED 99 + VAT
- Level Foundation
- Duration 27 hours
- Course by École polytechnique fédérale de Lausanne
- Total students 1,959 enrolled
-
Offered by
About
Introduction to unconstrained nonlinear optimization, Newton's algorithms and descent methods.
What you will learn
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.
Skills you learn
Auto Summary
Discover the fundamentals of unconstrained nonlinear optimization, focusing on Newton's algorithms and descent methods. This foundational course in Maths & Statistics, offered by edX, spans 27 hours and is available through Professional and Starter subscriptions. Ideal for those seeking to deepen their understanding of optimization principles and algorithms.
Michel Bierlaire