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Optimization: principles and algorithms - Unconstrained nonlinear optimization


Optimization: principles and algorithms - Unconstrained nonlinear optimization

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.

Meet the instructors

Michel Bierlaire


You'll find more information about EPFL MOOCs here. These courses in particular might be of interest to you:

Optimization: principles and algorithms - Linear optimization
Optimization: principles and algorithms - Network and discrete optimization

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