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Optimization: principles and algorithms - Unconstrained nonlinear optimization

1,306 people completed this program


Introduction to unconstrained nonlinear optimization, Newton’s algorithms and descent methods.

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.

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Optimization: principles and algorithms - Unconstrained nonlinear optimization
6 weeks
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