UE Numerical optimization

Degrees incorporating this pedagocial element :

Description

This program combines case studies coming from real life problems or models and lectures providing the mathematical and numerical backgrounds.

Contents:

  • Introduction, classification, examples.
  • Theoretical results: convexity and compacity, optimality conditions, KT theorem
  • Algorithmic for unconstrained optimisation (descent, line search, (quasi) Newton)
  • Algorithms for non differentiable problems
  • Algorithms for constrained optimisation: penalisatio, SQP methods
  • Applications

Prerequisites

linear algebra, differential calculus

Targeted skills

Recognise and classify optimisation problems

Solve optimisation problems using adequate algorithms and methods

Practical implementation

Knowledge assessment methods

Session 1 or single session - Knowledge testing

Type of teaching providedMethodTypeDuration (min)Coefficient
Teaching Unit (UE)CC 100/100
Teaching Unit (UE)CT Written - supervised work120100/100

Session 2 - Knowledge testing

Type of teaching providedMethodTypeDuration (min)Coefficient
Teaching Unit (UE)CC Calculation report100/100
Teaching Unit (UE)CT Written or Oral120100/100