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Probabilistic Algorithms (UNINE)

  • Enseignant(s):  
  • Titre en français: Probabilités d'algorithmes (UNINE)
  • Cours donné en: anglais
  • Crédits ECTS:
  • Horaire: Semestre d'automne 2018-2019, 0.0h. de cours (moyenne hebdomadaire)
      WARNING :   this is an old version of the syllabus, old versions contain   OBSOLETE   data.
  •  séances
  • Formations concernées:

 

Objectifs

A student attaining this course should be able: - to identify the randomized characteristic of an algorithm and to classify it as Monte Carlo or Las Vegas type - to identify an optimization problem where the measurements are affected by random noise - to select and to implement (using MATLAB environment) the appropriate stochastic algorithm for solving an optimization problem

Contenus

The course starts with an introduction of the concept of randomized algorithms (examples, types) and of random number generators, but the core of the course provides a survey of many of the most important methods in stochastic search and optimization. The first part of the course will concentrate on optimization heuristic approach (random search and non-linear simplex, simulated annealing, genetic algorithms and evolution strategies - including ant colony and particle swarm -, whereas the second part is dedicated to stochastic approximation approach (finite difference method, stochastic gradient, simultaneous perturbations). Although the theoretical bases of the algorithms are presented in a rigorous manner, the proofs of these results are not included. The implementation of the enumerated algorithms, for solving the included application-oriented examples, is made in MATLAB.

Références

R Introduction to Stochastic Search and Optimization, James C. Spall, 2003, John Wiley & Sons R Stochastic Optimization, J. Schneider and S. Kirkpatrick, 2006, Springer R MATLAB doc, http://www.mathworks.com/access/helpdesk/help/techdoc/index.html

Evaluation

1ère tentative

Examen:
Ecrit 2h00 heures
Documentation:
Non autorisée
Calculatrice:
Non autorisée
Evaluation:
Lab assignments: 40%, Mid-term and final written exam (2 hours): 60%

Rattrapage

Examen:
Ecrit 2h00 heures
Documentation:
Non autorisée
Calculatrice:
Non autorisée
Evaluation:
Examen écrit de 2heures compte à 100%


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