Optimization models and methods I

Teacher(s)

Glineur François;

Language

French

Content

Linear optimization:
Introduction, canonical formulations, polyhedral geometry, simplex algorithm, duality et sensitivity analysis, introduction to discrete optimization (branch & bound).
Nonlinear optimization:
Models : definitions and terminology, optimality conditions for unconstrained and constrained problems ; recognize and exploit convexity of a problem.
Methods : line-search methods for unconstrained problems (gradient, Newton and quasi-Newton methods) ; convergence properties (local and global) ; implementation details ; introduction to other types of methods.

Teaching methods

Due to the COVID-19 crisis, the information in this section is particularly likely to change.

This course is comprised of lectures, exercise sessions and computer labs, as well as a project to be carried out in small groups. Consulting is available for help with the project.

Evaluation methods

Due to the COVID-19 crisis, the information in this section is particularly likely to change.

Students will be evaluated with an individual written exam, based on the above-mentioned objectives. Students also carry out a project in small groups, whose evaluation is taken into account for the final grade. 

Online resources

Course documents (slides, notes and exercises) are available on Moodle : https://moodleucl.uclouvain.be/course/view.php?id=9200 

Bibliography

• Introduction to Linear Optimization, Dimitri Bertsimas and John Tsitsiklis, Athena Scientific, 1997.
• Linear Programming. Foundation and Extensions, Robert Vanderbei, Kluwer Academic Publishers, 1996.
• Integer Programming, Laurence Wolsey, Wiley, 1998.
• Numerical Optimization, Jorge Nocedal et Stephen J. Wright, Springer, 2006.
• Convex Optimization, Stephen Boyd et Lieven Vandenberghe, Cambridge University Press, 2004.

Faculty or entity

MAP