Mathematical treatment of diverse optimization techniques.
Objective
Advanced optimization theory and algorithms.
Content
1. Linear optimization: The geometry of linear programming, the simplex method for solving linear programming problems, Farkas' Lemma and infeasibility certificates, duality theory of linear programming.
2. Nonlinear optimization: Lagrange relaxation techniques, Newton method and gradient schemes for convex optimization.
3. Integer optimization: Ties between linear and integer optimization, total unimodularity, complexity theory, cutting plane theory.
4. Combinatorial optimization: Network flow problems, structural results and algorithms for matroids, matchings and, more generally, independence systems.
Performance assessment
Performance assessment information (valid until the course unit is held again)
The performance assessment is offered every session. Repetition possible without re-enrolling for the course unit.
Mode of examination
oral 30 minutes
Additional information on mode of examination
There is a mid-term examination. Participation at the mid-term examination is elective. The mark achieved at this mid-term examination either improves the final mark or has no influence on it.
This information can be updated until the beginning of the semester; information on the examination timetable is binding.