Mixed-Integer Programming
Gemischt-ganzzahlige Programmierung
8 CPirregularlyEN
Prerequisites
Recommended, as written
Knowledge in linear and mixed-integer optimization, e.g. knowledge of the content of modules • 13862 Optimierung und Operations Research • 11322 Optimierungsmethoden des Operations Research Knowledge of the simplex algorithm is implied.
Contents
Polyhedron theory, estimations in integer optimization, complexity theory, totally unimodular matrices, integer polyhedra and total dual integrity, cutting planes, branch-and-bound, Lagrange Relaxations, Benders decomposition, dynamic programming, stochastic programming, column generation methods, heuristics, nonlinear mixed-integer programming, applications
Learning outcomes
After successfully completing the module, students know the basic concepts (definitions, theorems and proofs) in the field of mixed-integer programming. They are able to formalize an applied optimization problem and solve it with mathematical means.
Assessment
• 4 intermediate tests, written during the lecture period, 30 minutes each The best 3 count 1/3 each for the final grade.
Literature (1)
- Current literature will be announced or handed out in the first lecture.
Remarks
• Study programme Mathematics M.Sc.: Compulsory elective module in complex „Optimization“ • Study programme Mathematik B.Sc.: Compulsory elective module in complex „Vertiefung“, in limited extend • Study programme Wirtschaftsmathematik B.Sc.: Compulsory elective module in complex „Vertiefung“, in limited extend
Source: BTU's module description · retrieved 29 Sep 2026Original