Main Content
Current Teaching
Here, we provide an overview of the current teaching offers of the working group during the summer term 2025.
Operations Research (4+2)
- Lecture: Wed 08:15 - 09:45 305 (B|07), Thu 12:15 - 13:45 HS B (H|05), P. Mehlitz
- Exercise class: Gr. 1 - Thu 16:30 - 18:00 04A23 (H|04), K. Kleiser; Fri 08:15 - 09:45 013 (B|07), T. Baake
- Tutorial: Gr. 1 - ???; Gr. 2 - ???
- Qualification: Knowledge according to courses Linear Algebra II is essential.
- Content: properties of linear optimization problems, solving linear optimization problems with the simplex method, duality theory, transportation problems, foundations of graph theory, shortest paths, minimum spanning trees, Euler tours, flow problems
- Exam: written exam of 120 minutes
- Material: Information and material for the course are provided via the associated ILIAS page.
Discrete Optimization (3+1)
- Lecture: Tue 10:15 - 11:45 03A20 (H|04), Thu 08:30 - 10:00 03A20 (H|04) every two weeks (beginning in the first week)
- Exercise class: Thu 08:30 - 10:00 03A20 (H|04) every two weeks (beginning in fourth week)
- Qualification: Knowledge according to course Linear Algebra II is essential. Knowledge according to the course Operations Research (previously: Linear Optimization) can be helpful.
- Content: models with integer conditions, branch and bound principle, cutting plane principle, approximation algorithms, matroids
- Exam: oral exam of 30 minutes, appointments will be arranged during the lecture
- Material: Information and material for the course are provided via the associated ILIAS page.
Preceding Teaching
Here, we provide an overview of the preceding teaching offers of the working group.
Continuous Optimization (4+2, winter term 2024/25)
- Lecture: Tue 10:15 - 11:45 04A30 (H|04), Wed 08:15 - 09:45 201 (B|07), P. Mehlitz
- Exercise class: Fri 08:00 - 09:30 03A21/04A24 (H|04), K. Kleiser
- Tutorial: Gr. 1 - Mon 14:00 - 15:30 04A23/04A24 (H|04), T. Baake; Gr. 2 - Tue 12:15 - 13:45 03A11/04A24 (H|04), K. Kleiser
- Qualification: Knowledge according to courses Linear Algebra II and Analysis II is essential.
- Content: convex sets and functions, separability and theorems of alternatives, necessary and sufficient optimality conditions of first and second order for constrained optimization problems, numerical methods for unconstrained optimization (general line search methods, steepest descent method, CG method, Newton method, Newton-like methods) and constrained optimization (penalty methods, multiplier-penalty methods)
- Exam: written exam of 120 minutes on 18/02/2025
- Material: Information and material for the course are provided via the associated ILIAS page.
Multiobjective Optimization (3+1, winter term 2024/25)
- Lecture: Tue 12:15 - 13:45 03A10 (H|04), Thu 10:15 - 11:45 03A10 (H|04) every two weeks (beginning in the first week)
- Exercise class: Thu 10:15 - 11:45 03A10 (H|04) every two weeks (beginning in second week)
- Qualification: Knowledge according to courses Linear Algebra II and Analysis II is essential. Knowledge according to the courses Operations Research (previously: Linear Optimization) and Continuous Optimization (previously: Nonlinear Optimization) can be helpful.
- Content: convex sets and functions, separability and theorems of alternatives, notions of efficiency, existence of efficient points, methods for the computation of efficient points (scalarization methods, bounding methods, compromise methods), linear multiobjective optimization, set optimization (vector and set approach with optimality conditions)
- Exam: oral exam of 30 minutes, appointments will be arranged during the lecture
- Material: Information and material for the course are provided via the associated ILIAS page.
Nonsmooth Analysis and Optimization (4+2, summer term 2024)
- Lecture (Tue 08:15 - 09:45 HS II A3, Wed. 14:15 - 15:45 SR XI C3) and Exercise class (Thu 12:15 - 14:45 HS VI A3): P. Mehlitz
- Qualification: Knowledge according to courses Linear Algebra II and Analysis II is essential. Knowledge according to the course Continuous Optimization can be helpful.
- Content: nonsmooth variational analysis according to Mordukhovich (normal directions, extremal principle, subdifferentiation, optimality conditions), algorithms of nonsmooth optimization (proximal gradient method, proximal multiplier-penalty method, ADMM), Newton differentiability and nonsmooth Newton methods
- Exam: oral exam of 30 minutes, appointments will be arranged during the lecture
- Material: Information and material for the course are provided via the associated ILIAS page.