Module MA4030-KP08, MA4030
Optimization (Opti)
Duration
1 Semester
Turnus of offer
each summer semester
Credit points
8
Course of studies, specific fields and terms:
- Minor in Teaching Mathematics, Bachelor of Arts 2023, compulsory, mathematics
- Bachelor CLS 2023, compulsory, mathematics
- Master Auditory Technology 2022, optional subject, mathematics
- Master MES 2020, optional subject, mathematics / natural sciences
- Bachelor Computer Science 2019, optional subject, Extended optional subjects
- Master Robotics and Autonomous Systems 2019, optional subject, Additionally recognized elective module
- Minor in Teaching Mathematics, Bachelor of Arts 2017, compulsory, mathematics
- Master Auditory Technology 2017, optional subject, mathematics
- Bachelor Computer Science 2016, optional subject, advanced curriculum
- Bachelor CLS 2016, compulsory, mathematics
- Master MES 2014, optional subject, mathematics / natural sciences
- Master MES 2011, optional subject, mathematics
- Master Computer Science 2012, optional subject, advanced curriculum numerical image processing
- Bachelor MES 2011, optional subject, medical engineering science
- Master Computer Science 2012, optional subject, advanced curriculum analysis
- Bachelor CLS 2010, compulsory, mathematics
Classes and lectures:
- Optimization (exercise, 2 SWS)
- Optimization (lecture, 4 SWS)
Workload:
- 130 hours private studies and exercises
- 90 hours in-classroom work
- 20 hours exam preparation
Contents of teaching:
- Linear optimization (simplex method)
- Unconstrained nonlinear optimization (gradient descent, conjugate gradients, Newton method, Quasi- Newton methods, globalization)
- Equality- and inquality-constrained nonlinear optimization (Lagrange multipliers, active set methods)
- Stochastic methods for machine learning
Qualification-goals/Competencies:
- Students can model real-life problems as optimization problems.
- They understand central optimization techniques.
- They can explain central optimization techniques.
- They can compare and assess central optimization techniques.
- They can implement central optimization techniques.
- They can assess numerical results.
- They can select suitable optimization techniques for practical problems.
- Interdisciplinary qualifications:
- Students can transfer theoretical concepts into practical solutions.
- They are experienced in implementation.
- They can think abstractly about practical problems.
Grading through:
- Written or oral exam as announced by the examiner
Is requisite for:
Responsible for this module:
Literature:
- J. Nocedal, S. Wright : Numerical Optimization Springer
- F. Jarre : Optimierung Springer
- C. Geiger : Theorie und Numerik restringierter Optimierungsaufgaben Springer
Language:
- offered only in German
Notes:
Prerequisites for attending the module:- None (Familiarity with the topics of the required modules is assumed, but the modules are not a formal prerequisite for attending the course).
Prerequisites for the exam:
- Examination prerequisites can be defined at the beginning of the semester. If preliminary work is defined, it must have been completed and positively evaluated before the first examination.
Examination:
- MA4030-L1: Optimization, written examination (90 min) or oral examination (30 min) as decided by examiner, 100 % of final mark
Last Updated:
31.08.2022