Duration:
1 Semester | Turnus of offer:
each summer semester | Credit points:
8 |
Course of studies, specific field and terms: - Master CLS 2023 (compulsory), 1st, 2nd, or 3rd semester
- Master CLS 2016 (compulsory), 1st, 2nd, or 3rd semester
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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
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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
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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.
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Grading through: - Written or oral exam as announced by the examiner
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Is requisite for: |
Requires: |
Responsible for this module: Teachers: |
Literature: - J. Nocedal, S. Wright: Numerical Optimization - Springer
- F. Jarre: Optimierung - Springer
- C. Geiger: Theorie und Numerik restringierter Optimierungsaufgaben - Springer
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Language: |
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: - Exercises and their presentation are ungraded preliminary examinations. These must have been completed and positively evaluated before the first examination. Examination: - MA4031-L1: Optimization, written examination (90 min) or oral examination (30 min) as decided by examiner, 100% of final mark Variant of MA4030, MA4030-KP08 for students who did not attend a course on optimization in their Bachelors program. |
Letzte Änderung: 4.2.2022 |
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