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Module guide

Module MA4030-KP08, MA4030

Optimization (Opti)

Duration:


1 Semester
Turnus of offer:


each summer semester
Credit points:


8
Course of studies, specific field and terms:
  • Minor in Teaching Mathematics, Bachelor of Arts 2023 (compulsory), mathematics, 8th semester
  • Bachelor CLS 2023 (compulsory), mathematics, 4th semester
  • Master MES 2020 (optional subject), mathematics / natural sciences, arbitrary semester
  • Bachelor Computer Science 2019 (optional subject), Extended optional subjects, arbitrary semester
  • Minor in Teaching Mathematics, Bachelor of Arts 2017 (compulsory), mathematics, 8th semester
  • Master Auditory Technology 2017 (optional subject), mathematics, 1st or 2nd semester
  • Bachelor Computer Science 2016 (optional subject), advanced curriculum, arbitrary semester
  • Bachelor CLS 2016 (compulsory), mathematics, 4th semester
  • Master MES 2014 (optional subject), mathematics / natural sciences, arbitrary semester
  • Master MES 2011 (optional subject), mathematics, 2nd semester
  • Master Computer Science 2012 (optional subject), advanced curriculum numerical image processing, 2nd or 3rd semester
  • Bachelor MES 2011 (optional subject), medical engineering science, 6th semester
  • Master Computer Science 2012 (optional subject), advanced curriculum analysis, 2nd or 3rd semester
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:
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
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

Letzte Änderung:
27.1.2022