- competitive analysis for deterministic and randomised algorithms,
- adversary concepts, adaptive and non-adaptive adversaries for randomised algorithms,
- amortised costs, potential method for costs analysis,
- competitive algorithms for paging / caching,
- online scheduling.
Introduction to Online Optimization (M, 4.5 LP)
|Module Number||Module Name||CP (Effort)|
|MAT-52-14A-M-7||Introduction to Online Optimization||4.5 CP (135 h)|
|CP, Effort||4.5 CP = 135 h|
|Position of the semester||1 Sem. irreg.|
|Level|| Master (Advanced)|
|Area of study||[MAT-OPT] Optimisation|
|Reference course of study||[MAT-88.105-SG] M.Sc. Mathematics|
|Type/SWS||Course Number||Title||Choice in |
|SL||SL is |
required for exa.
Introduction to Online Optimization
|P||42 h||93 h||-||-||PL1||4.5||irreg.|
- About [MAT-52-14A-K-7]: Title: "Introduction to Online Optimization"; Presence-Time: 42 h; Self-Study: 93 h
Examination achievement PL1
- Form of examination: oral examination (20-30 Min.)
- Examination Frequency: irregular (by arrangement)
- Examination number: 86347 ("Introduction to Online Optimization")
Evaluation of grades
The grade of the module examination is also the module grade.
Competencies / intended learning achievements
By completing the given exercises, they have developed a skilled, precise and independent handling of the terms, propositions and methods taught in the lecture. They have learnt how to apply the methods to new problems, analyze them and develop solution strategies independently or by team work.
- A. Borodin, R. El-Yaniv: Online Computation and Competitive Analysis,
- A. Fiat, G. J. Woeginger: Online Algorithms: The State of the Art,
- D. S. Hochbaum: Approximation Algorithms for NP-hard problems.
Requirements for attendance (informal)
- [MAT-10-1-M-2] Fundamentals of Mathematics (M, 28.0 LP)
- [MAT-14-13-M-3] Linear and Network Programming (M, 9.0 LP)
- [MAT-50-11-M-4] Integer Programming: Polyhedral Theory and Algorithms (M, 9.0 LP)