## Module Handbook

• Dynamischer Default-Fachbereich geändert auf MAT

# Module MAT-52-14A-M-7

## Module Identification

Module Number Module Name CP (Effort)
MAT-52-14A-M-7 Introduction to Online Optimization 4.5 CP (135 h)

## Basedata

CP, Effort 4.5 CP = 135 h 1 Sem. irreg. [7] Master (Advanced) [EN] English Krumke, Sven Oliver, Prof. Dr. (PROF | DEPT: MAT) Krumke, Sven Oliver, Prof. Dr. (PROF | DEPT: MAT) [MAT-OPT] Optimisation [MAT-88.105-SG] M.Sc. Mathematics [NORM] Active

## Courses

Type/SWS Course Number Title Choice in
Module-Part
Presence-Time /
Self-Study
SL SL is
required for exa.
PL CP Sem.
2V+1U MAT-52-14A-K-7
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")

## Contents

• competitive analysis for deterministic and randomised algorithms,
• amortised costs, potential method for costs analysis,
• competitive algorithms for paging / caching,
• online scheduling.

## Competencies / intended learning achievements

Upon successful completion of this module, the students know and understand the issues of online problems as well as the concept of competitive analysis. They have learned to analyse online problems for the existence of competitive algorithms and how to establish lower bounds for deterministic and randomised algorithms. They can derive and prove worst-case performance bounds for online algorithms. They understand the proofs presented in the lecture and are able to reproduce and explain them.

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.

## Literature

• 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.

None

## References to Module / Module Number [MAT-52-14A-M-7]

Module-Pool Name
[MAT-52-MPOOL-7] Specialisation Mathematical Optimisation (M.Sc.)
[MAT-AM-MPOOL-7] Applied Mathematics (Advanced Modules M.Sc.)