Bachelor Thesis

Bachelor Theses in Discrete Optimization

In general, a bachelor thesis is based on a mathematical article that is reviewed and expressed with own words. Frequently, this comes along with small implementation tasks or computational experiments.

The time horizon of a bachelor thesis is three months. Before starting, be sure to read the Handout on bachelor theses in discrete optimization. The work is usually registered when the student became acquainted with the topic. For the preparation of the thesis, a TeX template is available in Downloads. Alternatively, you an also use the template of the“Arbeitstechniken” lecture from WS15/16.

Requirements

The successful pass of the course “Introduction to Optimization” is required; in addition, the active participation in an optimization seminar is strongly recommended.

Contact

Prof. Pfetsch, Prof. Disser and staff members of the research group

Completed Theses

2019

  • Genetic Algorithms for the Dial-a-Ride Problem
    (Prof. Disser)
  • Solving the rectilinear distance problem
    (Prof. Pfetsch)
  • Scheduling for a processor sharing system with linear slowdown
    (Prof. Pfetsch)
  • Minimum equivalent precedence relation systems
    (Prof. Pfetsch)
  • Optimal ordering of statistically dependent tests
    (Prof. Pfetsch)
  • Hardness of the burning number of a graph
    (Prof. Pfetsch)

2018

  • Containment of Virus Expansion in Graphs
    (Prof. Disser)
  • State of the Art for the List Update Problem
    (Prof. Disser)
  • Shortest Distances on Undirected Graphs
    (Prof. Pfetsch)
  • Blocking Unions of arborescences
    (Prof. Pfetsch)
  • Efficient recovery of block-sparse signals
    (Prof. Pfetsch)
  • Matching Interdiction
    (Prof. Pfetsch)
  • Efficient solutions for weight-balanced partitioning problems
    (Prof. Pfetsch)
  • Approximation algorithms for maximum K-vertex cover
    (Prof. Pfetsch)
  • Machine Learning for Fraud Detection in E-Commerce
    (Prof. Pfetsch)
  • Shortest Paths for Planar Graphs
    (Prof. Pfetsch)
  • Covering problems in edge- and knot-weighted graphs
    (Prof. Pfetsch)
  • On the robust shortest path problems
    (Prof. Pfetsch)
  • An algorithm for solving parametric flow maximization problems
    (Prof. Pfetsch)
  • The K-Server Problem
    (Prof. Disser)
  • Single machine scheduling with supporting tasks
    (Prof. Pfetsch)
  • A testbed for online Dial-a-Ride on the line
    (Prof. Disser)
  • Anchored rectangle and square packings
    (Prof. Pfetsch)

2017

  • Improving Bounds for Incremental Maximization
    (Prof. Disser)
  • Max Flows in O(nm) Time, or Better
    (Prof. Pfetsch)
  • Routing in Netzwerken mit Kapazitäten
    (Prof. Pfetsch)
  • Belegungsplanung mit ressourcenabhängigen Bearbeitungszeiten
    (Prof. Pfetsch)
  • Polynomielle Approximationsschemata für das budgetierte Matching-Problem und das budgetierte Matroid-Intersektions-Problem
    (Prof. Pfetsch)
  • Polynomieller Netzwerksimplexalgorithmus für Kosten-minimale Flüsse
    (Prof. Pfetsch)
  • Scheduling Unrelated Parallel Machines and Graph Balancing
    (Prof. Pfetsch)
  • Berechnung kürzester Wege auf Flächen
    (Prof. Pfetsch)

2016

  • Optimale Approximation mit stückweise affinen Modellen
    (Prof. Pfetsch)
  • Gewichts-beschränkte kürzeste Wege Probleme
    (Prof. Pfetsch)
  • Der Seitenflächen-Algorithmus für lineare Optimierungsprobleme
    (Prof. Pfetsch)
  • Compact Flows
    (Prof. Pfetsch)
  • Solving Combinatorial Optimization Problems via Inclusion-Exclusion
    (Prof. Pfetsch)
  • The Stoer-Wagner algorithm for minimum cuts in undirected graphs
    (Prof. Pfetsch)
  • A recognition algorithm for unit interval graphs
    (Prof. Pfetsch)
  • Approximationsalgorithmen für das Scheduling auf parallelen Maschinen
    (Prof. Pfetsch)
  • Differences between maximum degrees and clique numbers in graphs
    (Prof. Pfetsch)
  • Image Segmentation via Minimum Cuts in Planar Graphs
    (Prof. Pfetsch)

2015

2014

2013

2012