# 186.856 Structural Decompositions and Algorithms This course is in all assigned curricula part of the STEOP.\$(function(){PrimeFaces.cw("Tooltip","widget_j_id_21",{id:"j_id_21",showEffect:"fade",hideEffect:"fade",target:"isAllSteop"});});This course is in at least 1 assigned curriculum part of the STEOP.\$(function(){PrimeFaces.cw("Tooltip","widget_j_id_23",{id:"j_id_23",showEffect:"fade",hideEffect:"fade",target:"isAnySteop"});}); 2024S 2023S 2022S 2021S 2020S 2019S 2017S 2016S

2021S, VU, 2.0h, 3.0EC

## Properties

• Semester hours: 2.0
• Credits: 3.0
• Type: VU Lecture and Exercise
• Format: Online

## Learning outcomes

After successful completion of the course, students are able to...

- design algorithms that use the decompositions associated with parameters such as treewidth, clique-width, and hypertree-width to solve problems efficiently.

- represent problems in artificial intelligence and database theory using different graph models (primal graph, incidence graph, hypergraph).

- prove upper and lower bounds for (some) width parameters in simple cases.

## Subject of course

Many combinatorial problems that are intractable in general can be efficiently solved on tree-like structures. Width parameters measuring "tree-likeness" and their corresponding decompositions can be used to design algorithms that are fast as long as the width of the input is reasonably small.

The course will cover common graph and hypergraph width parameters and some of their applications in AI and database theory. Specifically, we will discuss the graph measure tree-width and generalizations such as clique-width and rank-width. We then proceed to discuss hypergraph width parameters like hypertree-width and fractional hypertree-width.

We will also showcase applications of width parameters to problems such as propositional model counting, conjunctive query evaluation, and inference in Bayesian Networks.

## Teaching methods

Students solve exercise sheets based on pre-recorded video lectures. Solutions are subsequently presented and discussed during live video calls.

## Mode of examination

Immanent

ECTS Breakdown:

20 h video lectures and readings
45 h solving exercise sheets
10 h presentation of exercises
-------------------------------------
75 h total

## Lecturers

• Slivovsky, Friedrich

## Course dates

DayTimeDateLocationDescription
Fri11:00 - 12:0012.03.2021 https://tuwien.zoom.us/j/92820777449 (LIVE)Preliminary Meeting

## Examination modalities

Grading is based on the number of solved exercises and their presentation.

## Course registration

Begin End Deregistration end
10.02.2021 00:00 01.05.2021 00:00 01.05.2021 00:00

## Curricula

Study CodeObligationSemesterPrecon.Info
066 645 Data Science Not specified
066 646 Computational Science and Engineering Not specified
066 926 Business Informatics Mandatory elective
066 931 Logic and Computation Mandatory elective
066 937 Software Engineering & Internet Computing Mandatory elective
066 938 Computer Engineering Mandatory elective

## Literature

- Jörg Flum, Martin Grohe: Parameterized Complexity Theory. Texts in Theoretical Computer Science. An EATCS Series, Springer 2006.

- Marek Cygan, Fedor V. Fomin, Lukasz Kowalik, Daniel Lokshtanov, Dániel Marx, Marcin Pilipczuk, Michal Pilipczuk, Saket Saurabh: Parameterized Algorithms. Springer 2015.

## Previous knowledge

This course requires familiarity with fundamental graph theoretic definitions as well as basic knowledge of algorithmics and complexity theory. Knowledge of the topics covered in the "Algorithmics" course is an advantage.

English