In data science we distinguish:
- descriptive analysis: what is the current situation?
- predictive analysis: what is going to happen in the future?
- prescriptive analysis: which decision should I make?
This course and other courses in the field of algorithms focus on prescriptive analysis.
The purpose of this course is to teach topics that are:
- important for the working area of algorithms (in practice and theory)
- prerequisites for other courses in the COSC program
- not encountered by all students in the bachelor.
It therefore contains a broad range of topics.
In many real-life decision problems in e.g. (public) transportation, logistics, energy networks, healthcare, computer networks and education we want to select a very good solution from a large set of possible solutions.
In the course you learn how to model such problems and how to solve them by optimization algorithms. We focus on discrete models.
You shall learn about computational complexity and about meaning of exact optimization algorithms, heuristics and what-if analysis.
For deterministic problems, we study well-known algorithms from combinatorial optimization, including algorithms for linear programming, integer linear programming, online algorithms, shortest paths, approximation.
Course form
Lectures, self-study, exercises, assignments.
Literature
The material of the course consists of course notes and slides.
The following books are not mandatory but interesting for further reading:
- Laurence A. Wolsey, "Integer Programming", Wiley-Interscience publication, 1998, ISBN 0-471-28366-5.
- M.R. Garey and D.S. Johnson, "Computers and Intractability: A Guide to the Theory of NP-Completeness", W.H. Freeman and Company, New York, 1979, ISBN 0-7167-1044-7.
- John Kleinberg, Eva Tardos "Algorithm Design", Pearson/Addision Wesley, 2005. ISBN 0-321-29535-8.
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