Algorithms, Complexity, and Optimization (ALGOPT)
Designing efficient algorithms helps to explore the limits of what computers can achieve with real-world computational power.
Inefficient algorithms quickly become an issue especially when large volumes of data need to be processed and analysed, as is often the case today. Furthermore, if we are to rely on algorithms, we should be able to guarantee that they function well.
Research focus and application
Research within the scope of ALGOPT focuses on developing and analyzing algorithms with rigorous and verifiable performance guarantees. Performance guarantees typically relate to the quality of the output (optimal solutions or solutions that are at most a certain distance from optimality) and/or to the resources used by the algorithm (time/space complexity). Although much of the work has a theoretical flavour, this theory is used to drive the development of highly efficient algorithms in practise.
Techniques and algorithms developed within the scope of ALGOPT are applied to areas such as biology and bioinformatics, logistics, networking, parallel processing and (increasingly) machine learning.
Algorithms, Complexity and Optimization in practice:
- Combinatorial/discrete optimization
- Design and analysis of exact, parameterized, approximation, online and randomized algorithms
- Mathematical programming
- Operations research
- Graph theory
- Computational complexity
- Algorithmic game theory
The ALGOPT website is under construction: ALGOPT was previously embedded in the Networks and Strategic Optimization (NSO) group.
Dr. Steven Kelk
- Area coordinator
- Associate professor
Dr. Matúš Mihalák
- Assistant professor
Dr. Georgios Stamoulis
- Assistant professor
- Kelk, S., & Stamoulis, G. (2019). Integrality gaps for colorful matchings. Discrete Optimization, 32, 73-92. https://doi.org/10.1016/j.disopt.2018.12.003
- Van Iersel, L., Kelk, S., & Scornavacca, C. (2016). Kernelizations for the hybridization number problem on multiple nonbinary trees. Journal of Computer and System Sciences, 82(6), 1075-1089. https://doi.org/10.1016/j.jcss.2016.03.006
- Chalopin, J., Das, S., Disser, Y., Mihalák, M., & Widmayer, P. (2015). Mapping Simple Polygons: The Power of Telling Convex from Reflex. ACM Transactions on Algorithms, 11(4), 33.1-33.16. https://doi.org/10.1145/2700223