Research areas
Lead by Sebastian Pokutta, the research of IOL is divided into 5 distinct areas. They encompass optimization theory, algorithm design, machine learning, operations research, combinatorics, quantum computing, and practical applications in energy, transportation, and healthcare and are led by experienced researchers. Select one of them below to learn more:
![thrust logo](/images/logopic/thrusts/comp_small.png)
Computational Mathematics
computer-assistance in proofs; flag algebras in combinatorics; algebraic methods in computation; interactive theorem provers; learning-based heuristics
8 members
3 projects
26 publications
![thrust logo](/images/logopic/thrusts/opt_small.png)
Continuous Optimization
conditional gradient algorithms; non-smooth optimization; block-iterative and distributed optimization algorithms; accelerated methods; online learning algorithms
13 members
5 projects
50 publications
![thrust logo](/images/logopic/thrusts/int_small.png)
Integer Optimization
mixed-integer (nonlinear) programming; nonconvex optimization; exact linear programming; cutting planes; branch-and-bound; SCIP Optimization Suite
17 members
6 projects
53 publications
![thrust logo](/images/logopic/thrusts/quant_small.png)
Quantum Mathematics
tensor decompositions and tensor-based algorithms; operator theory (transfer operators); numerical methods PDE; data-driven and kernel-based techniques; Bell nonlocality; entanglement quantification
11 members
4 projects
12 publications
![thrust logo](/images/logopic/thrusts/learn_small.png)
Robust and Explainable Learning
robustness and interpretability of artificial intelligence; compression of deep neural networks; optimization methods for deep learning; learning for combinatorial optimization
17 members
6 projects
22 publications