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:
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
Continuous Optimization
conditional gradient algorithms; non-smooth optimization; block-iterative and distributed optimization algorithms; accelerated methods; online learning algorithms
9 members
5 projects
51 publications
Integer Optimization
mixed-integer (nonlinear) programming; nonconvex optimization; exact linear programming; cutting planes; branch-and-bound; SCIP Optimization Suite
15 members
6 projects
54 publications
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
9 members
4 projects
13 publications
Robust and Explainable Learning
robustness and interpretability of artificial intelligence; compression of deep neural networks; optimization methods for deep learning; learning for combinatorial optimization
13 members
6 projects
23 publications