Research areas
Lead by Sebastian Pokutta, the research of IOL is divided into 5 distinct areas. They encompasse 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:
Continuous Optimization
conditional gradient algorithms; non-smooth optimization; block-iterative and distributed optimization algorithms; accelerated methods; online learning algorithms
Integer Optimization
mixed-integer (nonlinear) programming; nonconvex optimization; exact linear programming; cutting planes; branch-and-bound; SCIP Optimization Suite
Robust and Explainable Learning
robustness and interpretability of artificial intelligence; compression of deep neural networks; optimization methods for deep learning; learning for combinatorial optimization
Computational Mathematics
computer-assistance in proofs; flag algebras in combinatorics; algebraic methods in computation; interactive theorem provers; learning-based heuristics
Quantum Mathematics
tensor decompositions and tensor-based algorithms; operator theory (transfer operators); numerical methods PDE; data-driven and kernel-based techniques