The aim of this project is to develop new algorithms and mathematical formulations that maintain the advantages of integer linear programming based code generation techniques while remaining computational feasible for real-world programs. This includes the application of well-known techniques from the operations research domain to decrease the required solver time such as cutting plane algorithms, column generation techniques, or Lagrangian relaxation. As some of the subproblems are known to be computationally hard for real-world instances, we want to use the developed models also to learn when and why established heuristics fail, and to develop efficient approximation algorithms and near-optimal techniques that remain computationally feasible even for large problems.
|Project LeaderResearch Group|
The project is funded by the Austrian Science Fund.