We shall attack the challenges described, lay the foundation for future applications, and establish a solid platform for experimental algorithmics. The vehicle will be continued development of carefully chosen generic methods with special emphasis on randomization, combinatorial optimization and approximation . Discrete optimization techniques are of vital importance for information systems and production plans. Much more is known about optimally solving highly-structured integer linear programs. However, existing software for this task is difficult to use because it requires substantial algorithmic ingenuity and advanced implementation.
Approximation is the approach of choice when a problem is known to be hard.
As opposed to heuristics with no guarantees, it can lead to practical algorithms with a solution demonstrably close to optimal. We shall produce methodologies for experimental algorithmics as well as a testbed for running reproducible experiments. Also, we shall provide tools for performance prediction, that are capable of informing users about the expected behaviour of their algorithms much more accurately than traditionally rough analysis. The work on randomization will address the precise quantification of efficiency, of probabilities of failure, and other similar questions, all at a fundamental level. Also, we will produce test sets for experimental algorithmics that are based on randomization.