Many real-world problems are modeled as computational problems, but unfortunately with incomplete data or knowledge. As such, they may admit a large number of solutions, and we have no way of finding the correct one. This issue is sometimes addressed by enumerating all solutions, which is infeasible for many practical problems. This project aims to construct a general methodology for finding the set of all, safe, sub-solutions that we can trust to be part of the correct solution. Our main motivation comes from Bioinformatics, from high-throughput sequencing of DNA. One main problem is to assemble this data back into the original DNA sequence. As sequencing technologies go from research labs to hospitals, we need solutions that are first of all accurate. We aim at a mathematical leap into understanding all that can be safely reported from the data. Our idea changes the perspective from which we address real-world problems and could spur a new line of research in Computer Science.

2019

2024