Parameterized Complexity is a research field that, by its own self-description, strives to provide practical, yet theoretically well-founded ways to deal with computationally hard problems (e.g., so-called NP-hard problems). However, the vast majority of the work in the field is purely theoretical — there is a great toolbox of interesting and powerful algorithmic methods, which have been proven to have … full description “Entry into the PACE Parameterized Algorithms and Computational Experiments Challenge (completed)”
Category: Algorithms
Machine Learning for Crystal Structure Prediction (completed)
Crystal Structure Prediction (CSP) is one of the major problems in computational chemistry with numerous applications in real life. This is essentially the (global) minimisation of a continuous, high-dimensional, complicated function. Many heuristic methods have been proposed for CSP and recently new methods based on Machine Learning were introduced. The goal of this project is … full description “Machine Learning for Crystal Structure Prediction (completed)”
Minimal Ontological Model Discovery (available)
Why is quantum theory more efficient in describing the small-scale world? The answer may be hidden in the structure of the large data acquired in quantum experiments. All we obtain in experimenting on a quantum system, e.g. quantum computers, is a large dataset of probabilities (called a MOP), p(k|M,P), showing the probability of outcome k … full description “Minimal Ontological Model Discovery (available)”
Mixed Nash Equilibria in Net Coordination Games (completed)
Net Coordination Games form a special class of many-player games with several applications in Theoretical Computer Sciene, Multi Agent Systems, and Semi Supervised Learning. Nash equilibria correspond to the stable outcomes and they are the prominent solution concept in games. It is known that Net Coordination Games possess a pure Nash equilibrium, but unfortunately, it … full description “Mixed Nash Equilibria in Net Coordination Games (completed)”