Sparse grid approximations and fitting using generalised combination techniques

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Sparse grid techniques provide an effective tool to deal with the computational curse of dimensionality which is a constant challenge in modelling complex data. The proposed research is aimed at the development and analysis of algorithms for data fitting with sparse grids using variants of the combination technique. The outcome of the research is a theory which will provide insights in the applicability, limitations and the convergence properties of the proposed algorithms. The outcomes will be widely applicable in modelling of large scale and complex data as is encountered in areas of bioinformatics, physics and experimental studies of complex systems.