Exploring the efficient frontier for massive genome data sets

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This project will develop optimization techniques to explore the balance between genetic improvement and genetic diversity using massive genome data sets. Efficient implementation of these techniques is largely dependent on numerical linear algebra, will have to exploit inherent properties of the data and, for the planned scale of data sets, will require the use of high-performance computing. For the very largest data sets, solution via exact methods may be intractable, in which case heuristic techniques derived from Machine Learning will be considered.