This section discusses optimization approaches for the efficient memory footprint reduction of machine learning algorithms that are written in the GNU R programming language. The presented optimization strategies target the memory management layer between the R interpreter and the operating system and reduce the memory overhead for large data structures by ensuring that memory will only be allocated for memory pages that are definitely required. The proposed approaches use additional information from the runtime environment, eg, the short-term usage pattern of a memory block, to guide optimization. The evaluation is based on statistical machine learning algorithms. When the memory consumption hits the point that the OS starts to swap out memory, optimization strategies are able to speed up computation by several orders of magnitude.