RALIB provides a library of general-purpose numerical algorithms for analyzing large, high dimensional, multimodal datasets. It currently includes a) the Randomized Singular Value Decomposition, b) the Randomized Approximate Nearest Neighbors, c) the Multiscale Singular Value Decomposition, d) the Heat Kernel Coordinates, and e) the Heat Kernel Function Estimation algorithms.
The algorithms are implemented as Fortran95 modules with OpenMP to utilize multiple cores/CPUs. It requires the BLAS and LAPACK libraries (preferrably multi-threaded and optimized implementations), and the gfortran compiler to build the software.
Estimated code upload date: Spring 2014
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