  Matrix data sets are common nowadays like in biomedical imaging where the
Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) modality produces data
sets of
  3D symmetric positive definite matrices anchored at voxel positions capturing
the anisotropic diffusion properties of water molecules in biological tissues.
The space of symmetric matrices can be partially ordered using the L\"owner
ordering, and computing extremal matrices dominating a given set of matrices is
a basic primitive used in matrix-valued signal processing. In this letter, we
design a fast and easy-to-implement iterative algorithm to approximate
arbitrarily finely these extremal matrices. Finally, we discuss on extensions
to matrix clustering.
