  The growth of big data in domains such as Earth Sciences, Social Networks,
Physical Sciences, etc. has lead to an immense need for efficient and scalable
linear algebra operations, e.g. Matrix inversion. Existing methods for
efficient and distributed matrix inversion using big data platforms rely on LU
decomposition based block-recursive algorithms. However, these algorithms are
complex and require a lot of side calculations, e.g. matrix multiplication, at
various levels of recursion. In this paper, we propose a different scheme based
on Strassen's matrix inversion algorithm (mentioned in Strassen's original
paper in 1969), which uses far fewer operations at each level of recursion. We
implement the proposed algorithm, and through extensive experimentation, show
that it is more efficient than the state of the art methods. Furthermore, we
provide a detailed theoretical analysis of the proposed algorithm, and derive
theoretical running times which match closely with the empirically observed
wall clock running times, thus explaining the U-shaped behaviour w.r.t.
block-sizes.
