  The advent of memristive devices offers a promising avenue for efficient and
scalable analog computing, particularly for linear algebra operations essential
in various scientific and engineering applications. This paper investigates the
potential of memristive crossbars in implementing matrix inversion algorithms.
We explore both static and dynamic approaches, emphasizing the advantages of
analog and in-memory computing for matrix operations beyond multiplication. Our
results demonstrate that memristive arrays can significantly reduce
computational complexity and power consumption compared to traditional digital
methods for certain matrix tasks. Furthermore, we address the challenges of
device variability, precision, and scalability, providing insights into the
practical implementation of these algorithms.
