  Matrix computation is ubiquitous in modern scientific and engineering fields.
Due to the high computational complexity in conventional digital computers,
matrix computation represents a heavy workload in many data-intensive
applications, e.g., machine learning, scientific computing, and wireless
communications. For fast, efficient matrix computations, analog computing with
resistive memory arrays has been proven to be a promising solution. In this
Tutorial, we present analog matrix computing (AMC) circuits based on crosspoint
resistive memory arrays. AMC circuits are able to carry out basic matrix
computations, including matrix multiplication, matrix inversion, pseudoinverse
and eigenvector computation, all with one single operation. We describe the
main design principles of the AMC circuits, such as local/global or
negative/positive feedback configurations, with/without external inputs.
Mapping strategies for matrices containing negative values will be presented.
The underlying requirements for circuit stability will be described via the
transfer function analysis, which also defines time complexity of the circuits
towards steady-state results. Lastly, typical applications, challenges, and
future trends of AMC circuits will be discussed.
