A matrix is a rectangular array of numbers treated as a single object. A block matrix is a matrix whose elements are themselves matrices, which are called submatrices. By allowing a matrix to be viewed at different levels of abstraction, the block matrix viewpoint enables elegant proofs of results and facilitates the development and understanding of numerical algorithms.

A block matrix is defined in terms of a partitioning, which breaks a matrix into contiguous pieces. The most common and important case is for an matrix to be partitioned as a block matrix (two block rows and two block columns). For , partitioning into blocks gives

where

and similarly for the other blocks. The diagonal blocks in a partitioning of a square matrix are usually square (but not necessarily so), and they do not have to be of the same dimensions. This same matrix could be partitioned as

where is a scalar, is a column vector, and is a row vector.

The sum of two block matrices and of the same dimension is obtained by adding blockwise as long as and have the same dimensions for all and , and the result has the same block structure: ,

The product of an matrix and an matrix can be computed as as long as the products are all defined. In this case the matrices and are said to be conformably partitioned for multiplication. Here, has as many block rows as and as many block columns as . For example,

as long as all the eight products are defined.

Block matrix notation is an essential tool in numerical linear algebra. Here are some examples of its usage.

## Matrix Factorization

For an matrix with nonzero element we can write

The first row and column of have the correct form for a unit lower triangular matrix and likewise the first row and column of have the correct form for an upper triangular matrix. If we can find an LU factorization of the Schur complement then is an LU factorization of . This construction is the basis of an inductive proof of the existence of an LU factorization (provided all the pivots are nonzero) and it also yields an algorithm for computing it.

The same type of construction applies to other factorizations, such as Cholesky factorization, QR factorization, and the Schur decomposition.

## Matrix Inverse

A useful formula for the inverse of a nonsingular block triangular matrix

is

which has the special case

If is upper triangular then so are and . By taking of dimension the nearest integer to this formula can be used to construct a divide and conquer algorithm for computing .

We note that , a fact that will be used in the next section.

## Determinantal Formulas

Block matrices provides elegant proofs of many results involving determinants. For example, consider the equations

which hold for any and such that and are defined. Taking determinants gives the formula . In particular we can take , , for -vectors and , giving .

## Constructing Matrices with Required Properties

We can sometimes build a matrix with certain desired properties by a block construction. For example, if is an involutory matrix () then

is a (block triangular) involutory matrix. And if and are any two matrices then

is involutory.

## The Anti Block Diagonal Trick

For matrices and consider the anti block diagonal matrix

Note that

Using these properties one can show a relation between the matrix sign function and the principal matrix square root:

This allows one to derive iterations for computing the matrix square root and its inverse from iterations for computing the matrix sign function.

It is easy to derive explicit formulas for all the powers of , and hence for any power series evaluated at . In particular, we have the formula

where denotes any square root of . With , this formula arises in the solution of the ordinary differential equation initial value problem , , ,

The most well known instance of the trick is when . The eigenvalues of

are plus and minus the singular values of , together with additional zeros if is with , and the eigenvectors of and the singular vectors of are also related. Consequently, by applying results or algorithms for symmetric matrices to one obtains results or algorithms for the singular value decomposition of .

## References

This is a minimal set of references, which contain further useful references within.

- Gene Golub and Charles F. Van Loan, Matrix Computations, fourth edition, Johns Hopkins University Press, Baltimore, MD, USA, 2013.
- Nicholas J. Higham, Functions of Matrices: Theory and Computation, Society for Industrial and Applied Mathematics, Philadelphia, PA, USA, 2008. (Sections 1.5 and 1.6 for the theory of matrix square roots.)
- Roger A. Horn and Charles R. Johnson, Matrix Analysis, second edition, Cambridge University Press, 2013. My review of the second edition.

## Related Blog Posts

This article is part of the “What Is” series, available from https://nhigham.com/category/what-is and in PDF form from the GitHub repository https://github.com/higham/what-is.