# Block matrix

In mathematics, a **block matrix** or a **partitioned matrix** is a matrix that is *interpreted* as having been broken into sections called **blocks** or **submatrices**.^{[1]} Intuitively, a matrix interpreted as a block matrix can be visualized as the original matrix with a collection of horizontal and vertical lines, which break it up, or partition it, into a collection of smaller matrices.^{[2]} Any matrix may be interpreted as a block matrix in one or more ways, with each interpretation defined by how its rows and columns are partitioned.

Block matrix algebra arises in general from biproducts in categories of matrices.^{[3]}

Or, using the Einstein notation that implicitly sums over repeated indices:

If a matrix is partitioned into four blocks, it can be inverted blockwise as follows:

where **A** and **D** are square of arbitrary size, and **B** and **C** are conformable for partitioning. Furthermore, **A** and the Schur complement of **A** in **P**: **P**/**A** = **D** − **CA**^{−1}**B** must be invertible.^{[6]}

Here, **D** and the Schur complement of **D** in **P**: **P**/**D** = **A** − **BD**^{−1}**C** must be invertible.

By the Weinstein–Aronszajn identity, one of the two matrices in the block-diagonal matrix is invertible exactly when the other is.

A **block diagonal matrix** is a block matrix that is a square matrix such that the main-diagonal blocks are square matrices and all off-diagonal blocks are zero matrices. That is, a block diagonal matrix **A** has the form

where **A**_{k} is a square matrix for all *k* = 1, ..., *n*. In other words, matrix **A** is the direct sum of **A**_{1}, ..., **A**_{n}. It can also be indicated as **A**_{1} ⊕ **A**_{2} ⊕ ... ⊕ **A**_{n} or diag(**A**_{1}, **A**_{2}, ..., **A**_{n}) (the latter being the same formalism used for a diagonal matrix). Any square matrix can trivially be considered a block diagonal matrix with only one block.

A block diagonal matrix is invertible if and only if each of its main-diagonal blocks are invertible, and in this case its inverse is another block diagonal matrix given by

A **block tridiagonal matrix** is another special block matrix, which is just like the block diagonal matrix a square matrix, having square matrices (blocks) in the lower diagonal, main diagonal and upper diagonal, with all other blocks being zero matrices. It is essentially a tridiagonal matrix but has submatrices in places of scalars. A block tridiagonal matrix **A** has the form

where **A**_{k}, **B**_{k} and **C**_{k} are square sub-matrices of the lower, main and upper diagonal respectively.

Block tridiagonal matrices are often encountered in numerical solutions of engineering problems (e.g., computational fluid dynamics). Optimized numerical methods for LU factorization are available and hence efficient solution algorithms for equation systems with a block tridiagonal matrix as coefficient matrix. The Thomas algorithm, used for efficient solution of equation systems involving a tridiagonal matrix can also be applied using matrix operations to block tridiagonal matrices (see also Block LU decomposition).

A **block Toeplitz matrix** is another special block matrix, which contains blocks that are repeated down the diagonals of the matrix, as a Toeplitz matrix has elements repeated down the diagonal.

This operation generalizes naturally to arbitrary dimensioned arrays (provided that **A** and **B** have the same number of dimensions).

Note that any element in the direct sum of two vector spaces of matrices could be represented as a direct sum of two matrices.

In linear algebra terms, the use of a block matrix corresponds to having a linear mapping thought of in terms of corresponding 'bunches' of basis vectors. That again matches the idea of having distinguished direct sum decompositions of the domain and range. It is always particularly significant if a block is the zero matrix; that carries the information that a summand maps into a sub-sum.

Given the interpretation *via* linear mappings and direct sums, there is a special type of block matrix that occurs for square matrices (the case *m* = *n*). For those we can assume an interpretation as an endomorphism of an *n*-dimensional space *V*; the block structure in which the bunching of rows and columns is the same is of importance because it corresponds to having a single direct sum decomposition on *V* (rather than two). In that case, for example, the diagonal blocks in the obvious sense are all square. This type of structure is required to describe the Jordan normal form.

This technique is used to cut down calculations of matrices, column-row expansions, and many computer science applications, including VLSI chip design. An example is the Strassen algorithm for fast matrix multiplication, as well as the Hamming(7,4) encoding for error detection and recovery in data transmissions.