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# Block matrix

In the mathematical discipline of matrix theory, a block matrix or a partitioned matrix is a partition of a matrix into rectangular smaller matrices called blocks. Looking at it another way, the matrix is written in terms of smaller matrices written side-by-side. A block matrix must conform to a consistent way of splitting up the rows, and the columns: we group the rows into some adjacent 'bunches', and the columns likewise. The partition is into the rectangles described by one bunch of adjacent rows crossing one bunch of adjacent columns. In other words, the matrix is split up by some horizontal and vertical lines that go all the way across.

## Example

The matrix

$mathbf\left\{P\right\} = begin\left\{bmatrix\right\}$
1 & 1 & 2 & 2 1 & 1 & 2 & 2 3 & 3 & 4 & 4 3 & 3 & 4 & 4end{bmatrix}

can be partitioned into 4 2×2 blocks

$mathbf\left\{P\right\}_\left\{11\right\} = begin\left\{bmatrix\right\}$
1 & 1 1 & 1 end{bmatrix}, mathbf{P}_{12} = begin{bmatrix} 2 & 2 2 & 2end{bmatrix}, mathbf{P}_{21} = begin{bmatrix} 3 & 3 3 & 3 end{bmatrix}, mathbf{P}_{22} = begin{bmatrix} 4 & 4 4 & 4end{bmatrix}.

The partitioned matrix can then be written as

$mathbf\left\{P\right\}_\left\{mathrm\left\{partitioned\right\}\right\} = begin\left\{bmatrix\right\}$
mathbf{P}_{11} & mathbf{P}_{12} mathbf{P}_{21} & mathbf{P}_{22}end{bmatrix}.

## Block Matrix Multiplication

A block partitioned matrix product can be formed involving operations only on the submatrices. Given an $\left(mtimes p\right)$ matrix $mathbf\left\{A\right\}$ with $q$ row partitions and $s$ column partitions


mathbf{A} = begin{bmatrix} mathbf{A}_{11} & mathbf{A}_{12} & cdots &mathbf{A}_{1s} mathbf{A}_{21} & mathbf{A}_{22} & cdots &mathbf{A}_{2s} vdots & vdots & ddots &vdots mathbf{A}_{q1} & mathbf{A}_{q2} & cdots &mathbf{A}_{qs}end{bmatrix}

and a $\left(ptimes n\right)$ matrix $mathbf\left\{B\right\}$ with $s$ row partitions and $r$ column partitions


mathbf{B} = begin{bmatrix} mathbf{B}_{11} & mathbf{B}_{12} & cdots &mathbf{B}_{1r} mathbf{B}_{21} & mathbf{B}_{22} & cdots &mathbf{B}_{2r} vdots & vdots & ddots &vdots mathbf{B}_{s1} & mathbf{B}_{s2} & cdots &mathbf{B}_{sr}end{bmatrix}, the matrix product


mathbf{C}=mathbf{A}mathbf{B}

can be formed blockwise, yielding $mathbf\left\{C\right\}$ as an $\left(mtimes n\right)$ matrix with $q$ row partitions and $r$ column partitions. The submatrix blocks of $mathbf\left\{C\right\}$ are calculated as


mathbf{C}_{alpha beta} = sum^s_{gamma=1}mathbf{A}_{alpha gamma}mathbf{B}_{gamma beta}.

## Block diagonal matrices

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


mathbf{A} = begin{bmatrix} mathbf{A}_{1} & 0 & cdots & 0 0 & mathbf{A}_{2} & cdots & 0 vdots & vdots & ddots & vdots 0 & 0 & cdots & mathbf{A}_{n} end{bmatrix}

where Ak is a square matrix; in other words, it is the direct sum of A1, …, An. It can also be indicated as A1 $oplus$ A2 $oplus,ldots,oplus$ An  or  diag(A1, A2,$ldots$, An)  (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.

For the determinant and trace, the following properties hold

$operatorname\left\{det\right\} mathbf\left\{A\right\} = operatorname\left\{det\right\} mathbf\left\{A\right\}_1 cdots operatorname\left\{det\right\} mathbf\left\{A\right\}_n$,
$operatorname\left\{trace\right\} mathbf\left\{A\right\} = operatorname\left\{trace\right\} mathbf\left\{A\right\}_1 +cdots +operatorname\left\{trace\right\} mathbf\left\{A\right\}_n$.

## Block tridiagonal matrices

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


mathbf{A} = begin{bmatrix} mathbf{B}_{1} & mathbf{C}_{1} & & & cdots & & 0 mathbf{A}_{2} & mathbf{B}_{2} & mathbf{C}_{2} & & & &
& ddots & ddots  & ddots  &         &         & vdots
& & mathbf{A}_{k} & mathbf{B}_{k} & mathbf{C}_{k} & & vdots & & & ddots & ddots & ddots & & & & & mathbf{A}_{n-1} & mathbf{B}_{n-1} & mathbf{C}_{n-1} 0 & & cdots & & & mathbf{A}_{n} & mathbf{B}_{n} end{bmatrix}

where Ak, Bk and Ck 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).

## Direct sum

For any arbitrary matrices A (of size m × n) and B (of size p × q), we have the direct sum of A and B, denoted by A $oplus$ B and defined as

mathbf{A} oplus mathbf{B} = begin{bmatrix} a_{11} & cdots & a_{1n} & 0 & cdots & 0
vdots & cdots & vdots & vdots & cdots & vdots
a_{m 1} & cdots & a_{mn} & 0 & cdots & 0 0 & cdots & 0 & b_{11} & cdots & b_{1q}
vdots & cdots & vdots & vdots & cdots & vdots
0 & cdots & 0 & b_{p1} & cdots & b_{pq} end{bmatrix}.

For instance,


begin{bmatrix}
1 & 3 & 2
2 & 3 & 1
end{bmatrix} oplus begin{bmatrix}
1 & 6
0 & 1
end{bmatrix} = begin{bmatrix}
1 & 3 & 2 & 0 & 0
2 & 3 & 1 & 0 & 0
0 & 0 & 0 & 1 & 6
0 & 0 & 0 & 0 & 1
end{bmatrix}.

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.

## Application

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.

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