Matrices are an important tool in algebra. A matrix nicely represents a homomorphism between two vector spaces with respect to a choice of bases for the vector spaces. Also matrices represent systems of linear equations.

In **GAP** matrices are represented by list of vectors (see Vectors).
The vectors must all have the same length, and their elements must lie in
a common field. The field may be the field of rationals (see
Rationals), a cyclotomic field (see Cyclotomics), a finite field (see
Finite Fields), or a library and/or user defined field (or ring) such
as a polynomial ring (see Polynomials).

The first section in this chapter describes the operations applicable to matrices (see Operations for Matrices). The next sections describes the function that tests whether an object is a matrix (see IsMat). The next sections describe the functions that create certain matrices (see IdentityMat, NullMat, TransposedMat, and KroneckerProduct). The next sections describe functions that compute certain characteristic values of matrices (see DimensionsMat, TraceMat, DeterminantMat, RankMat, and OrderMat). The next sections describe the functions that are related to the interpretation of a matrix as a system of linear equations (see TriangulizeMat, BaseMat, NullspaceMat, and SolutionMat). The last two sections describe the functions that diagonalize an integer matrix (see DiagonalizeMat and ElementaryDivisorsMat).

Because matrices are just a special case of lists, all operations and functions for lists are applicable to matrices also (see chapter Lists). This especially includes accessing elements of a matrix (see List Elements), changing elements of a matrix (see List Assignment), and comparing matrices (see Comparisons of Lists).

- Operations for Matrices
- IsMat
- IdentityMat
- NullMat
- TransposedMat
- KroneckerProduct
- DimensionsMat
- TraceMat
- DeterminantMat
- RankMat
- OrderMat
- TriangulizeMat
- BaseMat
- NullspaceMat
- SolutionMat
- DiagonalizeMat
- ElementaryDivisorsMat
- PrintArray

`mat` + `scalar`

`scalar` + `mat`

This forms evaluates to the sum of the matrix `mat` and the scalar
`scalar`. The elements of `mat` and `scalar` must lie in a common field.
The sum is a new matrix where each entry is the sum of the corresponding
entry of `mat` and `scalar`.

`mat1` + `mat2`

This form evaluates to the sum of the two matrices `mat1` and `mat2`,
which must have the same dimensions and whose elements must lie in a
common field. The sum is a new matrix where each entry is the sum of the
corresponding entries of `mat1` and `mat2`.

`mat` - `scalar`

`scalar` - `mat`

`mat1` - `mat2`

The definition for the `-`

operator are similar to the above definitions
for the `+`

operator, except that `-`

subtracts of course.

`mat` * `scalar`

`scalar` * `mat`

This forms evaluate to the product of the matrix `mat` and the scalar
`scalar`. The elements of `mat` and `scalar` must lie in a common field.
The product is a new matrix where each entry is the product of the
corresponding entries of `mat` and `scalar`.

`vec` * `mat`

This form evaluates to the product of the vector `vec` and the matrix
`mat`. The length of `vec` and the number of rows of `mat` must be
equal. The elements of `vec` and `mat` must lie in a common field. If
`vec` is a vector of length `n` and `mat` is a matrix with `n` rows and
`m` columns, the product is a new vector of length `m`. The element at
position `i` is the sum of

with `vec`[`l`] * `mat`[`l`][`i`]`l`
running from 1 to `n`.

`mat` * `vec`

This form evaluates to the product of the matrix `mat` and the vector
`vec`. The number of columns of `mat` and the length of `vec` must be
equal. The elements of `mat` and `vec` must lie in a common field. If
`mat` is a matrix with `m` rows and `n` columns and `vec` is a vector of
length `n`, the product is a new vector of length `m`. The element at
position `i` is the sum of

with `mat`[`i`][`l`] * `vec`[`l`]`l`
running from 1 to `n`.

`mat1` * `mat2`

This form evaluates to the product of the two matrices `mat1` and `mat2`.
The number of columns of `mat1` and the number of rows of `mat2` must be
equal. The elements of `mat1` and `mat2` must lie in a common field. If
`mat1` is a matrix with `m` rows and `n` columns and `mat2` is a matrix
with `n` rows and `o` columns, the result is a new matrix with `m` rows
and `o` columns. The element in row `i` at position `k` of the product
is the sum of

with `mat1`[`i`][`l`] * `mat2`[`l`][`k`]`l` running
from 1 to `n`.

`mat1` / `mat2`

`scalar` / `mat`

`mat` / `scalar`

`vec` / `mat`

In general

is defined as `left` / `right`

.
Thus in the above forms the right operand must always be invertable.
`left` * `right`^-1

`mat` ^ `int`

This form evaluates to the `int`-th power of the matrix `mat`. `mat`
must be a square matrix, `int` must be an integer. If `int` is negative,
`mat` must be invertible. If `int` is 0, the result is the identity
matrix, even if `mat` is not invertible.

`mat1` ^ `mat2`

This form evaluates to the conjugation of the matrix `mat1` by the matrix
`mat2`, i.e., to

. `mat2`^-1 * `mat1` * `mat2``mat2` must be
invertible and `mat1` must be such that these product can be computed.

`vec` ^ `mat`

This is in every respect equivalent to

. This
operations reflects the fact that matrices operate on the vector space by
multiplication from the right.
`vec` * `mat`

`scalar` + `matlist`

`matlist` + `scalar`

`scalar` - `matlist`

`matlist` - `scalar`

`scalar` * `matlist`

`matlist` * `scalar`

`matlist` / `scalar`

A scalar `scalar` may also be added, subtracted, multiplied with, or
divide into a whole list of matrices `matlist`. The result is a new list
of matrices where each matrix is the result of performing the operation
with the corresponding matrix in `matlist`.

`mat` * `matlist`

`matlist` * `mat`

A matrix `mat` may also be multiplied with a whole list of matrices
`matlist`. The result is a new list of matrices, where each matrix is
the product of `mat` and the corresponding matrix in `matlist`.

`matlist` / `mat`

This form evaluates to

. `matlist` * `mat`^-1`mat` must of course
be invertable.

`vec` * `matlist`

This form evaluates to the product of the vector `vec` and the list of
matrices `mat`. The length `l` of `vec` and `matlist` must be equal.
All matrices in `matlist` must have the same dimensions. The elements of
`vec` and the elements of the matrices in `matlist` must lie in a common
field. The product is the sum of

with
`vec`[`i`] * `matlist`[`i`]`i` running from 1 to `l`.

`Comm( `

`mat1`, `mat2` )

`Comm`

returns the commutator of the matrices `mat1` and `mat2`, i.e.,

. `mat1`^-1 * `mat2`^-1 * `mat1` * `mat2``mat1` and `mat2`
must be invertable and such that these product can be computed.

There is one exception to the rule that the operands or their elements
must lie in common field. It is allowed that one operand is a finite
field element, a finite field vector, a finite field matrix, or a list of
finite field matrices, and the other operand is an integer, an integer
vector, an integer matrix, or a list of integer matrices. In this case
the integers are interpreted as

, where `int` * `GF`.one`GF` is the
finite field (see Operations for Finite Field Elements).

For all the above operations the result is new, i.e., not identical to any other list (see Identical Lists). This is the case even if the result is equal to one of the operands, e.g., if you add zero to a matrix.

`IsMat( `

`obj` )

`IsMat`

return `true`

if `obj`, which can be an object of arbitrary type,
is a matrix and `false`

otherwise. Will cause an error if `obj` is an
unbound variable.

gap> IsMat( [ [ 1, 0 ], [ 0, 1 ] ] ); true # a matrix is a list of vectors gap> IsMat( [ [ 1, 2, 3, 4, 5 ] ] ); true gap> IsMat( [ [ Z(2)^0, 0*Z(2) ], [ 0*Z(2), Z(2)^0 ] ] ); true gap> IsMat( [ [ Z(2)^0, 0 ], [ 0, Z(2)^0 ] ] ); false # 'Z(2)\^0' and '0' do not lie in a common field gap> IsMat( [ 1, 0 ] ); false # a vector is not a matrix gap> IsMat( 1 ); false # neither is a scalar

`IdentityMat( `

`n` )

`IdentityMat( `

`n`, `F` )

`IdentityMat`

returns the identity matrix with `n` rows and `n` columns
over the field `F`. If no field is given, `IdentityMat`

returns the
identity matrix over the field of rationals. Each call to `IdentityMat`

returns a new matrix, so it is safe to modify the result.

gap> IdentityMat( 3 ); [ [ 1, 0, 0 ], [ 0, 1, 0 ], [ 0, 0, 1 ] ] gap> PrintArray( last ); [ [ 1, 0, 0 ], [ 0, 1, 0 ], [ 0, 0, 1 ] ] gap> PrintArray( IdentityMat( 3, GF(2) ) ); [ [ Z(2)^0, 0*Z(2), 0*Z(2) ], [ 0*Z(2), Z(2)^0, 0*Z(2) ], [ 0*Z(2), 0*Z(2), Z(2)^0 ] ]

`NullMat( `

`m`, `n` )

`NullMat( `

`m`, `n`, `F` )

`NullMat`

returns the null matrix with `m` rows and `n` columns over the
field `F`. If no field is given, `NullMat`

returns the null matrix over
the field of rationals. Each call to `NullMat`

returns a new matrix, so
it is safe to modify the result.

gap> PrintArray( NullMat( 2, 3 ) ); [ [ 0, 0, 0 ], [ 0, 0, 0 ] ] gap> PrintArray( NullMat( 2, 2, GF(2) ) ); [ [ 0*Z(2), 0*Z(2) ], [ 0*Z(2), 0*Z(2) ] ]

`TransposedMat( `

`mat` )

`TransposedMat`

returns the transposed of the matrix `mat`. The
transposed matrix is a new matrix `trn`, such that

is
`trn`[`i`][`k`]

.
`mat`[`k`][`i`]

gap> TransposedMat( [ [ 1, 2 ], [ 3, 4 ] ] ); [ [ 1, 3 ], [ 2, 4 ] ] gap> TransposedMat( [ [ 1..5 ] ] ); [ [ 1 ], [ 2 ], [ 3 ], [ 4 ], [ 5 ] ]

`KroneckerProduct( `

`mat1`, `mat2` )

`KroneckerProduct`

returns the Kronecker product of the two matrices
`mat1` and `mat2`. If `mat1` is a `m` by `n` matrix and `mat2` is a `o`
by `p` matrix, the Kronecker product is a

by `m`*`o`

matrix, such that the entry in row `n`*`p``(`

at position
`i1`-1)*`o`+`i2``(`

is `k1`-1)*`p`+`k2`

.
`mat1`[`i1`][`k1`] * `mat2`[`i2`][`k2`]

gap> mat1 := [ [ 0, -1, 1 ], [ -2, 0, -2 ] ];; gap> mat2 := [ [ 1, 1 ], [ 0, 1 ] ];; gap> PrintArray( KroneckerProduct( mat1, mat2 ) ); [ [ 0, 0, -1, -1, 1, 1 ], [ 0, 0, 0, -1, 0, 1 ], [ -2, -2, 0, 0, -2, -2 ], [ 0, -2, 0, 0, 0, -2 ] ]

`DimensionsMat( `

`mat` )

`DimensionsMat`

returns the dimensions of the matrix `mat` as a list of
two integers. The first entry is the number of rows of `mat`, the second
entry is the number of columns.

gap> DimensionsMat( [ [ 1, 2, 3 ], [ 4, 5, 6 ] ] ); [ 2, 3 ] gap> DimensionsMat( [ [ 1 .. 5 ] ] ); [ 1, 5 ]

`TraceMat( `

`mat` )

`TraceMat`

returns the trace of the square matrix `mat`. The trace is
the sum of all entries on the diagonal of `mat`.

gap> TraceMat( [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 7, 8, 9 ] ] ); 15 gap> TraceMat( IdentityMat( 4, GF(2) ) ); 0*Z(2)

`DeterminantMat( `

`mat` )

`DeterminantMat`

returns the determinant of the square matrix `mat`. The
determinant is defined by

*sum_{p in Symm(n)}{sign(p)prod_{i=1}^{n}{mat[i][i^p]}}*.

gap> DeterminantMat( [ [ 1, 2 ], [ 3, 4 ] ] ); -2 gap> DeterminantMat( [ [ 0*Z(3), Z(3)^0 ], [ Z(3)^0, Z(3) ] ] ); Z(3)

Note that `DeterminantMat`

does not use the above definition to compute
the result. Instead it performs a Gaussian elimination. For large
rational matrices this may take very long, because the entries may become
very large, even if the final result is a small integer.

`RankMat( `

`mat` )

`RankMat`

returns the rank of the matrix `mat`. The rank is defined as
the dimension of the vector space spanned by the rows of `mat`. It
follows that a `n` by `n` matrix is invertible exactly if its rank is
`n`.

gap> RankMat( [ [ 4, 1, 2 ], [ 3, -1, 4 ], [ -1, -2, 2 ] ] ); 2

Note that `RankMat`

performs a Gaussian elimination. For large rational
matrices this may take very long, because the entries may become very
large.

`OrderMat( `

`mat` )

`OrderMat`

returns the order of the invertible square matrix `mat`. The
order `ord` is the smallest positive integer such that

is
the identity.
`mat`^`ord`

gap> OrderMat( [ [ 0*Z(2), 0*Z(2), Z(2)^0 ], > [ Z(2)^0, Z(2)^0, 0*Z(2) ], > [ Z(2)^0, 0*Z(2), 0*Z(2) ] ] ); 4

`OrderMat`

first computes `ord1` such that the first standard basis
vector is mapped by

onto itself. It does this by
applying `mat`^`ord1``mat` repeatedly to the first standard basis vector. Then it
computes `mat1` as

. Then it computes `mat1`^`ord1``ord2` such that
the second standard basis vector is mapped by

onto
itself. This process is repeated until all basis vectors are mapped onto
themselves. `mat1`^`ord2``OrderMat`

warns you that the order may be infinite, when it
finds that the order must be larger than 1000.

`TriangulizeMat( `

`mat` )

`TriangulizeMat`

brings the matrix `mat` into upper triangular form.
Note that `mat` is changed and that nothing is returned. A matrix is in
upper triangular form when the first nonzero entry in each row is one and
lies further to the right than the first nonzero entry in the previous
row. Furthermore, above the first nonzero entry in each row all entries
are zero. Note that the matrix will have trailing zero rows if the rank
of `mat` is not maximal. The rows of the resulting matrix span the same
vectorspace than the rows of the original matrix `mat`.

gap> m := [ [ 0, -3, -1 ], [ -3, 0, -1 ], [ 2, -2, 0 ] ];; gap> TriangulizeMat( m ); m; [ [ 1, 0, 1/3 ], [ 0, 1, 1/3 ], [ 0, 0, 0 ] ]

Note that for large rational matrices `TriangulizeMat`

may take very
long, because the entries may become very large during the Gaussian
elimination, even if the final result contains only small integers.

`BaseMat( `

`mat` )

`BaseMat`

returns a standard base for the vector space spanned by the
rows of the matrix `mat`. The standard base is in upper triangular form.
That means that the first nonzero vector in each row is one and lies
further to the right than the first nonzero entry in the previous row.
Furthermore, above the first nonzero entry in each row all entries are
zero.

gap> BaseMat( [ [ 0, -3, -1 ], [ -3, 0, -1 ], [ 2, -2, 0 ] ] ); [ [ 1, 0, 1/3 ], [ 0, 1, 1/3 ] ]

Note that for large rational matrices `BaseMat`

may take very long,
because the entries may become very large during the Gaussian
elimination, even if the final result contains only small integers.

`NullspaceMat( `

`mat` )

`NullspaceMat`

returns a base for the nullspace of the matrix `mat`. The
nullspace is the set of vectors `vec` such that

is the
zero vector. The returned base is the standard base for the nullspace
(see BaseMat).
`vec` * `mat`

gap> NullspaceMat( [ [ 2, -4, 1 ], [ 0, 0, -4 ], [ 1, -2, -1 ] ] ); [ [ 1, 3/4, -2 ] ]

Note that for large rational matrices `NullspaceMat`

may take very long,
because the entries may become very large during the Gaussian
elimination, even if the final result only contains small integers.

`SolutionMat( `

`mat`, `vec` )

`SolutionMat`

returns one solution of the equation

or `x` * `mat` =
`vec``false`

if no such solution exists.

gap> SolutionMat( [ [ 2, -4, 1 ], [ 0, 0, -4 ], [ 1, -2, -1 ] ], > [ 10, -20, -10 ] ); [ 5, 15/4, 0 ] gap> SolutionMat( [ [ 2, -4, 1 ], [ 0, 0, -4 ], [ 1, -2, -1 ] ], > [ 10, 20, -10 ] ); false

Note that for large rational matrices `SolutionMat`

may take very long,
because the entries may become very large during the Gaussian
elimination, even if the final result only contains small integers.

`DiagonalizeMat( `

`mat` )

`DiagonalizeMat`

transforms the integer matrix `mat` by multiplication
with unimodular (i.e., determinant +1 or -1) integer matrices from the
left and from the right into diagonal form (i.e., only diagonal entries
are nonzero). Note that `DiagonalizeMat`

changes `mat` and returns
nothing. If there are several diagonal matrices to which `mat` is
equivalent, it is not specified which one is computed, except that all
zero entries on the diagonal are collected at the lower right end (see
ElementaryDivisorsMat).

gap> m := [ [ 0, -1, 1 ], [ -2, 0, -2 ], [ 2, -2, 4 ] ];; gap> DiagonalizeMat( m ); m; [ [ 1, 0, 0 ], [ 0, 2, 0 ], [ 0, 0, 0 ] ]

Note that for large integer matrices `DiagonalizeMat`

may take very long,
because the entries may become very large during the computation, even if
the final result only contains small integers.

`ElementaryDivisorsMat( `

`mat` )

`ElementaryDivisors`

returns a list of the elementary divisors, i.e., the
unique `d` with

divides `d`[`i`]

and `d`[`i`+1]`mat` is equivalent
to a diagonal matrix with the elements

on the diagonal (see
DiagonalizeMat).
`d`[`i`]

gap> m := [ [ 0, -1, 1 ], [ -2, 0, -2 ], [ 2, -2, 4 ] ];; gap> ElementaryDivisorsMat( m ); [ 1, 2, 0 ]

`PrintArray( `

`mat ` )

`PrintArray`

displays the matrix `mat` in a pretty way.

gap> m := [[1,2,3,4],[5,6,7,8],[9,10,11,12]]; [ [ 1, 2, 3, 4 ], [ 5, 6, 7, 8 ], [ 9, 10, 11, 12 ] ] gap> PrintArray( m ); [ [ 1, 2, 3, 4 ], [ 5, 6, 7, 8 ], [ 9, 10, 11, 12 ] ]

GAP 3.4.4

April 1997