Note: This page has been translated by MathWorks. Please click here

To view all translated materals including this page, select Japan from the country navigator on the bottom of this page.

To view all translated materals including this page, select Japan from the country navigator on the bottom of this page.

**MuPAD® notebooks are not recommended. Use MATLAB® live scripts instead.**

**MATLAB live scripts support most MuPAD functionality, though there are some differences. For more information, see Convert MuPAD Notebooks to MATLAB Live Scripts.**

Use only in the MuPAD Notebook Interface. **This
functionality does not run in MATLAB.**

An overview of all the available functions can be obtained by
using the MuPAD^{®} function `info`

. Here we see an extract of the functions
available in the linear algebra library (we do not state the whole
output generated by the call `info(linalg)`

, since
the library contains more than 40 different functions):

info(linalg)

Library 'linalg': the linear algebra package -- Interface: linalg::addCol, linalg::addRow, linalg::adjoint, linalg::angle, linalg::basis, linalg::charmat, linalg::charpoly, linalg::col, ...

After being exported, library functions can also be used by
their short notation. The function call `use`

`(linalg)`

exports
all functions of `linalg`

. After that one can use
the function name `gaussElim`

instead of `linalg::gaussElim`

,
for example.

Please note that user-defined procedures that use functions
of the library `linalg`

should always use the long
notation `linalg::functionname`

, in order to make
sure that the unambiguity of the function name is guaranteed.

The easiest way to define a matrix *A* is
using the command `matrix`

.
The following defines a 2×2 matrix:

A := matrix([[1, 2], [3, 2]])

Now, you can add or multiply matrices using the standard arithmetical operators of MuPAD:

A * A, 2 * A, 1/A

Further, you can use functions of the `linalg`

library:

linalg::det(A)

The domain type returned by `matrix`

is `Dom::Matrix`

`()`

:

domtype(A)

which is introduced in the following section.

The library `linalg`

is based on the domains `Dom::Matrix`

and `Dom::SquareMatrix`

. These
constructors enable the user to define matrices and they offer matrix
arithmetic and several functions for matrix manipulation.

A domain created by `Dom::Matrix`

represents
matrices of arbitrary rows and columns over a specified ring. The
domain constructor `Dom::Matrix`

expects a coefficient
ring of category `Cat::Rng`

(a
ring without unit) as argument. If no argument is given, the domain
of matrices is created that represents matrices over the field of
arithmetical expressions, i.e., the domain `Dom::ExpressionField`

`()`

.

Be careful with calculations with matrices over this coefficient
domain, because their entries usually do not have a unique representation
(e.g., there is more than one representation of zero). You can normalize
the components of such a matrix `A`

with the command ```
map(A,
normal )
```

.

The library Dom offers standard coefficient domains, such
as the field of rational numbers (`Dom::Rational`

), the ring of integers (`Dom::Integer`

), the residue
classes of integers (`Dom::IntegerMod`

`(n)`

)
for an integer `n`

, or even the rings of polynomials
(such as `Dom::DistributedPolynomial`

`(ind,R)`

or `Dom::Polynomial`

`(R)`

,
where `ind`

is the list of variables and `R`

is
the coefficient ring).

A domain created by the domain constructor `Dom::SquareMatrix`

represents
the ring of square matrices over a specified coefficient domain. `Dom::SquareMatrix`

expects
the number of rows of the square matrices and optionally a coefficient
ring of category `Cat::Rng`

.

There are several possibilities to define matrices of a domain
created by `Dom::Matrix`

or `Dom::SquareMatrix`

.
A matrix can be created by giving a two-dimensional array, a list
of the matrix components, or a function that generates the matrix
components:

delete a, b, c, d: A := matrix([[a, b], [c, d]])

The command `matrix`

actually
is an abbreviation for the domain `Dom::Matrix()`

.

To create diagonal matrices one should use the option `Diagonal`

(the
third argument of `matrix`

is either a function or
a list):

B := matrix(2, 2, [2, -2], Diagonal)

The following two examples show the meaning of the third argument:

delete x: matrix(2, 2, () -> x), matrix(2, 2, x)

The MuPAD arithmetical operators are used to perform matrix arithmetic:

A * B - 2 * B

1/A

Next we create the 2×2 generalized
Hilbert matrix (see also `linalg::hilbert`

)
as a matrix of the ring of two-dimensional square matrices:

MatQ2 := Dom::SquareMatrix(2, Dom::Rational)

H2 := MatQ2((i, j) -> 1/(i + j - 1))

A *vector* with *n* components
is a 1×*n* matrix
(a row vector) or a *n*×1 matrix
(a column vector).

The components of a matrix or a vector are accessed using the
index operator, i.e., `A[i,j]`

returns the component
of the row with index `i`

and column with index `j`

.

The input `A[i, j]:= x`

sets the (*i*, *j*)-th
component of the matrix `A`

to the value of `x`

.

The index operator can also be used to extract sub-matrices by giving ranges of integers as its arguments:

A := Dom::Matrix(Dom::Integer)( [[1, 2, 3], [4, 5, 6], [7, 8, 9]] )

A[1..3, 1..2], A[3..3, 1..3]

See also the function `linalg::submatrix`

.

The runtime of user-defined procedures that use functions of
the `linalg`

library and methods of the constructors `Dom::Matrix`

and `Dom::SquareMatrix`

can
be considerably improved in certain cases.

Some of the functions of the

`linalg`

library correspond to certain methods of the domain constructor`Dom::Matrix`

in their name and functionality. These functions are implemented by calling relevant methods of the domain to that they belong, apart from additional argument checking. These functions enable an user-friendly usage on the interactive level after exporting.However, in user-defined procedures the methods of the corresponding domain should be used directly to avoid additionally calls of procedures.

For example standard matrix manipulation functions such as deleting, extracting or swapping of rows and columns are defined as methods of the domain constructors

`Dom::Matrix`

and`Dom::SquareMatrix`

.The method

`"gaussElim"`

offers a Gaussian elimination process for each domain created by these constructors.When creating a new matrix the method

`"new"`

is called. It converts each component of the matrix explicitly into a component the component ring, which may be time consuming.However, matrices and vectors are often the results of computations, whose components already are elements of the component ring. Thus, the conversion of the entries is not necessary. To take this into account, the domain constructors

`Dom::Matrix`

and`Dom::SquareMatrix`

offer a method`"create"`

to define matrices in the usual way but without the conversion of the components.Please note that this method does not test its arguments. Thus it should be used with caution.

A further possibility of achieving better runtimes using functions of

`linalg`

or methods of the constructor`Dom::Matrix`

is to store functions and methods that are called more than once in local variables. This enables a faster access of these functions and methods.

The following example shows how a user-defined procedure using
functions of `linalg`

and methods of the domain constructor `Dom::Matrix`

may
look like. It computes the adjoint of a square matrix defined over
a commutative ring (see `Cat::CommutativeRing`

):

adjoint := proc(A) local n, R, i, j, a, Ai, Mat, // local variables to store often used methods det, delRow, delCol, Rnegate; begin if args(0) <> 1 then error("wrong number of arguments") end_if; Mat := A::dom; // the domain of A R := Mat::coeffRing; // the component ring of A n := Mat::matdim(A); // the dimension of A; faster than calling // 'linalg::matdim'! if testargs() then if Mat::hasProp(Cat::Matrix) <> TRUE then error("expecting a matrix") elif not R::hasProp( Cat::CommutativeRing ) then error("expecting matrix over a 'Cat::CommutativeRing'") elif n[1] <> n[2] then error("expecting a square matrix") end_if end_if; // store often used methods in local variables: det := linalg::det; delRow := Mat::delRow; // faster than calling 'linalg::delRow'! delCol := Mat::delCol; // faster than calling 'linalg::delCol'! Rnegate := R::_negate; // faster than using the '-' operator! n := Mat::matdim(A)[1]; // faster than calling 'linalg::nrows'! a := array(1..n, 1..n); for i from 1 to n do Ai := delCol(A, i); for j from 1 to n do a[i, j] := det(delRow(Ai, j)); if i + j mod 2 = 1 then a[i, j] := Rnegate(a[i, j]) end_if end_for end_for; // create a new matrix: use Mat::create instead of Mat::new // because the entries of the array are already elements of R return(Mat::create(a)) end_proc:

We give an example:

MatZ6 := Dom::Matrix(Dom::IntegerMod(6)): adjoint(MatZ6([[1, 5], [2, 4]]))

Was this topic helpful?