Linear Algebra¶

This chapter describes the commands for doing linear algebra. They can be used to manipulate vectors, represented as lists, and matrices, represented as lists of lists.

Dot(t1, t2)
t1 . t2

dot product of tensors

Param t1,t2

tensors (currently only vectors and matrices are supported)

Dot() returns the dot product (aka inner product) of two tensors t1 and t2. The last index of t1 and the first index of t2 are contracted. Currently Dot() works only for vectors and matrices. Inner product of two vectors, a matrix with a vector (and vice versa) or two matrices yields respectively a scalar, a vector or a matrix.

Example

In> Dot({1,2},{3,4})
Out> 11;
In> Dot({{1,2},{3,4}},{5,6})
Out> {17,39};
In> Dot({5,6},{{1,2},{3,4}})
Out> {23,34};
In> Dot({{1,2},{3,4}},{{5,6},{7,8}})
Out> {{19,22},{43,50}};


Or, using the . operator:

In> {1,2} . {3,4}
Out> 11;
In> {{1,2},{3,4}} . {5,6}
Out> {17,39};
In> {5,6} . {{1,2},{3,4}}
Out> {23,34};
In> {{1,2},{3,4}} . {{5,6},{7,8}}
Out> {{19,22},{43,50}};

CrossProduct(u, v)
u X v

cross outer product of vectors

Param u, v

three-dimensional vectors

The cross product of the vectors u and v is returned. Both u and v have to be three-dimensional.

Example

In> {a,b,c} X {d,e,f};
Out> {b*f-c*e,c*d-a*f,a*e-b*d};

Outer(t1, t2)
t1 o t2

outer tensor product

Param t1,t2

tensors (currently only vectors are supported)

Outer() returns the outer product of two tensors t1 and t2. Currently Outer() work works only for vectors, i.e. tensors of rank 1. The outer product of two vectors yields a matrix.

Example

In> Outer({1,2},{3,4,5})
Out> {{3,4,5},{6,8,10}};
In> Outer({a,b},{c,d})
Out> {{a*c,a*d},{b*c,b*d}};


Or, using the o operator:

In> {1,2} o {3,4,5}
Out> {{3,4,5},{6,8,10}};
In> {a,b} o {c,d}
Out> {{a*c,a*d},{b*c,b*d}};


Dot(), Cross()

ZeroVector(n)

create a vector with all zeroes

Param n

length of the vector to return

This command returns a vector of length n, filled with zeroes.

Example

In> ZeroVector(4)
Out> {0,0,0,0};

BaseVector(k, n)

base vector

Param k

index of the base vector to construct

Param n

dimension of the vector

This command returns the “k”-th base vector of dimension “n”. This is a vector of length “n” with all zeroes except for the “k”-th entry, which contains a 1.

Example

In> BaseVector(2,4)
Out> {0,1,0,0};

Identity(n)

make identity matrix

Param n

size of the matrix

This commands returns the identity matrix of size “n” by “n”. This matrix has ones on the diagonal while the other entries are zero.

Example

In> Identity(3)
Out> {{1,0,0},{0,1,0},{0,0,1}};

ZeroMatrix(n)

make a zero matrix

Param n

number of rows

Param m

number of columns

This command returns a matrix with n rows and m columns, completely filled with zeroes. If only given one parameter, it returns the square n by n zero matrix.

Example

In> ZeroMatrix(3,4)
Out> {{0,0,0,0},{0,0,0,0},{0,0,0,0}};
In> ZeroMatrix(3)
Out> {{0,0,0},{0,0,0},{0,0,0}};

Diagonal(A)

extract the diagonal from a matrix

Param A

matrix

This command returns a vector of the diagonal components of the matrix {A}.

Example

In> Diagonal(5*Identity(4))
Out> {5,5,5,5};
In> Diagonal(HilbertMatrix(3))
Out> {1,1/3,1/5};

DiagonalMatrix(d)

construct a diagonal matrix

Param d

list of values to put on the diagonal

This command constructs a diagonal matrix, that is a square matrix whose off-diagonal entries are all zero. The elements of the vector “d” are put on the diagonal.

Example

In> DiagonalMatrix(1 .. 4)
Out> {{1,0,0,0},{0,2,0,0},{0,0,3,0},{0,0,0,4}};

OrthogonalBasis(W)

create an orthogonal basis

Param W

A linearly independent set of row vectors (aka a matrix)

Given a linearly independent set {W} (constructed of rows vectors), this command returns an orthogonal basis {V} for {W}, which means that span(V) = span(W) and {InProduct(V[i],V[j]) = 0} when {i != j}. This function uses the Gram-Schmidt orthogonalization process.

Example

In> OrthogonalBasis({{1,1,0},{2,0,1},{2,2,1}})
Out> {{1,1,0},{1,-1,1},{-1/3,1/3,2/3}};


OrthonormalBasis(), InProduct()

OrthonormalBasis(W)

create an orthonormal basis

Param W

A linearly independent set of row vectors (aka a matrix)

Given a linearly independent set {W} (constructed of rows vectors), this command returns an orthonormal basis {V} for {W}. This is done by first using {OrthogonalBasis(W)}, then dividing each vector by its magnitude, so as the give them unit length.

Example

In> OrthonormalBasis({{1,1,0},{2,0,1},{2,2,1}})
Out> {{Sqrt(1/2),Sqrt(1/2),0},{Sqrt(1/3),-Sqrt(1/3),Sqrt(1/3)},
{-Sqrt(1/6),Sqrt(1/6),Sqrt(2/3)}};


OrthogonalBasis(), InProduct(), Normalize()

Normalize(v)

normalize a vector

Param v

a vector

Return the normalized (unit) vector parallel to {v}: a vector having the same direction but with length 1.

Example

In> v:=Normalize({3,4})
Out> {3/5,4/5};
In> v . v
Out> 1;


InProduct(), CrossProduct()

Transpose(M)

get transpose of a matrix

Param M

a matrix

{Transpose} returns the transpose of a matrix $$M$$. Because matrices are just lists of lists, this is a useful operation too for lists.

Example

In> Transpose({{a,b}})
Out> {{a},{b}};

Determinant(M)

determinant of a matrix

Param M

a matrix

Returns the determinant of a matrix M.

Example

In> A:=DiagonalMatrix(1 .. 4)
Out> {{1,0,0,0},{0,2,0,0},{0,0,3,0},{0,0,0,4}};
In> Determinant(A)
Out> 24;

Trace(M)

trace of a matrix

Param M

a matrix

{Trace} returns the trace of a matrix $$M$$ (defined as the sum of the elements on the diagonal of the matrix).

Example

In> A:=DiagonalMatrix(1 .. 4)
Out> {{1,0,0,0},{0,2,0,0},{0,0,3,0},{0,0,0,4}};
In> Trace(A)
Out> 10;

Inverse(M)

get inverse of a matrix

Param M

a matrix

Inverse returns the inverse of matrix $$M$$. The determinant of $$M$$ should be non-zero. Because this function uses {Determinant} for calculating the inverse of a matrix, you can supply matrices with non-numeric (symbolic) matrix elements.

Example

In> A:=DiagonalMatrix({a,b,c})
Out> {{a,0,0},{0,b,0},{0,0,c}};
In> B:=Inverse(A)
Out> {{(b*c)/(a*b*c),0,0},{0,(a*c)/(a*b*c),0},
{0,0,(a*b)/(a*b*c)}};
In> Simplify(B)
Out> {{1/a,0,0},{0,1/b,0},{0,0,1/c}};

Minor(M, i, j)

get principal minor of a matrix

Param M

a matrix

Param i}, {j

positive integers

Minor returns the minor of a matrix around the element $$i, j$$. The minor is the determinant of the matrix obtained from $$M$$ by deleting the $$i$$-th row and the $$j$$-th column.

Example

In> A := {{1,2,3}, {4,5,6}, {7,8,9}};
Out> {{1,2,3},{4,5,6},{7,8,9}};
In> PrettyForm(A);
/                    \
| ( 1 ) ( 2 ) ( 3 )  |
|                    |
| ( 4 ) ( 5 ) ( 6 )  |
|                    |
| ( 7 ) ( 8 ) ( 9 )  |
\                    /
Out> True;
In> Minor(A,1,2);
Out> -6;
In> Determinant({{2,3}, {8,9}});
Out> -6;

CoFactor(M, i, j)

cofactor of a matrix

Param M

a matrix

Param i}, {j

positive integers

{CoFactor} returns the cofactor of a matrix around the element $$i,j$$. The cofactor is the minor times $$(-1)^(i+j)$$.

Example

In> A := {{1,2,3}, {4,5,6}, {7,8,9}};
Out> {{1,2,3},{4,5,6},{7,8,9}};
In> PrettyForm(A);
/                    \
| ( 1 ) ( 2 ) ( 3 )  |
|                    |
| ( 4 ) ( 5 ) ( 6 )  |
|                    |
| ( 7 ) ( 8 ) ( 9 )  |
\                    /
Out> True;
In> CoFactor(A,1,2);
Out> 6;
In> Minor(A,1,2);
Out> -6;
In> Minor(A,1,2) * (-1)^(1+2);
Out> 6;

MatrixPower(mat, n)

get nth power of a square matrix

Param mat

a square matrix

Param n

an integer

{MatrixPower(mat,n)} returns the {n}th power of a square matrix {mat}. For positive {n} it evaluates dot products of {mat} with itself. For negative {n} the nth power of the inverse of {mat} is returned. For {n}=0 the identity matrix is returned.

SolveMatrix(M, v)

solve a linear system

Param M

a matrix

Param v

a vector

{SolveMatrix} returns the vector $$x$$ that satisfies the equation $$M*x = v$$. The determinant of $$M$$ should be non-zero.

Example

In> A := {{1,2}, {3,4}};
Out> {{1,2},{3,4}};
In> v := {5,6};
Out> {5,6};
In> x := SolveMatrix(A, v);
Out> {-4,9/2};
In> A * x;
Out> {5,6};

Sparsity(matrix)

get the sparsity of a matrix

Param matrix

a matrix

The function {Sparsity} returns a number between {0} and {1} which represents the percentage of zero entries in the matrix. Although there is no definite critical value, a sparsity of {0.75} or more is almost universally considered a “sparse” matrix. These type of matrices can be handled in a different manner than “full” matrices which speedup many calculations by orders of magnitude.

Example

In> Sparsity(Identity(2))
Out> 0.5;
In> Sparsity(Identity(10))
Out> 0.9;
In> Sparsity(HankelMatrix(10))
Out> 0.45;
In> Sparsity(HankelMatrix(100))
Out> 0.495;
In> Sparsity(HilbertMatrix(10))
Out> 0;
In> Sparsity(ZeroMatrix(10,10))
Out> 1;


Predicates¶

IsScalar(expr)

test for a scalar

Param expr

a mathematical object

{IsScalar} returns True if {expr} is a scalar, False otherwise. Something is considered to be a scalar if it’s not a list.

Example

In> IsScalar(7)
Out> True;
In> IsScalar(Sin(x)+x)
Out> True;
In> IsScalar({x,y})
Out> False;

IsVector([pred, ]expr)

test for a vector

Param expr

expression to test

Param pred

predicate test (e.g. IsNumber, IsInteger, …)

{IsVector(expr)} returns True if {expr} is a vector, False otherwise. Something is considered to be a vector if it’s a list of scalars. {IsVector(pred,expr)} returns True if {expr} is a vector and if the predicate test {pred} returns True when applied to every element of the vector {expr}, False otherwise.

Example

In> IsVector({a,b,c})
Out> True;
In> IsVector({a,{b},c})
Out> False;
In> IsVector(IsInteger,{1,2,3})
Out> True;
In> IsVector(IsInteger,{1,2.5,3})
Out> False;

IsMatrix([pred, ]expr)

test for a matrix

Param expr

expression to test

Param pred

predicate test (e.g. IsNumber, IsInteger, …)

{IsMatrix(expr)} returns True if {expr} is a matrix, False otherwise. Something is considered to be a matrix if it’s a list of vectors of equal length. {IsMatrix(pred,expr)} returns True if {expr} is a matrix and if the predicate test {pred} returns True when applied to every element of the matrix {expr}, False otherwise.

Example

In> IsMatrix(1)
Out> False;
In> IsMatrix({1,2})
Out> False;
In> IsMatrix({{1,2},{3,4}})
Out> True;
In> IsMatrix(IsRational,{{1,2},{3,4}})
Out> False;
In> IsMatrix(IsRational,{{1/2,2/3},{3/4,4/5}})
Out> True;


IsSquareMatrix([pred, ]expr)

test for a square matrix

Param expr

expression to test

Param pred

predicate test (e.g. IsNumber, IsInteger, …)

{IsSquareMatrix(expr)} returns True if {expr} is a square matrix, False otherwise. Something is considered to be a square matrix if it’s a matrix having the same number of rows and columns. {IsMatrix(pred,expr)} returns True if {expr} is a square matrix and if the predicate test {pred} returns True when applied to every element of the matrix {expr}, False otherwise.

Example

In> IsSquareMatrix({{1,2},{3,4}});
Out> True;
In> IsSquareMatrix({{1,2,3},{4,5,6}});
Out> False;
In> IsSquareMatrix(IsBoolean,{{1,2},{3,4}});
Out> False;
In> IsSquareMatrix(IsBoolean,{{True,False},{False,True}});
Out> True;

IsHermitian(A)

test for a Hermitian matrix

Param A

a square matrix

IsHermitian(A) returns True if {A} is Hermitian and False otherwise. $$A$$ is a Hermitian matrix iff Conjugate( Transpose $$A$$ )=:math:A. If $$A$$ is a real matrix, it must be symmetric to be Hermitian.

Example

In> IsHermitian({{0,I},{-I,0}})
Out> True;
In> IsHermitian({{0,I},{2,0}})
Out> False;

IsOrthogonal(A)

test for an orthogonal matrix

Param A

square matrix

{IsOrthogonal(A)} returns True if {A} is orthogonal and False otherwise. $$A$$ is orthogonal iff $$A$$) = Identity, or equivalently Inverse($$A$$) = Transpose($$A$$).

Example

In> A := {{1,2,2},{2,1,-2},{-2,2,-1}};
Out> {{1,2,2},{2,1,-2},{-2,2,-1}};
In> PrettyForm(A/3)
/                      \
| / 1 \  / 2 \ / 2 \   |
| | - |  | - | | - |   |
| \ 3 /  \ 3 / \ 3 /   |
|                      |
| / 2 \  / 1 \ / -2 \  |
| | - |  | - | | -- |  |
| \ 3 /  \ 3 / \ 3  /  |
|                      |
| / -2 \ / 2 \ / -1 \  |
| | -- | | - | | -- |  |
| \ 3  / \ 3 / \ 3  /  |
\                      /
Out> True;
In> IsOrthogonal(A/3)
Out> True;

IsDiagonal(A)

test for a diagonal matrix

Param A

a matrix

{IsDiagonal(A)} returns True if {A} is a diagonal square matrix and False otherwise.

Example

In> IsDiagonal(Identity(5))
Out> True;
In> IsDiagonal(HilbertMatrix(5))
Out> False;

IsLowerTriangular(A)

test for a lower triangular matrix

Param A

a matrix

A lower/upper triangular matrix is a square matrix which has all zero entries above/below the diagonal. {IsLowerTriangular(A)} returns True if {A} is a lower triangular matrix and False otherwise. {IsUpperTriangular(A)} returns True if {A} is an upper triangular matrix and False otherwise.

Example

In> IsUpperTriangular(Identity(5))
Out> True;
In> IsLowerTriangular(Identity(5))
Out> True;
In> IsLowerTriangular({{1,2},{0,1}})
Out> False;
In> IsUpperTriangular({{1,2},{0,1}})
Out> True;
A non-square matrix cannot be triangular:
In> IsUpperTriangular({{1,2,3},{0,1,2}})
Out> False;

IsSymmetric(A)

test for a symmetric matrix

Param A

a matrix

{IsSymmetric(A)} returns True if {A} is symmetric and False otherwise. $$A$$ is symmetric iff Transpose ($$A$$) =:math:A.

Example

In> A := {{1,0,0,0,1},{0,2,0,0,0},{0,0,3,0,0},
{0,0,0,4,0},{1,0,0,0,5}};
In> PrettyForm(A)
/                                \
| ( 1 ) ( 0 ) ( 0 ) ( 0 ) ( 1 )  |
|                                |
| ( 0 ) ( 2 ) ( 0 ) ( 0 ) ( 0 )  |
|                                |
| ( 0 ) ( 0 ) ( 3 ) ( 0 ) ( 0 )  |
|                                |
| ( 0 ) ( 0 ) ( 0 ) ( 4 ) ( 0 )  |
|                                |
| ( 1 ) ( 0 ) ( 0 ) ( 0 ) ( 5 )  |
\                                /
Out> True;
In> IsSymmetric(A)
Out> True;

IsSkewSymmetric(A)

test for a skew-symmetric matrix

Param A

a square matrix

{IsSkewSymmetric(A)} returns True if {A} is skew symmetric and False otherwise. $$A$$ is skew symmetric iff $$Transpose(A)$$ =:math:-A.

Example

In> A := {{0,-1},{1,0}}
Out> {{0,-1},{1,0}};
In> PrettyForm(%)
/               \
| ( 0 ) ( -1 )  |
|               |
| ( 1 ) ( 0 )   |
\               /
Out> True;
In> IsSkewSymmetric(A);
Out> True;

IsUnitary(A)

test for a unitary matrix

Param A

a square matrix

This function tries to find out if A is unitary. A matrix $$A$$ is orthogonal iff $$A^(-1)$$ = Transpose( Conjugate($$A$$) ). This is equivalent to the fact that the columns of $$A$$ build an orthonormal system (with respect to the scalar product defined by {InProduct}).

Example

In> IsUnitary({{0,I},{-I,0}})
Out> True;
In> IsUnitary({{0,I},{2,0}})
Out> False;

IsIdempotent(A)

test for an idempotent matrix

Param A

a square matrix

{IsIdempotent(A)} returns True if {A} is idempotent and False otherwise. $$A$$ is idempotent iff $$A^2=A$$. Note that this also implies that $$A$$ raised to any power is also equal to $$A$$.

Example

In> IsIdempotent(ZeroMatrix(10,10));
Out> True;
In> IsIdempotent(Identity(20))
Out> True;
Special matrices


Eigenproblem¶

CharacteristicEquation(matrix, var)

get characteristic polynomial of a matrix

Param matrix

a matrix

Param var

a free variable

CharacteristicEquation returns the characteristic equation of “matrix”, using “var”. The zeros of this equation are the eigenvalues of the matrix, Det(matrix-I*var);

Example

In> A:=DiagonalMatrix({a,b,c})
Out> {{a,0,0},{0,b,0},{0,0,c}};
In> B:=CharacteristicEquation(A,x)
Out> (a-x)*(b-x)*(c-x);
In> Expand(B,x)
Out> (b+a+c)*x^2-x^3-((b+a)*c+a*b)*x+a*b*c;

EigenValues(matrix)

get eigenvalues of a matrix

Param matrix

a square matrix

EigenValues returns the eigenvalues of a matrix. The eigenvalues x of a matrix M are the numbers such that $$M*v=x*v$$ for some vector. It first determines the characteristic equation, and then factorizes this equation, returning the roots of the characteristic equation Det(matrix-x*identity).

Example

In> M:={{1,2},{2,1}}
Out> {{1,2},{2,1}};
In> EigenValues(M)
Out> {3,-1};

EigenVectors(A, eigenvalues)

get eigenvectors of a matrix

Param matrix

a square matrix

Param eigenvalues

list of eigenvalues as returned by {EigenValues}

{EigenVectors} returns a list of the eigenvectors of a matrix. It uses the eigenvalues and the matrix to set up n equations with n unknowns for each eigenvalue, and then calls {Solve} to determine the values of each vector.

Example

In> M:={{1,2},{2,1}}
Out> {{1,2},{2,1}};
In> e:=EigenValues(M)
Out> {3,-1};
In> EigenVectors(M,e)
Out> {{-ki2/ -1,ki2},{-ki2,ki2}};


Matrix decompositions¶

Cholesky(A)

find the Cholesky decomposition

Param A

a square positive definite matrix

{Cholesky} returns a upper triangular matrix {R} such that {Transpose(R)*R = A}. The matrix {A} must be positive definite, {Cholesky} will notify the user if the matrix is not. Some families of positive definite matrices are all symmetric matrices, diagonal matrices with positive elements and Hilbert matrices.

Example

In> A:={{4,-2,4,2},{-2,10,-2,-7},{4,-2,8,4},{2,-7,4,7}}
Out> {{4,-2,4,2},{-2,10,-2,-7},{4,-2,8,4},{2,-7,4,7}};
In> R:=Cholesky(A);
Out> {{2,-1,2,1},{0,3,0,-2},{0,0,2,1},{0,0,0,1}};
In> Transpose(R)*R = A
Out> True;
In> Cholesky(4*Identity(5))
Out> {{2,0,0,0,0},{0,2,0,0,0},{0,0,2,0,0},{0,0,0,2,0},{0,0,0,0,2}};
In> Cholesky(HilbertMatrix(3))
Out> {{1,1/2,1/3},{0,Sqrt(1/12),Sqrt(1/12)},{0,0,Sqrt(1/180)}};
In> Cholesky(ToeplitzMatrix({1,2,3}))
In function "Check" :
CommandLine(1) : "Cholesky: Matrix is not positive definite"

LU(A)

find the LU decomposition

Param A

square matrix

LU() performs LU decomposition of a matrix.

Example

In> A := {{1,2}, {3,4}}
Out> {{1,2},{3,4}}
In> {l,u} := LU(A)
Out> {{{1,0},{3,1}},{{1,2},{0,-2}}}
In> IsLowerTriangular(l)
Out> True
In> IsUpperTriangular(u)
Out> True
In> l * u
Out> {{1,2},{3,4}}


LDU(), IsLowerTriangular(), IsUpperTriangular()

LDU(A)

find the LDU decomposition

Param A

square matrix

LDU() performs LDU decomposition of a matrix.

Example

In> A := {{1,2}, {3,4}}
Out> {{1,2},{3,4}}
In> {l,d,u} := LDU(A)
Out> {{{1,0},{3,1}},{{1,0},{0,-2}},{{1,2},{0,1}}}
In> IsLowerTriangular(l)
Out> True
In> IsDiagonal(d)
Out> True
In> IsUpperTriangular(u)
Out> True
In> l * d * u
Out> {{1,2},{3,4}}


Special matrices¶

VandermondeMatrix(vector)

create the Vandermonde matrix

Param vector

an $$n$$-dimensional vector

The function {VandermondeMatrix} calculates the Vandermonde matrix of a vector. The $$(i,j)$$-th element of the Vandermonde matrix is defined as $$i^(j-1)$$.

Example

In> VandermondeMatrix({1,2,3,4})
Out> {{1,1,1,1},{1,2,3,4},{1,4,9,16},{1,8,27,64}};
In>PrettyForm(%)
/                            \
| ( 1 ) ( 1 ) ( 1 )  ( 1 )   |
|                            |
| ( 1 ) ( 2 ) ( 3 )  ( 4 )   |
|                            |
| ( 1 ) ( 4 ) ( 9 )  ( 16 )  |
|                            |
| ( 1 ) ( 8 ) ( 27 ) ( 64 )  |
\                            /

HilbertMatrix(n)

create a Hilbert matrix

Param n,m

positive integers

The function {HilbertMatrix} returns the {n} by {m} Hilbert matrix if given two arguments, and the square {n} by {n} Hilbert matrix if given only one. The Hilbert matrix is defined as {A(i,j) = 1/(i+j-1)}. The Hilbert matrix is extremely sensitive to manipulate and invert numerically.

Example

In> PrettyForm(HilbertMatrix(4))
/                          \
| ( 1 ) / 1 \ / 1 \ / 1 \  |
|       | - | | - | | - |  |
|       \ 2 / \ 3 / \ 4 /  |
|                          |
| / 1 \ / 1 \ / 1 \ / 1 \  |
| | - | | - | | - | | - |  |
| \ 2 / \ 3 / \ 4 / \ 5 /  |
|                          |
| / 1 \ / 1 \ / 1 \ / 1 \  |
| | - | | - | | - | | - |  |
| \ 3 / \ 4 / \ 5 / \ 6 /  |
|                          |
| / 1 \ / 1 \ / 1 \ / 1 \  |
| | - | | - | | - | | - |  |
| \ 4 / \ 5 / \ 6 / \ 7 /  |
\                          /

HilbertInverseMatrix(n)

create a Hilbert inverse matrix

Param n

positive integer

The function {HilbertInverseMatrix} returns the {n} by {n} inverse of the corresponding Hilbert matrix. All Hilbert inverse matrices have integer entries that grow in magnitude rapidly.

Example

In> PrettyForm(HilbertInverseMatrix(4))
/                                         \
| ( 16 )   ( -120 )  ( 240 )   ( -140 )   |
|                                         |
| ( -120 ) ( 1200 )  ( -2700 ) ( 1680 )   |
|                                         |
| ( 240 )  ( -2700 ) ( 6480 )  ( -4200 )  |
|                                         |
| ( -140 ) ( 1680 )  ( -4200 ) ( 2800 )   |
\                                         /

ToeplitzMatrix(N)

create a Toeplitz matrix

Param N

an $$n$$-dimensional row vector

The function {ToeplitzMatrix} calculates the Toeplitz matrix given an $$n$$-dimensional row vector. This matrix has the same entries in all diagonal columns, from upper left to lower right.

Example

In> PrettyForm(ToeplitzMatrix({1,2,3,4,5}))
/                                \
| ( 1 ) ( 2 ) ( 3 ) ( 4 ) ( 5 )  |
|                                |
| ( 2 ) ( 1 ) ( 2 ) ( 3 ) ( 4 )  |
|                                |
| ( 3 ) ( 2 ) ( 1 ) ( 2 ) ( 3 )  |
|                                |
| ( 4 ) ( 3 ) ( 2 ) ( 1 ) ( 2 )  |
|                                |
| ( 5 ) ( 4 ) ( 3 ) ( 2 ) ( 1 )  |
\                                /

SylvesterMatrix(poly1, poly2, variable)

calculate the Sylvester matrix of two polynomials

Param poly1

polynomial

Param poly2

polynomial

Param variable

variable to express the matrix for

The function {SylvesterMatrix} calculates the Sylvester matrix for a pair of polynomials. The Sylvester matrix is closely related to the resultant, which is defined as the determinant of the Sylvester matrix. Two polynomials share common roots only if the resultant is zero.

Example

In> ex1:= x^2+2*x-a
Out> x^2+2*x-a;
In> ex2:= x^2+a*x-4
Out> x^2+a*x-4;
In> A:=SylvesterMatrix(ex1,ex2,x)
Out> {{1,2,-a,0},{0,1,2,-a},
{1,a,-4,0},{0,1,a,-4}};
In> B:=Determinant(A)
Out> 16-a^2*a- -8*a-4*a+a^2- -2*a^2-16-4*a;
In> Simplify(B)
Out> 3*a^2-a^3;
The above example shows that the two polynomials have common
zeros if :math: a = 3 :math:.