Linear Algebra

SAGE has extensive linear algebra capabilities.

Vector Spaces

The VectorSpace command creates a vector space class, from which one can create a subspace. Note the basis computed by Sage is row reduced.

sage: V = VectorSpace(GF(2),8)
sage: S = V.subspace([V([1,1,0,0,0,0,0,0]),V([1,0,0,0,0,1,1,0])])
sage: S.basis()
  [
   (1, 0, 0, 0, 0, 1, 1, 0),
   (0, 1, 0, 0, 0, 1, 1, 0)
  ]
sage: S.dimension()
  2

Matrix arithmetic

Computing matrix powers in Sage is illustrated by the example below.
sage: R = IntegerModRing(51) sage: M = MatrixSpace(R,3,3) sage: A = M([1,2,3, 4,5,6, 7,8,9]) sage: A^1000*A^1007 [ 3 3 3] [18 0 33] [33 48 12] sage: A^2007 [ 3 3 3] [18 0 33] [33 48 12]
Matrix addition and multiplication:
sage: R = IntegerModRing(51) sage: M = MatrixSpace(R,3,3) sage: A = M([1,2,3, 4,5,6, 7,8,9]) sage: B = M([1,0,1, 0,1,0, 1,1,1]) sage: A+B [ 2 2 4] [ 4 6 6] [ 8 9 10] sage: A*B [ 4 5 4] [10 11 10] [16 17 16] sage: -7*B [44 0 44] [ 0 44 0] [44 44 44]

Kernels

The kernel is computed by applying the kernel method to the matrix object. The following examples illustrate the syntax.
sage: M = MatrixSpace(IntegerRing(),4,2)(range(8)) sage: M.kernel() Free module of degree 4 and rank 2 over Integer Ring Echelon basis matrix: [ 1 0 -3 2] [ 0 1 -2 1]
A kernel of dimension one over \mathbb{Q} :
sage: A = MatrixSpace(RationalField(),3)(range(9)) sage: A.kernel() Vector space of degree 3 and dimension 1 over Rational Field Basis matrix: [ 1 -2 1]
A trivial kernel:
sage: A = MatrixSpace(RationalField(),2)([1,2,3,4]) sage: A.kernel() Vector space of degree 2 and dimension 0 over Rational Field Basis matrix: [] sage: M = MatrixSpace(RationalField(),0,2)(0) sage: M [] sage: M.kernel() Vector space of degree 0 and dimension 0 over Rational Field Basis matrix: [] sage: M = MatrixSpace(RationalField(),2,0)(0) sage: M.kernel() Vector space of dimension 2 over Rational Field
Kernel of a zero matrix:
sage: A = MatrixSpace(RationalField(),2)(0) sage: A.kernel() Vector space of degree 2 and dimension 2 over Rational Field Basis matrix: [1 0] [0 1]
Kernel of a non-square matrix:
sage: A = MatrixSpace(RationalField(),3,2)(range(6)) sage: A.kernel() Vector space of degree 3 and dimension 1 over Rational Field Basis matrix: [ 1 -2 1]
The 2-dimensional kernel of a matrix over a cyclotomic field:
sage: K = CyclotomicField(12); a = K.gen() sage: M = MatrixSpace(K,4,2)([1,-1, 0,-2, 0,-a^2-1, 0,a^2-1]) sage: M [ 1 -1] [ 0 -2] [ 0 -zeta12^2 - 1] [ 0 zeta12^2 - 1] sage: M.kernel() Vector space of degree 4 and dimension 2 over Cyclotomic Field of order 12 and degree 4 Basis matrix: [ 0 1 0 -2*zeta12^2] [ 0 0 1 -2*zeta12^2 + 1]
A nontrivial kernel over a complicated base field.
sage: K = FractionField(MPolynomialRing(RationalField(),2,'x')) sage: M = MatrixSpace(K, 2)([[K.gen(1),K.gen(0)], [K.gen(1), K.gen(0)]]) sage: M [x1 x0] [x1 x0] sage: M.kernel() Vector space of degree 2 and dimension 1 over Fraction Field of Multivariate Polynomial Ring in x0, x1 over Rational Field Basis matrix: [ 1 -1]
Other methods for integer matrices include: elementary_divisors, smith_form (for the Smith normal form), echelon (a method for integer matrices) for the Hermite normal form, frobenius for the Frobenius normal form (rational canonical form).
There a many methods for matrices over a field such as \mathbb{Q} or a finite field: row_span, nullity, transpose, swap_rows, matrix_from_columns, matrix_from_rows, among many others.

Eigenvectors and eigenvalues

How do you compute eigenvalues and eigenvectors using Sage?
Sage included both in the eigenspaces command, the output of which has several components, corresponding to the different eigenvalues.
sage: MS = MatrixSpace(QQ, 3, 3) sage: A = MS([[1,1,0],[0,2,0],[0,0, 3]]) sage: A [1 1 0] [0 2 0] [0 0 3] sage: A.eigenspaces() [ (3, [ (0, 0, 1) ]), (2, [ (0, 1, 0) ]), (1, [ (1, -1, 0) ]) ] sage: v1*A == r1*v1 True sage: v2*A == r2*v2 True sage: v3*A == r3*v3 True
Note that these are left eigenspaces. To get the right eigenspaces, first take the transpose:
sage: At = A.transpose() sage: v1 = At.eigenspaces()[0][1][0] sage: v2 = At.eigenspaces()[1][1][0] sage: v3 = At.eigenspaces()[2][1][0] sage: A*v1 == r1*v1 True sage: A*v2 == r2*v2 True sage: A*v3 == r3*v3 True

Row reduction

The row reduced echelon form of a matrix is computed as in the following example.
sage: M = MatrixSpace(RationalField(),2,3) sage: A = M([1,2,3, 4,5,6]) sage: A [1 2 3] [4 5 6] sage: A.parent() Full MatrixSpace of 2 by 3 dense matrices over Rational Field sage: A[0,2] = 389 sage: A [ 1 2 389] [ 4 5 6] sage: A.echelon_form() [ 1 0 -1933/3] [ 0 1 1550/3]

Characteristic polynomial

The characteristic polynomial is a Sage method for square matrices.
First a matrix over \mathbb{Z} :
sage: A = MatrixSpace(IntegerRing(),2)( [[1,2], [3,4]] ) sage: f = A.charpoly() sage: f x^2 - 5*x - 2 sage: f.parent() Univariate Polynomial Ring in x over Integer Ring
We compute the characteristic polynomial of a matrix over the polynomial ring \mathbb{Z}[a] :
sage: R = PolynomialRing(IntegerRing(),'a'); a = R.gen() sage: M = MatrixSpace(R,2)([[a,1], [a,a+1]]) sage: M [ a 1] [ a a + 1] sage: f = M.charpoly() sage: f x^2 + (-2*a - 1)*x + a^2 sage: f.parent() Univariate Polynomial Ring in x over Univariate Polynomial Ring in a over Integer Ring sage: M.trace() 2*a + 1 sage: M.determinant() a^2
We compute the characteristic polynomial of a matrix over the multi-variate polynomial ring \mathbb{Z}[u,v] :
sage: R.<u,v> = PolynomialRing(ZZ,2) sage: A = MatrixSpace(R,2)([u,v,u^2,v^2]) sage: f = A.charpoly(); f x^2 + (-1*v^2 - u)*x - u^2*v + u*v^2
It's a little difficult to distinguish the variables. To fix this, we might want to rename the indeterminate "Z". However, as of Sage 1.5, we can no longer reassign the name of a determinate in an instantiated polynomial ring. However, we can locally change the name in a with block:
sage: with localvars(f.parent(), 'Z'): ....: print(f) Z^2 + (-1*v^2 - u)*Z - u^2*v + u*v^2