线性代数

Sage 提供了线性代数中的标准构造,例如矩阵的特征多项式、阶梯形、迹、分解等。

创建矩阵和进行矩阵乘法非常简单自然:

sage: A = Matrix([[1,2,3],[3,2,1],[1,1,1]])
sage: w = vector([1,1,-4])
sage: w*A
(0, 0, 0)
sage: A*w
(-9, 1, -2)
sage: kernel(A)
Free module of degree 3 and rank 1 over Integer Ring
Echelon basis matrix:
[ 1  1 -4]
>>> from sage.all import *
>>> A = Matrix([[Integer(1),Integer(2),Integer(3)],[Integer(3),Integer(2),Integer(1)],[Integer(1),Integer(1),Integer(1)]])
>>> w = vector([Integer(1),Integer(1),-Integer(4)])
>>> w*A
(0, 0, 0)
>>> A*w
(-9, 1, -2)
>>> kernel(A)
Free module of degree 3 and rank 1 over Integer Ring
Echelon basis matrix:
[ 1  1 -4]

请注意,在 Sage 中,矩阵 \(A\) 的核是“左核”,即满足 \(wA=0\) 的向量空间 \(w\)

求解矩阵方程非常简单,使用 solve_right 方法即可。 运行 A.solve_right(Y) 将返回一个矩阵(或向量) \(X\),使得 \(AX=Y\)

sage: Y = vector([0, -4, -1])
sage: X = A.solve_right(Y)
sage: X
(-2, 1, 0)
sage: A * X   # checking our answer...
(0, -4, -1)
>>> from sage.all import *
>>> Y = vector([Integer(0), -Integer(4), -Integer(1)])
>>> X = A.solve_right(Y)
>>> X
(-2, 1, 0)
>>> A * X   # checking our answer...
(0, -4, -1)

倘若无解,Sage 会返回错误:

sage: A.solve_right(w)
Traceback (most recent call last):
...
ValueError: matrix equation has no solutions
>>> from sage.all import *
>>> A.solve_right(w)
Traceback (most recent call last):
...
ValueError: matrix equation has no solutions

同理,可以使用 A.solve_left(Y) 来求解方程 :\(XA=Y\) 中的 \(X\)

Sage 还可以计算特征值和特征向量:

sage: A = matrix([[0, 4], [-1, 0]])
sage: A.eigenvalues ()
[-2*I, 2*I]
sage: B = matrix([[1, 3], [3, 1]])
sage: B.eigenvectors_left()
[(4, [(1, 1)], 1), (-2, [(1, -1)], 1)]
>>> from sage.all import *
>>> A = matrix([[Integer(0), Integer(4)], [-Integer(1), Integer(0)]])
>>> A.eigenvalues ()
[-2*I, 2*I]
>>> B = matrix([[Integer(1), Integer(3)], [Integer(3), Integer(1)]])
>>> B.eigenvectors_left()
[(4, [(1, 1)], 1), (-2, [(1, -1)], 1)]

eigenvectors_left 的输出格式是一个包含三元组(特征值、特征向量、重数)的列表。) 特征值和特征向量可以通过 Maxima 在有理数域 QQ 或实数域 RR 上计算(见下文的 Maxima)。

基本环 所述,矩阵定义的环会影响其某些性质。 在下面的示例中,matrix 命令的第一个参数告诉 Sage 将矩阵视为整数矩阵(ZZ)、有理数矩阵(QQ)或实数矩阵(RR

sage: AZ = matrix(ZZ, [[2,0], [0,1]])
sage: AQ = matrix(QQ, [[2,0], [0,1]])
sage: AR = matrix(RR, [[2,0], [0,1]])
sage: AZ.echelon_form()
[2 0]
[0 1]
sage: AQ.echelon_form()
[1 0]
[0 1]
sage: AR.echelon_form()
[ 1.00000000000000 0.000000000000000]
[0.000000000000000  1.00000000000000]
>>> from sage.all import *
>>> AZ = matrix(ZZ, [[Integer(2),Integer(0)], [Integer(0),Integer(1)]])
>>> AQ = matrix(QQ, [[Integer(2),Integer(0)], [Integer(0),Integer(1)]])
>>> AR = matrix(RR, [[Integer(2),Integer(0)], [Integer(0),Integer(1)]])
>>> AZ.echelon_form()
[2 0]
[0 1]
>>> AQ.echelon_form()
[1 0]
[0 1]
>>> AR.echelon_form()
[ 1.00000000000000 0.000000000000000]
[0.000000000000000  1.00000000000000]

如果要计算浮点实数或复数矩阵的特征值和特征向量,矩阵应分别定义在 RDF (实双精度域)或 CDF (复双精度域)上。 如果没有指定环并且使用浮点实数或复数,则默认情况下矩阵定义在 RRCC 域上,这些域不支持所有情况的这些计算:

sage: ARDF = matrix(RDF, [[1.2, 2], [2, 3]])
sage: ARDF.eigenvalues()  # rel tol 8e-16
[-0.09317121994613098, 4.293171219946131]
sage: ACDF = matrix(CDF, [[1.2, I], [2, 3]])
sage: ACDF.eigenvectors_right()  # rel tol 3e-15
[(0.8818456983293743 - 0.8209140653434135*I, [(0.7505608183809549, -0.616145932704589 + 0.2387941530333261*I)], 1),
(3.3181543016706256 + 0.8209140653434133*I, [(0.14559469829270957 + 0.3756690858502104*I, 0.9152458258662108)], 1)]
>>> from sage.all import *
>>> ARDF = matrix(RDF, [[RealNumber('1.2'), Integer(2)], [Integer(2), Integer(3)]])
>>> ARDF.eigenvalues()  # rel tol 8e-16
[-0.09317121994613098, 4.293171219946131]
>>> ACDF = matrix(CDF, [[RealNumber('1.2'), I], [Integer(2), Integer(3)]])
>>> ACDF.eigenvectors_right()  # rel tol 3e-15
[(0.8818456983293743 - 0.8209140653434135*I, [(0.7505608183809549, -0.616145932704589 + 0.2387941530333261*I)], 1),
(3.3181543016706256 + 0.8209140653434133*I, [(0.14559469829270957 + 0.3756690858502104*I, 0.9152458258662108)], 1)]

矩阵空间

我们创建了一个定义在有理数域 \(\QQ\) 上的 \(3 \times 3\) 矩阵空间 \(\text{Mat}_{3\times 3}(\QQ)\):

sage: M = MatrixSpace(QQ,3)
sage: M
Full MatrixSpace of 3 by 3 dense matrices over Rational Field
>>> from sage.all import *
>>> M = MatrixSpace(QQ,Integer(3))
>>> M
Full MatrixSpace of 3 by 3 dense matrices over Rational Field

(要创建一个 \(3 \times 4\) 矩阵空间,可以使用 MatrixSpace(QQ,3,4)。 如果省略列数,则默认为行数,因此 MatrixSpace(QQ,3)MatrixSpace(QQ,3,3) 意义相同。) 矩阵空间有其规范基:

sage: B = M.basis()
sage: len(B)
9
sage: B[0,1]
[0 1 0]
[0 0 0]
[0 0 0]
>>> from sage.all import *
>>> B = M.basis()
>>> len(B)
9
>>> B[Integer(0),Integer(1)]
[0 1 0]
[0 0 0]
[0 0 0]

我们创建一个矩阵作为 M 的元素。

sage: A = M(range(9)); A
[0 1 2]
[3 4 5]
[6 7 8]
>>> from sage.all import *
>>> A = M(range(Integer(9))); A
[0 1 2]
[3 4 5]
[6 7 8]

接下来我们计算其简化行阶梯形和核。

sage: A.echelon_form()
[ 1  0 -1]
[ 0  1  2]
[ 0  0  0]
sage: A.kernel()
Vector space of degree 3 and dimension 1 over Rational Field
Basis matrix:
[ 1 -2  1]
>>> from sage.all import *
>>> A.echelon_form()
[ 1  0 -1]
[ 0  1  2]
[ 0  0  0]
>>> A.kernel()
Vector space of degree 3 and dimension 1 over Rational Field
Basis matrix:
[ 1 -2  1]

接着我们来演示在有限域上定义的矩阵的计算:

sage: M = MatrixSpace(GF(2),4,8)
sage: A = M([1,1,0,0, 1,1,1,1, 0,1,0,0, 1,0,1,1,
....:        0,0,1,0, 1,1,0,1, 0,0,1,1, 1,1,1,0])
sage: A
[1 1 0 0 1 1 1 1]
[0 1 0 0 1 0 1 1]
[0 0 1 0 1 1 0 1]
[0 0 1 1 1 1 1 0]
sage: rows = A.rows()
sage: A.columns()
[(1, 0, 0, 0), (1, 1, 0, 0), (0, 0, 1, 1), (0, 0, 0, 1),
 (1, 1, 1, 1), (1, 0, 1, 1), (1, 1, 0, 1), (1, 1, 1, 0)]
sage: rows
[(1, 1, 0, 0, 1, 1, 1, 1), (0, 1, 0, 0, 1, 0, 1, 1),
 (0, 0, 1, 0, 1, 1, 0, 1), (0, 0, 1, 1, 1, 1, 1, 0)]
>>> from sage.all import *
>>> M = MatrixSpace(GF(Integer(2)),Integer(4),Integer(8))
>>> A = M([Integer(1),Integer(1),Integer(0),Integer(0), Integer(1),Integer(1),Integer(1),Integer(1), Integer(0),Integer(1),Integer(0),Integer(0), Integer(1),Integer(0),Integer(1),Integer(1),
...        Integer(0),Integer(0),Integer(1),Integer(0), Integer(1),Integer(1),Integer(0),Integer(1), Integer(0),Integer(0),Integer(1),Integer(1), Integer(1),Integer(1),Integer(1),Integer(0)])
>>> A
[1 1 0 0 1 1 1 1]
[0 1 0 0 1 0 1 1]
[0 0 1 0 1 1 0 1]
[0 0 1 1 1 1 1 0]
>>> rows = A.rows()
>>> A.columns()
[(1, 0, 0, 0), (1, 1, 0, 0), (0, 0, 1, 1), (0, 0, 0, 1),
 (1, 1, 1, 1), (1, 0, 1, 1), (1, 1, 0, 1), (1, 1, 1, 0)]
>>> rows
[(1, 1, 0, 0, 1, 1, 1, 1), (0, 1, 0, 0, 1, 0, 1, 1),
 (0, 0, 1, 0, 1, 1, 0, 1), (0, 0, 1, 1, 1, 1, 1, 0)]

我们创建一个在有限域 \(\GF{2}\) 上由上述行生成的子空间。

sage: V = VectorSpace(GF(2),8)
sage: S = V.subspace(rows)
sage: S
Vector space of degree 8 and dimension 4 over Finite Field of size 2
Basis matrix:
[1 0 0 0 0 1 0 0]
[0 1 0 0 1 0 1 1]
[0 0 1 0 1 1 0 1]
[0 0 0 1 0 0 1 1]
sage: A.echelon_form()
[1 0 0 0 0 1 0 0]
[0 1 0 0 1 0 1 1]
[0 0 1 0 1 1 0 1]
[0 0 0 1 0 0 1 1]
>>> from sage.all import *
>>> V = VectorSpace(GF(Integer(2)),Integer(8))
>>> S = V.subspace(rows)
>>> S
Vector space of degree 8 and dimension 4 over Finite Field of size 2
Basis matrix:
[1 0 0 0 0 1 0 0]
[0 1 0 0 1 0 1 1]
[0 0 1 0 1 1 0 1]
[0 0 0 1 0 0 1 1]
>>> A.echelon_form()
[1 0 0 0 0 1 0 0]
[0 1 0 0 1 0 1 1]
[0 0 1 0 1 1 0 1]
[0 0 0 1 0 0 1 1]

Sage 使用的 \(S\) 的基是通过生成矩阵的简化行阶梯形的非零行获得的。

稀疏线性代数

Sage 支持在主理想域 (PIDs) 上的稀疏线性代数。

sage: M = MatrixSpace(QQ, 100, sparse=True)
sage: A = M.random_element(density = 0.05)
sage: E = A.echelon_form()
>>> from sage.all import *
>>> M = MatrixSpace(QQ, Integer(100), sparse=True)
>>> A = M.random_element(density = RealNumber('0.05'))
>>> E = A.echelon_form()

Sage 中的多模算法适用于方阵(但不适用于非方阵):

sage: M = MatrixSpace(QQ, 50, 100, sparse=True)
sage: A = M.random_element(density = 0.05)
sage: E = A.echelon_form()
sage: M = MatrixSpace(GF(2), 20, 40, sparse=True)
sage: A = M.random_element()
sage: E = A.echelon_form()
>>> from sage.all import *
>>> M = MatrixSpace(QQ, Integer(50), Integer(100), sparse=True)
>>> A = M.random_element(density = RealNumber('0.05'))
>>> E = A.echelon_form()
>>> M = MatrixSpace(GF(Integer(2)), Integer(20), Integer(40), sparse=True)
>>> A = M.random_element()
>>> E = A.echelon_form()

请注意,Python 是区分大小写的:

sage: M = MatrixSpace(QQ, 10,10, Sparse=True)
Traceback (most recent call last):
...
TypeError: ...__init__() got an unexpected keyword argument 'Sparse'...
>>> from sage.all import *
>>> M = MatrixSpace(QQ, Integer(10),Integer(10), Sparse=True)
Traceback (most recent call last):
...
TypeError: ...__init__() got an unexpected keyword argument 'Sparse'...