Python (NumPy, SciPy),找到矩阵的零空间 [英] Python (NumPy, SciPy), finding the null space of a matrix
问题描述
我试图找到给定矩阵的零空间(Ax=0 的解空间).我找到了两个例子,但我似乎无法让任何一个工作.此外,我无法理解他们正在做什么才能到达那里,所以我无法调试.我希望有人能引导我完成这个.
I'm trying to find the null space (solution space of Ax=0) of a given matrix. I've found two examples, but I can't seem to get either to work. Moreover, I can't understand what they're doing to get there, so I can't debug. I'm hoping someone might be able to walk me through this.
文档页面(numpy.linalg.svd
和 numpy.compress
) 对我来说是不透明的.我通过创建矩阵 C = [A|0]
、找到减少的行梯形形式并逐行求解变量来学习做到这一点.在这些示例中,我似乎无法理解它是如何完成的.
The documentation pages (numpy.linalg.svd
, and numpy.compress
) are opaque to me. I learned to do this by creating the matrix C = [A|0]
, finding the reduced row echelon form and solving for variables by row. I can't seem to follow how it's being done in these examples.
感谢所有帮助!
这是我的示例矩阵,与维基百科示例相同:
Here is my sample matrix, which is the same as the wikipedia example:
A = matrix([
[2,3,5],
[-4,2,3]
])
方法(在此处找到和
import scipy
from scipy import linalg, matrix
def null(A, eps=1e-15):
u, s, vh = scipy.linalg.svd(A)
null_mask = (s <= eps)
null_space = scipy.compress(null_mask, vh, axis=0)
return scipy.transpose(null_space)
当我尝试时,我得到一个空矩阵:
When I try it, I get back an empty matrix:
Python 2.6.6 (r266:84292, Sep 15 2010, 16:22:56)
[GCC 4.4.5] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import scipy
>>> from scipy import linalg, matrix
>>> def null(A, eps=1e-15):
... u, s, vh = scipy.linalg.svd(A)
... null_mask = (s <= eps)
... null_space = scipy.compress(null_mask, vh, axis=0)
... return scipy.transpose(null_space)
...
>>> A = matrix([
... [2,3,5],
... [-4,2,3]
... ])
>>>
>>> null(A)
array([], shape=(3, 0), dtype=float64)
>>>
推荐答案
它似乎对我来说工作正常:
It appears to be working okay for me:
A = matrix([[2,3,5],[-4,2,3],[0,0,0]])
A * null(A)
>>> [[ 4.02455846e-16]
>>> [ 1.94289029e-16]
>>> [ 0.00000000e+00]]
这篇关于Python (NumPy, SciPy),找到矩阵的零空间的文章就介绍到这了,希望我们推荐的答案对大家有所帮助,也希望大家多多支持IT屋!