A problem of dimension in Vector Space and It‘s nullspace
A easy Problem
This problem from a lemma that is Dimension Counting Theorem
\[dim(V) + dim(W) = dim(V\cap W) + dim(V+W)
\]
This lemma is easily to be proof by the concept of set of vectors, we cloud regard \(V\) as the set of vectors.
And the question is:
Now, Let‘s proof this problem:
- To proof \(v\) and \(w\) and \(u\) are independent, we could consider \(x_1v+x_2u+x_3w = 0\) if only \(x_1=x_2=x_3 = 0\)
- we cloud assume that part \(x\) from \(v\)‘s and \(u\)‘s is in \(V\), so the part from \(w\) is \(-x\).
- But the problem is, \(x\) is from \(V\), so, \(-x\) must from \(V\), so, \(-x\) must comes from \(u\)
- Now the combination of give 0 in \(x_1v+x_2u+x_3w = 0\) comes from only \(u\) and \(v\) , because \(x\) and \(-x\) both from \(V\), so coefficients in \(x_1\) and \(x_2\) must all 0, and \(x_3\) also is 0 because of the it from \(w\), and \(w\) is independent.
A Special Condition In Vector Space
This question has a special form in Vector Space and in Matrix,
We could write as \(Ax=0\) and define the Four Fundamental Subspaces of \(A\), we have a hypothesis that \(A\) is \(m \times n\) matrix, and define \(R\) = \(rref(A)\) which comes from Gaussian elimination.
- The row space is \(C (A^T )\), a subspace of \(R^n\)
- The column space is \(C(A)\), a subspace of \(R^m\)
- The nullspace is \(N (A)\), a subspace of \(R^n\)
- The left nullspace is \(N(A^T)\), a subspace of \(R^m\). This is the nullspace of \(A^T\) .
The most important things we consider is the relationship between rank of \(r\) , \(m\), \(n\) and the dimensions of this four subspace.
We cloud get below conclusions easily:
- The column space of \(A\) has dimension \(r\). The column rank equals the row rank.
- But we should pay attention the column space and the row space, the row space of \(A\) and \(R\) are the same, but the column didn‘t.
- \(A\) has the same nullspace as \(R\) Same dimension \(n - r\) and same basis.
- The left nullspace of \(A\) (the nullspace of \(A^T\) ) has dimension \(m - r\).
The question we want to interpret is the dimension of nullspace, for \(A\) and \(R\), why it is \(n-r\), we could use the formula above that:
\[dim(V) + dim(W) = dim(V\cap W) + dim(V+W)
\]
in this formula, \(V\) = row space, \(W\) = nullspace and there are all in \(R^n\). So all we want to proof is \(dim(V \cap W)\) is \(0\), how to proof this?
Assume that a vector \(x_i\) belongs to \(V\) and \(W\) at the same time, then, consider \(Ax=0\), then \(Ax_i = 0\) and
\[\left[ \begin{array}{c}
x_1\ \vdots\ x_i\ \vdots\ x_m\\end{array} \right] \ x_{i\ =\ 0}
\]
So, \(x_i = 0\) , \(dim(V \cap W) = 0\) ,
A problem of dimension in Vector Space and It's nullspace
原文:https://www.cnblogs.com/wevolf/p/12990685.html