Are the columns space and row space same for idempotent matrix?

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Discussion Overview

The discussion revolves around the properties of idempotent matrices, specifically whether the column space and row space are the same. Participants explore the implications of eigenvalues and eigenspaces in relation to the column and row spaces of idempotent matrices, addressing potential contradictions and misunderstandings in the reasoning presented.

Discussion Character

  • Debate/contested
  • Technical explanation

Main Points Raised

  • One participant asserts that for an idempotent matrix, the column space corresponds to the eigenspace of eigenvalue 1, while the null space corresponds to eigenvalue 0, leading to the conclusion that the row space and column space are the same.
  • Another participant challenges this conclusion by pointing out that the presence of an eigenvalue 0 implies a non-zero vector exists in the null space, contradicting the claim that the eigenspaces for distinct eigenvalues only intersect at the zero vector.
  • A later reply clarifies that the misunderstanding arose from a translation issue regarding the terms "kernel" and "null space," and questions the example of a specific matrix to illustrate the relationship between column and row spaces.
  • Further discussion highlights that subspaces should not be treated as sets, emphasizing that being complementary does not imply equality of subspaces, and provides an example to illustrate this point.

Areas of Agreement / Disagreement

Participants express disagreement regarding the conclusion that the column space and row space are the same for idempotent matrices. There is no consensus on the correctness of the initial argument, and multiple perspectives on the nature of subspaces are presented.

Contextual Notes

Participants note the importance of definitions and the conditions under which subspaces can be considered complementary or equal. The discussion reveals a need for clarity in the terminology used and the implications of eigenvalues on the properties of the matrix.

arpon
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Suppose, ##A## is an idempotent matrix, i.e, ##A^2=A##.
For idempotent matrix, the eigenvalues are ##1## and ##0##.
Here, the eigenspace corresponding to eigenvalue ##1## is the column space, and the eigenspace corresponding to eigenvalue ##0## is the null space.
But eigenspaces for distinct eigenvalues of a matrix have intersection ##\{0\}##.
So, null space and column space are complementary for idempotent matrix. That means the row space and column space are the same for idempotent matrix.
Is this argument correct?
 
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There's a contradiction in your conclusion. If you have an eigenvalue ##0##, then there is a vector ##v \neq 0## with ##A.v=0##, i.e. the eigenspace of eigenvalue ##0## cannot be ##\{0\}##.
 
fresh_42 said:
There's a contradiction in your conclusion. If you have an eigenvalue ##0##, then there is a vector ##v \neq 0## with ##A.v=0##, i.e. the eigenspace of eigenvalue ##0## cannot be ##\{0\}##.
I have not said that the eigenspace of eigenvalue ##0## is ##\{0\}##.
 
Yep, I translated it wrongly. I'm used to the term kernel for what you call nullspace. Sorry.

Before I also misinterpret column or row spaces I simply ask: What about ##\begin{bmatrix}1 & 0 \\ 1 & 0 \end{bmatrix}##?
 
Last edited:
fresh_42 said:
Yep, I translated it wrongly. I'm used to the term kernel for what you call nullspace. Sorry.

Before I also misinterpret column or row spaces I simply ask: What about ##\begin{bmatrix}1 & 0 \\ 1 & 0 \end{bmatrix}##?
The column space and row space are not the same in the example. But I cannot figure out what was wrong in my argument.
The row space of a matrix is the vector space spanned by the row vectors of the matrix and the column space of a matrix is the vector space spanned by the column vectors of the matrix
 
You must not handle subspaces like sets. The error is the usage of "complementary". As in the example you can have ##V=V_1 \oplus V_2## and ##V=V_1 \oplus V_3## without ##V_2## and ##V_3## even being contained in one another. Not to speak of being equal. Think of two lines through the origin. Together with, say the ##x-##axis each of them span the plane, although they only have ##0## in common.

You may conclude that ##V/V_1 \cong V_2 \cong V_3## are isomorphic, but not equal. To conclude equality, one always has to show a real inclusion ##V_2 \subseteq V_3##, too, when dealing with vector spaces.
 
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you seem to have argued that because two subspaces are complementary to the same subspace that they are equal. but "complementary" (in the algebraic sense) simply means as you say, their intersection is zero (and their dimensions are complementary). Can you give an example of two different subspaces of the x,y plane that both intersect the x-axis at only the origin? (it would be different if both subspaces were orthogonal complements of the same subspace.)
 
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