Proof of Column Extraction Theorem for Finding a Basis for Col(A)

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Discussion Overview

The discussion revolves around the Column Extraction Theorem, specifically focusing on how the columns of a matrix that correspond to leading ones in its reduced row echelon form (RREF) form a basis for the column space of the matrix, Col(A). The conversation includes theoretical aspects of linear maps, the implications of RREF, and examples illustrating these concepts.

Discussion Character

  • Technical explanation, Conceptual clarification, Debate/contested

Main Points Raised

  • Some participants state that the columns of A corresponding to leading ones in RREF form a basis for Col(A) and that the dimension of Col(A) equals the rank of A.
  • One participant explains that if a linear map is applied to a basis, the image spans the mapped space, using this to support the theorem.
  • Another participant notes that the ith column of a matrix represents the image of the ith standard basis vector.
  • It is proposed that columns without leading ones are linear combinations of those with leading ones, but clarification is sought on why this follows from the definition of RREF.
  • A later reply provides an example of an RREF matrix, illustrating which columns are basis columns and how non-basis columns can be expressed as linear combinations of basis columns.

Areas of Agreement / Disagreement

Participants generally agree on the theorem's statement and implications, but there is some uncertainty regarding the justification for why non-basis columns can be expressed as linear combinations of basis columns, leading to further questions and clarifications.

Contextual Notes

The discussion includes assumptions about the properties of linear maps and the structure of RREF, which may not be explicitly stated. The example provided illustrates the concepts but does not resolve the underlying questions about the justification for certain claims.

mattTch
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TL;DR
Let A be an m×n matrix. I am not sure why it's immediately obvious that the set B containing all and only the column vectors of R = RREF(A) which have leading ones, forms a basis for R. In particular, why is it the case that Span(B) = Col(R)? FYI: The linear independence of B is obvious to me.
Theorem: The columns of A which correspond to leading ones in the reduced row echelon form of A form a basis for Col(A). Moreover, dimCol(A)=rank(A).
 
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Consider:

1. If \alpha : V \to W is a linear map and B = \{b_i\} is a basis for V, then \alpha(B) spans \alpha(V). This follows from linearity of \alpha: If v \in V then v = \sum_i a_ib_i and <br /> \alpha(v) = \alpha\left(\sum _ia_i b_i\right) = \sum_i a_i \alpha(b_i).

2. The ith column of a matrix is the image of the ith standard basis vector.

3. It follows from the definition of RREF that columns which don't have a leading 1 are linear combinations of the columns which do.
 
Why does 3 follow from the definition of RREF?
 
mattTch said:
Why does 3 follow from the definition of RREF?
Can chip in? If I've understood your question this might help.

Consider an example RREF matrix:

##\begin{bmatrix}
1 & 0 & 2 & 0 & 8\\
0 & 1 & 7 & 0 & 3\\
0 & 0 & 0 & 1 & 2\\
0 & 0 & 0 & 0 & 0
\end{bmatrix} ##

Some columns contain a leading '1' (with zeroes for all other elements). These are the basis columns.

Other columns do not contain a leading '1'. These are non-basis columns.

The basis columns here are ##C_1 = \begin{bmatrix}
1\\
0\\
0\\
0
\end{bmatrix}##, ##C_2 = \begin{bmatrix}
0\\
1\\
0\\
0
\end{bmatrix}## and ##C_4 = \begin{bmatrix}
0\\
0\\
1\\
0
\end{bmatrix}##.

From inspection it should be clear that any non-basis column can be constructed as a linear combination of the basis columns, e.g. ##C_5 = 8C_1 + 3C_2 + 2C_4##.

That’s because every non-zero coefficient in a non-basis column is a simple multiple of the ‘1’ in a basis column.
 

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