How Can You Combine Bases from Subspaces in Linear Algebra?

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Discussion Overview

The discussion revolves around the concept of combining bases from subspaces in linear algebra, particularly in the context of orthogonality and the representation of vectors as linear combinations of basis vectors. Participants explore examples involving matrices and their subspaces, expressing confusion over the processes involved in deducing bases and understanding orthogonal relationships.

Discussion Character

  • Exploratory
  • Technical explanation
  • Conceptual clarification
  • Debate/contested
  • Homework-related

Main Points Raised

  • One participant presents a problem involving the decomposition of a vector into its components related to a matrix, questioning the validity and proof of this decomposition.
  • Another participant infers that (2,-1) serves as a basis vector for the null space, while (1,2) spans the orthogonal space, suggesting that any vector can be expressed as a linear combination of these basis vectors.
  • A participant expresses confusion regarding the extraction of pivot rows and columns from a given matrix, struggling with the elimination process and the notation of submatrices.
  • Repeated expressions of confusion about the examples provided, particularly regarding the relationship between the matrices and their respective bases.
  • One participant mentions that the examples are drawn from a textbook, indicating a learning context for their inquiries.
  • There is a request for clarification on the relevance of certain matrices to the problem at hand, highlighting a lack of understanding of their significance.

Areas of Agreement / Disagreement

Participants generally express confusion and seek clarification on various aspects of the topic. There is no consensus on the methods for deducing bases or the processes involved in the examples provided, indicating multiple competing views and unresolved questions.

Contextual Notes

Participants note limitations in their understanding of the elimination process and the notation used in submatrices, which may affect their ability to follow the examples presented. There are also unresolved questions regarding the significance of certain matrices in relation to the problem.

Who May Find This Useful

Readers interested in linear algebra, particularly those studying orthogonality and subspaces, may find the discussion relevant to their learning process.

applechu
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Hi:
I have a problem about combine bases from subspaces. This is part of orthogonality.
The examples as following:
For A=##\begin{bmatrix} 1 & 2 \\ 3 & 6 \end{bmatrix}## split x= ##\begin{bmatrix} 4 \\ 3 \end{bmatrix}## into ##x_r##+##x_n##=##\begin{bmatrix} 2 \\ 4 \end{bmatrix}+\begin{bmatrix} 2 \\ -1 \end{bmatrix}##

I don't know why it can split into ##x_r##+##x_n##, and how to prove that,
thanks a lot
 
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It looks like you haven't given us everything from your notes or problem, but I'll try to infer what is meant.

(2,-1) is a basis vector for the null space.

Orthogonal to that space is the space with spanning set (1,2)

(the slope is the negative reciprocal)

Then any vector, such as (4,3), can be written as a linear combination of these.

(4,3)=a(2,-1)+b(1,2).

Then solving the system of two linear equations for the two unknowns a and b, we get a=1, b=2.

Or was there something else you were trying to "prove"?
 
In fact, I feel I have stuck into the situation of learning linear algebra.
I read the part of orthogonality and four subspaces.
I feel confused about some examples, such as following:
B=##\begin{bmatrix} 1 & 2&3&4&5 \\ 1 & 2&4&5&6 \\ 1 & 2&4&5&6 \end{bmatrix}## conatins
##\begin{bmatrix} 1 &3 \\ 1& 4\end{bmatrix}## in the pivot rows and columns.
However, I can not deduce the process how it from. I try elimination, but it is not so directly perceived through the notation of submatrix, thanks
 
applechu said:
In fact, I feel I have stuck into the situation of learning linear algebra.
I read the part of orthogonality and four subspaces.
I feel confused about some examples, such as following:
B=##\begin{bmatrix} 1 & 2&3&4&5 \\ 1 & 2&4&5&6 \\ 1 & 2&4&5&6 \end{bmatrix}## conatins
##\begin{bmatrix} 1 &3 \\ 1& 4\end{bmatrix}## in the pivot rows and columns.
However, I can not deduce the process how it from. I try elimination, but it is not so directly perceived through the notation of submatrix, thanks
The form

##\begin{bmatrix} 1 & 2&3&4&5 \\ 0 & 0&1&1&1 \\ 0&0&0&0&0 \end{bmatrix}##, or ##\begin{bmatrix} 1 & 2&0&1&2 \\ 0 & 0&1&1&1 \\ 0&0&0&0&0 \end{bmatrix}##

Tell you to take the first and third columns as a basis for the column space,

##\begin{bmatrix} 1 \\ 1\\1\end{bmatrix}## and ##\begin{bmatrix} 3 \\ 4\\4\end{bmatrix}##

The last look similar to the 2 by 2 matrix you wrote, but I don't understand why you would want that matrix, can you tell us more about the problem?
 
It is a example from the book. I try to learn linear algebra from some books.
thanks a lot.
 
You say "I feel I have stuck into the situation of learning linear algebra." What you give, "subspaces", "basis", etc. is linear algebra. What course was this for?
 
For the chapter about orthogonality. Thanks.
 
I asked what course, if not Linear Algebra, not what chapter.
 

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