Proving Matrix A is a Spanning Set of R4

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Discussion Overview

The discussion revolves around the concept of spanning sets in linear algebra, specifically focusing on proving that a matrix or set of vectors spans R4. Participants explore examples, methods of proof, and clarify related concepts.

Discussion Character

  • Exploratory
  • Technical explanation
  • Conceptual clarification
  • Debate/contested
  • Mathematical reasoning

Main Points Raised

  • One participant seeks clarification on how to prove that a matrix with one column can span R4, noting a lack of examples in their textbook.
  • Another participant asserts that a single column cannot span R4, suggesting a set of four standard basis vectors as a simple spanning set.
  • A different participant proposes using four arbitrary vectors and mentions the block multiplication theorem as a method to prove spanning, expressing uncertainty about its application.
  • One participant suggests using Gaussian elimination to show that a linear combination of vectors can equal any vector in R4, indicating a method for proof.
  • Another participant introduces the dimension theorem, explaining that showing nullity equals zero would imply the column vectors span R4, emphasizing the importance of full rank.
  • A participant explains that to show a set spans a space, one must demonstrate that any vector in that space can be expressed as a linear combination of the set's vectors, providing an example with standard basis vectors.
  • One participant expresses satisfaction with the explanation provided, noting its similarity to concepts in R3.
  • A newcomer inquires about the implications of spanning sets for C^4 compared to R4 and questions whether a non-zero determinant indicates a spanning set.

Areas of Agreement / Disagreement

Participants express differing views on the ability of a single column matrix to span R4, with some asserting it cannot while others discuss methods to prove spanning for multiple vectors. The discussion remains unresolved regarding the application of the block multiplication theorem and the implications of determinants in spanning sets.

Contextual Notes

There are references to specific mathematical concepts such as Gaussian elimination and the dimension theorem, which may require additional context for full understanding. The discussion also touches on the distinction between real and complex vector spaces without a definitive conclusion.

Who May Find This Useful

This discussion may be useful for students and educators in linear algebra, particularly those interested in understanding spanning sets, methods of proof, and the relationship between vector spaces.

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Hi! I'm a bit confuse about spanning set.

could anyone give me an example of how to prove that a matrix with 1 column be a spanning set of R4? There isn't a clear example in the textbook.

ex: if A={[c1, c2, c3...,cn]} prove that it is span R4.
 
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If you mean a set with one matrice that spans all of R4, there is no such thing. If it doesn't matter how many matrices are in it, as long as it's one column, then the simplest spanning set I an think of is {[1 0 0 0], [0 1 0 0], [0 0 1 0], [0 0 0 1]}
 
No its not it. Let say I have four vectors A,B,C,D (i.e.ABCD could be anything of the sort [1,2,3,4], any real numbers). Now how do i prove that it would span R4?

The book said to used block multiplication theorem but I'm not sure how to use it.
 
If let's say A,B,C,D span R4 then c1A + c2B + c3C + c4D = [x,y,z,w] where the ci's are real numbers. You just have to prove (I would do it through Gaussian elimination) that it has solutions.
 
What is "block multiplication theorem" ? Certainly I haven't heard of that. One way you could do this is to invoke the dimension theorem for say matrix A whose columns are vectors a,b,c,d which you have to prove they span R4.

So dim(Colspace(A)) + dim( nullspace(A) ) = no. of columns of A.

So we need to show that nullity(A) = 0, since if rank(A) = 4 = dim(R4) then the column vectors span R4. So this reduces to showing that only the trivial solution satisfies Ax=0, since otherwise nullity(A) [tex]\neq 0[/tex].

EDIT: Given the above, note that you can easily check if A is of full rank. It's computationally quicker than verifying it by Gaussian elimination.
 
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To show that a set of vectors spans a space, you must show that any vector in the space can be written as a linear combination of vectors in that set.

If the set is something easy, such as S = {[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]}, you would prove it like this.

Let v = [x, y, z, w] be any vector in R^4. Since v = [x, y, z, w] = x * [1, 0, 0, 0] + y * [0, 1, 0, 0] + z * [0, 0, 1, 0] + w * [0, 0, 0, 1], v can be written as a linear combination of vectors in S. Thus, S spans R^4.

For less obvious sets, the proof might be a little more difficult, but ultimately, it involves showing that given [x, y, z, w], you can find proper coefficients for the linear combination to make it true.
 
that's awesome...thank you! that is excately what i was looking for, it make perfect sense because it is excately the samething as in R*3 ...
 
First ever Post!...

hi, just wondering what it would mean for a set of matrices to for a spanning set for C^4 as opposed to R^4

Also would it be right to say that a set of matrices form a spanning set when their determinant is non-zero?

thanks
 
just noticed this thread is a year old!...
 

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