Nullspace of non-zero 4x4 matrix

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Discussion Overview

The discussion revolves around the properties of the nullspace of a non-zero 4x4 matrix, particularly focusing on the implications of the rank-nullity theorem and the conditions under which the nullspace can contain linearly independent vectors. Participants explore the relationship between the rank of the matrix, the dimension of the nullspace, and the existence of solutions to homogeneous systems.

Discussion Character

  • Technical explanation
  • Conceptual clarification
  • Debate/contested

Main Points Raised

  • One participant asserts that the nullspace of a non-zero 4x4 matrix cannot contain 4 linearly independent vectors, reasoning that if the nullspace dimension were 4, it would imply 4 free variables, contradicting the matrix's size.
  • Another participant emphasizes that if a matrix has rank zero, it cannot be a non-zero matrix, suggesting that a nullspace dimension of 4 would imply the matrix is the zero matrix.
  • Some participants discuss the implications of the rank-nullity theorem, noting that if the rank is zero, the nullity must be 4, leading to the conclusion that the matrix must map all vectors to the zero vector.
  • There is a question raised about whether nullity must be at least 1, given that the nullspace always contains the zero vector, which is considered a non-empty solution.
  • Another participant points out the potential contradiction between having a matrix with linearly independent rows/columns (which would imply a nullity of 0) and the requirement for the nullspace to contain at least the zero vector.
  • Clarifications are made regarding the definition of nullity and the nature of the zero vector space, with one participant stating that the nullity is zero only when the nullspace contains only the zero vector.

Areas of Agreement / Disagreement

Participants express differing views on the implications of the rank-nullity theorem and the conditions under which a matrix can have a nullspace dimension of 4. The discussion remains unresolved regarding the relationship between nullity and the existence of linearly independent vectors in the nullspace.

Contextual Notes

Participants highlight the importance of understanding the definitions of rank, nullity, and the nature of vector spaces, particularly in relation to the conditions of linear independence and dependence. There are unresolved questions about the implications of these definitions in specific cases.

EvLer
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Hello everyone,
it's final's time next week :cry: , so I will be posting here more often than usual :biggrin:
Here is one problem I came across when doing review:

The nullspace of non-zero 4x4 matrix cannot contain a set of 4 lin. indep. vectors. (T/F)

The way I was thinking is that if I solve a homogeneous s-m with this matrix, and if the dimension of nullspace is 4, that means that there have to be 4 free variables in the homogeneous s-m, but matrix is just 4x4.
And then there is this rank-nullity theorem that n = rank(A) + nullity(A), so in this case rank(A) = 0, is it ever possible? My guess is not.
Does the same hold for dim of nullspace (nullity): it has to have at least one solution (trivial, where everyting = 0, but that does not mean that the nullity is an empty set!) ?
Is it correct?

Thanks in advance!
 
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Have you learned what bases are? If you have 4 linearly independent (nonzero) vectors, they form a basis for the space. The action of the matrix on any vector in your space is determined by the action on the 4 basis-vectors.
What does that mean for Av (A = matrix, v=vector), where v is any vector.

Ofcourse, the rank equation works fine too. What can you conclude from A if its rank is zero?
 
Galileo said:
Have you learned what bases are? If you have 4 linearly independent (nonzero) vectors, they form a basis for the space.
Yeah, I know about bases. And this part I understand.

The action of the matrix on any vector in your space is determined by the action on the 4 basis-vectors.
What does that mean for Av (A = matrix, v=vector), where v is any vector.
this is a bit blurry.

Ofcourse, the rank equation works fine too. What can you conclude from A if its rank is zero?
I can't say it's non-existent, but if it's rank = 0, dimens. of rowspace = 0!
Even if all the rows/cols are lin. dep. rank would be at least 1.
But if it's zero I dunno, it cannot be zero-vector, since it's one of the conditions. That's why ask.
n = dimesion of nullspace? but then what :confused:
 
But if it's zero I dunno, it cannot be zero-vector, since it's one of the conditions. That's why ask.

Precisely. The rank of a non-zero matrix is never zero (the ONLY vector space of dimension zero is the set containing only the zero vector, and its "basis" is the empty set). Thus if you find that null(A)=4 => rank(A)=0, you've answered the question.
 
Equivalently: a 4 by 4 matrix maps vectors from R4 to R4- which is 4 dimensional. The null space of any linear operator (or matrix) is a subspace of the domain space (here R4). If it contains 4 linearly independent vectors the its dimension is at least 4. In fact, the only 4 dimensional subspace of R4 is R4 itself. Saying the null space contains 4 independent vectors is simply saying the null space is R4 itself: the matrix maps every vector into the 0 vector and so is the 0 matrix.
 
Thanks!
Then a follow-up question: does nullity has to be at least 1? since it always has a solution as zero-vector? Which I assume to be non-empty, so it's 1.

On one hand it makes sense as I described it above, on the other hand, it doesn't: by rank-nullity thm that would mean that matrix contains at least one lin. dep. row/col: n = rank(A) + nullity(A).
But looking at a matrix A with all cols/rows lin. indep: rank(A) = n, which would mean nullity(A) = 0.

Is it a contradiction? Or am I missing something?

Thanks again.
 
The nullity of a matrix is the dimension of the nullspace of the matrix. The nullspace of any matrix contains the zero vector. In the case that the nullspace is the zero vector (and only in this case), the nullity of the matrix is 0, because (as I mentioned in my last post), the dimension of the vector space containing only the zero vector is zero (a basis for the vector space has zero elements - ie. the basis is the empty set).
 

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