Determine if linearly independent?

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Discussion Overview

The discussion revolves around determining the linear independence of a set of vectors in R4, specifically when given three vectors with four components each. Participants explore methods for assessing linear independence, including the use of matrices and row reduction techniques.

Discussion Character

  • Exploratory, Technical explanation, Debate/contested, Homework-related, Mathematical reasoning

Main Points Raised

  • One participant asks how to determine if a vector in R4 is linearly independent, noting the challenge of using a non-square matrix for determinant calculation.
  • Another participant clarifies that linear independence means the only solution to the corresponding equations is the trivial solution (x = y = z = 0).
  • It is suggested that row reduction can be used to ascertain independence, with the condition that if the last row reduces to 0 0 0, it does not necessarily imply dependence.
  • A later reply indicates that for three vectors in R4, the last row will always reduce to 0 0 0, and thus, additional conditions are needed to determine dependence.
  • One participant emphasizes that for n vectors to be independent, the first n rows of the row-reduced matrix must not all be zero, regardless of the number of components.

Areas of Agreement / Disagreement

Participants express differing views on the implications of row reduction results, particularly regarding the conditions under which vectors can be considered dependent or independent. The discussion remains unresolved on the precise criteria for linear independence in this context.

Contextual Notes

There are limitations regarding the assumptions made about the relationship between the number of vectors and their components, as well as the implications of row reduction outcomes on independence.

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Hi all,

I have been studying Linear Algebra for an upcoming exam, and one question has puzzled me slightly! How do you determine of a vector in R4 is linearly independent?

Given three vectors, each with 4 rows, I know you are meant to arrange them into a matrix, like this:

\[ \left( \begin{array}{ccc}<br /> a &amp; e &amp; i \\<br /> b &amp; f &amp; j \\<br /> c &amp; g &amp; k\\<br /> d &amp; h &amp; l\end{array} \right)\] <br />

In this case you are unable to find the determinant as it is not a square matrix. Are you meant to use row reduction instead? And if so, how do you ascertain whether it is independent or dependent? I'd appreciate any help clearing this up!

Cheers.
 
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Hi all,

I have been studying Linear Algebra for an upcoming exam, and one question has puzzled me slightly! How do you determine of a vector in R4 is linearly independent?

Given three vectors, each with 4 rows
You mean with 4 components, don't you?

, I know you are meant to arrange them into a matrix, like this:

\[ \left( \begin{array}{ccc}<br /> a &amp; e &amp; i \\<br /> b &amp; f &amp; j \\<br /> c &amp; g &amp; k\\<br /> d &amp; h &amp; l\end{array} \right)\] <br />

In this case you are unable to find the determinant as it is not a square matrix. Are you meant to use row reduction instead? And if so, how do you ascertain whether it is independent or dependent? I'd appreciate any help clearing this up!

Cheers.
Think about what "independent" means (that should be the first thing you learned).

A set of vectors (here three) are "independent" if and only the only solution to the equations xu yv+ zw 0 is x= y= z= 0. If the components are <a, b, c, d>, <e, f, g, h>, and <i, j, k, l> that is the same as the system of equations xa+ ye+ zi= 0, xb+ yg+ zf= 0, xc+ yg+ zk= 0, and xd+ yh+ zl= 0 which, in turn, is the same as the matrix equation
\begin{bmatrix}a &amp; e &amp; i \\ b &amp; g &amp; f \\ c &amp; g &amp; k \\ d &amp; h &amp; f\end{bmatrix}\begin{bmatrix}x \\ y \\ z\end{bmatrix}= \begin{bmatrix}0 \\ 0 \\ 0\end{bmatrix}

Obviously x= y= z= 0 is a solution so the whole question is whether or not there are any other solutions. You are right that if the matrix were square, we could look at the determinant: if it were non-zero, there would be only one solution, x= y= z= 0 and so the vectors would be independent. But with a non-square matrix, we need to row-reduce as you say. Here, because there are three equations with four unknowns, the last row will reduce to 0 0 0. If the third row does not also reduce to 0 0 0, then we can solve to get x= y= z= 0. If the third row does reduce to 0 0 0, then we have only two equations to solve for three unknowns and that will not have a unique solution.
 
A set of vectors (here three) are "independent" if and only the only solution to the equations xu yv+ zw 0 is x= y= z= 0. If the components are <a, b, c, d>, <e, f, g, h>, and <i, j, k, l> that is the same as the system of equations xa+ ye+ zi= 0, xb+ yg+ zf= 0, xc+ yg+ zk= 0, and xd+ yh+ zl= 0 which, in turn, is the same as the matrix equation
\begin{bmatrix}a &amp; e &amp; i \\ b &amp; g &amp; f \\ c &amp; g &amp; k \\ d &amp; h &amp; f\end{bmatrix}\begin{bmatrix}x \\ y \\ z\end{bmatrix}= \begin{bmatrix}0 \\ 0 \\ 0\end{bmatrix}

Minor correction: the right hand side should be

\left[\begin{array}{c}<br /> 0 \\<br /> 0 \\<br /> 0 \\<br /> 0\end{array}\right]
 
Brilliant! That makes perfect sense to me. Thanks very much for clearing that up. So if the last row does reduce to 0 0 0 after row reducing, then it's safe to say that the vectors are dependent?
 
In this particular, case, since there were only three vectors and four components, the last row will be 0 0 0 even if the vectors are independent. In order that they be dependent, the last two rows must be 0 0 0.

In order for n vectors to be independent, the first n rows of the matrix got by row-reducing formed by taking the vectors as columns must not be all 0s, no matter how many components (rows of the matrix) there are. Of course, if there are more vectors than components, there are not enough rows in the matrix for that to be true: if m> n, then a set of m vectors in a space of dimension n cannot be independent.
 
Thanks a million guys, that's really helped me (finally) understand this stuff.
 

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