Can RREF be used for any size matrix?

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    Echelon Matrices
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Discussion Overview

The discussion revolves around the applicability of the Reduced Row Echelon Form (RREF) algorithm to matrices of various sizes, including small matrices like 1x1 and 2x1. Participants explore whether RREF can be meaningfully applied to these matrices and the implications of doing so in the context of systems of equations.

Discussion Character

  • Exploratory, Technical explanation, Debate/contested

Main Points Raised

  • Some participants assert that the RREF algorithm can be applied to any matrix size, suggesting that even if the matrix is already in its final form, the application of the algorithm remains valid.
  • Others question the practicality of using RREF for very small matrices, such as 1x1 or 2x1, arguing that it may be excessive for simple systems of equations.
  • A participant explains that a 1x1 matrix can represent a single equation, while a 2x1 matrix could represent a system of two equations in one variable, but questions arise about the relevance of RREF in these cases.
  • There is a discussion about how matrices with only one column can represent systems of equations, with references to homogeneous systems where constant terms are zero.
  • Some participants challenge the representation of homogeneous systems, debating whether certain matrix forms accurately depict the systems being discussed.
  • One participant emphasizes that the RREF algorithm is fundamentally about matrix operations, which can be applied regardless of the context of the entries.
  • Another participant notes that definitions play a significant role in understanding the application of RREF, indicating that the algorithm's utility may depend on how the matrices are defined and used in context.

Areas of Agreement / Disagreement

Participants express differing views on the applicability and practicality of RREF for small matrices. While some agree that the algorithm can be applied universally, others contest its usefulness in specific cases, leading to an unresolved debate on the matter.

Contextual Notes

The discussion highlights limitations related to definitions and the context in which matrices are used, particularly concerning homogeneous versus nonhomogeneous systems. There is also uncertainty regarding the representation of systems by different matrix forms.

Mr Davis 97
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I understand that the RREF algorithm can be used on matrices representing systems of equations to find the solution set of that system. However, can this algorithm be used for any matrix of any size? For example, what if we, what if we had a 1x1 mattix, or a 2x1? What is the minimum size of a matrix for which RREF makes sense?
 
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You can always apply the algorithm. The matrix might be in its final form already, then nothing changes.
 
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Mr Davis 97 said:
I understand that the RREF algorithm can be used on matrices representing systems of equations to find the solution set of that system. However, can this algorithm be used for any matrix of any size? For example, what if we, what if we had a 1x1 mattix, or a 2x1? What is the minimum size of a matrix for which RREF makes sense?
Think about the system of equations a matrix represents. A 1x1 matrix represents a single equation such as 3x = 0. For an equation like 3x = 6, you would need an augmented 1x2 matrix. You could solve this by row reduction, but it seems like massive overkill.

A 2x1 matrix would represent a system of two equations in one variable, such as
3x = 0
2x = 0
The matrix itself would consist of a single column whose entries are 3 and 2, respectively. Again, you could use row reduction, and find that (surprise!) x = 0. For such simple systems, you could use RREF, but it doesn't make much sense.
 
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Alright, it makes sense. Just one further question. How does a matrix with only one column represent a system of equations? I see that you use zero when you right it out in equation form, but where is that coming from?
 
Mr Davis 97 said:
Alright, it makes sense. Just one further question. How does a matrix with only one column represent a system of equations? I see that you use zero when you right it out in equation form, but where is that coming from?
It would represent a homogeneous system, a system of equations where the constant terms are all zero.

So the system of two equations in one unknown
3x = 0
2x = 0
could be represented by this matrix:
##\begin{bmatrix} 3 \\ 2 \end{bmatrix}##

A nonhomogeneous system such as
4x = 10
2x = 4
could be represented by the augmented matrix
##\begin{bmatrix} 4 & | & 10 \\ 2 & | & 4\end{bmatrix}##
I hope it's obvious that this system has no solution.
 
Wouldn't ##\begin{bmatrix} 3 & 0 \\ 2 & 0\end{bmatrix}## represent the homogeneous system that you are talking about, and not ##\begin{bmatrix} 3 \\ 2 \end{bmatrix}##?
 
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Mr Davis 97 said:
Wouldn't ##\begin{bmatrix} 3 & 0 \\ 2 & 0\end{bmatrix}## represent the homogeneous system that you are talking about, and not ##\begin{bmatrix} 3 \\ 2 \end{bmatrix}##?

That's what I think too.
 
Hey Mr Davis 97.

The algorithm can be applied to an arbitrary matrix since all operations are based on multiplication (scalar multiplication) and addition (and subtraction) which can be done for any set of values.
 
This is getting a matter of definitions. The algorithm doesn't really care about what the entries represent.
 
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Mr Davis 97 said:
Wouldn't ##\begin{bmatrix} 3 & 0 \\ 2 & 0\end{bmatrix}## represent the homogeneous system that you are talking about, and not ##\begin{bmatrix} 3 \\ 2 \end{bmatrix}##?
Math_QED said:
That's what I think too.
Most of the linear algebra books I've seen use the matrix for the coefficients of the variables. If the system is nonhomogeneous (constants on the right sides of the equations, they use an augmented matrix.
If the system of equations is homogeneous, there's no point in dragging along a column of zeroes, none of which can change from any row operations.
 

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