Find the rank of this 3x3 matrix

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    3x3 Matrix rank
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Homework Help Overview

The discussion revolves around determining the rank of a 3x3 matrix, with participants exploring the concepts of linear independence and dependence among the matrix's columns.

Discussion Character

  • Conceptual clarification, Assumption checking

Approaches and Questions Raised

  • The original poster attempts to assert that all columns are linearly independent, leading to a conclusion of rank 3, while others question this assumption and point out the presence of a zero column, suggesting it affects linear independence.

Discussion Status

Participants are actively engaging with the original poster's reasoning, providing insights about linear dependence and the implications of having a zero column. There is a productive exploration of the definitions and properties related to matrix rank, but no consensus has been reached.

Contextual Notes

There is a mention of a non-zero subdeterminant and the determinant of the entire matrix being zero, indicating that the rank can be at most two. The discussion reflects a mix of assumptions and interpretations regarding the matrix's properties.

PainterGuy
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Homework Statement
Where am I going wrong with finding the rank?
Relevant Equations
Please check my attempt.
Hi,

I was trying to find the rank of following matrix.

1615088274129.png


I formed the following system and it seems like all three columns are linearly independent and hence the rank is 3. But the answer says the rank is '2'. Where am I going wrong? Thanks, in advance!

1615088442405.png
 
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Why do you think ##k_1=0## in your equation?

Equivalently, it is very obvious (after some experience) that the three columns are not linearly independent.
 
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One of the columns is the zero column (all entries zero). Doesn't that tell you something? Can the zero vector be linearly independent with any other vector(s)?
 
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There exists a non-zero subdeterminant (or minor) of order two and the determinant of the entire thing is 0. Equivalently, the rank is two. The second equality adds no information. The rank can be at most two.
 
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Reason you can't see it is probably you've done and got used to, routinised, other more or less difficult exercises but never met anything this trivial!

Just check definition of linear dependence "... said to be linearly dependent, if there exist scalars
{\displaystyle a_{1},a_{2},\dots ,a_{k},}
not all zero, such that

{\displaystyle a_{1}\mathbf {v} _{1}+a_{2}\mathbf {v} _{2}+\cdots +a_{k}\mathbf {v} _{k}=\mathbf {0} ,}

where
\mathbf {0}
denotes the zero vector."
 
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Thank you, everyone!

epenguin said:
Reason you can't see it is probably you've done and got used to, routinised, other more or less difficult exercises but never met anything this trivial!

Or, I'm just silly! :)

linear_dependence_vector.jpg

Source: Linear Algebra, 6th ed. by Seymour
 
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PainterGuy said:
Thank you, everyone!
Or, I'm just silly! :)
One of the first things you should learn about the rank of a matrix is to discard any rows (or colums) that are zero.
 
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