Linear Combinations and Span (Concept Question)

In summary: Ok, thanks for the advice.In summary, the statement "If a vector in \mathbb{R}^{m} is a linear combination of the columns of an m \hspace{1 mm} x \hspace{1 mm} n matrix A, then the columns of A span \mathbb{R}^{m}" is false. This can be proven using a specific counter example, such as a 3x2 matrix with a reduced row echelon form that does not have a pivot in every row.
  • #1
_N3WTON_
351
3

Homework Statement


Let [itex]A[/itex] be an [itex] m \hspace{1 mm} x \hspace{1 mm} n [/itex] matrix, and let [itex] \vec{b} [/itex] be a vector in [itex] \mathbb{R}^{m} [/itex]. Suppose that [itex] \vec{b} [/itex] is a linear combination of the columns of A. Then the columns of A span [itex] \mathbb{R}^{m} [/itex]

Homework Equations

The Attempt at a Solution


I said that this statement was true using the following theorem from my textbook:
Let [itex]A[/itex] be an [itex]m \hspace{1 mm}x \hspace{1 mm}n[/itex] matrix. Then the following statements are logically equivalent.
a) For each [itex]\vec{b}[/itex] in [itex]\mathbb{R}^{m}[/itex], the equation [itex]A \vec{x} = \vec{b} [/itex] has a solution
b) Each [itex]\vec{b} [/itex] in [itex] \mathbb{R}^{m} [/itex] is a linear combination of the columns of A
c) The columns of A span [itex]\mathbb{R}^{m}[/itex]
d) A has a pivot position in every row
However, my book says that this statement is false and I am not sure why. I think I am probably missing something obvious, but I'm not sure what.
 
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  • #2
_N3WTON_ said:
b) Each [itex]\vec{b} [/itex] in [itex] \mathbb{R}^{m} [/itex] is a linear combination of the columns of A
_N3WTON_ said:
let [itex] \vec{b} [/itex] be a vector in [itex] \mathbb{R}^{m} [/itex]. Suppose that [itex] \vec{b} [/itex] is a linear combination of the columns of A.
In the statement, [itex]\vec{b} [/itex] is a particular vector, and not any arbitrary vector in [itex] \mathbb{R}^{m} [/itex]; that is to say, it is not necessarily true that any vector in [itex] \mathbb{R}^{m} [/itex] can be expressed as a linear combination of the columns of A.
 
  • #3
Fightfish said:
In the statement, [itex]\vec{b} [/itex] is a particular vector, and not any arbitrary vector in [itex] \mathbb{R}^{m} [/itex]; that is to say, it is not necessarily true that any vector in [itex] \mathbb{R}^{m} [/itex] can be expressed as a linear combination of the columns of A.
ok, so I was wondering if this counter example would be a good way to verify that it is false? I picked an arbitrary matrix and an arbitrary vector:
$$
A =
\begin{bmatrix}
0 & 3\\
1& 5\\
2 &8
\end{bmatrix}
$$
$$ \vec{b} =
\begin{bmatrix}
1\\
2
\\5

\end{bmatrix} $$
I reduced A and found that there is not a pivot in every row, so I said that the columns of A do not span [itex] \mathbb{R}^{m} [/itex]. Is this a sufficient counter example?
 
  • #4
_N3WTON_ said:
ok, so I was wondering if this counter example would be a good way to verify that it is false? I picked an arbitrary matrix and an arbitrary vector:
$$
A =
\begin{bmatrix}
0 & 3\\
1& 5\\
2 &8
\end{bmatrix}
$$
$$ \vec{b} =
\begin{bmatrix}
1\\
2
\\5

\end{bmatrix} $$
I reduced A and found that there is not a pivot in every row, so I said that the columns of A do not span [itex] \mathbb{R}^{m} [/itex]. Is this a sufficient counter example?
I'm not sure it meets the conditions of the original problem, which states that ##\vec{b}## is a linear combination of the columns of A. In any case, the condition for ##\vec{b}## seems to me to be something of a red herring. Your 3 x 2 matrix clearly (I hope) can't span R3, since there are only two columns.
 
  • #5
Mark44 said:
I'm not sure it meets the conditions of the original problem, which states that ##\vec{b}## is a linear combination of the columns of A. In any case, the condition for ##\vec{b}## seems to me to be something of a red herring. Your 3 x 2 matrix clearly (I hope) can't span R3, since there are only two columns.
You're right that my example doesn't meet the given conditions. However, if I were to find an example that does meet the required conditions using a 3x2 matrix, could I then use that counter example to prove that the statement is false? Basically I'm still a little confused about where to go here...
 
  • #6
_N3WTON_ said:
You're right that my example doesn't meet the given conditions. However, if I were to find an example that does meet the required conditions using a 3x2 matrix, could I then use that counter example to prove that the statement is false?
Yes, I believe so.
 
  • #7
Mark44 said:
Yes, I believe so.
Awesome. I was thinking in this case it may be easier to use a matrix made up of stars and squares(like the kind used to determine echelon forms) rather than actually come up with a linear combination.
 
  • #8
_N3WTON_ said:
Awesome. I was thinking in this case it may be easier to use a matrix made up of stars and squares(like the kind used to determine echelon forms) rather than actually come up with a linear combination.
No, I would use a specific matrix.
 
  • #9
Mark44 said:
No, I would use a specific matrix.
Ok, thanks for the advice
 

FAQ: Linear Combinations and Span (Concept Question)

1. What is a linear combination?

A linear combination is a mathematical operation that involves multiplying a set of numbers by a set of coefficients and adding them together.

2. How is a linear combination related to span?

A linear combination is used to determine the span of a set of vectors. If all possible combinations of the vectors can be created using linear combinations, then the vectors span the entire space.

3. What is the significance of linear combinations in linear algebra?

Linear combinations form the basis for many concepts in linear algebra, such as vector spaces, linear transformations, and eigenvalues. They also have practical applications in fields such as physics, computer graphics, and machine learning.

4. Can a linear combination of vectors be unique?

Yes, a linear combination can be unique if the set of coefficients used is unique. However, if there are multiple combinations that result in the same vector, then the linear combination is not unique.

5. How can linear combinations be used to solve systems of equations?

Linear combinations can be used to express systems of equations in matrix form, making it easier to solve using tools such as Gaussian elimination or row reduction. This approach is known as the matrix method or the Gaussian elimination method.

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