Linear independence if there is a column of zeros

In summary: I'm trying to understand if a given 4-vector is linearly dependent on the zero-vector. For example, in the above matrix, is [1 2 0 4] linearly dependent on [0,1,2]?
  • #1
ti-84minus
7
0

Homework Statement


If, in a matrix, there is a column of all zeros, does this mean the given vector/matrix is linearly dependent?
An example would be:
[1 2 0 4]
[2 3 0 1]
[5 2 0 7]

A few questions to clear up some possible misconceptions:
1) The matrix above is a 4-dimensional vector?
2) The given vector has 4 unknowns, but a column of zeros, therefore the system is linearly dependent.

Thanks for your help!
 
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  • #2
I don't think the matrix you have is a vector, vectors are either row vectors (1xm matrices) or column vectors (nx1 matrices). For this system of linear equations, each row represents a different equation. The system is then linearly independent if each row is independent, ie. row reduction doesn't yield a row of zeros. A column of zeros doesn't affect this linear independence. This is what it affects: if you take a 4x1 column vector, say [a,b,c,d]^T, and multiply it on the left by your matrix, it maps this vector to a 3x1 vector based on the column vectors of your matrix. The zero-column (3rd column) of your matrix maps the third row of your [a,b,c,d]^T to 0 in all rows of its image, which ends up being [a+2b+4d,2a+3b+d,5a+2b+7d]^T.
 
  • #3
ti-84minus said:

Homework Statement


If, in a matrix, there is a column of all zeros, does this mean the given vector/matrix is linearly dependent?
A set of vectors can be linearly dependent or linearly independent, but it doesn't usually make any sense to describe a single vector as being dependent or independent.
ti-84minus said:
An example would be:
[1 2 0 4]
[2 3 0 1]
[5 2 0 7]

A few questions to clear up some possible misconceptions:
1) The matrix above is a 4-dimensional vector?
No. The matrix on its own belongs to a vector space of dimension 12. The rows of the matrix are vectors in R4, a 4-dimensional vector space. The columns of the matrix are vectors in R3, a 3-dimensional vector space.
ti-84minus said:
2) The given vector has 4 unknowns, but a column of zeros, therefore the system is linearly dependent.
What given vector? Does the matrix above represent a system of three equations in four unknowns?

You need to be more specific about what you're asking before we can give meaningful answers.
ti-84minus said:
Thanks for your help!
 

What is the concept of linear independence if there is a column of zeros?

Linear independence refers to the property of a set of vectors in a vector space where no vector in the set can be represented as a linear combination of other vectors in the set. This means that each vector in the set brings unique information and cannot be duplicated by a combination of other vectors. When there is a column of zeros in a matrix, it means that one of the vectors is a multiple of the zero vector and therefore, linearly dependent on the other vectors.

How can we determine if a set of vectors is linearly independent if there is a column of zeros?

To determine if a set of vectors is linearly independent, we can use the method of Gaussian elimination. By reducing the matrix to its row-echelon form, we can identify if there are any rows or columns that are linearly dependent. If there is a column of zeros, it means that the corresponding vector is linearly dependent on the other vectors in the set.

Can a set of vectors be linearly independent if there is a column of zeros?

No, a set of vectors cannot be linearly independent if there is a column of zeros. This is because the presence of a column of zeros indicates that one of the vectors is a multiple of the zero vector, which is always linearly dependent on other vectors. Therefore, the set cannot satisfy the criteria for linear independence.

What happens to linear independence when a column of zeros is added to a set of vectors?

When a column of zeros is added to a set of vectors, the linear independence of the set is not affected unless the zero vector is already present in the set. If the zero vector is not already present, then the added column of zeros will not change the linear independence of the set as long as the other vectors remain linearly independent.

How can understanding linear independence with a column of zeros be applied in real-world situations?

Understanding linear independence with a column of zeros is essential in many areas of science, such as physics, engineering, and data analysis. In physics, linear independence is crucial in determining the stability and equilibrium of systems. In engineering, it is used to analyze the strength and stability of structures. In data analysis, it helps to identify redundant variables and simplify models. Therefore, understanding linear independence with a column of zeros is essential in many practical applications.

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