Linear independence of columns of a matrix

In summary, the conversation discusses the linear independence of the columns of a given matrix. The presence of a row of zeros does not affect the linear independence of the columns, as it only indicates that the space spanned by the vectors is at most three-dimensional. A row of zeros may give away the answer in some cases, but not in this problem.
  • #1
TrippingBilly
27
0

Homework Statement


Are the columns of this matrix linearly independent?
1...3...-2
0...-8...11
0...0...1
0... 0... 0
(periods are just to make spacing clear)

The Attempt at a Solution


What is confusing me is the last row of zeros. If a set of vectors contains the zero vector, it is linearly dependent..but would this affect the linear independence of the columns of the matrix? If you augment the matrix with the zero vector, then the third row says that the only solution is the trivial one, which means that the columns of the matrix are linearly independent.
 
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  • #2
If the columns are linearly dependent, then the third column can be made by adding multiples of the first and second columns together. Can this be done?
 
  • #3
No it can't, so the fact that there is a row of zeros doesn't matter for the columns of the matrix..
 
  • #4
Well no, because you're not looking at the rows here. To put the question another way, is the set of vectors {(1 0 0 0), (3 -8 0 0), (-2 11 1 0)} linearly independent?
 
  • #5
A row of zeros is significant because it immediately tells us that the dimension of the space spanned by the column vectors must be less than the number of columns (i.e. the number of components of the vectors). In this case however, that only means that the space spanned by the vectors is at most three-dimensional, but you knew that already since there are only three vectors in this problem.

In other problems, a row of zeros may immediately give away the answer. For example: Are (a b 0), (c d 0) and (e f 0) linearly independent? No, they must be linearly dependent because there are three of them and they're all in the x-y plane, which is two-dimensional.
 

What is the definition of linear independence of columns of a matrix?

Linear independence of columns of a matrix refers to a set of columns in a matrix that cannot be expressed as a linear combination of other columns in the same matrix. In other words, no column in the set can be written as a combination of the other columns using scalar multiplication and addition.

Why is it important to determine the linear independence of columns of a matrix?

Determining the linear independence of columns of a matrix is important because it helps to identify the rank of the matrix, which is a crucial factor in solving systems of linear equations and finding solutions to problems in areas such as engineering, physics, and economics.

How can one test for linear independence of columns of a matrix?

To test for linear independence of columns in a matrix, one can use the determinant method or the pivot column method. In the determinant method, the determinant of the matrix is calculated and if it is equal to 0, the columns are linearly dependent. In the pivot column method, the matrix is put into reduced row echelon form and if there is a pivot in every column, the columns are linearly independent.

What is the difference between linear independence and linear dependence of columns of a matrix?

Linear independence refers to a set of columns in a matrix that cannot be expressed as a linear combination of other columns. On the other hand, linear dependence refers to a set of columns in a matrix that can be expressed as a linear combination of other columns.

Can a matrix have both linearly independent and linearly dependent columns?

Yes, a matrix can have both linearly independent and linearly dependent columns. For example, in a 3x3 matrix, the first two columns may be linearly independent while the third column is a linear combination of the first two columns.

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