How Does Matrix Rank Relate to Vector Independence and Dimensionality?

In summary, the rank of matrix A formed by taking n vectors from R^m as its columns is n if the vectors are linearly independent. The relationship between m and n is that n is equal to the dimension of the column space, and m is less than or equal to n. However, if the vectors span R^m instead, the rank of A is m and m=n. If the vectors form a basis for R^m, then m=n.
  • #1
physicsss
319
0
Suppose that a matrix A is formed by taking n vectors from R^m as its columns.

a) if these vectors are linearly independent, what is the rank of A and what is the relationship between m and n?

is the rank the same as the dimension of the column space, or n, and m less than or equal to n?

b) if these vectors span R^m instead, what is the rank of A and what is the relationship between m and n?

is the rank m and m=n?

c) if these vectors form a basis for R^m, what is the relationship between m and n then?

is m=n?
 
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  • #2
physicsss said:
Suppose that a matrix A is formed by taking n vectors from R^m as its columns.

a) if these vectors are linearly independent, what is the rank of A and what is the relationship between m and n?

is the rank the same as the dimension of the column space, or n, and m less than or equal to n?

b) if these vectors span R^m instead, what is the rank of A and what is the relationship between m and n?

is the rank m and m=n?

c) if these vectors form a basis for R^m, what is the relationship between m and n then?

is m=n?
A:R^n->R^m
a)
The first part is easy to see rank(A)=n=dim(span(colums))=coulumspace
as you said but n<=m as it must be. If m<n R^m has a linearly independent basis with more than m vectors, but this cannot be as we know the standard basis has m vectors and for finite diminsional vector spaces two linearly independent spaning sets have the same finite number of elements.
b)
That is right
c)
this is also right.
 
  • #3


a) If the n vectors are linearly independent, the rank of A would be n. In this case, the dimension of the column space would also be n. The relationship between m and n would be that m is equal to or greater than n, as the vectors are taken from R^m.

b) If the n vectors span R^m, the rank of A would be m. In this case, m would be equal to n, as the vectors are taken from R^m and are able to span the entire space. Therefore, the relationship between m and n would be that m is equal to n.

c) If the n vectors form a basis for R^m, then m would also be equal to n. This is because a basis consists of linearly independent vectors that span the entire space. Therefore, the relationship between m and n would be that m is equal to n.
 

What is a matrix rank?

A matrix rank is a measure of the dimensionality of a matrix. It represents the maximum number of linearly independent rows or columns in a matrix.

How is matrix rank calculated?

To calculate the rank of a matrix, you can use various methods such as Gaussian elimination, row reduction, or the determinant method. These methods involve performing mathematical operations on the matrix to reduce it to a simpler form and then counting the number of non-zero rows or columns.

Why is matrix rank important?

Matrix rank is important because it provides information about the properties of a matrix and its solutions. It can be used to determine if a system of linear equations has a unique solution, the number of free variables in a system, and the dimension of the column and row spaces of a matrix.

What are the possible ranks of a matrix?

The rank of a matrix can range from 0 to the minimum of the number of rows and columns in the matrix. A rank of 0 indicates that all the elements in the matrix are 0, and a rank equal to the number of rows or columns indicates that all the rows or columns are linearly independent.

How can I use matrix rank in real-world applications?

Matrix rank has various applications in fields such as engineering, physics, computer science, and economics. It can be used to solve systems of linear equations, analyze networks and circuits, compress images, and perform data analysis. It is also used in machine learning algorithms and computer graphics.

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