How Does Matrix Rank Relate to Vector Independence and Dimensionality?

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The discussion clarifies the relationship between matrix rank, vector independence, and dimensionality in the context of linear algebra. When n vectors from R^m are linearly independent, the rank of matrix A is n, with m being greater than or equal to n. If the vectors span R^m, the rank is m, leading to the conclusion that m equals n. Furthermore, when the vectors form a basis for R^m, m is also equal to n, confirming that a basis consists of linearly independent vectors that span the entire space.

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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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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.
 


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.
 

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