Range space of linear mappings

In summary, we are asked to prove that the rank of the composition of two linear mappings, M o L, is always less than or equal to the rank of the first mapping, L. An example is given where the rank of M o L is less than the rank of both M and L. To solve this, we can use the fact that the range of a linear mapping is a subspace with a dimension equal to its rank. By applying L and then M, we can see that the range of L may not be the entire space of Rm, leading to a smaller range for M to act on and therefore a smaller rank for M o L. To find an example, we can use matrices from R^2 to R
  • #1
Jennifer1990
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0

Homework Statement


Let L : Rn --> Rm and M : Rm --> Rp be linear mappings.
a)Prove that rank( M o L) <= rank(L).
b)Give an example such that the rank(M o L) < rank(M) and rank(L)

Homework Equations


None


The Attempt at a Solution


a)I see that (M o L) takes all vectors in Rn and maps them to vectors in Rm then maps these vectors to vectors in Rp. (L) also takes all vectors in Rn and maps them to Rm. From this, i get the impression that rank(M o L) = rank (L) because the quantity of vectors should not change when (M o L) maps vectors in Rm to Rp.

b)Is there a method to get such a matrix or do I have to use trial and error?
 
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  • #2
Hints:

For part a:
Note that the range of M is a subspace of Rp with dimension Rank(M).
Likewise, the range of L is a subspace of Rm with dimension Rank(L).
For the composition ML, notice that after L is applied, the range of L is not necessarily all of Rm. Moreover, when you next apply M, it is only acting on that subspace, range of L.

For part b, try matrices from R^2 to R^2. Make both of them with rank 1, yet the composition has rank 0.
 

1. What is the range space of a linear mapping?

The range space of a linear mapping is the set of all possible output values that the mapping can produce. It is also known as the image space.

2. How is the range space related to the domain of a linear mapping?

The range space is a subset of the codomain, which is the set of all possible input values for the mapping. The domain of the mapping is a subset of the range space, meaning that it only includes the input values for which the mapping produces an output.

3. Can the range space of a linear mapping be empty?

Yes, it is possible for the range space of a linear mapping to be empty. This means that the mapping has no output values for any of its input values. In this case, the mapping is not considered to be a surjective (onto) mapping.

4. How is the range space of a linear mapping affected by its dimension?

The dimension of the range space is equal to the number of linearly independent columns in the matrix representing the linear mapping. This means that the dimension of the range space can vary depending on the number of linearly independent columns, and it can also be equal to the dimension of the codomain.

5. What is the significance of the range space in linear algebra?

The range space is an important concept in linear algebra because it helps us understand the behavior of linear mappings and their relationship to their input and output values. It also allows us to determine if a mapping is invertible, and if so, what the inverse mapping would look like.

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