Direct sum of nullspace and range

In summary, the statement "N(T) \bigoplus R(T) = V" where V is the source of the linear map T:V → V is not always true. The null space and range may have dimensions that add up to the dimension of V, but they can overlap. This can be seen in the example of the linear map T(x,y) = (y,0) from R2 to R2, where N(T)+R(T) is just the x-axis. Therefore, the statement is not sufficient to conclude that T is invertible. A counterexample to this claim can be found in the construction of a projection in two dimensions.
  • #1
Bipolarity
776
2
Is this true? I am studying direct sums and was wondering if the following statement holds? It seems to be true if one considers the proof of the dimension theorem, but I need to be sure, so I can steer my proof toward a particular direction.

## N(T) \bigoplus R(T) = V ## where ##V## is the source of the linear map ##T:V → V## ?

BiP
 
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  • #2
It's not. The null space and range have dimensions which add up to the dimension of V, but they can overlap. For example consider the map

T(x,y) = (y,0).

This is a linear map from R2 to R2. The null space is (x,0) for x in R and the range is (x,0) for x in R, so N(T)+R(T) is just the x-axis
 
  • #3
Office_Shredder said:
It's not. The null space and range have dimensions which add up to the dimension of V, but they can overlap. For example consider the map

T(x,y) = (y,0).

This is a linear map from R2 to R2. The null space is (x,0) for x in R and the range is (x,0) for x in R, so N(T)+R(T) is just the x-axis

I see. Suppose that you knew the statement to be true. Then could you conclude that T is invertible?

BiP
 
  • #4
It's not hard to construct a projection in two dimensions which is a counterexample to that claim
 
  • #5
olarBear, the statement you have provided is true. The direct sum of the nullspace and range of a linear map T is equal to the source space V. This can be proven using the dimension theorem, which states that for any linear map T: V → W, the dimension of the nullspace of T plus the dimension of the range of T is equal to the dimension of the source space V. In other words, the dimensions of the nullspace and range of T add up to the dimension of the source space. Therefore, the direct sum of the nullspace and range of T must equal the source space V. I hope this helps guide your proof in the right direction.
 

What is the direct sum of nullspace and range?

The direct sum of nullspace and range is a concept in linear algebra that describes the combination of two subspaces of a given vector space. It is defined as the set of all possible linear combinations of the basis vectors of the nullspace and range, and is denoted by the symbol ⊕.

What is the significance of the direct sum of nullspace and range?

The direct sum of nullspace and range is significant because it allows us to decompose a vector space into two smaller subspaces that are mutually exclusive and together span the entire space. This decomposition can help in solving systems of linear equations and understanding the structure of a vector space.

How is the direct sum of nullspace and range calculated?

The direct sum of nullspace and range can be calculated by first finding the nullspace and range of a given matrix. Then, the basis vectors of the nullspace and range are combined to form a new basis for the direct sum. The resulting direct sum is a new subspace that contains all possible linear combinations of the basis vectors from the nullspace and range.

What is the relationship between the direct sum of nullspace and range and the rank-nullity theorem?

The direct sum of nullspace and range is closely related to the rank-nullity theorem, which states that the dimension of a vector space is equal to the sum of the dimensions of its nullspace and range. In other words, the direct sum of nullspace and range has a dimension that is equal to the rank of the original matrix.

Can the direct sum of nullspace and range be used to solve systems of linear equations?

Yes, the direct sum of nullspace and range can be used to solve systems of linear equations. By decomposing a vector space into its nullspace and range, we can find a basis for the direct sum that can be used to solve the equations. This can be particularly useful when dealing with large systems of equations, as it allows us to break them down into smaller, more manageable subspaces.

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