Proving Equal Ranks in Linear Maps: The Case of T^2 = TT and T(V) = V

In summary, we are given a linear map T(V)=V in a finite-dimensional vector space. We define T^2 as the composite TT of T with itself and similarly T^(i+1) = TT^i for all i >=1. We are also given that Rank (T) = Rank (T^2). We are asked to prove that Im(T) = Im(T^2), that U_i is nonsingular for all i>=1, and that Rank (T) = Rank (T^i) for all i >= 1. Using induction and the given information, we can conclude that Im(T) = Im(T^2), U_i is nonsingular for all i>=1, and Rank (T)
  • #1
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Homework Statement


let T(V)=V be a linear map, where V is a finite-dimensional vector space. Then T^2 is defined to be the composite TT of T with itself, and similarly T^(i+1) = TT^i for all i >=1. Suppose Rank (T) = Rank (T^2)


Homework Equations



a) prove that Im(T) = Im(T^2)

b) for i>=1, let U_i(Im(T))=Im(T) be definied as the restriction of T^i to the subspace Im(T) of V. Show that U_i is nonsingular for all i

c) Deduce that Rank (T) = Rank (T^i) for all i >= 1

The Attempt at a Solution



a) since Rank (T) = Rank (T^2), then
dim( Im(T) ) = dim ( Im(T^2) )
since I am (T) = V and TT(V) = T(V) = V = Im(T^2)
so Im(T) = I am (T^2) because V=V
-does that make sense?-

b) U_i (Im(T)) = Im(T)
so U_i (V) = V
since V is mapped to itself, U_i has to be an identity matrix
and identity matrix has an inverse because its determinant is not zero,
so U_i is nonsingular
-does that make sense?-

c) since T is a linear map that V to itself, T^i (V) = V for all i>=1,
implies that image would be the same
hence dimension of image is the same
so the Rank (T) = Rank (T^i) for all i>=1
-again, does that make sense?-
 
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  • #2
b) Just because T(V)=V does not mean T is the identity. It doesn't mean "Tv=v for all v in V". It just means it's a surjective map. However, you can conclude that T is invertible.

c) Right idea, but you should probably use induction explicitly.
 

Related to Proving Equal Ranks in Linear Maps: The Case of T^2 = TT and T(V) = V

1. What is a vector space?

A vector space is a mathematical structure that consists of a set of vectors, which can be added together and multiplied by scalars. It follows a set of axioms, such as closure under addition and scalar multiplication, and has properties such as associativity and distributivity.

2. How is a vector space different from a matrix space?

A vector space is a generalization of a matrix space, as it includes not only matrices, but also other types of objects (such as polynomials and functions) that can be added and multiplied in a similar way. In a matrix space, the operations are restricted to only matrices.

3. What is a mapping in the context of vector spaces?

A mapping, also known as a linear transformation, is a function that takes a vector from one vector space and maps it to another vector space while preserving the vector space structure. It can be represented by a matrix and has properties such as linearity and preservation of operations.

4. How are vector spaces and mappings used in real-world applications?

Vector spaces and mappings are used in various fields, such as physics, computer graphics, and machine learning. They provide a way to represent and manipulate data, such as images and signals, and can be used to solve problems involving linear equations and optimization.

5. What is the importance of basis vectors in vector spaces?

Basis vectors are a set of linearly independent vectors that span the vector space. They are important because they allow us to represent any vector in the space as a linear combination of the basis vectors. This makes it easier to perform operations and calculations in the vector space.

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