A Linear Transformation $T: U\to V$ cannot be Injective if $\dim(U) > \dim(V)$

Linear Transformation problems and solutions

Problem 541

Let $U$ and $V$ be finite dimensional vector spaces over a scalar field $\F$.
Consider a linear transformation $T:U\to V$.

Prove that if $\dim(U) > \dim(V)$, then $T$ cannot be injective (one-to-one).

 
LoadingAdd to solve later

Sponsored Links


Hints.

You may use the folowing facts.

A linear transformation $T: U\to V$ is injective if and only if the nullity of $T$ is zero.

For the proof of this fact, see the post ↴
A Linear Transformation is Injective (One-To-One) if and only if the Nullity is Zero

Rank-Nullity Theorem
For a linear transformation $T: U \to V$, we have
\[\nullity(T)+\rk(T)=\dim(U).\]

We give two proofs. The first one uses the rank-nullity theorem.
The second one avoids using the theorem.

Proof 1.

By the rank-nullity theorem, we have
\[\nullity(T)+\rk(T)=\dim(U).\] Note that the rank of $T$ is the dimension of the range $\calR(T)$, which is a subspace of the vector space of $V$.
Hence it yields that
\[\rk(T)=\dim(\calR(T)) \leq \dim(V).\]

It follows that
\begin{align*}
\nullity(T)=\dim(U)-\rk(T) > \dim(U)-\dim(V) > 0,
\end{align*}
where the last inequality follows from the assumption.

This implies that the nullity is nonzero, hence $T$ is not injective.
(See the hints above.)

Proof 2.

In the second proof, we prove $T$ is injective without using the rank-nullity theorem.

Seeking a contradiction, assume that $T: U\to V$ is injective.
Let $\{\mathbf{u}_1, \dots, \mathbf{u}_n\}$ be a basis of the vector space $U$, where $n=\dim(U)$.
Put $\mathbf{v}_1=T(\mathbf{u}_1), \dots, \mathbf{v}_n=T(\mathbf{u}_n)$.


We claim that the vectors $\mathbf{v}_1, \dots, \mathbf{v}_n$ are linearly independent in $V$.
To see this, consider the linear combination
\[c_1\mathbf{v}_1+\cdots+c_n \mathbf{v}_n=\mathbf{0}_V,\] where $c_1, \dots, c_n$ are scalars in $\F$.
Then we have
\begin{align*}
\mathbf{0}_V&=c_1\mathbf{v}_1+\cdots+c_n \mathbf{v}_n\\
&=c_1T(\mathbf{u}_1)+\cdots+c_n T(\mathbf{u}_n)\\
&=T(c_1\mathbf{u}_1+\cdots + c_n \mathbf{u}_n).
\end{align*}
Since $T$ is assumed to be injective, we must have
\[c_1\mathbf{u}_1+\cdots + c_n \mathbf{u}_n=\mathbf{0}_U.\] Because $\mathbf{u}_1, \dots, \mathbf{u}_n$ are basis vectors, they are linearly independent.
It follows that
\[c_1=\cdots=c_n=0,\] and hence the vectors $\mathbf{v}_1, \dots, \mathbf{v}_n$ are linearly independent.


As the vector space $V$ contains $n$ linearly independent vectors, we see that
\[\dim(V) \geq n=\dim(U),\] which contradicts the assumption that $\dim(U) > \dim(V)$.
Therefore, $T$ cannot be injective.


LoadingAdd to solve later

Sponsored Links

More from my site

  • Quiz 7. Find a Basis of the Range, Rank, and Nullity of a MatrixQuiz 7. Find a Basis of the Range, Rank, and Nullity of a Matrix (a) Let $A=\begin{bmatrix} 1 & 3 & 0 & 0 \\ 1 &3 & 1 & 2 \\ 1 & 3 & 1 & 2 \end{bmatrix}$. Find a basis for the range $\calR(A)$ of $A$ that consists of columns of $A$. (b) Find the rank and nullity of the matrix $A$ in part (a).   Solution. (a) […]
  • Idempotent Matrices are DiagonalizableIdempotent Matrices are Diagonalizable Let $A$ be an $n\times n$ idempotent matrix, that is, $A^2=A$. Then prove that $A$ is diagonalizable.   We give three proofs of this problem. The first one proves that $\R^n$ is a direct sum of eigenspaces of $A$, hence $A$ is diagonalizable. The second proof proves […]
  • Linear Transformation to 1-Dimensional Vector Space and Its KernelLinear Transformation to 1-Dimensional Vector Space and Its Kernel Let $n$ be a positive integer. Let $T:\R^n \to \R$ be a non-zero linear transformation. Prove the followings. (a) The nullity of $T$ is $n-1$. That is, the dimension of the nullspace of $T$ is $n-1$. (b) Let $B=\{\mathbf{v}_1, \cdots, \mathbf{v}_{n-1}\}$ be a basis of the […]
  • Orthonormal Basis of Null Space and Row SpaceOrthonormal Basis of Null Space and Row Space Let $A=\begin{bmatrix} 1 & 0 & 1 \\ 0 &1 &0 \end{bmatrix}$. (a) Find an orthonormal basis of the null space of $A$. (b) Find the rank of $A$. (c) Find an orthonormal basis of the row space of $A$. (The Ohio State University, Linear Algebra Exam […]
  • Determine Bases for Nullspaces $\calN(A)$ and $\calN(A^{T}A)$Determine Bases for Nullspaces $\calN(A)$ and $\calN(A^{T}A)$ Determine bases for $\calN(A)$ and $\calN(A^{T}A)$ when \[ A= \begin{bmatrix} 1 & 2 & 1 \\ 1 & 1 & 3 \\ 0 & 0 & 0 \end{bmatrix} . \] Then, determine the ranks and nullities of the matrices $A$ and $A^{\trans}A$.   Solution. We will first […]
  • Rank and Nullity of a Matrix, Nullity of TransposeRank and Nullity of a Matrix, Nullity of Transpose Let $A$ be an $m\times n$ matrix. The nullspace of $A$ is denoted by $\calN(A)$. The dimension of the nullspace of $A$ is called the nullity of $A$. Prove the followings. (a) $\calN(A)=\calN(A^{\trans}A)$. (b) $\rk(A)=\rk(A^{\trans}A)$.   Hint. For part (b), […]
  • The Range and Null Space of the Zero Transformation of Vector SpacesThe Range and Null Space of the Zero Transformation of Vector Spaces Let $U$ and $V$ be vector spaces over a scalar field $\F$. Define the map $T:U\to V$ by $T(\mathbf{u})=\mathbf{0}_V$ for each vector $\mathbf{u}\in U$. (a) Prove that $T:U\to V$ is a linear transformation. (Hence, $T$ is called the zero transformation.) (b) Determine […]
  • In which $\R^k$, are the Nullspace and Range Subspaces?In which $\R^k$, are the Nullspace and Range Subspaces? Let $A$ be an $m \times n$ matrix. Suppose that the nullspace of $A$ is a plane in $\R^3$ and the range is spanned by a nonzero vector $\mathbf{v}$ in $\R^5$. Determine $m$ and $n$. Also, find the rank and nullity of $A$.   Solution. For an $m \times n$ matrix $A$, the […]

You may also like...

Leave a Reply

Your email address will not be published. Required fields are marked *

This site uses Akismet to reduce spam. Learn how your comment data is processed.

More in Linear Algebra
Linear Transformation problems and solutions
A Linear Transformation is Injective (One-To-One) if and only if the Nullity is Zero

Let $U$ and $V$ be vector spaces over a scalar field $\F$. Let $T: U \to V$ be a linear...

Close