Example of a linear transformation L which is injective but not surj, or vice versa

In summary, An example of a linear vector space V and a linear transformation L: V-> V that is injective but not surjective is the transformation D on the vector space ℝ[X] and an example of a linear transformation L: V-> V that is surjective but not injective is the transformation I on the vector space ℝ[X]. These examples demonstrate the equivalence between injectivity and surjectivity in finite-dimensional vector spaces.
  • #1
damabo
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Homework Statement


Give an example of a linear vector space V and a linear transformation L: V-> V that is 1.injective, but not surjective (or 2. vice versa)


Homework Equations



-If L:V-> V is a linear transformation of a finitedimensional vector space, then
L is surjective, L is injective and L is bijective are equivalent
- ker(L)={0} is equivalent with L is injective (in which case ker(L) is 0-dimensional)
-dimV=dim(ker(L))+dim(Im(L)), if (ℝ,V,+),(ℝ,W,+) finite dimensional vector spaces and L maps V on W linearly.

The Attempt at a Solution



I guess that the word finite is the most relevant, so that the equation of the dimensions does not apply for infinitedimensional vector spaces.
an example of an infinitedimensional vectorspace would be (ℝ,ℝ[X],+) if ℝ[X] is a polynomial of infinite degree, with a basis β={1,X,X^2,...,X^n,...}.

so suppose L:ℝ[X]->ℝ[X] is a transformation.
1. example of surjective, but not injective function:
Perhaps D if D is the differential operator such that a_0+a_1X+a_2X^2+... -> a_1 + 2a_2X + ...
not injective: clearly 1 != 2, yet D(1)=D(2)
surjective: choose p in ℝ[X]. p=a_1+2a_2X+3a_2X^2... for certain a_i in ℝ. is there a p_0 in ℝ[X] such that D(p_0)=p? indeed, choose a_0 randomly, and choose a_1,a_2,a_3,... in ℝ. define p_0= a_0+a_1X+a_2X^2+... then clearly p_0 [itex]\in[/itex] ℝ[X], and D(p_0)=p.
 
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  • #2


(continued)
2. example of an injective, but not surjective function:
how about I, if I is the integration function, such that I: a_0+a_1X+a_2X^2 ->a_0X + a_1(X^2)/2+ a_2(X^3)/3 + ...
not surjective: is there a p in ℝ[X] such that there is no p_0 in ℝ[X] so that I(p_0)=p (or equivalently D(p)=p_0)?
In other words, is there a p whose derivative is not a real polynomial?
I don't think this is possible.
 
  • #3


Let [itex]V=\mathbb{R}^2[/itex]. Every vector in such space can be described as [itex]\mathbf{v}=(r\cos{\phi},r\sin{\phi})[/itex], where [itex]r\in[0;\infty)\: \wedge \:\phi\in[0;2\pi)[/itex]. Let:
[itex]L:V\rightarrow V;\quad L((r\cos{\phi},r\sin{\phi}))=(r\cos{\frac{\phi}{2}},r\sin{\frac{\phi}{2}})[/itex]
Due to function [itex]f(x)=\frac{x}{2}[/itex] being injective, [itex]L[/itex] is also injective. Furthermore, because none of [itex]L(\mathbf{v})[/itex] have [itex]\phi > \pi[/itex], [itex]L[/itex] is not surjective.
 
  • #4


none of the L(v) have [itex]\phi[/itex]/2 > [itex]\pi[/itex] ?
 
  • #5


Ah, yes, sorry, I've written it too quickly. More precisely: none of vectors from [itex]L[/itex] image has angle greater than [itex]\pi[/itex]. Basically the image is a half-plane.
 
  • #6


ah ok. Get it.
 

1. What is a linear transformation?

A linear transformation is a mathematical function that maps one vector space to another while preserving the algebraic operations of addition and scalar multiplication. It is often represented by a matrix and can be used to model real-world situations in fields such as physics, engineering, and economics.

2. What does it mean for a linear transformation to be injective?

A linear transformation is injective if each element in the domain maps to a unique element in the range. In other words, no two distinct elements in the domain are mapped to the same element in the range. This is also known as the "one-to-one" property.

3. Can a linear transformation be injective but not surjective?

Yes, it is possible for a linear transformation to be injective but not surjective. This means that there are elements in the range that are not mapped to by any element in the domain. In other words, the transformation does not cover the entire range of the vector space.

4. What is an example of a linear transformation that is injective but not surjective?

A simple example of a linear transformation that is injective but not surjective is the function f(x) = x^2 from the real numbers to the non-negative real numbers. This transformation is injective because each input (x) has a unique output (x^2), but it is not surjective because there are non-negative real numbers that are not outputs of the function (e.g. -2 does not have a square root in the non-negative real numbers).

5. How does the injectivity and surjectivity of a linear transformation affect its inverse?

If a linear transformation is both injective and surjective, it is considered to be bijective and its inverse exists. However, if a transformation is only injective or only surjective, its inverse may not exist or may not be unique. For example, an injective but not surjective transformation may have an inverse that only maps a subset of the range back to the domain. Similarly, a surjective but not injective transformation may have an inverse that maps multiple elements in the domain to the same element in the range.

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