Aren't all linear operators one-to-one and onto?

In summary, the conversation discusses the properties of linear operators and their relationship to one-to-one and onto mappings. It is noted that not all linear operators are one-to-one, and a counterexample is given for the differentiation operator in the infinite-dimensional space of smooth functions. The concept of projection operators is also mentioned, and their effect on the injectivity and surjectivity of linear operators is explained. Additionally, it is mentioned that any linear surjection with a kernel restricts to an isomorphism on a complementary subspace, and the inverse isomorphism is injective and not surjective.
  • #1
pellman
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Let W be a vector space and let A be a linear operator W --> W. Isn't it the case that for any such A, the kernel of A is the zero vector and the range is all of W? And that it is one-to-one from linearity? I ask because an author I am reading goes through a lot of steps to show that a certain operator is one-to-one and onto yet I thought it was a given for any linear operator.
 
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  • #2
What if ##A = \frac{d}{dx}##? One to One is bijective, right?
I don't believe that that operator is one to one, if you consider it acting on ln(x) in W. If you differentiate, you get 1/x, and the domain of those two functions are different (assuming no absolute value sign on ln(x)). ln(x): (0, inf) and 1/x: (-inf,0) U (0,inf). This is an injective mapping, which is not 1 to 1.
 
  • #3
So I think it depends on W, is what I'm gathering. If W is a vector space that includes all real functions, then no, it's not 1 to 1. I'm not 100% sure, though. Someone will likely step in and correct me on this =/
 
  • #4
pellman said:
Let W be a vector space and let A be a linear operator W --> W. Isn't it the case that for any such A, the kernel of A is the zero vector and the range is all of W? And that it is one-to-one from linearity? I ask because an author I am reading goes through a lot of steps to show that a certain operator is one-to-one and onto yet I thought it was a given for any linear operator.
T(a):=0 is linear: T(a+b)=0=T(a)+T(b); T(ca)=0=cT(a). Projection operators are not 1-1: P(a,b,c)=P(a,b,d) ; ## c \neq d ## For P(x,y,z):=P(x,y,0). Or, take a matrix with linearly-dependent rows/columns. EDITIt is not injective (independently of the choice of basis).
 
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  • #5
Projection operators! Yes. thank you for that counter example.
 
  • #6
pellman said:
Projection operators! Yes. thank you for that counter example.

A linear operator (from finite dimensional W to W) is 1-1 iff it is onto. That may be what you were thinking.
 
  • #7
PeroK said:
A linear operator (from finite dimensional W to W) is 1-1 iff it is onto. That may be what you were thinking.
Yes. I just didn't remember that not all linear operators are one-to-one.
 
  • #8
"finite dimensional W" is important here. It is not true if you drop that requirement.
 
  • #9
the most important linear operator is D = differentiation, on the (infinite dimensional) linear space W of smooth functions. Then notice that D of a constant function is zero, so it is not 1-1. (it is onto however.)
 
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  • #10
Add integration with the requirement f(0)=0 and you have the example for 1-1 but not onto.
 
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  • #11
nice remark.

here we have the subspace Z of those smooth functions that equal zero at 0, and the complementary (real) line R of constant functions. then the space of all smooth functions is a direct product of Z and R, and D is 1-1 on Z and onto all smooth functions. the inverse of this restriction therefore, i.e. integration with f(0) = 0, is an injection from all smooth functions onto the subspace Z.

So differentiation D is essentially projection of all smooth functions onto the subspace Z, followed by an isomorphism from Z to all smooth functions. And the special integration above is essentially the inverse isomorphism of all smooth functions onto Z, followed by inclusion of Z into all smooth functions.

In general any linear surjection which has a kernel, restricts to an isomorphism on any subspace complementary to that kernel. Then the inmverse isomorphism is injective and not surjective.
 

Related to Aren't all linear operators one-to-one and onto?

1. What is the definition of a linear operator?

A linear operator is a function that operates on vectors in a vector space, and satisfies two properties: additivity and homogeneity. This means that for any two vectors in the space and any scalar, the operator must produce the sum or scalar multiple of those vectors.

2. How does a linear operator relate to one-to-one and onto functions?

A linear operator can be thought of as a generalization of a one-to-one and onto function. While a one-to-one function maps each input to a unique output, and an onto function maps to every element in the range, a linear operator can map from one vector space to another, and may not necessarily have a one-to-one or onto relationship.

3. Is every linear operator one-to-one and onto?

No, not all linear operators are one-to-one and onto. For a linear operator to be one-to-one, each input vector must produce a unique output vector. However, this is not always the case, as some linear operators may map multiple input vectors to the same output vector. Additionally, for a linear operator to be onto, it must map to every element in the range. Again, this is not always true for all linear operators.

4. What are some examples of linear operators that are not one-to-one and onto?

Some examples of linear operators that are not one-to-one and onto include projection operators, which map a higher-dimensional space onto a lower-dimensional subspace, and zero operators, which map all vectors to the zero vector.

5. How can one determine if a linear operator is one-to-one and onto?

To determine if a linear operator is one-to-one and onto, one can check the kernel and range of the operator. The kernel, also known as the null space, is the set of all vectors that are mapped to the zero vector by the operator. If the kernel contains only the zero vector, then the operator is one-to-one. The range, also known as the image, is the set of all vectors that are mapped to by the operator. If the range contains all vectors in the output space, then the operator is onto.

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