Linear Algebra - one to one and onto question

In summary, the three mappings f are not one-to-one and onto. This means that there are elements in the output space that do not have corresponding elements in the input space, and there are also elements in the input space that map to the same element in the output space. Therefore, f is not an isomorphism.
  • #1
zeion
466
1

Homework Statement



Determine whether each of the following mappings f is onto or one-to-one. Is f an isomorphism?

1) f maps R2 to R3 defined by f(x, y) = (x, y, x+y)
2) f maps R3 to R(1x3) defined by f(x,y,z) = [x^2, y^2, z^2]
3) f maps R4 to P2(R) defined by f(a,b,c,d) = a+(b-c)x+dx^2

Homework Equations





The Attempt at a Solution



I'm not sure how to do these.. I understand somewhat about one-to-one and onto, but these notations kind of confuse me.. For onto, do I just need to look at the outcome and see if it spans the space? And for one-to-one, I look at rather every component from the source appears in every component in the result?

1) Not one-to-one, onto.
2) Not one to one, onto.
3) Not one to one, onto.
 
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  • #2
zeion said:

Homework Statement



Determine whether each of the following mappings f is onto or one-to-one. Is f an isomorphism?

1) f maps R2 to R3 defined by f(x, y) = (x, y, x+y)
2) f maps R3 to R(1x3) defined by f(x,y,z) = [x^2, y^2, z^2]
3) f maps R4 to P2(R) defined by f(a,b,c,d) = a+(b-c)x+dx^2

Homework Equations





The Attempt at a Solution



I'm not sure how to do these.. I understand somewhat about one-to-one and onto, but these notations kind of confuse me..
You need to understand the definitions of these terms more than just somewhat. Go back and look at the definitions of one-to-one and onto, and any examples there are in your book.
zeion said:
For onto, do I just need to look at the outcome and see if it spans the space?
Pretty much. More precisely, if f is a map from U to V, f is onto V if, for any v in V, there is a u in U such that f(u) = v. IOW, no matter what thing you pick in the output space, there is a thing in the input space that maps to it. For example, if f:R --> R is defined by f(x) = x2, f is not onto, since -1 is in R (the output space), but there is no real number x in R (the input space) such that f(x = -1.
zeion said:
And for one-to-one, I look at rather every component from the source appears in every component in the result?
That's not how one-to-one-ness is defined. One definition is that if a != b, then f(a) != f(b). Using the same function as my previous example, f is not one-to-one, since f(2) = f(-2).
zeion said:
1) Not one-to-one, onto.
2) Not one to one, onto.
3) Not one to one, onto.
 

1. What is the difference between a one-to-one and an onto mapping in linear algebra?

In linear algebra, a one-to-one mapping, also known as an injection, is a function that maps each element in its domain to a unique element in its range. This means that no two elements in the domain can map to the same element in the range. Whereas, an onto mapping, also known as a surjection, is a function that maps each element in its range to at least one element in its domain. This means that every element in the range has at least one pre-image in the domain.

2. How can I determine if a linear transformation is one-to-one or onto?

To determine if a linear transformation is one-to-one, you can check if the kernel, or null space, of the transformation is equal to the zero vector. If it is, then the transformation is one-to-one. To determine if a linear transformation is onto, you can check if the range, or image, of the transformation is equal to the entire codomain. If it is, then the transformation is onto.

3. Can a linear transformation be both one-to-one and onto?

Yes, a linear transformation can be both one-to-one and onto. This type of transformation is known as a bijection, which is a function that is both injective and surjective. In other words, a bijection maps each element in its domain to a unique element in its range and vice versa.

4. How do one-to-one and onto mappings affect the dimensions of vector spaces?

If a linear transformation is one-to-one, it means that the dimension of the range is equal to the dimension of the domain. This is because, for every input in the domain, there is a unique output in the range, and therefore the number of vectors in the domain and range must be equal. On the other hand, if a linear transformation is onto, it means that the dimension of the range is equal to the dimension of the codomain. This is because every element in the codomain is being mapped to, and therefore the range must contain all possible outputs, resulting in the same dimension as the codomain.

5. How are one-to-one and onto mappings used in real-life applications?

One-to-one and onto mappings are commonly used in coding and data compression algorithms, such as JPEG and MP3. They are also used in cryptography to ensure secure communication between parties. In addition, these concepts are important in machine learning and data analysis, where one-to-one and onto transformations are used to simplify and analyze large datasets.

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