Please explain isomorphism with respect to vector spaces.

In summary: An isomorphism is a one-to-one and onto function that preserves the structure of the vector space it is defined on.
  • #1
mrroboto
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Can someone explain isomorphism to me, with respect to vector spaces. Thanks!
 
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  • #2
mrroboto said:
Can someone explain isomorphism to me, with respect to vector spaces. Thanks!

Are you looking for a "layman's terms" explanation? You can think of an isomorphism (with respect to a vector space) as a one-to-one map from the vector space onto itself that preserves all the properties of that vector space. The image of the vector space under this map is "identical" to the original vector space. These maps are often associated with invertible matrices.

Here's an example:

Consider [tex]f:\mathbb{R}^2 \to \mathbb{R}^2[/tex] defined by

[tex]f(v) = v\cdot \left[ \begin{array}{cc} 1 & 0 \\ 0 & -1 \end{array} \right][/tex]

where [tex] v [/tex] is a point in the plane written as row vector. That is, if [tex]v=(x,y)[/tex], then [tex]f(v) = f((x,y)) = (x,-y)[/tex]. Under this map, the "y" values of the Cartesian plane are negated, while the "x" values remain the same.
 
  • #3
An isomorphism from a vector space V to W is a function that is one-to-one and onto that also preserves the operation of the vector spaces.

For example, a bijective linear map is an isomorphism between vector spaces.

http://en.wikipedia.org/wiki/Isomorphism#Practical_example
 
  • #4
in general, isomorphisms preserve some kind of structure. If you've heard of a homomorphism, an isomorphism is just a 1-1 and onto homomorphism.

Any linear transformation between vector spaces is actually just a homomorphism of vector spaces. When it's an invertible, 1-1, onto homomorphism (linear transformation) then it's an isomorphism.
 
  • #5
In a very real sense, two vector spaces (or groups, or semigroups, or fields) are "isomorphic" if they are exactly the same, except the elements, operations, etc., are named differently.

If a "mathematical structure"- a set of objects [itex]\{x_0, x_1, ...\}[/itex] with operations {+, *, ...} is "isomorphic" to another such structure- a set [itex]\{y_0, y_1,...\}[/itex] with operations {+', *', ...} then there is a function, f, that maps each x to a y and each operation to a corresponding ' operation so that the operations are "preserved". If I were to 're-name' x, f(x), and rename each operation with its corresponding operation, then the "x" structure would be indistinguishable from the "y" structure.
 

1. What is isomorphism in the context of vector spaces?

Isomorphism is a mathematical concept that describes a relationship between two vector spaces. It means that the two vector spaces have the same structure and can be mapped onto each other in a one-to-one and onto manner. This means that every vector in one space can be mapped to a unique vector in the other space, and vice versa.

2. How is isomorphism different from similarity?

Isomorphism and similarity are often confused, but they are not the same concept. While isomorphism describes a structural relationship between two vector spaces, similarity describes a relationship between two objects within the same space. In other words, two objects are similar if they have the same shape or form, while two vector spaces are isomorphic if they have the same structure.

3. What is an isomorphism mapping?

An isomorphism mapping is a function that maps vectors from one vector space to another in a one-to-one and onto manner. It is a bijective mapping, meaning that every vector in one space has a unique corresponding vector in the other space and vice versa. Isomorphism mappings preserve the structure of vector spaces, meaning that the operations of addition and scalar multiplication are preserved.

4. How is isomorphism useful in mathematics?

Isomorphism is a fundamental concept in mathematics and has many applications. It allows mathematicians to study different vector spaces that have the same structure by using the properties of one space to understand the other. This can simplify complex problems and make them more manageable. Isomorphism is also used in other areas of mathematics, such as group theory and topology, to study relationships between different mathematical structures.

5. Can two vector spaces be isomorphic if they have different dimensions?

No, two vector spaces cannot be isomorphic if they have different dimensions. Isomorphism requires a one-to-one and onto mapping, which means that the number of elements in each space must be the same. If the dimensions are different, there will not be a bijection between the spaces, and they cannot be isomorphic. However, it is possible for two vector spaces to have the same dimension and still not be isomorphic, as they may have different structures.

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