Please explain isomorphism with respect to vector spaces.

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Discussion Overview

The discussion centers around the concept of isomorphism in the context of vector spaces, exploring its definition, properties, and implications. Participants seek to clarify the nature of isomorphisms and how they relate to linear transformations and mathematical structures.

Discussion Character

  • Conceptual clarification
  • Technical explanation
  • Debate/contested

Main Points Raised

  • Some participants describe isomorphism as a one-to-one map from a vector space onto itself that preserves all properties of the vector space, often associated with invertible matrices.
  • Others emphasize that an isomorphism is a function that is both one-to-one and onto, preserving the operations of the vector spaces involved.
  • A participant notes that isomorphisms can be viewed as a specific type of homomorphism, specifically a bijective linear map.
  • Another viewpoint suggests that two vector spaces are isomorphic if they are structurally identical, differing only in the naming of elements and operations, with a function mapping each element and operation accordingly.

Areas of Agreement / Disagreement

Participants express various interpretations of isomorphism, with some focusing on its mapping properties and others on its structural implications. No consensus is reached on a singular definition or understanding of isomorphism in vector spaces.

Contextual Notes

Participants reference different aspects of isomorphisms, such as linear transformations and homomorphisms, without resolving the nuances between these concepts. The discussion reflects a range of assumptions about the definitions and implications of isomorphism.

mrroboto
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Can someone explain isomorphism to me, with respect to vector spaces. Thanks!
 
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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.
 
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.
 
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.
 

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