How do change of basis matrices work in linear algebra?

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Discussion Overview

The discussion centers on the concept of change of basis matrices in linear algebra, specifically how to translate coordinates from one basis to another. Participants explore the theoretical underpinnings, practical examples, and intuitive understanding of this process, particularly in the context of R².

Discussion Character

  • Exploratory
  • Technical explanation
  • Conceptual clarification
  • Mathematical reasoning

Main Points Raised

  • One participant explains that a change of basis matrix is a square matrix whose rows consist of the coordinates of the original basis vectors expressed in terms of the new basis vectors.
  • Another participant questions how to compute the transformation of a vector from one basis to another, highlighting the ambiguity in treating vectors as row or column vectors and suggesting a need for a concrete example.
  • A different participant introduces the idea of factoring transformations into rotations, translations, and scaling operations, suggesting a geometric interpretation of these transformations.
  • A participant reiterates the definition of the change of basis matrix, emphasizing that each vector in the original basis can be expressed as a linear combination of the new basis vectors, with the coefficients forming the columns of the change of basis matrix.
  • An example is provided using specific vectors in R² to illustrate how to construct the change of basis matrix, detailing the linear combinations needed to express the original basis vectors in terms of the new basis.

Areas of Agreement / Disagreement

Participants express varying degrees of understanding and clarity regarding the definition and computation of change of basis matrices. There is no consensus on the best approach to represent vectors as row or column vectors, and the discussion remains unresolved on certain technical aspects.

Contextual Notes

Some participants note the dependence on definitions regarding vector representation (row vs. column), which may affect the computation of the change of basis matrix. Additionally, the examples provided may not cover all possible scenarios or complexities in higher dimensions.

aaaa202
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Given a basis A = {a1,a2...an} we can always translate coordinates originally expressed with this basis to another basis A' = {a1',a2'...an'}. To do this we simply do some matrix-multiplication and it turns out that the change of basis matrix equals a square matrix whose rows are the coordinates of the original basis vectors written in terms of the new basis-vectors. I'm finding this a little hard to understand intuitively - can someone give me an example from maybe R^2 that shows why this is in an intuitive manner.
 
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aaaa202 said:
that the change of basis matrix equals a square matrix whose rows are the coordinates of the original basis vectors written in terms of the new basis-vectors.

That depends on how we define a "change of basis matrix". For example if I have the row vector [itex]V_a[/itex] in basis [itex]A[/itex] and [itex]S[/itex] is "the change of basis matrix from basis [itex]A[/itex] to basis [itex]B[/itex] then how do I find that vector expessed in basis [itex]B[/itex]?

Do I compute [itex](V_a)( S)[/itex] or do I compute [itex](S)(V_a)^T[/itex] ? It depends on whether the course materials like to treat vectors as row vectors or as column vectors.

You should be able to construct a 2x2 example.

Let
[itex]a_1 = S_{11} b_1 + S_{12} b_2[/itex]
[itex]a_2 = S_{21} b_1 + S_{22} b_2[/itex]

What is the vector [itex]4 a_1 + 3 a_2[/itex] equal to in the [itex]B[/itex] basis?
 
You can factor out transformations in terms of rotations, translations, and scaling operations.

By composing maps, you can see geometrically what the translation is doing by treating each map separately in terms of its geometric effect on a vector.
 
aaaa202 said:
Given a basis A = {a1,a2...an} we can always translate coordinates originally expressed with this basis to another basis A' = {a1',a2'...an'}. To do this we simply do some matrix-multiplication and it turns out that the change of basis matrix equals a square matrix whose rows are the coordinates of the original basis vectors written in terms of the new basis-vectors. I'm finding this a little hard to understand intuitively - can someone give me an example from maybe R^2 that shows why this is in an intuitive manner.

Each vector in the first basis can be written as a linear combination of the vectors in the second. The coefficients of this linear combination are a column in the change of basis matrix.
The set of columns, one for each basis vector in the first basis, make up the change of basis matrix.
Multiplying a basis vector by the matrix just writes the vector out in terms of the second basis.

E.g. take for the first basis of R^2 the vectors (1,0) and (0,1) and the second basis (1,-1) and (1.1).

Then (1,0) = 1.2 (1,-1) + 1/2(1,1) so the first column of the matrix is (1/2 1/2).

The second column is (-1/2 1/2)
 
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