Multiplication Of Vectors with a Vector As An Element

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Discussion Overview

The discussion centers around the multiplication of vectors, specifically when one of the elements of a vector is itself a vector. Participants explore the implications of this scenario on matrix multiplication rules and the definition of dot products.

Discussion Character

  • Technical explanation
  • Debate/contested

Main Points Raised

  • One participant questions whether the expression cTc is defined when c contains a vector as an element, and seeks clarification on how to calculate a*a.
  • Another participant suggests that to write vectors correctly, one should consider them as column matrices, and discusses the implications of transposing vectors for matrix multiplication.
  • A different participant challenges the validity of having a vector as an element within another vector, arguing that components of a vector must be scalars in a given basis.
  • One participant inquires if the vector a must be a column vector to conform to standard definitions.
  • Another participant explains that the dot product of two vectors can be understood as the transpose of one vector multiplied by the other, emphasizing the conditions under which this multiplication is defined.

Areas of Agreement / Disagreement

Participants express differing views on whether a vector can contain another vector as an element, and there is no consensus on the implications of this for matrix multiplication and dot products.

Contextual Notes

There are unresolved assumptions regarding the definitions of vectors and their components, as well as the notation used in matrix operations.

Fribbles
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Hi,

If I have a vector c = [ a , 1 ]T where the element a is a vector. If I multiply c by its transpose:

cTc

is this defined? How do I calculate the a*a? Matrix multiplication rules would say that a * a is undefined because it is a nx1 matrix multiplied by a nx1 matrix. Or is the convention that it is the dot product?

Thank you in advance for your help!
 
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In order to write vectors like that, you will need to think of the original vector, v, as a column matrix, say, v= \begin{bmatrix} x\\ y\end{bmatrix} so that its transpose is a row matrix: v*= \begin{bmatrix}x & y\end{bmatrix}. Then the product, v*u, is the matrix product \begin{bmatrix} x & y\end{bmatrix}\begin{bmatrix}a \\ b \end{bmatrix}= ax+ by.

Of course, that is only notation. If you want to write the vector v as a row, fine. Then its transpose is a column and you have to write the product in the other order. Or you can go with the more abstract concept- given a vector in a vector space, V, there exist an isomorphism from V to its dual, V*, the set of linear functions from V to the underlying field. In that case "v*u" is the linear function corresponding to v applied the vector u.
 
Are you sure this is a well-defined vector, though?
The matrix notation, \vec{u} = \begin{bmatrix}x_1 \\ x_2 \\ \vdots \\ x_n\end{bmatrix} is defined such that x_1, \dots, x_n are the components of your vector in a given basis of your vector space, so they must be scalars.
That's why I don't think having a vector there makes much sense.
 
Thank you for the replies...so then does, a, have to be a column vector?
 
when you take the dot product of two vectors a.b what you're really doing is the transpose of a and then doing matrix multiplication

so a.b === a^t b

if a is 1xn and b is nx1 then a^t is 1xn and so the matrix multiplication is defined (and gives you back a 1x1 matrix aka a real number)
 

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