How to multiply matrix with row vector?

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    Matrix Row Vector
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Discussion Overview

The discussion centers on the multiplication of a 3x3 matrix with a row vector, exploring the conditions under which such operations are valid and the implications of different multiplication configurations. The scope includes mathematical reasoning and conceptual clarification regarding matrix dimensions and operations.

Discussion Character

  • Technical explanation
  • Conceptual clarification
  • Debate/contested

Main Points Raised

  • One participant asks how to calculate the multiplication of a 3x3 matrix with a 3-column row vector.
  • Another participant states that matrix multiplication can only occur between a matrix and a column vector or between a row vector and a matrix, providing a formula for the multiplication of a 3x3 matrix with a 3x1 column vector.
  • A different participant emphasizes the importance of matching inner dimensions, explaining that a row vector is a 1x3 matrix and can multiply a 3x3 matrix to yield a 1x3 result, while a 1x3 multiplied by a 3x1 results in a 1x1 output, akin to a dot product.
  • One participant suggests that various "tricks" could be used to manipulate the row vector into a form compatible with the matrix multiplication, questioning the mathematical or physical significance of such manipulations.
  • Another participant reinforces the importance of understanding the fundamental rule of 'row times column', arguing that using tricks may lead to confusion and providing examples of the outcomes of different multiplication configurations.

Areas of Agreement / Disagreement

Participants express differing views on the validity and implications of manipulating matrix and vector dimensions for multiplication. There is no consensus on the appropriateness of using tricks to achieve the multiplication.

Contextual Notes

Participants highlight the need for clarity regarding the definitions of row and column vectors, as well as the significance of the resulting mathematical objects from different multiplication approaches. Limitations in understanding the implications of these operations are noted.

entropy1
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How do I calculate a 3x3 matrix multiplication with a 3 column row vector, like below?

##
\begin{bmatrix}
A11 & A12 & A13\\
A21 & A22 & A23\\
A31 & A32 & A33
\end{bmatrix}\begin{bmatrix}
B1 & B2 & B3
\end{bmatrix}
##
 
Last edited:
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You can only multiply matrix times column or row times matrix.
$$
\begin{pmatrix}a_{11}&a_{12}&a_{13}\\a_{21}&a_{22}&a_{23}\\a_{31}&a_{32}&a_{33}\end{pmatrix}\cdot
\begin{pmatrix}b_{1}\\b_{2}\\b_{3}\end{pmatrix}=
\begin{pmatrix}a_{11}b_{1}+a_{12}b_{2}+a_{13}b_{3}\\a_{21}b_{1}+a_{22}b_{2}+a_{23}b_{3}\\a_{31}b_{1}+a_{32}b_{2}+a_{33}b_{3}\end{pmatrix}
$$
 
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The "inner dimensions" must match. A column vector is a 3x1 [3 rows by 1 column] while a row vector is a 1x3 matrix.

Use the outer dimensions to get dimension of the resulting matrix.

So you can multiply a (1x3) by a (3x3) and get a 1x3.

Multiply a 1x3 by a 3x1 and get a 1x1... essentially the same as dot product of two vectors
 
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I guess you can use various tricks, like assuming your row vector is the first row of a 3X3 matrix with zeros in the other places, or that the matrix on the left is made up of three “independent” column vectors, etc. However, you have to ask yourself what mathematical or physical objects do I get from such tricks?
 
Last edited:
apostolosdt said:
However, you have to ask yourself what mathematical or physical objects do I get from such tricks?
That's the point! It is important to understand the rule: 'row times column'. This has a meaning in itself, so "tricks" may cause more confusion than they solve. E.g.
$$
\begin{pmatrix}a&b&c\end{pmatrix}\cdot \begin{pmatrix}x\\y\\z\end{pmatrix} =\bigl \langle \begin{pmatrix}a&b&c\end{pmatrix}\, , \,\begin{pmatrix}x&y&z\end{pmatrix} \bigr\rangle = ax+by+cz \in \mathbb{R}
$$
is a number, e.g. a real number if the vectors have real components, whereas
$$
\begin{pmatrix}a\\b\\c\end{pmatrix}\cdot \begin{pmatrix}x&y&z\end{pmatrix}=\begin{pmatrix}a\\b\\c\end{pmatrix}\otimes \begin{pmatrix}x&y&z\end{pmatrix}=\begin{pmatrix}ax&ay&az\\bx&by&bz\\cx&cy&cz\end{pmatrix} \in \operatorname{M}(2,\mathbb{R})
$$
is a rank-##1## matrix, i.e. e.g. a ##(1,1)##-tensor.
 

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