Why are certain matrices referred to as n x n?

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Discussion Overview

The discussion revolves around the terminology used to describe matrices, specifically the common reference to "n x n" matrices compared to "m x m" matrices. Participants explore the implications of these terms in mathematical contexts, including linear transformations and notation conventions.

Discussion Character

  • Conceptual clarification
  • Debate/contested

Main Points Raised

  • Some participants note that "rectangular" matrices represent linear transformations from Rm to Rn, highlighting that transformations between different dimensions cannot be one-to-one or onto, which relates to the solvability of equations.
  • One participant suggests that "n" is commonly used in mathematical notation to define terms, while "m" is less frequently referenced, indicating a preference for "n x n" in educational contexts.
  • Another viewpoint expresses that there is no mathematical significance in preferring "n x n" over "m x m," suggesting that any symbol could be used to denote matrix dimensions, and the choice is largely a matter of convention.
  • One participant mentions that in different languages, such as Chinese, the notation tends to align with English conventions, but expresses a desire to use alternative alphabets to avoid limitations in notation across fields.

Areas of Agreement / Disagreement

Participants do not reach a consensus on the significance of using "n x n" versus "m x m." There are multiple competing views regarding the importance of these terms and their implications in mathematical contexts.

Contextual Notes

Some limitations in the discussion include the dependence on definitions of dimensions and the potential confusion arising from notation conventions across different languages and fields.

Nusc
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Why are certain matrices referred to as n x n?

I rarely see anything refer to an m x m matrix, on what circumstance do we see this? Or why isn't this used for anything?
 
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Yes, "rectangular" matrices represent linear transformations from Rm to Rn. The problem is that linear transformations from spaces of different dimensions can't be 1 to 1 or onto- that is, they don't have inverses. It is basically the same problem as solving m equations in n unknowns. If m< n, there are not enough equations and many different solutions. If n< m, there are too many equations- in general there is no solution. It is only n equations in n unknowns that have solutions.
 
in math n is usually the first symbol used to define the mth term... thus u see M_nxn rather than M_mxm. Its like
[] "i" is usually the first counter followed by j,k,l
[] "a" is coefficient followed by b,c,d
[] "x" for spatial quantities.
[] F for functions G,H
[] theta for angles
etc. who designed them i don't know but its the way you usually learn.
 
in other words, there is no reason at all of any mathematical ,importance, you can say m by m all you want, there is no difference. indsed in logic a bound variable also known as a "dummy" variable, it could be phrased, "& by & matrix, where '&' is the number of rows and columns".

i.e. the same is expressed as well by " [ ] by [ ] matrix where [ ] = the number of rows and columns," and you can put any symbol at all in all three brackets [ ]. but people have limited imaginations, and many students are confused by creativity, so we try to keep it simple. obviously in languages written in other alphabets you are less likely to find "n by n".

i'll try to remember to look in "vectornye prostranstvye" a russian text i have written in the cyrillic alphabet.
 
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actually in chinese they tend to go towards the english notations. these days. I want to use the chinese alphabet though...cuz using the roman and greek has limitations when you cross fields.
 

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