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I'm writing a mathematical library; and I have an idea where I want to automatically turn column matrices and row matrices to vectors, with all of the mathematical properties of a vector.

Answer I'm looking for:

Someone with good mathematical reasoning explaining why:

column matrices, column vectors, row matrices, row vectors should not be treated as the same thing. (The library will ofcourse understand operations like [[1,2],[3,4]] * [1,2], where [1,2] is a vector)


some kind of showcase or example where it is impossible for a library that can't differentiate between row vectors and column vectors to know which one of several possible answers are correct.


some kind of evidence that it is in fact possible to do this.

please note: inner vector multiplication will be easily integrated by using a special function for that function rather than the * sign.

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Why would you want to not distinguish the two in your program? It seems to me that it would be extra work to have to convert the result to a vector, every time a computation returns a column matrix. In addition, the distinction can make it easier to pinpoint mistakes, like type checking in programming languages. – Jørgen Fogh Jan 19 '15 at 16:53

One "silly" example is the product of a column matrix times a row matrix. Consider: $$ \left[\begin{array}{c} 1 \\ 2 \\ 3 \end{array}\right] \left[\begin{array}{ccc} 4 & 5 & 6 \end{array}\right]$$. By the rules of matrix multiplication, we obtain the $3 \times 3$ matrix: $$ \left[\begin{array}{ccc} 4 & 5 & 6 \\ 8 & 10 & 12 \\ 12 & 15 & 18 \end{array}\right]$$ However, if I had "forgotten" than my original matrices were column and row matrices, respectively, then I might have considered them as vectors and (perhaps) computed the inner product: $$ (1, 2, 3) \cdot (4, 5, 6) = 32$$. By the way, if one works entirely in terms of matrices, and considers any vectors to be a column matrix, then the inner product can be defined by $\mathbf{v} \cdot \mathbf{w} = \mathbf{v}^T\mathbf{w}$, which is a standard practice in most linear algebra texts.

Hope this helps!

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As a technical note, this only occurs because you rely on convention to determine which product to compute. If you had specified the operation explicitly, this issue could not occur. – Jørgen Fogh Jan 19 '15 at 16:50

Don't expect to find any important mathematical distinction between them: these objects differ only at the level of notation and convention. They will form isomorphic (i.e. structurally equivalent) vector spaces.

There's quite a number of these things:

  • $n \times 1$ (column) matrices and column vectors of length $n$, e.g. \[\left(\begin{array}{c} 1 \\ 2 \\ 3 \\ \end{array}\right).\] A matrix uses two indices $A(i,j)$ say (where, in this case, index $j$ can only take on one value), whereas a column vector only has one (this distinction can matter e.g. in computer algebra systems).

  • $1 \times n$ (row) matrices and row vectors of length $n$, e.g. \[\left(\begin{array}{ccc} 1 & 2 & 3 \\ \end{array}\right).\] Same difference as with column matrices and column vectors.

  • 1-dimensional array of length $n$ (or a $k$-dimensional array where $k-1$ indices can take on only one value and one index can take on $n$ values).

  • Sequences of length $n$, ordered lists of length $n$, or ordered $n$-multisets, e.g. $(1,2,3)$.

  • Functions $f:\{1,2,\ldots,n\} \rightarrow S$, e.g. $f(x)=x$ and $n=3$.

  • Coefficients of polynomials of degree $n-1$ with a single indeterminate $x$, e.g. $1+2x+3x^2$.

The key ingredient in each case is that there is a 1-st element, a 2-nd element, up to n-th element. Individual definitions will have their own conventions (such as how matrix multiplication works), and will be easier to use in different contexts.

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