Projective coordinates vs vectors

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The discussion clarifies the distinction between projective coordinates and affine coordinates in the context of linear transformations. Projective coordinates, represented as [x:y:z], are equivalent under scalar multiplication, while affine coordinates, represented as (x,y), do not share this property. Matrix multiplication can represent translations in affine space but does not yield linear transformations due to the inherent differences in these coordinate systems. The confusion arises from the misinterpretation of translations as linear operations, which is addressed by recognizing that affine maps are compositions of translations and linear maps.

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There is a technical distinction between a vector and the coordinates of a vector. Are projective (also called "affine") coordinates the coordinates of vectors?

I'm thinking of how translation is accomplished by matrix multiplication. For example the point (x,y) in 2-D is given coordinates (x,y,1) and translation by (h,v) is represented as:
\begin{pmatrix} 1&0&h \\ 0&1&v \\ 0&0&1 \end{pmatrix} \begin{pmatrix} x \\ y\\ 1 \end{pmatrix} = \begin{pmatrix} x+h \\ y+v \\ 1 \end{pmatrix}.

Students are told that matrix multiplication performs a linear transformation on a vector space and also disturbed by the exercise showing that translation by a (non-zero) vector is not a linear transformation . What are the saving legalisms here?
 
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Projective and affine coordinates are definitely not the same thing. Projective space (in 2 dimensions let's say) is the set of all points [x:y:z] where [a:b:c] and [x:y:z] are considered equivalent if there is some number k such that ak=x, bk=y, ck=z.

It is the case that if you restrict to the set of [x:y:1] that there is an obvious bijection between these points and points of the form (x,y) with no equivalence relation - these (x,y) coordinates are what is called affine space.

At first glance that matrix multiplication appears to make translation a linear operation, but the set of points of the form (x,y,1) is not a vector space in any way that will make that matrix multiplication a linear transformation. For example it is tempting to just declare (x,y,1)+(a,b,1) = (a+x,y+b,1) but then if the matrix is T, we no longer get that T(x,y,1) + T(a,b,1) = T(a+x,y+b,1) since the left hand side will be shifted by twice what the right hand side is shifted by.
 
When I taught an intro class in Linear Algebra, I told the students that , while innacurate, any expression of x of the type ax+b is linear, and that the linearity has to see with the fact that x is raised to the first degree. When someone pinned me down and wanted more of an explanation , I said that we were actually dealing with affine maps, which are the composition of a translation and a linear map. But I agree with you, it is confusing.
 

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