(2) 
x = (x_{1},x_{2}) = x_{1}(1,0) + x_{2}(0,1)
Apply now (1) to (2): f(x) = x_{1}f(1,0) + x_{2}f(0,1), where f(1,0) that
should more correctly be written as f((1,0)), is the result of applying f to the vector (1,0). A similar
remark holds for f(0,1) and f(x_{1},x_{2}) in the following. Let f(1,0)=(f_{11},f_{21}) and f(0,1)=(f_{12},f_{22}). Then
(3) 
f(x) = f(x_{1}, x_{2}) = x_{1}(f_{11}, f_{21}) + x_{2}(f_{12}, f_{22}) = (x_{1}f_{11} + x_{2}f_{12}, x_{1}f_{21} + x_{2}f_{22})
Assume g is another linear transformation and g(1,0)=(g_{11}, g_{21}) and g(0,1)=(g_{12}, g_{22}). Then by (3),
(4) 
g(f(x)) = ((x_{1}f_{11} + x_{2}f_{12})g_{11} + (x_{1}f_{21} + x_{2}f_{22})g_{12}, (x_{1}f_{11} + x_{2}f_{12})g_{12} + (x_{1}f_{21} + x_{2}f_{22})g_{22})
or, after regrouping,
(4') 
g(f(x)) = (x_{1}(f_{11}g_{11} + f_{21}g_{12}) + x_{2}(f_{12}g_{11} + f_{22}g_{12}), x_{1}(f_{11}g_{12} + f_{21}g_{22}) + x_{2}(f_{12}g_{12} + f_{22}g_{22}))
We see that a composition g(f(x)) of two linear transformations is in turn linear. Furthermore,
(5) 
g(f(1,0)) = (f_{11}g_{11} + f_{21}g_{12}, f_{11}g_{21} + f_{21}g_{22}), and

g(f(0,1)) = (f_{12}g_{11} + f_{22}g_{12}, f_{12}g_{21} + f_{22}g_{22})
Now, a pair of numbers x_{1},x_{2} might be written as either a vertical (2x1 matrix) or a horizontal (1x2 matrix) vector.
Given the limitations of HTML, the horizontal convention is a real life saver and has been used so far. However, I must note that the vertical notations are, by far, more common.
Depending on the notations (3) and (5) may be rewritten variously in a vectormatrix format:
x  (x_{1}, x_{2})  
F   
f(x)  xF  Fx 
g(f(x))  xFG  GFx 
What we arrived at is that a linear transformation of a vector space may be expressed as
a product of a matrix and a vector. Composition of two linear transformations is represented
by a product of the corresponding matrices. The claim is more general than what was actually shown.
R^{2} is known as an arithmetic vector space. The set of all combinations
rsin(x) + scos(x), where x changes over some interval, is another example of a 2dimensional vector
space whose elements look differently from those of R^{2}. However, as we already remarked,
vector spaces of the same dimensionality are isomorphic, and one way to establish a
correspondence (isomorphism) between them is by selecting bases and identifying their
vectors with tuples of coordinates. For example, a vector rsin(x) + scos(x) could be identified
with an ordered pair (r,s). Under this correspondence, sin(x) and cos(x) appear as (1,0) and (0,1), respectively.
To sum up, selecting bases in vector spaces leads to identification of vectors with tuples
of coordinates (horizontal or vertical). Horizontal tuples are usually called row vectors whereas the vertical
ones are known as column vectors. Matrices turn up as representing linear transformations
for a fixed pair of bases. To obtain the result of applying a transformation to a vector multiply
the corresponding matrix and the tuple. The product of two matrices represents the composition (product)
of two transformations (functions.)
Reference
 H.Eves, Great Moments in Mathematics After 1650, MAA, 1983
What Can Be Multiplied?
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