n. These polynomials can be viewed as vectors in a finite dimensional vector space
V of dimension n+1 since they can be represented as linear combinations of the set of vectors
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Coordinates of a vector in an abstract vector space. Coordinate vector.
An abstract vector space V is called finite dimensional of dimension n if there exist n members v1, v2, ... , vn such that every member v of V can be written as a linear combination
v = a1v1 + a2v2 + .... + anvn
(where the a’s belong to some number field F) and there exist no set of fewer than n members of V with the same property. The vectors v1, v2, ... , vn are said to form a basis for V.
If a set of n vectors v1, v2, ... , vn form a basis for a vector space V over a field F, then for each v of V the representation
v = a1v1 + a2v2 + .... + anvn
of v as a linear combination over F of v1, v2, ... , vn is unique. The numbers a1, a2, ... , an are called the coordinates of vector v with respect to the basis { v1, v2, ... , vn } of vector space V. The n-tuple (a1, a2, ... , an ) is called the coordinate vector of v relative to the basis {v1, v2, ... , vn } . Thus the coordinate vector of a vector in n dimensional space is a set of n numbers (a1, a2, ... , an ) which define the vector relative to a particular basis { v1, v2, ... , vn }.
Example. Consider the set of all polynomials
a0 + a1x + a2x2 + ... + anxn
of degree
n. These polynomials can be viewed as vectors in a finite dimensional vector space
V of dimension n+1 since they can be represented as linear combinations of the set of vectors
1, x, x2, ... , xn
and the vectors 1, x, x2, ... , xn are linearly independent and their number is n+1. The set {1, x, x2, ... , xn } can be taken as a basis for this space V and any vector in V corresponds to a polynomial
a0 + a1x + a2x2 + ... + anxn
with coordinates a0, a1, a2, ... , an relative to the basis {1, x, x2, ... , xn }.
References.
Hohn, Elem, Matrix Theory. p.87
Lipchutz. Linear Algebra. p. 92
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