Linear Combinations and Span
The notion of linear combination is the engine of linear algebra. It tells us what vectors we can "reach" using addition and scalar multiplication.
Definitions
Let be a vector space over . A linear combination of vectors is any vector of the form
where are scalars.
The span (or linear span) of a set is the set of all linear combinations of vectors in :
By convention, .
We say spans (or is a spanning set for ) if .
For any subset , is a subspace of . Moreover, it is the smallest subspace containing : if is any subspace with , then .
contains (take all coefficients to be ). If and are in , then and are in . By the subspace test, is a subspace. Since every subspace containing must contain all linear combinations of elements of , the minimality follows.
Examples
because every .
Also : the third vector is redundant since .
, which is the line through the origin with slope .
, which is the plane in .
because . Two parallel vectors span only a line, not a plane. Geometrically, adding a redundant vector does not enlarge the span.
, the space of polynomials of degree at most 2.
Alternatively, as well, since: , , and .
The four matrices
span , since any matrix .
Do the vectors , , span ?
We check: (since ). So , which is a plane in . These three vectors do not span .
In , is the set of all functions with . This can also be written as .
, the space of all polynomials. This is an infinite-dimensional example: no finite subset spans all of .
In , is a 3-dimensional subspace. These functions are linearly independent (as can be verified using a Wronskian determinant).
is the -plane in , not all of . The vector cannot be expressed as a linear combination of and .
The subspace can be parametrized by setting , , giving . So
Properties of span
If , then . Adding more vectors can only enlarge the span.
. The span of a subspace is itself.
A spanning set may contain redundant vectors. The quest for a spanning set with no redundancy leads to the notion of a basis, which in turn requires linear independence.