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Linear span

The span of a set of vectors, also called linear span, is the linear space formed by all the vectors that can be written as linear combinations of the vectors belonging to the given set.

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Let us start with a formal definition of span.

Definition Let [eq1] be n vectors. Their linear span is the set $S$ of all the linear combinations [eq2] that can be obtained by arbitrarily choosing n scalars $lpha _{1}$, ...,$lpha _{n}$.

A very simple example of a linear span follows.

Example Let $x_{1}$ and $x_{2}$ be $2	imes 1$ column vectors defined as follows.[eq3]Let x be a linear combination of $x_{1}$ and $x_{2}$ with coefficients $lpha _{1}$ and $lpha _{2}$. Then,[eq4]Thus, the linear span $S$ is the set of all vectors $S$ that can be written as[eq5]where $lpha _{1}$ and $lpha _{2}$ are two arbitrary real numbers. In other words, $S=U{211d} ^{2}$.

A linear span is a linear space

The following proposition, although elementary, is extremely important.

Proposition The linear span of a set of vectors is a linear space.


Let $S$ be the linear span of n vectors [eq6]. Then, $S$ is the set of all vectors x that can be represented as a linear combination[eq7]Take two vectors $s_{1}$ and $s_{2}$ belonging to $S$. Then, there exist coefficients [eq8] and [eq9] such that[eq10]The span $S$ is a linear space if and only if for any two coefficients $eta _{1}$ and $eta _{2}$ the linear combination[eq11]also belongs to $S$. But,[eq12]Thus, the linear combination [eq13]can itself be expressed as a linear combination of the vectors [eq14] with coefficients [eq15], ..., [eq16]. As a consequence, it belongs to the span $S$. In summary, we have proved that any linear combination of vectors belonging to the span $S$ also belongs to the span $S$. This means that $S$ is a linear space.

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