Let’s say we have:

The columns of lie in the same plane such that they are dependent, and the columns of are independent, (see Linear Independence and Vector Combinations).

There are many vectors in that don’t lie in the plane; these vectors cannot be written as a linear combination of the columns of and so correspond to values of for which has no solution . The linear combinations of the columns of form a 2-dimensional subspace of .

This plane of combinations of (the columns of ) can be described as “all vectors ”. But we know that vectors for which satisfy the condition (from this example). So the plane of all combinations of and consists of all vectors whose combinations sum to .

On the other hand, if we take , such that we have columns

we get the entire space ; the equation has a solution for every in . We say that form a basis for .

Basis, Vector Space, Subspace

A basis for is a collection of independent vectors in . Equivalently, a basis is a collection of vectors whose combinations cover the whole space. Or, a collection of vectors forms a basis whenever a matrix has those vectors as its columns is invertible.

  • A vector space is a collection of vectors that is closed under linear combinations.
  • A subspace is a vector space inside another vector space; a plane through the origin in is an example of a subspace.
    • A subspace could be equal to the space it’s contained in
    • The small space contains only the zero vector

The subspaces of are:

  • The origin
  • A line through the origin
  • A plane through the origin
  • All of