## Linear Vector Spaces: Change of Basis

#### Learning Outcomes

the components of a vector in different ONBs__Calculate__

### Change of Basis

In this section, we will introduce the concept of transformation between coordinate systems. The components of vectors in a linear vector spaces depend on the chosen basis set of vectors. If an alternate basis set is chosen, the components of the vector change according to a transformation matrix that is calculated based on the relationship between the vectors in the initial basis set and those in the alternate basis set. Before the coordinate transformation matrices are introduced, we will introduce the Kronecker delta(), which is a tool that will shorten the derivations below. By we mean the following function of and :

Let be an orthonormal set of basis vectors in . Then, such that:

The numbers are called the components of u in the orthonormal basis set and each component can be obtained using the dot product operation:

Then, the vector can also be written as:

Let be a different orthonormal set of basis vectors in , and let be the representation of the vector in the new coordinate system. In this case, we have a new set of components such that:

In order to find the relationship between the components and we first find the relationship between the basis vectors. As a first step, each basis vector will be expressed in terms of the original basis vectors as was done above for :

Let denote the matrix of numbers . This matrix of numbers is the matrix of transformation between the components in the basis sets and . Let denote the transpose of , i.e.:

We will now show that the rows of and are orthonormal.

Indeed, since we know that the set is orthonormal, we have:

i.e., if we multiply the components of the row of by the components of the row of , we get when and we get 1 when .

If we use I to denote the identity matrix (i.e., with 1 in the diagonal components and 0 in the off diagonal components ) then we have:

By expressing the basis vectors in terms of the orthonormal basis set of vectors and using the same argument above we have:

Thus, the matrix is an example of a special type of operators (orthogonal matrices) which will be studied later. This matrix can be used to relate the components of with the components of as follows:

This last relationship can be written in matrix form as follows. If u represents the components of a vector in the orthonormal basis set and represents the components of that vector in the orthonormal basis set , then, these components are related by the matrix :

The JSXgraph tool in the Matrix Representation and Change of Basis is a useful for the illustration of the concept and calculations of coordinate transformations in 2D.