This article needs additional citations for verification. Please help improve this article by adding citations to reliable sources. Unsourced material may be challenged and removed.Find sources: "Change of basis" – news · newspapers · books · scholar · JSTOR (November 2017) (Learn how and when to remove this template message)

In mathematics, an ordered basis of a vector space of finite dimension n allows representing uniquely any element of the vector space by a coordinate vector, which is a sequence of n scalars called coordinates. If two different bases are considered, the coordinate vector that represents a vector v on one basis is, in general, different from the coordinate vector that represents v on the other basis. A **change of basis** consists of converting every assertion expressed in terms of coordinates relative to one basis into an assertion expressed in terms of coordinates relative to the other basis.^{[1]}^{[2]}^{[3]}

Such a conversion results from the *change-of-basis formula* which expresses the coordinates relative to one basis in terms of coordinates relative to the other basis. Using matrices, this formula can be written

where "old" and "new" refer respectively to the firstly defined basis and the other basis, and are the column vectors of the coordinates of the same vector on the two bases, and is the **change-of-basis matrix** (also called **transition matrix**), which is the matrix whose columns are the coordinates of the new basis vectors on the old basis.

This article deals mainly with finite-dimensional vector spaces. However, many of the principles are also valid for infinite-dimensional vector spaces.

Let be a basis of a finite-dimensional vector space V over a field F.^{[a]}

For *j* = 1, ..., *n*, one can define a vector *w*_{j} by its coordinates over

Let

be the matrix whose jth column is formed by the coordinates of *w*_{j}. (Here and in what follows, the index i refers always to the rows of A and the while the index j refers always to the columns of A and the such a convention is useful for avoiding errors in explicit computations.)

Setting one has that is a basis of V if and only if the matrix A is invertible, or equivalently if it has a nonzero determinant. In this case, A is said to be the *change-of-basis matrix* from the basis to the basis

Given a vector let be the coordinates of over and its coordinates over that is

(One could take the same summation index for the two sums, but choosing systematically the indexes i for the old basis and j for the new one makes clearer the formulas that follows, and helps avoiding errors in proofs and explicit computations.)

The *change-of-basis formula* expresses the coordinates over the old basis in terms of the coordinates over the new basis. With above notation, it is

In terms of matrices, the change of basis formula is

where and are the column vectors of the coordinates of z over and respectively.

*Proof:* Using the above definition of the change-of basis matrix, one has

As the change-of-basis formula results from the uniqueness of the decomposition of a vector over a basis.

Consider the Euclidean vector space Its standard basis consists of the vectors and If one rotates them by an angle of t, one gets a *new basis* formed by and

So, the change-of-basis matrix is

The change-of-basis formula asserts that, if are the new coordinates of a vector then one has

That is,

This may be verified by writing

Normally, a matrix represents a linear map, and the product of a matrix and a column vector represents the function application of the corresponding linear map to the vector whose coordinates form the column vector. The change-of-basis formula is a specific case of this general principle, although this is not immediately clear from its definition and proof.

When one says that a matrix *represents* a linear map, one refers implicitly to bases of implied vector spaces, and to the fact that the choice of a basis induces an isomorphism between a vector space and *F*^{n}, where F is the field of scalars. When only one basis is considered for each vector space, it is worth to leave this isomorphism implicit, and to work up to an isomorphism. As several bases of the same vector space are considered here, a more accurate wording is required.

Let F be a field, the set of the n-tuples is a F-vector space whose addition and scalar multiplication are defined component-wise. Its standard basis is the basis that has as its ith element the tuple with all components equal to 0 except the ith that is 1.

A basis of a F-vector space V defines a linear isomorphism by

Conversely, such a linear isomorphism defines a basis, which is the image by of the standard basis of

Let be the "old basis" of a change of basis, and the associated isomorphism. Given a change-of basis matrix A, one could consider it the matrix of an endomorphism of Finally, define

(where denotes function composition), and

A straightforward verification shows that this definition of is the same as that of the preceding section.

Now, by composing the equation with on the left and on the right, one gets

It follows that, for one has

which is the change-of-basis formula expressed in terms of linear maps instead of coordinates.

A function that has a vector space as its domain is commonly specified as a multivariate function whose variables are the coordinates on some basis of the vector on which the function is applied.

When the basis is changed, the expression of the function is changed. This change can be computed by substituting the "old" coordinates for their expressions in terms of the "new" coordinates. More precisely, if *f*(**x**) is the expression of the function in terms of the old coordinates, and if **x** = *A***y** is the change-of-base formula, then *f*(*A***y**) is the expression of the same function in terms of the new coordinates.

The fact that the change-of-basis formula expresses the old coordinates in terms of the new one may seem unnatural, but appears as useful, as no matrix inversion is needed here.

As the change-of-basis formula involves only linear functions, many function properties are kept by a change of basis. This allows defining these properties as properties of functions of a variable vector that are not related to any specific basis. So, a function whose domain is a vector space or a subset of it is

- a linear function,
- a polynomial function,
- a continuous function,
- a differentiable function,
- a smooth function,
- an analytic function,

if the multivariate function that represents it on some basis—and thus on every basis—has the same property.

This is specially useful in the theory of manifolds, as this allows extending the concepts of continuous, differentiable, smooth and analytic functions to functions that are defined on a manifold.

Consider a linear map *T*: *W* → *V* from a vector space W of dimension n to a vector space V of dimension m. It is represented on "old" bases of V and W by a *m*×*n* matrix M. A change of bases is defined by an *m*×*m* change-of-basis matrix P for V, and an *n*×*n* change-of-basis matrix Q for W.

On the "new" bases, the matrix of T is

This is a straightforward consequence of the change-of-basis formula.

Endomorphisms, are linear maps from a vector space V to itself. For a change of basis, the formula of the preceding section applies, with the same change-of-basis matrix on both sides of the formula. That is, if M is the square matrix of an endomorphism of V over an "old" basis, and P is a change-of-basis matrix, then the matrix of the endomorphism on the "new" basis is

As every invertible matrix can be used as a change-of-basis matrix, this implies that two matrices are similar if and only if they represent the same endomorphism on two different bases.

A *bilinear form* on a vector space *V* over a field F is a function *V* × *V* → F which is linear in both arguments. That is, *B* : *V* × *V* → F is bilinear if the maps
and
are linear for every fixed

The matrix **B** of a bilinear form B on a basis (the "old" basis in what follows) is the matrix whose entry of the ith row and jth column is *B*(*i*, *j*). It follows that if **v** and **w** are the column vectors of the coordinates of two vectors v and w, one has

where denotes the transpose of the matrix **v**.

If P is a change of basis matrix, then a straightforward computation shows that the matrix of the bilinear form on the new basis is

A symmetric bilinear form is a bilinear form B such that for every v and w in V. It follows that the matrix of B on any basis is symmetric. This implies that the property of being a symmetric matrix must be kept by the above change-of-base formula. One can also check this by noting that the transpose of a matrix product is the product of the transposes computed in the reverse order. In particular,

and the two members of this equation equal if the matrix **B** is symmetric.

If the characteristic of the ground field F is not two, then for every symmetric bilinear form there is a basis for which the matrix is diagonal. Moreover, the resulting nonzero entries on the diagonal are defined up to the multiplication by a square. So, if the ground field is the field of the real numbers, these nonzero entries can be chosen to be either 1 or –1. Sylvester's law of inertia is a theorem that asserts that the numbers of 1 and of –1 depends only on the bilinear form, and not of the change of basis.

Symmetric bilinear forms over the reals are often encountered in geometry and physics, typically in the study of quadrics and of the inertia of a rigid body. In these cases, orthonormal bases are specially useful; this means that one generally prefer to restrict changes of basis to those that have an orthogonal change-of-base matrix, that is, a matrix such that Such matrices have the fundamental property that the change-of-base formula is the same for a symmetric bilinear form and the endomorphism that is represented by the same symmetric matrix. The Spectral theorem asserts that, given such a symmetric matrix, there is an orthogonal change of basis such that the resulting matrix (of both the bilinear form and the endomorphism) is a diagonal matrix with the eigenvalues of the initial matrix on the diagonal. It follows that, over the reals, if the matrix of an endomorphism is symmetric, then it is diagonalizable.