for all indices i and j. The identical term Vandermonde matrix was used for the transpose of the above matrix by Macon and Spitzbart (1958). The Vandermonde matrix used for the Discrete Fourier Transform matrix satisfies both definitions.
The determinant of a square Vandermonde matrix (where m = n) can be expressed as
This is called the Vandermonde determinant or Vandermonde polynomial. It is non-zero if and only if all are distinct.
The Vandermonde determinant was sometimes called the discriminant, although, presently, the discriminant of a polynomial is the square of the Vandermonde determinant of the roots of the polynomial. The Vandermonde determinant is an alternating form in the , meaning that exchanging two changes the sign, while permuting the by an even permutation does not change the value of the determinant. It thus depends on the choice of an order for the , while its square, the discriminant, does not depend on any order, and this implies, by Galois theory, that the discriminant is a polynomial function of the coefficients of the polynomial that has the as roots.
The main property of a square Vandermonde matrix
is that its determinant has the simple form
Three proofs of this equality are given below. The first one uses polynomial properties, especially the unique factorization property of multivariate polynomials. Although conceptually simple, it involves non-elementary concepts of abstract algebra. The second proof does not requires any explicit computation, but involves the concepts of the determinant of a linear map and change of basis. It provides also the structure of the LU decomposition of the Vandermonde matrix. The third one is more elementary and more complicated, as using only elementary row and column operations.
Using polynomial properties
By the Leibniz formula, det(V) is a polynomial in the with integer coefficients. All entries of the ith column have total degree i – 1. Thus, again by the Leibniz formula, all terms of the determinant have total degree
(that is the determinant is a homogeneous polynomial of this degree).
If, for i ≠ j, one substitutes for , one gets a matrix with two equal rows, which has thus a zero determinant. Thus, by the factor theorem, is a divisor of det(V). By the unique factorization property of multivariate polynomials, the product of all divides det(V), that is
where Q is a polynomial. As the product of all and det(V) have the same degree the polynomial Q is, in fact, a constant. This constant is one, because the product of the diagonal entries of V is which is also the monomial that is obtained by taking the first term of all factors in This proves that
Using linear maps
be the linear map defined by
The Vandermonde matrix is the matrix of on the canonical bases of and
Changing the basis of amounts to multiply (on the right) the Vandermonde matrix by a change-of-basis matrix M. This does not changes the determinant, if the determinant of M is 1.
The polynomials are monic of respective degrees 0, 1, ..., n – 1. Their matrix on the monomial basis is an upper-triangular matrix U (if the monomials are ordered in increasing degrees), with all diagonal entries equal to one. This matrix is thus a change-of-basis matrix of determinant one. The matrix of on this new basis is
Thus Vandermonde determinant equals the determinant of this matrix, which is the product of its diagonal entries.
This proves the desired equality. Moreover, one gets the LU decomposition of V as
By row and column operations
This second proof is based on the fact that, if one adds to a row (or a column) of a matrix the product by a scalar of another row (or column), the determinant remains unchanged.
If one subtracts the first row of V from all the other rows, the determinant is not changed, and the new matrix has the form
where is a row matrix, is a column of zeros, and A is a square matrix, such that
The entry of the (i – 1)th row and the (j – 1)th column of A (that is the ith row and the jth column of the whole matrix) is
Dividing out from the (i – 1)th row of A, for i = 2, ..., n, one gets a matrix B such that
The coefficient of the (i – 1)th row and the (j – 1)th column of B is
for i = 2, ..., n, and setting
Thus, subtracting, for j running from n down to 2, the (j – 2)th column of B multiplied by from the (j – 1)th column, one gets an (n – 1) × (n – 1) Vandermonde matrix in which has the same determinant as B. Iterating this process on this smaller Vandermonde matrix, one gets eventually the desired expression of det(V) as the product of the
An m × n rectangular Vandermonde matrix such that m ≥ n has maximum rank n if and only if there are n of the xi that are distinct.
The Vandermonde matrix evaluates a polynomial at a set of points; formally, it is the matrix of the linear map that maps the vector of coefficients of a polynomial to the vector of the values of the polynomial at the values appearing in the Vandermonde matrix. The non-vanishing of the Vandermonde determinant for distinct points shows that, for distinct points, the map from coefficients to values at those points is a one-to-one correspondence, and thus that the polynomial interpolation problem is solvable with a unique solution; this result is called the unisolvence theorem, and is a special case of the Chinese remainder theorem for polynomials.
This may be useful in polynomial interpolation, since inverting the Vandermonde matrix allows expressing the coefficients of the polynomial in terms of the  and the values of the polynomial at the . However, the interpolation polynomial is generally easier to compute with the Lagrange interpolation formula, which may be used for deriving a formula for the inverse of a Vandermonde matrix.
When the values belong to a finite field, then the Vandermonde determinant is also called a Moore determinant and has specific properties that are used, for example, in the theory of BCH code and Reed–Solomon error correction codes.
The Laughlin wavefunction with filling factor one (appearing in the Quantum Hall effect), by the formula for the Vandermonde determinant, can be seen to be a Slater determinant. This is not true anymore for filling factors different from one, i.e., in the fractional Quantum Hall effect.
Confluent Vandermonde matrices
As described before, a Vandermonde matrix describes the linear algebra interpolation problem of finding the coefficients of a polynomial of degree based on the values , where are distinct points. If are not distinct, then this problem does not have a unique solution (which is reflected by the fact that the corresponding Vandermonde matrix is singular). However, if we give the values of the derivatives at the repeated points, then the problem can have a unique solution. For example, the problem
where is a polynomial of degree , has a unique solution for all . In general, suppose that are (not necessarily distinct) numbers, and suppose for ease of notation that equal values come in continuous sequences in the list. That is
where and are distinct. Then the corresponding interpolation problem is
And the corresponding matrix for this problem is called a confluent Vandermonde matrices. In our case (which is the general case, up to permuting the rows of the matrix) the formula for it is given as follows: if , then for some (unique) (we consider ). Then, we have
This generalization of the Vandermonde matrix makes it non-singular (such that there exists a unique solution to the system of equations) while retaining most properties of the Vandermonde matrix. Its rows are derivatives (of some order) of the original Vandermonde rows.
Another way to receive this formula is to let some of the 's go arbitrarily close to each other. For example, if , then letting in the original Vandermonde matrix, the difference between the first and second rows yields the corresponding row in the confluent Vandermonde matrix. This allows us to link the generalized interpolation problem (given value and derivatives on a point) to the original case where all points are distinct: Being given is similar to being given where is very small.
- Schur polynomial – a generalization
- Alternant matrix
- Lagrange polynomial
- List of matrices
- Moore determinant over a finite field
- Vieta's formulas
- Roger A. Horn and Charles R. Johnson (1991), Topics in matrix analysis, Cambridge University Press. See Section 6.1.
- DFT matrix,https://en.wikipedia.org/wiki/DFT_matrix
- Turner, L. Richard (August 1966). Inverse of the Vandermonde matrix with applications (PDF).
- Macon, N.; A. Spitzbart (February 1958). "Inverses of Vandermonde Matrices". The American Mathematical Monthly. 65 (2): 95–100. doi:10.2307/2308881. JSTOR 2308881.
- "Inverse of Vandermonde Matrix". 2018.
- François Viète (1540-1603), Vieta's formulas, https://en.wikipedia.org/wiki/Vieta%27s_formulas
- Press, WH; Teukolsky, SA; Vetterling, WT; Flannery, BP (2007). "Section 2.8.1. Vandermonde Matrices". Numerical Recipes: The Art of Scientific Computing (3rd ed.). New York: Cambridge University Press. ISBN 978-0-521-88068-8.
- Inverse of Vandermonde Matrix (2018),https://proofwiki.org/wiki/Inverse_of_Vandermonde_Matrix
- Fulton, William; Harris, Joe (1991). Representation theory. A first course. Graduate Texts in Mathematics, Readings in Mathematics. 129. New York: Springer-Verlag. doi:10.1007/978-1-4612-0979-9. ISBN 978-0-387-97495-8. MR 1153249. OCLC 246650103. Lecture 4 reviews the representation theory of symmetric groups, including the role of the Vandermonde determinant.
- Ycart, Bernard (2013), "A case of mathematical eponymy: the Vandermonde determinant", Revue d'Histoire des Mathématiques, 13, arXiv:1204.4716, Bibcode:2012arXiv1204.4716Y.
- Vandermonde matrix at ProofWiki