Brahmagupta polynomials are a class of polynomials associated with the Brahmagupa matrix which in turn is associated with the
Brahmagupta's identity
In algebra, Brahmagupta's identity says that, for given n, the product of two numbers of the form a^2+nb^2 is itself a number of that form. In other words, the set of such numbers is closed under multiplication. Specifically:
:\begin
\left(a^2 + ...
. The concept and terminology were introduced by E. R. Suryanarayan, University of Rhode Island, Kingston in a paper published in 1996.
These polynomials have several interesting properties and have found applications in
tiling problems and in the problem of finding
Heronian triangles in which the lengths of the sides are consecutive integers.
Definition
Brahmagupta's identity
In algebra,
Brahmagupta's identity says that, for given integer N, the product of two numbers of the form
is again a number of the form. More precisely, we have
:
This identity can be used to generate infinitely many solutions to the
Pell's equation. It can also be used to generate successively better rational approximations to square roots of arbitrary integers.
Brahmagupta matrix
If, for an arbitrary real number
, we define the matrix
:
then, Brahmagupta's identity can be expressed in the following form:
:
The matrix
is called the Brahmagupta matrix.
Brahmagupta polynomials
Let
be as above. Then, it can be seen by induction that the matrix
can be written in the form
:
Here,
and
are polynomials in
. These polynomials are called the Brahmagupta polynomials. The first few of the polynomials are listed below:
:
Properties
A few elementary properties of the Brahmagupta polynomials are summarized here. More advanced properties are discussed in the paper by Suryanarayan.
Recurrence relations
The polynomials
and
satisfy the following recurrence relations:
*
*
*
*
*
*
Exact expressions
The
eigenvalue
In linear algebra, an eigenvector () or characteristic vector of a linear transformation is a nonzero vector that changes at most by a scalar factor when that linear transformation is applied to it. The corresponding eigenvalue, often denoted b ...
s of
are
and the corresponding
eigenvectors are
. Hence
:
.
It follows that
:
.
This yields the following exact expressions for
and
:
*