** Overview**

Polynomials are incredibly useful mathematical tools as they are simply defined, can be calculated quickly on computer systems and represent a tremendous variety of functions. They can be differentiated and integrated easily, and can be pieced together to form spline curves that can approximate any function to any accuracy desired. Most students are introducted to polynomials at a very early stage in their studies of mathematics, and would probably recall them in the form below:

In general, any polynomial function that has degree less than or equal to , can be written in this way, and the reasons are simply

- The set of polynomials of degree less than or equal to forms a vector space: polynomials can be added together, can be multiplied by a scalar, and all the vector space properties hold.
- The set of functions form a basis for this vector space - that is, any polynomial of degree less than or equal to can be uniquely written as a linear combinations of these functions.

In these notes we discuss another of the commonly used bases for the
space of polynomials, the * Bernstein basis*, and discuss its
many useful properties.

For a pdf version of these notes look here.

The Bernstein polynomials of degree are defined by

These polynomials are quite easy to write down: the coefficients can be obtained from Pascal's triangle; the exponents on the term increase by one as increases; and the exponents on the term decrease by one as increases. In the simple cases, we obtain

- The Bernstein polynomials of degree 1 are

and can be plotted for as - The Bernstein polynomials of degree 2 are

and can be plotted for as - The Bernstein polynomials of degree 3 are

and can be plotted for as

** A Recursive Definition of the Bernstein Polynomials**

The Bernstein polynomials of degree can be defined by blending together two Bernstein polynomials of degree . That is, the th th-degree Bernstein polynomial can be written as

** The Bernstein Polynomials are All Non-Negative**

A function is non-negative over an interval if for . In the case of the Bernstein polynomials of degree , each is non-negative over the interval . To show this we use the recursive definition property above and mathematical induction.

It is easily seen that the functions and are both non-negative for . If we assume that all Bernstein polynomials of degree less than are non-negative, then by using the recursive definition of the Bernstein polynomial, we can write

In this process, we have also shown that each of the Bernstein polynomials is * positive* when .

** The Bernstein Polynomials form a Partition of Unity**

A set of functions is said to partition unity if they sum to one for all values of . The Bernstein polynomials of degree form a partition of unity in that they all sum to one.

To show that this is true, it is easiest to first show a slightly different fact: for each , the sum of the Bernstein polynomials of degree is equal to the sum of the Bernstein polynomials of degree . That is,

(where we have utilized ).

Once we have established this equality, it is simple to write

The partition of unity is a very important property when utilizing Bernstein polynomials in geometric modeling and computer graphics. In particular, for any set of points , , , , in three-dimensional space, and for any , the expression

Any of the lower-degree Bernstein polynomials (degree ) can be expressed as a linear combination of Bernstein polynomials of degree . In particular, any Bernstein polynomial of degree can be written as a linear combination of Bernstein polynomials of degree . We first note that

and

and finally

Using this final equation, we can write an arbitrary Bernstein polynomial in terms of Bernstein polynomials of higher degree. That is,

which expresses a Bernstein polynomial of degree in terms of a linear combination of Bernstein polynomials of degree . We can easily extend this to show that any Bernstein polynomial of degree (less than ) can be written as a linear combination of Bernstein polynomials of degree - e.g., a Bernstein polynomial of degree can be expressed as a linear combination of two Bernstein polynomials of degree , each of which can be expressed as a linear combination of two Bernstein polynomials of degree , etc.

** Converting from the Bernstein Basis to the Power Basis**

Since the power basis forms a basis for the space of polynomials of degree less than or equal to , any Bernstein polynomial of degree can be written in terms of the power basis. This can be directly calculated using the definition of the Bernstein polynomials and the binomial theorem, as follows:

where we have used the binomial theorem to expand .

To show that each power basis element can be written as a linear combination of Bernstein Polynomials, we use the degree elevation formulas and induction to calculate:

where the induction hypothesis was used in the second step.

** Derivatives**

Derivatives of the th degree Bernstein polynomials are polynomials of degree . Using the definition of the Bernstein polynomial we can show that this derivative can be written as a linear combination of Bernstein polynomials. In particular

That is, the derivative of a Bernstein polynomial can be expressed as the degree of the polynomial, multiplied by the difference of two Bernstein polynomials of degree .

** The Bernstein Polynomials as a Basis**

Why do the Bernstein polynomials of order form a basis for the space of polynomials of degree less than or equal to ?

- 1.
- They span the space of polynomials - any polynomial of degree
less than or equal to
can be written as a linear combination of
the Bernstein polynomials.
This is easily seen if one realizes that The power basis spans the space of polynomials and any member of the power basis can be written as a linear combination of Bernstein polynomials.

- 2.
- They are linearly independent - that is, if there exist constants
so that the identity
If this were true, then we could write

Since the power basis is a linearly independent set, we must have that

which implies that ( is clearly zero, substituting this in the second equation gives , substituting these two into the third equation gives ...)

** A Matrix Representation for Bernstein Polynomials**

In many applications, a matrix formulation for the Bernstein polynomials is useful. These are straightforward to develop if one only looks at a linear combination in terms of dot products.

Given a polynomial written as a linear combination of the Bernstein basis functions

In the quadratic case (), the matrix representation is

2000-11-28