Let be a Borel measure on the real line. Usually we impose two conditions on this measure:

(i) the support contains at least infinitely many points,

(ii) for all we have (This is called the finite moment condition.)

We shall see in a minute why these restrictions are needed. The following theorem says that there is a sequence of polynomials mutually orthogonal to each other. (No surprise there, they are called *orthogonal polynomials*, OP in short, our main object of study.)

**Theorem 1. (Existence of orthonormal polynomials) **There is a unique sequence of polynomials in such that (i) , (ii) , where if and otherwise.

**Proof. **The finite moment condition implies that all polynomials are an element of , therefore we can put the vector system into the Gram-Schmidt process. It is easy to see that the output satisfies the conditions (i) and (ii).

Notice that if the support of is a finite set, then the Gram-Schmidt process stops after a finite number of steps. The sequence of polynomials are called the orthonormal polynomials with respect to . Sometimes I write *orthogonal* instead of *orthonormal* when it is not important. I also indicate the dependence on the measure when it is necessary. Probably the most famous example of orthogonal polynomials are the Chebyshev polynomials. They are defined as

.

Although it is not obvious, they are indeed polynomials. They are also orthogonal with respect to the weight supported on the interval . As the title of this post indicates, we are interested in the zeros of OPs. It is easy to see that the zeros of are exactly

What happens if we construct a measure which puts a weight into each zero of ? With this in mind, define as

where is the Dirac measure. For arbitrary we can calculate the number of zeros less than to obtain

,

from which it is immediate that . This means that the measures weakly converge to a measure with distribution function . But this is exactly the measure for which the Chebyshev polynomials are orthogonal! In other words, if we take the zeros of and put weights into each zero, the resulting measure converges to the original measure!

**Question.** Is there a similar phenomenon for measures other than ?

This question will be partially answered right now. This example with the Chebyshev polynomials is a little misleading, because the measure is a special one. For more general measures, we have the following theorem.

**Theorem 2. **Let be a Borel probability measure supported on , and suppose that , where a.e. on . Then for the orthonormal polynomials , we have

,

and the measure defined as

,

where are the zeros of , converges weakly to the measure .

A more general theorem can be found at [WVA], Theorem 1.2. This theorem was rather surprising for me, because under certain assumptions, no matter which measure we start from, the discrete measure obtained from the zeros of orthogonal polynomials converges weakly to a special measure. That measure is called the equilibrium measure for the interval , and you can find more about them, for example, in the book [TR].

I conclude this post with a few questions. Some of them will be answered next time, but for the others, I am yet to find the answer.

**Questions.
1. **Is the assumption really necessary in Theorem 2?

**2.**What about measures supported on more general sets? For example, is there a similar theorem for measures on the unit circle?

**3.**The discrete measure defined with has uniform weights. What if we use different weights?

**4.**For exactly which measures can we prove the analogue of Theorem 2?

**References.**

[TR] Thomas Ransford, Potential theory in theory in the complex plane, Cambridge University Press, Cambridge, 1995

[WVA] Walter Van Assche, Asymptotics for orthogonal polynomials, Lecture Notes in Mathematics, Springer-Verlag, 1987

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