In my recent work on skew-orthogonal polynomials I was interested in deriving a Christoffel-Darboux type formula. To this end Thomas Bothner referred me to a paper of his in collaboration with Marco Bertola which contains a nice derivation of the Christoffel-Darboux formula using only complex analysis arguments (Theorem 2.8). I will present this argument in the simpler context of orthogonal polynomials rather than that of biorthogonal polynomials considered by the authors.

Let us recall the Riemann-Hilbert problem for orthogonal polynomials. Given a measurable function \(w : \mathbb{R} \to [0,+\infty]\) such that \(\int_\mathbb{R} w(x) \lvert x \rvert ^k \, dx < +\infty\) for all \(k \in \mathbb{N}\) and where \(w(x) > 0\) on some open set we construct a sequence of monic polynomials \(P_n(x) = x^n + \mathcal{O}(x^{n-1})\) such that

\[\int_\mathbb{R} P_n(x)x^k w(x)\, dx = 0\]

for all \(k=0, \dots, n-1\). We call \(P_n\) the \(n\)th monic orthogonal polynomial with respect to \(w\).

We let \(h_n = \int_\mathbb{R} P_n(x)x^n w(x)\, dx\) be the (squared) \(L^2(w)\) norm of \(P_n\). We may reformulate this as a Riemann-Hilbert problem.

Riemann-Hilbert problem for orthogonal polynomials (Fokas-Its-Kitaev, 1992):

Find a matrix valued function \(X_n : \mathbb{C} \setminus \mathbb{R} \to \mathbb{C}^{2 \times 2}\) such that

  1. \(X_n\) is analytic (entry-wise) on \(\mathbb{C} \setminus \mathbb{R}\).
  2. \(X_n\) has continuous non-tangential boundary values up to \(\mathbb{R}\) from above (\(+\)) and below (\(-\)). We label these \(X_n^\pm (x) = \lim_{\epsilon \downarrow 0} X_n(x\pm i \epsilon)\) for \(x \in \mathbb{R}\).
  3. These boundary values are related by the jump condition \(X_n^+(x) = X_n^-(x) \left( \begin{matrix} 1 & w(x) \\ 0 & 1\end{matrix} \right)\)
  4. Finally, \(X_n\) is normalised at infinity by the scaling as \(z \to \infty\)
\[X_n(z) = \left( \mathbb{I}+\mathcal{O}(z^{-1}) \right) \left( \begin{matrix} z^n & 0 \\ 0 & z^{-n} \end{matrix} \right)\]

Proposition: The above RHP has a unique solution given by (for \(n\geq 1\))

\[X_n(z) = \left( \begin{matrix} P_n(z) & C_\mathbb{R} \left( P_n w\right)(z) \\ - 2\pi i h_{n-1}^{-1} P_{n-1}(z) & - 2\pi i h_{n-1}^{-1} C_\mathbb{R} \left( P_{n-1} w\right)(z) \end{matrix} \right)\]

where

\[C_\mathbb{R}(f)(z) = \frac{1}{2\pi i} \int_\mathbb{R} \frac{f(x)}{x-z}\, dx\]

is the Cauchy transform of the function \(f\). Furthermore \(\det X_n(z) = 1\) identically. If \(n=0\) the solution is \(X_0(z) = \left( \begin{matrix} 1 & C_\mathbb{R} \left( w\right)(z) \\ 0 & 1 \end{matrix} \right)\). \(\triangle\)

That \(\det X_n(z) = 1\) can be seen directly from the RHP since \(\det X_n(z)\) has no jump across \(\mathbb{R}\) and has continuous boundary values, and so by Morera’s theorem is entire. \(\det X_n(z) \to 1\) as \(z \to \infty\) and so by Liouville’s theorem is identically \(1\). This implies that the RHP can have at most one solution. The reader may then verify that the above is a solution.

Because \(\det X_n(z) = 1\) we can introduce a “dual” Riemann-Hilbert problem \(\widehat{X_n}(z) = X_n(z)^{-\mathsf{T}}\) (inverse transpose). \(\widehat{X_n}\) solves the following RHP.

Dual Riemann-Hilbert problem for orthogonal polynomials:

Find a matrix valued function \(\widehat{X_n} : \mathbb{C} \setminus \mathbb{R} \to \mathbb{C}^{2 \times 2}\) such that

  1. \(\widehat{X_n}\) is analytic (entry-wise) on \(\mathbb{C} \setminus \mathbb{R}\).
  2. \(\widehat{X_n}\) has continuous non-tangential boundary values up to \(\mathbb{R}\) from above (\(+\)) and below (\(-\)). We label these \(\widehat{X_n}^\pm (x) = \lim_{\epsilon \downarrow 0} \widehat{X_n}(x\pm i \epsilon)\) for \(x \in \mathbb{R}\).
  3. These boundary values are related by the jump condition \(\widehat{X_n}^+(x) = \widehat{X_n}^-(x) \left( \begin{matrix} 1 & 0 \\ -w(x) & 1\end{matrix} \right)\)
  4. Finally, \(\widehat{X_n}\) is normalised at infinity by the scaling as \(z \to \infty\)
\[\widehat{X_n}(z) = \left( \mathbb{I}+\mathcal{O}(z^{-1}) \right) \left( \begin{matrix} z^{-n} & 0 \\ 0 & z^n \end{matrix} \right)\]

Indeed, we know the unique solution of the dual RHP,

\[\widehat{X_n}(z) = \left( \begin{matrix} -2\pi i h_{n-1}^{-1} C_\mathbb{R} \left( P_{n-1} w\right)(z) & 2\pi i h_{n-1}^{-1} P_{n-1}(z) \\ -C_\mathbb{R} \left( P_n w\right)(z) & P_n(z) \end{matrix} \right)\]

Let us now derive a pair of recursion relations. We note that \(X_{n+1}\) satisfies properties 1-3, differing only on property 4. Thus if we let \(\Delta_n(z) = X_{n+1}(z) X_n(z)^{-1}\) we see that \(\Delta_n\) has no jump across the real axis, has continuous boundary values, and is analytic on \(\mathbb{C}\setminus \mathbb{R}\). It is thus entire by Morera’s theorem. Let us expand this at infinity. Let \(X_n(z) = \left( \mathbb{I}+A_n z^{-1} + \mathcal{O}(z^{-2}) \right)\left( \begin{matrix} z^n & 0 \\ 0 & z^{-n}\end{matrix} \right)\). Then

\[\Delta_n(z) = z E_1 + A_{n+1}E_1 - A_n E_1 + \mathcal{O}(z^{-1})\]

where \(E_1 = \left( \begin{matrix} 1 & 0 \\ 0 & 0\end{matrix} \right)\). However since \(\Delta_n\) is entire the \(\mathcal{O}(z^{-1})\) term is identically zero, so we have

\[X_{n+1}(z) = \left( z E_1 + A_{n+1}E_1 - E_1 A_n \right)X_{n}(z)\]

This is the famous “three term recurrence” for orthogonal polynomials, derived by complex analysis arguments. By a similar argument we find

\[\widehat{X_{n+1}}(z) = \left( z E_2 - A_{n+1}^\mathsf{T} E_2 + E_2 A_n^\mathsf{T} \right)\widehat{X_{n}}(z)\]

where \(E_2 = \left( \begin{matrix} 0 & 0 \\ 0 & 1\end{matrix} \right)\). Let us now consider quantity

\[Y_n(z,w) := X_n(z)^{-1}X_n(w) = \widehat{X_n}(z)^{\mathsf{T}}X_n(w)\]

Using our recursion relations for \(\widehat{X_n}\) and \(X_n\) we may relate \(Y_n\) and \(Y_{n+1}\) by \(Y_{n+1}(z) = X_n^{-1}(z) \Delta_n(z)^{-1} \Delta_n(w) X_n(w)\). \(\Delta_n(z)^{-1} \Delta_n(w)\) is a polynomial in two variables, moreover

\[\Delta_n(z)^{-1} \Delta_n(w) = (z-w) (A_{n+1})_{21} E_{21} + \mathbb{I}\]

where \(E_{21} = \left( \begin{matrix} 0 & 0 \\ 1 & 0\end{matrix} \right)\). By our formula for solution \(X_n\) we find \((A_{n+1})_{21} = - 2\pi i h_n^{-1}\), and so

\[Y_{n+1}(z,w) = Y_n(z,w) - \frac{2\pi i}{h_n} (z-w) X_n^{-1}(z) E_{21} X_n(w)\]

If we now take the \((2,1)\) matrix element of both sides we find

\[Y_{n+1}(z,w)_{21} = Y_n(z,w)_{21} - 2\pi i \frac{P_n(z)P_n(w)}{h_n} (z-w)\]

If we now sum both sides from \(n=0\) to \(n=N-1\) we find

\[Y_{N}(z,w)_{21} = Y_0(z,w)_{21} - 2\pi i (z-w) \sum_{n=0}^{N-1} \frac{P_n(z)P_n(w)}{h_n}\]

Then from our solution \(Y_0(z,w)_{21} = 0\) we find

\[\boxed{ \sum_{n=0}^{N-1} \frac{P_n(z)P_n(w)}{h_n} = -\frac{1}{2\pi i} \frac{(X_N(z)^{-1}X_N(w))_{21}}{z-w}}\]

which is the Christoffel-Darboux formula.

Remark: This way of writing the Christoffel-Darboux formula mirrors nicely with what happens for \(\beta = 4\). Here the relevant quantity that encodes eigenvalue correlation functions is the “pre-kernel,” written as a sum over skew-orthogonal polynomials. Namely,

\(\sum_{k=0}^{n-1} \frac{ P_{2k}(x) e^{-V(x)} \frac{d}{dy}\left( P_{2k+1}(y) e^{-V(y)} \right) - P_{2k+1}(x) e^{-V(x)} \frac{d}{dy}\left( P_{2k}(y) e^{-V(y)} \right)}{2 h_k}\) \(= - \frac{e^{-V(x)-V(y)}}{4\pi i} \frac{(A_n(x)^{-1}A_n(y))_{21}}{x-y}\)

where \(P_k\) is the \(k\)th monic skew-orthogonal polynomial, \(h_k\) is the skew-norm, and \(A_n\) is a Riemann-Hilbert problem introduced in my recent paper.