GMRES for oscillatory matrix-valued differential equations

We investigate the use of Krylov subspace methods to solve linear, oscillatory ODEs. When we apply a Krylov subspace method to a properly formulated equation, we retain the asymptotic accuracy of the asymptotic expansion whilst converging to the exact solution. We will demonstrate the effectiveness of this method by computing Error and Mathieu functions

Computing the Hilbert transform and its inverse

We construct a new method for approximating Hilbert transforms and their inverse throughout the complex plane. Both problems can be formulated as Riemann-Hilbert problems via Plemelj's lemma. Using this framework, we re-derive existing approaches for computing Hilbert transforms over the real line and unit interval, with the added benefit that we can compute the Hilbert transform in the complex plane. We then demonstrate the power of this approach by generalizing to the half line. Combining two half lines, we can compute the Hilbert transform of a more general class of functions on the real line than is possible with existing methods

Computation of equilibrium measures

We present a new way of computing equilibrium measures, based on the Riemann-Hilbert formulation. For equilibrium measures whose support is a single interval, the simple algorithm consists of a Newton-Raphson iteration where each step only involves fast cosine transforms. The approach is then generalized for multiple intervals

Fast, numerically stable computation of oscillatory integrals with stationary points

We present a numerically stable way to compute oscillatory integrals of the form $\int{-1}^{1} f(x)e^{i\omega g(x)}dx$. For each additional frequency, only a small, well-conditioned linear system with a Hessenberg matrix must be solved, and the amount of work needed decreases as the frequency increases. Moreover, we can modify the method for computing oscillatory integrals with stationary points. This is the first stable algorithm for oscillatory integrals with stationary points which does not lose accuracy as the frequency increases and does not require deformation into the complex plane

A general framework for solving Riemann-Hilbert problems\ud numerically

A new, numerical framework for the approximation of solutions to matrix-valued Riemann-Hilbert problems is developed, based on a recent method for the homogeneous Painlev\'e II Riemann- Hilbert problem. We demonstrate its effectiveness by computing solutions to other Painlev\'e transcendents.\ud \ud An implementation in MATHEMATICA is made available online

Numerical solution of Riemann-Hilbert problems: Painleve II

We describe a new spectral method for solving matrix-valued Riemann-Hilbert problems numerically. We demonstrate the effectiveness of this approach by computing solutions to the homogeneous Painleve II equation. This can be used to relate initial conditions with asymptotic behaviour

Orthogonal structure on a quadratic curve

Orthogonal polynomials on quadratic curves in the plane are studied. These include orthogonal polynomials on ellipses, parabolas, hyperbolas, and two lines. For an integral with respect to an appropriate weight function defined on any quadratic curve, an explicit basis of orthogonal polynomials is constructed in terms of two families of orthogonal polynomials in one variable. Convergence of the Fourier orthogonal expansions is also studied in each case. As an application, we see that the resulting bases can be used to interpolate functions on the real line with singularities of the form $|x|$, $\sqrt{x^2+ \epsilon^2}$, or $1/x$, with exponential convergence

A fast and well-conditioned spectral method

A novel spectral method is developed for the direct solution of linear ordinary differential equations with variable coefficients. The method leads to matrices which are almost banded, and a numerical solver is presented that takes $O(m^{2}n)$ operations, where $m$ is the number of Chebyshev points needed to resolve the coefficients of the differential operator and $n$ is the number of Chebyshev points needed to resolve the solution to the differential equation. We prove stability of the method by relating it to a diagonally preconditioned system which has a bounded condition number, in a suitable norm. For Dirichlet boundary conditions, this reduces to stability in the standard 2-norm