Chebyshev Expansions for Solutions of Linear Differential Equations

A Chebyshev expansion is a series in the basis of Chebyshev polynomials of the first kind. When such a series solves a linear differential equation, its coefficients satisfy a linear recurrence equation. We interpret this equation as the numerator of a fraction of linear recurrence operators. This interpretation lets us give a simple view of previous algorithms, analyze their complexity, and design a faster one for large orders

Rational chebyshev spectral methods for unbounded solutions on an infinite interval using polynomial-growth special basis functions

AbstractIn the method of matched asymptotic expansions, one is often forced to compute solutions which grow as a polynomial in y as |y| → ∞. Similarly, the integral or repeated integral of a bounded function f(y) is generally unbounded also. The kth integral of a function f(y) solves . We describe a two-part algorithm for solving linear differential equations on y ϵ [−∞, ∞] where u(y) grows as a polynomial as |y| → ∞. First, perform an explicit, analytic transformation to a new unknown v so that v is bounded. Second, expand v as a rational Chebyshev series and apply a pseudospectral or Galerkin discretization. (For our examples, it is convenient to perform a preliminary step of splitting the problem into uncoupled equations for the parts of u which are symmetric and antisymmetric with respect to y = 0, but although this is very helpful when applicable, it is not necessary.) For the integral and interated integrals and for constant coefficient differential equations in general, the Galerkin matrices are banded with very low bandwidth. We derive an improvement on the “last coefficient error estimate” of the author's book which applies to series with a subgeometric rate of convergence, as is normally true of rational Chebyshev expansions

An algorithm for the rapid numerical evaluation of Bessel functions of real orders and arguments

We describe a method for the rapid numerical evaluation of the Bessel functions of the first and second kinds of nonnegative real orders and positive arguments. Our algorithm makes use of the well-known observation that although the Bessel functions themselves are expensive to represent via piecewise polynomial expansions, the logarithms of certain solutions of Bessel's equation are not. We exploit this observation by numerically precomputing the logarithms of carefully chosen Bessel functions and representing them with piecewise bivariate Chebyshev expansions. Our scheme is able to evaluate Bessel functions of orders between $0$ and 1\sep,000\sep,000\sep,000 at essentially any positive real argument. In that regime, it is competitive with existing methods for the rapid evaluation of Bessel functions and has several advantages over them. First, our approach is quite general and can be readily applied to many other special functions which satisfy second order ordinary differential equations. Second, by calculating the logarithms of the Bessel functions rather than the Bessel functions themselves, we avoid many issues which arise from numerical overflow and underflow. Third, in the oscillatory regime, our algorithm calculates the values of a nonoscillatory phase function for Bessel's differential equation and its derivative. These quantities are useful for computing the zeros of Bessel functions, as well as for rapidly applying the Fourier-Bessel transform. The results of extensive numerical experiments demonstrating the efficacy of our algorithm are presented. A Fortran package which includes our code for evaluating the Bessel functions as well as our code for all of the numerical experiments described here is publically available

On the numerical calculation of the roots of special functions satisfying second order ordinary differential equations

We describe a method for calculating the roots of special functions satisfying second order linear ordinary differential equations. It exploits the recent observation that the solutions of a large class of such equations can be represented via nonoscillatory phase functions, even in the high-frequency regime. Our algorithm achieves near machine precision accuracy and the time required to compute one root of a solution is independent of the frequency of oscillations of that solution. Moreover, despite its great generality, our approach is competitive with specialized, state-of-the-art methods for the construction of Gaussian quadrature rules of large orders when it used in such a capacity. The performance of the scheme is illustrated with several numerical experiments and a Fortran implementation of our algorithm is available at the author's website