Increasing the Reliability of Adaptive Quadrature Using Explicit Interpolants

We present two new adaptive quadrature routines. Both routines differ from previously published algorithms in many aspects, most significantly in how they represent the integrand, how they treat non-numerical values of the integrand, how they deal with improper divergent integrals and how they estimate the integration error. The main focus of these improvements is to increase the reliability of the algorithms without significantly impacting their efficiency. Both algorithms are implemented in Matlab and tested using both the "families" suggested by Lyness and Kaganove and the battery test used by Gander and Gautschi and Kahaner. They are shown to be more reliable, albeit in some cases less efficient, than other commonly-used adaptive integrators.Comment: 32 pages, submitted to ACM Transactions on Mathematical Softwar

FATODE: A Library for Forward, Adjoint, and Tangent Linear Integration of ODEs

FATODE is a FORTRAN library for the integration of ordinary differential equations with direct and adjoint sensitivity analysis capabilities. The paper describes the capabilities, implementation, code organization, and usage of this package. FATODE implements four families of methods -- explicit Runge-Kutta for nonstiff problems and fully implicit Runge-Kutta, singly diagonally implicit Runge-Kutta, and Rosenbrock for stiff problems. Each family contains several methods with different orders of accuracy; users can add new methods by simply providing their coefficients. For each family the forward, adjoint, and tangent linear models are implemented. General purpose solvers for dense and sparse linear algebra are used; users can easily incorporate problem-tailored linear algebra routines. The performance of the package is demonstrated on several test problems. To the best of our knowledge FATODE is the first publicly available general purpose package that offers forward and adjoint sensitivity analysis capabilities in the context of Runge Kutta methods. A wide range of applications are expected to benefit from its use; examples include parameter estimation, data assimilation, optimal control, and uncertainty quantification

DSN advanced receiver: Breadboard description and test results

A breadboard Advanced Receiver for use in the Deep Space Network was designed, built, and tested in the laboratory. Field testing was also performed during Voyager Uranus encounter at DSS-13. The development of the breadboard is intended to lead towards implementation of the new receiver throughout the network. The receiver is described on a functional level and then in terms of more specific hardware and software architecture. The results of performance tests in the laboratory and in the field are given. Finally, there is a discussion of suggested improvements for the next phase of development

Performance of likelihood-based estimation methods for multilevel binary regression models.

By means of a fractional factorial simulation experiment, we compare the performance of Penalised Quasi-Likelihood, Non-Adaptive Gaussian Quadrature and Adaptive Gaussian Quadrature in estimating parameters for multi-level logistic regression models. The comparison is done in terms of bias, mean squared error, numerical convergence, and computational efficiency. It turns out that, in terms of Mean Squared Error, standard versions of the Quadrature methods perform relatively poor in comparison with Penalized Quasi-Likelihood.Bias; Binary regression; Convergence; Efficiency; Factorial; Fractional factorial experiment; Gaussian quadrature; Logistic regression; Methods; Model; Models; Monte Carlo simulation; Multilevel analysis; Penalised quasi-likelihood; Performance; Regression; Simulation;

Performance of likelihood-based estimation methods for multilevel binary regression models.

By means of a fractional factorial simulation experiment, we. compare the performance of penalised quasi-likelihood (PQL), non-adaptive Gaussian quadrature and adaptive Gaussian quadrature in estimating parameters for multilevel logistic regression models. The comparison is done in terms of bias, mean-squared error (MSE), numerical convergence and computational efficiency. It turns out that in terms of MSE, standard versions of the quadrature methods per-form relatively poorly in comparison with PQL.Bias; Binary regression; Convergence; Efficiency; Factorial; Fractional factorial experiment; Gaussian quadrature; Logistic regression; Methods; Model; Models; Monte Carlo simulation; Multilevel analysis; Parameters; Penalised quasi-likelihood; Performance; Regression; Simulation;

On the subdivision strategy in adaptive quadrature algorithms

AbstractThe subdivision procedure used in most available adaptive quadrature codes is a simple bisection of the chosen interval. Thus the interval is divided in two equally sized parts. In this paper we present a subdivision strategy which gives three nonequally sized parts. The subdivision points are found using only available information. The strategy has been implemented in the QUADPACK code DQAG and tested using the “performance profile” testing technique. We present test results showing a significant reduction in the number of function evaluations compared to the standard bisection procedure on most test families of integrands

Chebfun and numerical quadrature

Chebfun is a Matlab-based software system that overloads Matlab’s discrete operations for vectors and matrices to analogous continuous operations for functions and operators. We begin by describing Chebfun’s fast capabilities for Clenshaw–Curtis and also Gauss–Legendre, –Jacobi, –Hermite, and –Laguerre quadrature, based on algorithms of Waldvogel and Glaser, Liu, and Rokhlin. Then we consider how such methods can be applied to quadrature problems including 2D integrals over rectangles, fractional derivatives and integrals, functions defined on unbounded intervals, and the fast computation of weights for barycentric interpolation