In many kinds of numerical computations, it is convenient to introduce approximate functions. Approximate functions can be thought of as generalizations of ordinary ...

Implicit Runge–Kutta methods have a number of desirable properties. The Gauss–Legendre methods, for example, are self-adjoint, meaning that they provide the same solution ...

Parallel computing in Mathematica is based on launching and controlling multiple Mathematica kernel (worker) processes from within a single master Mathematica, providing a ...

TransformationFunction[data] represents a transformation function that applies geometric and other transformations.

Mathematica has a flexible system for specifying arbitrary symbolic assumptions about variables. It uses a wide range of sophisticated algorithms to infer the consequences of ...

Mathematica's handling of polynomial systems is a tour de force of algebraic computation. Building on mathematical results spanning more than a century, Mathematica for the ...

LaunchKernels[] launches all currently configured parallel subkernels.LaunchKernels[n] launches n local subkernels on the current computer.LaunchKernels[des] launches a ...

The World Wide Web is increasingly being used for communication between applications. The programmatic interfaces made available over the web for application-to-application ...

CUDALink allows Mathematica to use the CUDA parallel computing architecture on Graphical Processing Units (GPUs). It contains functions that use CUDA-enabled GPUs to boost ...

This package contains functions for measuring the performance of Mathematica on your computer and for producing a comparison report that includes benchmark results for other ...