Numerical algorithms for constrained nonlinear optimization can be broadly categorized into gradient-based methods and direct search methods. Gradient search methods use ...

The functions described here are among the most commonly used discrete univariate statistical distributions. You can compute their densities, means, variances, and other ...

This loads packages containing some test problems and utility functions. One of the first and simplest methods for solving initial value problems was proposed by Euler: ...

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 ...

When a differential system has a certain structure, it is advantageous if a numerical integration method preserves the structure. In certain situations it is useful to solve ...

Mathematica contains some powerful primitives for making structural changes to expressions. You can use these primitives both to implement mathematical properties such as ...

ZTest[data] tests whether the mean of the data is zero. ZTest[{data_1, data_2}] tests whether the means of data_1 and data_2 are equal.ZTest[dspec, \[Sigma]] tests for zero ...

As of Version 8, much of the functionality covered by Combinatorica has been implemented in the Mathematica kernel. BooleanAlgebra CodeToLabeledTree HasseDiagram ...

ListContourPlot[array] generates a contour plot from an array of height values. ListContourPlot[{{x_1, y_1, f_1}, {x_2, y_2, f_2}, ...}] generates a contour plot from values ...

ListDensityPlot[array] generates a smooth density plot from an array of values. ListDensityPlot[{{x_1, y_1, f_1}, {x_2, y_2, f_2}, ...}] generates a density plot with values ...