RSolve[eqn, a[n], n] solves a recurrence equation for a[n]. RSolve[{eqn_1, eqn_2, ...}, {a_1[n], a_2[n], ...}, n] solves a system of recurrence equations. RSolve[eqn, a[n_1, ...

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

Wolfram LibraryLink allows dynamic libraries to be directly loaded into the Mathematica kernel so that functions in the libraries can be immediately called from Mathematica. ...

The numerical method of lines is a technique for solving partial differential equations by discretizing in all but one dimension, and then integrating the semi-discrete ...

This section is designed to discuss how to make compiled functions run efficiently. It will cover features that make them run faster, as well as problems that can make them ...

BernoulliB[n] gives the Bernoulli number B_n. BernoulliB[n, x] gives the Bernoulli polynomial B_n (x).

Blend[{col_1, col_2}, x] gives a color obtained by blending a fraction 1 - x of color col_1 and x of color col_2.Blend[{col_1, col_2, col_3, ...}, x] linearly interpolates ...

Convolve[f, g, x, y] gives the convolution with respect to x of the expressions f and g.Convolve[f, g, {x_1, x_2, ...}, {y_1, y_2, ...}] gives the multidimensional ...

FindArgMax[f, x] gives the position x_max of a local maximum of f.FindArgMax[f, {x, x_0}] gives the position x_max of a local maximum of f, found by a search starting from ...

FindArgMin[f, x] gives the position x_min of a local minimum of f.FindArgMin[f, {x, x_0}] gives the position x_min of a local minimum of f, found by a search starting from ...