This tutorial covers advanced features of the Manipulate command. It assumes that you have read "Introduction to Manipulate" and thus have a good idea what the command is for ...

Linear programming problems are optimization problems where the objective function and constraints are all linear. Mathematica has a collection of algorithms for solving ...

This section is concerned with the way that CUDALink is set up and configured for your machine. It will also help to track down and correct problems. CUDALink is designed to ...

LineBreakChart[{{date_1, p_1}, {date_2, p_2}, ...}] makes a line break chart with prices p_i at date date_i.LineBreakChart[{" name", daterange}] makes a line break chart of ...

DSolve[eqn, y, x] solves a differential equation for the function y, with independent variable x. DSolve[{eqn_1, eqn_2, ...}, {y_1, y_2, ...}, x] solves a list of ...

ExponentialDistribution[\[Lambda]] represents an exponential distribution with scale inversely proportional to parameter \[Lambda].

NormalDistribution[\[Mu], \[Sigma]] represents a normal (Gaussian) distribution with mean \[Mu] and standard deviation \[Sigma].NormalDistribution[] represents a normal ...

Plot[f, {x, x_min, x_max}] generates a plot of f as a function of x from x_min to x_max. Plot[{f_1, f_2, ...}, {x, x_min, x_max}] plots several functions f_i.

WeibullDistribution[\[Alpha], \[Beta]] represents a Weibull distribution with shape parameter \[Alpha] and scale parameter \[Beta].WeibullDistribution[\[Alpha], \[Beta], ...

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