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

Mathematica has a collection of commands that do unconstrained optimization (FindMinimum and FindMaximum) and solve nonlinear equations (FindRoot) and nonlinear fitting ...

The function FindRoot has a Jacobian option; the functions FindMinimum, FindMaximum, and FindFit have a Gradient option; and the "Newton" method has a method option Hessian. ...

All the test problems presented in [MGH81] have been coded into Mathematica in the Optimization`UnconstrainedProblems` package. A data structure is used so that the problems ...

A trust region method has a region around the current search point, where the quadratic model for "local minimization" is "trusted" to be correct and steps are chosen to stay ...

Probability[pred, x \[Distributed] dist] gives the probability for an event that satisfies the predicate pred under the assumption that x follows the probability distribution ...

The ability to generate pseudorandom numbers is important for simulating events, estimating probabilities and other quantities, making randomized assignments or selections, ...

ParametricPlot3D[{f_x, f_y, f_z}, {u, u_min, u_max}] produces a three-dimensional space curve parametrized by a variable u which runs from u_min to u_max. ...

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

Mathematica provides a broad range of powerful constructs for laying out content on a screen or page. They are designed to be immediately useful for the beginner, yet also ...