Exact global optimization problems can be solved exactly using Minimize and Maximize. This computes the radius of the circle, centered at the origin, circumscribed about the ...

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 ...