Groups admit many different representations. In particular, all finite groups can be represented as permutation groups, that is, they are always isomorphic to a subgroup of ...

Mathematica can run parallel kernels in a number of different ways; locally on the same machine or remote on other machines connected in a network. Furthermore, the network ...

The function FindClusters finds clusters in a dataset based on a distance or dissimilarity function. This package contains functions for generating cluster hierarchies and ...

ParetoDistribution[k, \[Alpha]] represents a Pareto distribution with minimum value parameter k and shape parameter \[Alpha].ParetoDistribution[k, \[Alpha], \[Mu]] represents ...

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

A method like "Newton's" method chooses a step, but the validity of that step only goes as far as the Newton quadratic model for the function really reflects the function. ...

One significant advantage Mathematica provides is that it can symbolically compute derivatives. This means that when you specify Method->"Newton" and the function is ...

Mathematically, sufficient conditions for a local minimum of a smooth function are quite straightforward: x^* is a local minimum if ∇f(x^*)=0 and the Hessian ∇^2f(x^*) is ...

Eigensystem[m] gives a list {values, vectors} of the eigenvalues and eigenvectors of the square matrix m. Eigensystem[{m, a}] gives the generalized eigenvalues and ...

GammaDistribution[\[Alpha], \[Beta]] represents a gamma distribution with shape parameter \[Alpha] and scale parameter \[Beta].GammaDistribution[\[Alpha], \[Beta], \[Gamma], ...