Numerical Nonlinear Local Optimization (Mathematica Tutorial)

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

CUDA Functions (CUDALink Tutorial)

CUDALink is a built-in Mathematica package that provides a simple and powerful interface for using CUDA within Mathematica's streamlined work flow. CUDALink provides you with ...

Eigensystem (Built-in Mathematica Symbol)

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 (Built-in Mathematica Symbol)

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

Product (Built-in Mathematica Symbol)

Product[f, {i, i_max}] evaluates the product \[Product]i = 1 i_max f. Product[f, {i, i_min, i_max}] starts with i = i_min. Product[f, {i, i_min, i_max, di}] uses steps di. ...

SmoothKernelDistribution (Built-in Mathematica Symbol)

SmoothKernelDistribution[{x_1, x_2, ...}] represents a smooth kernel distribution based on the data values x_i.SmoothKernelDistribution[{{x_1, y_1, ...}, {x_2, y_2, ...}, ...

WaveletBestBasis (Built-in Mathematica Symbol)

WaveletBestBasis[dwd] computes a best basis representation in the DiscreteWaveletData object dwd.WaveletBestBasis[dwd, cspec] computes a best basis representation using the ...

MathieuCharacteristicExponent (Built-in Mathematica Symbol)

MathieuCharacteristicExponent[a, q] gives the characteristic exponent r for Mathieu functions with characteristic value a and parameter q.

Concurrency: Managing Parallel Processes (Parallel Package Tutorial)

A process is simply a Mathematica expression being evaluated. A processor is a parallel kernel that performs such evaluations. The command ParallelEvaluate discussed in the ...

Sum (Built-in Mathematica Symbol)

Sum[f, {i, i_max}] evaluates the sum \[Sum]i = 1 i_max f. Sum[f, {i, i_min, i_max}] starts with i = i_min. Sum[f, {i, i_min, i_max, di}] uses steps d i. Sum[f, {i, {i_1, i_2, ...