Together (Built-in Mathematica Symbol)

Together[expr] puts terms in a sum over a common denominator, and cancels factors in the result.

Eigenvalues and Eigenvectors (Mathematica Tutorial)

Eigenvalues and eigenvectors. The eigenvalues of a matrix m are the values λ_i for which one can find nonzero vectors v_i such that m.v_i λ_iv_i. The eigenvectors are the ...

MathLink and External Program ... (Mathematica Overview)

Introduction to MathLink How MathLink Is Used Installing Existing MathLink-Compatible Programs

Naming and Finding Files (Mathematica Tutorial)

The precise details of the naming of files differ from one computer system to another. Nevertheless, Mathematica provides some fairly general mechanisms that work on all ...

BiorthogonalSplineWavelet (Built-in Mathematica Symbol)

BiorthogonalSplineWavelet[] represents a biorthogonal spline wavelet of order 4 and dual order 2.BiorthogonalSplineWavelet[n, m] represents a biorthogonal spline wavelet of ...

GraphPlot (Built-in Mathematica Symbol)

GraphPlot[{v i 1 -> v j 1, v i 2 -> v j 2, ...}] generates a plot of the graph in which vertex v_ik is connected to vertex v_jk.GraphPlot[{{v i 1 -> v j 1, lbl_1}, ...}] ...

ReverseBiorthogonalSplineWavelet (Built-in Mathematica Symbol)

ReverseBiorthogonalSplineWavelet[] represents a reverse biorthogonal spline wavelet of order 4 and dual order 2.ReverseBiorthogonalSplineWavelet[n, m] represents a reverse ...

NIntegrate Integration Rules (Mathematica Tutorial)

An integration rule computes an estimate of an integral over a region, typically using a weighted sum. In the context of NIntegrate usage, an integration rule object provides ...

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 ...

LinearModelFit (Built-in Mathematica Symbol)

LinearModelFit[{y_1, y_2, ...}, {f_1, f_2, ...}, x] constructs a linear model of the form \[Beta]_0 + \[Beta]_1 f_1 + \[Beta]_2 f_2 + ... that fits the y_i for successive x ...