ListLogPlot[{y_1, y_2, ...}] makes a log plot of the y_i, assumed to correspond to x coordinates 1, 2, ....ListLogPlot[{{x_1, y_1}, {x_2, y_2}, ...}] makes a log plot of the ...
The Mathematica function NIntegrate is a general numerical integrator. It can handle a wide range of one-dimensional and multidimensional integrals. Finding a numerical ...
The default behavior for a function in Mathematica is carefully chosen to be suitable for the vast majority of cases. Mathematica also gives you fine-grained control over the ...
LogLinearPlot[f, {x, x_min, x_max}] generates a log-linear plot of f as a function of x from x_min to x_max. LogLinearPlot[{f_1, f_2, ...}, {x, x_min, x_max}] generates ...
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. ...
VectorPlot3D[{v_x, v_y, v_z}, {x, x_min, x_max}, {y, y_min, y_max}, {z, z_min, z_max}] generates a 3D vector plot of the vector field {v_x, v_y, v_z} as a function of x, y, ...
There are many situations where one wants to find a formula that best fits a given set of data. One way to do this in Mathematica is to use Fit. Basic linear fitting. Here is ...
Supporting a large number of numerical integration methods for differential equations is a lot of work. In order to cut down on maintenance and duplication of code, common ...
FindMaximum[f, x] searches for a local maximum in f, starting from an automatically selected point.FindMaximum[f, {x, x_0}] searches for a local maximum in f, starting from ...
FindMinimum[f, x] searches for a local minimum in f, starting from an automatically selected point.FindMinimum[f, {x, x_0}] searches for a local minimum in f, starting from ...