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 ...

NDSolve[eqns, y, {x, x_min, x_max}] finds a numerical solution to the ordinary differential equations eqns for the function y with the independent variable x in the range ...

MapIndexed[f, expr] applies f to the elements of expr, giving the part specification of each element as a second argument to f. MapIndexed[f, expr, levelspec] applies f to ...

NestWhileList[f, expr, test] generates a list of the results of applying f repeatedly, starting with expr, and continuing until applying test to the result no longer yields ...

When numerically solving Hamiltonian dynamical systems it is advantageous if the numerical method yields a symplectic map. If the Hamiltonian can be written in separable ...

Many differential equations exhibit some form of stiffness, which restricts the step size and hence effectiveness of explicit solution methods. A number of implicit methods ...

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

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 ...

LogPlot[f, {x, x_min, x_max}] generates a log plot of f as a function of x from x_min to x_max. LogPlot[{f_1, f_2, ...}, {x, x_min, x_max}] generates log plots of several ...