diff( f, x ) — numerical derivative of a real or complex function at x
diff( f, x, n ) — nthorder numerical derivative of a real or complex function at x
D( f, x ) — numerical derivative of a real or complex function at x
D( f, x, n ) — nthorder numerical derivative of a real or complex function at x
gradient( f, point ) — numerical gradient of a real or complex function of multiple variables at the correspondingly dimensioned point
findExtremum( f, point ) — numerical minimum of a real function of multiple variables by gradient descent at the correspondingly dimensioned point
findExtremum( f, point, { findMaximum: true } ) — numerical maximum of a real function of multiple variables by gradient ascent at the correspondingly dimensioned point
integrate( f, [a,b] ) — numerical integral of a real or complex function on the interval [a,b] by an adaptive Simpson algorithm
integrate( f, [a,b], options ) — numerical integral of a real or complex function on the interval [a,b]; options include
method  one of 'eulermaclaurin' 'romberg' , 'adaptivesimpson' 'tanhsinh' 'gaussian' ; default 'adaptivesimpson' 
tolerance  default 10^{−10} 
avoidEndpoints  set to true to displace endpoints by tolerance

discreteIntegral( values, step ) — numerical integral over discrete real values separated by step using EulerMaclaurin summation
polynomial( x, coefficients ) — value of polynomial with real or complex coefficients at x by Horner’s rule with the coefficient of the highest power first
polynomial( x, coefficients, true
) — value of polynomial with real or complex coefficients and its derivative at x by Horner’s rule returned as { polynomial: polynomial, derivative: derivative }
partialBell( n, k, arguments ) — partial Bell polynomial with integer indices n and k and an array of length n−k+1 of real arguments
findRoot( f, [a,b] ) — numerical root of a real function on the interval [a,b] by bisection
findRoot( f, a ) — numerical root of a real or complex function starting from a by Newton’s method
findRoots( functions, point ) — numerical roots of an array of real functions starting from the correspondingly dimensioned point by Newton’s method
spline( points ) — interpolating cubic spline over the array of twodimensional points returned as a function
spline( points, value ) — interpolating cubic spline over the array of twodimensional points with a value of 'function'
, 'derivative'
or 'integral'
returned as a function
ode( f, y_{0}, [x_{0},x_{1}] ) — numerical solution of the system dy/dx = f(x,y), y(x_{0}) = y_{0} on the specified interval. The function and initial condition should be vectorized for higherorder systems. The solution is returned as an array of arrays of data points, with the independent variable as the first item in each data point array.
ode( f, y_{0}, [x_{0},x_{1}], step, method ) — numerical solution of the system dy/dx = f(x,y), y(x_{0}) = y_{0} on the specified interval with specified step size and a method of 'euler'
or 'rungekutta'
fourierSinCoefficient( f, n ) — Fourier sine coefficient of index n of a continuous real function on the interval [0,2π]
fourierSinCoefficient( f, n, period ) — Fourier sine coefficient of index n of a continuous real function on the interval [0,period]
fourierSinCoefficient( points, n ) — Fourier sine coefficient of index n of an array of discrete twodimensional points
fourierCosCoefficient( f, n ) — Fourier cosine coefficient of index n of a continuous real function on the interval [0,2π]
fourierCosCoefficient( f, n, period ) — Fourier cosine coefficient of index n of a continuous real function on the interval [0,period]
fourierCosCoefficient( points, n ) — Fourier cosine coefficient of index n of an array of discrete twodimensional points