fnorm: Function Norm

In pracma: Practical Numerical Math Functions

Function Norm

Description

The fnorm function calculates several different types of function norms for depending on the argument p.

Usage

fnorm(f, g, x1, x2, p = 2, npoints = 100)

Arguments

f, g	functions given by name or string.
x1, x2	endpoints of the interval.
p	Numeric scalar or Inf, -Inf; default is 2.
npoints	number of points to be considered in the interval.

Details

fnorm returns a scalar that gives some measure of the distance of two functions f and g on the interval [x1, x2].

It takes npoints equidistant points in the interval, computes the function values for f and g and applies Norm to their difference.

Especially p=Inf returns the maximum norm, while fnorm(f, g, x1, x2, p = 1, npoints) / npoints would return some estimate of the mean distance.

Value

Numeric scalar (or Inf), or NA if one of these functions returns NA.

Note

Another kind of ‘mean’ distance could be calculated by integrating the difference f-g and dividing through the length of the interval.

See Also

Norm

Examples

xp <- seq(-1, 1, length.out = 6)
yp <- runge(xp)
p5 <- polyfit(xp, yp, 5)
f5 <- function(x) polyval(p5, x)
fnorm(runge, f5, -1, 1, p = Inf)                  #=> 0.4303246
fnorm(runge, f5, -1, 1, p = Inf, npoints = 1000)  #=> 0.4326690

# Compute mean distance using fnorm:
fnorm(runge, f5, -1, 1, p = 1, 1000) / 1000       #=> 0.1094193

# Compute mean distance by integration:
fn <- function(x) abs(runge(x) - f5(x))
integrate(fn, -1, 1)$value / 2                    #=> 0.1095285

pracma documentation built on Nov. 10, 2023, 1:14 a.m.