2nthrt (problem 3.4.6)

Percentage Accurate: 54.0% → 87.1%

Time: 19.8s

Alternatives: 16

Speedup: 2.5×

• could not determine a ground truth

  1. x = -6.566980741094054e+148
  2. n = -1044881291.2221259

Specification

Math

\[\begin{array}{l} \\ {\left(x + 1\right)}^{\left(\frac{1}{n}\right)} - {x}^{\left(\frac{1}{n}\right)} \end{array} \]

FPCore

(FPCore (x n)
 :precision binary64
 (- (pow (+ x 1.0) (/ 1.0 n)) (pow x (/ 1.0 n))))

C

double code(double x, double n) {
	return pow((x + 1.0), (1.0 / n)) - pow(x, (1.0 / n));
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x, n)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: n
    code = ((x + 1.0d0) ** (1.0d0 / n)) - (x ** (1.0d0 / n))
end function

Java

public static double code(double x, double n) {
	return Math.pow((x + 1.0), (1.0 / n)) - Math.pow(x, (1.0 / n));
}

Python

def code(x, n):
	return math.pow((x + 1.0), (1.0 / n)) - math.pow(x, (1.0 / n))

Julia

function code(x, n)
	return Float64((Float64(x + 1.0) ^ Float64(1.0 / n)) - (x ^ Float64(1.0 / n)))
end

MATLAB

function tmp = code(x, n)
	tmp = ((x + 1.0) ^ (1.0 / n)) - (x ^ (1.0 / n));
end

Wolfram

code[x_, n_] := N[(N[Power[N[(x + 1.0), $MachinePrecision], N[(1.0 / n), $MachinePrecision]], $MachinePrecision] - N[Power[x, N[(1.0 / n), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]

Initial Program: 54.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ {\left(x + 1\right)}^{\left(\frac{1}{n}\right)} - {x}^{\left(\frac{1}{n}\right)} \end{array} \]

FPCore

(FPCore (x n)
 :precision binary64
 (- (pow (+ x 1.0) (/ 1.0 n)) (pow x (/ 1.0 n))))

C

double code(double x, double n) {
	return pow((x + 1.0), (1.0 / n)) - pow(x, (1.0 / n));
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x, n)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: n
    code = ((x + 1.0d0) ** (1.0d0 / n)) - (x ** (1.0d0 / n))
end function

Java

public static double code(double x, double n) {
	return Math.pow((x + 1.0), (1.0 / n)) - Math.pow(x, (1.0 / n));
}

Python

def code(x, n):
	return math.pow((x + 1.0), (1.0 / n)) - math.pow(x, (1.0 / n))

Julia

function code(x, n)
	return Float64((Float64(x + 1.0) ^ Float64(1.0 / n)) - (x ^ Float64(1.0 / n)))
end

MATLAB

function tmp = code(x, n)
	tmp = ((x + 1.0) ^ (1.0 / n)) - (x ^ (1.0 / n));
end

Wolfram

code[x_, n_] := N[(N[Power[N[(x + 1.0), $MachinePrecision], N[(1.0 / n), $MachinePrecision]], $MachinePrecision] - N[Power[x, N[(1.0 / n), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]

Alternative 1: 87.1% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\frac{1}{n} \leq -5 \cdot 10^{-20}:\\ \;\;\;\;\frac{e^{\frac{\log x}{n}}}{n \cdot x}\\ \mathbf{elif}\;\frac{1}{n} \leq 4 \cdot 10^{-78}:\\ \;\;\;\;-\frac{\log \left(\frac{x}{1 + x}\right)}{n}\\ \mathbf{elif}\;\frac{1}{n} \leq 10^{-13}:\\ \;\;\;\;\frac{\frac{n + \log x}{n}}{n \cdot x}\\ \mathbf{elif}\;\frac{1}{n} \leq 2 \cdot 10^{+172}:\\ \;\;\;\;{\left(x + 1\right)}^{\left(\frac{1}{n}\right)} - {x}^{\left(\frac{1}{n}\right)}\\ \mathbf{else}:\\ \;\;\;\;\frac{n \cdot \log \left(\frac{1 + x}{x}\right)}{n \cdot n}\\ \end{array} \end{array} \]

FPCore

(FPCore (x n)
 :precision binary64
 (if (<= (/ 1.0 n) -5e-20)
   (/ (exp (/ (log x) n)) (* n x))
   (if (<= (/ 1.0 n) 4e-78)
     (- (/ (log (/ x (+ 1.0 x))) n))
     (if (<= (/ 1.0 n) 1e-13)
       (/ (/ (+ n (log x)) n) (* n x))
       (if (<= (/ 1.0 n) 2e+172)
         (- (pow (+ x 1.0) (/ 1.0 n)) (pow x (/ 1.0 n)))
         (/ (* n (log (/ (+ 1.0 x) x))) (* n n)))))))

C

double code(double x, double n) {
	double tmp;
	if ((1.0 / n) <= -5e-20) {
		tmp = exp((log(x) / n)) / (n * x);
	} else if ((1.0 / n) <= 4e-78) {
		tmp = -(log((x / (1.0 + x))) / n);
	} else if ((1.0 / n) <= 1e-13) {
		tmp = ((n + log(x)) / n) / (n * x);
	} else if ((1.0 / n) <= 2e+172) {
		tmp = pow((x + 1.0), (1.0 / n)) - pow(x, (1.0 / n));
	} else {
		tmp = (n * log(((1.0 + x) / x))) / (n * n);
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x, n)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: n
    real(8) :: tmp
    if ((1.0d0 / n) <= (-5d-20)) then
        tmp = exp((log(x) / n)) / (n * x)
    else if ((1.0d0 / n) <= 4d-78) then
        tmp = -(log((x / (1.0d0 + x))) / n)
    else if ((1.0d0 / n) <= 1d-13) then
        tmp = ((n + log(x)) / n) / (n * x)
    else if ((1.0d0 / n) <= 2d+172) then
        tmp = ((x + 1.0d0) ** (1.0d0 / n)) - (x ** (1.0d0 / n))
    else
        tmp = (n * log(((1.0d0 + x) / x))) / (n * n)
    end if
    code = tmp
end function

Java

public static double code(double x, double n) {
	double tmp;
	if ((1.0 / n) <= -5e-20) {
		tmp = Math.exp((Math.log(x) / n)) / (n * x);
	} else if ((1.0 / n) <= 4e-78) {
		tmp = -(Math.log((x / (1.0 + x))) / n);
	} else if ((1.0 / n) <= 1e-13) {
		tmp = ((n + Math.log(x)) / n) / (n * x);
	} else if ((1.0 / n) <= 2e+172) {
		tmp = Math.pow((x + 1.0), (1.0 / n)) - Math.pow(x, (1.0 / n));
	} else {
		tmp = (n * Math.log(((1.0 + x) / x))) / (n * n);
	}
	return tmp;
}

Python

def code(x, n):
	tmp = 0
	if (1.0 / n) <= -5e-20:
		tmp = math.exp((math.log(x) / n)) / (n * x)
	elif (1.0 / n) <= 4e-78:
		tmp = -(math.log((x / (1.0 + x))) / n)
	elif (1.0 / n) <= 1e-13:
		tmp = ((n + math.log(x)) / n) / (n * x)
	elif (1.0 / n) <= 2e+172:
		tmp = math.pow((x + 1.0), (1.0 / n)) - math.pow(x, (1.0 / n))
	else:
		tmp = (n * math.log(((1.0 + x) / x))) / (n * n)
	return tmp

Julia

function code(x, n)
	tmp = 0.0
	if (Float64(1.0 / n) <= -5e-20)
		tmp = Float64(exp(Float64(log(x) / n)) / Float64(n * x));
	elseif (Float64(1.0 / n) <= 4e-78)
		tmp = Float64(-Float64(log(Float64(x / Float64(1.0 + x))) / n));
	elseif (Float64(1.0 / n) <= 1e-13)
		tmp = Float64(Float64(Float64(n + log(x)) / n) / Float64(n * x));
	elseif (Float64(1.0 / n) <= 2e+172)
		tmp = Float64((Float64(x + 1.0) ^ Float64(1.0 / n)) - (x ^ Float64(1.0 / n)));
	else
		tmp = Float64(Float64(n * log(Float64(Float64(1.0 + x) / x))) / Float64(n * n));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, n)
	tmp = 0.0;
	if ((1.0 / n) <= -5e-20)
		tmp = exp((log(x) / n)) / (n * x);
	elseif ((1.0 / n) <= 4e-78)
		tmp = -(log((x / (1.0 + x))) / n);
	elseif ((1.0 / n) <= 1e-13)
		tmp = ((n + log(x)) / n) / (n * x);
	elseif ((1.0 / n) <= 2e+172)
		tmp = ((x + 1.0) ^ (1.0 / n)) - (x ^ (1.0 / n));
	else
		tmp = (n * log(((1.0 + x) / x))) / (n * n);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, n_] := If[LessEqual[N[(1.0 / n), $MachinePrecision], -5e-20], N[(N[Exp[N[(N[Log[x], $MachinePrecision] / n), $MachinePrecision]], $MachinePrecision] / N[(n * x), $MachinePrecision]), $MachinePrecision], If[LessEqual[N[(1.0 / n), $MachinePrecision], 4e-78], (-N[(N[Log[N[(x / N[(1.0 + x), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / n), $MachinePrecision]), If[LessEqual[N[(1.0 / n), $MachinePrecision], 1e-13], N[(N[(N[(n + N[Log[x], $MachinePrecision]), $MachinePrecision] / n), $MachinePrecision] / N[(n * x), $MachinePrecision]), $MachinePrecision], If[LessEqual[N[(1.0 / n), $MachinePrecision], 2e+172], N[(N[Power[N[(x + 1.0), $MachinePrecision], N[(1.0 / n), $MachinePrecision]], $MachinePrecision] - N[Power[x, N[(1.0 / n), $MachinePrecision]], $MachinePrecision]), $MachinePrecision], N[(N[(n * N[Log[N[(N[(1.0 + x), $MachinePrecision] / x), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] / N[(n * n), $MachinePrecision]), $MachinePrecision]]]]]

Derivation

1. Split input into 5 regimes

2. if (/.f64 #s(literal 1 binary64) n) < -4.9999999999999999e-20

   1. Initial program 96.1%

      \[{\left(x + 1\right)}^{\left(\frac{1}{n}\right)} - {x}^{\left(\frac{1}{n}\right)} \]

   2. Taylor expanded in x around inf

      \[\leadsto \color{blue}{\frac{e^{-1 \cdot \frac{\log \left(\frac{1}{x}\right)}{n}}}{n \cdot x}} \]
   3. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{e^{-1 \cdot \frac{\log \left(\frac{1}{x}\right)}{n}}}{\color{blue}{n \cdot x}} \]

      2. lower-exp.f64 N/A

         \[\leadsto \frac{e^{-1 \cdot \frac{\log \left(\frac{1}{x}\right)}{n}}}{\color{blue}{n} \cdot x} \]

      3. mul-1-neg N/A

         \[\leadsto \frac{e^{\mathsf{neg}\left(\frac{\log \left(\frac{1}{x}\right)}{n}\right)}}{n \cdot x} \]

      4. log-rec N/A

         \[\leadsto \frac{e^{\mathsf{neg}\left(\frac{\mathsf{neg}\left(\log x\right)}{n}\right)}}{n \cdot x} \]

      5. mul-1-neg N/A

         \[\leadsto \frac{e^{\mathsf{neg}\left(\frac{-1 \cdot \log x}{n}\right)}}{n \cdot x} \]

      6. lower-neg.f64 N/A

         \[\leadsto \frac{e^{-\frac{-1 \cdot \log x}{n}}}{n \cdot x} \]

      7. lower-/.f64 N/A

         \[\leadsto \frac{e^{-\frac{-1 \cdot \log x}{n}}}{n \cdot x} \]

      8. mul-1-neg N/A

         \[\leadsto \frac{e^{-\frac{\mathsf{neg}\left(\log x\right)}{n}}}{n \cdot x} \]

      9. lower-neg.f64 N/A

         \[\leadsto \frac{e^{-\frac{-\log x}{n}}}{n \cdot x} \]

      10. lower-log.f64 N/A

          \[\leadsto \frac{e^{-\frac{-\log x}{n}}}{n \cdot x} \]

      11. lower-*.f64 97.3

          \[\leadsto \frac{e^{-\frac{-\log x}{n}}}{n \cdot \color{blue}{x}} \]

   4. Applied rewrites 97.3%

      \[\leadsto \color{blue}{\frac{e^{-\frac{-\log x}{n}}}{n \cdot x}} \]

   5. Taylor expanded in x around 0

      \[\leadsto \frac{e^{\frac{\log x}{n}}}{n \cdot x} \]
   6. Step-by-step derivation

      1. lift-log.f64 N/A

         \[\leadsto \frac{e^{\frac{\log x}{n}}}{n \cdot x} \]

      2. lift-/.f64 97.3

         \[\leadsto \frac{e^{\frac{\log x}{n}}}{n \cdot x} \]

   7. Applied rewrites 97.3%

      \[\leadsto \frac{e^{\frac{\log x}{n}}}{n \cdot x} \]

   if -4.9999999999999999e-20 < (/.f64 #s(literal 1 binary64) n) < 4e-78

   1. Initial program 31.6%

      \[{\left(x + 1\right)}^{\left(\frac{1}{n}\right)} - {x}^{\left(\frac{1}{n}\right)} \]

   2. Taylor expanded in n around -inf

      \[\leadsto \color{blue}{-1 \cdot \frac{\left(-1 \cdot \log \left(1 + x\right) + -1 \cdot \frac{\left(-1 \cdot \frac{\frac{-1}{6} \cdot {\log \left(1 + x\right)}^{3} - \frac{-1}{6} \cdot {\log x}^{3}}{n} + \frac{1}{2} \cdot {\log \left(1 + x\right)}^{2}\right) - \frac{1}{2} \cdot {\log x}^{2}}{n}\right) - -1 \cdot \log x}{n}} \]

   4. Applied rewrites 80.8%

      \[\leadsto \color{blue}{-\frac{\left(\left(-\frac{\left(-\frac{-0.16666666666666666 \cdot \left({\log \left(1 + x\right)}^{3} - {\log x}^{3}\right)}{n}\right) + 0.5 \cdot \left(\log \left(1 + x\right) \cdot \log \left(1 + x\right) - \log x \cdot \log x\right)}{n}\right) + \left(-\log \left(1 + x\right)\right)\right) + \log x}{n}} \]

   5. Taylor expanded in n around inf

      \[\leadsto -\frac{\log x - \log \left(1 + x\right)}{n} \]
   6. Step-by-step derivation

      1. diff-log N/A

         \[\leadsto -\frac{\log \left(\frac{x}{1 + x}\right)}{n} \]

      2. lower-log.f64 N/A

         \[\leadsto -\frac{\log \left(\frac{x}{1 + x}\right)}{n} \]

      3. lower-/.f64 N/A

         \[\leadsto -\frac{\log \left(\frac{x}{1 + x}\right)}{n} \]

      4. lift-+.f64 81.0

         \[\leadsto -\frac{\log \left(\frac{x}{1 + x}\right)}{n} \]

   7. Applied rewrites 81.0%

      \[\leadsto -\frac{\log \left(\frac{x}{1 + x}\right)}{n} \]

   if 4e-78 < (/.f64 #s(literal 1 binary64) n) < 1e-13

   1. Initial program 11.7%

      \[{\left(x + 1\right)}^{\left(\frac{1}{n}\right)} - {x}^{\left(\frac{1}{n}\right)} \]

   2. Taylor expanded in x around inf

      \[\leadsto \color{blue}{\frac{e^{-1 \cdot \frac{\log \left(\frac{1}{x}\right)}{n}}}{n \cdot x}} \]
   3. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{e^{-1 \cdot \frac{\log \left(\frac{1}{x}\right)}{n}}}{\color{blue}{n \cdot x}} \]

      2. lower-exp.f64 N/A

         \[\leadsto \frac{e^{-1 \cdot \frac{\log \left(\frac{1}{x}\right)}{n}}}{\color{blue}{n} \cdot x} \]

      3. mul-1-neg N/A

         \[\leadsto \frac{e^{\mathsf{neg}\left(\frac{\log \left(\frac{1}{x}\right)}{n}\right)}}{n \cdot x} \]

      4. log-rec N/A

         \[\leadsto \frac{e^{\mathsf{neg}\left(\frac{\mathsf{neg}\left(\log x\right)}{n}\right)}}{n \cdot x} \]

      5. mul-1-neg N/A

         \[\leadsto \frac{e^{\mathsf{neg}\left(\frac{-1 \cdot \log x}{n}\right)}}{n \cdot x} \]

      6. lower-neg.f64 N/A

         \[\leadsto \frac{e^{-\frac{-1 \cdot \log x}{n}}}{n \cdot x} \]

      7. lower-/.f64 N/A

         \[\leadsto \frac{e^{-\frac{-1 \cdot \log x}{n}}}{n \cdot x} \]

      8. mul-1-neg N/A

         \[\leadsto \frac{e^{-\frac{\mathsf{neg}\left(\log x\right)}{n}}}{n \cdot x} \]

      9. lower-neg.f64 N/A

         \[\leadsto \frac{e^{-\frac{-\log x}{n}}}{n \cdot x} \]

      10. lower-log.f64 N/A

          \[\leadsto \frac{e^{-\frac{-\log x}{n}}}{n \cdot x} \]

      11. lower-*.f64 51.8

          \[\leadsto \frac{e^{-\frac{-\log x}{n}}}{n \cdot \color{blue}{x}} \]

   4. Applied rewrites 51.8%

      \[\leadsto \color{blue}{\frac{e^{-\frac{-\log x}{n}}}{n \cdot x}} \]

   5. Taylor expanded in n around inf

      \[\leadsto \frac{1 + \frac{\log x}{n}}{\color{blue}{n} \cdot x} \]
   6. Step-by-step derivation

      1. lower-+.f64 N/A

         \[\leadsto \frac{1 + \frac{\log x}{n}}{n \cdot x} \]

      2. lift-log.f64 N/A

         \[\leadsto \frac{1 + \frac{\log x}{n}}{n \cdot x} \]

      3. lift-/.f64 51.8

         \[\leadsto \frac{1 + \frac{\log x}{n}}{n \cdot x} \]

   7. Applied rewrites 51.8%

      \[\leadsto \frac{1 + \frac{\log x}{n}}{\color{blue}{n} \cdot x} \]

   8. Taylor expanded in n around 0

      \[\leadsto \frac{\frac{n + \log x}{n}}{n \cdot x} \]
   9. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{\frac{n + \log x}{n}}{n \cdot x} \]

      2. lower-+.f64 N/A

         \[\leadsto \frac{\frac{n + \log x}{n}}{n \cdot x} \]

      3. lift-log.f64 51.8

         \[\leadsto \frac{\frac{n + \log x}{n}}{n \cdot x} \]

   10. Applied rewrites 51.8%

       \[\leadsto \frac{\frac{n + \log x}{n}}{n \cdot x} \]

   if 1e-13 < (/.f64 #s(literal 1 binary64) n) < 2.0000000000000002e172

   1. Initial program 71.6%

      \[{\left(x + 1\right)}^{\left(\frac{1}{n}\right)} - {x}^{\left(\frac{1}{n}\right)} \]

   if 2.0000000000000002e172 < (/.f64 #s(literal 1 binary64) n)

   1. Initial program 28.6%

      \[{\left(x + 1\right)}^{\left(\frac{1}{n}\right)} - {x}^{\left(\frac{1}{n}\right)} \]

   2. Taylor expanded in n around -inf

      \[\leadsto \color{blue}{-1 \cdot \frac{\left(-1 \cdot \log \left(1 + x\right) + -1 \cdot \frac{\frac{1}{2} \cdot {\log \left(1 + x\right)}^{2} - \frac{1}{2} \cdot {\log x}^{2}}{n}\right) - -1 \cdot \log x}{n}} \]

   4. Applied rewrites 0.2%

      \[\leadsto \color{blue}{-\frac{\left(\left(-\frac{0.5 \cdot \left(\log \left(1 + x\right) \cdot \log \left(1 + x\right) - \log x \cdot \log x\right)}{n}\right) + \left(-\log \left(1 + x\right)\right)\right) + \log x}{n}} \]

   5. Taylor expanded in n around 0

      \[\leadsto \frac{\frac{1}{2} \cdot \left({\log \left(1 + x\right)}^{2} - {\log x}^{2}\right) + n \cdot \left(\log \left(1 + x\right) - \log x\right)}{\color{blue}{{n}^{2}}} \]
   6. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{\frac{1}{2} \cdot \left({\log \left(1 + x\right)}^{2} - {\log x}^{2}\right) + n \cdot \left(\log \left(1 + x\right) - \log x\right)}{{n}^{\color{blue}{2}}} \]

   7. Applied rewrites 0.2%

      \[\leadsto \frac{\mathsf{fma}\left(0.5, \log \left(1 + x\right) \cdot \log \left(1 + x\right) - \log x \cdot \log x, n \cdot \log \left(\frac{1 + x}{x}\right)\right)}{\color{blue}{n \cdot n}} \]

   8. Taylor expanded in n around inf

      \[\leadsto \frac{n \cdot \log \left(\frac{1 + x}{x}\right)}{n \cdot n} \]
   9. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \frac{n \cdot \log \left(\frac{1 + x}{x}\right)}{n \cdot n} \]

      2. lift-+.f64 N/A

         \[\leadsto \frac{n \cdot \log \left(\frac{1 + x}{x}\right)}{n \cdot n} \]

      3. lift-log.f64 N/A

         \[\leadsto \frac{n \cdot \log \left(\frac{1 + x}{x}\right)}{n \cdot n} \]

      4. lift-*.f64 76.9

         \[\leadsto \frac{n \cdot \log \left(\frac{1 + x}{x}\right)}{n \cdot n} \]

   10. Applied rewrites 76.9%

       \[\leadsto \frac{n \cdot \log \left(\frac{1 + x}{x}\right)}{n \cdot n} \]
3. Recombined 5 regimes into one program.
4. Add Preprocessing

Alternative 2: 86.3% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\frac{1}{n} \leq -5 \cdot 10^{-20}:\\ \;\;\;\;\frac{e^{\frac{\log x}{n}}}{n \cdot x}\\ \mathbf{elif}\;\frac{1}{n} \leq 4 \cdot 10^{-78}:\\ \;\;\;\;-\frac{\log \left(\frac{x}{1 + x}\right)}{n}\\ \mathbf{elif}\;\frac{1}{n} \leq 10^{-13}:\\ \;\;\;\;\frac{\frac{n + \log x}{n}}{n \cdot x}\\ \mathbf{elif}\;\frac{1}{n} \leq 2 \cdot 10^{+172}:\\ \;\;\;\;\left(\frac{x}{n} + 1\right) - {x}^{\left(\frac{1}{n}\right)}\\ \mathbf{else}:\\ \;\;\;\;\frac{n \cdot \log \left(\frac{1 + x}{x}\right)}{n \cdot n}\\ \end{array} \end{array} \]

FPCore

(FPCore (x n)
 :precision binary64
 (if (<= (/ 1.0 n) -5e-20)
   (/ (exp (/ (log x) n)) (* n x))
   (if (<= (/ 1.0 n) 4e-78)
     (- (/ (log (/ x (+ 1.0 x))) n))
     (if (<= (/ 1.0 n) 1e-13)
       (/ (/ (+ n (log x)) n) (* n x))
       (if (<= (/ 1.0 n) 2e+172)
         (- (+ (/ x n) 1.0) (pow x (/ 1.0 n)))
         (/ (* n (log (/ (+ 1.0 x) x))) (* n n)))))))

C

double code(double x, double n) {
	double tmp;
	if ((1.0 / n) <= -5e-20) {
		tmp = exp((log(x) / n)) / (n * x);
	} else if ((1.0 / n) <= 4e-78) {
		tmp = -(log((x / (1.0 + x))) / n);
	} else if ((1.0 / n) <= 1e-13) {
		tmp = ((n + log(x)) / n) / (n * x);
	} else if ((1.0 / n) <= 2e+172) {
		tmp = ((x / n) + 1.0) - pow(x, (1.0 / n));
	} else {
		tmp = (n * log(((1.0 + x) / x))) / (n * n);
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x, n)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: n
    real(8) :: tmp
    if ((1.0d0 / n) <= (-5d-20)) then
        tmp = exp((log(x) / n)) / (n * x)
    else if ((1.0d0 / n) <= 4d-78) then
        tmp = -(log((x / (1.0d0 + x))) / n)
    else if ((1.0d0 / n) <= 1d-13) then
        tmp = ((n + log(x)) / n) / (n * x)
    else if ((1.0d0 / n) <= 2d+172) then
        tmp = ((x / n) + 1.0d0) - (x ** (1.0d0 / n))
    else
        tmp = (n * log(((1.0d0 + x) / x))) / (n * n)
    end if
    code = tmp
end function

Java

public static double code(double x, double n) {
	double tmp;
	if ((1.0 / n) <= -5e-20) {
		tmp = Math.exp((Math.log(x) / n)) / (n * x);
	} else if ((1.0 / n) <= 4e-78) {
		tmp = -(Math.log((x / (1.0 + x))) / n);
	} else if ((1.0 / n) <= 1e-13) {
		tmp = ((n + Math.log(x)) / n) / (n * x);
	} else if ((1.0 / n) <= 2e+172) {
		tmp = ((x / n) + 1.0) - Math.pow(x, (1.0 / n));
	} else {
		tmp = (n * Math.log(((1.0 + x) / x))) / (n * n);
	}
	return tmp;
}

Python

def code(x, n):
	tmp = 0
	if (1.0 / n) <= -5e-20:
		tmp = math.exp((math.log(x) / n)) / (n * x)
	elif (1.0 / n) <= 4e-78:
		tmp = -(math.log((x / (1.0 + x))) / n)
	elif (1.0 / n) <= 1e-13:
		tmp = ((n + math.log(x)) / n) / (n * x)
	elif (1.0 / n) <= 2e+172:
		tmp = ((x / n) + 1.0) - math.pow(x, (1.0 / n))
	else:
		tmp = (n * math.log(((1.0 + x) / x))) / (n * n)
	return tmp

Julia

function code(x, n)
	tmp = 0.0
	if (Float64(1.0 / n) <= -5e-20)
		tmp = Float64(exp(Float64(log(x) / n)) / Float64(n * x));
	elseif (Float64(1.0 / n) <= 4e-78)
		tmp = Float64(-Float64(log(Float64(x / Float64(1.0 + x))) / n));
	elseif (Float64(1.0 / n) <= 1e-13)
		tmp = Float64(Float64(Float64(n + log(x)) / n) / Float64(n * x));
	elseif (Float64(1.0 / n) <= 2e+172)
		tmp = Float64(Float64(Float64(x / n) + 1.0) - (x ^ Float64(1.0 / n)));
	else
		tmp = Float64(Float64(n * log(Float64(Float64(1.0 + x) / x))) / Float64(n * n));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, n)
	tmp = 0.0;
	if ((1.0 / n) <= -5e-20)
		tmp = exp((log(x) / n)) / (n * x);
	elseif ((1.0 / n) <= 4e-78)
		tmp = -(log((x / (1.0 + x))) / n);
	elseif ((1.0 / n) <= 1e-13)
		tmp = ((n + log(x)) / n) / (n * x);
	elseif ((1.0 / n) <= 2e+172)
		tmp = ((x / n) + 1.0) - (x ^ (1.0 / n));
	else
		tmp = (n * log(((1.0 + x) / x))) / (n * n);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, n_] := If[LessEqual[N[(1.0 / n), $MachinePrecision], -5e-20], N[(N[Exp[N[(N[Log[x], $MachinePrecision] / n), $MachinePrecision]], $MachinePrecision] / N[(n * x), $MachinePrecision]), $MachinePrecision], If[LessEqual[N[(1.0 / n), $MachinePrecision], 4e-78], (-N[(N[Log[N[(x / N[(1.0 + x), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / n), $MachinePrecision]), If[LessEqual[N[(1.0 / n), $MachinePrecision], 1e-13], N[(N[(N[(n + N[Log[x], $MachinePrecision]), $MachinePrecision] / n), $MachinePrecision] / N[(n * x), $MachinePrecision]), $MachinePrecision], If[LessEqual[N[(1.0 / n), $MachinePrecision], 2e+172], N[(N[(N[(x / n), $MachinePrecision] + 1.0), $MachinePrecision] - N[Power[x, N[(1.0 / n), $MachinePrecision]], $MachinePrecision]), $MachinePrecision], N[(N[(n * N[Log[N[(N[(1.0 + x), $MachinePrecision] / x), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] / N[(n * n), $MachinePrecision]), $MachinePrecision]]]]]

Derivation

1. Split input into 5 regimes

2. if (/.f64 #s(literal 1 binary64) n) < -4.9999999999999999e-20

   1. Initial program 96.1%

      \[{\left(x + 1\right)}^{\left(\frac{1}{n}\right)} - {x}^{\left(\frac{1}{n}\right)} \]

   2. Taylor expanded in x around inf

      \[\leadsto \color{blue}{\frac{e^{-1 \cdot \frac{\log \left(\frac{1}{x}\right)}{n}}}{n \cdot x}} \]
   3. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{e^{-1 \cdot \frac{\log \left(\frac{1}{x}\right)}{n}}}{\color{blue}{n \cdot x}} \]

      2. lower-exp.f64 N/A

         \[\leadsto \frac{e^{-1 \cdot \frac{\log \left(\frac{1}{x}\right)}{n}}}{\color{blue}{n} \cdot x} \]

      3. mul-1-neg N/A

         \[\leadsto \frac{e^{\mathsf{neg}\left(\frac{\log \left(\frac{1}{x}\right)}{n}\right)}}{n \cdot x} \]

      4. log-rec N/A

         \[\leadsto \frac{e^{\mathsf{neg}\left(\frac{\mathsf{neg}\left(\log x\right)}{n}\right)}}{n \cdot x} \]

      5. mul-1-neg N/A

         \[\leadsto \frac{e^{\mathsf{neg}\left(\frac{-1 \cdot \log x}{n}\right)}}{n \cdot x} \]

      6. lower-neg.f64 N/A

         \[\leadsto \frac{e^{-\frac{-1 \cdot \log x}{n}}}{n \cdot x} \]

      7. lower-/.f64 N/A

         \[\leadsto \frac{e^{-\frac{-1 \cdot \log x}{n}}}{n \cdot x} \]

      8. mul-1-neg N/A

         \[\leadsto \frac{e^{-\frac{\mathsf{neg}\left(\log x\right)}{n}}}{n \cdot x} \]

      9. lower-neg.f64 N/A

         \[\leadsto \frac{e^{-\frac{-\log x}{n}}}{n \cdot x} \]

      10. lower-log.f64 N/A

          \[\leadsto \frac{e^{-\frac{-\log x}{n}}}{n \cdot x} \]

      11. lower-*.f64 97.3

          \[\leadsto \frac{e^{-\frac{-\log x}{n}}}{n \cdot \color{blue}{x}} \]

   4. Applied rewrites 97.3%

      \[\leadsto \color{blue}{\frac{e^{-\frac{-\log x}{n}}}{n \cdot x}} \]

   5. Taylor expanded in x around 0

      \[\leadsto \frac{e^{\frac{\log x}{n}}}{n \cdot x} \]
   6. Step-by-step derivation

      1. lift-log.f64 N/A

         \[\leadsto \frac{e^{\frac{\log x}{n}}}{n \cdot x} \]

      2. lift-/.f64 97.3

         \[\leadsto \frac{e^{\frac{\log x}{n}}}{n \cdot x} \]

   7. Applied rewrites 97.3%

      \[\leadsto \frac{e^{\frac{\log x}{n}}}{n \cdot x} \]

   if -4.9999999999999999e-20 < (/.f64 #s(literal 1 binary64) n) < 4e-78

   1. Initial program 31.6%

      \[{\left(x + 1\right)}^{\left(\frac{1}{n}\right)} - {x}^{\left(\frac{1}{n}\right)} \]

   2. Taylor expanded in n around -inf

      \[\leadsto \color{blue}{-1 \cdot \frac{\left(-1 \cdot \log \left(1 + x\right) + -1 \cdot \frac{\left(-1 \cdot \frac{\frac{-1}{6} \cdot {\log \left(1 + x\right)}^{3} - \frac{-1}{6} \cdot {\log x}^{3}}{n} + \frac{1}{2} \cdot {\log \left(1 + x\right)}^{2}\right) - \frac{1}{2} \cdot {\log x}^{2}}{n}\right) - -1 \cdot \log x}{n}} \]

   4. Applied rewrites 80.8%

      \[\leadsto \color{blue}{-\frac{\left(\left(-\frac{\left(-\frac{-0.16666666666666666 \cdot \left({\log \left(1 + x\right)}^{3} - {\log x}^{3}\right)}{n}\right) + 0.5 \cdot \left(\log \left(1 + x\right) \cdot \log \left(1 + x\right) - \log x \cdot \log x\right)}{n}\right) + \left(-\log \left(1 + x\right)\right)\right) + \log x}{n}} \]

   5. Taylor expanded in n around inf

      \[\leadsto -\frac{\log x - \log \left(1 + x\right)}{n} \]
   6. Step-by-step derivation

      1. diff-log N/A

         \[\leadsto -\frac{\log \left(\frac{x}{1 + x}\right)}{n} \]

      2. lower-log.f64 N/A

         \[\leadsto -\frac{\log \left(\frac{x}{1 + x}\right)}{n} \]

      3. lower-/.f64 N/A

         \[\leadsto -\frac{\log \left(\frac{x}{1 + x}\right)}{n} \]

      4. lift-+.f64 81.0

         \[\leadsto -\frac{\log \left(\frac{x}{1 + x}\right)}{n} \]

   7. Applied rewrites 81.0%

      \[\leadsto -\frac{\log \left(\frac{x}{1 + x}\right)}{n} \]

   if 4e-78 < (/.f64 #s(literal 1 binary64) n) < 1e-13

   1. Initial program 11.7%

      \[{\left(x + 1\right)}^{\left(\frac{1}{n}\right)} - {x}^{\left(\frac{1}{n}\right)} \]

   2. Taylor expanded in x around inf

      \[\leadsto \color{blue}{\frac{e^{-1 \cdot \frac{\log \left(\frac{1}{x}\right)}{n}}}{n \cdot x}} \]
   3. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{e^{-1 \cdot \frac{\log \left(\frac{1}{x}\right)}{n}}}{\color{blue}{n \cdot x}} \]

      2. lower-exp.f64 N/A

         \[\leadsto \frac{e^{-1 \cdot \frac{\log \left(\frac{1}{x}\right)}{n}}}{\color{blue}{n} \cdot x} \]

      3. mul-1-neg N/A

         \[\leadsto \frac{e^{\mathsf{neg}\left(\frac{\log \left(\frac{1}{x}\right)}{n}\right)}}{n \cdot x} \]

      4. log-rec N/A

         \[\leadsto \frac{e^{\mathsf{neg}\left(\frac{\mathsf{neg}\left(\log x\right)}{n}\right)}}{n \cdot x} \]

      5. mul-1-neg N/A

         \[\leadsto \frac{e^{\mathsf{neg}\left(\frac{-1 \cdot \log x}{n}\right)}}{n \cdot x} \]

      6. lower-neg.f64 N/A

         \[\leadsto \frac{e^{-\frac{-1 \cdot \log x}{n}}}{n \cdot x} \]

      7. lower-/.f64 N/A

         \[\leadsto \frac{e^{-\frac{-1 \cdot \log x}{n}}}{n \cdot x} \]

      8. mul-1-neg N/A

         \[\leadsto \frac{e^{-\frac{\mathsf{neg}\left(\log x\right)}{n}}}{n \cdot x} \]

      9. lower-neg.f64 N/A

         \[\leadsto \frac{e^{-\frac{-\log x}{n}}}{n \cdot x} \]

      10. lower-log.f64 N/A

          \[\leadsto \frac{e^{-\frac{-\log x}{n}}}{n \cdot x} \]

      11. lower-*.f64 51.8

          \[\leadsto \frac{e^{-\frac{-\log x}{n}}}{n \cdot \color{blue}{x}} \]

   4. Applied rewrites 51.8%

      \[\leadsto \color{blue}{\frac{e^{-\frac{-\log x}{n}}}{n \cdot x}} \]

   5. Taylor expanded in n around inf

      \[\leadsto \frac{1 + \frac{\log x}{n}}{\color{blue}{n} \cdot x} \]
   6. Step-by-step derivation

      1. lower-+.f64 N/A

         \[\leadsto \frac{1 + \frac{\log x}{n}}{n \cdot x} \]

      2. lift-log.f64 N/A

         \[\leadsto \frac{1 + \frac{\log x}{n}}{n \cdot x} \]

      3. lift-/.f64 51.8

         \[\leadsto \frac{1 + \frac{\log x}{n}}{n \cdot x} \]

   7. Applied rewrites 51.8%

      \[\leadsto \frac{1 + \frac{\log x}{n}}{\color{blue}{n} \cdot x} \]

   8. Taylor expanded in n around 0

      \[\leadsto \frac{\frac{n + \log x}{n}}{n \cdot x} \]
   9. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{\frac{n + \log x}{n}}{n \cdot x} \]

      2. lower-+.f64 N/A

         \[\leadsto \frac{\frac{n + \log x}{n}}{n \cdot x} \]

      3. lift-log.f64 51.8

         \[\leadsto \frac{\frac{n + \log x}{n}}{n \cdot x} \]

   10. Applied rewrites 51.8%

       \[\leadsto \frac{\frac{n + \log x}{n}}{n \cdot x} \]

   if 1e-13 < (/.f64 #s(literal 1 binary64) n) < 2.0000000000000002e172

   1. Initial program 71.6%

      \[{\left(x + 1\right)}^{\left(\frac{1}{n}\right)} - {x}^{\left(\frac{1}{n}\right)} \]

   2. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\left(1 + \frac{x}{n}\right)} - {x}^{\left(\frac{1}{n}\right)} \]
   3. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \left(\frac{x}{n} + \color{blue}{1}\right) - {x}^{\left(\frac{1}{n}\right)} \]

      2. lower-+.f64 N/A

         \[\leadsto \left(\frac{x}{n} + \color{blue}{1}\right) - {x}^{\left(\frac{1}{n}\right)} \]

      3. lower-/.f64 68.2

         \[\leadsto \left(\frac{x}{n} + 1\right) - {x}^{\left(\frac{1}{n}\right)} \]

   4. Applied rewrites 68.2%

      \[\leadsto \color{blue}{\left(\frac{x}{n} + 1\right)} - {x}^{\left(\frac{1}{n}\right)} \]

   if 2.0000000000000002e172 < (/.f64 #s(literal 1 binary64) n)

   1. Initial program 28.6%

      \[{\left(x + 1\right)}^{\left(\frac{1}{n}\right)} - {x}^{\left(\frac{1}{n}\right)} \]

   2. Taylor expanded in n around -inf

      \[\leadsto \color{blue}{-1 \cdot \frac{\left(-1 \cdot \log \left(1 + x\right) + -1 \cdot \frac{\frac{1}{2} \cdot {\log \left(1 + x\right)}^{2} - \frac{1}{2} \cdot {\log x}^{2}}{n}\right) - -1 \cdot \log x}{n}} \]

   4. Applied rewrites 0.2%

      \[\leadsto \color{blue}{-\frac{\left(\left(-\frac{0.5 \cdot \left(\log \left(1 + x\right) \cdot \log \left(1 + x\right) - \log x \cdot \log x\right)}{n}\right) + \left(-\log \left(1 + x\right)\right)\right) + \log x}{n}} \]

   5. Taylor expanded in n around 0

      \[\leadsto \frac{\frac{1}{2} \cdot \left({\log \left(1 + x\right)}^{2} - {\log x}^{2}\right) + n \cdot \left(\log \left(1 + x\right) - \log x\right)}{\color{blue}{{n}^{2}}} \]
   6. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{\frac{1}{2} \cdot \left({\log \left(1 + x\right)}^{2} - {\log x}^{2}\right) + n \cdot \left(\log \left(1 + x\right) - \log x\right)}{{n}^{\color{blue}{2}}} \]

   7. Applied rewrites 0.2%

      \[\leadsto \frac{\mathsf{fma}\left(0.5, \log \left(1 + x\right) \cdot \log \left(1 + x\right) - \log x \cdot \log x, n \cdot \log \left(\frac{1 + x}{x}\right)\right)}{\color{blue}{n \cdot n}} \]

   8. Taylor expanded in n around inf

      \[\leadsto \frac{n \cdot \log \left(\frac{1 + x}{x}\right)}{n \cdot n} \]
   9. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \frac{n \cdot \log \left(\frac{1 + x}{x}\right)}{n \cdot n} \]

      2. lift-+.f64 N/A

         \[\leadsto \frac{n \cdot \log \left(\frac{1 + x}{x}\right)}{n \cdot n} \]

      3. lift-log.f64 N/A

         \[\leadsto \frac{n \cdot \log \left(\frac{1 + x}{x}\right)}{n \cdot n} \]

      4. lift-*.f64 76.9

         \[\leadsto \frac{n \cdot \log \left(\frac{1 + x}{x}\right)}{n \cdot n} \]

   10. Applied rewrites 76.9%

       \[\leadsto \frac{n \cdot \log \left(\frac{1 + x}{x}\right)}{n \cdot n} \]
3. Recombined 5 regimes into one program.
4. Add Preprocessing

Alternative 3: 83.4% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\frac{1}{n} \leq -5 \cdot 10^{-20}:\\ \;\;\;\;\frac{e^{\frac{\log x}{n}}}{n \cdot x}\\ \mathbf{elif}\;\frac{1}{n} \leq 4 \cdot 10^{-78}:\\ \;\;\;\;-\frac{\log \left(\frac{x}{1 + x}\right)}{n}\\ \mathbf{elif}\;\frac{1}{n} \leq 10^{-13}:\\ \;\;\;\;\frac{\frac{n + \log x}{n}}{n \cdot x}\\ \mathbf{elif}\;\frac{1}{n} \leq 2 \cdot 10^{+172}:\\ \;\;\;\;1 - {x}^{\left(\frac{1}{n}\right)}\\ \mathbf{else}:\\ \;\;\;\;\frac{n \cdot \log \left(\frac{1 + x}{x}\right)}{n \cdot n}\\ \end{array} \end{array} \]

FPCore

(FPCore (x n)
 :precision binary64
 (if (<= (/ 1.0 n) -5e-20)
   (/ (exp (/ (log x) n)) (* n x))
   (if (<= (/ 1.0 n) 4e-78)
     (- (/ (log (/ x (+ 1.0 x))) n))
     (if (<= (/ 1.0 n) 1e-13)
       (/ (/ (+ n (log x)) n) (* n x))
       (if (<= (/ 1.0 n) 2e+172)
         (- 1.0 (pow x (/ 1.0 n)))
         (/ (* n (log (/ (+ 1.0 x) x))) (* n n)))))))

C

double code(double x, double n) {
	double tmp;
	if ((1.0 / n) <= -5e-20) {
		tmp = exp((log(x) / n)) / (n * x);
	} else if ((1.0 / n) <= 4e-78) {
		tmp = -(log((x / (1.0 + x))) / n);
	} else if ((1.0 / n) <= 1e-13) {
		tmp = ((n + log(x)) / n) / (n * x);
	} else if ((1.0 / n) <= 2e+172) {
		tmp = 1.0 - pow(x, (1.0 / n));
	} else {
		tmp = (n * log(((1.0 + x) / x))) / (n * n);
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x, n)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: n
    real(8) :: tmp
    if ((1.0d0 / n) <= (-5d-20)) then
        tmp = exp((log(x) / n)) / (n * x)
    else if ((1.0d0 / n) <= 4d-78) then
        tmp = -(log((x / (1.0d0 + x))) / n)
    else if ((1.0d0 / n) <= 1d-13) then
        tmp = ((n + log(x)) / n) / (n * x)
    else if ((1.0d0 / n) <= 2d+172) then
        tmp = 1.0d0 - (x ** (1.0d0 / n))
    else
        tmp = (n * log(((1.0d0 + x) / x))) / (n * n)
    end if
    code = tmp
end function

Java

public static double code(double x, double n) {
	double tmp;
	if ((1.0 / n) <= -5e-20) {
		tmp = Math.exp((Math.log(x) / n)) / (n * x);
	} else if ((1.0 / n) <= 4e-78) {
		tmp = -(Math.log((x / (1.0 + x))) / n);
	} else if ((1.0 / n) <= 1e-13) {
		tmp = ((n + Math.log(x)) / n) / (n * x);
	} else if ((1.0 / n) <= 2e+172) {
		tmp = 1.0 - Math.pow(x, (1.0 / n));
	} else {
		tmp = (n * Math.log(((1.0 + x) / x))) / (n * n);
	}
	return tmp;
}

Python

def code(x, n):
	tmp = 0
	if (1.0 / n) <= -5e-20:
		tmp = math.exp((math.log(x) / n)) / (n * x)
	elif (1.0 / n) <= 4e-78:
		tmp = -(math.log((x / (1.0 + x))) / n)
	elif (1.0 / n) <= 1e-13:
		tmp = ((n + math.log(x)) / n) / (n * x)
	elif (1.0 / n) <= 2e+172:
		tmp = 1.0 - math.pow(x, (1.0 / n))
	else:
		tmp = (n * math.log(((1.0 + x) / x))) / (n * n)
	return tmp

Julia

function code(x, n)
	tmp = 0.0
	if (Float64(1.0 / n) <= -5e-20)
		tmp = Float64(exp(Float64(log(x) / n)) / Float64(n * x));
	elseif (Float64(1.0 / n) <= 4e-78)
		tmp = Float64(-Float64(log(Float64(x / Float64(1.0 + x))) / n));
	elseif (Float64(1.0 / n) <= 1e-13)
		tmp = Float64(Float64(Float64(n + log(x)) / n) / Float64(n * x));
	elseif (Float64(1.0 / n) <= 2e+172)
		tmp = Float64(1.0 - (x ^ Float64(1.0 / n)));
	else
		tmp = Float64(Float64(n * log(Float64(Float64(1.0 + x) / x))) / Float64(n * n));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, n)
	tmp = 0.0;
	if ((1.0 / n) <= -5e-20)
		tmp = exp((log(x) / n)) / (n * x);
	elseif ((1.0 / n) <= 4e-78)
		tmp = -(log((x / (1.0 + x))) / n);
	elseif ((1.0 / n) <= 1e-13)
		tmp = ((n + log(x)) / n) / (n * x);
	elseif ((1.0 / n) <= 2e+172)
		tmp = 1.0 - (x ^ (1.0 / n));
	else
		tmp = (n * log(((1.0 + x) / x))) / (n * n);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, n_] := If[LessEqual[N[(1.0 / n), $MachinePrecision], -5e-20], N[(N[Exp[N[(N[Log[x], $MachinePrecision] / n), $MachinePrecision]], $MachinePrecision] / N[(n * x), $MachinePrecision]), $MachinePrecision], If[LessEqual[N[(1.0 / n), $MachinePrecision], 4e-78], (-N[(N[Log[N[(x / N[(1.0 + x), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / n), $MachinePrecision]), If[LessEqual[N[(1.0 / n), $MachinePrecision], 1e-13], N[(N[(N[(n + N[Log[x], $MachinePrecision]), $MachinePrecision] / n), $MachinePrecision] / N[(n * x), $MachinePrecision]), $MachinePrecision], If[LessEqual[N[(1.0 / n), $MachinePrecision], 2e+172], N[(1.0 - N[Power[x, N[(1.0 / n), $MachinePrecision]], $MachinePrecision]), $MachinePrecision], N[(N[(n * N[Log[N[(N[(1.0 + x), $MachinePrecision] / x), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] / N[(n * n), $MachinePrecision]), $MachinePrecision]]]]]

Derivation

1. Split input into 5 regimes

2. if (/.f64 #s(literal 1 binary64) n) < -4.9999999999999999e-20

   1. Initial program 96.1%

      \[{\left(x + 1\right)}^{\left(\frac{1}{n}\right)} - {x}^{\left(\frac{1}{n}\right)} \]

   2. Taylor expanded in x around inf

      \[\leadsto \color{blue}{\frac{e^{-1 \cdot \frac{\log \left(\frac{1}{x}\right)}{n}}}{n \cdot x}} \]
   3. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{e^{-1 \cdot \frac{\log \left(\frac{1}{x}\right)}{n}}}{\color{blue}{n \cdot x}} \]

      2. lower-exp.f64 N/A

         \[\leadsto \frac{e^{-1 \cdot \frac{\log \left(\frac{1}{x}\right)}{n}}}{\color{blue}{n} \cdot x} \]

      3. mul-1-neg N/A

         \[\leadsto \frac{e^{\mathsf{neg}\left(\frac{\log \left(\frac{1}{x}\right)}{n}\right)}}{n \cdot x} \]

      4. log-rec N/A

         \[\leadsto \frac{e^{\mathsf{neg}\left(\frac{\mathsf{neg}\left(\log x\right)}{n}\right)}}{n \cdot x} \]

      5. mul-1-neg N/A

         \[\leadsto \frac{e^{\mathsf{neg}\left(\frac{-1 \cdot \log x}{n}\right)}}{n \cdot x} \]

      6. lower-neg.f64 N/A

         \[\leadsto \frac{e^{-\frac{-1 \cdot \log x}{n}}}{n \cdot x} \]

      7. lower-/.f64 N/A

         \[\leadsto \frac{e^{-\frac{-1 \cdot \log x}{n}}}{n \cdot x} \]

      8. mul-1-neg N/A

         \[\leadsto \frac{e^{-\frac{\mathsf{neg}\left(\log x\right)}{n}}}{n \cdot x} \]

      9. lower-neg.f64 N/A

         \[\leadsto \frac{e^{-\frac{-\log x}{n}}}{n \cdot x} \]

      10. lower-log.f64 N/A

          \[\leadsto \frac{e^{-\frac{-\log x}{n}}}{n \cdot x} \]

      11. lower-*.f64 97.3

          \[\leadsto \frac{e^{-\frac{-\log x}{n}}}{n \cdot \color{blue}{x}} \]

   4. Applied rewrites 97.3%

      \[\leadsto \color{blue}{\frac{e^{-\frac{-\log x}{n}}}{n \cdot x}} \]

   5. Taylor expanded in x around 0

      \[\leadsto \frac{e^{\frac{\log x}{n}}}{n \cdot x} \]
   6. Step-by-step derivation

      1. lift-log.f64 N/A

         \[\leadsto \frac{e^{\frac{\log x}{n}}}{n \cdot x} \]

      2. lift-/.f64 97.3

         \[\leadsto \frac{e^{\frac{\log x}{n}}}{n \cdot x} \]

   7. Applied rewrites 97.3%

      \[\leadsto \frac{e^{\frac{\log x}{n}}}{n \cdot x} \]

   if -4.9999999999999999e-20 < (/.f64 #s(literal 1 binary64) n) < 4e-78

   1. Initial program 31.6%

      \[{\left(x + 1\right)}^{\left(\frac{1}{n}\right)} - {x}^{\left(\frac{1}{n}\right)} \]

   2. Taylor expanded in n around -inf

      \[\leadsto \color{blue}{-1 \cdot \frac{\left(-1 \cdot \log \left(1 + x\right) + -1 \cdot \frac{\left(-1 \cdot \frac{\frac{-1}{6} \cdot {\log \left(1 + x\right)}^{3} - \frac{-1}{6} \cdot {\log x}^{3}}{n} + \frac{1}{2} \cdot {\log \left(1 + x\right)}^{2}\right) - \frac{1}{2} \cdot {\log x}^{2}}{n}\right) - -1 \cdot \log x}{n}} \]

   4. Applied rewrites 80.8%

      \[\leadsto \color{blue}{-\frac{\left(\left(-\frac{\left(-\frac{-0.16666666666666666 \cdot \left({\log \left(1 + x\right)}^{3} - {\log x}^{3}\right)}{n}\right) + 0.5 \cdot \left(\log \left(1 + x\right) \cdot \log \left(1 + x\right) - \log x \cdot \log x\right)}{n}\right) + \left(-\log \left(1 + x\right)\right)\right) + \log x}{n}} \]

   5. Taylor expanded in n around inf

      \[\leadsto -\frac{\log x - \log \left(1 + x\right)}{n} \]
   6. Step-by-step derivation

      1. diff-log N/A

         \[\leadsto -\frac{\log \left(\frac{x}{1 + x}\right)}{n} \]

      2. lower-log.f64 N/A

         \[\leadsto -\frac{\log \left(\frac{x}{1 + x}\right)}{n} \]

      3. lower-/.f64 N/A

         \[\leadsto -\frac{\log \left(\frac{x}{1 + x}\right)}{n} \]

      4. lift-+.f64 81.0

         \[\leadsto -\frac{\log \left(\frac{x}{1 + x}\right)}{n} \]

   7. Applied rewrites 81.0%

      \[\leadsto -\frac{\log \left(\frac{x}{1 + x}\right)}{n} \]

   if 4e-78 < (/.f64 #s(literal 1 binary64) n) < 1e-13

   1. Initial program 11.7%

      \[{\left(x + 1\right)}^{\left(\frac{1}{n}\right)} - {x}^{\left(\frac{1}{n}\right)} \]

   2. Taylor expanded in x around inf

      \[\leadsto \color{blue}{\frac{e^{-1 \cdot \frac{\log \left(\frac{1}{x}\right)}{n}}}{n \cdot x}} \]
   3. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{e^{-1 \cdot \frac{\log \left(\frac{1}{x}\right)}{n}}}{\color{blue}{n \cdot x}} \]

      2. lower-exp.f64 N/A

         \[\leadsto \frac{e^{-1 \cdot \frac{\log \left(\frac{1}{x}\right)}{n}}}{\color{blue}{n} \cdot x} \]

      3. mul-1-neg N/A

         \[\leadsto \frac{e^{\mathsf{neg}\left(\frac{\log \left(\frac{1}{x}\right)}{n}\right)}}{n \cdot x} \]

      4. log-rec N/A

         \[\leadsto \frac{e^{\mathsf{neg}\left(\frac{\mathsf{neg}\left(\log x\right)}{n}\right)}}{n \cdot x} \]

      5. mul-1-neg N/A

         \[\leadsto \frac{e^{\mathsf{neg}\left(\frac{-1 \cdot \log x}{n}\right)}}{n \cdot x} \]

      6. lower-neg.f64 N/A

         \[\leadsto \frac{e^{-\frac{-1 \cdot \log x}{n}}}{n \cdot x} \]

      7. lower-/.f64 N/A

         \[\leadsto \frac{e^{-\frac{-1 \cdot \log x}{n}}}{n \cdot x} \]

      8. mul-1-neg N/A

         \[\leadsto \frac{e^{-\frac{\mathsf{neg}\left(\log x\right)}{n}}}{n \cdot x} \]

      9. lower-neg.f64 N/A

         \[\leadsto \frac{e^{-\frac{-\log x}{n}}}{n \cdot x} \]

      10. lower-log.f64 N/A

          \[\leadsto \frac{e^{-\frac{-\log x}{n}}}{n \cdot x} \]

      11. lower-*.f64 51.8

          \[\leadsto \frac{e^{-\frac{-\log x}{n}}}{n \cdot \color{blue}{x}} \]

   4. Applied rewrites 51.8%

      \[\leadsto \color{blue}{\frac{e^{-\frac{-\log x}{n}}}{n \cdot x}} \]

   5. Taylor expanded in n around inf

      \[\leadsto \frac{1 + \frac{\log x}{n}}{\color{blue}{n} \cdot x} \]
   6. Step-by-step derivation

      1. lower-+.f64 N/A

         \[\leadsto \frac{1 + \frac{\log x}{n}}{n \cdot x} \]

      2. lift-log.f64 N/A

         \[\leadsto \frac{1 + \frac{\log x}{n}}{n \cdot x} \]

      3. lift-/.f64 51.8

         \[\leadsto \frac{1 + \frac{\log x}{n}}{n \cdot x} \]

   7. Applied rewrites 51.8%

      \[\leadsto \frac{1 + \frac{\log x}{n}}{\color{blue}{n} \cdot x} \]

   8. Taylor expanded in n around 0

      \[\leadsto \frac{\frac{n + \log x}{n}}{n \cdot x} \]
   9. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{\frac{n + \log x}{n}}{n \cdot x} \]

      2. lower-+.f64 N/A

         \[\leadsto \frac{\frac{n + \log x}{n}}{n \cdot x} \]

      3. lift-log.f64 51.8

         \[\leadsto \frac{\frac{n + \log x}{n}}{n \cdot x} \]

   10. Applied rewrites 51.8%

       \[\leadsto \frac{\frac{n + \log x}{n}}{n \cdot x} \]

   if 1e-13 < (/.f64 #s(literal 1 binary64) n) < 2.0000000000000002e172

   1. Initial program 71.6%

      \[{\left(x + 1\right)}^{\left(\frac{1}{n}\right)} - {x}^{\left(\frac{1}{n}\right)} \]

   2. Taylor expanded in x around 0

      \[\leadsto \color{blue}{1} - {x}^{\left(\frac{1}{n}\right)} \]
   3. Step-by-step derivation

   4. Applied rewrites 67.1%

      \[\leadsto \color{blue}{1} - {x}^{\left(\frac{1}{n}\right)} \]

   if 2.0000000000000002e172 < (/.f64 #s(literal 1 binary64) n)

   1. Initial program 28.6%

      \[{\left(x + 1\right)}^{\left(\frac{1}{n}\right)} - {x}^{\left(\frac{1}{n}\right)} \]

   2. Taylor expanded in n around -inf

      \[\leadsto \color{blue}{-1 \cdot \frac{\left(-1 \cdot \log \left(1 + x\right) + -1 \cdot \frac{\frac{1}{2} \cdot {\log \left(1 + x\right)}^{2} - \frac{1}{2} \cdot {\log x}^{2}}{n}\right) - -1 \cdot \log x}{n}} \]

   4. Applied rewrites 0.2%

      \[\leadsto \color{blue}{-\frac{\left(\left(-\frac{0.5 \cdot \left(\log \left(1 + x\right) \cdot \log \left(1 + x\right) - \log x \cdot \log x\right)}{n}\right) + \left(-\log \left(1 + x\right)\right)\right) + \log x}{n}} \]

   5. Taylor expanded in n around 0

      \[\leadsto \frac{\frac{1}{2} \cdot \left({\log \left(1 + x\right)}^{2} - {\log x}^{2}\right) + n \cdot \left(\log \left(1 + x\right) - \log x\right)}{\color{blue}{{n}^{2}}} \]
   6. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{\frac{1}{2} \cdot \left({\log \left(1 + x\right)}^{2} - {\log x}^{2}\right) + n \cdot \left(\log \left(1 + x\right) - \log x\right)}{{n}^{\color{blue}{2}}} \]

   7. Applied rewrites 0.2%

      \[\leadsto \frac{\mathsf{fma}\left(0.5, \log \left(1 + x\right) \cdot \log \left(1 + x\right) - \log x \cdot \log x, n \cdot \log \left(\frac{1 + x}{x}\right)\right)}{\color{blue}{n \cdot n}} \]

   8. Taylor expanded in n around inf

      \[\leadsto \frac{n \cdot \log \left(\frac{1 + x}{x}\right)}{n \cdot n} \]
   9. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \frac{n \cdot \log \left(\frac{1 + x}{x}\right)}{n \cdot n} \]

      2. lift-+.f64 N/A

         \[\leadsto \frac{n \cdot \log \left(\frac{1 + x}{x}\right)}{n \cdot n} \]

      3. lift-log.f64 N/A

         \[\leadsto \frac{n \cdot \log \left(\frac{1 + x}{x}\right)}{n \cdot n} \]

      4. lift-*.f64 76.9

         \[\leadsto \frac{n \cdot \log \left(\frac{1 + x}{x}\right)}{n \cdot n} \]

   10. Applied rewrites 76.9%

       \[\leadsto \frac{n \cdot \log \left(\frac{1 + x}{x}\right)}{n \cdot n} \]
3. Recombined 5 regimes into one program.
4. Add Preprocessing

Alternative 4: 83.1% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \log \left(1 + x\right)\\ \mathbf{if}\;x \leq 10000000:\\ \;\;\;\;-\frac{\left(\left(-\frac{\left(-\frac{-0.16666666666666666 \cdot \left({t\_0}^{3} - {\log x}^{3}\right)}{n}\right) + 0.5 \cdot \left(t\_0 \cdot t\_0 - \log x \cdot \log x\right)}{n}\right) + \left(-t\_0\right)\right) + \log x}{n}\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{e^{\frac{\log x}{n}}}{n}}{x}\\ \end{array} \end{array} \]

FPCore

(FPCore (x n)
 :precision binary64
 (let* ((t_0 (log (+ 1.0 x))))
   (if (<= x 10000000.0)
     (-
      (/
       (+
        (+
         (-
          (/
           (+
            (-
             (/
              (* -0.16666666666666666 (- (pow t_0 3.0) (pow (log x) 3.0)))
              n))
            (* 0.5 (- (* t_0 t_0) (* (log x) (log x)))))
           n))
         (- t_0))
        (log x))
       n))
     (/ (/ (exp (/ (log x) n)) n) x))))

C

double code(double x, double n) {
	double t_0 = log((1.0 + x));
	double tmp;
	if (x <= 10000000.0) {
		tmp = -(((-((-((-0.16666666666666666 * (pow(t_0, 3.0) - pow(log(x), 3.0))) / n) + (0.5 * ((t_0 * t_0) - (log(x) * log(x))))) / n) + -t_0) + log(x)) / n);
	} else {
		tmp = (exp((log(x) / n)) / n) / x;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x, n)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: n
    real(8) :: t_0
    real(8) :: tmp
    t_0 = log((1.0d0 + x))
    if (x <= 10000000.0d0) then
        tmp = -(((-((-(((-0.16666666666666666d0) * ((t_0 ** 3.0d0) - (log(x) ** 3.0d0))) / n) + (0.5d0 * ((t_0 * t_0) - (log(x) * log(x))))) / n) + -t_0) + log(x)) / n)
    else
        tmp = (exp((log(x) / n)) / n) / x
    end if
    code = tmp
end function

Java

public static double code(double x, double n) {
	double t_0 = Math.log((1.0 + x));
	double tmp;
	if (x <= 10000000.0) {
		tmp = -(((-((-((-0.16666666666666666 * (Math.pow(t_0, 3.0) - Math.pow(Math.log(x), 3.0))) / n) + (0.5 * ((t_0 * t_0) - (Math.log(x) * Math.log(x))))) / n) + -t_0) + Math.log(x)) / n);
	} else {
		tmp = (Math.exp((Math.log(x) / n)) / n) / x;
	}
	return tmp;
}

Python

def code(x, n):
	t_0 = math.log((1.0 + x))
	tmp = 0
	if x <= 10000000.0:
		tmp = -(((-((-((-0.16666666666666666 * (math.pow(t_0, 3.0) - math.pow(math.log(x), 3.0))) / n) + (0.5 * ((t_0 * t_0) - (math.log(x) * math.log(x))))) / n) + -t_0) + math.log(x)) / n)
	else:
		tmp = (math.exp((math.log(x) / n)) / n) / x
	return tmp

Julia

function code(x, n)
	t_0 = log(Float64(1.0 + x))
	tmp = 0.0
	if (x <= 10000000.0)
		tmp = Float64(-Float64(Float64(Float64(Float64(-Float64(Float64(Float64(-Float64(Float64(-0.16666666666666666 * Float64((t_0 ^ 3.0) - (log(x) ^ 3.0))) / n)) + Float64(0.5 * Float64(Float64(t_0 * t_0) - Float64(log(x) * log(x))))) / n)) + Float64(-t_0)) + log(x)) / n));
	else
		tmp = Float64(Float64(exp(Float64(log(x) / n)) / n) / x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, n)
	t_0 = log((1.0 + x));
	tmp = 0.0;
	if (x <= 10000000.0)
		tmp = -(((-((-((-0.16666666666666666 * ((t_0 ^ 3.0) - (log(x) ^ 3.0))) / n) + (0.5 * ((t_0 * t_0) - (log(x) * log(x))))) / n) + -t_0) + log(x)) / n);
	else
		tmp = (exp((log(x) / n)) / n) / x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, n_] := Block[{t$95$0 = N[Log[N[(1.0 + x), $MachinePrecision]], $MachinePrecision]}, If[LessEqual[x, 10000000.0], (-N[(N[(N[((-N[(N[((-N[(N[(-0.16666666666666666 * N[(N[Power[t$95$0, 3.0], $MachinePrecision] - N[Power[N[Log[x], $MachinePrecision], 3.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / n), $MachinePrecision]) + N[(0.5 * N[(N[(t$95$0 * t$95$0), $MachinePrecision] - N[(N[Log[x], $MachinePrecision] * N[Log[x], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / n), $MachinePrecision]) + (-t$95$0)), $MachinePrecision] + N[Log[x], $MachinePrecision]), $MachinePrecision] / n), $MachinePrecision]), N[(N[(N[Exp[N[(N[Log[x], $MachinePrecision] / n), $MachinePrecision]], $MachinePrecision] / n), $MachinePrecision] / x), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if x < 1e7

   1. Initial program 43.5%

      \[{\left(x + 1\right)}^{\left(\frac{1}{n}\right)} - {x}^{\left(\frac{1}{n}\right)} \]

   2. Taylor expanded in n around -inf

      \[\leadsto \color{blue}{-1 \cdot \frac{\left(-1 \cdot \log \left(1 + x\right) + -1 \cdot \frac{\left(-1 \cdot \frac{\frac{-1}{6} \cdot {\log \left(1 + x\right)}^{3} - \frac{-1}{6} \cdot {\log x}^{3}}{n} + \frac{1}{2} \cdot {\log \left(1 + x\right)}^{2}\right) - \frac{1}{2} \cdot {\log x}^{2}}{n}\right) - -1 \cdot \log x}{n}} \]

   4. Applied rewrites 78.2%

      \[\leadsto \color{blue}{-\frac{\left(\left(-\frac{\left(-\frac{-0.16666666666666666 \cdot \left({\log \left(1 + x\right)}^{3} - {\log x}^{3}\right)}{n}\right) + 0.5 \cdot \left(\log \left(1 + x\right) \cdot \log \left(1 + x\right) - \log x \cdot \log x\right)}{n}\right) + \left(-\log \left(1 + x\right)\right)\right) + \log x}{n}} \]

   if 1e7 < x

   1. Initial program 68.7%

      \[{\left(x + 1\right)}^{\left(\frac{1}{n}\right)} - {x}^{\left(\frac{1}{n}\right)} \]

   2. Taylor expanded in x around inf

      \[\leadsto \color{blue}{\frac{e^{-1 \cdot \frac{\log \left(\frac{1}{x}\right)}{n}}}{n \cdot x}} \]
   3. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{e^{-1 \cdot \frac{\log \left(\frac{1}{x}\right)}{n}}}{\color{blue}{n \cdot x}} \]

      2. lower-exp.f64 N/A

         \[\leadsto \frac{e^{-1 \cdot \frac{\log \left(\frac{1}{x}\right)}{n}}}{\color{blue}{n} \cdot x} \]

      3. mul-1-neg N/A

         \[\leadsto \frac{e^{\mathsf{neg}\left(\frac{\log \left(\frac{1}{x}\right)}{n}\right)}}{n \cdot x} \]

      4. log-rec N/A

         \[\leadsto \frac{e^{\mathsf{neg}\left(\frac{\mathsf{neg}\left(\log x\right)}{n}\right)}}{n \cdot x} \]

      5. mul-1-neg N/A

         \[\leadsto \frac{e^{\mathsf{neg}\left(\frac{-1 \cdot \log x}{n}\right)}}{n \cdot x} \]

      6. lower-neg.f64 N/A

         \[\leadsto \frac{e^{-\frac{-1 \cdot \log x}{n}}}{n \cdot x} \]

      7. lower-/.f64 N/A

         \[\leadsto \frac{e^{-\frac{-1 \cdot \log x}{n}}}{n \cdot x} \]

      8. mul-1-neg N/A

         \[\leadsto \frac{e^{-\frac{\mathsf{neg}\left(\log x\right)}{n}}}{n \cdot x} \]

      9. lower-neg.f64 N/A

         \[\leadsto \frac{e^{-\frac{-\log x}{n}}}{n \cdot x} \]

      10. lower-log.f64 N/A

          \[\leadsto \frac{e^{-\frac{-\log x}{n}}}{n \cdot x} \]

      11. lower-*.f64 98.3

          \[\leadsto \frac{e^{-\frac{-\log x}{n}}}{n \cdot \color{blue}{x}} \]

   4. Applied rewrites 98.3%

      \[\leadsto \color{blue}{\frac{e^{-\frac{-\log x}{n}}}{n \cdot x}} \]

   5. Taylor expanded in x around 0

      \[\leadsto \frac{e^{\frac{\log x}{n}}}{n \cdot x} \]
   6. Step-by-step derivation

      1. lift-log.f64 N/A

         \[\leadsto \frac{e^{\frac{\log x}{n}}}{n \cdot x} \]

      2. lift-/.f64 98.3

         \[\leadsto \frac{e^{\frac{\log x}{n}}}{n \cdot x} \]

   7. Applied rewrites 98.3%

      \[\leadsto \frac{e^{\frac{\log x}{n}}}{n \cdot x} \]
   8. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \frac{e^{\frac{\log x}{n}}}{n \cdot \color{blue}{x}} \]

      2. lift-/.f64 N/A

         \[\leadsto \frac{e^{\frac{\log x}{n}}}{\color{blue}{n \cdot x}} \]

      3. associate-/r* N/A

         \[\leadsto \frac{\frac{e^{\frac{\log x}{n}}}{n}}{\color{blue}{x}} \]

      4. lower-/.f64 N/A

         \[\leadsto \frac{\frac{e^{\frac{\log x}{n}}}{n}}{\color{blue}{x}} \]

      5. lower-/.f64 99.5

         \[\leadsto \frac{\frac{e^{\frac{\log x}{n}}}{n}}{x} \]

   9. Applied rewrites 99.5%

      \[\leadsto \frac{\frac{e^{\frac{\log x}{n}}}{n}}{\color{blue}{x}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 5: 83.0% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq 0.0006:\\ \;\;\;\;-\frac{\log x + \mathsf{fma}\left(0.16666666666666666, \frac{{\log x}^{3}}{n \cdot n}, 0.5 \cdot \frac{\log x \cdot \log x}{n}\right)}{n}\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{e^{\frac{\log x}{n}}}{n}}{x}\\ \end{array} \end{array} \]

FPCore

(FPCore (x n)
 :precision binary64
 (if (<= x 0.0006)
   (-
    (/
     (+
      (log x)
      (fma
       0.16666666666666666
       (/ (pow (log x) 3.0) (* n n))
       (* 0.5 (/ (* (log x) (log x)) n))))
     n))
   (/ (/ (exp (/ (log x) n)) n) x)))

C

double code(double x, double n) {
	double tmp;
	if (x <= 0.0006) {
		tmp = -((log(x) + fma(0.16666666666666666, (pow(log(x), 3.0) / (n * n)), (0.5 * ((log(x) * log(x)) / n)))) / n);
	} else {
		tmp = (exp((log(x) / n)) / n) / x;
	}
	return tmp;
}

Julia

function code(x, n)
	tmp = 0.0
	if (x <= 0.0006)
		tmp = Float64(-Float64(Float64(log(x) + fma(0.16666666666666666, Float64((log(x) ^ 3.0) / Float64(n * n)), Float64(0.5 * Float64(Float64(log(x) * log(x)) / n)))) / n));
	else
		tmp = Float64(Float64(exp(Float64(log(x) / n)) / n) / x);
	end
	return tmp
end

Wolfram

code[x_, n_] := If[LessEqual[x, 0.0006], (-N[(N[(N[Log[x], $MachinePrecision] + N[(0.16666666666666666 * N[(N[Power[N[Log[x], $MachinePrecision], 3.0], $MachinePrecision] / N[(n * n), $MachinePrecision]), $MachinePrecision] + N[(0.5 * N[(N[(N[Log[x], $MachinePrecision] * N[Log[x], $MachinePrecision]), $MachinePrecision] / n), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / n), $MachinePrecision]), N[(N[(N[Exp[N[(N[Log[x], $MachinePrecision] / n), $MachinePrecision]], $MachinePrecision] / n), $MachinePrecision] / x), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < 5.99999999999999947e-4

   1. Initial program 43.6%

      \[{\left(x + 1\right)}^{\left(\frac{1}{n}\right)} - {x}^{\left(\frac{1}{n}\right)} \]

   2. Taylor expanded in n around -inf

      \[\leadsto \color{blue}{-1 \cdot \frac{\left(-1 \cdot \log \left(1 + x\right) + -1 \cdot \frac{\left(-1 \cdot \frac{\frac{-1}{6} \cdot {\log \left(1 + x\right)}^{3} - \frac{-1}{6} \cdot {\log x}^{3}}{n} + \frac{1}{2} \cdot {\log \left(1 + x\right)}^{2}\right) - \frac{1}{2} \cdot {\log x}^{2}}{n}\right) - -1 \cdot \log x}{n}} \]

   4. Applied rewrites 78.4%

      \[\leadsto \color{blue}{-\frac{\left(\left(-\frac{\left(-\frac{-0.16666666666666666 \cdot \left({\log \left(1 + x\right)}^{3} - {\log x}^{3}\right)}{n}\right) + 0.5 \cdot \left(\log \left(1 + x\right) \cdot \log \left(1 + x\right) - \log x \cdot \log x\right)}{n}\right) + \left(-\log \left(1 + x\right)\right)\right) + \log x}{n}} \]

   5. Taylor expanded in n around inf

      \[\leadsto -\frac{\log x - \log \left(1 + x\right)}{n} \]
   6. Step-by-step derivation

      1. diff-log N/A

         \[\leadsto -\frac{\log \left(\frac{x}{1 + x}\right)}{n} \]

      2. lower-log.f64 N/A

         \[\leadsto -\frac{\log \left(\frac{x}{1 + x}\right)}{n} \]

      3. lower-/.f64 N/A

         \[\leadsto -\frac{\log \left(\frac{x}{1 + x}\right)}{n} \]

      4. lift-+.f64 52.0

         \[\leadsto -\frac{\log \left(\frac{x}{1 + x}\right)}{n} \]

   7. Applied rewrites 52.0%

      \[\leadsto -\frac{\log \left(\frac{x}{1 + x}\right)}{n} \]

   8. Taylor expanded in x around 0

      \[\leadsto -\frac{\left(\log x + \frac{1}{6} \cdot \frac{{\log x}^{3}}{{n}^{2}}\right) - \frac{-1}{2} \cdot \frac{{\log x}^{2}}{n}}{n} \]
   9. Step-by-step derivation

      1. associate--l+ N/A

         \[\leadsto -\frac{\log x + \left(\frac{1}{6} \cdot \frac{{\log x}^{3}}{{n}^{2}} - \frac{-1}{2} \cdot \frac{{\log x}^{2}}{n}\right)}{n} \]

      2. metadata-eval N/A

         \[\leadsto -\frac{\log x + \left(\frac{1}{6} \cdot \frac{{\log x}^{3}}{{n}^{2}} - \left(\mathsf{neg}\left(\frac{1}{2}\right)\right) \cdot \frac{{\log x}^{2}}{n}\right)}{n} \]

      3. fp-cancel-sign-sub-inv N/A

         \[\leadsto -\frac{\log x + \left(\frac{1}{6} \cdot \frac{{\log x}^{3}}{{n}^{2}} + \frac{1}{2} \cdot \frac{{\log x}^{2}}{n}\right)}{n} \]

      4. lower-+.f64 N/A

         \[\leadsto -\frac{\log x + \left(\frac{1}{6} \cdot \frac{{\log x}^{3}}{{n}^{2}} + \frac{1}{2} \cdot \frac{{\log x}^{2}}{n}\right)}{n} \]

      5. lift-log.f64 N/A

         \[\leadsto -\frac{\log x + \left(\frac{1}{6} \cdot \frac{{\log x}^{3}}{{n}^{2}} + \frac{1}{2} \cdot \frac{{\log x}^{2}}{n}\right)}{n} \]

      6. lower-fma.f64 N/A

         \[\leadsto -\frac{\log x + \mathsf{fma}\left(\frac{1}{6}, \frac{{\log x}^{3}}{{n}^{2}}, \frac{1}{2} \cdot \frac{{\log x}^{2}}{n}\right)}{n} \]

   10. Applied rewrites 77.7%

       \[\leadsto -\frac{\log x + \mathsf{fma}\left(0.16666666666666666, \frac{{\log x}^{3}}{n \cdot n}, 0.5 \cdot \frac{\log x \cdot \log x}{n}\right)}{n} \]

   if 5.99999999999999947e-4 < x

   1. Initial program 67.8%

      \[{\left(x + 1\right)}^{\left(\frac{1}{n}\right)} - {x}^{\left(\frac{1}{n}\right)} \]

   2. Taylor expanded in x around inf

      \[\leadsto \color{blue}{\frac{e^{-1 \cdot \frac{\log \left(\frac{1}{x}\right)}{n}}}{n \cdot x}} \]
   3. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{e^{-1 \cdot \frac{\log \left(\frac{1}{x}\right)}{n}}}{\color{blue}{n \cdot x}} \]

      2. lower-exp.f64 N/A

         \[\leadsto \frac{e^{-1 \cdot \frac{\log \left(\frac{1}{x}\right)}{n}}}{\color{blue}{n} \cdot x} \]

      3. mul-1-neg N/A

         \[\leadsto \frac{e^{\mathsf{neg}\left(\frac{\log \left(\frac{1}{x}\right)}{n}\right)}}{n \cdot x} \]

      4. log-rec N/A

         \[\leadsto \frac{e^{\mathsf{neg}\left(\frac{\mathsf{neg}\left(\log x\right)}{n}\right)}}{n \cdot x} \]

      5. mul-1-neg N/A

         \[\leadsto \frac{e^{\mathsf{neg}\left(\frac{-1 \cdot \log x}{n}\right)}}{n \cdot x} \]

      6. lower-neg.f64 N/A

         \[\leadsto \frac{e^{-\frac{-1 \cdot \log x}{n}}}{n \cdot x} \]

      7. lower-/.f64 N/A

         \[\leadsto \frac{e^{-\frac{-1 \cdot \log x}{n}}}{n \cdot x} \]

      8. mul-1-neg N/A

         \[\leadsto \frac{e^{-\frac{\mathsf{neg}\left(\log x\right)}{n}}}{n \cdot x} \]

      9. lower-neg.f64 N/A

         \[\leadsto \frac{e^{-\frac{-\log x}{n}}}{n \cdot x} \]

      10. lower-log.f64 N/A

          \[\leadsto \frac{e^{-\frac{-\log x}{n}}}{n \cdot x} \]

      11. lower-*.f64 96.5

          \[\leadsto \frac{e^{-\frac{-\log x}{n}}}{n \cdot \color{blue}{x}} \]

   4. Applied rewrites 96.5%

      \[\leadsto \color{blue}{\frac{e^{-\frac{-\log x}{n}}}{n \cdot x}} \]

   5. Taylor expanded in x around 0

      \[\leadsto \frac{e^{\frac{\log x}{n}}}{n \cdot x} \]
   6. Step-by-step derivation

      1. lift-log.f64 N/A

         \[\leadsto \frac{e^{\frac{\log x}{n}}}{n \cdot x} \]

      2. lift-/.f64 96.5

         \[\leadsto \frac{e^{\frac{\log x}{n}}}{n \cdot x} \]

   7. Applied rewrites 96.5%

      \[\leadsto \frac{e^{\frac{\log x}{n}}}{n \cdot x} \]
   8. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \frac{e^{\frac{\log x}{n}}}{n \cdot \color{blue}{x}} \]

      2. lift-/.f64 N/A

         \[\leadsto \frac{e^{\frac{\log x}{n}}}{\color{blue}{n \cdot x}} \]

      3. associate-/r* N/A

         \[\leadsto \frac{\frac{e^{\frac{\log x}{n}}}{n}}{\color{blue}{x}} \]

      4. lower-/.f64 N/A

         \[\leadsto \frac{\frac{e^{\frac{\log x}{n}}}{n}}{\color{blue}{x}} \]

      5. lower-/.f64 97.6

         \[\leadsto \frac{\frac{e^{\frac{\log x}{n}}}{n}}{x} \]

   9. Applied rewrites 97.6%

      \[\leadsto \frac{\frac{e^{\frac{\log x}{n}}}{n}}{\color{blue}{x}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 6: 79.0% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := {x}^{\left(\frac{1}{n}\right)}\\ t_1 := {\left(x + 1\right)}^{\left(\frac{1}{n}\right)} - t\_0\\ t_2 := 1 - t\_0\\ \mathbf{if}\;t\_1 \leq -\infty:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;t\_1 \leq 0:\\ \;\;\;\;-\frac{\log \left(\frac{x}{1 + x}\right)}{n}\\ \mathbf{else}:\\ \;\;\;\;t\_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x n)
 :precision binary64
 (let* ((t_0 (pow x (/ 1.0 n)))
        (t_1 (- (pow (+ x 1.0) (/ 1.0 n)) t_0))
        (t_2 (- 1.0 t_0)))
   (if (<= t_1 (- INFINITY))
     t_2
     (if (<= t_1 0.0) (- (/ (log (/ x (+ 1.0 x))) n)) t_2))))

C

double code(double x, double n) {
	double t_0 = pow(x, (1.0 / n));
	double t_1 = pow((x + 1.0), (1.0 / n)) - t_0;
	double t_2 = 1.0 - t_0;
	double tmp;
	if (t_1 <= -((double) INFINITY)) {
		tmp = t_2;
	} else if (t_1 <= 0.0) {
		tmp = -(log((x / (1.0 + x))) / n);
	} else {
		tmp = t_2;
	}
	return tmp;
}

Java

public static double code(double x, double n) {
	double t_0 = Math.pow(x, (1.0 / n));
	double t_1 = Math.pow((x + 1.0), (1.0 / n)) - t_0;
	double t_2 = 1.0 - t_0;
	double tmp;
	if (t_1 <= -Double.POSITIVE_INFINITY) {
		tmp = t_2;
	} else if (t_1 <= 0.0) {
		tmp = -(Math.log((x / (1.0 + x))) / n);
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, n):
	t_0 = math.pow(x, (1.0 / n))
	t_1 = math.pow((x + 1.0), (1.0 / n)) - t_0
	t_2 = 1.0 - t_0
	tmp = 0
	if t_1 <= -math.inf:
		tmp = t_2
	elif t_1 <= 0.0:
		tmp = -(math.log((x / (1.0 + x))) / n)
	else:
		tmp = t_2
	return tmp

Julia

function code(x, n)
	t_0 = x ^ Float64(1.0 / n)
	t_1 = Float64((Float64(x + 1.0) ^ Float64(1.0 / n)) - t_0)
	t_2 = Float64(1.0 - t_0)
	tmp = 0.0
	if (t_1 <= Float64(-Inf))
		tmp = t_2;
	elseif (t_1 <= 0.0)
		tmp = Float64(-Float64(log(Float64(x / Float64(1.0 + x))) / n));
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, n)
	t_0 = x ^ (1.0 / n);
	t_1 = ((x + 1.0) ^ (1.0 / n)) - t_0;
	t_2 = 1.0 - t_0;
	tmp = 0.0;
	if (t_1 <= -Inf)
		tmp = t_2;
	elseif (t_1 <= 0.0)
		tmp = -(log((x / (1.0 + x))) / n);
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, n_] := Block[{t$95$0 = N[Power[x, N[(1.0 / n), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$1 = N[(N[Power[N[(x + 1.0), $MachinePrecision], N[(1.0 / n), $MachinePrecision]], $MachinePrecision] - t$95$0), $MachinePrecision]}, Block[{t$95$2 = N[(1.0 - t$95$0), $MachinePrecision]}, If[LessEqual[t$95$1, (-Infinity)], t$95$2, If[LessEqual[t$95$1, 0.0], (-N[(N[Log[N[(x / N[(1.0 + x), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / n), $MachinePrecision]), t$95$2]]]]]

Derivation

1. Split input into 2 regimes

2. if (-.f64 (pow.f64 (+.f64 x #s(literal 1 binary64)) (/.f64 #s(literal 1 binary64) n)) (pow.f64 x (/.f64 #s(literal 1 binary64) n))) < -inf.0 or 0.0 < (-.f64 (pow.f64 (+.f64 x #s(literal 1 binary64)) (/.f64 #s(literal 1 binary64) n)) (pow.f64 x (/.f64 #s(literal 1 binary64) n)))

   1. Initial program 78.3%

      \[{\left(x + 1\right)}^{\left(\frac{1}{n}\right)} - {x}^{\left(\frac{1}{n}\right)} \]

   2. Taylor expanded in x around 0

      \[\leadsto \color{blue}{1} - {x}^{\left(\frac{1}{n}\right)} \]
   3. Step-by-step derivation

   4. Applied rewrites 75.9%

      \[\leadsto \color{blue}{1} - {x}^{\left(\frac{1}{n}\right)} \]

   if -inf.0 < (-.f64 (pow.f64 (+.f64 x #s(literal 1 binary64)) (/.f64 #s(literal 1 binary64) n)) (pow.f64 x (/.f64 #s(literal 1 binary64) n))) < 0.0

   1. Initial program 43.8%

      \[{\left(x + 1\right)}^{\left(\frac{1}{n}\right)} - {x}^{\left(\frac{1}{n}\right)} \]

   2. Taylor expanded in n around -inf

      \[\leadsto \color{blue}{-1 \cdot \frac{\left(-1 \cdot \log \left(1 + x\right) + -1 \cdot \frac{\left(-1 \cdot \frac{\frac{-1}{6} \cdot {\log \left(1 + x\right)}^{3} - \frac{-1}{6} \cdot {\log x}^{3}}{n} + \frac{1}{2} \cdot {\log \left(1 + x\right)}^{2}\right) - \frac{1}{2} \cdot {\log x}^{2}}{n}\right) - -1 \cdot \log x}{n}} \]

   4. Applied rewrites 80.5%

      \[\leadsto \color{blue}{-\frac{\left(\left(-\frac{\left(-\frac{-0.16666666666666666 \cdot \left({\log \left(1 + x\right)}^{3} - {\log x}^{3}\right)}{n}\right) + 0.5 \cdot \left(\log \left(1 + x\right) \cdot \log \left(1 + x\right) - \log x \cdot \log x\right)}{n}\right) + \left(-\log \left(1 + x\right)\right)\right) + \log x}{n}} \]

   5. Taylor expanded in n around inf

      \[\leadsto -\frac{\log x - \log \left(1 + x\right)}{n} \]
   6. Step-by-step derivation

      1. diff-log N/A

         \[\leadsto -\frac{\log \left(\frac{x}{1 + x}\right)}{n} \]

      2. lower-log.f64 N/A

         \[\leadsto -\frac{\log \left(\frac{x}{1 + x}\right)}{n} \]

      3. lower-/.f64 N/A

         \[\leadsto -\frac{\log \left(\frac{x}{1 + x}\right)}{n} \]

      4. lift-+.f64 80.3

         \[\leadsto -\frac{\log \left(\frac{x}{1 + x}\right)}{n} \]

   7. Applied rewrites 80.3%

      \[\leadsto -\frac{\log \left(\frac{x}{1 + x}\right)}{n} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 7: 74.9% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := {\left(x + 1\right)}^{\left(\frac{1}{n}\right)} - {x}^{\left(\frac{1}{n}\right)}\\ \mathbf{if}\;t\_0 \leq -\infty:\\ \;\;\;\;\frac{0.3333333333333333}{n \cdot \left(\left(x \cdot x\right) \cdot x\right)}\\ \mathbf{elif}\;t\_0 \leq 1.6 \cdot 10^{-6}:\\ \;\;\;\;-\frac{\log \left(\frac{x}{1 + x}\right)}{n}\\ \mathbf{else}:\\ \;\;\;\;\frac{n \cdot \log \left(\frac{1 + x}{x}\right)}{n \cdot n}\\ \end{array} \end{array} \]

FPCore

(FPCore (x n)
 :precision binary64
 (let* ((t_0 (- (pow (+ x 1.0) (/ 1.0 n)) (pow x (/ 1.0 n)))))
   (if (<= t_0 (- INFINITY))
     (/ 0.3333333333333333 (* n (* (* x x) x)))
     (if (<= t_0 1.6e-6)
       (- (/ (log (/ x (+ 1.0 x))) n))
       (/ (* n (log (/ (+ 1.0 x) x))) (* n n))))))

C

double code(double x, double n) {
	double t_0 = pow((x + 1.0), (1.0 / n)) - pow(x, (1.0 / n));
	double tmp;
	if (t_0 <= -((double) INFINITY)) {
		tmp = 0.3333333333333333 / (n * ((x * x) * x));
	} else if (t_0 <= 1.6e-6) {
		tmp = -(log((x / (1.0 + x))) / n);
	} else {
		tmp = (n * log(((1.0 + x) / x))) / (n * n);
	}
	return tmp;
}

Java

public static double code(double x, double n) {
	double t_0 = Math.pow((x + 1.0), (1.0 / n)) - Math.pow(x, (1.0 / n));
	double tmp;
	if (t_0 <= -Double.POSITIVE_INFINITY) {
		tmp = 0.3333333333333333 / (n * ((x * x) * x));
	} else if (t_0 <= 1.6e-6) {
		tmp = -(Math.log((x / (1.0 + x))) / n);
	} else {
		tmp = (n * Math.log(((1.0 + x) / x))) / (n * n);
	}
	return tmp;
}

Python

def code(x, n):
	t_0 = math.pow((x + 1.0), (1.0 / n)) - math.pow(x, (1.0 / n))
	tmp = 0
	if t_0 <= -math.inf:
		tmp = 0.3333333333333333 / (n * ((x * x) * x))
	elif t_0 <= 1.6e-6:
		tmp = -(math.log((x / (1.0 + x))) / n)
	else:
		tmp = (n * math.log(((1.0 + x) / x))) / (n * n)
	return tmp

Julia

function code(x, n)
	t_0 = Float64((Float64(x + 1.0) ^ Float64(1.0 / n)) - (x ^ Float64(1.0 / n)))
	tmp = 0.0
	if (t_0 <= Float64(-Inf))
		tmp = Float64(0.3333333333333333 / Float64(n * Float64(Float64(x * x) * x)));
	elseif (t_0 <= 1.6e-6)
		tmp = Float64(-Float64(log(Float64(x / Float64(1.0 + x))) / n));
	else
		tmp = Float64(Float64(n * log(Float64(Float64(1.0 + x) / x))) / Float64(n * n));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, n)
	t_0 = ((x + 1.0) ^ (1.0 / n)) - (x ^ (1.0 / n));
	tmp = 0.0;
	if (t_0 <= -Inf)
		tmp = 0.3333333333333333 / (n * ((x * x) * x));
	elseif (t_0 <= 1.6e-6)
		tmp = -(log((x / (1.0 + x))) / n);
	else
		tmp = (n * log(((1.0 + x) / x))) / (n * n);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, n_] := Block[{t$95$0 = N[(N[Power[N[(x + 1.0), $MachinePrecision], N[(1.0 / n), $MachinePrecision]], $MachinePrecision] - N[Power[x, N[(1.0 / n), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$0, (-Infinity)], N[(0.3333333333333333 / N[(n * N[(N[(x * x), $MachinePrecision] * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$0, 1.6e-6], (-N[(N[Log[N[(x / N[(1.0 + x), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / n), $MachinePrecision]), N[(N[(n * N[Log[N[(N[(1.0 + x), $MachinePrecision] / x), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] / N[(n * n), $MachinePrecision]), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if (-.f64 (pow.f64 (+.f64 x #s(literal 1 binary64)) (/.f64 #s(literal 1 binary64) n)) (pow.f64 x (/.f64 #s(literal 1 binary64) n))) < -inf.0

   1. Initial program 100.0%

      \[{\left(x + 1\right)}^{\left(\frac{1}{n}\right)} - {x}^{\left(\frac{1}{n}\right)} \]

   2. Taylor expanded in n around inf

      \[\leadsto \color{blue}{\frac{\log \left(1 + x\right) - \log x}{n}} \]
   3. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{\log \left(1 + x\right) - \log x}{\color{blue}{n}} \]

      2. diff-log N/A

         \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

      3. lower-log.f64 N/A

         \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

      4. +-commutative N/A

         \[\leadsto \frac{\log \left(\frac{x + 1}{x}\right)}{n} \]

      5. lower-/.f64 N/A

         \[\leadsto \frac{\log \left(\frac{x + 1}{x}\right)}{n} \]

      6. +-commutative N/A

         \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

      7. lower-+.f64 6.1

         \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

   4. Applied rewrites 6.1%

      \[\leadsto \color{blue}{\frac{\log \left(\frac{1 + x}{x}\right)}{n}} \]

   5. Taylor expanded in x around inf

      \[\leadsto \frac{\left(\frac{\frac{1}{3}}{n \cdot {x}^{2}} + \frac{1}{n}\right) - \frac{\frac{1}{2}}{n \cdot x}}{\color{blue}{x}} \]
   6. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{\left(\frac{\frac{1}{3}}{n \cdot {x}^{2}} + \frac{1}{n}\right) - \frac{\frac{1}{2}}{n \cdot x}}{x} \]

      2. lower--.f64 N/A

         \[\leadsto \frac{\left(\frac{\frac{1}{3}}{n \cdot {x}^{2}} + \frac{1}{n}\right) - \frac{\frac{1}{2}}{n \cdot x}}{x} \]

      3. lower-+.f64 N/A

         \[\leadsto \frac{\left(\frac{\frac{1}{3}}{n \cdot {x}^{2}} + \frac{1}{n}\right) - \frac{\frac{1}{2}}{n \cdot x}}{x} \]

      4. lower-/.f64 N/A

         \[\leadsto \frac{\left(\frac{\frac{1}{3}}{n \cdot {x}^{2}} + \frac{1}{n}\right) - \frac{\frac{1}{2}}{n \cdot x}}{x} \]

      5. lower-*.f64 N/A

         \[\leadsto \frac{\left(\frac{\frac{1}{3}}{n \cdot {x}^{2}} + \frac{1}{n}\right) - \frac{\frac{1}{2}}{n \cdot x}}{x} \]

      6. unpow2 N/A

         \[\leadsto \frac{\left(\frac{\frac{1}{3}}{n \cdot \left(x \cdot x\right)} + \frac{1}{n}\right) - \frac{\frac{1}{2}}{n \cdot x}}{x} \]

      7. lower-*.f64 N/A

         \[\leadsto \frac{\left(\frac{\frac{1}{3}}{n \cdot \left(x \cdot x\right)} + \frac{1}{n}\right) - \frac{\frac{1}{2}}{n \cdot x}}{x} \]

      8. lift-/.f64 N/A

         \[\leadsto \frac{\left(\frac{\frac{1}{3}}{n \cdot \left(x \cdot x\right)} + \frac{1}{n}\right) - \frac{\frac{1}{2}}{n \cdot x}}{x} \]

      9. lower-/.f64 N/A

         \[\leadsto \frac{\left(\frac{\frac{1}{3}}{n \cdot \left(x \cdot x\right)} + \frac{1}{n}\right) - \frac{\frac{1}{2}}{n \cdot x}}{x} \]

      10. lift-*.f64 31.3

          \[\leadsto \frac{\left(\frac{0.3333333333333333}{n \cdot \left(x \cdot x\right)} + \frac{1}{n}\right) - \frac{0.5}{n \cdot x}}{x} \]

   7. Applied rewrites 31.3%

      \[\leadsto \frac{\left(\frac{0.3333333333333333}{n \cdot \left(x \cdot x\right)} + \frac{1}{n}\right) - \frac{0.5}{n \cdot x}}{\color{blue}{x}} \]

   8. Taylor expanded in x around 0

      \[\leadsto \frac{\frac{1}{3}}{n \cdot \color{blue}{{x}^{3}}} \]
   9. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{\frac{1}{3}}{n \cdot {x}^{\color{blue}{3}}} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{\frac{1}{3}}{n \cdot {x}^{3}} \]

      3. unpow3 N/A

         \[\leadsto \frac{\frac{1}{3}}{n \cdot \left(\left(x \cdot x\right) \cdot x\right)} \]

      4. pow2 N/A

         \[\leadsto \frac{\frac{1}{3}}{n \cdot \left({x}^{2} \cdot x\right)} \]

      5. lower-*.f64 N/A

         \[\leadsto \frac{\frac{1}{3}}{n \cdot \left({x}^{2} \cdot x\right)} \]

      6. pow2 N/A

         \[\leadsto \frac{\frac{1}{3}}{n \cdot \left(\left(x \cdot x\right) \cdot x\right)} \]

      7. lift-*.f64 84.0

         \[\leadsto \frac{0.3333333333333333}{n \cdot \left(\left(x \cdot x\right) \cdot x\right)} \]

   10. Applied rewrites 84.0%

       \[\leadsto \frac{0.3333333333333333}{n \cdot \color{blue}{\left(\left(x \cdot x\right) \cdot x\right)}} \]

   if -inf.0 < (-.f64 (pow.f64 (+.f64 x #s(literal 1 binary64)) (/.f64 #s(literal 1 binary64) n)) (pow.f64 x (/.f64 #s(literal 1 binary64) n))) < 1.5999999999999999e-6

   1. Initial program 43.8%

      \[{\left(x + 1\right)}^{\left(\frac{1}{n}\right)} - {x}^{\left(\frac{1}{n}\right)} \]

   2. Taylor expanded in n around -inf

      \[\leadsto \color{blue}{-1 \cdot \frac{\left(-1 \cdot \log \left(1 + x\right) + -1 \cdot \frac{\left(-1 \cdot \frac{\frac{-1}{6} \cdot {\log \left(1 + x\right)}^{3} - \frac{-1}{6} \cdot {\log x}^{3}}{n} + \frac{1}{2} \cdot {\log \left(1 + x\right)}^{2}\right) - \frac{1}{2} \cdot {\log x}^{2}}{n}\right) - -1 \cdot \log x}{n}} \]

   4. Applied rewrites 80.6%

      \[\leadsto \color{blue}{-\frac{\left(\left(-\frac{\left(-\frac{-0.16666666666666666 \cdot \left({\log \left(1 + x\right)}^{3} - {\log x}^{3}\right)}{n}\right) + 0.5 \cdot \left(\log \left(1 + x\right) \cdot \log \left(1 + x\right) - \log x \cdot \log x\right)}{n}\right) + \left(-\log \left(1 + x\right)\right)\right) + \log x}{n}} \]

   5. Taylor expanded in n around inf

      \[\leadsto -\frac{\log x - \log \left(1 + x\right)}{n} \]
   6. Step-by-step derivation

      1. diff-log N/A

         \[\leadsto -\frac{\log \left(\frac{x}{1 + x}\right)}{n} \]

      2. lower-log.f64 N/A

         \[\leadsto -\frac{\log \left(\frac{x}{1 + x}\right)}{n} \]

      3. lower-/.f64 N/A

         \[\leadsto -\frac{\log \left(\frac{x}{1 + x}\right)}{n} \]

      4. lift-+.f64 80.2

         \[\leadsto -\frac{\log \left(\frac{x}{1 + x}\right)}{n} \]

   7. Applied rewrites 80.2%

      \[\leadsto -\frac{\log \left(\frac{x}{1 + x}\right)}{n} \]

   if 1.5999999999999999e-6 < (-.f64 (pow.f64 (+.f64 x #s(literal 1 binary64)) (/.f64 #s(literal 1 binary64) n)) (pow.f64 x (/.f64 #s(literal 1 binary64) n)))

   1. Initial program 56.2%

      \[{\left(x + 1\right)}^{\left(\frac{1}{n}\right)} - {x}^{\left(\frac{1}{n}\right)} \]

   2. Taylor expanded in n around -inf

      \[\leadsto \color{blue}{-1 \cdot \frac{\left(-1 \cdot \log \left(1 + x\right) + -1 \cdot \frac{\frac{1}{2} \cdot {\log \left(1 + x\right)}^{2} - \frac{1}{2} \cdot {\log x}^{2}}{n}\right) - -1 \cdot \log x}{n}} \]

   4. Applied rewrites 1.5%

      \[\leadsto \color{blue}{-\frac{\left(\left(-\frac{0.5 \cdot \left(\log \left(1 + x\right) \cdot \log \left(1 + x\right) - \log x \cdot \log x\right)}{n}\right) + \left(-\log \left(1 + x\right)\right)\right) + \log x}{n}} \]

   5. Taylor expanded in n around 0

      \[\leadsto \frac{\frac{1}{2} \cdot \left({\log \left(1 + x\right)}^{2} - {\log x}^{2}\right) + n \cdot \left(\log \left(1 + x\right) - \log x\right)}{\color{blue}{{n}^{2}}} \]
   6. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{\frac{1}{2} \cdot \left({\log \left(1 + x\right)}^{2} - {\log x}^{2}\right) + n \cdot \left(\log \left(1 + x\right) - \log x\right)}{{n}^{\color{blue}{2}}} \]

   7. Applied rewrites 1.5%

      \[\leadsto \frac{\mathsf{fma}\left(0.5, \log \left(1 + x\right) \cdot \log \left(1 + x\right) - \log x \cdot \log x, n \cdot \log \left(\frac{1 + x}{x}\right)\right)}{\color{blue}{n \cdot n}} \]

   8. Taylor expanded in n around inf

      \[\leadsto \frac{n \cdot \log \left(\frac{1 + x}{x}\right)}{n \cdot n} \]
   9. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \frac{n \cdot \log \left(\frac{1 + x}{x}\right)}{n \cdot n} \]

      2. lift-+.f64 N/A

         \[\leadsto \frac{n \cdot \log \left(\frac{1 + x}{x}\right)}{n \cdot n} \]

      3. lift-log.f64 N/A

         \[\leadsto \frac{n \cdot \log \left(\frac{1 + x}{x}\right)}{n \cdot n} \]

      4. lift-*.f64 38.5

         \[\leadsto \frac{n \cdot \log \left(\frac{1 + x}{x}\right)}{n \cdot n} \]

   10. Applied rewrites 38.5%

       \[\leadsto \frac{n \cdot \log \left(\frac{1 + x}{x}\right)}{n \cdot n} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 8: 74.7% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := {\left(x + 1\right)}^{\left(\frac{1}{n}\right)} - {x}^{\left(\frac{1}{n}\right)}\\ \mathbf{if}\;t\_0 \leq -\infty:\\ \;\;\;\;\frac{0.3333333333333333}{n \cdot \left(\left(x \cdot x\right) \cdot x\right)}\\ \mathbf{elif}\;t\_0 \leq 0.9994104013186538:\\ \;\;\;\;-\frac{\log \left(\frac{x}{1 + x}\right)}{n}\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{0.3333333333333333}{\left(n \cdot x\right) \cdot x}}{x}\\ \end{array} \end{array} \]

FPCore

(FPCore (x n)
 :precision binary64
 (let* ((t_0 (- (pow (+ x 1.0) (/ 1.0 n)) (pow x (/ 1.0 n)))))
   (if (<= t_0 (- INFINITY))
     (/ 0.3333333333333333 (* n (* (* x x) x)))
     (if (<= t_0 0.9994104013186538)
       (- (/ (log (/ x (+ 1.0 x))) n))
       (/ (/ 0.3333333333333333 (* (* n x) x)) x)))))

C

double code(double x, double n) {
	double t_0 = pow((x + 1.0), (1.0 / n)) - pow(x, (1.0 / n));
	double tmp;
	if (t_0 <= -((double) INFINITY)) {
		tmp = 0.3333333333333333 / (n * ((x * x) * x));
	} else if (t_0 <= 0.9994104013186538) {
		tmp = -(log((x / (1.0 + x))) / n);
	} else {
		tmp = (0.3333333333333333 / ((n * x) * x)) / x;
	}
	return tmp;
}

Java

public static double code(double x, double n) {
	double t_0 = Math.pow((x + 1.0), (1.0 / n)) - Math.pow(x, (1.0 / n));
	double tmp;
	if (t_0 <= -Double.POSITIVE_INFINITY) {
		tmp = 0.3333333333333333 / (n * ((x * x) * x));
	} else if (t_0 <= 0.9994104013186538) {
		tmp = -(Math.log((x / (1.0 + x))) / n);
	} else {
		tmp = (0.3333333333333333 / ((n * x) * x)) / x;
	}
	return tmp;
}

Python

def code(x, n):
	t_0 = math.pow((x + 1.0), (1.0 / n)) - math.pow(x, (1.0 / n))
	tmp = 0
	if t_0 <= -math.inf:
		tmp = 0.3333333333333333 / (n * ((x * x) * x))
	elif t_0 <= 0.9994104013186538:
		tmp = -(math.log((x / (1.0 + x))) / n)
	else:
		tmp = (0.3333333333333333 / ((n * x) * x)) / x
	return tmp

Julia

function code(x, n)
	t_0 = Float64((Float64(x + 1.0) ^ Float64(1.0 / n)) - (x ^ Float64(1.0 / n)))
	tmp = 0.0
	if (t_0 <= Float64(-Inf))
		tmp = Float64(0.3333333333333333 / Float64(n * Float64(Float64(x * x) * x)));
	elseif (t_0 <= 0.9994104013186538)
		tmp = Float64(-Float64(log(Float64(x / Float64(1.0 + x))) / n));
	else
		tmp = Float64(Float64(0.3333333333333333 / Float64(Float64(n * x) * x)) / x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, n)
	t_0 = ((x + 1.0) ^ (1.0 / n)) - (x ^ (1.0 / n));
	tmp = 0.0;
	if (t_0 <= -Inf)
		tmp = 0.3333333333333333 / (n * ((x * x) * x));
	elseif (t_0 <= 0.9994104013186538)
		tmp = -(log((x / (1.0 + x))) / n);
	else
		tmp = (0.3333333333333333 / ((n * x) * x)) / x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, n_] := Block[{t$95$0 = N[(N[Power[N[(x + 1.0), $MachinePrecision], N[(1.0 / n), $MachinePrecision]], $MachinePrecision] - N[Power[x, N[(1.0 / n), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$0, (-Infinity)], N[(0.3333333333333333 / N[(n * N[(N[(x * x), $MachinePrecision] * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$0, 0.9994104013186538], (-N[(N[Log[N[(x / N[(1.0 + x), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / n), $MachinePrecision]), N[(N[(0.3333333333333333 / N[(N[(n * x), $MachinePrecision] * x), $MachinePrecision]), $MachinePrecision] / x), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if (-.f64 (pow.f64 (+.f64 x #s(literal 1 binary64)) (/.f64 #s(literal 1 binary64) n)) (pow.f64 x (/.f64 #s(literal 1 binary64) n))) < -inf.0

   1. Initial program 100.0%

      \[{\left(x + 1\right)}^{\left(\frac{1}{n}\right)} - {x}^{\left(\frac{1}{n}\right)} \]

   2. Taylor expanded in n around inf

      \[\leadsto \color{blue}{\frac{\log \left(1 + x\right) - \log x}{n}} \]
   3. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{\log \left(1 + x\right) - \log x}{\color{blue}{n}} \]

      2. diff-log N/A

         \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

      3. lower-log.f64 N/A

         \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

      4. +-commutative N/A

         \[\leadsto \frac{\log \left(\frac{x + 1}{x}\right)}{n} \]

      5. lower-/.f64 N/A

         \[\leadsto \frac{\log \left(\frac{x + 1}{x}\right)}{n} \]

      6. +-commutative N/A

         \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

      7. lower-+.f64 6.1

         \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

   4. Applied rewrites 6.1%

      \[\leadsto \color{blue}{\frac{\log \left(\frac{1 + x}{x}\right)}{n}} \]

   5. Taylor expanded in x around inf

      \[\leadsto \frac{\left(\frac{\frac{1}{3}}{n \cdot {x}^{2}} + \frac{1}{n}\right) - \frac{\frac{1}{2}}{n \cdot x}}{\color{blue}{x}} \]
   6. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{\left(\frac{\frac{1}{3}}{n \cdot {x}^{2}} + \frac{1}{n}\right) - \frac{\frac{1}{2}}{n \cdot x}}{x} \]

      2. lower--.f64 N/A

         \[\leadsto \frac{\left(\frac{\frac{1}{3}}{n \cdot {x}^{2}} + \frac{1}{n}\right) - \frac{\frac{1}{2}}{n \cdot x}}{x} \]

      3. lower-+.f64 N/A

         \[\leadsto \frac{\left(\frac{\frac{1}{3}}{n \cdot {x}^{2}} + \frac{1}{n}\right) - \frac{\frac{1}{2}}{n \cdot x}}{x} \]

      4. lower-/.f64 N/A

         \[\leadsto \frac{\left(\frac{\frac{1}{3}}{n \cdot {x}^{2}} + \frac{1}{n}\right) - \frac{\frac{1}{2}}{n \cdot x}}{x} \]

      5. lower-*.f64 N/A

         \[\leadsto \frac{\left(\frac{\frac{1}{3}}{n \cdot {x}^{2}} + \frac{1}{n}\right) - \frac{\frac{1}{2}}{n \cdot x}}{x} \]

      6. unpow2 N/A

         \[\leadsto \frac{\left(\frac{\frac{1}{3}}{n \cdot \left(x \cdot x\right)} + \frac{1}{n}\right) - \frac{\frac{1}{2}}{n \cdot x}}{x} \]

      7. lower-*.f64 N/A

         \[\leadsto \frac{\left(\frac{\frac{1}{3}}{n \cdot \left(x \cdot x\right)} + \frac{1}{n}\right) - \frac{\frac{1}{2}}{n \cdot x}}{x} \]

      8. lift-/.f64 N/A

         \[\leadsto \frac{\left(\frac{\frac{1}{3}}{n \cdot \left(x \cdot x\right)} + \frac{1}{n}\right) - \frac{\frac{1}{2}}{n \cdot x}}{x} \]

      9. lower-/.f64 N/A

         \[\leadsto \frac{\left(\frac{\frac{1}{3}}{n \cdot \left(x \cdot x\right)} + \frac{1}{n}\right) - \frac{\frac{1}{2}}{n \cdot x}}{x} \]

      10. lift-*.f64 31.3

          \[\leadsto \frac{\left(\frac{0.3333333333333333}{n \cdot \left(x \cdot x\right)} + \frac{1}{n}\right) - \frac{0.5}{n \cdot x}}{x} \]

   7. Applied rewrites 31.3%

      \[\leadsto \frac{\left(\frac{0.3333333333333333}{n \cdot \left(x \cdot x\right)} + \frac{1}{n}\right) - \frac{0.5}{n \cdot x}}{\color{blue}{x}} \]

   8. Taylor expanded in x around 0

      \[\leadsto \frac{\frac{1}{3}}{n \cdot \color{blue}{{x}^{3}}} \]
   9. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{\frac{1}{3}}{n \cdot {x}^{\color{blue}{3}}} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{\frac{1}{3}}{n \cdot {x}^{3}} \]

      3. unpow3 N/A

         \[\leadsto \frac{\frac{1}{3}}{n \cdot \left(\left(x \cdot x\right) \cdot x\right)} \]

      4. pow2 N/A

         \[\leadsto \frac{\frac{1}{3}}{n \cdot \left({x}^{2} \cdot x\right)} \]

      5. lower-*.f64 N/A

         \[\leadsto \frac{\frac{1}{3}}{n \cdot \left({x}^{2} \cdot x\right)} \]

      6. pow2 N/A

         \[\leadsto \frac{\frac{1}{3}}{n \cdot \left(\left(x \cdot x\right) \cdot x\right)} \]

      7. lift-*.f64 84.0

         \[\leadsto \frac{0.3333333333333333}{n \cdot \left(\left(x \cdot x\right) \cdot x\right)} \]

   10. Applied rewrites 84.0%

       \[\leadsto \frac{0.3333333333333333}{n \cdot \color{blue}{\left(\left(x \cdot x\right) \cdot x\right)}} \]

   if -inf.0 < (-.f64 (pow.f64 (+.f64 x #s(literal 1 binary64)) (/.f64 #s(literal 1 binary64) n)) (pow.f64 x (/.f64 #s(literal 1 binary64) n))) < 0.99941040131865377

   1. Initial program 44.0%

      \[{\left(x + 1\right)}^{\left(\frac{1}{n}\right)} - {x}^{\left(\frac{1}{n}\right)} \]

   2. Taylor expanded in n around -inf

      \[\leadsto \color{blue}{-1 \cdot \frac{\left(-1 \cdot \log \left(1 + x\right) + -1 \cdot \frac{\left(-1 \cdot \frac{\frac{-1}{6} \cdot {\log \left(1 + x\right)}^{3} - \frac{-1}{6} \cdot {\log x}^{3}}{n} + \frac{1}{2} \cdot {\log \left(1 + x\right)}^{2}\right) - \frac{1}{2} \cdot {\log x}^{2}}{n}\right) - -1 \cdot \log x}{n}} \]

   4. Applied rewrites 80.6%

      \[\leadsto \color{blue}{-\frac{\left(\left(-\frac{\left(-\frac{-0.16666666666666666 \cdot \left({\log \left(1 + x\right)}^{3} - {\log x}^{3}\right)}{n}\right) + 0.5 \cdot \left(\log \left(1 + x\right) \cdot \log \left(1 + x\right) - \log x \cdot \log x\right)}{n}\right) + \left(-\log \left(1 + x\right)\right)\right) + \log x}{n}} \]

   5. Taylor expanded in n around inf

      \[\leadsto -\frac{\log x - \log \left(1 + x\right)}{n} \]
   6. Step-by-step derivation

      1. diff-log N/A

         \[\leadsto -\frac{\log \left(\frac{x}{1 + x}\right)}{n} \]

      2. lower-log.f64 N/A

         \[\leadsto -\frac{\log \left(\frac{x}{1 + x}\right)}{n} \]

      3. lower-/.f64 N/A

         \[\leadsto -\frac{\log \left(\frac{x}{1 + x}\right)}{n} \]

      4. lift-+.f64 80.1

         \[\leadsto -\frac{\log \left(\frac{x}{1 + x}\right)}{n} \]

   7. Applied rewrites 80.1%

      \[\leadsto -\frac{\log \left(\frac{x}{1 + x}\right)}{n} \]

   if 0.99941040131865377 < (-.f64 (pow.f64 (+.f64 x #s(literal 1 binary64)) (/.f64 #s(literal 1 binary64) n)) (pow.f64 x (/.f64 #s(literal 1 binary64) n)))

   1. Initial program 55.7%

      \[{\left(x + 1\right)}^{\left(\frac{1}{n}\right)} - {x}^{\left(\frac{1}{n}\right)} \]

   2. Taylor expanded in n around inf

      \[\leadsto \color{blue}{\frac{\log \left(1 + x\right) - \log x}{n}} \]
   3. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{\log \left(1 + x\right) - \log x}{\color{blue}{n}} \]

      2. diff-log N/A

         \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

      3. lower-log.f64 N/A

         \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

      4. +-commutative N/A

         \[\leadsto \frac{\log \left(\frac{x + 1}{x}\right)}{n} \]

      5. lower-/.f64 N/A

         \[\leadsto \frac{\log \left(\frac{x + 1}{x}\right)}{n} \]

      6. +-commutative N/A

         \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

      7. lower-+.f64 6.7

         \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

   4. Applied rewrites 6.7%

      \[\leadsto \color{blue}{\frac{\log \left(\frac{1 + x}{x}\right)}{n}} \]

   5. Taylor expanded in x around inf

      \[\leadsto \frac{\left(\frac{\frac{1}{3}}{n \cdot {x}^{2}} + \frac{1}{n}\right) - \frac{\frac{1}{2}}{n \cdot x}}{\color{blue}{x}} \]
   6. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{\left(\frac{\frac{1}{3}}{n \cdot {x}^{2}} + \frac{1}{n}\right) - \frac{\frac{1}{2}}{n \cdot x}}{x} \]

      2. lower--.f64 N/A

         \[\leadsto \frac{\left(\frac{\frac{1}{3}}{n \cdot {x}^{2}} + \frac{1}{n}\right) - \frac{\frac{1}{2}}{n \cdot x}}{x} \]

      3. lower-+.f64 N/A

         \[\leadsto \frac{\left(\frac{\frac{1}{3}}{n \cdot {x}^{2}} + \frac{1}{n}\right) - \frac{\frac{1}{2}}{n \cdot x}}{x} \]

      4. lower-/.f64 N/A

         \[\leadsto \frac{\left(\frac{\frac{1}{3}}{n \cdot {x}^{2}} + \frac{1}{n}\right) - \frac{\frac{1}{2}}{n \cdot x}}{x} \]

      5. lower-*.f64 N/A

         \[\leadsto \frac{\left(\frac{\frac{1}{3}}{n \cdot {x}^{2}} + \frac{1}{n}\right) - \frac{\frac{1}{2}}{n \cdot x}}{x} \]

      6. unpow2 N/A

         \[\leadsto \frac{\left(\frac{\frac{1}{3}}{n \cdot \left(x \cdot x\right)} + \frac{1}{n}\right) - \frac{\frac{1}{2}}{n \cdot x}}{x} \]

      7. lower-*.f64 N/A

         \[\leadsto \frac{\left(\frac{\frac{1}{3}}{n \cdot \left(x \cdot x\right)} + \frac{1}{n}\right) - \frac{\frac{1}{2}}{n \cdot x}}{x} \]

      8. lift-/.f64 N/A

         \[\leadsto \frac{\left(\frac{\frac{1}{3}}{n \cdot \left(x \cdot x\right)} + \frac{1}{n}\right) - \frac{\frac{1}{2}}{n \cdot x}}{x} \]

      9. lower-/.f64 N/A

         \[\leadsto \frac{\left(\frac{\frac{1}{3}}{n \cdot \left(x \cdot x\right)} + \frac{1}{n}\right) - \frac{\frac{1}{2}}{n \cdot x}}{x} \]

      10. lift-*.f64 14.0

          \[\leadsto \frac{\left(\frac{0.3333333333333333}{n \cdot \left(x \cdot x\right)} + \frac{1}{n}\right) - \frac{0.5}{n \cdot x}}{x} \]

   7. Applied rewrites 14.0%

      \[\leadsto \frac{\left(\frac{0.3333333333333333}{n \cdot \left(x \cdot x\right)} + \frac{1}{n}\right) - \frac{0.5}{n \cdot x}}{\color{blue}{x}} \]

   8. Taylor expanded in x around inf

      \[\leadsto \frac{\frac{1}{n}}{x} \]
   9. Step-by-step derivation

      1. lift-/.f64 25.7

         \[\leadsto \frac{\frac{1}{n}}{x} \]

   10. Applied rewrites 25.7%

       \[\leadsto \frac{\frac{1}{n}}{x} \]

   11. Taylor expanded in x around 0

       \[\leadsto \frac{\frac{\frac{1}{3}}{n \cdot {x}^{2}}}{x} \]
   12. Step-by-step derivation

       1. associate--l+ N/A

          \[\leadsto \frac{\frac{\frac{1}{3}}{n \cdot {x}^{2}}}{x} \]

       2. pow2 N/A

          \[\leadsto \frac{\frac{\frac{1}{3}}{n \cdot {x}^{2}}}{x} \]

       3. pow-to-exp N/A

          \[\leadsto \frac{\frac{\frac{1}{3}}{n \cdot {x}^{2}}}{x} \]

       4. associate--l+ N/A

          \[\leadsto \frac{\frac{\frac{1}{3}}{n \cdot {x}^{2}}}{x} \]

       5. lower-/.f64 N/A

          \[\leadsto \frac{\frac{\frac{1}{3}}{n \cdot {x}^{2}}}{x} \]

       6. pow2 N/A

          \[\leadsto \frac{\frac{\frac{1}{3}}{n \cdot \left(x \cdot x\right)}}{x} \]

       7. associate-*r* N/A

          \[\leadsto \frac{\frac{\frac{1}{3}}{\left(n \cdot x\right) \cdot x}}{x} \]

       8. lower-*.f64 N/A

          \[\leadsto \frac{\frac{\frac{1}{3}}{\left(n \cdot x\right) \cdot x}}{x} \]

       9. lift-*.f64 37.0

          \[\leadsto \frac{\frac{0.3333333333333333}{\left(n \cdot x\right) \cdot x}}{x} \]

   13. Applied rewrites 37.0%

       \[\leadsto \frac{\frac{0.3333333333333333}{\left(n \cdot x\right) \cdot x}}{x} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 9: 74.7% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := {\left(x + 1\right)}^{\left(\frac{1}{n}\right)} - {x}^{\left(\frac{1}{n}\right)}\\ \mathbf{if}\;t\_0 \leq -\infty:\\ \;\;\;\;\frac{0.3333333333333333}{n \cdot \left(\left(x \cdot x\right) \cdot x\right)}\\ \mathbf{elif}\;t\_0 \leq 0.9994104013186538:\\ \;\;\;\;\frac{\log \left(\frac{1 + x}{x}\right)}{n}\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{0.3333333333333333}{\left(n \cdot x\right) \cdot x}}{x}\\ \end{array} \end{array} \]

FPCore

(FPCore (x n)
 :precision binary64
 (let* ((t_0 (- (pow (+ x 1.0) (/ 1.0 n)) (pow x (/ 1.0 n)))))
   (if (<= t_0 (- INFINITY))
     (/ 0.3333333333333333 (* n (* (* x x) x)))
     (if (<= t_0 0.9994104013186538)
       (/ (log (/ (+ 1.0 x) x)) n)
       (/ (/ 0.3333333333333333 (* (* n x) x)) x)))))

C

double code(double x, double n) {
	double t_0 = pow((x + 1.0), (1.0 / n)) - pow(x, (1.0 / n));
	double tmp;
	if (t_0 <= -((double) INFINITY)) {
		tmp = 0.3333333333333333 / (n * ((x * x) * x));
	} else if (t_0 <= 0.9994104013186538) {
		tmp = log(((1.0 + x) / x)) / n;
	} else {
		tmp = (0.3333333333333333 / ((n * x) * x)) / x;
	}
	return tmp;
}

Java

public static double code(double x, double n) {
	double t_0 = Math.pow((x + 1.0), (1.0 / n)) - Math.pow(x, (1.0 / n));
	double tmp;
	if (t_0 <= -Double.POSITIVE_INFINITY) {
		tmp = 0.3333333333333333 / (n * ((x * x) * x));
	} else if (t_0 <= 0.9994104013186538) {
		tmp = Math.log(((1.0 + x) / x)) / n;
	} else {
		tmp = (0.3333333333333333 / ((n * x) * x)) / x;
	}
	return tmp;
}

Python

def code(x, n):
	t_0 = math.pow((x + 1.0), (1.0 / n)) - math.pow(x, (1.0 / n))
	tmp = 0
	if t_0 <= -math.inf:
		tmp = 0.3333333333333333 / (n * ((x * x) * x))
	elif t_0 <= 0.9994104013186538:
		tmp = math.log(((1.0 + x) / x)) / n
	else:
		tmp = (0.3333333333333333 / ((n * x) * x)) / x
	return tmp

Julia

function code(x, n)
	t_0 = Float64((Float64(x + 1.0) ^ Float64(1.0 / n)) - (x ^ Float64(1.0 / n)))
	tmp = 0.0
	if (t_0 <= Float64(-Inf))
		tmp = Float64(0.3333333333333333 / Float64(n * Float64(Float64(x * x) * x)));
	elseif (t_0 <= 0.9994104013186538)
		tmp = Float64(log(Float64(Float64(1.0 + x) / x)) / n);
	else
		tmp = Float64(Float64(0.3333333333333333 / Float64(Float64(n * x) * x)) / x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, n)
	t_0 = ((x + 1.0) ^ (1.0 / n)) - (x ^ (1.0 / n));
	tmp = 0.0;
	if (t_0 <= -Inf)
		tmp = 0.3333333333333333 / (n * ((x * x) * x));
	elseif (t_0 <= 0.9994104013186538)
		tmp = log(((1.0 + x) / x)) / n;
	else
		tmp = (0.3333333333333333 / ((n * x) * x)) / x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, n_] := Block[{t$95$0 = N[(N[Power[N[(x + 1.0), $MachinePrecision], N[(1.0 / n), $MachinePrecision]], $MachinePrecision] - N[Power[x, N[(1.0 / n), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$0, (-Infinity)], N[(0.3333333333333333 / N[(n * N[(N[(x * x), $MachinePrecision] * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$0, 0.9994104013186538], N[(N[Log[N[(N[(1.0 + x), $MachinePrecision] / x), $MachinePrecision]], $MachinePrecision] / n), $MachinePrecision], N[(N[(0.3333333333333333 / N[(N[(n * x), $MachinePrecision] * x), $MachinePrecision]), $MachinePrecision] / x), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if (-.f64 (pow.f64 (+.f64 x #s(literal 1 binary64)) (/.f64 #s(literal 1 binary64) n)) (pow.f64 x (/.f64 #s(literal 1 binary64) n))) < -inf.0

   1. Initial program 100.0%

      \[{\left(x + 1\right)}^{\left(\frac{1}{n}\right)} - {x}^{\left(\frac{1}{n}\right)} \]

   2. Taylor expanded in n around inf

      \[\leadsto \color{blue}{\frac{\log \left(1 + x\right) - \log x}{n}} \]
   3. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{\log \left(1 + x\right) - \log x}{\color{blue}{n}} \]

      2. diff-log N/A

         \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

      3. lower-log.f64 N/A

         \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

      4. +-commutative N/A

         \[\leadsto \frac{\log \left(\frac{x + 1}{x}\right)}{n} \]

      5. lower-/.f64 N/A

         \[\leadsto \frac{\log \left(\frac{x + 1}{x}\right)}{n} \]

      6. +-commutative N/A

         \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

      7. lower-+.f64 6.1

         \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

   4. Applied rewrites 6.1%

      \[\leadsto \color{blue}{\frac{\log \left(\frac{1 + x}{x}\right)}{n}} \]

   5. Taylor expanded in x around inf

      \[\leadsto \frac{\left(\frac{\frac{1}{3}}{n \cdot {x}^{2}} + \frac{1}{n}\right) - \frac{\frac{1}{2}}{n \cdot x}}{\color{blue}{x}} \]
   6. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{\left(\frac{\frac{1}{3}}{n \cdot {x}^{2}} + \frac{1}{n}\right) - \frac{\frac{1}{2}}{n \cdot x}}{x} \]

      2. lower--.f64 N/A

         \[\leadsto \frac{\left(\frac{\frac{1}{3}}{n \cdot {x}^{2}} + \frac{1}{n}\right) - \frac{\frac{1}{2}}{n \cdot x}}{x} \]

      3. lower-+.f64 N/A

         \[\leadsto \frac{\left(\frac{\frac{1}{3}}{n \cdot {x}^{2}} + \frac{1}{n}\right) - \frac{\frac{1}{2}}{n \cdot x}}{x} \]

      4. lower-/.f64 N/A

         \[\leadsto \frac{\left(\frac{\frac{1}{3}}{n \cdot {x}^{2}} + \frac{1}{n}\right) - \frac{\frac{1}{2}}{n \cdot x}}{x} \]

      5. lower-*.f64 N/A

         \[\leadsto \frac{\left(\frac{\frac{1}{3}}{n \cdot {x}^{2}} + \frac{1}{n}\right) - \frac{\frac{1}{2}}{n \cdot x}}{x} \]

      6. unpow2 N/A

         \[\leadsto \frac{\left(\frac{\frac{1}{3}}{n \cdot \left(x \cdot x\right)} + \frac{1}{n}\right) - \frac{\frac{1}{2}}{n \cdot x}}{x} \]

      7. lower-*.f64 N/A

         \[\leadsto \frac{\left(\frac{\frac{1}{3}}{n \cdot \left(x \cdot x\right)} + \frac{1}{n}\right) - \frac{\frac{1}{2}}{n \cdot x}}{x} \]

      8. lift-/.f64 N/A

         \[\leadsto \frac{\left(\frac{\frac{1}{3}}{n \cdot \left(x \cdot x\right)} + \frac{1}{n}\right) - \frac{\frac{1}{2}}{n \cdot x}}{x} \]

      9. lower-/.f64 N/A

         \[\leadsto \frac{\left(\frac{\frac{1}{3}}{n \cdot \left(x \cdot x\right)} + \frac{1}{n}\right) - \frac{\frac{1}{2}}{n \cdot x}}{x} \]

      10. lift-*.f64 31.3

          \[\leadsto \frac{\left(\frac{0.3333333333333333}{n \cdot \left(x \cdot x\right)} + \frac{1}{n}\right) - \frac{0.5}{n \cdot x}}{x} \]

   7. Applied rewrites 31.3%

      \[\leadsto \frac{\left(\frac{0.3333333333333333}{n \cdot \left(x \cdot x\right)} + \frac{1}{n}\right) - \frac{0.5}{n \cdot x}}{\color{blue}{x}} \]

   8. Taylor expanded in x around 0

      \[\leadsto \frac{\frac{1}{3}}{n \cdot \color{blue}{{x}^{3}}} \]
   9. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{\frac{1}{3}}{n \cdot {x}^{\color{blue}{3}}} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{\frac{1}{3}}{n \cdot {x}^{3}} \]

      3. unpow3 N/A

         \[\leadsto \frac{\frac{1}{3}}{n \cdot \left(\left(x \cdot x\right) \cdot x\right)} \]

      4. pow2 N/A

         \[\leadsto \frac{\frac{1}{3}}{n \cdot \left({x}^{2} \cdot x\right)} \]

      5. lower-*.f64 N/A

         \[\leadsto \frac{\frac{1}{3}}{n \cdot \left({x}^{2} \cdot x\right)} \]

      6. pow2 N/A

         \[\leadsto \frac{\frac{1}{3}}{n \cdot \left(\left(x \cdot x\right) \cdot x\right)} \]

      7. lift-*.f64 84.0

         \[\leadsto \frac{0.3333333333333333}{n \cdot \left(\left(x \cdot x\right) \cdot x\right)} \]

   10. Applied rewrites 84.0%

       \[\leadsto \frac{0.3333333333333333}{n \cdot \color{blue}{\left(\left(x \cdot x\right) \cdot x\right)}} \]

   if -inf.0 < (-.f64 (pow.f64 (+.f64 x #s(literal 1 binary64)) (/.f64 #s(literal 1 binary64) n)) (pow.f64 x (/.f64 #s(literal 1 binary64) n))) < 0.99941040131865377

   1. Initial program 44.0%

      \[{\left(x + 1\right)}^{\left(\frac{1}{n}\right)} - {x}^{\left(\frac{1}{n}\right)} \]

   2. Taylor expanded in n around inf

      \[\leadsto \color{blue}{\frac{\log \left(1 + x\right) - \log x}{n}} \]
   3. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{\log \left(1 + x\right) - \log x}{\color{blue}{n}} \]

      2. diff-log N/A

         \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

      3. lower-log.f64 N/A

         \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

      4. +-commutative N/A

         \[\leadsto \frac{\log \left(\frac{x + 1}{x}\right)}{n} \]

      5. lower-/.f64 N/A

         \[\leadsto \frac{\log \left(\frac{x + 1}{x}\right)}{n} \]

      6. +-commutative N/A

         \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

      7. lower-+.f64 80.1

         \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

   4. Applied rewrites 80.1%

      \[\leadsto \color{blue}{\frac{\log \left(\frac{1 + x}{x}\right)}{n}} \]

   if 0.99941040131865377 < (-.f64 (pow.f64 (+.f64 x #s(literal 1 binary64)) (/.f64 #s(literal 1 binary64) n)) (pow.f64 x (/.f64 #s(literal 1 binary64) n)))

   1. Initial program 55.7%

      \[{\left(x + 1\right)}^{\left(\frac{1}{n}\right)} - {x}^{\left(\frac{1}{n}\right)} \]

   2. Taylor expanded in n around inf

      \[\leadsto \color{blue}{\frac{\log \left(1 + x\right) - \log x}{n}} \]
   3. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{\log \left(1 + x\right) - \log x}{\color{blue}{n}} \]

      2. diff-log N/A

         \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

      3. lower-log.f64 N/A

         \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

      4. +-commutative N/A

         \[\leadsto \frac{\log \left(\frac{x + 1}{x}\right)}{n} \]

      5. lower-/.f64 N/A

         \[\leadsto \frac{\log \left(\frac{x + 1}{x}\right)}{n} \]

      6. +-commutative N/A

         \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

      7. lower-+.f64 6.7

         \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

   4. Applied rewrites 6.7%

      \[\leadsto \color{blue}{\frac{\log \left(\frac{1 + x}{x}\right)}{n}} \]

   5. Taylor expanded in x around inf

      \[\leadsto \frac{\left(\frac{\frac{1}{3}}{n \cdot {x}^{2}} + \frac{1}{n}\right) - \frac{\frac{1}{2}}{n \cdot x}}{\color{blue}{x}} \]
   6. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{\left(\frac{\frac{1}{3}}{n \cdot {x}^{2}} + \frac{1}{n}\right) - \frac{\frac{1}{2}}{n \cdot x}}{x} \]

      2. lower--.f64 N/A

         \[\leadsto \frac{\left(\frac{\frac{1}{3}}{n \cdot {x}^{2}} + \frac{1}{n}\right) - \frac{\frac{1}{2}}{n \cdot x}}{x} \]

      3. lower-+.f64 N/A

         \[\leadsto \frac{\left(\frac{\frac{1}{3}}{n \cdot {x}^{2}} + \frac{1}{n}\right) - \frac{\frac{1}{2}}{n \cdot x}}{x} \]

      4. lower-/.f64 N/A

         \[\leadsto \frac{\left(\frac{\frac{1}{3}}{n \cdot {x}^{2}} + \frac{1}{n}\right) - \frac{\frac{1}{2}}{n \cdot x}}{x} \]

      5. lower-*.f64 N/A

         \[\leadsto \frac{\left(\frac{\frac{1}{3}}{n \cdot {x}^{2}} + \frac{1}{n}\right) - \frac{\frac{1}{2}}{n \cdot x}}{x} \]

      6. unpow2 N/A

         \[\leadsto \frac{\left(\frac{\frac{1}{3}}{n \cdot \left(x \cdot x\right)} + \frac{1}{n}\right) - \frac{\frac{1}{2}}{n \cdot x}}{x} \]

      7. lower-*.f64 N/A

         \[\leadsto \frac{\left(\frac{\frac{1}{3}}{n \cdot \left(x \cdot x\right)} + \frac{1}{n}\right) - \frac{\frac{1}{2}}{n \cdot x}}{x} \]

      8. lift-/.f64 N/A

         \[\leadsto \frac{\left(\frac{\frac{1}{3}}{n \cdot \left(x \cdot x\right)} + \frac{1}{n}\right) - \frac{\frac{1}{2}}{n \cdot x}}{x} \]

      9. lower-/.f64 N/A

         \[\leadsto \frac{\left(\frac{\frac{1}{3}}{n \cdot \left(x \cdot x\right)} + \frac{1}{n}\right) - \frac{\frac{1}{2}}{n \cdot x}}{x} \]

      10. lift-*.f64 14.0

          \[\leadsto \frac{\left(\frac{0.3333333333333333}{n \cdot \left(x \cdot x\right)} + \frac{1}{n}\right) - \frac{0.5}{n \cdot x}}{x} \]

   7. Applied rewrites 14.0%

      \[\leadsto \frac{\left(\frac{0.3333333333333333}{n \cdot \left(x \cdot x\right)} + \frac{1}{n}\right) - \frac{0.5}{n \cdot x}}{\color{blue}{x}} \]

   8. Taylor expanded in x around inf

      \[\leadsto \frac{\frac{1}{n}}{x} \]
   9. Step-by-step derivation

      1. lift-/.f64 25.7

         \[\leadsto \frac{\frac{1}{n}}{x} \]

   10. Applied rewrites 25.7%

       \[\leadsto \frac{\frac{1}{n}}{x} \]

   11. Taylor expanded in x around 0

       \[\leadsto \frac{\frac{\frac{1}{3}}{n \cdot {x}^{2}}}{x} \]
   12. Step-by-step derivation

       1. associate--l+ N/A

          \[\leadsto \frac{\frac{\frac{1}{3}}{n \cdot {x}^{2}}}{x} \]

       2. pow2 N/A

          \[\leadsto \frac{\frac{\frac{1}{3}}{n \cdot {x}^{2}}}{x} \]

       3. pow-to-exp N/A

          \[\leadsto \frac{\frac{\frac{1}{3}}{n \cdot {x}^{2}}}{x} \]

       4. associate--l+ N/A

          \[\leadsto \frac{\frac{\frac{1}{3}}{n \cdot {x}^{2}}}{x} \]

       5. lower-/.f64 N/A

          \[\leadsto \frac{\frac{\frac{1}{3}}{n \cdot {x}^{2}}}{x} \]

       6. pow2 N/A

          \[\leadsto \frac{\frac{\frac{1}{3}}{n \cdot \left(x \cdot x\right)}}{x} \]

       7. associate-*r* N/A

          \[\leadsto \frac{\frac{\frac{1}{3}}{\left(n \cdot x\right) \cdot x}}{x} \]

       8. lower-*.f64 N/A

          \[\leadsto \frac{\frac{\frac{1}{3}}{\left(n \cdot x\right) \cdot x}}{x} \]

       9. lift-*.f64 37.0

          \[\leadsto \frac{\frac{0.3333333333333333}{\left(n \cdot x\right) \cdot x}}{x} \]

   13. Applied rewrites 37.0%

       \[\leadsto \frac{\frac{0.3333333333333333}{\left(n \cdot x\right) \cdot x}}{x} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 10: 59.5% accurate, 2.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq 1:\\ \;\;\;\;\frac{x + \left(-\log x\right)}{n}\\ \mathbf{elif}\;x \leq 3.2 \cdot 10^{+31}:\\ \;\;\;\;-\frac{\frac{\frac{0.5}{x} - 1}{x}}{n}\\ \mathbf{else}:\\ \;\;\;\;\frac{\log 1}{n}\\ \end{array} \end{array} \]

FPCore

(FPCore (x n)
 :precision binary64
 (if (<= x 1.0)
   (/ (+ x (- (log x))) n)
   (if (<= x 3.2e+31) (- (/ (/ (- (/ 0.5 x) 1.0) x) n)) (/ (log 1.0) n))))

C

double code(double x, double n) {
	double tmp;
	if (x <= 1.0) {
		tmp = (x + -log(x)) / n;
	} else if (x <= 3.2e+31) {
		tmp = -((((0.5 / x) - 1.0) / x) / n);
	} else {
		tmp = log(1.0) / n;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x, n)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: n
    real(8) :: tmp
    if (x <= 1.0d0) then
        tmp = (x + -log(x)) / n
    else if (x <= 3.2d+31) then
        tmp = -((((0.5d0 / x) - 1.0d0) / x) / n)
    else
        tmp = log(1.0d0) / n
    end if
    code = tmp
end function

Java

public static double code(double x, double n) {
	double tmp;
	if (x <= 1.0) {
		tmp = (x + -Math.log(x)) / n;
	} else if (x <= 3.2e+31) {
		tmp = -((((0.5 / x) - 1.0) / x) / n);
	} else {
		tmp = Math.log(1.0) / n;
	}
	return tmp;
}

Python

def code(x, n):
	tmp = 0
	if x <= 1.0:
		tmp = (x + -math.log(x)) / n
	elif x <= 3.2e+31:
		tmp = -((((0.5 / x) - 1.0) / x) / n)
	else:
		tmp = math.log(1.0) / n
	return tmp

Julia

function code(x, n)
	tmp = 0.0
	if (x <= 1.0)
		tmp = Float64(Float64(x + Float64(-log(x))) / n);
	elseif (x <= 3.2e+31)
		tmp = Float64(-Float64(Float64(Float64(Float64(0.5 / x) - 1.0) / x) / n));
	else
		tmp = Float64(log(1.0) / n);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, n)
	tmp = 0.0;
	if (x <= 1.0)
		tmp = (x + -log(x)) / n;
	elseif (x <= 3.2e+31)
		tmp = -((((0.5 / x) - 1.0) / x) / n);
	else
		tmp = log(1.0) / n;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, n_] := If[LessEqual[x, 1.0], N[(N[(x + (-N[Log[x], $MachinePrecision])), $MachinePrecision] / n), $MachinePrecision], If[LessEqual[x, 3.2e+31], (-N[(N[(N[(N[(0.5 / x), $MachinePrecision] - 1.0), $MachinePrecision] / x), $MachinePrecision] / n), $MachinePrecision]), N[(N[Log[1.0], $MachinePrecision] / n), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if x < 1

   1. Initial program 43.7%

      \[{\left(x + 1\right)}^{\left(\frac{1}{n}\right)} - {x}^{\left(\frac{1}{n}\right)} \]

   2. Taylor expanded in n around inf

      \[\leadsto \color{blue}{\frac{\log \left(1 + x\right) - \log x}{n}} \]
   3. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{\log \left(1 + x\right) - \log x}{\color{blue}{n}} \]

      2. diff-log N/A

         \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

      3. lower-log.f64 N/A

         \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

      4. +-commutative N/A

         \[\leadsto \frac{\log \left(\frac{x + 1}{x}\right)}{n} \]

      5. lower-/.f64 N/A

         \[\leadsto \frac{\log \left(\frac{x + 1}{x}\right)}{n} \]

      6. +-commutative N/A

         \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

      7. lower-+.f64 52.0

         \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

   4. Applied rewrites 52.0%

      \[\leadsto \color{blue}{\frac{\log \left(\frac{1 + x}{x}\right)}{n}} \]

   5. Taylor expanded in x around 0

      \[\leadsto \frac{x + -1 \cdot \log x}{n} \]
   6. Step-by-step derivation

      1. log-pow-rev N/A

         \[\leadsto \frac{x + \log \left({x}^{-1}\right)}{n} \]

      2. inv-pow N/A

         \[\leadsto \frac{x + \log \left(\frac{1}{x}\right)}{n} \]

      3. lower-+.f64 N/A

         \[\leadsto \frac{x + \log \left(\frac{1}{x}\right)}{n} \]

      4. neg-log N/A

         \[\leadsto \frac{x + \left(\mathsf{neg}\left(\log x\right)\right)}{n} \]

      5. lift-neg.f64 N/A

         \[\leadsto \frac{x + \left(-\log x\right)}{n} \]

      6. lift-log.f64 51.5

         \[\leadsto \frac{x + \left(-\log x\right)}{n} \]

   7. Applied rewrites 51.5%

      \[\leadsto \frac{x + \left(-\log x\right)}{n} \]

   if 1 < x < 3.2000000000000001e31

   1. Initial program 36.5%

      \[{\left(x + 1\right)}^{\left(\frac{1}{n}\right)} - {x}^{\left(\frac{1}{n}\right)} \]

   2. Taylor expanded in n around -inf

      \[\leadsto \color{blue}{-1 \cdot \frac{\left(-1 \cdot \log \left(1 + x\right) + -1 \cdot \frac{\left(-1 \cdot \frac{\frac{-1}{6} \cdot {\log \left(1 + x\right)}^{3} - \frac{-1}{6} \cdot {\log x}^{3}}{n} + \frac{1}{2} \cdot {\log \left(1 + x\right)}^{2}\right) - \frac{1}{2} \cdot {\log x}^{2}}{n}\right) - -1 \cdot \log x}{n}} \]

   4. Applied rewrites 39.2%

      \[\leadsto \color{blue}{-\frac{\left(\left(-\frac{\left(-\frac{-0.16666666666666666 \cdot \left({\log \left(1 + x\right)}^{3} - {\log x}^{3}\right)}{n}\right) + 0.5 \cdot \left(\log \left(1 + x\right) \cdot \log \left(1 + x\right) - \log x \cdot \log x\right)}{n}\right) + \left(-\log \left(1 + x\right)\right)\right) + \log x}{n}} \]

   5. Taylor expanded in n around inf

      \[\leadsto -\frac{\log x - \log \left(1 + x\right)}{n} \]
   6. Step-by-step derivation

      1. diff-log N/A

         \[\leadsto -\frac{\log \left(\frac{x}{1 + x}\right)}{n} \]

      2. lower-log.f64 N/A

         \[\leadsto -\frac{\log \left(\frac{x}{1 + x}\right)}{n} \]

      3. lower-/.f64 N/A

         \[\leadsto -\frac{\log \left(\frac{x}{1 + x}\right)}{n} \]

      4. lift-+.f64 38.8

         \[\leadsto -\frac{\log \left(\frac{x}{1 + x}\right)}{n} \]

   7. Applied rewrites 38.8%

      \[\leadsto -\frac{\log \left(\frac{x}{1 + x}\right)}{n} \]

   8. Taylor expanded in x around inf

      \[\leadsto -\frac{\frac{\frac{1}{2} \cdot \frac{1}{x} - 1}{x}}{n} \]
   9. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto -\frac{\frac{\frac{1}{2} \cdot \frac{1}{x} - 1}{x}}{n} \]

      2. lower--.f64 N/A

         \[\leadsto -\frac{\frac{\frac{1}{2} \cdot \frac{1}{x} - 1}{x}}{n} \]

      3. associate-*r/ N/A

         \[\leadsto -\frac{\frac{\frac{\frac{1}{2} \cdot 1}{x} - 1}{x}}{n} \]

      4. metadata-eval N/A

         \[\leadsto -\frac{\frac{\frac{\frac{1}{2}}{x} - 1}{x}}{n} \]

      5. lower-/.f64 60.8

         \[\leadsto -\frac{\frac{\frac{0.5}{x} - 1}{x}}{n} \]

   10. Applied rewrites 60.8%

       \[\leadsto -\frac{\frac{\frac{0.5}{x} - 1}{x}}{n} \]

   if 3.2000000000000001e31 < x

   1. Initial program 71.2%

      \[{\left(x + 1\right)}^{\left(\frac{1}{n}\right)} - {x}^{\left(\frac{1}{n}\right)} \]

   2. Taylor expanded in n around inf

      \[\leadsto \color{blue}{\frac{\log \left(1 + x\right) - \log x}{n}} \]
   3. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{\log \left(1 + x\right) - \log x}{\color{blue}{n}} \]

      2. diff-log N/A

         \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

      3. lower-log.f64 N/A

         \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

      4. +-commutative N/A

         \[\leadsto \frac{\log \left(\frac{x + 1}{x}\right)}{n} \]

      5. lower-/.f64 N/A

         \[\leadsto \frac{\log \left(\frac{x + 1}{x}\right)}{n} \]

      6. +-commutative N/A

         \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

      7. lower-+.f64 71.2

         \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

   4. Applied rewrites 71.2%

      \[\leadsto \color{blue}{\frac{\log \left(\frac{1 + x}{x}\right)}{n}} \]

   5. Taylor expanded in x around inf

      \[\leadsto \frac{\log 1}{n} \]
   6. Step-by-step derivation

   7. Applied rewrites 71.2%

      \[\leadsto \frac{\log 1}{n} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 11: 59.2% accurate, 2.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq 1:\\ \;\;\;\;\frac{x + \left(-\log x\right)}{n}\\ \mathbf{elif}\;x \leq 3.2 \cdot 10^{+31}:\\ \;\;\;\;\frac{\frac{1}{x}}{n}\\ \mathbf{else}:\\ \;\;\;\;\frac{\log 1}{n}\\ \end{array} \end{array} \]

FPCore

(FPCore (x n)
 :precision binary64
 (if (<= x 1.0)
   (/ (+ x (- (log x))) n)
   (if (<= x 3.2e+31) (/ (/ 1.0 x) n) (/ (log 1.0) n))))

C

double code(double x, double n) {
	double tmp;
	if (x <= 1.0) {
		tmp = (x + -log(x)) / n;
	} else if (x <= 3.2e+31) {
		tmp = (1.0 / x) / n;
	} else {
		tmp = log(1.0) / n;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x, n)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: n
    real(8) :: tmp
    if (x <= 1.0d0) then
        tmp = (x + -log(x)) / n
    else if (x <= 3.2d+31) then
        tmp = (1.0d0 / x) / n
    else
        tmp = log(1.0d0) / n
    end if
    code = tmp
end function

Java

public static double code(double x, double n) {
	double tmp;
	if (x <= 1.0) {
		tmp = (x + -Math.log(x)) / n;
	} else if (x <= 3.2e+31) {
		tmp = (1.0 / x) / n;
	} else {
		tmp = Math.log(1.0) / n;
	}
	return tmp;
}

Python

def code(x, n):
	tmp = 0
	if x <= 1.0:
		tmp = (x + -math.log(x)) / n
	elif x <= 3.2e+31:
		tmp = (1.0 / x) / n
	else:
		tmp = math.log(1.0) / n
	return tmp

Julia

function code(x, n)
	tmp = 0.0
	if (x <= 1.0)
		tmp = Float64(Float64(x + Float64(-log(x))) / n);
	elseif (x <= 3.2e+31)
		tmp = Float64(Float64(1.0 / x) / n);
	else
		tmp = Float64(log(1.0) / n);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, n)
	tmp = 0.0;
	if (x <= 1.0)
		tmp = (x + -log(x)) / n;
	elseif (x <= 3.2e+31)
		tmp = (1.0 / x) / n;
	else
		tmp = log(1.0) / n;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, n_] := If[LessEqual[x, 1.0], N[(N[(x + (-N[Log[x], $MachinePrecision])), $MachinePrecision] / n), $MachinePrecision], If[LessEqual[x, 3.2e+31], N[(N[(1.0 / x), $MachinePrecision] / n), $MachinePrecision], N[(N[Log[1.0], $MachinePrecision] / n), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if x < 1

   1. Initial program 43.7%

      \[{\left(x + 1\right)}^{\left(\frac{1}{n}\right)} - {x}^{\left(\frac{1}{n}\right)} \]

   2. Taylor expanded in n around inf

      \[\leadsto \color{blue}{\frac{\log \left(1 + x\right) - \log x}{n}} \]
   3. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{\log \left(1 + x\right) - \log x}{\color{blue}{n}} \]

      2. diff-log N/A

         \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

      3. lower-log.f64 N/A

         \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

      4. +-commutative N/A

         \[\leadsto \frac{\log \left(\frac{x + 1}{x}\right)}{n} \]

      5. lower-/.f64 N/A

         \[\leadsto \frac{\log \left(\frac{x + 1}{x}\right)}{n} \]

      6. +-commutative N/A

         \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

      7. lower-+.f64 52.0

         \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

   4. Applied rewrites 52.0%

      \[\leadsto \color{blue}{\frac{\log \left(\frac{1 + x}{x}\right)}{n}} \]

   5. Taylor expanded in x around 0

      \[\leadsto \frac{x + -1 \cdot \log x}{n} \]
   6. Step-by-step derivation

      1. log-pow-rev N/A

         \[\leadsto \frac{x + \log \left({x}^{-1}\right)}{n} \]

      2. inv-pow N/A

         \[\leadsto \frac{x + \log \left(\frac{1}{x}\right)}{n} \]

      3. lower-+.f64 N/A

         \[\leadsto \frac{x + \log \left(\frac{1}{x}\right)}{n} \]

      4. neg-log N/A

         \[\leadsto \frac{x + \left(\mathsf{neg}\left(\log x\right)\right)}{n} \]

      5. lift-neg.f64 N/A

         \[\leadsto \frac{x + \left(-\log x\right)}{n} \]

      6. lift-log.f64 51.5

         \[\leadsto \frac{x + \left(-\log x\right)}{n} \]

   7. Applied rewrites 51.5%

      \[\leadsto \frac{x + \left(-\log x\right)}{n} \]

   if 1 < x < 3.2000000000000001e31

   1. Initial program 36.5%

      \[{\left(x + 1\right)}^{\left(\frac{1}{n}\right)} - {x}^{\left(\frac{1}{n}\right)} \]

   2. Taylor expanded in n around inf

      \[\leadsto \color{blue}{\frac{\log \left(1 + x\right) - \log x}{n}} \]
   3. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{\log \left(1 + x\right) - \log x}{\color{blue}{n}} \]

      2. diff-log N/A

         \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

      3. lower-log.f64 N/A

         \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

      4. +-commutative N/A

         \[\leadsto \frac{\log \left(\frac{x + 1}{x}\right)}{n} \]

      5. lower-/.f64 N/A

         \[\leadsto \frac{\log \left(\frac{x + 1}{x}\right)}{n} \]

      6. +-commutative N/A

         \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

      7. lower-+.f64 38.3

         \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

   4. Applied rewrites 38.3%

      \[\leadsto \color{blue}{\frac{\log \left(\frac{1 + x}{x}\right)}{n}} \]

   5. Taylor expanded in x around inf

      \[\leadsto \frac{\frac{1}{x}}{n} \]
   6. Step-by-step derivation

      1. lower-/.f64 55.2

         \[\leadsto \frac{\frac{1}{x}}{n} \]

   7. Applied rewrites 55.2%

      \[\leadsto \frac{\frac{1}{x}}{n} \]

   if 3.2000000000000001e31 < x

   1. Initial program 71.2%

      \[{\left(x + 1\right)}^{\left(\frac{1}{n}\right)} - {x}^{\left(\frac{1}{n}\right)} \]

   2. Taylor expanded in n around inf

      \[\leadsto \color{blue}{\frac{\log \left(1 + x\right) - \log x}{n}} \]
   3. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{\log \left(1 + x\right) - \log x}{\color{blue}{n}} \]

      2. diff-log N/A

         \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

      3. lower-log.f64 N/A

         \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

      4. +-commutative N/A

         \[\leadsto \frac{\log \left(\frac{x + 1}{x}\right)}{n} \]

      5. lower-/.f64 N/A

         \[\leadsto \frac{\log \left(\frac{x + 1}{x}\right)}{n} \]

      6. +-commutative N/A

         \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

      7. lower-+.f64 71.2

         \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

   4. Applied rewrites 71.2%

      \[\leadsto \color{blue}{\frac{\log \left(\frac{1 + x}{x}\right)}{n}} \]

   5. Taylor expanded in x around inf

      \[\leadsto \frac{\log 1}{n} \]
   6. Step-by-step derivation

   7. Applied rewrites 71.2%

      \[\leadsto \frac{\log 1}{n} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 12: 59.0% accurate, 2.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq 0.55:\\ \;\;\;\;\frac{-\log x}{n}\\ \mathbf{elif}\;x \leq 3.2 \cdot 10^{+31}:\\ \;\;\;\;\frac{\frac{1}{x}}{n}\\ \mathbf{else}:\\ \;\;\;\;\frac{\log 1}{n}\\ \end{array} \end{array} \]

FPCore

(FPCore (x n)
 :precision binary64
 (if (<= x 0.55)
   (/ (- (log x)) n)
   (if (<= x 3.2e+31) (/ (/ 1.0 x) n) (/ (log 1.0) n))))

C

double code(double x, double n) {
	double tmp;
	if (x <= 0.55) {
		tmp = -log(x) / n;
	} else if (x <= 3.2e+31) {
		tmp = (1.0 / x) / n;
	} else {
		tmp = log(1.0) / n;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x, n)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: n
    real(8) :: tmp
    if (x <= 0.55d0) then
        tmp = -log(x) / n
    else if (x <= 3.2d+31) then
        tmp = (1.0d0 / x) / n
    else
        tmp = log(1.0d0) / n
    end if
    code = tmp
end function

Java

public static double code(double x, double n) {
	double tmp;
	if (x <= 0.55) {
		tmp = -Math.log(x) / n;
	} else if (x <= 3.2e+31) {
		tmp = (1.0 / x) / n;
	} else {
		tmp = Math.log(1.0) / n;
	}
	return tmp;
}

Python

def code(x, n):
	tmp = 0
	if x <= 0.55:
		tmp = -math.log(x) / n
	elif x <= 3.2e+31:
		tmp = (1.0 / x) / n
	else:
		tmp = math.log(1.0) / n
	return tmp

Julia

function code(x, n)
	tmp = 0.0
	if (x <= 0.55)
		tmp = Float64(Float64(-log(x)) / n);
	elseif (x <= 3.2e+31)
		tmp = Float64(Float64(1.0 / x) / n);
	else
		tmp = Float64(log(1.0) / n);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, n)
	tmp = 0.0;
	if (x <= 0.55)
		tmp = -log(x) / n;
	elseif (x <= 3.2e+31)
		tmp = (1.0 / x) / n;
	else
		tmp = log(1.0) / n;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, n_] := If[LessEqual[x, 0.55], N[((-N[Log[x], $MachinePrecision]) / n), $MachinePrecision], If[LessEqual[x, 3.2e+31], N[(N[(1.0 / x), $MachinePrecision] / n), $MachinePrecision], N[(N[Log[1.0], $MachinePrecision] / n), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if x < 0.55000000000000004

   1. Initial program 43.7%

      \[{\left(x + 1\right)}^{\left(\frac{1}{n}\right)} - {x}^{\left(\frac{1}{n}\right)} \]

   2. Taylor expanded in n around inf

      \[\leadsto \color{blue}{\frac{\log \left(1 + x\right) - \log x}{n}} \]
   3. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{\log \left(1 + x\right) - \log x}{\color{blue}{n}} \]

      2. diff-log N/A

         \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

      3. lower-log.f64 N/A

         \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

      4. +-commutative N/A

         \[\leadsto \frac{\log \left(\frac{x + 1}{x}\right)}{n} \]

      5. lower-/.f64 N/A

         \[\leadsto \frac{\log \left(\frac{x + 1}{x}\right)}{n} \]

      6. +-commutative N/A

         \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

      7. lower-+.f64 51.9

         \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

   4. Applied rewrites 51.9%

      \[\leadsto \color{blue}{\frac{\log \left(\frac{1 + x}{x}\right)}{n}} \]

   5. Taylor expanded in x around 0

      \[\leadsto \frac{-1 \cdot \log x}{n} \]
   6. Step-by-step derivation

      1. log-pow-rev N/A

         \[\leadsto \frac{\log \left({x}^{-1}\right)}{n} \]

      2. inv-pow N/A

         \[\leadsto \frac{\log \left(\frac{1}{x}\right)}{n} \]

      3. neg-log N/A

         \[\leadsto \frac{\mathsf{neg}\left(\log x\right)}{n} \]

      4. lift-neg.f64 N/A

         \[\leadsto \frac{-\log x}{n} \]

      5. lift-log.f64 51.1

         \[\leadsto \frac{-\log x}{n} \]

   7. Applied rewrites 51.1%

      \[\leadsto \frac{-\log x}{n} \]

   if 0.55000000000000004 < x < 3.2000000000000001e31

   1. Initial program 36.2%

      \[{\left(x + 1\right)}^{\left(\frac{1}{n}\right)} - {x}^{\left(\frac{1}{n}\right)} \]

   2. Taylor expanded in n around inf

      \[\leadsto \color{blue}{\frac{\log \left(1 + x\right) - \log x}{n}} \]
   3. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{\log \left(1 + x\right) - \log x}{\color{blue}{n}} \]

      2. diff-log N/A

         \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

      3. lower-log.f64 N/A

         \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

      4. +-commutative N/A

         \[\leadsto \frac{\log \left(\frac{x + 1}{x}\right)}{n} \]

      5. lower-/.f64 N/A

         \[\leadsto \frac{\log \left(\frac{x + 1}{x}\right)}{n} \]

      6. +-commutative N/A

         \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

      7. lower-+.f64 38.9

         \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

   4. Applied rewrites 38.9%

      \[\leadsto \color{blue}{\frac{\log \left(\frac{1 + x}{x}\right)}{n}} \]

   5. Taylor expanded in x around inf

      \[\leadsto \frac{\frac{1}{x}}{n} \]
   6. Step-by-step derivation

      1. lower-/.f64 54.9

         \[\leadsto \frac{\frac{1}{x}}{n} \]

   7. Applied rewrites 54.9%

      \[\leadsto \frac{\frac{1}{x}}{n} \]

   if 3.2000000000000001e31 < x

   1. Initial program 71.2%

      \[{\left(x + 1\right)}^{\left(\frac{1}{n}\right)} - {x}^{\left(\frac{1}{n}\right)} \]

   2. Taylor expanded in n around inf

      \[\leadsto \color{blue}{\frac{\log \left(1 + x\right) - \log x}{n}} \]
   3. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{\log \left(1 + x\right) - \log x}{\color{blue}{n}} \]

      2. diff-log N/A

         \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

      3. lower-log.f64 N/A

         \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

      4. +-commutative N/A

         \[\leadsto \frac{\log \left(\frac{x + 1}{x}\right)}{n} \]

      5. lower-/.f64 N/A

         \[\leadsto \frac{\log \left(\frac{x + 1}{x}\right)}{n} \]

      6. +-commutative N/A

         \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

      7. lower-+.f64 71.2

         \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

   4. Applied rewrites 71.2%

      \[\leadsto \color{blue}{\frac{\log \left(\frac{1 + x}{x}\right)}{n}} \]

   5. Taylor expanded in x around inf

      \[\leadsto \frac{\log 1}{n} \]
   6. Step-by-step derivation

   7. Applied rewrites 71.2%

      \[\leadsto \frac{\log 1}{n} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 13: 46.4% accurate, 2.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\frac{1}{n} \leq -50:\\ \;\;\;\;\frac{\log 1}{n}\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{1}{n}}{x}\\ \end{array} \end{array} \]

FPCore

(FPCore (x n)
 :precision binary64
 (if (<= (/ 1.0 n) -50.0) (/ (log 1.0) n) (/ (/ 1.0 n) x)))

C

double code(double x, double n) {
	double tmp;
	if ((1.0 / n) <= -50.0) {
		tmp = log(1.0) / n;
	} else {
		tmp = (1.0 / n) / x;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x, n)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: n
    real(8) :: tmp
    if ((1.0d0 / n) <= (-50.0d0)) then
        tmp = log(1.0d0) / n
    else
        tmp = (1.0d0 / n) / x
    end if
    code = tmp
end function

Java

public static double code(double x, double n) {
	double tmp;
	if ((1.0 / n) <= -50.0) {
		tmp = Math.log(1.0) / n;
	} else {
		tmp = (1.0 / n) / x;
	}
	return tmp;
}

Python

def code(x, n):
	tmp = 0
	if (1.0 / n) <= -50.0:
		tmp = math.log(1.0) / n
	else:
		tmp = (1.0 / n) / x
	return tmp

Julia

function code(x, n)
	tmp = 0.0
	if (Float64(1.0 / n) <= -50.0)
		tmp = Float64(log(1.0) / n);
	else
		tmp = Float64(Float64(1.0 / n) / x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, n)
	tmp = 0.0;
	if ((1.0 / n) <= -50.0)
		tmp = log(1.0) / n;
	else
		tmp = (1.0 / n) / x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, n_] := If[LessEqual[N[(1.0 / n), $MachinePrecision], -50.0], N[(N[Log[1.0], $MachinePrecision] / n), $MachinePrecision], N[(N[(1.0 / n), $MachinePrecision] / x), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 #s(literal 1 binary64) n) < -50

   1. Initial program 100.0%

      \[{\left(x + 1\right)}^{\left(\frac{1}{n}\right)} - {x}^{\left(\frac{1}{n}\right)} \]

   2. Taylor expanded in n around inf

      \[\leadsto \color{blue}{\frac{\log \left(1 + x\right) - \log x}{n}} \]
   3. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{\log \left(1 + x\right) - \log x}{\color{blue}{n}} \]

      2. diff-log N/A

         \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

      3. lower-log.f64 N/A

         \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

      4. +-commutative N/A

         \[\leadsto \frac{\log \left(\frac{x + 1}{x}\right)}{n} \]

      5. lower-/.f64 N/A

         \[\leadsto \frac{\log \left(\frac{x + 1}{x}\right)}{n} \]

      6. +-commutative N/A

         \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

      7. lower-+.f64 49.9

         \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

   4. Applied rewrites 49.9%

      \[\leadsto \color{blue}{\frac{\log \left(\frac{1 + x}{x}\right)}{n}} \]

   5. Taylor expanded in x around inf

      \[\leadsto \frac{\log 1}{n} \]
   6. Step-by-step derivation

   7. Applied rewrites 49.8%

      \[\leadsto \frac{\log 1}{n} \]

   if -50 < (/.f64 #s(literal 1 binary64) n)

   1. Initial program 35.0%

      \[{\left(x + 1\right)}^{\left(\frac{1}{n}\right)} - {x}^{\left(\frac{1}{n}\right)} \]

   2. Taylor expanded in n around inf

      \[\leadsto \color{blue}{\frac{\log \left(1 + x\right) - \log x}{n}} \]
   3. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{\log \left(1 + x\right) - \log x}{\color{blue}{n}} \]

      2. diff-log N/A

         \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

      3. lower-log.f64 N/A

         \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

      4. +-commutative N/A

         \[\leadsto \frac{\log \left(\frac{x + 1}{x}\right)}{n} \]

      5. lower-/.f64 N/A

         \[\leadsto \frac{\log \left(\frac{x + 1}{x}\right)}{n} \]

      6. +-commutative N/A

         \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

      7. lower-+.f64 62.5

         \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

   4. Applied rewrites 62.5%

      \[\leadsto \color{blue}{\frac{\log \left(\frac{1 + x}{x}\right)}{n}} \]

   5. Taylor expanded in x around inf

      \[\leadsto \frac{\left(\frac{\frac{1}{3}}{n \cdot {x}^{2}} + \frac{1}{n}\right) - \frac{\frac{1}{2}}{n \cdot x}}{\color{blue}{x}} \]
   6. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{\left(\frac{\frac{1}{3}}{n \cdot {x}^{2}} + \frac{1}{n}\right) - \frac{\frac{1}{2}}{n \cdot x}}{x} \]

      2. lower--.f64 N/A

         \[\leadsto \frac{\left(\frac{\frac{1}{3}}{n \cdot {x}^{2}} + \frac{1}{n}\right) - \frac{\frac{1}{2}}{n \cdot x}}{x} \]

      3. lower-+.f64 N/A

         \[\leadsto \frac{\left(\frac{\frac{1}{3}}{n \cdot {x}^{2}} + \frac{1}{n}\right) - \frac{\frac{1}{2}}{n \cdot x}}{x} \]

      4. lower-/.f64 N/A

         \[\leadsto \frac{\left(\frac{\frac{1}{3}}{n \cdot {x}^{2}} + \frac{1}{n}\right) - \frac{\frac{1}{2}}{n \cdot x}}{x} \]

      5. lower-*.f64 N/A

         \[\leadsto \frac{\left(\frac{\frac{1}{3}}{n \cdot {x}^{2}} + \frac{1}{n}\right) - \frac{\frac{1}{2}}{n \cdot x}}{x} \]

      6. unpow2 N/A

         \[\leadsto \frac{\left(\frac{\frac{1}{3}}{n \cdot \left(x \cdot x\right)} + \frac{1}{n}\right) - \frac{\frac{1}{2}}{n \cdot x}}{x} \]

      7. lower-*.f64 N/A

         \[\leadsto \frac{\left(\frac{\frac{1}{3}}{n \cdot \left(x \cdot x\right)} + \frac{1}{n}\right) - \frac{\frac{1}{2}}{n \cdot x}}{x} \]

      8. lift-/.f64 N/A

         \[\leadsto \frac{\left(\frac{\frac{1}{3}}{n \cdot \left(x \cdot x\right)} + \frac{1}{n}\right) - \frac{\frac{1}{2}}{n \cdot x}}{x} \]

      9. lower-/.f64 N/A

         \[\leadsto \frac{\left(\frac{\frac{1}{3}}{n \cdot \left(x \cdot x\right)} + \frac{1}{n}\right) - \frac{\frac{1}{2}}{n \cdot x}}{x} \]

      10. lift-*.f64 42.3

          \[\leadsto \frac{\left(\frac{0.3333333333333333}{n \cdot \left(x \cdot x\right)} + \frac{1}{n}\right) - \frac{0.5}{n \cdot x}}{x} \]

   7. Applied rewrites 42.3%

      \[\leadsto \frac{\left(\frac{0.3333333333333333}{n \cdot \left(x \cdot x\right)} + \frac{1}{n}\right) - \frac{0.5}{n \cdot x}}{\color{blue}{x}} \]

   8. Taylor expanded in x around inf

      \[\leadsto \frac{\frac{1}{n}}{x} \]
   9. Step-by-step derivation

      1. lift-/.f64 45.0

         \[\leadsto \frac{\frac{1}{n}}{x} \]

   10. Applied rewrites 45.0%

       \[\leadsto \frac{\frac{1}{n}}{x} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 14: 40.7% accurate, 5.8× speedup

Math

\[\begin{array}{l} \\ \frac{\frac{1}{n}}{x} \end{array} \]

FPCore

(FPCore (x n) :precision binary64 (/ (/ 1.0 n) x))

C

double code(double x, double n) {
	return (1.0 / n) / x;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x, n)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: n
    code = (1.0d0 / n) / x
end function

Java

public static double code(double x, double n) {
	return (1.0 / n) / x;
}

Python

def code(x, n):
	return (1.0 / n) / x

Julia

function code(x, n)
	return Float64(Float64(1.0 / n) / x)
end

MATLAB

function tmp = code(x, n)
	tmp = (1.0 / n) / x;
end

Wolfram

code[x_, n_] := N[(N[(1.0 / n), $MachinePrecision] / x), $MachinePrecision]

Derivation

1. Initial program 54.0%

   \[{\left(x + 1\right)}^{\left(\frac{1}{n}\right)} - {x}^{\left(\frac{1}{n}\right)} \]

2. Taylor expanded in n around inf

   \[\leadsto \color{blue}{\frac{\log \left(1 + x\right) - \log x}{n}} \]
3. Step-by-step derivation

   1. lower-/.f64 N/A

      \[\leadsto \frac{\log \left(1 + x\right) - \log x}{\color{blue}{n}} \]

   2. diff-log N/A

      \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

   3. lower-log.f64 N/A

      \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

   4. +-commutative N/A

      \[\leadsto \frac{\log \left(\frac{x + 1}{x}\right)}{n} \]

   5. lower-/.f64 N/A

      \[\leadsto \frac{\log \left(\frac{x + 1}{x}\right)}{n} \]

   6. +-commutative N/A

      \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

   7. lower-+.f64 58.8

      \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

4. Applied rewrites 58.8%

   \[\leadsto \color{blue}{\frac{\log \left(\frac{1 + x}{x}\right)}{n}} \]

5. Taylor expanded in x around inf

   \[\leadsto \frac{\left(\frac{\frac{1}{3}}{n \cdot {x}^{2}} + \frac{1}{n}\right) - \frac{\frac{1}{2}}{n \cdot x}}{\color{blue}{x}} \]
6. Step-by-step derivation

   1. lower-/.f64 N/A

      \[\leadsto \frac{\left(\frac{\frac{1}{3}}{n \cdot {x}^{2}} + \frac{1}{n}\right) - \frac{\frac{1}{2}}{n \cdot x}}{x} \]

   2. lower--.f64 N/A

      \[\leadsto \frac{\left(\frac{\frac{1}{3}}{n \cdot {x}^{2}} + \frac{1}{n}\right) - \frac{\frac{1}{2}}{n \cdot x}}{x} \]

   3. lower-+.f64 N/A

      \[\leadsto \frac{\left(\frac{\frac{1}{3}}{n \cdot {x}^{2}} + \frac{1}{n}\right) - \frac{\frac{1}{2}}{n \cdot x}}{x} \]

   4. lower-/.f64 N/A

      \[\leadsto \frac{\left(\frac{\frac{1}{3}}{n \cdot {x}^{2}} + \frac{1}{n}\right) - \frac{\frac{1}{2}}{n \cdot x}}{x} \]

   5. lower-*.f64 N/A

      \[\leadsto \frac{\left(\frac{\frac{1}{3}}{n \cdot {x}^{2}} + \frac{1}{n}\right) - \frac{\frac{1}{2}}{n \cdot x}}{x} \]

   6. unpow2 N/A

      \[\leadsto \frac{\left(\frac{\frac{1}{3}}{n \cdot \left(x \cdot x\right)} + \frac{1}{n}\right) - \frac{\frac{1}{2}}{n \cdot x}}{x} \]

   7. lower-*.f64 N/A

      \[\leadsto \frac{\left(\frac{\frac{1}{3}}{n \cdot \left(x \cdot x\right)} + \frac{1}{n}\right) - \frac{\frac{1}{2}}{n \cdot x}}{x} \]

   8. lift-/.f64 N/A

      \[\leadsto \frac{\left(\frac{\frac{1}{3}}{n \cdot \left(x \cdot x\right)} + \frac{1}{n}\right) - \frac{\frac{1}{2}}{n \cdot x}}{x} \]

   9. lower-/.f64 N/A

      \[\leadsto \frac{\left(\frac{\frac{1}{3}}{n \cdot \left(x \cdot x\right)} + \frac{1}{n}\right) - \frac{\frac{1}{2}}{n \cdot x}}{x} \]

   10. lift-*.f64 35.1

       \[\leadsto \frac{\left(\frac{0.3333333333333333}{n \cdot \left(x \cdot x\right)} + \frac{1}{n}\right) - \frac{0.5}{n \cdot x}}{x} \]

7. Applied rewrites 35.1%

   \[\leadsto \frac{\left(\frac{0.3333333333333333}{n \cdot \left(x \cdot x\right)} + \frac{1}{n}\right) - \frac{0.5}{n \cdot x}}{\color{blue}{x}} \]

8. Taylor expanded in x around inf

   \[\leadsto \frac{\frac{1}{n}}{x} \]
9. Step-by-step derivation

   1. lift-/.f64 40.7

      \[\leadsto \frac{\frac{1}{n}}{x} \]

10. Applied rewrites 40.7%

    \[\leadsto \frac{\frac{1}{n}}{x} \]
11. Add Preprocessing

Alternative 15: 40.2% accurate, 6.1× speedup

Math

\[\begin{array}{l} \\ \frac{1}{n \cdot x} \end{array} \]

FPCore

(FPCore (x n) :precision binary64 (/ 1.0 (* n x)))

C

double code(double x, double n) {
	return 1.0 / (n * x);
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x, n)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: n
    code = 1.0d0 / (n * x)
end function

Java

public static double code(double x, double n) {
	return 1.0 / (n * x);
}

Python

def code(x, n):
	return 1.0 / (n * x)

Julia

function code(x, n)
	return Float64(1.0 / Float64(n * x))
end

MATLAB

function tmp = code(x, n)
	tmp = 1.0 / (n * x);
end

Wolfram

code[x_, n_] := N[(1.0 / N[(n * x), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 54.0%

   \[{\left(x + 1\right)}^{\left(\frac{1}{n}\right)} - {x}^{\left(\frac{1}{n}\right)} \]

2. Taylor expanded in x around inf

   \[\leadsto \color{blue}{\frac{e^{-1 \cdot \frac{\log \left(\frac{1}{x}\right)}{n}}}{n \cdot x}} \]
3. Step-by-step derivation

   1. lower-/.f64 N/A

      \[\leadsto \frac{e^{-1 \cdot \frac{\log \left(\frac{1}{x}\right)}{n}}}{\color{blue}{n \cdot x}} \]

   2. lower-exp.f64 N/A

      \[\leadsto \frac{e^{-1 \cdot \frac{\log \left(\frac{1}{x}\right)}{n}}}{\color{blue}{n} \cdot x} \]

   3. mul-1-neg N/A

      \[\leadsto \frac{e^{\mathsf{neg}\left(\frac{\log \left(\frac{1}{x}\right)}{n}\right)}}{n \cdot x} \]

   4. log-rec N/A

      \[\leadsto \frac{e^{\mathsf{neg}\left(\frac{\mathsf{neg}\left(\log x\right)}{n}\right)}}{n \cdot x} \]

   5. mul-1-neg N/A

      \[\leadsto \frac{e^{\mathsf{neg}\left(\frac{-1 \cdot \log x}{n}\right)}}{n \cdot x} \]

   6. lower-neg.f64 N/A

      \[\leadsto \frac{e^{-\frac{-1 \cdot \log x}{n}}}{n \cdot x} \]

   7. lower-/.f64 N/A

      \[\leadsto \frac{e^{-\frac{-1 \cdot \log x}{n}}}{n \cdot x} \]

   8. mul-1-neg N/A

      \[\leadsto \frac{e^{-\frac{\mathsf{neg}\left(\log x\right)}{n}}}{n \cdot x} \]

   9. lower-neg.f64 N/A

      \[\leadsto \frac{e^{-\frac{-\log x}{n}}}{n \cdot x} \]

   10. lower-log.f64 N/A

       \[\leadsto \frac{e^{-\frac{-\log x}{n}}}{n \cdot x} \]

   11. lower-*.f64 58.2

       \[\leadsto \frac{e^{-\frac{-\log x}{n}}}{n \cdot \color{blue}{x}} \]

4. Applied rewrites 58.2%

   \[\leadsto \color{blue}{\frac{e^{-\frac{-\log x}{n}}}{n \cdot x}} \]

5. Taylor expanded in n around inf

   \[\leadsto \frac{1}{\color{blue}{n} \cdot x} \]
6. Step-by-step derivation

7. Applied rewrites 40.2%

   \[\leadsto \frac{1}{\color{blue}{n} \cdot x} \]
8. Add Preprocessing

Alternative 16: 4.5% accurate, 10.3× speedup

Math

\[\begin{array}{l} \\ \frac{x}{n} \end{array} \]

FPCore

(FPCore (x n) :precision binary64 (/ x n))

C

double code(double x, double n) {
	return x / n;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x, n)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: n
    code = x / n
end function

Java

public static double code(double x, double n) {
	return x / n;
}

Python

def code(x, n):
	return x / n

Julia

function code(x, n)
	return Float64(x / n)
end

MATLAB

function tmp = code(x, n)
	tmp = x / n;
end

Wolfram

code[x_, n_] := N[(x / n), $MachinePrecision]

Derivation

1. Initial program 54.0%

   \[{\left(x + 1\right)}^{\left(\frac{1}{n}\right)} - {x}^{\left(\frac{1}{n}\right)} \]

2. Taylor expanded in n around inf

   \[\leadsto \color{blue}{\frac{\log \left(1 + x\right) - \log x}{n}} \]
3. Step-by-step derivation

   1. lower-/.f64 N/A

      \[\leadsto \frac{\log \left(1 + x\right) - \log x}{\color{blue}{n}} \]

   2. diff-log N/A

      \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

   3. lower-log.f64 N/A

      \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

   4. +-commutative N/A

      \[\leadsto \frac{\log \left(\frac{x + 1}{x}\right)}{n} \]

   5. lower-/.f64 N/A

      \[\leadsto \frac{\log \left(\frac{x + 1}{x}\right)}{n} \]

   6. +-commutative N/A

      \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

   7. lower-+.f64 58.8

      \[\leadsto \frac{\log \left(\frac{1 + x}{x}\right)}{n} \]

4. Applied rewrites 58.8%

   \[\leadsto \color{blue}{\frac{\log \left(\frac{1 + x}{x}\right)}{n}} \]

5. Taylor expanded in x around 0

   \[\leadsto -1 \cdot \frac{\log x}{n} + \color{blue}{\frac{x}{n}} \]
6. Step-by-step derivation

   1. lower-fma.f64 N/A

      \[\leadsto \mathsf{fma}\left(-1, \frac{\log x}{\color{blue}{n}}, \frac{x}{n}\right) \]

   2. lift-log.f64 N/A

      \[\leadsto \mathsf{fma}\left(-1, \frac{\log x}{n}, \frac{x}{n}\right) \]

   3. lift-/.f64 N/A

      \[\leadsto \mathsf{fma}\left(-1, \frac{\log x}{n}, \frac{x}{n}\right) \]

   4. lower-/.f64 31.0

      \[\leadsto \mathsf{fma}\left(-1, \frac{\log x}{n}, \frac{x}{n}\right) \]

7. Applied rewrites 31.0%

   \[\leadsto \mathsf{fma}\left(-1, \color{blue}{\frac{\log x}{n}}, \frac{x}{n}\right) \]

8. Taylor expanded in x around inf

   \[\leadsto \frac{x}{n} \]
9. Step-by-step derivation

   1. lift-/.f64 4.5

      \[\leadsto \frac{x}{n} \]

10. Applied rewrites 4.5%

    \[\leadsto \frac{x}{n} \]
11. Add Preprocessing

Reproduce

herbie shell --seed 2025122 
(FPCore (x n)
  :name "2nthrt (problem 3.4.6)"
  :precision binary64
  (- (pow (+ x 1.0) (/ 1.0 n)) (pow x (/ 1.0 n))))