2nthrt (problem 3.4.6)

Percentage Accurate: 53.5% → 91.0%

Time: 19.8s

Alternatives: 11

Speedup: 2.5×

• could not determine a ground truth

  1. x = -1.4760332064509476e+299
  2. n = -1.4569639930012854e+155

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: 53.5% 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: 91.0% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := -\log x\\ t_1 := \frac{\log x}{n}\\ \mathbf{if}\;x \leq 5.2 \cdot 10^{-41}:\\ \;\;\;\;-\mathsf{expm1}\left(t\_1\right)\\ \mathbf{elif}\;x \leq 2200:\\ \;\;\;\;-\frac{\left(\left(-\frac{\frac{\mathsf{fma}\left(-1, t\_0, \mathsf{fma}\left(0.5, \frac{0.9166666666666666 + 0.5 \cdot t\_0}{\left(x \cdot x\right) \cdot x}, \mathsf{fma}\left(0.5, \frac{1 + t\_0}{x}, 0.5 \cdot \frac{-0.6666666666666666 \cdot t\_0 - 1}{x \cdot x}\right)\right)\right)}{x}}{n}\right) + \left(-\log \left(x - -1\right)\right)\right) - t\_0}{n}\\ \mathbf{else}:\\ \;\;\;\;\frac{e^{t\_1}}{n \cdot x}\\ \end{array} \end{array} \]

FPCore

(FPCore (x n)
 :precision binary64
 (let* ((t_0 (- (log x))) (t_1 (/ (log x) n)))
   (if (<= x 5.2e-41)
     (- (expm1 t_1))
     (if (<= x 2200.0)
       (-
        (/
         (-
          (+
           (-
            (/
             (/
              (fma
               -1.0
               t_0
               (fma
                0.5
                (/ (+ 0.9166666666666666 (* 0.5 t_0)) (* (* x x) x))
                (fma
                 0.5
                 (/ (+ 1.0 t_0) x)
                 (* 0.5 (/ (- (* -0.6666666666666666 t_0) 1.0) (* x x))))))
              x)
             n))
           (- (log (- x -1.0))))
          t_0)
         n))
       (/ (exp t_1) (* n x))))))

C

double code(double x, double n) {
	double t_0 = -log(x);
	double t_1 = log(x) / n;
	double tmp;
	if (x <= 5.2e-41) {
		tmp = -expm1(t_1);
	} else if (x <= 2200.0) {
		tmp = -(((-((fma(-1.0, t_0, fma(0.5, ((0.9166666666666666 + (0.5 * t_0)) / ((x * x) * x)), fma(0.5, ((1.0 + t_0) / x), (0.5 * (((-0.6666666666666666 * t_0) - 1.0) / (x * x)))))) / x) / n) + -log((x - -1.0))) - t_0) / n);
	} else {
		tmp = exp(t_1) / (n * x);
	}
	return tmp;
}

Julia

function code(x, n)
	t_0 = Float64(-log(x))
	t_1 = Float64(log(x) / n)
	tmp = 0.0
	if (x <= 5.2e-41)
		tmp = Float64(-expm1(t_1));
	elseif (x <= 2200.0)
		tmp = Float64(-Float64(Float64(Float64(Float64(-Float64(Float64(fma(-1.0, t_0, fma(0.5, Float64(Float64(0.9166666666666666 + Float64(0.5 * t_0)) / Float64(Float64(x * x) * x)), fma(0.5, Float64(Float64(1.0 + t_0) / x), Float64(0.5 * Float64(Float64(Float64(-0.6666666666666666 * t_0) - 1.0) / Float64(x * x)))))) / x) / n)) + Float64(-log(Float64(x - -1.0)))) - t_0) / n));
	else
		tmp = Float64(exp(t_1) / Float64(n * x));
	end
	return tmp
end

Wolfram

code[x_, n_] := Block[{t$95$0 = (-N[Log[x], $MachinePrecision])}, Block[{t$95$1 = N[(N[Log[x], $MachinePrecision] / n), $MachinePrecision]}, If[LessEqual[x, 5.2e-41], (-N[(Exp[t$95$1] - 1), $MachinePrecision]), If[LessEqual[x, 2200.0], (-N[(N[(N[((-N[(N[(N[(-1.0 * t$95$0 + N[(0.5 * N[(N[(0.9166666666666666 + N[(0.5 * t$95$0), $MachinePrecision]), $MachinePrecision] / N[(N[(x * x), $MachinePrecision] * x), $MachinePrecision]), $MachinePrecision] + N[(0.5 * N[(N[(1.0 + t$95$0), $MachinePrecision] / x), $MachinePrecision] + N[(0.5 * N[(N[(N[(-0.6666666666666666 * t$95$0), $MachinePrecision] - 1.0), $MachinePrecision] / N[(x * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / x), $MachinePrecision] / n), $MachinePrecision]) + (-N[Log[N[(x - -1.0), $MachinePrecision]], $MachinePrecision])), $MachinePrecision] - t$95$0), $MachinePrecision] / n), $MachinePrecision]), N[(N[Exp[t$95$1], $MachinePrecision] / N[(n * x), $MachinePrecision]), $MachinePrecision]]]]]

Derivation

1. Split input into 3 regimes

2. if x < 5.1999999999999999e-41

   1. Initial program 53.5%

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

   2. Taylor expanded in x around 0

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

      1. sub-negate-rev N/A

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

      2. lower-neg.f64 N/A

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

      3. lower-expm1.f64 N/A

         \[\leadsto -\mathsf{expm1}\left(\frac{\log x}{n}\right) \]

      4. lower-/.f64 N/A

         \[\leadsto -\mathsf{expm1}\left(\frac{\log x}{n}\right) \]

      5. lower-log.f64 50.6

         \[\leadsto -\mathsf{expm1}\left(\frac{\log x}{n}\right) \]

   4. Applied rewrites 50.6%

      \[\leadsto \color{blue}{-\mathsf{expm1}\left(\frac{\log x}{n}\right)} \]

   if 5.1999999999999999e-41 < x < 2200

   1. Initial program 53.5%

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

   2. Taylor expanded in n around -inf

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

      1. mul-1-neg N/A

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

      2. lower-neg.f64 N/A

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

      3. lower-/.f64 N/A

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

   4. Applied rewrites 64.7%

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

   5. Taylor expanded in x around inf

      \[\leadsto -\frac{\left(\left(-\frac{\frac{-1 \cdot \log \left(\frac{1}{x}\right) + \left(\frac{1}{2} \cdot \frac{\frac{11}{12} + \frac{1}{2} \cdot \log \left(\frac{1}{x}\right)}{{x}^{3}} + \left(\frac{1}{2} \cdot \frac{1 + \log \left(\frac{1}{x}\right)}{x} + \frac{1}{2} \cdot \frac{\frac{-2}{3} \cdot \log \left(\frac{1}{x}\right) - 1}{{x}^{2}}\right)\right)}{x}}{n}\right) + \left(-\log \left(x - -1\right)\right)\right) - \left(-\log x\right)}{n} \]

   7. Applied rewrites 30.5%

      \[\leadsto -\frac{\left(\left(-\frac{\frac{\mathsf{fma}\left(-1, -\log x, \mathsf{fma}\left(0.5, \frac{0.9166666666666666 + 0.5 \cdot \left(-\log x\right)}{\left(x \cdot x\right) \cdot x}, \mathsf{fma}\left(0.5, \frac{1 + \left(-\log x\right)}{x}, 0.5 \cdot \frac{-0.6666666666666666 \cdot \left(-\log x\right) - 1}{x \cdot x}\right)\right)\right)}{x}}{n}\right) + \left(-\log \left(x - -1\right)\right)\right) - \left(-\log x\right)}{n} \]

   if 2200 < x

   1. Initial program 53.5%

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

   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 58.3

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

   4. Applied rewrites 58.3%

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

   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 58.3

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

   7. Applied rewrites 58.3%

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

Alternative 2: 90.1% accurate, 1.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{\log x}{n}\\ \mathbf{if}\;x \leq 0.6:\\ \;\;\;\;-\mathsf{expm1}\left(t\_0\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{e^{t\_0}}{n \cdot x}\\ \end{array} \end{array} \]

FPCore

(FPCore (x n)
 :precision binary64
 (let* ((t_0 (/ (log x) n)))
   (if (<= x 0.6) (- (expm1 t_0)) (/ (exp t_0) (* n x)))))

C

double code(double x, double n) {
	double t_0 = log(x) / n;
	double tmp;
	if (x <= 0.6) {
		tmp = -expm1(t_0);
	} else {
		tmp = exp(t_0) / (n * x);
	}
	return tmp;
}

Java

public static double code(double x, double n) {
	double t_0 = Math.log(x) / n;
	double tmp;
	if (x <= 0.6) {
		tmp = -Math.expm1(t_0);
	} else {
		tmp = Math.exp(t_0) / (n * x);
	}
	return tmp;
}

Python

def code(x, n):
	t_0 = math.log(x) / n
	tmp = 0
	if x <= 0.6:
		tmp = -math.expm1(t_0)
	else:
		tmp = math.exp(t_0) / (n * x)
	return tmp

Julia

function code(x, n)
	t_0 = Float64(log(x) / n)
	tmp = 0.0
	if (x <= 0.6)
		tmp = Float64(-expm1(t_0));
	else
		tmp = Float64(exp(t_0) / Float64(n * x));
	end
	return tmp
end

Wolfram

code[x_, n_] := Block[{t$95$0 = N[(N[Log[x], $MachinePrecision] / n), $MachinePrecision]}, If[LessEqual[x, 0.6], (-N[(Exp[t$95$0] - 1), $MachinePrecision]), N[(N[Exp[t$95$0], $MachinePrecision] / N[(n * x), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if x < 0.599999999999999978

   1. Initial program 53.5%

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

   2. Taylor expanded in x around 0

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

      1. sub-negate-rev N/A

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

      2. lower-neg.f64 N/A

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

      3. lower-expm1.f64 N/A

         \[\leadsto -\mathsf{expm1}\left(\frac{\log x}{n}\right) \]

      4. lower-/.f64 N/A

         \[\leadsto -\mathsf{expm1}\left(\frac{\log x}{n}\right) \]

      5. lower-log.f64 50.6

         \[\leadsto -\mathsf{expm1}\left(\frac{\log x}{n}\right) \]

   4. Applied rewrites 50.6%

      \[\leadsto \color{blue}{-\mathsf{expm1}\left(\frac{\log x}{n}\right)} \]

   if 0.599999999999999978 < x

   1. Initial program 53.5%

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

   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 58.3

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

   4. Applied rewrites 58.3%

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

   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 58.3

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

   7. Applied rewrites 58.3%

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

Alternative 3: 79.6% accurate, 1.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq 0.0055:\\ \;\;\;\;-\mathsf{expm1}\left(\frac{\log x}{n}\right)\\ \mathbf{elif}\;x \leq 7.4 \cdot 10^{+236}:\\ \;\;\;\;\frac{\frac{1 + \frac{\frac{0.3333333333333333}{x} - 0.5}{x}}{x}}{n}\\ \mathbf{else}:\\ \;\;\;\;\frac{-0.5}{\left(x \cdot x\right) \cdot n}\\ \end{array} \end{array} \]

FPCore

(FPCore (x n)
 :precision binary64
 (if (<= x 0.0055)
   (- (expm1 (/ (log x) n)))
   (if (<= x 7.4e+236)
     (/ (/ (+ 1.0 (/ (- (/ 0.3333333333333333 x) 0.5) x)) x) n)
     (/ -0.5 (* (* x x) n)))))

C

double code(double x, double n) {
	double tmp;
	if (x <= 0.0055) {
		tmp = -expm1((log(x) / n));
	} else if (x <= 7.4e+236) {
		tmp = ((1.0 + (((0.3333333333333333 / x) - 0.5) / x)) / x) / n;
	} else {
		tmp = -0.5 / ((x * x) * n);
	}
	return tmp;
}

Java

public static double code(double x, double n) {
	double tmp;
	if (x <= 0.0055) {
		tmp = -Math.expm1((Math.log(x) / n));
	} else if (x <= 7.4e+236) {
		tmp = ((1.0 + (((0.3333333333333333 / x) - 0.5) / x)) / x) / n;
	} else {
		tmp = -0.5 / ((x * x) * n);
	}
	return tmp;
}

Python

def code(x, n):
	tmp = 0
	if x <= 0.0055:
		tmp = -math.expm1((math.log(x) / n))
	elif x <= 7.4e+236:
		tmp = ((1.0 + (((0.3333333333333333 / x) - 0.5) / x)) / x) / n
	else:
		tmp = -0.5 / ((x * x) * n)
	return tmp

Julia

function code(x, n)
	tmp = 0.0
	if (x <= 0.0055)
		tmp = Float64(-expm1(Float64(log(x) / n)));
	elseif (x <= 7.4e+236)
		tmp = Float64(Float64(Float64(1.0 + Float64(Float64(Float64(0.3333333333333333 / x) - 0.5) / x)) / x) / n);
	else
		tmp = Float64(-0.5 / Float64(Float64(x * x) * n));
	end
	return tmp
end

Wolfram

code[x_, n_] := If[LessEqual[x, 0.0055], (-N[(Exp[N[(N[Log[x], $MachinePrecision] / n), $MachinePrecision]] - 1), $MachinePrecision]), If[LessEqual[x, 7.4e+236], N[(N[(N[(1.0 + N[(N[(N[(0.3333333333333333 / x), $MachinePrecision] - 0.5), $MachinePrecision] / x), $MachinePrecision]), $MachinePrecision] / x), $MachinePrecision] / n), $MachinePrecision], N[(-0.5 / N[(N[(x * x), $MachinePrecision] * n), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if x < 0.0054999999999999997

   1. Initial program 53.5%

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

   2. Taylor expanded in x around 0

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

      1. sub-negate-rev N/A

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

      2. lower-neg.f64 N/A

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

      3. lower-expm1.f64 N/A

         \[\leadsto -\mathsf{expm1}\left(\frac{\log x}{n}\right) \]

      4. lower-/.f64 N/A

         \[\leadsto -\mathsf{expm1}\left(\frac{\log x}{n}\right) \]

      5. lower-log.f64 50.6

         \[\leadsto -\mathsf{expm1}\left(\frac{\log x}{n}\right) \]

   4. Applied rewrites 50.6%

      \[\leadsto \color{blue}{-\mathsf{expm1}\left(\frac{\log x}{n}\right)} \]

   if 0.0054999999999999997 < x < 7.40000000000000028e236

   1. Initial program 53.5%

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

   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. add-flip N/A

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

      7. metadata-eval N/A

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

      8. lower--.f64 58.6

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

   4. Applied rewrites 58.6%

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

   5. Taylor expanded in x around -inf

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

      1. lower-*.f64 N/A

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

      2. lower-/.f64 N/A

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

      3. lower--.f64 N/A

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

      4. lower-*.f64 N/A

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

      5. lower-/.f64 N/A

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

      6. lower--.f64 N/A

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

      7. lower-*.f64 N/A

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

      8. lower-/.f64 46.7

         \[\leadsto \frac{-1 \cdot \frac{-1 \cdot \frac{0.3333333333333333 \cdot \frac{1}{x} - 0.5}{x} - 1}{x}}{n} \]

   7. Applied rewrites 46.7%

      \[\leadsto \frac{-1 \cdot \frac{-1 \cdot \frac{0.3333333333333333 \cdot \frac{1}{x} - 0.5}{x} - 1}{x}}{n} \]

   8. Taylor expanded in x around 0

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

      1. lower-/.f64 N/A

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

      2. unpow2 N/A

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

      3. lower-*.f64 46.4

         \[\leadsto \frac{-1 \cdot \frac{-1 \cdot \frac{0.3333333333333333}{x \cdot x} - 1}{x}}{n} \]

   10. Applied rewrites 46.4%

       \[\leadsto \frac{-1 \cdot \frac{-1 \cdot \frac{0.3333333333333333}{x \cdot x} - 1}{x}}{n} \]

   11. Taylor expanded in x around inf

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

       1. lower-/.f64 N/A

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

       2. associate--l+ N/A

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

       3. pow2 N/A

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

       4. associate-/r* N/A

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

       5. mult-flip-rev N/A

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

       6. mult-flip-rev N/A

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

       7. div-sub N/A

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

       8. lower-+.f64 N/A

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

       9. lower-/.f64 N/A

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

       10. lower--.f64 N/A

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

       11. mult-flip-rev N/A

           \[\leadsto \frac{\frac{1 + \frac{\frac{\frac{1}{3}}{x} - \frac{1}{2}}{x}}{x}}{n} \]

       12. lower-/.f64 46.7

           \[\leadsto \frac{\frac{1 + \frac{\frac{0.3333333333333333}{x} - 0.5}{x}}{x}}{n} \]

   13. Applied rewrites 46.7%

       \[\leadsto \frac{\frac{1 + \frac{\frac{0.3333333333333333}{x} - 0.5}{x}}{x}}{n} \]

   if 7.40000000000000028e236 < x

   1. Initial program 53.5%

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

   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. add-flip N/A

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

      7. metadata-eval N/A

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

      8. lower--.f64 58.6

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

   4. Applied rewrites 58.6%

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

   5. Taylor expanded in x around inf

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

      1. lower-/.f64 N/A

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

      2. lift-*.f64 40.6

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

   7. Applied rewrites 40.6%

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

   8. Taylor expanded in x around inf

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

      1. lower-/.f64 N/A

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

      2. lower--.f64 N/A

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

      3. lift-/.f64 N/A

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

      4. mult-flip-rev N/A

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

      5. lower-/.f64 N/A

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

      6. lift-*.f64 28.9

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

   10. Applied rewrites 28.9%

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

   11. Taylor expanded in x around 0

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

       1. lower-/.f64 N/A

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

       2. *-commutative N/A

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

       3. lower-*.f64 N/A

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

       4. pow2 N/A

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

       5. lift-*.f64 22.8

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

   13. Applied rewrites 22.8%

       \[\leadsto \frac{-0.5}{\left(x \cdot x\right) \cdot \color{blue}{n}} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 4: 79.5% accurate, 1.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq 1:\\ \;\;\;\;-\mathsf{expm1}\left(\frac{\log x}{n}\right)\\ \mathbf{elif}\;x \leq 7.4 \cdot 10^{+236}:\\ \;\;\;\;\frac{\frac{1}{n} - \frac{0.5}{n \cdot x}}{x}\\ \mathbf{else}:\\ \;\;\;\;\frac{-0.5}{\left(x \cdot x\right) \cdot n}\\ \end{array} \end{array} \]

FPCore

(FPCore (x n)
 :precision binary64
 (if (<= x 1.0)
   (- (expm1 (/ (log x) n)))
   (if (<= x 7.4e+236)
     (/ (- (/ 1.0 n) (/ 0.5 (* n x))) x)
     (/ -0.5 (* (* x x) n)))))

C

double code(double x, double n) {
	double tmp;
	if (x <= 1.0) {
		tmp = -expm1((log(x) / n));
	} else if (x <= 7.4e+236) {
		tmp = ((1.0 / n) - (0.5 / (n * x))) / x;
	} else {
		tmp = -0.5 / ((x * x) * n);
	}
	return tmp;
}

Java

public static double code(double x, double n) {
	double tmp;
	if (x <= 1.0) {
		tmp = -Math.expm1((Math.log(x) / n));
	} else if (x <= 7.4e+236) {
		tmp = ((1.0 / n) - (0.5 / (n * x))) / x;
	} else {
		tmp = -0.5 / ((x * x) * n);
	}
	return tmp;
}

Python

def code(x, n):
	tmp = 0
	if x <= 1.0:
		tmp = -math.expm1((math.log(x) / n))
	elif x <= 7.4e+236:
		tmp = ((1.0 / n) - (0.5 / (n * x))) / x
	else:
		tmp = -0.5 / ((x * x) * n)
	return tmp

Julia

function code(x, n)
	tmp = 0.0
	if (x <= 1.0)
		tmp = Float64(-expm1(Float64(log(x) / n)));
	elseif (x <= 7.4e+236)
		tmp = Float64(Float64(Float64(1.0 / n) - Float64(0.5 / Float64(n * x))) / x);
	else
		tmp = Float64(-0.5 / Float64(Float64(x * x) * n));
	end
	return tmp
end

Wolfram

code[x_, n_] := If[LessEqual[x, 1.0], (-N[(Exp[N[(N[Log[x], $MachinePrecision] / n), $MachinePrecision]] - 1), $MachinePrecision]), If[LessEqual[x, 7.4e+236], N[(N[(N[(1.0 / n), $MachinePrecision] - N[(0.5 / N[(n * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / x), $MachinePrecision], N[(-0.5 / N[(N[(x * x), $MachinePrecision] * n), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if x < 1

   1. Initial program 53.5%

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

   2. Taylor expanded in x around 0

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

      1. sub-negate-rev N/A

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

      2. lower-neg.f64 N/A

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

      3. lower-expm1.f64 N/A

         \[\leadsto -\mathsf{expm1}\left(\frac{\log x}{n}\right) \]

      4. lower-/.f64 N/A

         \[\leadsto -\mathsf{expm1}\left(\frac{\log x}{n}\right) \]

      5. lower-log.f64 50.6

         \[\leadsto -\mathsf{expm1}\left(\frac{\log x}{n}\right) \]

   4. Applied rewrites 50.6%

      \[\leadsto \color{blue}{-\mathsf{expm1}\left(\frac{\log x}{n}\right)} \]

   if 1 < x < 7.40000000000000028e236

   1. Initial program 53.5%

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

   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. add-flip N/A

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

      7. metadata-eval N/A

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

      8. lower--.f64 58.6

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

   4. Applied rewrites 58.6%

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

   5. Taylor expanded in x around inf

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

      1. lower-/.f64 N/A

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

      2. lift-*.f64 40.6

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

   7. Applied rewrites 40.6%

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

   8. Taylor expanded in x around inf

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

      1. lower-/.f64 N/A

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

      2. lower--.f64 N/A

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

      3. lift-/.f64 N/A

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

      4. mult-flip-rev N/A

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

      5. lower-/.f64 N/A

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

      6. lift-*.f64 28.9

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

   10. Applied rewrites 28.9%

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

   if 7.40000000000000028e236 < x

   1. Initial program 53.5%

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

   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. add-flip N/A

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

      7. metadata-eval N/A

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

      8. lower--.f64 58.6

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

   4. Applied rewrites 58.6%

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

   5. Taylor expanded in x around inf

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

      1. lower-/.f64 N/A

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

      2. lift-*.f64 40.6

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

   7. Applied rewrites 40.6%

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

   8. Taylor expanded in x around inf

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

      1. lower-/.f64 N/A

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

      2. lower--.f64 N/A

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

      3. lift-/.f64 N/A

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

      4. mult-flip-rev N/A

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

      5. lower-/.f64 N/A

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

      6. lift-*.f64 28.9

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

   10. Applied rewrites 28.9%

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

   11. Taylor expanded in x around 0

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

       1. lower-/.f64 N/A

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

       2. *-commutative N/A

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

       3. lower-*.f64 N/A

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

       4. pow2 N/A

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

       5. lift-*.f64 22.8

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

   13. Applied rewrites 22.8%

       \[\leadsto \frac{-0.5}{\left(x \cdot x\right) \cdot \color{blue}{n}} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 5: 74.1% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := {\left(x + 1\right)}^{\left(\frac{1}{n}\right)} - {x}^{\left(\frac{1}{n}\right)}\\ t_1 := \frac{\frac{0.3333333333333333}{\left(x \cdot x\right) \cdot x}}{n}\\ \mathbf{if}\;t\_0 \leq -\infty:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;t\_0 \leq 0.002:\\ \;\;\;\;\frac{\log \left(\frac{x - -1}{x}\right)}{n}\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x n)
 :precision binary64
 (let* ((t_0 (- (pow (+ x 1.0) (/ 1.0 n)) (pow x (/ 1.0 n))))
        (t_1 (/ (/ 0.3333333333333333 (* (* x x) x)) n)))
   (if (<= t_0 (- INFINITY))
     t_1
     (if (<= t_0 0.002) (/ (log (/ (- x -1.0) x)) n) t_1))))

C

double code(double x, double n) {
	double t_0 = pow((x + 1.0), (1.0 / n)) - pow(x, (1.0 / n));
	double t_1 = (0.3333333333333333 / ((x * x) * x)) / n;
	double tmp;
	if (t_0 <= -((double) INFINITY)) {
		tmp = t_1;
	} else if (t_0 <= 0.002) {
		tmp = log(((x - -1.0) / x)) / n;
	} else {
		tmp = t_1;
	}
	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 t_1 = (0.3333333333333333 / ((x * x) * x)) / n;
	double tmp;
	if (t_0 <= -Double.POSITIVE_INFINITY) {
		tmp = t_1;
	} else if (t_0 <= 0.002) {
		tmp = Math.log(((x - -1.0) / x)) / n;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, n):
	t_0 = math.pow((x + 1.0), (1.0 / n)) - math.pow(x, (1.0 / n))
	t_1 = (0.3333333333333333 / ((x * x) * x)) / n
	tmp = 0
	if t_0 <= -math.inf:
		tmp = t_1
	elif t_0 <= 0.002:
		tmp = math.log(((x - -1.0) / x)) / n
	else:
		tmp = t_1
	return tmp

Julia

function code(x, n)
	t_0 = Float64((Float64(x + 1.0) ^ Float64(1.0 / n)) - (x ^ Float64(1.0 / n)))
	t_1 = Float64(Float64(0.3333333333333333 / Float64(Float64(x * x) * x)) / n)
	tmp = 0.0
	if (t_0 <= Float64(-Inf))
		tmp = t_1;
	elseif (t_0 <= 0.002)
		tmp = Float64(log(Float64(Float64(x - -1.0) / x)) / n);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, n)
	t_0 = ((x + 1.0) ^ (1.0 / n)) - (x ^ (1.0 / n));
	t_1 = (0.3333333333333333 / ((x * x) * x)) / n;
	tmp = 0.0;
	if (t_0 <= -Inf)
		tmp = t_1;
	elseif (t_0 <= 0.002)
		tmp = log(((x - -1.0) / x)) / n;
	else
		tmp = t_1;
	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]}, Block[{t$95$1 = N[(N[(0.3333333333333333 / N[(N[(x * x), $MachinePrecision] * x), $MachinePrecision]), $MachinePrecision] / n), $MachinePrecision]}, If[LessEqual[t$95$0, (-Infinity)], t$95$1, If[LessEqual[t$95$0, 0.002], N[(N[Log[N[(N[(x - -1.0), $MachinePrecision] / x), $MachinePrecision]], $MachinePrecision] / n), $MachinePrecision], t$95$1]]]]

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 2e-3 < (-.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 53.5%

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

   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. add-flip N/A

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

      7. metadata-eval N/A

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

      8. lower--.f64 58.6

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

   4. Applied rewrites 58.6%

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

   5. Taylor expanded in x around -inf

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

      1. lower-*.f64 N/A

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

      2. lower-/.f64 N/A

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

      3. lower--.f64 N/A

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

      4. lower-*.f64 N/A

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

      5. lower-/.f64 N/A

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

      6. lower--.f64 N/A

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

      7. lower-*.f64 N/A

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

      8. lower-/.f64 46.7

         \[\leadsto \frac{-1 \cdot \frac{-1 \cdot \frac{0.3333333333333333 \cdot \frac{1}{x} - 0.5}{x} - 1}{x}}{n} \]

   7. Applied rewrites 46.7%

      \[\leadsto \frac{-1 \cdot \frac{-1 \cdot \frac{0.3333333333333333 \cdot \frac{1}{x} - 0.5}{x} - 1}{x}}{n} \]

   8. Taylor expanded in x around 0

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

      1. lower-/.f64 N/A

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

      2. unpow3 N/A

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

      3. unpow2 N/A

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

      4. lower-*.f64 N/A

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

      5. unpow2 N/A

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

      6. lower-*.f64 43.3

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

   10. Applied rewrites 43.3%

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

   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))) < 2e-3

   1. Initial program 53.5%

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

   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. add-flip N/A

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

      7. metadata-eval N/A

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

      8. lower--.f64 58.6

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

   4. Applied rewrites 58.6%

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

Alternative 6: 59.2% accurate, 1.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{\frac{1}{n}}{x}\\ \mathbf{if}\;n \leq -1.6:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;n \leq 2.25 \cdot 10^{-99}:\\ \;\;\;\;\frac{\frac{0.3333333333333333}{\left(x \cdot x\right) \cdot x}}{n}\\ \mathbf{elif}\;n \leq 1.2 \cdot 10^{+182}:\\ \;\;\;\;t\_0\\ \mathbf{else}:\\ \;\;\;\;\frac{\log \left(\frac{1}{x}\right)}{n}\\ \end{array} \end{array} \]

FPCore

(FPCore (x n)
 :precision binary64
 (let* ((t_0 (/ (/ 1.0 n) x)))
   (if (<= n -1.6)
     t_0
     (if (<= n 2.25e-99)
       (/ (/ 0.3333333333333333 (* (* x x) x)) n)
       (if (<= n 1.2e+182) t_0 (/ (log (/ 1.0 x)) n))))))

C

double code(double x, double n) {
	double t_0 = (1.0 / n) / x;
	double tmp;
	if (n <= -1.6) {
		tmp = t_0;
	} else if (n <= 2.25e-99) {
		tmp = (0.3333333333333333 / ((x * x) * x)) / n;
	} else if (n <= 1.2e+182) {
		tmp = t_0;
	} else {
		tmp = log((1.0 / x)) / 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) :: t_0
    real(8) :: tmp
    t_0 = (1.0d0 / n) / x
    if (n <= (-1.6d0)) then
        tmp = t_0
    else if (n <= 2.25d-99) then
        tmp = (0.3333333333333333d0 / ((x * x) * x)) / n
    else if (n <= 1.2d+182) then
        tmp = t_0
    else
        tmp = log((1.0d0 / x)) / n
    end if
    code = tmp
end function

Java

public static double code(double x, double n) {
	double t_0 = (1.0 / n) / x;
	double tmp;
	if (n <= -1.6) {
		tmp = t_0;
	} else if (n <= 2.25e-99) {
		tmp = (0.3333333333333333 / ((x * x) * x)) / n;
	} else if (n <= 1.2e+182) {
		tmp = t_0;
	} else {
		tmp = Math.log((1.0 / x)) / n;
	}
	return tmp;
}

Python

def code(x, n):
	t_0 = (1.0 / n) / x
	tmp = 0
	if n <= -1.6:
		tmp = t_0
	elif n <= 2.25e-99:
		tmp = (0.3333333333333333 / ((x * x) * x)) / n
	elif n <= 1.2e+182:
		tmp = t_0
	else:
		tmp = math.log((1.0 / x)) / n
	return tmp

Julia

function code(x, n)
	t_0 = Float64(Float64(1.0 / n) / x)
	tmp = 0.0
	if (n <= -1.6)
		tmp = t_0;
	elseif (n <= 2.25e-99)
		tmp = Float64(Float64(0.3333333333333333 / Float64(Float64(x * x) * x)) / n);
	elseif (n <= 1.2e+182)
		tmp = t_0;
	else
		tmp = Float64(log(Float64(1.0 / x)) / n);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, n)
	t_0 = (1.0 / n) / x;
	tmp = 0.0;
	if (n <= -1.6)
		tmp = t_0;
	elseif (n <= 2.25e-99)
		tmp = (0.3333333333333333 / ((x * x) * x)) / n;
	elseif (n <= 1.2e+182)
		tmp = t_0;
	else
		tmp = log((1.0 / x)) / n;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, n_] := Block[{t$95$0 = N[(N[(1.0 / n), $MachinePrecision] / x), $MachinePrecision]}, If[LessEqual[n, -1.6], t$95$0, If[LessEqual[n, 2.25e-99], N[(N[(0.3333333333333333 / N[(N[(x * x), $MachinePrecision] * x), $MachinePrecision]), $MachinePrecision] / n), $MachinePrecision], If[LessEqual[n, 1.2e+182], t$95$0, N[(N[Log[N[(1.0 / x), $MachinePrecision]], $MachinePrecision] / n), $MachinePrecision]]]]]

Derivation

1. Split input into 3 regimes

2. if n < -1.6000000000000001 or 2.2500000000000001e-99 < n < 1.20000000000000005e182

   1. Initial program 53.5%

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

   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. add-flip N/A

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

      7. metadata-eval N/A

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

      8. lower--.f64 58.6

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

   4. Applied rewrites 58.6%

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

   5. Taylor expanded in x around inf

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

      1. lower-/.f64 N/A

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

      2. lift-*.f64 40.6

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

   7. Applied rewrites 40.6%

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

      1. lift-*.f64 N/A

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

      2. lift-/.f64 N/A

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

      3. associate-/r* N/A

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

      4. lower-/.f64 N/A

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

      5. lift-/.f64 41.3

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

   9. Applied rewrites 41.3%

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

   if -1.6000000000000001 < n < 2.2500000000000001e-99

   1. Initial program 53.5%

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

   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. add-flip N/A

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

      7. metadata-eval N/A

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

      8. lower--.f64 58.6

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

   4. Applied rewrites 58.6%

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

   5. Taylor expanded in x around -inf

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

      1. lower-*.f64 N/A

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

      2. lower-/.f64 N/A

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

      3. lower--.f64 N/A

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

      4. lower-*.f64 N/A

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

      5. lower-/.f64 N/A

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

      6. lower--.f64 N/A

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

      7. lower-*.f64 N/A

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

      8. lower-/.f64 46.7

         \[\leadsto \frac{-1 \cdot \frac{-1 \cdot \frac{0.3333333333333333 \cdot \frac{1}{x} - 0.5}{x} - 1}{x}}{n} \]

   7. Applied rewrites 46.7%

      \[\leadsto \frac{-1 \cdot \frac{-1 \cdot \frac{0.3333333333333333 \cdot \frac{1}{x} - 0.5}{x} - 1}{x}}{n} \]

   8. Taylor expanded in x around 0

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

      1. lower-/.f64 N/A

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

      2. unpow3 N/A

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

      3. unpow2 N/A

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

      4. lower-*.f64 N/A

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

      5. unpow2 N/A

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

      6. lower-*.f64 43.3

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

   10. Applied rewrites 43.3%

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

   if 1.20000000000000005e182 < n

   1. Initial program 53.5%

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

   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. add-flip N/A

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

      7. metadata-eval N/A

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

      8. lower--.f64 58.6

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

   4. Applied rewrites 58.6%

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

   5. Taylor expanded in x around 0

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

   7. Applied rewrites 30.4%

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

Alternative 7: 59.2% accurate, 2.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq 0.0055:\\ \;\;\;\;\frac{\log \left(\frac{1}{x}\right)}{n}\\ \mathbf{elif}\;x \leq 7.4 \cdot 10^{+236}:\\ \;\;\;\;\frac{\frac{1}{n}}{x}\\ \mathbf{else}:\\ \;\;\;\;\frac{-0.5}{\left(x \cdot x\right) \cdot n}\\ \end{array} \end{array} \]

FPCore

(FPCore (x n)
 :precision binary64
 (if (<= x 0.0055)
   (/ (log (/ 1.0 x)) n)
   (if (<= x 7.4e+236) (/ (/ 1.0 n) x) (/ -0.5 (* (* x x) n)))))

C

double code(double x, double n) {
	double tmp;
	if (x <= 0.0055) {
		tmp = log((1.0 / x)) / n;
	} else if (x <= 7.4e+236) {
		tmp = (1.0 / n) / x;
	} else {
		tmp = -0.5 / ((x * x) * 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 <= 0.0055d0) then
        tmp = log((1.0d0 / x)) / n
    else if (x <= 7.4d+236) then
        tmp = (1.0d0 / n) / x
    else
        tmp = (-0.5d0) / ((x * x) * n)
    end if
    code = tmp
end function

Java

public static double code(double x, double n) {
	double tmp;
	if (x <= 0.0055) {
		tmp = Math.log((1.0 / x)) / n;
	} else if (x <= 7.4e+236) {
		tmp = (1.0 / n) / x;
	} else {
		tmp = -0.5 / ((x * x) * n);
	}
	return tmp;
}

Python

def code(x, n):
	tmp = 0
	if x <= 0.0055:
		tmp = math.log((1.0 / x)) / n
	elif x <= 7.4e+236:
		tmp = (1.0 / n) / x
	else:
		tmp = -0.5 / ((x * x) * n)
	return tmp

Julia

function code(x, n)
	tmp = 0.0
	if (x <= 0.0055)
		tmp = Float64(log(Float64(1.0 / x)) / n);
	elseif (x <= 7.4e+236)
		tmp = Float64(Float64(1.0 / n) / x);
	else
		tmp = Float64(-0.5 / Float64(Float64(x * x) * n));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, n)
	tmp = 0.0;
	if (x <= 0.0055)
		tmp = log((1.0 / x)) / n;
	elseif (x <= 7.4e+236)
		tmp = (1.0 / n) / x;
	else
		tmp = -0.5 / ((x * x) * n);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, n_] := If[LessEqual[x, 0.0055], N[(N[Log[N[(1.0 / x), $MachinePrecision]], $MachinePrecision] / n), $MachinePrecision], If[LessEqual[x, 7.4e+236], N[(N[(1.0 / n), $MachinePrecision] / x), $MachinePrecision], N[(-0.5 / N[(N[(x * x), $MachinePrecision] * n), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if x < 0.0054999999999999997

   1. Initial program 53.5%

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

   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. add-flip N/A

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

      7. metadata-eval N/A

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

      8. lower--.f64 58.6

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

   4. Applied rewrites 58.6%

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

   5. Taylor expanded in x around 0

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

   7. Applied rewrites 30.4%

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

   if 0.0054999999999999997 < x < 7.40000000000000028e236

   1. Initial program 53.5%

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

   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. add-flip N/A

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

      7. metadata-eval N/A

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

      8. lower--.f64 58.6

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

   4. Applied rewrites 58.6%

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

   5. Taylor expanded in x around inf

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

      1. lower-/.f64 N/A

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

      2. lift-*.f64 40.6

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

   7. Applied rewrites 40.6%

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

      1. lift-*.f64 N/A

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

      2. lift-/.f64 N/A

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

      3. associate-/r* N/A

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

      4. lower-/.f64 N/A

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

      5. lift-/.f64 41.3

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

   9. Applied rewrites 41.3%

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

   if 7.40000000000000028e236 < x

   1. Initial program 53.5%

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

   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. add-flip N/A

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

      7. metadata-eval N/A

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

      8. lower--.f64 58.6

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

   4. Applied rewrites 58.6%

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

   5. Taylor expanded in x around inf

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

      1. lower-/.f64 N/A

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

      2. lift-*.f64 40.6

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

   7. Applied rewrites 40.6%

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

   8. Taylor expanded in x around inf

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

      1. lower-/.f64 N/A

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

      2. lower--.f64 N/A

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

      3. lift-/.f64 N/A

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

      4. mult-flip-rev N/A

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

      5. lower-/.f64 N/A

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

      6. lift-*.f64 28.9

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

   10. Applied rewrites 28.9%

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

   11. Taylor expanded in x around 0

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

       1. lower-/.f64 N/A

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

       2. *-commutative N/A

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

       3. lower-*.f64 N/A

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

       4. pow2 N/A

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

       5. lift-*.f64 22.8

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

   13. Applied rewrites 22.8%

       \[\leadsto \frac{-0.5}{\left(x \cdot x\right) \cdot \color{blue}{n}} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 8: 56.6% accurate, 2.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq 0.0055:\\ \;\;\;\;\frac{-\log x}{n}\\ \mathbf{elif}\;x \leq 7.4 \cdot 10^{+236}:\\ \;\;\;\;\frac{\frac{1}{n}}{x}\\ \mathbf{else}:\\ \;\;\;\;\frac{-0.5}{\left(x \cdot x\right) \cdot n}\\ \end{array} \end{array} \]

FPCore

(FPCore (x n)
 :precision binary64
 (if (<= x 0.0055)
   (/ (- (log x)) n)
   (if (<= x 7.4e+236) (/ (/ 1.0 n) x) (/ -0.5 (* (* x x) n)))))

C

double code(double x, double n) {
	double tmp;
	if (x <= 0.0055) {
		tmp = -log(x) / n;
	} else if (x <= 7.4e+236) {
		tmp = (1.0 / n) / x;
	} else {
		tmp = -0.5 / ((x * x) * 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 <= 0.0055d0) then
        tmp = -log(x) / n
    else if (x <= 7.4d+236) then
        tmp = (1.0d0 / n) / x
    else
        tmp = (-0.5d0) / ((x * x) * n)
    end if
    code = tmp
end function

Java

public static double code(double x, double n) {
	double tmp;
	if (x <= 0.0055) {
		tmp = -Math.log(x) / n;
	} else if (x <= 7.4e+236) {
		tmp = (1.0 / n) / x;
	} else {
		tmp = -0.5 / ((x * x) * n);
	}
	return tmp;
}

Python

def code(x, n):
	tmp = 0
	if x <= 0.0055:
		tmp = -math.log(x) / n
	elif x <= 7.4e+236:
		tmp = (1.0 / n) / x
	else:
		tmp = -0.5 / ((x * x) * n)
	return tmp

Julia

function code(x, n)
	tmp = 0.0
	if (x <= 0.0055)
		tmp = Float64(Float64(-log(x)) / n);
	elseif (x <= 7.4e+236)
		tmp = Float64(Float64(1.0 / n) / x);
	else
		tmp = Float64(-0.5 / Float64(Float64(x * x) * n));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, n)
	tmp = 0.0;
	if (x <= 0.0055)
		tmp = -log(x) / n;
	elseif (x <= 7.4e+236)
		tmp = (1.0 / n) / x;
	else
		tmp = -0.5 / ((x * x) * n);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, n_] := If[LessEqual[x, 0.0055], N[((-N[Log[x], $MachinePrecision]) / n), $MachinePrecision], If[LessEqual[x, 7.4e+236], N[(N[(1.0 / n), $MachinePrecision] / x), $MachinePrecision], N[(-0.5 / N[(N[(x * x), $MachinePrecision] * n), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if x < 0.0054999999999999997

   1. Initial program 53.5%

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

   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. add-flip N/A

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

      7. metadata-eval N/A

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

      8. lower--.f64 58.6

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

   4. Applied rewrites 58.6%

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

   5. Taylor expanded in x around -inf

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

      1. lower-*.f64 N/A

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

      2. lower-/.f64 N/A

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

      3. lower--.f64 N/A

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

      4. lower-*.f64 N/A

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

      5. lower-/.f64 N/A

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

      6. lower--.f64 N/A

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

      7. lower-*.f64 N/A

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

      8. lower-/.f64 46.7

         \[\leadsto \frac{-1 \cdot \frac{-1 \cdot \frac{0.3333333333333333 \cdot \frac{1}{x} - 0.5}{x} - 1}{x}}{n} \]

   7. Applied rewrites 46.7%

      \[\leadsto \frac{-1 \cdot \frac{-1 \cdot \frac{0.3333333333333333 \cdot \frac{1}{x} - 0.5}{x} - 1}{x}}{n} \]

   8. Taylor expanded in x around 0

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

      1. mul-1-neg N/A

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

      2. lower-neg.f64 N/A

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

      3. lift-log.f64 30.4

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

   10. Applied rewrites 30.4%

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

   if 0.0054999999999999997 < x < 7.40000000000000028e236

   1. Initial program 53.5%

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

   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. add-flip N/A

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

      7. metadata-eval N/A

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

      8. lower--.f64 58.6

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

   4. Applied rewrites 58.6%

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

   5. Taylor expanded in x around inf

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

      1. lower-/.f64 N/A

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

      2. lift-*.f64 40.6

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

   7. Applied rewrites 40.6%

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

      1. lift-*.f64 N/A

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

      2. lift-/.f64 N/A

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

      3. associate-/r* N/A

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

      4. lower-/.f64 N/A

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

      5. lift-/.f64 41.3

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

   9. Applied rewrites 41.3%

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

   if 7.40000000000000028e236 < x

   1. Initial program 53.5%

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

   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. add-flip N/A

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

      7. metadata-eval N/A

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

      8. lower--.f64 58.6

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

   4. Applied rewrites 58.6%

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

   5. Taylor expanded in x around inf

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

      1. lower-/.f64 N/A

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

      2. lift-*.f64 40.6

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

   7. Applied rewrites 40.6%

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

   8. Taylor expanded in x around inf

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

      1. lower-/.f64 N/A

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

      2. lower--.f64 N/A

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

      3. lift-/.f64 N/A

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

      4. mult-flip-rev N/A

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

      5. lower-/.f64 N/A

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

      6. lift-*.f64 28.9

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

   10. Applied rewrites 28.9%

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

   11. Taylor expanded in x around 0

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

       1. lower-/.f64 N/A

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

       2. *-commutative N/A

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

       3. lower-*.f64 N/A

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

       4. pow2 N/A

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

       5. lift-*.f64 22.8

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

   13. Applied rewrites 22.8%

       \[\leadsto \frac{-0.5}{\left(x \cdot x\right) \cdot \color{blue}{n}} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 9: 43.7% accurate, 3.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq 7.4 \cdot 10^{+236}:\\ \;\;\;\;\frac{\frac{1}{n}}{x}\\ \mathbf{else}:\\ \;\;\;\;\frac{-0.5}{\left(x \cdot x\right) \cdot n}\\ \end{array} \end{array} \]

FPCore

(FPCore (x n)
 :precision binary64
 (if (<= x 7.4e+236) (/ (/ 1.0 n) x) (/ -0.5 (* (* x x) n))))

C

double code(double x, double n) {
	double tmp;
	if (x <= 7.4e+236) {
		tmp = (1.0 / n) / x;
	} else {
		tmp = -0.5 / ((x * x) * 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 <= 7.4d+236) then
        tmp = (1.0d0 / n) / x
    else
        tmp = (-0.5d0) / ((x * x) * n)
    end if
    code = tmp
end function

Java

public static double code(double x, double n) {
	double tmp;
	if (x <= 7.4e+236) {
		tmp = (1.0 / n) / x;
	} else {
		tmp = -0.5 / ((x * x) * n);
	}
	return tmp;
}

Python

def code(x, n):
	tmp = 0
	if x <= 7.4e+236:
		tmp = (1.0 / n) / x
	else:
		tmp = -0.5 / ((x * x) * n)
	return tmp

Julia

function code(x, n)
	tmp = 0.0
	if (x <= 7.4e+236)
		tmp = Float64(Float64(1.0 / n) / x);
	else
		tmp = Float64(-0.5 / Float64(Float64(x * x) * n));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, n)
	tmp = 0.0;
	if (x <= 7.4e+236)
		tmp = (1.0 / n) / x;
	else
		tmp = -0.5 / ((x * x) * n);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, n_] := If[LessEqual[x, 7.4e+236], N[(N[(1.0 / n), $MachinePrecision] / x), $MachinePrecision], N[(-0.5 / N[(N[(x * x), $MachinePrecision] * n), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < 7.40000000000000028e236

   1. Initial program 53.5%

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

   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. add-flip N/A

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

      7. metadata-eval N/A

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

      8. lower--.f64 58.6

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

   4. Applied rewrites 58.6%

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

   5. Taylor expanded in x around inf

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

      1. lower-/.f64 N/A

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

      2. lift-*.f64 40.6

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

   7. Applied rewrites 40.6%

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

      1. lift-*.f64 N/A

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

      2. lift-/.f64 N/A

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

      3. associate-/r* N/A

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

      4. lower-/.f64 N/A

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

      5. lift-/.f64 41.3

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

   9. Applied rewrites 41.3%

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

   if 7.40000000000000028e236 < x

   1. Initial program 53.5%

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

   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. add-flip N/A

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

      7. metadata-eval N/A

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

      8. lower--.f64 58.6

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

   4. Applied rewrites 58.6%

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

   5. Taylor expanded in x around inf

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

      1. lower-/.f64 N/A

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

      2. lift-*.f64 40.6

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

   7. Applied rewrites 40.6%

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

   8. Taylor expanded in x around inf

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

      1. lower-/.f64 N/A

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

      2. lower--.f64 N/A

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

      3. lift-/.f64 N/A

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

      4. mult-flip-rev N/A

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

      5. lower-/.f64 N/A

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

      6. lift-*.f64 28.9

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

   10. Applied rewrites 28.9%

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

   11. Taylor expanded in x around 0

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

       1. lower-/.f64 N/A

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

       2. *-commutative N/A

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

       3. lower-*.f64 N/A

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

       4. pow2 N/A

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

       5. lift-*.f64 22.8

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

   13. Applied rewrites 22.8%

       \[\leadsto \frac{-0.5}{\left(x \cdot x\right) \cdot \color{blue}{n}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 10: 41.3% 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 53.5%

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

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. add-flip N/A

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

   7. metadata-eval N/A

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

   8. lower--.f64 58.6

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

4. Applied rewrites 58.6%

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

5. Taylor expanded in x around inf

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

   1. lower-/.f64 N/A

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

   2. lift-*.f64 40.6

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

7. Applied rewrites 40.6%

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

   1. lift-*.f64 N/A

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

   2. lift-/.f64 N/A

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

   3. associate-/r* N/A

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

   4. lower-/.f64 N/A

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

   5. lift-/.f64 41.3

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

9. Applied rewrites 41.3%

   \[\leadsto \frac{\frac{1}{n}}{x} \]
10. Add Preprocessing

Alternative 11: 40.6% 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 53.5%

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

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. add-flip N/A

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

   7. metadata-eval N/A

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

   8. lower--.f64 58.6

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

4. Applied rewrites 58.6%

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

5. Taylor expanded in x around inf

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

   1. lower-/.f64 N/A

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

   2. lift-*.f64 40.6

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

7. Applied rewrites 40.6%

   \[\leadsto \frac{1}{\color{blue}{n \cdot x}} \]
8. Add Preprocessing

Reproduce

herbie shell --seed 2025132 
(FPCore (x n)
  :name "2nthrt (problem 3.4.6)"
  :precision binary64
  (- (pow (+ x 1.0) (/ 1.0 n)) (pow x (/ 1.0 n))))