2nthrt (problem 3.4.6)

Percentage Accurate: 54.6% → 91.2%

Time: 17.9s

Alternatives: 11

Speedup: 2.3×

• could not determine a ground truth

  1. x = -7.81690487369606e-83
  2. n = 100738267306057780.0

Specification

Math

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

FPCore

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

C

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

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 54.6% 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: 91.2% accurate, 1.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{\log x}{n}\\ \mathbf{if}\;x \leq 0.71:\\ \;\;\;\;-\mathsf{expm1}\left(t\_0\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{e^{t\_0}}{n \cdot x}\\ \end{array} \end{array} \]

FPCore

(FPCore (x n)
 :precision binary64
 (let* ((t_0 (/ (log x) n)))
   (if (<= x 0.71) (- (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 <= 0.71) {
		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 <= 0.71) {
		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 <= 0.71:
		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 <= 0.71)
		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, 0.71], (-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 < 0.70999999999999996

   1. Initial program 43.6%

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

   2. Taylor expanded in x around 0

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

      1. negate-sub2 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 86.6

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

   4. Applied rewrites 86.6%

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

   if 0.70999999999999996 < x

   1. Initial program 68.9%

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

   2. Taylor expanded in x around inf

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

      1. lower-/.f64 N/A

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

      2. lower-exp.f64 N/A

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

      3. mul-1-neg N/A

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

      4. log-rec N/A

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

      5. mul-1-neg N/A

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

      6. lower-neg.f64 N/A

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

      7. lower-/.f64 N/A

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

      8. mul-1-neg N/A

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

      9. lower-neg.f64 N/A

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

      10. lower-log.f64 N/A

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

      11. lower-*.f64 97.3

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

   4. Applied rewrites 97.3%

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

      1. lift-neg.f64 N/A

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

      2. lift-/.f64 N/A

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

      3. lift-log.f64 N/A

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

      4. lift-neg.f64 N/A

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

      5. distribute-neg-frac2 N/A

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

      6. frac-2neg N/A

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

      7. lower-/.f64 N/A

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

      8. lift-log.f64 97.3

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

   6. Applied rewrites 97.3%

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

Alternative 2: 78.1% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq 1:\\ \;\;\;\;-\mathsf{expm1}\left(\frac{\log x}{n}\right)\\ \mathbf{elif}\;x \leq 10^{+49}:\\ \;\;\;\;1 - 1\\ \mathbf{elif}\;x \leq 8.5 \cdot 10^{+245}:\\ \;\;\;\;\frac{\mathsf{fma}\left(-1, \frac{-\log x}{n \cdot n}, \frac{1}{n}\right)}{x}\\ \mathbf{else}:\\ \;\;\;\;\frac{\log \left(1 + x\right) - \log x}{n}\\ \end{array} \end{array} \]

FPCore

(FPCore (x n)
 :precision binary64
 (if (<= x 1.0)
   (- (expm1 (/ (log x) n)))
   (if (<= x 1e+49)
     (- 1.0 1.0)
     (if (<= x 8.5e+245)
       (/ (fma -1.0 (/ (- (log x)) (* n n)) (/ 1.0 n)) x)
       (/ (- (log (+ 1.0 x)) (log x)) n)))))

C

double code(double x, double n) {
	double tmp;
	if (x <= 1.0) {
		tmp = -expm1((log(x) / n));
	} else if (x <= 1e+49) {
		tmp = 1.0 - 1.0;
	} else if (x <= 8.5e+245) {
		tmp = fma(-1.0, (-log(x) / (n * n)), (1.0 / n)) / x;
	} else {
		tmp = (log((1.0 + x)) - log(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 <= 1e+49)
		tmp = Float64(1.0 - 1.0);
	elseif (x <= 8.5e+245)
		tmp = Float64(fma(-1.0, Float64(Float64(-log(x)) / Float64(n * n)), Float64(1.0 / n)) / x);
	else
		tmp = Float64(Float64(log(Float64(1.0 + x)) - log(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, 1e+49], N[(1.0 - 1.0), $MachinePrecision], If[LessEqual[x, 8.5e+245], N[(N[(-1.0 * N[((-N[Log[x], $MachinePrecision]) / N[(n * n), $MachinePrecision]), $MachinePrecision] + N[(1.0 / n), $MachinePrecision]), $MachinePrecision] / x), $MachinePrecision], N[(N[(N[Log[N[(1.0 + x), $MachinePrecision]], $MachinePrecision] - N[Log[x], $MachinePrecision]), $MachinePrecision] / n), $MachinePrecision]]]]

Derivation

1. Split input into 4 regimes

2. if x < 1

   1. Initial program 43.6%

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

   2. Taylor expanded in x around 0

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

      1. negate-sub2 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 86.5

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

   4. Applied rewrites 86.5%

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

   if 1 < x < 9.99999999999999946e48

   1. Initial program 42.3%

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

   2. Taylor expanded in n around inf

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

   4. Applied rewrites 7.1%

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

   5. Taylor expanded in x around 0

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

   7. Applied rewrites 42.2%

      \[\leadsto \color{blue}{1} - 1 \]

   if 9.99999999999999946e48 < x < 8.49999999999999971e245

   1. Initial program 68.5%

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

   2. Taylor expanded in n around -inf

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

   4. Applied rewrites 68.5%

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

   5. Taylor expanded in x around inf

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

      1. lower-/.f64 N/A

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

      2. lower-fma.f64 N/A

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

      3. lower-/.f64 N/A

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

      4. neg-log N/A

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

      5. lift-neg.f64 N/A

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

      6. lift-log.f64 N/A

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

      7. pow2 N/A

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

      8. lift-*.f64 N/A

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

      9. lift-/.f64 65.7

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

   7. Applied rewrites 65.7%

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

   if 8.49999999999999971e245 < x

   1. Initial program 93.6%

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

   2. Taylor expanded in n around inf

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

      1. lower-/.f64 N/A

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

      2. lower--.f64 N/A

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

      3. +-commutative N/A

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

      4. lower-log.f64 N/A

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

      5. +-commutative N/A

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

      6. lower-+.f64 N/A

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

      7. lower-log.f64 93.6

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

   4. Applied rewrites 93.6%

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

Alternative 3: 78.0% accurate, 1.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{\log x}{n}\\ \mathbf{if}\;x \leq 1:\\ \;\;\;\;-\mathsf{expm1}\left(t\_0\right)\\ \mathbf{elif}\;x \leq 10^{+49}:\\ \;\;\;\;1 - 1\\ \mathbf{elif}\;x \leq 8.5 \cdot 10^{+245}:\\ \;\;\;\;\frac{t\_0 + 1}{n \cdot x}\\ \mathbf{else}:\\ \;\;\;\;1 - 1\\ \end{array} \end{array} \]

FPCore

(FPCore (x n)
 :precision binary64
 (let* ((t_0 (/ (log x) n)))
   (if (<= x 1.0)
     (- (expm1 t_0))
     (if (<= x 1e+49)
       (- 1.0 1.0)
       (if (<= x 8.5e+245) (/ (+ t_0 1.0) (* n x)) (- 1.0 1.0))))))

C

double code(double x, double n) {
	double t_0 = log(x) / n;
	double tmp;
	if (x <= 1.0) {
		tmp = -expm1(t_0);
	} else if (x <= 1e+49) {
		tmp = 1.0 - 1.0;
	} else if (x <= 8.5e+245) {
		tmp = (t_0 + 1.0) / (n * x);
	} else {
		tmp = 1.0 - 1.0;
	}
	return tmp;
}

Java

public static double code(double x, double n) {
	double t_0 = Math.log(x) / n;
	double tmp;
	if (x <= 1.0) {
		tmp = -Math.expm1(t_0);
	} else if (x <= 1e+49) {
		tmp = 1.0 - 1.0;
	} else if (x <= 8.5e+245) {
		tmp = (t_0 + 1.0) / (n * x);
	} else {
		tmp = 1.0 - 1.0;
	}
	return tmp;
}

Python

def code(x, n):
	t_0 = math.log(x) / n
	tmp = 0
	if x <= 1.0:
		tmp = -math.expm1(t_0)
	elif x <= 1e+49:
		tmp = 1.0 - 1.0
	elif x <= 8.5e+245:
		tmp = (t_0 + 1.0) / (n * x)
	else:
		tmp = 1.0 - 1.0
	return tmp

Julia

function code(x, n)
	t_0 = Float64(log(x) / n)
	tmp = 0.0
	if (x <= 1.0)
		tmp = Float64(-expm1(t_0));
	elseif (x <= 1e+49)
		tmp = Float64(1.0 - 1.0);
	elseif (x <= 8.5e+245)
		tmp = Float64(Float64(t_0 + 1.0) / Float64(n * x));
	else
		tmp = Float64(1.0 - 1.0);
	end
	return tmp
end

Wolfram

code[x_, n_] := Block[{t$95$0 = N[(N[Log[x], $MachinePrecision] / n), $MachinePrecision]}, If[LessEqual[x, 1.0], (-N[(Exp[t$95$0] - 1), $MachinePrecision]), If[LessEqual[x, 1e+49], N[(1.0 - 1.0), $MachinePrecision], If[LessEqual[x, 8.5e+245], N[(N[(t$95$0 + 1.0), $MachinePrecision] / N[(n * x), $MachinePrecision]), $MachinePrecision], N[(1.0 - 1.0), $MachinePrecision]]]]]

Derivation

1. Split input into 3 regimes

2. if x < 1

   1. Initial program 43.6%

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

   2. Taylor expanded in x around 0

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

      1. negate-sub2 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 86.5

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

   4. Applied rewrites 86.5%

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

   if 1 < x < 9.99999999999999946e48 or 8.49999999999999971e245 < x

   1. Initial program 69.6%

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

   2. Taylor expanded in n around inf

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

   4. Applied rewrites 34.5%

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

   5. Taylor expanded in x around 0

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

   7. Applied rewrites 69.6%

      \[\leadsto \color{blue}{1} - 1 \]

   if 9.99999999999999946e48 < x < 8.49999999999999971e245

   1. Initial program 68.5%

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

   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 98.6

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

   4. Applied rewrites 98.6%

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

   5. Taylor expanded in n around inf

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

      1. +-commutative N/A

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

      2. lower-+.f64 N/A

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

      3. lower-/.f64 N/A

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

      4. lift-log.f64 64.5

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

   7. Applied rewrites 64.5%

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

Alternative 4: 77.8% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{\log x}{n}\\ \mathbf{if}\;x \leq 1:\\ \;\;\;\;-\mathsf{expm1}\left(t\_0\right)\\ \mathbf{elif}\;x \leq 10^{+49}:\\ \;\;\;\;1 - 1\\ \mathbf{elif}\;x \leq 8.5 \cdot 10^{+245}:\\ \;\;\;\;\frac{t\_0 + 1}{n \cdot x}\\ \mathbf{else}:\\ \;\;\;\;\frac{\log \left(1 + x\right) - \log x}{n}\\ \end{array} \end{array} \]

FPCore

(FPCore (x n)
 :precision binary64
 (let* ((t_0 (/ (log x) n)))
   (if (<= x 1.0)
     (- (expm1 t_0))
     (if (<= x 1e+49)
       (- 1.0 1.0)
       (if (<= x 8.5e+245)
         (/ (+ t_0 1.0) (* n x))
         (/ (- (log (+ 1.0 x)) (log x)) n))))))

C

double code(double x, double n) {
	double t_0 = log(x) / n;
	double tmp;
	if (x <= 1.0) {
		tmp = -expm1(t_0);
	} else if (x <= 1e+49) {
		tmp = 1.0 - 1.0;
	} else if (x <= 8.5e+245) {
		tmp = (t_0 + 1.0) / (n * x);
	} else {
		tmp = (log((1.0 + x)) - log(x)) / n;
	}
	return tmp;
}

Java

public static double code(double x, double n) {
	double t_0 = Math.log(x) / n;
	double tmp;
	if (x <= 1.0) {
		tmp = -Math.expm1(t_0);
	} else if (x <= 1e+49) {
		tmp = 1.0 - 1.0;
	} else if (x <= 8.5e+245) {
		tmp = (t_0 + 1.0) / (n * x);
	} else {
		tmp = (Math.log((1.0 + x)) - Math.log(x)) / n;
	}
	return tmp;
}

Python

def code(x, n):
	t_0 = math.log(x) / n
	tmp = 0
	if x <= 1.0:
		tmp = -math.expm1(t_0)
	elif x <= 1e+49:
		tmp = 1.0 - 1.0
	elif x <= 8.5e+245:
		tmp = (t_0 + 1.0) / (n * x)
	else:
		tmp = (math.log((1.0 + x)) - math.log(x)) / n
	return tmp

Julia

function code(x, n)
	t_0 = Float64(log(x) / n)
	tmp = 0.0
	if (x <= 1.0)
		tmp = Float64(-expm1(t_0));
	elseif (x <= 1e+49)
		tmp = Float64(1.0 - 1.0);
	elseif (x <= 8.5e+245)
		tmp = Float64(Float64(t_0 + 1.0) / Float64(n * x));
	else
		tmp = Float64(Float64(log(Float64(1.0 + x)) - log(x)) / n);
	end
	return tmp
end

Wolfram

code[x_, n_] := Block[{t$95$0 = N[(N[Log[x], $MachinePrecision] / n), $MachinePrecision]}, If[LessEqual[x, 1.0], (-N[(Exp[t$95$0] - 1), $MachinePrecision]), If[LessEqual[x, 1e+49], N[(1.0 - 1.0), $MachinePrecision], If[LessEqual[x, 8.5e+245], N[(N[(t$95$0 + 1.0), $MachinePrecision] / N[(n * x), $MachinePrecision]), $MachinePrecision], N[(N[(N[Log[N[(1.0 + x), $MachinePrecision]], $MachinePrecision] - N[Log[x], $MachinePrecision]), $MachinePrecision] / n), $MachinePrecision]]]]]

Derivation

1. Split input into 4 regimes

2. if x < 1

   1. Initial program 43.6%

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

   2. Taylor expanded in x around 0

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

      1. negate-sub2 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 86.5

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

   4. Applied rewrites 86.5%

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

   if 1 < x < 9.99999999999999946e48

   1. Initial program 42.3%

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

   2. Taylor expanded in n around inf

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

   4. Applied rewrites 7.1%

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

   5. Taylor expanded in x around 0

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

   7. Applied rewrites 42.2%

      \[\leadsto \color{blue}{1} - 1 \]

   if 9.99999999999999946e48 < x < 8.49999999999999971e245

   1. Initial program 68.5%

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

   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 98.6

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

   4. Applied rewrites 98.6%

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

   5. Taylor expanded in n around inf

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

      1. +-commutative N/A

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

      2. lower-+.f64 N/A

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

      3. lower-/.f64 N/A

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

      4. lift-log.f64 64.5

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

   7. Applied rewrites 64.5%

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

   if 8.49999999999999971e245 < x

   1. Initial program 93.6%

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

   2. Taylor expanded in n around inf

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

      1. lower-/.f64 N/A

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

      2. lower--.f64 N/A

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

      3. +-commutative N/A

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

      4. lower-log.f64 N/A

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

      5. +-commutative N/A

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

      6. lower-+.f64 N/A

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

      7. lower-log.f64 93.6

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

   4. Applied rewrites 93.6%

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

Alternative 5: 77.8% accurate, 2.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq 1:\\ \;\;\;\;-\mathsf{expm1}\left(\frac{\log x}{n}\right)\\ \mathbf{elif}\;x \leq 10^{+49}:\\ \;\;\;\;1 - 1\\ \mathbf{elif}\;x \leq 8.5 \cdot 10^{+245}:\\ \;\;\;\;\frac{\frac{1}{n}}{x}\\ \mathbf{else}:\\ \;\;\;\;1 - 1\\ \end{array} \end{array} \]

FPCore

(FPCore (x n)
 :precision binary64
 (if (<= x 1.0)
   (- (expm1 (/ (log x) n)))
   (if (<= x 1e+49)
     (- 1.0 1.0)
     (if (<= x 8.5e+245) (/ (/ 1.0 n) x) (- 1.0 1.0)))))

C

double code(double x, double n) {
	double tmp;
	if (x <= 1.0) {
		tmp = -expm1((log(x) / n));
	} else if (x <= 1e+49) {
		tmp = 1.0 - 1.0;
	} else if (x <= 8.5e+245) {
		tmp = (1.0 / n) / x;
	} else {
		tmp = 1.0 - 1.0;
	}
	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 <= 1e+49) {
		tmp = 1.0 - 1.0;
	} else if (x <= 8.5e+245) {
		tmp = (1.0 / n) / x;
	} else {
		tmp = 1.0 - 1.0;
	}
	return tmp;
}

Python

def code(x, n):
	tmp = 0
	if x <= 1.0:
		tmp = -math.expm1((math.log(x) / n))
	elif x <= 1e+49:
		tmp = 1.0 - 1.0
	elif x <= 8.5e+245:
		tmp = (1.0 / n) / x
	else:
		tmp = 1.0 - 1.0
	return tmp

Julia

function code(x, n)
	tmp = 0.0
	if (x <= 1.0)
		tmp = Float64(-expm1(Float64(log(x) / n)));
	elseif (x <= 1e+49)
		tmp = Float64(1.0 - 1.0);
	elseif (x <= 8.5e+245)
		tmp = Float64(Float64(1.0 / n) / x);
	else
		tmp = Float64(1.0 - 1.0);
	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, 1e+49], N[(1.0 - 1.0), $MachinePrecision], If[LessEqual[x, 8.5e+245], N[(N[(1.0 / n), $MachinePrecision] / x), $MachinePrecision], N[(1.0 - 1.0), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if x < 1

   1. Initial program 43.6%

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

   2. Taylor expanded in x around 0

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

      1. negate-sub2 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 86.5

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

   4. Applied rewrites 86.5%

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

   if 1 < x < 9.99999999999999946e48 or 8.49999999999999971e245 < x

   1. Initial program 69.6%

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

   2. Taylor expanded in n around inf

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

   4. Applied rewrites 34.5%

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

   5. Taylor expanded in x around 0

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

   7. Applied rewrites 69.6%

      \[\leadsto \color{blue}{1} - 1 \]

   if 9.99999999999999946e48 < x < 8.49999999999999971e245

   1. Initial program 68.5%

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

   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. lower--.f64 N/A

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

      3. +-commutative N/A

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

      4. lower-log.f64 N/A

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

      5. +-commutative N/A

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

      6. lower-+.f64 N/A

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

      7. lower-log.f64 68.5

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

   4. Applied rewrites 68.5%

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

   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 64.0

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

   7. Applied rewrites 64.0%

      \[\leadsto \frac{1}{\color{blue}{n \cdot x}} \]
   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. lower-/.f64 65.3

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

   9. Applied rewrites 65.3%

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

Alternative 6: 58.0% accurate, 2.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq 2.7:\\ \;\;\;\;\left(-\frac{\log x}{n}\right) + \frac{x}{n}\\ \mathbf{elif}\;x \leq 10^{+49}:\\ \;\;\;\;1 - 1\\ \mathbf{elif}\;x \leq 8.5 \cdot 10^{+245}:\\ \;\;\;\;\frac{\frac{1}{n}}{x}\\ \mathbf{else}:\\ \;\;\;\;1 - 1\\ \end{array} \end{array} \]

FPCore

(FPCore (x n)
 :precision binary64
 (if (<= x 2.7)
   (+ (- (/ (log x) n)) (/ x n))
   (if (<= x 1e+49)
     (- 1.0 1.0)
     (if (<= x 8.5e+245) (/ (/ 1.0 n) x) (- 1.0 1.0)))))

C

double code(double x, double n) {
	double tmp;
	if (x <= 2.7) {
		tmp = -(log(x) / n) + (x / n);
	} else if (x <= 1e+49) {
		tmp = 1.0 - 1.0;
	} else if (x <= 8.5e+245) {
		tmp = (1.0 / n) / x;
	} else {
		tmp = 1.0 - 1.0;
	}
	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 <= 2.7d0) then
        tmp = -(log(x) / n) + (x / n)
    else if (x <= 1d+49) then
        tmp = 1.0d0 - 1.0d0
    else if (x <= 8.5d+245) then
        tmp = (1.0d0 / n) / x
    else
        tmp = 1.0d0 - 1.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double n) {
	double tmp;
	if (x <= 2.7) {
		tmp = -(Math.log(x) / n) + (x / n);
	} else if (x <= 1e+49) {
		tmp = 1.0 - 1.0;
	} else if (x <= 8.5e+245) {
		tmp = (1.0 / n) / x;
	} else {
		tmp = 1.0 - 1.0;
	}
	return tmp;
}

Python

def code(x, n):
	tmp = 0
	if x <= 2.7:
		tmp = -(math.log(x) / n) + (x / n)
	elif x <= 1e+49:
		tmp = 1.0 - 1.0
	elif x <= 8.5e+245:
		tmp = (1.0 / n) / x
	else:
		tmp = 1.0 - 1.0
	return tmp

Julia

function code(x, n)
	tmp = 0.0
	if (x <= 2.7)
		tmp = Float64(Float64(-Float64(log(x) / n)) + Float64(x / n));
	elseif (x <= 1e+49)
		tmp = Float64(1.0 - 1.0);
	elseif (x <= 8.5e+245)
		tmp = Float64(Float64(1.0 / n) / x);
	else
		tmp = Float64(1.0 - 1.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, n)
	tmp = 0.0;
	if (x <= 2.7)
		tmp = -(log(x) / n) + (x / n);
	elseif (x <= 1e+49)
		tmp = 1.0 - 1.0;
	elseif (x <= 8.5e+245)
		tmp = (1.0 / n) / x;
	else
		tmp = 1.0 - 1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, n_] := If[LessEqual[x, 2.7], N[((-N[(N[Log[x], $MachinePrecision] / n), $MachinePrecision]) + N[(x / n), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 1e+49], N[(1.0 - 1.0), $MachinePrecision], If[LessEqual[x, 8.5e+245], N[(N[(1.0 / n), $MachinePrecision] / x), $MachinePrecision], N[(1.0 - 1.0), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if x < 2.7000000000000002

   1. Initial program 43.5%

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

   2. Taylor expanded in n around inf

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

      1. lower-/.f64 N/A

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

      2. lower--.f64 N/A

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

      3. +-commutative N/A

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

      4. lower-log.f64 N/A

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

      5. +-commutative N/A

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

      6. lower-+.f64 N/A

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

      7. lower-log.f64 51.8

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

   4. Applied rewrites 51.8%

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

   5. Taylor expanded in x around 0

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

      1. lower-+.f64 N/A

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

      2. mul-1-neg N/A

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

      3. lower-neg.f64 N/A

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

      4. lower-/.f64 N/A

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

      5. lift-log.f64 N/A

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

      6. lower-/.f64 51.2

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

   7. Applied rewrites 51.2%

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

   if 2.7000000000000002 < x < 9.99999999999999946e48 or 8.49999999999999971e245 < x

   1. Initial program 69.9%

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

   2. Taylor expanded in n around inf

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

   4. Applied rewrites 34.6%

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

   5. Taylor expanded in x around 0

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

   7. Applied rewrites 69.9%

      \[\leadsto \color{blue}{1} - 1 \]

   if 9.99999999999999946e48 < x < 8.49999999999999971e245

   1. Initial program 68.5%

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

   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. lower--.f64 N/A

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

      3. +-commutative N/A

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

      4. lower-log.f64 N/A

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

      5. +-commutative N/A

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

      6. lower-+.f64 N/A

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

      7. lower-log.f64 68.5

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

   4. Applied rewrites 68.5%

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

   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 64.0

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

   7. Applied rewrites 64.0%

      \[\leadsto \frac{1}{\color{blue}{n \cdot x}} \]
   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. lower-/.f64 65.3

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

   9. Applied rewrites 65.3%

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

Alternative 7: 58.0% accurate, 2.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq 2.7:\\ \;\;\;\;\frac{x - \log x}{n}\\ \mathbf{elif}\;x \leq 10^{+49}:\\ \;\;\;\;1 - 1\\ \mathbf{elif}\;x \leq 8.5 \cdot 10^{+245}:\\ \;\;\;\;\frac{\frac{1}{n}}{x}\\ \mathbf{else}:\\ \;\;\;\;1 - 1\\ \end{array} \end{array} \]

FPCore

(FPCore (x n)
 :precision binary64
 (if (<= x 2.7)
   (/ (- x (log x)) n)
   (if (<= x 1e+49)
     (- 1.0 1.0)
     (if (<= x 8.5e+245) (/ (/ 1.0 n) x) (- 1.0 1.0)))))

C

double code(double x, double n) {
	double tmp;
	if (x <= 2.7) {
		tmp = (x - log(x)) / n;
	} else if (x <= 1e+49) {
		tmp = 1.0 - 1.0;
	} else if (x <= 8.5e+245) {
		tmp = (1.0 / n) / x;
	} else {
		tmp = 1.0 - 1.0;
	}
	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 <= 2.7d0) then
        tmp = (x - log(x)) / n
    else if (x <= 1d+49) then
        tmp = 1.0d0 - 1.0d0
    else if (x <= 8.5d+245) then
        tmp = (1.0d0 / n) / x
    else
        tmp = 1.0d0 - 1.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double n) {
	double tmp;
	if (x <= 2.7) {
		tmp = (x - Math.log(x)) / n;
	} else if (x <= 1e+49) {
		tmp = 1.0 - 1.0;
	} else if (x <= 8.5e+245) {
		tmp = (1.0 / n) / x;
	} else {
		tmp = 1.0 - 1.0;
	}
	return tmp;
}

Python

def code(x, n):
	tmp = 0
	if x <= 2.7:
		tmp = (x - math.log(x)) / n
	elif x <= 1e+49:
		tmp = 1.0 - 1.0
	elif x <= 8.5e+245:
		tmp = (1.0 / n) / x
	else:
		tmp = 1.0 - 1.0
	return tmp

Julia

function code(x, n)
	tmp = 0.0
	if (x <= 2.7)
		tmp = Float64(Float64(x - log(x)) / n);
	elseif (x <= 1e+49)
		tmp = Float64(1.0 - 1.0);
	elseif (x <= 8.5e+245)
		tmp = Float64(Float64(1.0 / n) / x);
	else
		tmp = Float64(1.0 - 1.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, n)
	tmp = 0.0;
	if (x <= 2.7)
		tmp = (x - log(x)) / n;
	elseif (x <= 1e+49)
		tmp = 1.0 - 1.0;
	elseif (x <= 8.5e+245)
		tmp = (1.0 / n) / x;
	else
		tmp = 1.0 - 1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, n_] := If[LessEqual[x, 2.7], N[(N[(x - N[Log[x], $MachinePrecision]), $MachinePrecision] / n), $MachinePrecision], If[LessEqual[x, 1e+49], N[(1.0 - 1.0), $MachinePrecision], If[LessEqual[x, 8.5e+245], N[(N[(1.0 / n), $MachinePrecision] / x), $MachinePrecision], N[(1.0 - 1.0), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if x < 2.7000000000000002

   1. Initial program 43.5%

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

   2. Taylor expanded in n around inf

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

      1. lower-/.f64 N/A

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

      2. lower--.f64 N/A

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

      3. +-commutative N/A

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

      4. lower-log.f64 N/A

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

      5. +-commutative N/A

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

      6. lower-+.f64 N/A

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

      7. lower-log.f64 51.8

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

   4. Applied rewrites 51.8%

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

   5. Taylor expanded in x around 0

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

   7. Applied rewrites 51.2%

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

   if 2.7000000000000002 < x < 9.99999999999999946e48 or 8.49999999999999971e245 < x

   1. Initial program 69.9%

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

   2. Taylor expanded in n around inf

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

   4. Applied rewrites 34.6%

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

   5. Taylor expanded in x around 0

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

   7. Applied rewrites 69.9%

      \[\leadsto \color{blue}{1} - 1 \]

   if 9.99999999999999946e48 < x < 8.49999999999999971e245

   1. Initial program 68.5%

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

   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. lower--.f64 N/A

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

      3. +-commutative N/A

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

      4. lower-log.f64 N/A

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

      5. +-commutative N/A

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

      6. lower-+.f64 N/A

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

      7. lower-log.f64 68.5

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

   4. Applied rewrites 68.5%

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

   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 64.0

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

   7. Applied rewrites 64.0%

      \[\leadsto \frac{1}{\color{blue}{n \cdot x}} \]
   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. lower-/.f64 65.3

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

   9. Applied rewrites 65.3%

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

Alternative 8: 57.8% accurate, 2.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq 1:\\ \;\;\;\;-\frac{\log x}{n}\\ \mathbf{elif}\;x \leq 10^{+49}:\\ \;\;\;\;1 - 1\\ \mathbf{elif}\;x \leq 8.5 \cdot 10^{+245}:\\ \;\;\;\;\frac{\frac{1}{n}}{x}\\ \mathbf{else}:\\ \;\;\;\;1 - 1\\ \end{array} \end{array} \]

FPCore

(FPCore (x n)
 :precision binary64
 (if (<= x 1.0)
   (- (/ (log x) n))
   (if (<= x 1e+49)
     (- 1.0 1.0)
     (if (<= x 8.5e+245) (/ (/ 1.0 n) x) (- 1.0 1.0)))))

C

double code(double x, double n) {
	double tmp;
	if (x <= 1.0) {
		tmp = -(log(x) / n);
	} else if (x <= 1e+49) {
		tmp = 1.0 - 1.0;
	} else if (x <= 8.5e+245) {
		tmp = (1.0 / n) / x;
	} else {
		tmp = 1.0 - 1.0;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, n)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: n
    real(8) :: tmp
    if (x <= 1.0d0) then
        tmp = -(log(x) / n)
    else if (x <= 1d+49) then
        tmp = 1.0d0 - 1.0d0
    else if (x <= 8.5d+245) then
        tmp = (1.0d0 / n) / x
    else
        tmp = 1.0d0 - 1.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double n) {
	double tmp;
	if (x <= 1.0) {
		tmp = -(Math.log(x) / n);
	} else if (x <= 1e+49) {
		tmp = 1.0 - 1.0;
	} else if (x <= 8.5e+245) {
		tmp = (1.0 / n) / x;
	} else {
		tmp = 1.0 - 1.0;
	}
	return tmp;
}

Python

def code(x, n):
	tmp = 0
	if x <= 1.0:
		tmp = -(math.log(x) / n)
	elif x <= 1e+49:
		tmp = 1.0 - 1.0
	elif x <= 8.5e+245:
		tmp = (1.0 / n) / x
	else:
		tmp = 1.0 - 1.0
	return tmp

Julia

function code(x, n)
	tmp = 0.0
	if (x <= 1.0)
		tmp = Float64(-Float64(log(x) / n));
	elseif (x <= 1e+49)
		tmp = Float64(1.0 - 1.0);
	elseif (x <= 8.5e+245)
		tmp = Float64(Float64(1.0 / n) / x);
	else
		tmp = Float64(1.0 - 1.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, n)
	tmp = 0.0;
	if (x <= 1.0)
		tmp = -(log(x) / n);
	elseif (x <= 1e+49)
		tmp = 1.0 - 1.0;
	elseif (x <= 8.5e+245)
		tmp = (1.0 / n) / x;
	else
		tmp = 1.0 - 1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, n_] := If[LessEqual[x, 1.0], (-N[(N[Log[x], $MachinePrecision] / n), $MachinePrecision]), If[LessEqual[x, 1e+49], N[(1.0 - 1.0), $MachinePrecision], If[LessEqual[x, 8.5e+245], N[(N[(1.0 / n), $MachinePrecision] / x), $MachinePrecision], N[(1.0 - 1.0), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if x < 1

   1. Initial program 43.6%

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

   2. Taylor expanded in x around 0

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

      1. negate-sub2 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 86.5

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

   4. Applied rewrites 86.5%

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

   5. Taylor expanded in n around inf

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

      1. lift-log.f64 N/A

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

      2. lift-/.f64 50.8

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

   7. Applied rewrites 50.8%

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

   if 1 < x < 9.99999999999999946e48 or 8.49999999999999971e245 < x

   1. Initial program 69.6%

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

   2. Taylor expanded in n around inf

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

   4. Applied rewrites 34.5%

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

   5. Taylor expanded in x around 0

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

   7. Applied rewrites 69.6%

      \[\leadsto \color{blue}{1} - 1 \]

   if 9.99999999999999946e48 < x < 8.49999999999999971e245

   1. Initial program 68.5%

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

   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. lower--.f64 N/A

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

      3. +-commutative N/A

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

      4. lower-log.f64 N/A

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

      5. +-commutative N/A

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

      6. lower-+.f64 N/A

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

      7. lower-log.f64 68.5

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

   4. Applied rewrites 68.5%

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

   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 64.0

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

   7. Applied rewrites 64.0%

      \[\leadsto \frac{1}{\color{blue}{n \cdot x}} \]
   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. lower-/.f64 65.3

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

   9. Applied rewrites 65.3%

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

Alternative 9: 43.9% accurate, 2.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{\frac{1}{n}}{x}\\ \mathbf{if}\;n \leq -5.4 \cdot 10^{-33}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;n \leq -1.45 \cdot 10^{-131}:\\ \;\;\;\;1 - 1\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x n)
 :precision binary64
 (let* ((t_0 (/ (/ 1.0 n) x)))
   (if (<= n -5.4e-33) t_0 (if (<= n -1.45e-131) (- 1.0 1.0) t_0))))

C

double code(double x, double n) {
	double t_0 = (1.0 / n) / x;
	double tmp;
	if (n <= -5.4e-33) {
		tmp = t_0;
	} else if (n <= -1.45e-131) {
		tmp = 1.0 - 1.0;
	} else {
		tmp = t_0;
	}
	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 <= (-5.4d-33)) then
        tmp = t_0
    else if (n <= (-1.45d-131)) then
        tmp = 1.0d0 - 1.0d0
    else
        tmp = t_0
    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 <= -5.4e-33) {
		tmp = t_0;
	} else if (n <= -1.45e-131) {
		tmp = 1.0 - 1.0;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, n):
	t_0 = (1.0 / n) / x
	tmp = 0
	if n <= -5.4e-33:
		tmp = t_0
	elif n <= -1.45e-131:
		tmp = 1.0 - 1.0
	else:
		tmp = t_0
	return tmp

Julia

function code(x, n)
	t_0 = Float64(Float64(1.0 / n) / x)
	tmp = 0.0
	if (n <= -5.4e-33)
		tmp = t_0;
	elseif (n <= -1.45e-131)
		tmp = Float64(1.0 - 1.0);
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, n)
	t_0 = (1.0 / n) / x;
	tmp = 0.0;
	if (n <= -5.4e-33)
		tmp = t_0;
	elseif (n <= -1.45e-131)
		tmp = 1.0 - 1.0;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, n_] := Block[{t$95$0 = N[(N[(1.0 / n), $MachinePrecision] / x), $MachinePrecision]}, If[LessEqual[n, -5.4e-33], t$95$0, If[LessEqual[n, -1.45e-131], N[(1.0 - 1.0), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if n < -5.4000000000000002e-33 or -1.4500000000000001e-131 < n

   1. Initial program 49.9%

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

   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. lower--.f64 N/A

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

      3. +-commutative N/A

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

      4. lower-log.f64 N/A

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

      5. +-commutative N/A

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

      6. lower-+.f64 N/A

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

      7. lower-log.f64 60.0

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

   4. Applied rewrites 60.0%

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

   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 42.5

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

   7. Applied rewrites 42.5%

      \[\leadsto \frac{1}{\color{blue}{n \cdot x}} \]
   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. lower-/.f64 43.1

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

   9. Applied rewrites 43.1%

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

   if -5.4000000000000002e-33 < n < -1.4500000000000001e-131

   1. Initial program 100.0%

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

   2. Taylor expanded in n around inf

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

   4. Applied rewrites 2.4%

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

   5. Taylor expanded in x around 0

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

   7. Applied rewrites 52.6%

      \[\leadsto \color{blue}{1} - 1 \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 10: 43.4% accurate, 3.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{1}{n \cdot x}\\ \mathbf{if}\;n \leq -5.4 \cdot 10^{-33}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;n \leq -1.45 \cdot 10^{-131}:\\ \;\;\;\;1 - 1\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x n)
 :precision binary64
 (let* ((t_0 (/ 1.0 (* n x))))
   (if (<= n -5.4e-33) t_0 (if (<= n -1.45e-131) (- 1.0 1.0) t_0))))

C

double code(double x, double n) {
	double t_0 = 1.0 / (n * x);
	double tmp;
	if (n <= -5.4e-33) {
		tmp = t_0;
	} else if (n <= -1.45e-131) {
		tmp = 1.0 - 1.0;
	} else {
		tmp = t_0;
	}
	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 <= (-5.4d-33)) then
        tmp = t_0
    else if (n <= (-1.45d-131)) then
        tmp = 1.0d0 - 1.0d0
    else
        tmp = t_0
    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 <= -5.4e-33) {
		tmp = t_0;
	} else if (n <= -1.45e-131) {
		tmp = 1.0 - 1.0;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, n):
	t_0 = 1.0 / (n * x)
	tmp = 0
	if n <= -5.4e-33:
		tmp = t_0
	elif n <= -1.45e-131:
		tmp = 1.0 - 1.0
	else:
		tmp = t_0
	return tmp

Julia

function code(x, n)
	t_0 = Float64(1.0 / Float64(n * x))
	tmp = 0.0
	if (n <= -5.4e-33)
		tmp = t_0;
	elseif (n <= -1.45e-131)
		tmp = Float64(1.0 - 1.0);
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, n)
	t_0 = 1.0 / (n * x);
	tmp = 0.0;
	if (n <= -5.4e-33)
		tmp = t_0;
	elseif (n <= -1.45e-131)
		tmp = 1.0 - 1.0;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, n_] := Block[{t$95$0 = N[(1.0 / N[(n * x), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[n, -5.4e-33], t$95$0, If[LessEqual[n, -1.45e-131], N[(1.0 - 1.0), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if n < -5.4000000000000002e-33 or -1.4500000000000001e-131 < n

   1. Initial program 49.9%

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

   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. lower--.f64 N/A

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

      3. +-commutative N/A

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

      4. lower-log.f64 N/A

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

      5. +-commutative N/A

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

      6. lower-+.f64 N/A

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

      7. lower-log.f64 60.0

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

   4. Applied rewrites 60.0%

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

   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 42.5

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

   7. Applied rewrites 42.5%

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

   if -5.4000000000000002e-33 < n < -1.4500000000000001e-131

   1. Initial program 100.0%

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

   2. Taylor expanded in n around inf

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

   4. Applied rewrites 2.4%

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

   5. Taylor expanded in x around 0

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

   7. Applied rewrites 52.6%

      \[\leadsto \color{blue}{1} - 1 \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 11: 32.0% accurate, 12.4× speedup

Math

\[\begin{array}{l} \\ 1 - 1 \end{array} \]

FPCore

(FPCore (x n) :precision binary64 (- 1.0 1.0))

C

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

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 - 1.0d0
end function

Java

public static double code(double x, double n) {
	return 1.0 - 1.0;
}

Python

def code(x, n):
	return 1.0 - 1.0

Julia

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

MATLAB

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

Wolfram

code[x_, n_] := N[(1.0 - 1.0), $MachinePrecision]

Derivation

1. Initial program 54.6%

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

2. Taylor expanded in n around inf

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

4. Applied rewrites 18.2%

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

5. Taylor expanded in x around 0

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

7. Applied rewrites 32.0%

   \[\leadsto \color{blue}{1} - 1 \]
8. Add Preprocessing

Reproduce

herbie shell --seed 2025116 
(FPCore (x n)
  :name "2nthrt (problem 3.4.6)"
  :precision binary64
  (- (pow (+ x 1.0) (/ 1.0 n)) (pow x (/ 1.0 n))))