Numeric.AD.Rank1.Halley:findZero from ad-4.2.4

Percentage Accurate: 81.1% → 94.4%

Time: 4.4s

Alternatives: 5

Speedup: 1.6×

Specification

Math

\[\begin{array}{l} \\ x - \frac{\left(y \cdot 2\right) \cdot z}{\left(z \cdot 2\right) \cdot z - y \cdot t} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (- x (/ (* (* y 2.0) z) (- (* (* z 2.0) z) (* y t)))))

C

double code(double x, double y, double z, double t) {
	return x - (((y * 2.0) * z) / (((z * 2.0) * z) - (y * t)));
}

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, y, z, t)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    code = x - (((y * 2.0d0) * z) / (((z * 2.0d0) * z) - (y * t)))
end function

Java

public static double code(double x, double y, double z, double t) {
	return x - (((y * 2.0) * z) / (((z * 2.0) * z) - (y * t)));
}

Python

def code(x, y, z, t):
	return x - (((y * 2.0) * z) / (((z * 2.0) * z) - (y * t)))

Julia

function code(x, y, z, t)
	return Float64(x - Float64(Float64(Float64(y * 2.0) * z) / Float64(Float64(Float64(z * 2.0) * z) - Float64(y * t))))
end

MATLAB

function tmp = code(x, y, z, t)
	tmp = x - (((y * 2.0) * z) / (((z * 2.0) * z) - (y * t)));
end

Wolfram

code[x_, y_, z_, t_] := N[(x - N[(N[(N[(y * 2.0), $MachinePrecision] * z), $MachinePrecision] / N[(N[(N[(z * 2.0), $MachinePrecision] * z), $MachinePrecision] - N[(y * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Initial Program: 81.1% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ x - \frac{\left(y \cdot 2\right) \cdot z}{\left(z \cdot 2\right) \cdot z - y \cdot t} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (- x (/ (* (* y 2.0) z) (- (* (* z 2.0) z) (* y t)))))

C

double code(double x, double y, double z, double t) {
	return x - (((y * 2.0) * z) / (((z * 2.0) * z) - (y * t)));
}

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, y, z, t)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    code = x - (((y * 2.0d0) * z) / (((z * 2.0d0) * z) - (y * t)))
end function

Java

public static double code(double x, double y, double z, double t) {
	return x - (((y * 2.0) * z) / (((z * 2.0) * z) - (y * t)));
}

Python

def code(x, y, z, t):
	return x - (((y * 2.0) * z) / (((z * 2.0) * z) - (y * t)))

Julia

function code(x, y, z, t)
	return Float64(x - Float64(Float64(Float64(y * 2.0) * z) / Float64(Float64(Float64(z * 2.0) * z) - Float64(y * t))))
end

MATLAB

function tmp = code(x, y, z, t)
	tmp = x - (((y * 2.0) * z) / (((z * 2.0) * z) - (y * t)));
end

Wolfram

code[x_, y_, z_, t_] := N[(x - N[(N[(N[(y * 2.0), $MachinePrecision] * z), $MachinePrecision] / N[(N[(N[(z * 2.0), $MachinePrecision] * z), $MachinePrecision] - N[(y * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Alternative 1: 94.4% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\frac{\left(y \cdot 2\right) \cdot z}{\left(z \cdot 2\right) \cdot z - y \cdot t} \leq 4 \cdot 10^{+214}:\\ \;\;\;\;x - \left(y + y\right) \cdot \frac{z}{\mathsf{fma}\left(z \cdot 2, z, \left(-y\right) \cdot t\right)}\\ \mathbf{else}:\\ \;\;\;\;x - \frac{y}{z}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= (/ (* (* y 2.0) z) (- (* (* z 2.0) z) (* y t))) 4e+214)
   (- x (* (+ y y) (/ z (fma (* z 2.0) z (* (- y) t)))))
   (- x (/ y z))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((((y * 2.0) * z) / (((z * 2.0) * z) - (y * t))) <= 4e+214) {
		tmp = x - ((y + y) * (z / fma((z * 2.0), z, (-y * t))));
	} else {
		tmp = x - (y / z);
	}
	return tmp;
}

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (Float64(Float64(Float64(y * 2.0) * z) / Float64(Float64(Float64(z * 2.0) * z) - Float64(y * t))) <= 4e+214)
		tmp = Float64(x - Float64(Float64(y + y) * Float64(z / fma(Float64(z * 2.0), z, Float64(Float64(-y) * t)))));
	else
		tmp = Float64(x - Float64(y / z));
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[N[(N[(N[(y * 2.0), $MachinePrecision] * z), $MachinePrecision] / N[(N[(N[(z * 2.0), $MachinePrecision] * z), $MachinePrecision] - N[(y * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 4e+214], N[(x - N[(N[(y + y), $MachinePrecision] * N[(z / N[(N[(z * 2.0), $MachinePrecision] * z + N[((-y) * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x - N[(y / z), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 (*.f64 (*.f64 y #s(literal 2 binary64)) z) (-.f64 (*.f64 (*.f64 z #s(literal 2 binary64)) z) (*.f64 y t))) < 3.9999999999999998e214

   1. Initial program 96.6%

      \[x - \frac{\left(y \cdot 2\right) \cdot z}{\left(z \cdot 2\right) \cdot z - y \cdot t} \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto x - \color{blue}{\frac{\left(y \cdot 2\right) \cdot z}{\left(z \cdot 2\right) \cdot z - y \cdot t}} \]

      2. lift-*.f64 N/A

         \[\leadsto x - \frac{\color{blue}{\left(y \cdot 2\right)} \cdot z}{\left(z \cdot 2\right) \cdot z - y \cdot t} \]

      3. lift-*.f64 N/A

         \[\leadsto x - \frac{\color{blue}{\left(y \cdot 2\right) \cdot z}}{\left(z \cdot 2\right) \cdot z - y \cdot t} \]

      4. lift--.f64 N/A

         \[\leadsto x - \frac{\left(y \cdot 2\right) \cdot z}{\color{blue}{\left(z \cdot 2\right) \cdot z - y \cdot t}} \]

      5. lift-*.f64 N/A

         \[\leadsto x - \frac{\left(y \cdot 2\right) \cdot z}{\color{blue}{\left(z \cdot 2\right) \cdot z} - y \cdot t} \]

      6. lift-*.f64 N/A

         \[\leadsto x - \frac{\left(y \cdot 2\right) \cdot z}{\color{blue}{\left(z \cdot 2\right)} \cdot z - y \cdot t} \]

      7. lift-*.f64 N/A

         \[\leadsto x - \frac{\left(y \cdot 2\right) \cdot z}{\left(z \cdot 2\right) \cdot z - \color{blue}{y \cdot t}} \]

      8. associate-/l* N/A

         \[\leadsto x - \color{blue}{\left(y \cdot 2\right) \cdot \frac{z}{\left(z \cdot 2\right) \cdot z - y \cdot t}} \]

      9. lower-*.f64 N/A

         \[\leadsto x - \color{blue}{\left(y \cdot 2\right) \cdot \frac{z}{\left(z \cdot 2\right) \cdot z - y \cdot t}} \]

      10. *-commutative N/A

          \[\leadsto x - \color{blue}{\left(2 \cdot y\right)} \cdot \frac{z}{\left(z \cdot 2\right) \cdot z - y \cdot t} \]

      11. lower-*.f64 N/A

          \[\leadsto x - \color{blue}{\left(2 \cdot y\right)} \cdot \frac{z}{\left(z \cdot 2\right) \cdot z - y \cdot t} \]

      12. lower-/.f64 N/A

          \[\leadsto x - \left(2 \cdot y\right) \cdot \color{blue}{\frac{z}{\left(z \cdot 2\right) \cdot z - y \cdot t}} \]

      13. lift-*.f64 N/A

          \[\leadsto x - \left(2 \cdot y\right) \cdot \frac{z}{\color{blue}{\left(z \cdot 2\right)} \cdot z - y \cdot t} \]

      14. lift-*.f64 N/A

          \[\leadsto x - \left(2 \cdot y\right) \cdot \frac{z}{\color{blue}{\left(z \cdot 2\right) \cdot z} - y \cdot t} \]

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

          \[\leadsto x - \left(2 \cdot y\right) \cdot \frac{z}{\color{blue}{\left(z \cdot 2\right) \cdot z + \left(\mathsf{neg}\left(y\right)\right) \cdot t}} \]

      16. lift-*.f64 N/A

          \[\leadsto x - \left(2 \cdot y\right) \cdot \frac{z}{\color{blue}{\left(z \cdot 2\right) \cdot z} + \left(\mathsf{neg}\left(y\right)\right) \cdot t} \]

      17. lift-*.f64 N/A

          \[\leadsto x - \left(2 \cdot y\right) \cdot \frac{z}{\color{blue}{\left(z \cdot 2\right)} \cdot z + \left(\mathsf{neg}\left(y\right)\right) \cdot t} \]

      18. *-commutative N/A

          \[\leadsto x - \left(2 \cdot y\right) \cdot \frac{z}{\color{blue}{\left(2 \cdot z\right)} \cdot z + \left(\mathsf{neg}\left(y\right)\right) \cdot t} \]

      19. lower-fma.f64 N/A

          \[\leadsto x - \left(2 \cdot y\right) \cdot \frac{z}{\color{blue}{\mathsf{fma}\left(2 \cdot z, z, \left(\mathsf{neg}\left(y\right)\right) \cdot t\right)}} \]

      20. *-commutative N/A

          \[\leadsto x - \left(2 \cdot y\right) \cdot \frac{z}{\mathsf{fma}\left(\color{blue}{z \cdot 2}, z, \left(\mathsf{neg}\left(y\right)\right) \cdot t\right)} \]

      21. lift-*.f64 N/A

          \[\leadsto x - \left(2 \cdot y\right) \cdot \frac{z}{\mathsf{fma}\left(\color{blue}{z \cdot 2}, z, \left(\mathsf{neg}\left(y\right)\right) \cdot t\right)} \]

      22. mul-1-neg N/A

          \[\leadsto x - \left(2 \cdot y\right) \cdot \frac{z}{\mathsf{fma}\left(z \cdot 2, z, \color{blue}{\left(-1 \cdot y\right)} \cdot t\right)} \]

      23. lower-*.f64 N/A

          \[\leadsto x - \left(2 \cdot y\right) \cdot \frac{z}{\mathsf{fma}\left(z \cdot 2, z, \color{blue}{\left(-1 \cdot y\right) \cdot t}\right)} \]

      24. mul-1-neg N/A

          \[\leadsto x - \left(2 \cdot y\right) \cdot \frac{z}{\mathsf{fma}\left(z \cdot 2, z, \color{blue}{\left(\mathsf{neg}\left(y\right)\right)} \cdot t\right)} \]

      25. lower-neg.f64 97.9

          \[\leadsto x - \left(2 \cdot y\right) \cdot \frac{z}{\mathsf{fma}\left(z \cdot 2, z, \color{blue}{\left(-y\right)} \cdot t\right)} \]

   4. Applied rewrites 97.9%

      \[\leadsto x - \color{blue}{\left(2 \cdot y\right) \cdot \frac{z}{\mathsf{fma}\left(z \cdot 2, z, \left(-y\right) \cdot t\right)}} \]
   5. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto x - \color{blue}{\left(2 \cdot y\right)} \cdot \frac{z}{\mathsf{fma}\left(z \cdot 2, z, \left(-y\right) \cdot t\right)} \]

      2. count-2-rev N/A

         \[\leadsto x - \color{blue}{\left(y + y\right)} \cdot \frac{z}{\mathsf{fma}\left(z \cdot 2, z, \left(-y\right) \cdot t\right)} \]

      3. lower-+.f64 97.9

         \[\leadsto x - \color{blue}{\left(y + y\right)} \cdot \frac{z}{\mathsf{fma}\left(z \cdot 2, z, \left(-y\right) \cdot t\right)} \]

   6. Applied rewrites 97.9%

      \[\leadsto x - \color{blue}{\left(y + y\right)} \cdot \frac{z}{\mathsf{fma}\left(z \cdot 2, z, \left(-y\right) \cdot t\right)} \]

   if 3.9999999999999998e214 < (/.f64 (*.f64 (*.f64 y #s(literal 2 binary64)) z) (-.f64 (*.f64 (*.f64 z #s(literal 2 binary64)) z) (*.f64 y t)))

   1. Initial program 0.3%

      \[x - \frac{\left(y \cdot 2\right) \cdot z}{\left(z \cdot 2\right) \cdot z - y \cdot t} \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0

      \[\leadsto x - \color{blue}{\frac{y}{z}} \]
   4. Step-by-step derivation

      1. lower-/.f64 64.7

         \[\leadsto x - \frac{y}{\color{blue}{z}} \]

   5. Applied rewrites 64.7%

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

Alternative 2: 89.3% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -3.7 \cdot 10^{-50} \lor \neg \left(z \leq 6.2 \cdot 10^{+39}\right):\\ \;\;\;\;x - \frac{y}{z}\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(\frac{z}{t}, 2, x\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= z -3.7e-50) (not (<= z 6.2e+39)))
   (- x (/ y z))
   (fma (/ z t) 2.0 x)))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((z <= -3.7e-50) || !(z <= 6.2e+39)) {
		tmp = x - (y / z);
	} else {
		tmp = fma((z / t), 2.0, x);
	}
	return tmp;
}

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((z <= -3.7e-50) || !(z <= 6.2e+39))
		tmp = Float64(x - Float64(y / z));
	else
		tmp = fma(Float64(z / t), 2.0, x);
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[z, -3.7e-50], N[Not[LessEqual[z, 6.2e+39]], $MachinePrecision]], N[(x - N[(y / z), $MachinePrecision]), $MachinePrecision], N[(N[(z / t), $MachinePrecision] * 2.0 + x), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if z < -3.7000000000000001e-50 or 6.2000000000000005e39 < z

   1. Initial program 73.7%

      \[x - \frac{\left(y \cdot 2\right) \cdot z}{\left(z \cdot 2\right) \cdot z - y \cdot t} \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0

      \[\leadsto x - \color{blue}{\frac{y}{z}} \]
   4. Step-by-step derivation

      1. lower-/.f64 87.6

         \[\leadsto x - \frac{y}{\color{blue}{z}} \]

   5. Applied rewrites 87.6%

      \[\leadsto x - \color{blue}{\frac{y}{z}} \]

   if -3.7000000000000001e-50 < z < 6.2000000000000005e39

   1. Initial program 90.7%

      \[x - \frac{\left(y \cdot 2\right) \cdot z}{\left(z \cdot 2\right) \cdot z - y \cdot t} \]
   2. Add Preprocessing

   3. Taylor expanded in y around inf

      \[\leadsto \color{blue}{x - -2 \cdot \frac{z}{t}} \]
   4. Step-by-step derivation

      1. metadata-eval N/A

         \[\leadsto x - \left(\mathsf{neg}\left(2\right)\right) \cdot \frac{\color{blue}{z}}{t} \]

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

         \[\leadsto x + \color{blue}{2 \cdot \frac{z}{t}} \]

      3. +-commutative N/A

         \[\leadsto 2 \cdot \frac{z}{t} + \color{blue}{x} \]

      4. *-commutative N/A

         \[\leadsto \frac{z}{t} \cdot 2 + x \]

      5. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(\frac{z}{t}, \color{blue}{2}, x\right) \]

      6. lower-/.f64 85.2

         \[\leadsto \mathsf{fma}\left(\frac{z}{t}, 2, x\right) \]

   5. Applied rewrites 85.2%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{z}{t}, 2, x\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 86.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -3.7 \cdot 10^{-50} \lor \neg \left(z \leq 6.2 \cdot 10^{+39}\right):\\ \;\;\;\;x - \frac{y}{z}\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(\frac{z}{t}, 2, x\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 82.4% accurate, 1.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -3.3 \cdot 10^{-50} \lor \neg \left(z \leq 1.1 \cdot 10^{+42}\right):\\ \;\;\;\;x - \frac{y}{z}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= z -3.3e-50) (not (<= z 1.1e+42))) (- x (/ y z)) x))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((z <= -3.3e-50) || !(z <= 1.1e+42)) {
		tmp = x - (y / z);
	} else {
		tmp = x;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, t)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: tmp
    if ((z <= (-3.3d-50)) .or. (.not. (z <= 1.1d+42))) then
        tmp = x - (y / z)
    else
        tmp = x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if ((z <= -3.3e-50) || !(z <= 1.1e+42)) {
		tmp = x - (y / z);
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (z <= -3.3e-50) or not (z <= 1.1e+42):
		tmp = x - (y / z)
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((z <= -3.3e-50) || !(z <= 1.1e+42))
		tmp = Float64(x - Float64(y / z));
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((z <= -3.3e-50) || ~((z <= 1.1e+42)))
		tmp = x - (y / z);
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[z, -3.3e-50], N[Not[LessEqual[z, 1.1e+42]], $MachinePrecision]], N[(x - N[(y / z), $MachinePrecision]), $MachinePrecision], x]

Derivation

1. Split input into 2 regimes

2. if z < -3.2999999999999998e-50 or 1.1000000000000001e42 < z

   1. Initial program 73.7%

      \[x - \frac{\left(y \cdot 2\right) \cdot z}{\left(z \cdot 2\right) \cdot z - y \cdot t} \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0

      \[\leadsto x - \color{blue}{\frac{y}{z}} \]
   4. Step-by-step derivation

      1. lower-/.f64 87.6

         \[\leadsto x - \frac{y}{\color{blue}{z}} \]

   5. Applied rewrites 87.6%

      \[\leadsto x - \color{blue}{\frac{y}{z}} \]

   if -3.2999999999999998e-50 < z < 1.1000000000000001e42

   1. Initial program 90.7%

      \[x - \frac{\left(y \cdot 2\right) \cdot z}{\left(z \cdot 2\right) \cdot z - y \cdot t} \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

      \[\leadsto \color{blue}{x} \]
   4. Step-by-step derivation

   5. Applied rewrites 76.2%

      \[\leadsto \color{blue}{x} \]
3. Recombined 2 regimes into one program.

4. Final simplification 81.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -3.3 \cdot 10^{-50} \lor \neg \left(z \leq 1.1 \cdot 10^{+42}\right):\\ \;\;\;\;x - \frac{y}{z}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 75.5% accurate, 1.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1.45 \cdot 10^{-239} \lor \neg \left(x \leq 1.3 \cdot 10^{-225}\right):\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;\frac{-y}{z}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= x -1.45e-239) (not (<= x 1.3e-225))) x (/ (- y) z)))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((x <= -1.45e-239) || !(x <= 1.3e-225)) {
		tmp = x;
	} else {
		tmp = -y / z;
	}
	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, y, z, t)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: tmp
    if ((x <= (-1.45d-239)) .or. (.not. (x <= 1.3d-225))) then
        tmp = x
    else
        tmp = -y / z
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if ((x <= -1.45e-239) || !(x <= 1.3e-225)) {
		tmp = x;
	} else {
		tmp = -y / z;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (x <= -1.45e-239) or not (x <= 1.3e-225):
		tmp = x
	else:
		tmp = -y / z
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((x <= -1.45e-239) || !(x <= 1.3e-225))
		tmp = x;
	else
		tmp = Float64(Float64(-y) / z);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((x <= -1.45e-239) || ~((x <= 1.3e-225)))
		tmp = x;
	else
		tmp = -y / z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[x, -1.45e-239], N[Not[LessEqual[x, 1.3e-225]], $MachinePrecision]], x, N[((-y) / z), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < -1.4500000000000001e-239 or 1.30000000000000007e-225 < x

   1. Initial program 84.5%

      \[x - \frac{\left(y \cdot 2\right) \cdot z}{\left(z \cdot 2\right) \cdot z - y \cdot t} \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

      \[\leadsto \color{blue}{x} \]
   4. Step-by-step derivation

   5. Applied rewrites 79.1%

      \[\leadsto \color{blue}{x} \]

   if -1.4500000000000001e-239 < x < 1.30000000000000007e-225

   1. Initial program 67.5%

      \[x - \frac{\left(y \cdot 2\right) \cdot z}{\left(z \cdot 2\right) \cdot z - y \cdot t} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{-2 \cdot \frac{y \cdot z}{2 \cdot {z}^{2} - t \cdot y}} \]
   4. Step-by-step derivation

      1. associate-*r/ N/A

         \[\leadsto \frac{-2 \cdot \left(y \cdot z\right)}{\color{blue}{2 \cdot {z}^{2} - t \cdot y}} \]

      2. lower-/.f64 N/A

         \[\leadsto \frac{-2 \cdot \left(y \cdot z\right)}{\color{blue}{2 \cdot {z}^{2} - t \cdot y}} \]

      3. lower-*.f64 N/A

         \[\leadsto \frac{-2 \cdot \left(y \cdot z\right)}{\color{blue}{2 \cdot {z}^{2}} - t \cdot y} \]

      4. *-commutative N/A

         \[\leadsto \frac{-2 \cdot \left(z \cdot y\right)}{2 \cdot \color{blue}{{z}^{2}} - t \cdot y} \]

      5. lower-*.f64 N/A

         \[\leadsto \frac{-2 \cdot \left(z \cdot y\right)}{2 \cdot \color{blue}{{z}^{2}} - t \cdot y} \]

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

         \[\leadsto \frac{-2 \cdot \left(z \cdot y\right)}{2 \cdot {z}^{2} + \color{blue}{\left(\mathsf{neg}\left(t\right)\right) \cdot y}} \]

      7. mul-1-neg N/A

         \[\leadsto \frac{-2 \cdot \left(z \cdot y\right)}{2 \cdot {z}^{2} + \left(-1 \cdot t\right) \cdot y} \]

      8. associate-*r* N/A

         \[\leadsto \frac{-2 \cdot \left(z \cdot y\right)}{2 \cdot {z}^{2} + -1 \cdot \color{blue}{\left(t \cdot y\right)}} \]

      9. +-commutative N/A

         \[\leadsto \frac{-2 \cdot \left(z \cdot y\right)}{-1 \cdot \left(t \cdot y\right) + \color{blue}{2 \cdot {z}^{2}}} \]

      10. associate-*r* N/A

          \[\leadsto \frac{-2 \cdot \left(z \cdot y\right)}{\left(-1 \cdot t\right) \cdot y + \color{blue}{2} \cdot {z}^{2}} \]

      11. mul-1-neg N/A

          \[\leadsto \frac{-2 \cdot \left(z \cdot y\right)}{\left(\mathsf{neg}\left(t\right)\right) \cdot y + 2 \cdot {z}^{2}} \]

      12. lower-fma.f64 N/A

          \[\leadsto \frac{-2 \cdot \left(z \cdot y\right)}{\mathsf{fma}\left(\mathsf{neg}\left(t\right), \color{blue}{y}, 2 \cdot {z}^{2}\right)} \]

      13. lower-neg.f64 N/A

          \[\leadsto \frac{-2 \cdot \left(z \cdot y\right)}{\mathsf{fma}\left(-t, y, 2 \cdot {z}^{2}\right)} \]

      14. *-commutative N/A

          \[\leadsto \frac{-2 \cdot \left(z \cdot y\right)}{\mathsf{fma}\left(-t, y, {z}^{2} \cdot 2\right)} \]

      15. lower-*.f64 N/A

          \[\leadsto \frac{-2 \cdot \left(z \cdot y\right)}{\mathsf{fma}\left(-t, y, {z}^{2} \cdot 2\right)} \]

      16. unpow2 N/A

          \[\leadsto \frac{-2 \cdot \left(z \cdot y\right)}{\mathsf{fma}\left(-t, y, \left(z \cdot z\right) \cdot 2\right)} \]

      17. lower-*.f64 41.1

          \[\leadsto \frac{-2 \cdot \left(z \cdot y\right)}{\mathsf{fma}\left(-t, y, \left(z \cdot z\right) \cdot 2\right)} \]

   5. Applied rewrites 41.1%

      \[\leadsto \color{blue}{\frac{-2 \cdot \left(z \cdot y\right)}{\mathsf{fma}\left(-t, y, \left(z \cdot z\right) \cdot 2\right)}} \]

   6. Taylor expanded in y around 0

      \[\leadsto -1 \cdot \color{blue}{\frac{y}{z}} \]
   7. Step-by-step derivation

      1. associate-*r/ N/A

         \[\leadsto \frac{-1 \cdot y}{z} \]

      2. mul-1-neg N/A

         \[\leadsto \frac{\mathsf{neg}\left(y\right)}{z} \]

      3. lower-/.f64 N/A

         \[\leadsto \frac{\mathsf{neg}\left(y\right)}{z} \]

      4. lift-neg.f64 55.7

         \[\leadsto \frac{-y}{z} \]

   8. Applied rewrites 55.7%

      \[\leadsto \frac{-y}{\color{blue}{z}} \]
3. Recombined 2 regimes into one program.

4. Final simplification 75.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -1.45 \cdot 10^{-239} \lor \neg \left(x \leq 1.3 \cdot 10^{-225}\right):\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;\frac{-y}{z}\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 75.5% accurate, 43.0× speedup

Math

\[\begin{array}{l} \\ x \end{array} \]

FPCore

(FPCore (x y z t) :precision binary64 x)

C

double code(double x, double y, double z, double t) {
	return 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, y, z, t)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    code = x
end function

Java

public static double code(double x, double y, double z, double t) {
	return x;
}

Python

def code(x, y, z, t):
	return x

Julia

function code(x, y, z, t)
	return x
end

MATLAB

function tmp = code(x, y, z, t)
	tmp = x;
end

Wolfram

code[x_, y_, z_, t_] := x

Derivation

1. Initial program 82.3%

   \[x - \frac{\left(y \cdot 2\right) \cdot z}{\left(z \cdot 2\right) \cdot z - y \cdot t} \]
2. Add Preprocessing

3. Taylor expanded in x around inf

   \[\leadsto \color{blue}{x} \]
4. Step-by-step derivation

5. Applied rewrites 72.6%

   \[\leadsto \color{blue}{x} \]
6. Add Preprocessing

Developer Target 1: 99.9% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ x - \frac{1}{\frac{z}{y} - \frac{\frac{t}{2}}{z}} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (- x (/ 1.0 (- (/ z y) (/ (/ t 2.0) z)))))

C

double code(double x, double y, double z, double t) {
	return x - (1.0 / ((z / y) - ((t / 2.0) / z)));
}

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, y, z, t)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    code = x - (1.0d0 / ((z / y) - ((t / 2.0d0) / z)))
end function

Java

public static double code(double x, double y, double z, double t) {
	return x - (1.0 / ((z / y) - ((t / 2.0) / z)));
}

Python

def code(x, y, z, t):
	return x - (1.0 / ((z / y) - ((t / 2.0) / z)))

Julia

function code(x, y, z, t)
	return Float64(x - Float64(1.0 / Float64(Float64(z / y) - Float64(Float64(t / 2.0) / z))))
end

MATLAB

function tmp = code(x, y, z, t)
	tmp = x - (1.0 / ((z / y) - ((t / 2.0) / z)));
end

Wolfram

code[x_, y_, z_, t_] := N[(x - N[(1.0 / N[(N[(z / y), $MachinePrecision] - N[(N[(t / 2.0), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Reproduce

herbie shell --seed 2025064 
(FPCore (x y z t)
  :name "Numeric.AD.Rank1.Halley:findZero from ad-4.2.4"
  :precision binary64

  :alt
  (! :herbie-platform default (- x (/ 1 (- (/ z y) (/ (/ t 2) z)))))

  (- x (/ (* (* y 2.0) z) (- (* (* z 2.0) z) (* y t)))))