fabs fraction 1

Percentage Accurate: 91.5% → 99.9%

Time: 3.0s

Alternatives: 9

Speedup: 0.9×

• 20.5% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

\[\begin{array}{l} \\ \left|\frac{x + 4}{y} - \frac{x}{y} \cdot z\right| \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (fabs (- (/ (+ x 4.0) y) (* (/ x y) z))))

C

double code(double x, double y, double z) {
	return fabs((((x + 4.0) / y) - ((x / y) * 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)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = abs((((x + 4.0d0) / y) - ((x / y) * z)))
end function

Java

public static double code(double x, double y, double z) {
	return Math.abs((((x + 4.0) / y) - ((x / y) * z)));
}

Python

def code(x, y, z):
	return math.fabs((((x + 4.0) / y) - ((x / y) * z)))

Julia

function code(x, y, z)
	return abs(Float64(Float64(Float64(x + 4.0) / y) - Float64(Float64(x / y) * z)))
end

MATLAB

function tmp = code(x, y, z)
	tmp = abs((((x + 4.0) / y) - ((x / y) * z)));
end

Wolfram

code[x_, y_, z_] := N[Abs[N[(N[(N[(x + 4.0), $MachinePrecision] / y), $MachinePrecision] - N[(N[(x / y), $MachinePrecision] * z), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]

Initial Program: 91.5% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \left|\frac{x + 4}{y} - \frac{x}{y} \cdot z\right| \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (fabs (- (/ (+ x 4.0) y) (* (/ x y) z))))

C

double code(double x, double y, double z) {
	return fabs((((x + 4.0) / y) - ((x / y) * 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)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = abs((((x + 4.0d0) / y) - ((x / y) * z)))
end function

Java

public static double code(double x, double y, double z) {
	return Math.abs((((x + 4.0) / y) - ((x / y) * z)));
}

Python

def code(x, y, z):
	return math.fabs((((x + 4.0) / y) - ((x / y) * z)))

Julia

function code(x, y, z)
	return abs(Float64(Float64(Float64(x + 4.0) / y) - Float64(Float64(x / y) * z)))
end

MATLAB

function tmp = code(x, y, z)
	tmp = abs((((x + 4.0) / y) - ((x / y) * z)));
end

Wolfram

code[x_, y_, z_] := N[Abs[N[(N[(N[(x + 4.0), $MachinePrecision] / y), $MachinePrecision] - N[(N[(x / y), $MachinePrecision] * z), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]

Alternative 1: 99.9% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left|-\frac{x}{y} \cdot \left(z + -1\right)\right|\\ \mathbf{if}\;x \leq -112000000000:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;x \leq 2 \cdot 10^{+16}:\\ \;\;\;\;\left|\frac{\mathsf{fma}\left(z, x, -4 - x\right)}{y}\right|\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (fabs (- (* (/ x y) (+ z -1.0))))))
   (if (<= x -112000000000.0)
     t_0
     (if (<= x 2e+16) (fabs (/ (fma z x (- -4.0 x)) y)) t_0))))

C

double code(double x, double y, double z) {
	double t_0 = fabs(-((x / y) * (z + -1.0)));
	double tmp;
	if (x <= -112000000000.0) {
		tmp = t_0;
	} else if (x <= 2e+16) {
		tmp = fabs((fma(z, x, (-4.0 - x)) / y));
	} else {
		tmp = t_0;
	}
	return tmp;
}

Julia

function code(x, y, z)
	t_0 = abs(Float64(-Float64(Float64(x / y) * Float64(z + -1.0))))
	tmp = 0.0
	if (x <= -112000000000.0)
		tmp = t_0;
	elseif (x <= 2e+16)
		tmp = abs(Float64(fma(z, x, Float64(-4.0 - x)) / y));
	else
		tmp = t_0;
	end
	return tmp
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[Abs[(-N[(N[(x / y), $MachinePrecision] * N[(z + -1.0), $MachinePrecision]), $MachinePrecision])], $MachinePrecision]}, If[LessEqual[x, -112000000000.0], t$95$0, If[LessEqual[x, 2e+16], N[Abs[N[(N[(z * x + N[(-4.0 - x), $MachinePrecision]), $MachinePrecision] / y), $MachinePrecision]], $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if x < -1.12e11 or 2e16 < x

   1. Initial program 86.1%

      \[\left|\frac{x + 4}{y} - \frac{x}{y} \cdot z\right| \]

   2. Taylor expanded in x around inf

      \[\leadsto \left|\color{blue}{x \cdot \left(\frac{1}{y} - \frac{z}{y}\right)}\right| \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left|\left(\frac{1}{y} - \frac{z}{y}\right) \cdot \color{blue}{x}\right| \]

      2. lower-*.f64 N/A

         \[\leadsto \left|\left(\frac{1}{y} - \frac{z}{y}\right) \cdot \color{blue}{x}\right| \]

      3. sub-div N/A

         \[\leadsto \left|\frac{1 - z}{y} \cdot x\right| \]

      4. lower-/.f64 N/A

         \[\leadsto \left|\frac{1 - z}{y} \cdot x\right| \]

      5. lower--.f64 99.7

         \[\leadsto \left|\frac{1 - z}{y} \cdot x\right| \]

   4. Applied rewrites 99.7%

      \[\leadsto \left|\color{blue}{\frac{1 - z}{y} \cdot x}\right| \]
   5. Step-by-step derivation

      1. lift--.f64 N/A

         \[\leadsto \left|\frac{1 - z}{y} \cdot x\right| \]

      2. lift-/.f64 N/A

         \[\leadsto \left|\frac{1 - z}{y} \cdot x\right| \]

      3. lower-*.f64 N/A

         \[\leadsto \left|\frac{1 - z}{y} \cdot \color{blue}{x}\right| \]

      4. *-commutative N/A

         \[\leadsto \left|x \cdot \color{blue}{\frac{1 - z}{y}}\right| \]

      5. sub-div N/A

         \[\leadsto \left|x \cdot \left(\frac{1}{y} - \color{blue}{\frac{z}{y}}\right)\right| \]

      6. *-lft-identity N/A

         \[\leadsto \left|x \cdot \left(\frac{1}{y} - \frac{1 \cdot z}{y}\right)\right| \]

      7. div-sub N/A

         \[\leadsto \left|x \cdot \frac{1 - 1 \cdot z}{\color{blue}{y}}\right| \]

      8. metadata-eval N/A

         \[\leadsto \left|x \cdot \frac{1 - \left(\mathsf{neg}\left(-1\right)\right) \cdot z}{y}\right| \]

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

         \[\leadsto \left|x \cdot \frac{1 + -1 \cdot z}{y}\right| \]

      10. associate-/l* N/A

          \[\leadsto \left|\frac{x \cdot \left(1 + -1 \cdot z\right)}{\color{blue}{y}}\right| \]

      11. frac-2neg N/A

          \[\leadsto \left|\frac{\mathsf{neg}\left(x \cdot \left(1 + -1 \cdot z\right)\right)}{\color{blue}{\mathsf{neg}\left(y\right)}}\right| \]

      12. distribute-frac-neg2 N/A

          \[\leadsto \left|\mathsf{neg}\left(\frac{\mathsf{neg}\left(x \cdot \left(1 + -1 \cdot z\right)\right)}{y}\right)\right| \]

   6. Applied rewrites 99.8%

      \[\leadsto \left|-\frac{x}{y} \cdot \left(z + -1\right)\right| \]

   if -1.12e11 < x < 2e16

   1. Initial program 96.3%

      \[\left|\frac{x + 4}{y} - \frac{x}{y} \cdot z\right| \]
   2. Step-by-step derivation

      1. lift-fabs.f64 N/A

         \[\leadsto \color{blue}{\left|\frac{x + 4}{y} - \frac{x}{y} \cdot z\right|} \]

      2. lift--.f64 N/A

         \[\leadsto \left|\color{blue}{\frac{x + 4}{y} - \frac{x}{y} \cdot z}\right| \]

      3. lift-+.f64 N/A

         \[\leadsto \left|\frac{\color{blue}{x + 4}}{y} - \frac{x}{y} \cdot z\right| \]

      4. lift-/.f64 N/A

         \[\leadsto \left|\color{blue}{\frac{x + 4}{y}} - \frac{x}{y} \cdot z\right| \]

      5. lift-*.f64 N/A

         \[\leadsto \left|\frac{x + 4}{y} - \color{blue}{\frac{x}{y} \cdot z}\right| \]

      6. lift-/.f64 N/A

         \[\leadsto \left|\frac{x + 4}{y} - \color{blue}{\frac{x}{y}} \cdot z\right| \]

      7. neg-fabs N/A

         \[\leadsto \color{blue}{\left|\mathsf{neg}\left(\left(\frac{x + 4}{y} - \frac{x}{y} \cdot z\right)\right)\right|} \]

      8. +-commutative N/A

         \[\leadsto \left|\mathsf{neg}\left(\left(\frac{\color{blue}{4 + x}}{y} - \frac{x}{y} \cdot z\right)\right)\right| \]

      9. associate-*l/ N/A

         \[\leadsto \left|\mathsf{neg}\left(\left(\frac{4 + x}{y} - \color{blue}{\frac{x \cdot z}{y}}\right)\right)\right| \]

      10. div-sub N/A

          \[\leadsto \left|\mathsf{neg}\left(\color{blue}{\frac{\left(4 + x\right) - x \cdot z}{y}}\right)\right| \]

      11. distribute-neg-frac N/A

          \[\leadsto \left|\color{blue}{\frac{\mathsf{neg}\left(\left(\left(4 + x\right) - x \cdot z\right)\right)}{y}}\right| \]

      12. mul-1-neg N/A

          \[\leadsto \left|\frac{\color{blue}{-1 \cdot \left(\left(4 + x\right) - x \cdot z\right)}}{y}\right| \]

      13. distribute-lft-out-- N/A

          \[\leadsto \left|\frac{\color{blue}{-1 \cdot \left(4 + x\right) - -1 \cdot \left(x \cdot z\right)}}{y}\right| \]

      14. lower-fabs.f64 N/A

          \[\leadsto \color{blue}{\left|\frac{-1 \cdot \left(4 + x\right) - -1 \cdot \left(x \cdot z\right)}{y}\right|} \]

      15. distribute-lft-out-- N/A

          \[\leadsto \left|\frac{\color{blue}{-1 \cdot \left(\left(4 + x\right) - x \cdot z\right)}}{y}\right| \]

      16. mul-1-neg N/A

          \[\leadsto \left|\frac{\color{blue}{\mathsf{neg}\left(\left(\left(4 + x\right) - x \cdot z\right)\right)}}{y}\right| \]

   3. Applied rewrites 99.9%

      \[\leadsto \color{blue}{\left|\frac{\mathsf{fma}\left(z, x, -4 - x\right)}{y}\right|} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 2: 98.8% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq 2.15 \cdot 10^{+63}:\\ \;\;\;\;\left|\frac{\mathsf{fma}\left(z, x, -4 - x\right)}{y}\right|\\ \mathbf{else}:\\ \;\;\;\;\left|\mathsf{fma}\left(\frac{1 - z}{y}, x, \frac{4}{y}\right)\right|\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= y 2.15e+63)
   (fabs (/ (fma z x (- -4.0 x)) y))
   (fabs (fma (/ (- 1.0 z) y) x (/ 4.0 y)))))

C

double code(double x, double y, double z) {
	double tmp;
	if (y <= 2.15e+63) {
		tmp = fabs((fma(z, x, (-4.0 - x)) / y));
	} else {
		tmp = fabs(fma(((1.0 - z) / y), x, (4.0 / y)));
	}
	return tmp;
}

Julia

function code(x, y, z)
	tmp = 0.0
	if (y <= 2.15e+63)
		tmp = abs(Float64(fma(z, x, Float64(-4.0 - x)) / y));
	else
		tmp = abs(fma(Float64(Float64(1.0 - z) / y), x, Float64(4.0 / y)));
	end
	return tmp
end

Wolfram

code[x_, y_, z_] := If[LessEqual[y, 2.15e+63], N[Abs[N[(N[(z * x + N[(-4.0 - x), $MachinePrecision]), $MachinePrecision] / y), $MachinePrecision]], $MachinePrecision], N[Abs[N[(N[(N[(1.0 - z), $MachinePrecision] / y), $MachinePrecision] * x + N[(4.0 / y), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < 2.15e63

   1. Initial program 90.5%

      \[\left|\frac{x + 4}{y} - \frac{x}{y} \cdot z\right| \]
   2. Step-by-step derivation

      1. lift-fabs.f64 N/A

         \[\leadsto \color{blue}{\left|\frac{x + 4}{y} - \frac{x}{y} \cdot z\right|} \]

      2. lift--.f64 N/A

         \[\leadsto \left|\color{blue}{\frac{x + 4}{y} - \frac{x}{y} \cdot z}\right| \]

      3. lift-+.f64 N/A

         \[\leadsto \left|\frac{\color{blue}{x + 4}}{y} - \frac{x}{y} \cdot z\right| \]

      4. lift-/.f64 N/A

         \[\leadsto \left|\color{blue}{\frac{x + 4}{y}} - \frac{x}{y} \cdot z\right| \]

      5. lift-*.f64 N/A

         \[\leadsto \left|\frac{x + 4}{y} - \color{blue}{\frac{x}{y} \cdot z}\right| \]

      6. lift-/.f64 N/A

         \[\leadsto \left|\frac{x + 4}{y} - \color{blue}{\frac{x}{y}} \cdot z\right| \]

      7. neg-fabs N/A

         \[\leadsto \color{blue}{\left|\mathsf{neg}\left(\left(\frac{x + 4}{y} - \frac{x}{y} \cdot z\right)\right)\right|} \]

      8. +-commutative N/A

         \[\leadsto \left|\mathsf{neg}\left(\left(\frac{\color{blue}{4 + x}}{y} - \frac{x}{y} \cdot z\right)\right)\right| \]

      9. associate-*l/ N/A

         \[\leadsto \left|\mathsf{neg}\left(\left(\frac{4 + x}{y} - \color{blue}{\frac{x \cdot z}{y}}\right)\right)\right| \]

      10. div-sub N/A

          \[\leadsto \left|\mathsf{neg}\left(\color{blue}{\frac{\left(4 + x\right) - x \cdot z}{y}}\right)\right| \]

      11. distribute-neg-frac N/A

          \[\leadsto \left|\color{blue}{\frac{\mathsf{neg}\left(\left(\left(4 + x\right) - x \cdot z\right)\right)}{y}}\right| \]

      12. mul-1-neg N/A

          \[\leadsto \left|\frac{\color{blue}{-1 \cdot \left(\left(4 + x\right) - x \cdot z\right)}}{y}\right| \]

      13. distribute-lft-out-- N/A

          \[\leadsto \left|\frac{\color{blue}{-1 \cdot \left(4 + x\right) - -1 \cdot \left(x \cdot z\right)}}{y}\right| \]

      14. lower-fabs.f64 N/A

          \[\leadsto \color{blue}{\left|\frac{-1 \cdot \left(4 + x\right) - -1 \cdot \left(x \cdot z\right)}{y}\right|} \]

      15. distribute-lft-out-- N/A

          \[\leadsto \left|\frac{\color{blue}{-1 \cdot \left(\left(4 + x\right) - x \cdot z\right)}}{y}\right| \]

      16. mul-1-neg N/A

          \[\leadsto \left|\frac{\color{blue}{\mathsf{neg}\left(\left(\left(4 + x\right) - x \cdot z\right)\right)}}{y}\right| \]

   3. Applied rewrites 97.4%

      \[\leadsto \color{blue}{\left|\frac{\mathsf{fma}\left(z, x, -4 - x\right)}{y}\right|} \]

   if 2.15e63 < y

   1. Initial program 95.3%

      \[\left|\frac{x + 4}{y} - \frac{x}{y} \cdot z\right| \]
   2. Step-by-step derivation

      1. lift--.f64 N/A

         \[\leadsto \left|\color{blue}{\frac{x + 4}{y} - \frac{x}{y} \cdot z}\right| \]

      2. lift-+.f64 N/A

         \[\leadsto \left|\frac{\color{blue}{x + 4}}{y} - \frac{x}{y} \cdot z\right| \]

      3. lift-/.f64 N/A

         \[\leadsto \left|\color{blue}{\frac{x + 4}{y}} - \frac{x}{y} \cdot z\right| \]

      4. lift-*.f64 N/A

         \[\leadsto \left|\frac{x + 4}{y} - \color{blue}{\frac{x}{y} \cdot z}\right| \]

      5. lift-/.f64 N/A

         \[\leadsto \left|\frac{x + 4}{y} - \color{blue}{\frac{x}{y}} \cdot z\right| \]

      6. associate-*l/ N/A

         \[\leadsto \left|\frac{x + 4}{y} - \color{blue}{\frac{x \cdot z}{y}}\right| \]

      7. div-add N/A

         \[\leadsto \left|\color{blue}{\left(\frac{x}{y} + \frac{4}{y}\right)} - \frac{x \cdot z}{y}\right| \]

      8. metadata-eval N/A

         \[\leadsto \left|\left(\frac{x}{y} + \frac{\color{blue}{4 \cdot 1}}{y}\right) - \frac{x \cdot z}{y}\right| \]

      9. associate-*r/ N/A

         \[\leadsto \left|\left(\frac{x}{y} + \color{blue}{4 \cdot \frac{1}{y}}\right) - \frac{x \cdot z}{y}\right| \]

      10. +-commutative N/A

          \[\leadsto \left|\color{blue}{\left(4 \cdot \frac{1}{y} + \frac{x}{y}\right)} - \frac{x \cdot z}{y}\right| \]

      11. associate--l+ N/A

          \[\leadsto \left|\color{blue}{4 \cdot \frac{1}{y} + \left(\frac{x}{y} - \frac{x \cdot z}{y}\right)}\right| \]

      12. +-commutative N/A

          \[\leadsto \left|\color{blue}{\left(\frac{x}{y} - \frac{x \cdot z}{y}\right) + 4 \cdot \frac{1}{y}}\right| \]

   3. Applied rewrites 99.7%

      \[\leadsto \left|\color{blue}{\mathsf{fma}\left(\frac{1 - z}{y}, x, \frac{4}{y}\right)}\right| \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 3: 98.6% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left|-\frac{x}{y} \cdot \left(z + -1\right)\right|\\ \mathbf{if}\;x \leq -6400000:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;x \leq 3.3 \cdot 10^{-10}:\\ \;\;\;\;\left|\frac{\mathsf{fma}\left(-z, x, 4\right)}{y}\right|\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (fabs (- (* (/ x y) (+ z -1.0))))))
   (if (<= x -6400000.0)
     t_0
     (if (<= x 3.3e-10) (fabs (/ (fma (- z) x 4.0) y)) t_0))))

C

double code(double x, double y, double z) {
	double t_0 = fabs(-((x / y) * (z + -1.0)));
	double tmp;
	if (x <= -6400000.0) {
		tmp = t_0;
	} else if (x <= 3.3e-10) {
		tmp = fabs((fma(-z, x, 4.0) / y));
	} else {
		tmp = t_0;
	}
	return tmp;
}

Julia

function code(x, y, z)
	t_0 = abs(Float64(-Float64(Float64(x / y) * Float64(z + -1.0))))
	tmp = 0.0
	if (x <= -6400000.0)
		tmp = t_0;
	elseif (x <= 3.3e-10)
		tmp = abs(Float64(fma(Float64(-z), x, 4.0) / y));
	else
		tmp = t_0;
	end
	return tmp
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[Abs[(-N[(N[(x / y), $MachinePrecision] * N[(z + -1.0), $MachinePrecision]), $MachinePrecision])], $MachinePrecision]}, If[LessEqual[x, -6400000.0], t$95$0, If[LessEqual[x, 3.3e-10], N[Abs[N[(N[((-z) * x + 4.0), $MachinePrecision] / y), $MachinePrecision]], $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if x < -6.4e6 or 3.3e-10 < x

   1. Initial program 86.8%

      \[\left|\frac{x + 4}{y} - \frac{x}{y} \cdot z\right| \]

   2. Taylor expanded in x around inf

      \[\leadsto \left|\color{blue}{x \cdot \left(\frac{1}{y} - \frac{z}{y}\right)}\right| \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left|\left(\frac{1}{y} - \frac{z}{y}\right) \cdot \color{blue}{x}\right| \]

      2. lower-*.f64 N/A

         \[\leadsto \left|\left(\frac{1}{y} - \frac{z}{y}\right) \cdot \color{blue}{x}\right| \]

      3. sub-div N/A

         \[\leadsto \left|\frac{1 - z}{y} \cdot x\right| \]

      4. lower-/.f64 N/A

         \[\leadsto \left|\frac{1 - z}{y} \cdot x\right| \]

      5. lower--.f64 98.4

         \[\leadsto \left|\frac{1 - z}{y} \cdot x\right| \]

   4. Applied rewrites 98.4%

      \[\leadsto \left|\color{blue}{\frac{1 - z}{y} \cdot x}\right| \]
   5. Step-by-step derivation

      1. lift--.f64 N/A

         \[\leadsto \left|\frac{1 - z}{y} \cdot x\right| \]

      2. lift-/.f64 N/A

         \[\leadsto \left|\frac{1 - z}{y} \cdot x\right| \]

      3. lower-*.f64 N/A

         \[\leadsto \left|\frac{1 - z}{y} \cdot \color{blue}{x}\right| \]

      4. *-commutative N/A

         \[\leadsto \left|x \cdot \color{blue}{\frac{1 - z}{y}}\right| \]

      5. sub-div N/A

         \[\leadsto \left|x \cdot \left(\frac{1}{y} - \color{blue}{\frac{z}{y}}\right)\right| \]

      6. *-lft-identity N/A

         \[\leadsto \left|x \cdot \left(\frac{1}{y} - \frac{1 \cdot z}{y}\right)\right| \]

      7. div-sub N/A

         \[\leadsto \left|x \cdot \frac{1 - 1 \cdot z}{\color{blue}{y}}\right| \]

      8. metadata-eval N/A

         \[\leadsto \left|x \cdot \frac{1 - \left(\mathsf{neg}\left(-1\right)\right) \cdot z}{y}\right| \]

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

         \[\leadsto \left|x \cdot \frac{1 + -1 \cdot z}{y}\right| \]

      10. associate-/l* N/A

          \[\leadsto \left|\frac{x \cdot \left(1 + -1 \cdot z\right)}{\color{blue}{y}}\right| \]

      11. frac-2neg N/A

          \[\leadsto \left|\frac{\mathsf{neg}\left(x \cdot \left(1 + -1 \cdot z\right)\right)}{\color{blue}{\mathsf{neg}\left(y\right)}}\right| \]

      12. distribute-frac-neg2 N/A

          \[\leadsto \left|\mathsf{neg}\left(\frac{\mathsf{neg}\left(x \cdot \left(1 + -1 \cdot z\right)\right)}{y}\right)\right| \]

   6. Applied rewrites 98.5%

      \[\leadsto \left|-\frac{x}{y} \cdot \left(z + -1\right)\right| \]

   if -6.4e6 < x < 3.3e-10

   1. Initial program 96.2%

      \[\left|\frac{x + 4}{y} - \frac{x}{y} \cdot z\right| \]
   2. Step-by-step derivation

      1. lift--.f64 N/A

         \[\leadsto \left|\color{blue}{\frac{x + 4}{y} - \frac{x}{y} \cdot z}\right| \]

      2. lift-+.f64 N/A

         \[\leadsto \left|\frac{\color{blue}{x + 4}}{y} - \frac{x}{y} \cdot z\right| \]

      3. lift-/.f64 N/A

         \[\leadsto \left|\color{blue}{\frac{x + 4}{y}} - \frac{x}{y} \cdot z\right| \]

      4. lift-*.f64 N/A

         \[\leadsto \left|\frac{x + 4}{y} - \color{blue}{\frac{x}{y} \cdot z}\right| \]

      5. lift-/.f64 N/A

         \[\leadsto \left|\frac{x + 4}{y} - \color{blue}{\frac{x}{y}} \cdot z\right| \]

      6. associate-*l/ N/A

         \[\leadsto \left|\frac{x + 4}{y} - \color{blue}{\frac{x \cdot z}{y}}\right| \]

      7. div-add N/A

         \[\leadsto \left|\color{blue}{\left(\frac{x}{y} + \frac{4}{y}\right)} - \frac{x \cdot z}{y}\right| \]

      8. metadata-eval N/A

         \[\leadsto \left|\left(\frac{x}{y} + \frac{\color{blue}{4 \cdot 1}}{y}\right) - \frac{x \cdot z}{y}\right| \]

      9. associate-*r/ N/A

         \[\leadsto \left|\left(\frac{x}{y} + \color{blue}{4 \cdot \frac{1}{y}}\right) - \frac{x \cdot z}{y}\right| \]

      10. +-commutative N/A

          \[\leadsto \left|\color{blue}{\left(4 \cdot \frac{1}{y} + \frac{x}{y}\right)} - \frac{x \cdot z}{y}\right| \]

      11. associate--l+ N/A

          \[\leadsto \left|\color{blue}{4 \cdot \frac{1}{y} + \left(\frac{x}{y} - \frac{x \cdot z}{y}\right)}\right| \]

      12. +-commutative N/A

          \[\leadsto \left|\color{blue}{\left(\frac{x}{y} - \frac{x \cdot z}{y}\right) + 4 \cdot \frac{1}{y}}\right| \]

   3. Applied rewrites 92.5%

      \[\leadsto \left|\color{blue}{\mathsf{fma}\left(\frac{1 - z}{y}, x, \frac{4}{y}\right)}\right| \]
   4. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \left|\mathsf{fma}\left(\frac{1 - z}{y}, x, \color{blue}{\frac{4}{y}}\right)\right| \]

      2. lift-fma.f64 N/A

         \[\leadsto \left|\color{blue}{\frac{1 - z}{y} \cdot x + \frac{4}{y}}\right| \]

      3. lift--.f64 N/A

         \[\leadsto \left|\frac{\color{blue}{1 - z}}{y} \cdot x + \frac{4}{y}\right| \]

      4. lift-/.f64 N/A

         \[\leadsto \left|\color{blue}{\frac{1 - z}{y}} \cdot x + \frac{4}{y}\right| \]

      5. *-commutative N/A

         \[\leadsto \left|\color{blue}{x \cdot \frac{1 - z}{y}} + \frac{4}{y}\right| \]

      6. associate-/l* N/A

         \[\leadsto \left|\color{blue}{\frac{x \cdot \left(1 - z\right)}{y}} + \frac{4}{y}\right| \]

      7. div-add-rev N/A

         \[\leadsto \left|\color{blue}{\frac{x \cdot \left(1 - z\right) + 4}{y}}\right| \]

      8. +-commutative N/A

         \[\leadsto \left|\frac{\color{blue}{4 + x \cdot \left(1 - z\right)}}{y}\right| \]

      9. lower-/.f64 N/A

         \[\leadsto \left|\color{blue}{\frac{4 + x \cdot \left(1 - z\right)}{y}}\right| \]

      10. +-commutative N/A

          \[\leadsto \left|\frac{\color{blue}{x \cdot \left(1 - z\right) + 4}}{y}\right| \]

      11. *-commutative N/A

          \[\leadsto \left|\frac{\color{blue}{\left(1 - z\right) \cdot x} + 4}{y}\right| \]

      12. lower-fma.f64 N/A

          \[\leadsto \left|\frac{\color{blue}{\mathsf{fma}\left(1 - z, x, 4\right)}}{y}\right| \]

      13. lift--.f64 99.9

          \[\leadsto \left|\frac{\mathsf{fma}\left(\color{blue}{1 - z}, x, 4\right)}{y}\right| \]

   5. Applied rewrites 99.9%

      \[\leadsto \left|\color{blue}{\frac{\mathsf{fma}\left(1 - z, x, 4\right)}{y}}\right| \]

   6. Taylor expanded in z around inf

      \[\leadsto \left|\frac{\mathsf{fma}\left(\color{blue}{-1 \cdot z}, x, 4\right)}{y}\right| \]
   7. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \left|\frac{\mathsf{fma}\left(\mathsf{neg}\left(z\right), x, 4\right)}{y}\right| \]

      2. lower-neg.f64 99.0

         \[\leadsto \left|\frac{\mathsf{fma}\left(-z, x, 4\right)}{y}\right| \]

   8. Applied rewrites 99.0%

      \[\leadsto \left|\frac{\mathsf{fma}\left(\color{blue}{-z}, x, 4\right)}{y}\right| \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 4: 97.9% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left|\frac{1 - z}{y} \cdot x\right|\\ \mathbf{if}\;x \leq -21000000000000:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;x \leq 3.9:\\ \;\;\;\;\left|\frac{\mathsf{fma}\left(-z, x, 4\right)}{y}\right|\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (fabs (* (/ (- 1.0 z) y) x))))
   (if (<= x -21000000000000.0)
     t_0
     (if (<= x 3.9) (fabs (/ (fma (- z) x 4.0) y)) t_0))))

C

double code(double x, double y, double z) {
	double t_0 = fabs((((1.0 - z) / y) * x));
	double tmp;
	if (x <= -21000000000000.0) {
		tmp = t_0;
	} else if (x <= 3.9) {
		tmp = fabs((fma(-z, x, 4.0) / y));
	} else {
		tmp = t_0;
	}
	return tmp;
}

Julia

function code(x, y, z)
	t_0 = abs(Float64(Float64(Float64(1.0 - z) / y) * x))
	tmp = 0.0
	if (x <= -21000000000000.0)
		tmp = t_0;
	elseif (x <= 3.9)
		tmp = abs(Float64(fma(Float64(-z), x, 4.0) / y));
	else
		tmp = t_0;
	end
	return tmp
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[Abs[N[(N[(N[(1.0 - z), $MachinePrecision] / y), $MachinePrecision] * x), $MachinePrecision]], $MachinePrecision]}, If[LessEqual[x, -21000000000000.0], t$95$0, If[LessEqual[x, 3.9], N[Abs[N[(N[((-z) * x + 4.0), $MachinePrecision] / y), $MachinePrecision]], $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if x < -2.1e13 or 3.89999999999999991 < x

   1. Initial program 86.4%

      \[\left|\frac{x + 4}{y} - \frac{x}{y} \cdot z\right| \]

   2. Taylor expanded in x around inf

      \[\leadsto \left|\color{blue}{x \cdot \left(\frac{1}{y} - \frac{z}{y}\right)}\right| \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left|\left(\frac{1}{y} - \frac{z}{y}\right) \cdot \color{blue}{x}\right| \]

      2. lower-*.f64 N/A

         \[\leadsto \left|\left(\frac{1}{y} - \frac{z}{y}\right) \cdot \color{blue}{x}\right| \]

      3. sub-div N/A

         \[\leadsto \left|\frac{1 - z}{y} \cdot x\right| \]

      4. lower-/.f64 N/A

         \[\leadsto \left|\frac{1 - z}{y} \cdot x\right| \]

      5. lower--.f64 99.2

         \[\leadsto \left|\frac{1 - z}{y} \cdot x\right| \]

   4. Applied rewrites 99.2%

      \[\leadsto \left|\color{blue}{\frac{1 - z}{y} \cdot x}\right| \]

   if -2.1e13 < x < 3.89999999999999991

   1. Initial program 96.3%

      \[\left|\frac{x + 4}{y} - \frac{x}{y} \cdot z\right| \]
   2. Step-by-step derivation

      1. lift--.f64 N/A

         \[\leadsto \left|\color{blue}{\frac{x + 4}{y} - \frac{x}{y} \cdot z}\right| \]

      2. lift-+.f64 N/A

         \[\leadsto \left|\frac{\color{blue}{x + 4}}{y} - \frac{x}{y} \cdot z\right| \]

      3. lift-/.f64 N/A

         \[\leadsto \left|\color{blue}{\frac{x + 4}{y}} - \frac{x}{y} \cdot z\right| \]

      4. lift-*.f64 N/A

         \[\leadsto \left|\frac{x + 4}{y} - \color{blue}{\frac{x}{y} \cdot z}\right| \]

      5. lift-/.f64 N/A

         \[\leadsto \left|\frac{x + 4}{y} - \color{blue}{\frac{x}{y}} \cdot z\right| \]

      6. associate-*l/ N/A

         \[\leadsto \left|\frac{x + 4}{y} - \color{blue}{\frac{x \cdot z}{y}}\right| \]

      7. div-add N/A

         \[\leadsto \left|\color{blue}{\left(\frac{x}{y} + \frac{4}{y}\right)} - \frac{x \cdot z}{y}\right| \]

      8. metadata-eval N/A

         \[\leadsto \left|\left(\frac{x}{y} + \frac{\color{blue}{4 \cdot 1}}{y}\right) - \frac{x \cdot z}{y}\right| \]

      9. associate-*r/ N/A

         \[\leadsto \left|\left(\frac{x}{y} + \color{blue}{4 \cdot \frac{1}{y}}\right) - \frac{x \cdot z}{y}\right| \]

      10. +-commutative N/A

          \[\leadsto \left|\color{blue}{\left(4 \cdot \frac{1}{y} + \frac{x}{y}\right)} - \frac{x \cdot z}{y}\right| \]

      11. associate--l+ N/A

          \[\leadsto \left|\color{blue}{4 \cdot \frac{1}{y} + \left(\frac{x}{y} - \frac{x \cdot z}{y}\right)}\right| \]

      12. +-commutative N/A

          \[\leadsto \left|\color{blue}{\left(\frac{x}{y} - \frac{x \cdot z}{y}\right) + 4 \cdot \frac{1}{y}}\right| \]

   3. Applied rewrites 92.7%

      \[\leadsto \left|\color{blue}{\mathsf{fma}\left(\frac{1 - z}{y}, x, \frac{4}{y}\right)}\right| \]
   4. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \left|\mathsf{fma}\left(\frac{1 - z}{y}, x, \color{blue}{\frac{4}{y}}\right)\right| \]

      2. lift-fma.f64 N/A

         \[\leadsto \left|\color{blue}{\frac{1 - z}{y} \cdot x + \frac{4}{y}}\right| \]

      3. lift--.f64 N/A

         \[\leadsto \left|\frac{\color{blue}{1 - z}}{y} \cdot x + \frac{4}{y}\right| \]

      4. lift-/.f64 N/A

         \[\leadsto \left|\color{blue}{\frac{1 - z}{y}} \cdot x + \frac{4}{y}\right| \]

      5. *-commutative N/A

         \[\leadsto \left|\color{blue}{x \cdot \frac{1 - z}{y}} + \frac{4}{y}\right| \]

      6. associate-/l* N/A

         \[\leadsto \left|\color{blue}{\frac{x \cdot \left(1 - z\right)}{y}} + \frac{4}{y}\right| \]

      7. div-add-rev N/A

         \[\leadsto \left|\color{blue}{\frac{x \cdot \left(1 - z\right) + 4}{y}}\right| \]

      8. +-commutative N/A

         \[\leadsto \left|\frac{\color{blue}{4 + x \cdot \left(1 - z\right)}}{y}\right| \]

      9. lower-/.f64 N/A

         \[\leadsto \left|\color{blue}{\frac{4 + x \cdot \left(1 - z\right)}{y}}\right| \]

      10. +-commutative N/A

          \[\leadsto \left|\frac{\color{blue}{x \cdot \left(1 - z\right) + 4}}{y}\right| \]

      11. *-commutative N/A

          \[\leadsto \left|\frac{\color{blue}{\left(1 - z\right) \cdot x} + 4}{y}\right| \]

      12. lower-fma.f64 N/A

          \[\leadsto \left|\frac{\color{blue}{\mathsf{fma}\left(1 - z, x, 4\right)}}{y}\right| \]

      13. lift--.f64 99.9

          \[\leadsto \left|\frac{\mathsf{fma}\left(\color{blue}{1 - z}, x, 4\right)}{y}\right| \]

   5. Applied rewrites 99.9%

      \[\leadsto \left|\color{blue}{\frac{\mathsf{fma}\left(1 - z, x, 4\right)}{y}}\right| \]

   6. Taylor expanded in z around inf

      \[\leadsto \left|\frac{\mathsf{fma}\left(\color{blue}{-1 \cdot z}, x, 4\right)}{y}\right| \]
   7. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \left|\frac{\mathsf{fma}\left(\mathsf{neg}\left(z\right), x, 4\right)}{y}\right| \]

      2. lower-neg.f64 98.0

         \[\leadsto \left|\frac{\mathsf{fma}\left(-z, x, 4\right)}{y}\right| \]

   8. Applied rewrites 98.0%

      \[\leadsto \left|\frac{\mathsf{fma}\left(\color{blue}{-z}, x, 4\right)}{y}\right| \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 5: 85.5% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -2.85 \cdot 10^{+53}:\\ \;\;\;\;\left|\frac{-x}{y} \cdot z\right|\\ \mathbf{elif}\;z \leq 1.05 \cdot 10^{+48}:\\ \;\;\;\;\left|\frac{x - -4}{y}\right|\\ \mathbf{else}:\\ \;\;\;\;\left|\left(-x\right) \cdot \frac{z}{y}\right|\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= z -2.85e+53)
   (fabs (* (/ (- x) y) z))
   (if (<= z 1.05e+48) (fabs (/ (- x -4.0) y)) (fabs (* (- x) (/ z y))))))

C

double code(double x, double y, double z) {
	double tmp;
	if (z <= -2.85e+53) {
		tmp = fabs(((-x / y) * z));
	} else if (z <= 1.05e+48) {
		tmp = fabs(((x - -4.0) / y));
	} else {
		tmp = fabs((-x * (z / y)));
	}
	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)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if (z <= (-2.85d+53)) then
        tmp = abs(((-x / y) * z))
    else if (z <= 1.05d+48) then
        tmp = abs(((x - (-4.0d0)) / y))
    else
        tmp = abs((-x * (z / y)))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (z <= -2.85e+53) {
		tmp = Math.abs(((-x / y) * z));
	} else if (z <= 1.05e+48) {
		tmp = Math.abs(((x - -4.0) / y));
	} else {
		tmp = Math.abs((-x * (z / y)));
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if z <= -2.85e+53:
		tmp = math.fabs(((-x / y) * z))
	elif z <= 1.05e+48:
		tmp = math.fabs(((x - -4.0) / y))
	else:
		tmp = math.fabs((-x * (z / y)))
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (z <= -2.85e+53)
		tmp = abs(Float64(Float64(Float64(-x) / y) * z));
	elseif (z <= 1.05e+48)
		tmp = abs(Float64(Float64(x - -4.0) / y));
	else
		tmp = abs(Float64(Float64(-x) * Float64(z / y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (z <= -2.85e+53)
		tmp = abs(((-x / y) * z));
	elseif (z <= 1.05e+48)
		tmp = abs(((x - -4.0) / y));
	else
		tmp = abs((-x * (z / y)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[z, -2.85e+53], N[Abs[N[(N[((-x) / y), $MachinePrecision] * z), $MachinePrecision]], $MachinePrecision], If[LessEqual[z, 1.05e+48], N[Abs[N[(N[(x - -4.0), $MachinePrecision] / y), $MachinePrecision]], $MachinePrecision], N[Abs[N[((-x) * N[(z / y), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if z < -2.85000000000000009e53

   1. Initial program 94.9%

      \[\left|\frac{x + 4}{y} - \frac{x}{y} \cdot z\right| \]

   2. Taylor expanded in z around inf

      \[\leadsto \left|\color{blue}{-1 \cdot \frac{x \cdot z}{y}}\right| \]
   3. Step-by-step derivation

      1. mul-1-neg N/A

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

      2. associate-/l* N/A

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

      3. distribute-lft-neg-in N/A

         \[\leadsto \left|\left(\mathsf{neg}\left(x\right)\right) \cdot \color{blue}{\frac{z}{y}}\right| \]

      4. lower-*.f64 N/A

         \[\leadsto \left|\left(\mathsf{neg}\left(x\right)\right) \cdot \color{blue}{\frac{z}{y}}\right| \]

      5. lower-neg.f64 N/A

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

      6. lower-/.f64 75.1

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

   4. Applied rewrites 75.1%

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

      1. lift-/.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. associate-*r/ N/A

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

      4. associate-*l/ N/A

         \[\leadsto \left|\frac{-x}{y} \cdot \color{blue}{z}\right| \]

      5. lift-neg.f64 N/A

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

      6. mul-1-neg N/A

         \[\leadsto \left|\frac{-1 \cdot x}{y} \cdot z\right| \]

      7. associate-*r/ N/A

         \[\leadsto \left|\left(-1 \cdot \frac{x}{y}\right) \cdot z\right| \]

      8. lower-*.f64 N/A

         \[\leadsto \left|\left(-1 \cdot \frac{x}{y}\right) \cdot \color{blue}{z}\right| \]

      9. associate-*r/ N/A

         \[\leadsto \left|\frac{-1 \cdot x}{y} \cdot z\right| \]

      10. mul-1-neg N/A

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

      11. lift-neg.f64 N/A

          \[\leadsto \left|\frac{-x}{y} \cdot z\right| \]

      12. lower-/.f64 74.5

          \[\leadsto \left|\frac{-x}{y} \cdot z\right| \]

   6. Applied rewrites 74.5%

      \[\leadsto \left|\frac{-x}{y} \cdot \color{blue}{z}\right| \]

   if -2.85000000000000009e53 < z < 1.0499999999999999e48

   1. Initial program 93.7%

      \[\left|\frac{x + 4}{y} - \frac{x}{y} \cdot z\right| \]

   2. Taylor expanded in z around 0

      \[\leadsto \left|\color{blue}{4 \cdot \frac{1}{y} + \frac{x}{y}}\right| \]
   3. Step-by-step derivation

      1. associate-*r/ N/A

         \[\leadsto \left|\frac{4 \cdot 1}{y} + \frac{\color{blue}{x}}{y}\right| \]

      2. metadata-eval N/A

         \[\leadsto \left|\frac{4}{y} + \frac{x}{y}\right| \]

      3. div-add N/A

         \[\leadsto \left|\frac{4 + x}{\color{blue}{y}}\right| \]

      4. lower-/.f64 N/A

         \[\leadsto \left|\frac{4 + x}{\color{blue}{y}}\right| \]

      5. +-commutative N/A

         \[\leadsto \left|\frac{x + 4}{y}\right| \]

      6. metadata-eval N/A

         \[\leadsto \left|\frac{x + 4 \cdot 1}{y}\right| \]

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

         \[\leadsto \left|\frac{x - \left(\mathsf{neg}\left(4\right)\right) \cdot 1}{y}\right| \]

      8. distribute-lft-neg-in N/A

         \[\leadsto \left|\frac{x - \left(\mathsf{neg}\left(4 \cdot 1\right)\right)}{y}\right| \]

      9. metadata-eval N/A

         \[\leadsto \left|\frac{x - \left(\mathsf{neg}\left(4\right)\right)}{y}\right| \]

      10. lower--.f64 N/A

          \[\leadsto \left|\frac{x - \left(\mathsf{neg}\left(4\right)\right)}{y}\right| \]

      11. metadata-eval 93.0

          \[\leadsto \left|\frac{x - -4}{y}\right| \]

   4. Applied rewrites 93.0%

      \[\leadsto \left|\color{blue}{\frac{x - -4}{y}}\right| \]

   if 1.0499999999999999e48 < z

   1. Initial program 82.3%

      \[\left|\frac{x + 4}{y} - \frac{x}{y} \cdot z\right| \]

   2. Taylor expanded in z around inf

      \[\leadsto \left|\color{blue}{-1 \cdot \frac{x \cdot z}{y}}\right| \]
   3. Step-by-step derivation

      1. mul-1-neg N/A

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

      2. associate-/l* N/A

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

      3. distribute-lft-neg-in N/A

         \[\leadsto \left|\left(\mathsf{neg}\left(x\right)\right) \cdot \color{blue}{\frac{z}{y}}\right| \]

      4. lower-*.f64 N/A

         \[\leadsto \left|\left(\mathsf{neg}\left(x\right)\right) \cdot \color{blue}{\frac{z}{y}}\right| \]

      5. lower-neg.f64 N/A

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

      6. lower-/.f64 74.6

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

   4. Applied rewrites 74.6%

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

Alternative 6: 85.4% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left|\left(-x\right) \cdot \frac{z}{y}\right|\\ \mathbf{if}\;z \leq -4.1 \cdot 10^{+37}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;z \leq 1.05 \cdot 10^{+48}:\\ \;\;\;\;\left|\frac{x - -4}{y}\right|\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (fabs (* (- x) (/ z y)))))
   (if (<= z -4.1e+37) t_0 (if (<= z 1.05e+48) (fabs (/ (- x -4.0) y)) t_0))))

C

double code(double x, double y, double z) {
	double t_0 = fabs((-x * (z / y)));
	double tmp;
	if (z <= -4.1e+37) {
		tmp = t_0;
	} else if (z <= 1.05e+48) {
		tmp = fabs(((x - -4.0) / y));
	} 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, y, z)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: t_0
    real(8) :: tmp
    t_0 = abs((-x * (z / y)))
    if (z <= (-4.1d+37)) then
        tmp = t_0
    else if (z <= 1.05d+48) then
        tmp = abs(((x - (-4.0d0)) / y))
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = Math.abs((-x * (z / y)));
	double tmp;
	if (z <= -4.1e+37) {
		tmp = t_0;
	} else if (z <= 1.05e+48) {
		tmp = Math.abs(((x - -4.0) / y));
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = math.fabs((-x * (z / y)))
	tmp = 0
	if z <= -4.1e+37:
		tmp = t_0
	elif z <= 1.05e+48:
		tmp = math.fabs(((x - -4.0) / y))
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y, z)
	t_0 = abs(Float64(Float64(-x) * Float64(z / y)))
	tmp = 0.0
	if (z <= -4.1e+37)
		tmp = t_0;
	elseif (z <= 1.05e+48)
		tmp = abs(Float64(Float64(x - -4.0) / y));
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = abs((-x * (z / y)));
	tmp = 0.0;
	if (z <= -4.1e+37)
		tmp = t_0;
	elseif (z <= 1.05e+48)
		tmp = abs(((x - -4.0) / y));
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[Abs[N[((-x) * N[(z / y), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]}, If[LessEqual[z, -4.1e+37], t$95$0, If[LessEqual[z, 1.05e+48], N[Abs[N[(N[(x - -4.0), $MachinePrecision] / y), $MachinePrecision]], $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if z < -4.0999999999999998e37 or 1.0499999999999999e48 < z

   1. Initial program 88.7%

      \[\left|\frac{x + 4}{y} - \frac{x}{y} \cdot z\right| \]

   2. Taylor expanded in z around inf

      \[\leadsto \left|\color{blue}{-1 \cdot \frac{x \cdot z}{y}}\right| \]
   3. Step-by-step derivation

      1. mul-1-neg N/A

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

      2. associate-/l* N/A

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

      3. distribute-lft-neg-in N/A

         \[\leadsto \left|\left(\mathsf{neg}\left(x\right)\right) \cdot \color{blue}{\frac{z}{y}}\right| \]

      4. lower-*.f64 N/A

         \[\leadsto \left|\left(\mathsf{neg}\left(x\right)\right) \cdot \color{blue}{\frac{z}{y}}\right| \]

      5. lower-neg.f64 N/A

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

      6. lower-/.f64 74.3

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

   4. Applied rewrites 74.3%

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

   if -4.0999999999999998e37 < z < 1.0499999999999999e48

   1. Initial program 93.6%

      \[\left|\frac{x + 4}{y} - \frac{x}{y} \cdot z\right| \]

   2. Taylor expanded in z around 0

      \[\leadsto \left|\color{blue}{4 \cdot \frac{1}{y} + \frac{x}{y}}\right| \]
   3. Step-by-step derivation

      1. associate-*r/ N/A

         \[\leadsto \left|\frac{4 \cdot 1}{y} + \frac{\color{blue}{x}}{y}\right| \]

      2. metadata-eval N/A

         \[\leadsto \left|\frac{4}{y} + \frac{x}{y}\right| \]

      3. div-add N/A

         \[\leadsto \left|\frac{4 + x}{\color{blue}{y}}\right| \]

      4. lower-/.f64 N/A

         \[\leadsto \left|\frac{4 + x}{\color{blue}{y}}\right| \]

      5. +-commutative N/A

         \[\leadsto \left|\frac{x + 4}{y}\right| \]

      6. metadata-eval N/A

         \[\leadsto \left|\frac{x + 4 \cdot 1}{y}\right| \]

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

         \[\leadsto \left|\frac{x - \left(\mathsf{neg}\left(4\right)\right) \cdot 1}{y}\right| \]

      8. distribute-lft-neg-in N/A

         \[\leadsto \left|\frac{x - \left(\mathsf{neg}\left(4 \cdot 1\right)\right)}{y}\right| \]

      9. metadata-eval N/A

         \[\leadsto \left|\frac{x - \left(\mathsf{neg}\left(4\right)\right)}{y}\right| \]

      10. lower--.f64 N/A

          \[\leadsto \left|\frac{x - \left(\mathsf{neg}\left(4\right)\right)}{y}\right| \]

      11. metadata-eval 93.9

          \[\leadsto \left|\frac{x - -4}{y}\right| \]

   4. Applied rewrites 93.9%

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

Alternative 7: 69.8% accurate, 2.1× speedup

Math

\[\begin{array}{l} \\ \left|\frac{x - -4}{y}\right| \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (fabs (/ (- x -4.0) y)))

C

double code(double x, double y, double z) {
	return fabs(((x - -4.0) / y));
}

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)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = abs(((x - (-4.0d0)) / y))
end function

Java

public static double code(double x, double y, double z) {
	return Math.abs(((x - -4.0) / y));
}

Python

def code(x, y, z):
	return math.fabs(((x - -4.0) / y))

Julia

function code(x, y, z)
	return abs(Float64(Float64(x - -4.0) / y))
end

MATLAB

function tmp = code(x, y, z)
	tmp = abs(((x - -4.0) / y));
end

Wolfram

code[x_, y_, z_] := N[Abs[N[(N[(x - -4.0), $MachinePrecision] / y), $MachinePrecision]], $MachinePrecision]

Derivation

1. Initial program 91.5%

   \[\left|\frac{x + 4}{y} - \frac{x}{y} \cdot z\right| \]

2. Taylor expanded in z around 0

   \[\leadsto \left|\color{blue}{4 \cdot \frac{1}{y} + \frac{x}{y}}\right| \]
3. Step-by-step derivation

   1. associate-*r/ N/A

      \[\leadsto \left|\frac{4 \cdot 1}{y} + \frac{\color{blue}{x}}{y}\right| \]

   2. metadata-eval N/A

      \[\leadsto \left|\frac{4}{y} + \frac{x}{y}\right| \]

   3. div-add N/A

      \[\leadsto \left|\frac{4 + x}{\color{blue}{y}}\right| \]

   4. lower-/.f64 N/A

      \[\leadsto \left|\frac{4 + x}{\color{blue}{y}}\right| \]

   5. +-commutative N/A

      \[\leadsto \left|\frac{x + 4}{y}\right| \]

   6. metadata-eval N/A

      \[\leadsto \left|\frac{x + 4 \cdot 1}{y}\right| \]

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

      \[\leadsto \left|\frac{x - \left(\mathsf{neg}\left(4\right)\right) \cdot 1}{y}\right| \]

   8. distribute-lft-neg-in N/A

      \[\leadsto \left|\frac{x - \left(\mathsf{neg}\left(4 \cdot 1\right)\right)}{y}\right| \]

   9. metadata-eval N/A

      \[\leadsto \left|\frac{x - \left(\mathsf{neg}\left(4\right)\right)}{y}\right| \]

   10. lower--.f64 N/A

       \[\leadsto \left|\frac{x - \left(\mathsf{neg}\left(4\right)\right)}{y}\right| \]

   11. metadata-eval 69.8

       \[\leadsto \left|\frac{x - -4}{y}\right| \]

4. Applied rewrites 69.8%

   \[\leadsto \left|\color{blue}{\frac{x - -4}{y}}\right| \]
5. Add Preprocessing

Alternative 8: 68.8% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left|\frac{x}{y}\right|\\ \mathbf{if}\;x \leq -1.55:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;x \leq 4:\\ \;\;\;\;\left|\frac{4}{y}\right|\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (fabs (/ x y))))
   (if (<= x -1.55) t_0 (if (<= x 4.0) (fabs (/ 4.0 y)) t_0))))

C

double code(double x, double y, double z) {
	double t_0 = fabs((x / y));
	double tmp;
	if (x <= -1.55) {
		tmp = t_0;
	} else if (x <= 4.0) {
		tmp = fabs((4.0 / y));
	} 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, y, z)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: t_0
    real(8) :: tmp
    t_0 = abs((x / y))
    if (x <= (-1.55d0)) then
        tmp = t_0
    else if (x <= 4.0d0) then
        tmp = abs((4.0d0 / y))
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = Math.abs((x / y));
	double tmp;
	if (x <= -1.55) {
		tmp = t_0;
	} else if (x <= 4.0) {
		tmp = Math.abs((4.0 / y));
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = math.fabs((x / y))
	tmp = 0
	if x <= -1.55:
		tmp = t_0
	elif x <= 4.0:
		tmp = math.fabs((4.0 / y))
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y, z)
	t_0 = abs(Float64(x / y))
	tmp = 0.0
	if (x <= -1.55)
		tmp = t_0;
	elseif (x <= 4.0)
		tmp = abs(Float64(4.0 / y));
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = abs((x / y));
	tmp = 0.0;
	if (x <= -1.55)
		tmp = t_0;
	elseif (x <= 4.0)
		tmp = abs((4.0 / y));
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[Abs[N[(x / y), $MachinePrecision]], $MachinePrecision]}, If[LessEqual[x, -1.55], t$95$0, If[LessEqual[x, 4.0], N[Abs[N[(4.0 / y), $MachinePrecision]], $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if x < -1.55000000000000004 or 4 < x

   1. Initial program 86.7%

      \[\left|\frac{x + 4}{y} - \frac{x}{y} \cdot z\right| \]

   2. Taylor expanded in z around 0

      \[\leadsto \left|\color{blue}{4 \cdot \frac{1}{y} + \frac{x}{y}}\right| \]
   3. Step-by-step derivation

      1. associate-*r/ N/A

         \[\leadsto \left|\frac{4 \cdot 1}{y} + \frac{\color{blue}{x}}{y}\right| \]

      2. metadata-eval N/A

         \[\leadsto \left|\frac{4}{y} + \frac{x}{y}\right| \]

      3. div-add N/A

         \[\leadsto \left|\frac{4 + x}{\color{blue}{y}}\right| \]

      4. lower-/.f64 N/A

         \[\leadsto \left|\frac{4 + x}{\color{blue}{y}}\right| \]

      5. +-commutative N/A

         \[\leadsto \left|\frac{x + 4}{y}\right| \]

      6. metadata-eval N/A

         \[\leadsto \left|\frac{x + 4 \cdot 1}{y}\right| \]

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

         \[\leadsto \left|\frac{x - \left(\mathsf{neg}\left(4\right)\right) \cdot 1}{y}\right| \]

      8. distribute-lft-neg-in N/A

         \[\leadsto \left|\frac{x - \left(\mathsf{neg}\left(4 \cdot 1\right)\right)}{y}\right| \]

      9. metadata-eval N/A

         \[\leadsto \left|\frac{x - \left(\mathsf{neg}\left(4\right)\right)}{y}\right| \]

      10. lower--.f64 N/A

          \[\leadsto \left|\frac{x - \left(\mathsf{neg}\left(4\right)\right)}{y}\right| \]

      11. metadata-eval 63.8

          \[\leadsto \left|\frac{x - -4}{y}\right| \]

   4. Applied rewrites 63.8%

      \[\leadsto \left|\color{blue}{\frac{x - -4}{y}}\right| \]

   5. Taylor expanded in x around inf

      \[\leadsto \left|\frac{x}{y}\right| \]
   6. Step-by-step derivation

   7. Applied rewrites 62.8%

      \[\leadsto \left|\frac{x}{y}\right| \]

   if -1.55000000000000004 < x < 4

   1. Initial program 96.2%

      \[\left|\frac{x + 4}{y} - \frac{x}{y} \cdot z\right| \]

   2. Taylor expanded in x around 0

      \[\leadsto \left|\color{blue}{\frac{4}{y}}\right| \]
   3. Step-by-step derivation

      1. lower-/.f64 74.8

         \[\leadsto \left|\frac{4}{\color{blue}{y}}\right| \]

   4. Applied rewrites 74.8%

      \[\leadsto \left|\color{blue}{\frac{4}{y}}\right| \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 9: 40.2% accurate, 3.1× speedup

Math

\[\begin{array}{l} \\ \left|\frac{4}{y}\right| \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (fabs (/ 4.0 y)))

C

double code(double x, double y, double z) {
	return fabs((4.0 / y));
}

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)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = abs((4.0d0 / y))
end function

Java

public static double code(double x, double y, double z) {
	return Math.abs((4.0 / y));
}

Python

def code(x, y, z):
	return math.fabs((4.0 / y))

Julia

function code(x, y, z)
	return abs(Float64(4.0 / y))
end

MATLAB

function tmp = code(x, y, z)
	tmp = abs((4.0 / y));
end

Wolfram

code[x_, y_, z_] := N[Abs[N[(4.0 / y), $MachinePrecision]], $MachinePrecision]

Derivation

1. Initial program 91.5%

   \[\left|\frac{x + 4}{y} - \frac{x}{y} \cdot z\right| \]

2. Taylor expanded in x around 0

   \[\leadsto \left|\color{blue}{\frac{4}{y}}\right| \]
3. Step-by-step derivation

   1. lower-/.f64 40.2

      \[\leadsto \left|\frac{4}{\color{blue}{y}}\right| \]

4. Applied rewrites 40.2%

   \[\leadsto \left|\color{blue}{\frac{4}{y}}\right| \]
5. Add Preprocessing

Reproduce

herbie shell --seed 2025112 
(FPCore (x y z)
  :name "fabs fraction 1"
  :precision binary64
  (fabs (- (/ (+ x 4.0) y) (* (/ x y) z))))