fabs fraction 1

Percentage Accurate: 91.3% → 99.9%

Time: 3.2s

Alternatives: 10

Speedup: 1.1×

• 20.8% 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: 91.3% 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, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left|\frac{x + 4}{y} - \frac{x}{y} \cdot z\right|\\ \mathbf{if}\;t\_0 \leq 2 \cdot 10^{-19}:\\ \;\;\;\;\left|\frac{\mathsf{fma}\left(z, x, -4 - x\right)}{y}\right|\\ \mathbf{elif}\;t\_0 \leq \infty:\\ \;\;\;\;\left|\mathsf{fma}\left(\frac{x}{y}, z, \frac{-4 - x}{y}\right)\right|\\ \mathbf{else}:\\ \;\;\;\;\left|\frac{x}{y}\right|\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (fabs (- (/ (+ x 4.0) y) (* (/ x y) z)))))
   (if (<= t_0 2e-19)
     (fabs (/ (fma z x (- -4.0 x)) y))
     (if (<= t_0 INFINITY)
       (fabs (fma (/ x y) z (/ (- -4.0 x) y)))
       (fabs (/ x y))))))

C

double code(double x, double y, double z) {
	double t_0 = fabs((((x + 4.0) / y) - ((x / y) * z)));
	double tmp;
	if (t_0 <= 2e-19) {
		tmp = fabs((fma(z, x, (-4.0 - x)) / y));
	} else if (t_0 <= ((double) INFINITY)) {
		tmp = fabs(fma((x / y), z, ((-4.0 - x) / y)));
	} else {
		tmp = fabs((x / y));
	}
	return tmp;
}

Julia

function code(x, y, z)
	t_0 = abs(Float64(Float64(Float64(x + 4.0) / y) - Float64(Float64(x / y) * z)))
	tmp = 0.0
	if (t_0 <= 2e-19)
		tmp = abs(Float64(fma(z, x, Float64(-4.0 - x)) / y));
	elseif (t_0 <= Inf)
		tmp = abs(fma(Float64(x / y), z, Float64(Float64(-4.0 - x) / y)));
	else
		tmp = abs(Float64(x / y));
	end
	return tmp
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[Abs[N[(N[(N[(x + 4.0), $MachinePrecision] / y), $MachinePrecision] - N[(N[(x / y), $MachinePrecision] * z), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]}, If[LessEqual[t$95$0, 2e-19], N[Abs[N[(N[(z * x + N[(-4.0 - x), $MachinePrecision]), $MachinePrecision] / y), $MachinePrecision]], $MachinePrecision], If[LessEqual[t$95$0, Infinity], N[Abs[N[(N[(x / y), $MachinePrecision] * z + N[(N[(-4.0 - x), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision]], $MachinePrecision], N[Abs[N[(x / y), $MachinePrecision]], $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if (fabs.f64 (-.f64 (/.f64 (+.f64 x #s(literal 4 binary64)) y) (*.f64 (/.f64 x y) z))) < 2e-19

   1. Initial program 92.5%

      \[\left|\frac{x + 4}{y} - \frac{x}{y} \cdot z\right| \]
   2. Add Preprocessing
   3. 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

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

      17. lower-/.f64 N/A

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

   4. Applied rewrites 99.9%

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

   if 2e-19 < (fabs.f64 (-.f64 (/.f64 (+.f64 x #s(literal 4 binary64)) y) (*.f64 (/.f64 x y) z))) < +inf.0

   1. Initial program 99.9%

      \[\left|\frac{x + 4}{y} - \frac{x}{y} \cdot z\right| \]
   2. Add Preprocessing
   3. 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

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

      17. lower-/.f64 N/A

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

   4. Applied rewrites 93.7%

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

      1. lift-/.f64 N/A

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

      2. lift--.f64 N/A

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

      3. lift-fma.f64 N/A

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

      4. *-commutative N/A

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

      5. div-add-rev N/A

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

      6. associate-*l/ N/A

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

      7. lower-fma.f64 N/A

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

      8. lift-/.f64 N/A

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

      9. lower-/.f64 N/A

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

      10. lift--.f64 99.9

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

   6. Applied rewrites 99.9%

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

   if +inf.0 < (fabs.f64 (-.f64 (/.f64 (+.f64 x #s(literal 4 binary64)) y) (*.f64 (/.f64 x y) z)))

   1. Initial program 0.0%

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

   3. Taylor expanded in z around 0

      \[\leadsto \left|\color{blue}{4 \cdot \frac{1}{y} + \frac{x}{y}}\right| \]
   4. 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 100.0

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

   5. Applied rewrites 100.0%

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

   6. Taylor expanded in x around inf

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

   8. Applied rewrites 100.0%

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

Alternative 2: 97.8% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq 4.2 \cdot 10^{-7}:\\ \;\;\;\;\left|\frac{\mathsf{fma}\left(1 - z, x, 4\right)}{y}\right|\\ \mathbf{else}:\\ \;\;\;\;\left|\mathsf{fma}\left(\frac{1 - z}{y}, x, \frac{4}{y}\right)\right|\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= y 4.2e-7)
   (fabs (/ (fma (- 1.0 z) x 4.0) y))
   (fabs (fma (/ (- 1.0 z) y) x (/ 4.0 y)))))

C

double code(double x, double y, double z) {
	double tmp;
	if (y <= 4.2e-7) {
		tmp = fabs((fma((1.0 - z), x, 4.0) / 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 <= 4.2e-7)
		tmp = abs(Float64(fma(Float64(1.0 - z), x, 4.0) / 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, 4.2e-7], N[Abs[N[(N[(N[(1.0 - z), $MachinePrecision] * x + 4.0), $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 < 4.2e-7

   1. Initial program 89.7%

      \[\left|\frac{x + 4}{y} - \frac{x}{y} \cdot z\right| \]
   2. Add Preprocessing
   3. 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

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

   4. Applied rewrites 94.4%

      \[\leadsto \left|\color{blue}{\mathsf{fma}\left(\frac{1 - z}{y}, x, \frac{4}{y}\right)}\right| \]
   5. 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 97.1

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

   6. Applied rewrites 97.1%

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

   if 4.2e-7 < y

   1. Initial program 95.9%

      \[\left|\frac{x + 4}{y} - \frac{x}{y} \cdot z\right| \]
   2. Add Preprocessing
   3. 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

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

   4. Applied rewrites 99.8%

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

Alternative 3: 99.7% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left|\frac{1 - z}{y} \cdot x\right|\\ \mathbf{if}\;x \leq -9.5 \cdot 10^{+86}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;x \leq 3.9 \cdot 10^{+23}:\\ \;\;\;\;\left|\frac{\mathsf{fma}\left(1 - z, x, 4\right)}{y}\right|\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (fabs (* (/ (- 1.0 z) y) x))))
   (if (<= x -9.5e+86)
     t_0
     (if (<= x 3.9e+23) (fabs (/ (fma (- 1.0 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 <= -9.5e+86) {
		tmp = t_0;
	} else if (x <= 3.9e+23) {
		tmp = fabs((fma((1.0 - 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 <= -9.5e+86)
		tmp = t_0;
	elseif (x <= 3.9e+23)
		tmp = abs(Float64(fma(Float64(1.0 - 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, -9.5e+86], t$95$0, If[LessEqual[x, 3.9e+23], N[Abs[N[(N[(N[(1.0 - z), $MachinePrecision] * x + 4.0), $MachinePrecision] / y), $MachinePrecision]], $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if x < -9.50000000000000028e86 or 3.9e23 < x

   1. Initial program 84.8%

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

   3. Taylor expanded in x around inf

      \[\leadsto \left|\color{blue}{x \cdot \left(\frac{1}{y} - \frac{z}{y}\right)}\right| \]
   4. 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 99.8

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

   5. Applied rewrites 99.8%

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

   if -9.50000000000000028e86 < x < 3.9e23

   1. Initial program 95.9%

      \[\left|\frac{x + 4}{y} - \frac{x}{y} \cdot z\right| \]
   2. Add Preprocessing
   3. 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

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

   4. Applied rewrites 93.0%

      \[\leadsto \left|\color{blue}{\mathsf{fma}\left(\frac{1 - z}{y}, x, \frac{4}{y}\right)}\right| \]
   5. 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 99.6

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

   6. Applied rewrites 99.6%

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

Alternative 4: 99.7% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left|\frac{1 - z}{y} \cdot x\right|\\ \mathbf{if}\;x \leq -9.5 \cdot 10^{+86}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;x \leq 5 \cdot 10^{+23}:\\ \;\;\;\;\left|\frac{\mathsf{fma}\left(z, x, -4 - x\right)}{y}\right|\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (fabs (* (/ (- 1.0 z) y) x))))
   (if (<= x -9.5e+86)
     t_0
     (if (<= x 5e+23) (fabs (/ (fma z x (- -4.0 x)) 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 <= -9.5e+86) {
		tmp = t_0;
	} else if (x <= 5e+23) {
		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(1.0 - z) / y) * x))
	tmp = 0.0
	if (x <= -9.5e+86)
		tmp = t_0;
	elseif (x <= 5e+23)
		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[(N[(1.0 - z), $MachinePrecision] / y), $MachinePrecision] * x), $MachinePrecision]], $MachinePrecision]}, If[LessEqual[x, -9.5e+86], t$95$0, If[LessEqual[x, 5e+23], 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 < -9.50000000000000028e86 or 4.9999999999999999e23 < x

   1. Initial program 84.8%

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

   3. Taylor expanded in x around inf

      \[\leadsto \left|\color{blue}{x \cdot \left(\frac{1}{y} - \frac{z}{y}\right)}\right| \]
   4. 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 99.8

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

   5. Applied rewrites 99.8%

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

   if -9.50000000000000028e86 < x < 4.9999999999999999e23

   1. Initial program 95.9%

      \[\left|\frac{x + 4}{y} - \frac{x}{y} \cdot z\right| \]
   2. Add Preprocessing
   3. 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

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

      17. lower-/.f64 N/A

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

   4. Applied rewrites 99.6%

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

Alternative 5: 98.7% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left|\frac{1 - z}{y} \cdot x\right|\\ \mathbf{if}\;x \leq -14500000:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;x \leq 3.7:\\ \;\;\;\;\left|\frac{\mathsf{fma}\left(z, x, -4\right)}{y}\right|\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (fabs (* (/ (- 1.0 z) y) x))))
   (if (<= x -14500000.0)
     t_0
     (if (<= x 3.7) (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 <= -14500000.0) {
		tmp = t_0;
	} else if (x <= 3.7) {
		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 <= -14500000.0)
		tmp = t_0;
	elseif (x <= 3.7)
		tmp = abs(Float64(fma(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, -14500000.0], t$95$0, If[LessEqual[x, 3.7], 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 < -1.45e7 or 3.7000000000000002 < x

   1. Initial program 86.9%

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

   3. Taylor expanded in x around inf

      \[\leadsto \left|\color{blue}{x \cdot \left(\frac{1}{y} - \frac{z}{y}\right)}\right| \]
   4. 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 99.1

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

   5. Applied rewrites 99.1%

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

   if -1.45e7 < x < 3.7000000000000002

   1. Initial program 95.7%

      \[\left|\frac{x + 4}{y} - \frac{x}{y} \cdot z\right| \]
   2. Add Preprocessing
   3. 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

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

      17. lower-/.f64 N/A

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

   4. Applied rewrites 99.9%

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

   5. Taylor expanded in x around 0

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

   7. Applied rewrites 98.4%

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

Alternative 6: 94.3% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left|\frac{\mathsf{fma}\left(z, x, -4\right)}{y}\right|\\ \mathbf{if}\;z \leq -2.5 \cdot 10^{+24}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;z \leq 2.6:\\ \;\;\;\;\left|\frac{x - -4}{y}\right|\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (fabs (/ (fma z x -4.0) y))))
   (if (<= z -2.5e+24) t_0 (if (<= z 2.6) (fabs (/ (- x -4.0) y)) t_0))))

C

double code(double x, double y, double z) {
	double t_0 = fabs((fma(z, x, -4.0) / y));
	double tmp;
	if (z <= -2.5e+24) {
		tmp = t_0;
	} else if (z <= 2.6) {
		tmp = fabs(((x - -4.0) / y));
	} else {
		tmp = t_0;
	}
	return tmp;
}

Julia

function code(x, y, z)
	t_0 = abs(Float64(fma(z, x, -4.0) / y))
	tmp = 0.0
	if (z <= -2.5e+24)
		tmp = t_0;
	elseif (z <= 2.6)
		tmp = abs(Float64(Float64(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[(z * x + -4.0), $MachinePrecision] / y), $MachinePrecision]], $MachinePrecision]}, If[LessEqual[z, -2.5e+24], t$95$0, If[LessEqual[z, 2.6], N[Abs[N[(N[(x - -4.0), $MachinePrecision] / y), $MachinePrecision]], $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if z < -2.50000000000000023e24 or 2.60000000000000009 < z

   1. Initial program 88.4%

      \[\left|\frac{x + 4}{y} - \frac{x}{y} \cdot z\right| \]
   2. Add Preprocessing
   3. 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

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

      17. lower-/.f64 N/A

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

   4. Applied rewrites 91.3%

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

   5. Taylor expanded in x around 0

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

   7. Applied rewrites 90.9%

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

   if -2.50000000000000023e24 < z < 2.60000000000000009

   1. Initial program 94.0%

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

   3. Taylor expanded in z around 0

      \[\leadsto \left|\color{blue}{4 \cdot \frac{1}{y} + \frac{x}{y}}\right| \]
   4. 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 97.5

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

   5. Applied rewrites 97.5%

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

Alternative 7: 85.5% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left|\frac{x}{y} \cdot z\right|\\ \mathbf{if}\;z \leq -2.65 \cdot 10^{+112}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;z \leq 2 \cdot 10^{+65}:\\ \;\;\;\;\left|\frac{x - -4}{y}\right|\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (fabs (* (/ x y) z))))
   (if (<= z -2.65e+112) t_0 (if (<= z 2e+65) (fabs (/ (- x -4.0) y)) t_0))))

C

double code(double x, double y, double z) {
	double t_0 = fabs(((x / y) * z));
	double tmp;
	if (z <= -2.65e+112) {
		tmp = t_0;
	} else if (z <= 2e+65) {
		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 / y) * z))
    if (z <= (-2.65d+112)) then
        tmp = t_0
    else if (z <= 2d+65) 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 / y) * z));
	double tmp;
	if (z <= -2.65e+112) {
		tmp = t_0;
	} else if (z <= 2e+65) {
		tmp = Math.abs(((x - -4.0) / y));
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = math.fabs(((x / y) * z))
	tmp = 0
	if z <= -2.65e+112:
		tmp = t_0
	elif z <= 2e+65:
		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 / y) * z))
	tmp = 0.0
	if (z <= -2.65e+112)
		tmp = t_0;
	elseif (z <= 2e+65)
		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 / y) * z));
	tmp = 0.0;
	if (z <= -2.65e+112)
		tmp = t_0;
	elseif (z <= 2e+65)
		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[(N[(x / y), $MachinePrecision] * z), $MachinePrecision]], $MachinePrecision]}, If[LessEqual[z, -2.65e+112], t$95$0, If[LessEqual[z, 2e+65], N[Abs[N[(N[(x - -4.0), $MachinePrecision] / y), $MachinePrecision]], $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if z < -2.65000000000000009e112 or 2e65 < z

   1. Initial program 86.7%

      \[\left|\frac{x + 4}{y} - \frac{x}{y} \cdot z\right| \]
   2. Add Preprocessing
   3. 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. fabs-sub N/A

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

      8. lower-fabs.f64 N/A

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

      9. associate-*l/ N/A

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

      10. +-commutative N/A

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

      11. sub-div N/A

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

      12. lower-/.f64 N/A

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

      13. lower--.f64 N/A

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

      14. *-commutative N/A

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

      15. lower-*.f64 N/A

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

      16. +-commutative N/A

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

      17. metadata-eval N/A

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

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

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

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

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

      20. metadata-eval N/A

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

      21. lower--.f64 N/A

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

      22. metadata-eval 89.7

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

   4. Applied rewrites 89.7%

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

   5. Taylor expanded in z around inf

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

      1. associate-*l/ N/A

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

      2. lower-*.f64 N/A

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

      3. lift-/.f64 77.3

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

   7. Applied rewrites 77.3%

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

   if -2.65000000000000009e112 < z < 2e65

   1. Initial program 93.9%

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

   3. Taylor expanded in z around 0

      \[\leadsto \left|\color{blue}{4 \cdot \frac{1}{y} + \frac{x}{y}}\right| \]
   4. 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 90.1

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

   5. Applied rewrites 90.1%

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

Alternative 8: 68.9% accurate, 1.4× 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 87.0%

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

   3. Taylor expanded in z around 0

      \[\leadsto \left|\color{blue}{4 \cdot \frac{1}{y} + \frac{x}{y}}\right| \]
   4. 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 64.1

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

   5. Applied rewrites 64.1%

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

   6. Taylor expanded in x around inf

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

   8. Applied rewrites 63.1%

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

   if -1.55000000000000004 < x < 4

   1. Initial program 95.7%

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

   3. Taylor expanded in x around 0

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

      1. lower-/.f64 74.9

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

   5. Applied rewrites 74.9%

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

Alternative 9: 69.9% 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 91.3%

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

3. Taylor expanded in z around 0

   \[\leadsto \left|\color{blue}{4 \cdot \frac{1}{y} + \frac{x}{y}}\right| \]
4. 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 69.9

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

5. Applied rewrites 69.9%

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

Alternative 10: 39.7% accurate, 2.6× 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 91.3%

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

3. Taylor expanded in x around 0

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

   1. lower-/.f64 39.7

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

5. Applied rewrites 39.7%

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

Reproduce

herbie shell --seed 2025089 
(FPCore (x y z)
  :name "fabs fraction 1"
  :precision binary64
  (fabs (- (/ (+ x 4.0) y) (* (/ x y) z))))