Diagrams.TwoD.Apollonian:initialConfig from diagrams-contrib-1.3.0.5, A

Percentage Accurate: 68.9% → 99.9%

Time: 7.6s

Alternatives: 5

Speedup: 1.2×

• 41.1% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

\[\frac{\left(x \cdot x + y \cdot y\right) - z \cdot z}{y \cdot 2} \]

FPCore

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

C

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

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 68.9% accurate, 1.0× speedup

Math

\[\frac{\left(x \cdot x + y \cdot y\right) - z \cdot z}{y \cdot 2} \]

FPCore

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

C

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

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 99.9% accurate, 1.2× speedup

Math

\[\mathsf{fma}\left(\frac{x - z}{y}, z + x, y\right) \cdot 0.5 \]

FPCore

(FPCore (x y z) :precision binary64 (* (fma (/ (- x z) y) (+ z x) y) 0.5))

C

double code(double x, double y, double z) {
	return fma(((x - z) / y), (z + x), y) * 0.5;
}

Julia

function code(x, y, z)
	return Float64(fma(Float64(Float64(x - z) / y), Float64(z + x), y) * 0.5)
end

Wolfram

code[x_, y_, z_] := N[(N[(N[(N[(x - z), $MachinePrecision] / y), $MachinePrecision] * N[(z + x), $MachinePrecision] + y), $MachinePrecision] * 0.5), $MachinePrecision]

Derivation

1. Initial program 68.9%

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

   1. lift--.f64 N/A

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

   2. lift-+.f64 N/A

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

   3. +-commutative N/A

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

   4. associate--l+ N/A

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

   5. +-commutative N/A

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

   6. lift-*.f64 N/A

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

   7. lift-*.f64 N/A

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

   8. difference-of-squares N/A

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

   9. lower-fma.f64 N/A

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

   10. lower-+.f64 N/A

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

   11. lower--.f64 74.0

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

   12. lift-*.f64 N/A

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

   13. *-commutative N/A

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

   14. count-2-rev N/A

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

   15. lower-+.f64 74.0

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

3. Applied rewrites 74.0%

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

   1. lift-/.f64 N/A

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

   2. lift-+.f64 N/A

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

   3. count-2 N/A

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

   4. *-commutative N/A

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

   5. associate-/l/ N/A

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

   6. lift-fma.f64 N/A

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

   7. lift-*.f64 N/A

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

   8. +-commutative N/A

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

   9. lift-*.f64 N/A

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

   10. add-to-fraction N/A

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

   11. lift-/.f64 N/A

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

   12. lift-+.f64 N/A

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

   13. mult-flip N/A

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

   14. metadata-eval N/A

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

   15. lower-*.f64 89.3

       \[\leadsto \color{blue}{\left(y + \frac{\left(x + z\right) \cdot \left(x - z\right)}{y}\right) \cdot 0.5} \]

5. Applied rewrites 99.9%

   \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{x - z}{y}, z + x, y\right) \cdot 0.5} \]
6. Add Preprocessing

Alternative 2: 98.6% accurate, 0.2× speedup

Math

\[\begin{array}{l} t_0 := \left|x\right| - \left|z\right|\\ t_1 := \frac{\left(\left|x\right| \cdot \left|x\right| + \left|y\right| \cdot \left|y\right|\right) - \left|z\right| \cdot \left|z\right|}{\left|y\right| \cdot 2}\\ \mathsf{copysign}\left(1, y\right) \cdot \begin{array}{l} \mathbf{if}\;t\_1 \leq 0:\\ \;\;\;\;\mathsf{fma}\left(\left|z\right|, \frac{-0.5}{\left|y\right|} \cdot \left|z\right|, \left|y\right| \cdot 0.5\right)\\ \mathbf{elif}\;t\_1 \leq \infty:\\ \;\;\;\;\mathsf{fma}\left(t\_0, \frac{\left|x\right|}{\left|y\right|}, \left|y\right|\right) \cdot 0.5\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(t\_0, \frac{\left|z\right|}{\left|y\right|}, \left|y\right|\right) \cdot 0.5\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (- (fabs x) (fabs z)))
        (t_1
         (/
          (-
           (+ (* (fabs x) (fabs x)) (* (fabs y) (fabs y)))
           (* (fabs z) (fabs z)))
          (* (fabs y) 2.0))))
   (*
    (copysign 1.0 y)
    (if (<= t_1 0.0)
      (fma (fabs z) (* (/ -0.5 (fabs y)) (fabs z)) (* (fabs y) 0.5))
      (if (<= t_1 INFINITY)
        (* (fma t_0 (/ (fabs x) (fabs y)) (fabs y)) 0.5)
        (* (fma t_0 (/ (fabs z) (fabs y)) (fabs y)) 0.5))))))

C

double code(double x, double y, double z) {
	double t_0 = fabs(x) - fabs(z);
	double t_1 = (((fabs(x) * fabs(x)) + (fabs(y) * fabs(y))) - (fabs(z) * fabs(z))) / (fabs(y) * 2.0);
	double tmp;
	if (t_1 <= 0.0) {
		tmp = fma(fabs(z), ((-0.5 / fabs(y)) * fabs(z)), (fabs(y) * 0.5));
	} else if (t_1 <= ((double) INFINITY)) {
		tmp = fma(t_0, (fabs(x) / fabs(y)), fabs(y)) * 0.5;
	} else {
		tmp = fma(t_0, (fabs(z) / fabs(y)), fabs(y)) * 0.5;
	}
	return copysign(1.0, y) * tmp;
}

Julia

function code(x, y, z)
	t_0 = Float64(abs(x) - abs(z))
	t_1 = Float64(Float64(Float64(Float64(abs(x) * abs(x)) + Float64(abs(y) * abs(y))) - Float64(abs(z) * abs(z))) / Float64(abs(y) * 2.0))
	tmp = 0.0
	if (t_1 <= 0.0)
		tmp = fma(abs(z), Float64(Float64(-0.5 / abs(y)) * abs(z)), Float64(abs(y) * 0.5));
	elseif (t_1 <= Inf)
		tmp = Float64(fma(t_0, Float64(abs(x) / abs(y)), abs(y)) * 0.5);
	else
		tmp = Float64(fma(t_0, Float64(abs(z) / abs(y)), abs(y)) * 0.5);
	end
	return Float64(copysign(1.0, y) * tmp)
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(N[Abs[x], $MachinePrecision] - N[Abs[z], $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(N[(N[(N[(N[Abs[x], $MachinePrecision] * N[Abs[x], $MachinePrecision]), $MachinePrecision] + N[(N[Abs[y], $MachinePrecision] * N[Abs[y], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(N[Abs[z], $MachinePrecision] * N[Abs[z], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(N[Abs[y], $MachinePrecision] * 2.0), $MachinePrecision]), $MachinePrecision]}, N[(N[With[{TMP1 = Abs[1.0], TMP2 = Sign[y]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision] * If[LessEqual[t$95$1, 0.0], N[(N[Abs[z], $MachinePrecision] * N[(N[(-0.5 / N[Abs[y], $MachinePrecision]), $MachinePrecision] * N[Abs[z], $MachinePrecision]), $MachinePrecision] + N[(N[Abs[y], $MachinePrecision] * 0.5), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$1, Infinity], N[(N[(t$95$0 * N[(N[Abs[x], $MachinePrecision] / N[Abs[y], $MachinePrecision]), $MachinePrecision] + N[Abs[y], $MachinePrecision]), $MachinePrecision] * 0.5), $MachinePrecision], N[(N[(t$95$0 * N[(N[Abs[z], $MachinePrecision] / N[Abs[y], $MachinePrecision]), $MachinePrecision] + N[Abs[y], $MachinePrecision]), $MachinePrecision] * 0.5), $MachinePrecision]]]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if (/.f64 (-.f64 (+.f64 (*.f64 x x) (*.f64 y y)) (*.f64 z z)) (*.f64 y #s(literal 2 binary64))) < 0.0

   1. Initial program 68.9%

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

      1. lift-/.f64 N/A

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

      2. frac-2neg N/A

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

      3. lift--.f64 N/A

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

      4. sub-negate-rev N/A

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

      5. sub-flip N/A

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

      6. div-add N/A

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

      7. distribute-neg-frac2 N/A

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

      8. lift-*.f64 N/A

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

      9. associate-/l* N/A

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

      10. distribute-rgt-neg-in N/A

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

      11. distribute-frac-neg N/A

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

      12. frac-2neg N/A

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

      13. lower-fma.f64 N/A

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

   3. Applied rewrites 87.5%

      \[\leadsto \color{blue}{\mathsf{fma}\left(z, \frac{z}{-2 \cdot y}, \left(y + \frac{x \cdot x}{y}\right) \cdot 0.5\right)} \]

   4. Taylor expanded in x around 0

      \[\leadsto \mathsf{fma}\left(z, \frac{z}{-2 \cdot y}, \color{blue}{y} \cdot \frac{1}{2}\right) \]
   5. Step-by-step derivation

   6. Applied rewrites 67.4%

      \[\leadsto \mathsf{fma}\left(z, \frac{z}{-2 \cdot y}, \color{blue}{y} \cdot 0.5\right) \]
   7. Step-by-step derivation

      1. lift-/.f64 N/A

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

      2. mult-flip N/A

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

      3. *-commutative N/A

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

      4. lower-*.f64 N/A

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

      5. lift-*.f64 N/A

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

      6. associate-/r* N/A

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

      7. lower-/.f64 N/A

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

      8. metadata-eval 67.4

         \[\leadsto \mathsf{fma}\left(z, \frac{\color{blue}{-0.5}}{y} \cdot z, y \cdot 0.5\right) \]

   8. Applied rewrites 67.4%

      \[\leadsto \mathsf{fma}\left(z, \color{blue}{\frac{-0.5}{y} \cdot z}, y \cdot 0.5\right) \]

   if 0.0 < (/.f64 (-.f64 (+.f64 (*.f64 x x) (*.f64 y y)) (*.f64 z z)) (*.f64 y #s(literal 2 binary64))) < +inf.0

   1. Initial program 68.9%

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

      1. lift--.f64 N/A

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

      2. lift-+.f64 N/A

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

      3. +-commutative N/A

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

      4. associate--l+ N/A

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

      5. +-commutative N/A

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

      6. lift-*.f64 N/A

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

      7. lift-*.f64 N/A

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

      8. difference-of-squares N/A

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

      9. lower-fma.f64 N/A

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

      10. lower-+.f64 N/A

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

      11. lower--.f64 74.0

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

      12. lift-*.f64 N/A

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

      13. *-commutative N/A

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

      14. count-2-rev N/A

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

      15. lower-+.f64 74.0

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

   3. Applied rewrites 74.0%

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

      1. lift-/.f64 N/A

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

      2. lift-+.f64 N/A

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

      3. count-2 N/A

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

      4. *-commutative N/A

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

      5. associate-/l/ N/A

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

      6. lift-fma.f64 N/A

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

      7. lift-*.f64 N/A

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

      8. +-commutative N/A

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

      9. lift-*.f64 N/A

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

      10. add-to-fraction N/A

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

      11. lift-/.f64 N/A

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

      12. lift-+.f64 N/A

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

      13. mult-flip N/A

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

      14. metadata-eval N/A

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

      15. lower-*.f64 89.3

          \[\leadsto \color{blue}{\left(y + \frac{\left(x + z\right) \cdot \left(x - z\right)}{y}\right) \cdot 0.5} \]

   5. Applied rewrites 99.9%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{x - z}{y}, z + x, y\right) \cdot 0.5} \]
   6. Step-by-step derivation

      1. lift-fma.f64 N/A

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

      2. lift-/.f64 N/A

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

      3. mult-flip N/A

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

      4. lift-+.f64 N/A

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

      5. +-commutative N/A

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

      6. lift-+.f64 N/A

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

      7. associate-*l* N/A

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

      8. lower-fma.f64 N/A

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

      9. lower-*.f64 N/A

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

      10. lower-/.f64 99.8

          \[\leadsto \mathsf{fma}\left(x - z, \color{blue}{\frac{1}{y}} \cdot \left(x + z\right), y\right) \cdot 0.5 \]

      11. lift-+.f64 N/A

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

      12. +-commutative N/A

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

      13. lift-+.f64 99.8

          \[\leadsto \mathsf{fma}\left(x - z, \frac{1}{y} \cdot \color{blue}{\left(z + x\right)}, y\right) \cdot 0.5 \]

   7. Applied rewrites 99.8%

      \[\leadsto \color{blue}{\mathsf{fma}\left(x - z, \frac{1}{y} \cdot \left(z + x\right), y\right)} \cdot 0.5 \]

   8. Taylor expanded in x around inf

      \[\leadsto \mathsf{fma}\left(x - z, \color{blue}{\frac{x}{y}}, y\right) \cdot \frac{1}{2} \]
   9. Step-by-step derivation

      1. lower-/.f64 71.2

         \[\leadsto \mathsf{fma}\left(x - z, \frac{x}{\color{blue}{y}}, y\right) \cdot 0.5 \]

   10. Applied rewrites 71.2%

       \[\leadsto \mathsf{fma}\left(x - z, \color{blue}{\frac{x}{y}}, y\right) \cdot 0.5 \]

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

   1. Initial program 68.9%

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

      1. lift--.f64 N/A

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

      2. lift-+.f64 N/A

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

      3. +-commutative N/A

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

      4. associate--l+ N/A

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

      5. +-commutative N/A

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

      6. lift-*.f64 N/A

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

      7. lift-*.f64 N/A

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

      8. difference-of-squares N/A

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

      9. lower-fma.f64 N/A

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

      10. lower-+.f64 N/A

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

      11. lower--.f64 74.0

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

      12. lift-*.f64 N/A

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

      13. *-commutative N/A

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

      14. count-2-rev N/A

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

      15. lower-+.f64 74.0

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

   3. Applied rewrites 74.0%

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

      1. lift-/.f64 N/A

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

      2. lift-+.f64 N/A

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

      3. count-2 N/A

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

      4. *-commutative N/A

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

      5. associate-/l/ N/A

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

      6. lift-fma.f64 N/A

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

      7. lift-*.f64 N/A

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

      8. +-commutative N/A

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

      9. lift-*.f64 N/A

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

      10. add-to-fraction N/A

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

      11. lift-/.f64 N/A

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

      12. lift-+.f64 N/A

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

      13. mult-flip N/A

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

      14. metadata-eval N/A

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

      15. lower-*.f64 89.3

          \[\leadsto \color{blue}{\left(y + \frac{\left(x + z\right) \cdot \left(x - z\right)}{y}\right) \cdot 0.5} \]

   5. Applied rewrites 99.9%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{x - z}{y}, z + x, y\right) \cdot 0.5} \]

   6. Taylor expanded in x around 0

      \[\leadsto \mathsf{fma}\left(\frac{x - z}{y}, \color{blue}{z}, y\right) \cdot \frac{1}{2} \]
   7. Step-by-step derivation

   8. Applied rewrites 73.7%

      \[\leadsto \mathsf{fma}\left(\frac{x - z}{y}, \color{blue}{z}, y\right) \cdot 0.5 \]
   9. Step-by-step derivation

      1. lift-fma.f64 N/A

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

      2. lift-/.f64 N/A

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

      3. associate-*l/ N/A

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

      4. associate-/l* N/A

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

      5. lower-fma.f64 N/A

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

      6. lower-/.f64 71.7

         \[\leadsto \mathsf{fma}\left(x - z, \color{blue}{\frac{z}{y}}, y\right) \cdot 0.5 \]

   10. Applied rewrites 71.7%

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

Alternative 3: 98.5% accurate, 0.2× speedup

Math

\[\begin{array}{l} t_0 := \frac{\left(\left|x\right| \cdot \left|x\right| + \left|y\right| \cdot \left|y\right|\right) - \left|z\right| \cdot \left|z\right|}{\left|y\right| \cdot 2}\\ t_1 := \left|x\right| - \left|z\right|\\ t_2 := \mathsf{fma}\left(t\_1, \frac{\left|z\right|}{\left|y\right|}, \left|y\right|\right) \cdot 0.5\\ \mathsf{copysign}\left(1, y\right) \cdot \begin{array}{l} \mathbf{if}\;t\_0 \leq 0:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;t\_0 \leq \infty:\\ \;\;\;\;\mathsf{fma}\left(t\_1, \frac{\left|x\right|}{\left|y\right|}, \left|y\right|\right) \cdot 0.5\\ \mathbf{else}:\\ \;\;\;\;t\_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0
         (/
          (-
           (+ (* (fabs x) (fabs x)) (* (fabs y) (fabs y)))
           (* (fabs z) (fabs z)))
          (* (fabs y) 2.0)))
        (t_1 (- (fabs x) (fabs z)))
        (t_2 (* (fma t_1 (/ (fabs z) (fabs y)) (fabs y)) 0.5)))
   (*
    (copysign 1.0 y)
    (if (<= t_0 0.0)
      t_2
      (if (<= t_0 INFINITY)
        (* (fma t_1 (/ (fabs x) (fabs y)) (fabs y)) 0.5)
        t_2)))))

C

double code(double x, double y, double z) {
	double t_0 = (((fabs(x) * fabs(x)) + (fabs(y) * fabs(y))) - (fabs(z) * fabs(z))) / (fabs(y) * 2.0);
	double t_1 = fabs(x) - fabs(z);
	double t_2 = fma(t_1, (fabs(z) / fabs(y)), fabs(y)) * 0.5;
	double tmp;
	if (t_0 <= 0.0) {
		tmp = t_2;
	} else if (t_0 <= ((double) INFINITY)) {
		tmp = fma(t_1, (fabs(x) / fabs(y)), fabs(y)) * 0.5;
	} else {
		tmp = t_2;
	}
	return copysign(1.0, y) * tmp;
}

Julia

function code(x, y, z)
	t_0 = Float64(Float64(Float64(Float64(abs(x) * abs(x)) + Float64(abs(y) * abs(y))) - Float64(abs(z) * abs(z))) / Float64(abs(y) * 2.0))
	t_1 = Float64(abs(x) - abs(z))
	t_2 = Float64(fma(t_1, Float64(abs(z) / abs(y)), abs(y)) * 0.5)
	tmp = 0.0
	if (t_0 <= 0.0)
		tmp = t_2;
	elseif (t_0 <= Inf)
		tmp = Float64(fma(t_1, Float64(abs(x) / abs(y)), abs(y)) * 0.5);
	else
		tmp = t_2;
	end
	return Float64(copysign(1.0, y) * tmp)
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(N[(N[(N[(N[Abs[x], $MachinePrecision] * N[Abs[x], $MachinePrecision]), $MachinePrecision] + N[(N[Abs[y], $MachinePrecision] * N[Abs[y], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(N[Abs[z], $MachinePrecision] * N[Abs[z], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(N[Abs[y], $MachinePrecision] * 2.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(N[Abs[x], $MachinePrecision] - N[Abs[z], $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(t$95$1 * N[(N[Abs[z], $MachinePrecision] / N[Abs[y], $MachinePrecision]), $MachinePrecision] + N[Abs[y], $MachinePrecision]), $MachinePrecision] * 0.5), $MachinePrecision]}, N[(N[With[{TMP1 = Abs[1.0], TMP2 = Sign[y]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision] * If[LessEqual[t$95$0, 0.0], t$95$2, If[LessEqual[t$95$0, Infinity], N[(N[(t$95$1 * N[(N[Abs[x], $MachinePrecision] / N[Abs[y], $MachinePrecision]), $MachinePrecision] + N[Abs[y], $MachinePrecision]), $MachinePrecision] * 0.5), $MachinePrecision], t$95$2]]), $MachinePrecision]]]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 (-.f64 (+.f64 (*.f64 x x) (*.f64 y y)) (*.f64 z z)) (*.f64 y #s(literal 2 binary64))) < 0.0 or +inf.0 < (/.f64 (-.f64 (+.f64 (*.f64 x x) (*.f64 y y)) (*.f64 z z)) (*.f64 y #s(literal 2 binary64)))

   1. Initial program 68.9%

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

      1. lift--.f64 N/A

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

      2. lift-+.f64 N/A

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

      3. +-commutative N/A

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

      4. associate--l+ N/A

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

      5. +-commutative N/A

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

      6. lift-*.f64 N/A

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

      7. lift-*.f64 N/A

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

      8. difference-of-squares N/A

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

      9. lower-fma.f64 N/A

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

      10. lower-+.f64 N/A

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

      11. lower--.f64 74.0

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

      12. lift-*.f64 N/A

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

      13. *-commutative N/A

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

      14. count-2-rev N/A

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

      15. lower-+.f64 74.0

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

   3. Applied rewrites 74.0%

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

      1. lift-/.f64 N/A

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

      2. lift-+.f64 N/A

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

      3. count-2 N/A

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

      4. *-commutative N/A

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

      5. associate-/l/ N/A

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

      6. lift-fma.f64 N/A

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

      7. lift-*.f64 N/A

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

      8. +-commutative N/A

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

      9. lift-*.f64 N/A

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

      10. add-to-fraction N/A

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

      11. lift-/.f64 N/A

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

      12. lift-+.f64 N/A

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

      13. mult-flip N/A

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

      14. metadata-eval N/A

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

      15. lower-*.f64 89.3

          \[\leadsto \color{blue}{\left(y + \frac{\left(x + z\right) \cdot \left(x - z\right)}{y}\right) \cdot 0.5} \]

   5. Applied rewrites 99.9%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{x - z}{y}, z + x, y\right) \cdot 0.5} \]

   6. Taylor expanded in x around 0

      \[\leadsto \mathsf{fma}\left(\frac{x - z}{y}, \color{blue}{z}, y\right) \cdot \frac{1}{2} \]
   7. Step-by-step derivation

   8. Applied rewrites 73.7%

      \[\leadsto \mathsf{fma}\left(\frac{x - z}{y}, \color{blue}{z}, y\right) \cdot 0.5 \]
   9. Step-by-step derivation

      1. lift-fma.f64 N/A

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

      2. lift-/.f64 N/A

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

      3. associate-*l/ N/A

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

      4. associate-/l* N/A

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

      5. lower-fma.f64 N/A

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

      6. lower-/.f64 71.7

         \[\leadsto \mathsf{fma}\left(x - z, \color{blue}{\frac{z}{y}}, y\right) \cdot 0.5 \]

   10. Applied rewrites 71.7%

       \[\leadsto \color{blue}{\mathsf{fma}\left(x - z, \frac{z}{y}, y\right)} \cdot 0.5 \]

   if 0.0 < (/.f64 (-.f64 (+.f64 (*.f64 x x) (*.f64 y y)) (*.f64 z z)) (*.f64 y #s(literal 2 binary64))) < +inf.0

   1. Initial program 68.9%

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

      1. lift--.f64 N/A

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

      2. lift-+.f64 N/A

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

      3. +-commutative N/A

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

      4. associate--l+ N/A

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

      5. +-commutative N/A

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

      6. lift-*.f64 N/A

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

      7. lift-*.f64 N/A

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

      8. difference-of-squares N/A

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

      9. lower-fma.f64 N/A

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

      10. lower-+.f64 N/A

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

      11. lower--.f64 74.0

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

      12. lift-*.f64 N/A

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

      13. *-commutative N/A

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

      14. count-2-rev N/A

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

      15. lower-+.f64 74.0

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

   3. Applied rewrites 74.0%

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

      1. lift-/.f64 N/A

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

      2. lift-+.f64 N/A

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

      3. count-2 N/A

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

      4. *-commutative N/A

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

      5. associate-/l/ N/A

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

      6. lift-fma.f64 N/A

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

      7. lift-*.f64 N/A

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

      8. +-commutative N/A

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

      9. lift-*.f64 N/A

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

      10. add-to-fraction N/A

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

      11. lift-/.f64 N/A

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

      12. lift-+.f64 N/A

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

      13. mult-flip N/A

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

      14. metadata-eval N/A

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

      15. lower-*.f64 89.3

          \[\leadsto \color{blue}{\left(y + \frac{\left(x + z\right) \cdot \left(x - z\right)}{y}\right) \cdot 0.5} \]

   5. Applied rewrites 99.9%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{x - z}{y}, z + x, y\right) \cdot 0.5} \]
   6. Step-by-step derivation

      1. lift-fma.f64 N/A

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

      2. lift-/.f64 N/A

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

      3. mult-flip N/A

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

      4. lift-+.f64 N/A

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

      5. +-commutative N/A

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

      6. lift-+.f64 N/A

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

      7. associate-*l* N/A

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

      8. lower-fma.f64 N/A

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

      9. lower-*.f64 N/A

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

      10. lower-/.f64 99.8

          \[\leadsto \mathsf{fma}\left(x - z, \color{blue}{\frac{1}{y}} \cdot \left(x + z\right), y\right) \cdot 0.5 \]

      11. lift-+.f64 N/A

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

      12. +-commutative N/A

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

      13. lift-+.f64 99.8

          \[\leadsto \mathsf{fma}\left(x - z, \frac{1}{y} \cdot \color{blue}{\left(z + x\right)}, y\right) \cdot 0.5 \]

   7. Applied rewrites 99.8%

      \[\leadsto \color{blue}{\mathsf{fma}\left(x - z, \frac{1}{y} \cdot \left(z + x\right), y\right)} \cdot 0.5 \]

   8. Taylor expanded in x around inf

      \[\leadsto \mathsf{fma}\left(x - z, \color{blue}{\frac{x}{y}}, y\right) \cdot \frac{1}{2} \]
   9. Step-by-step derivation

      1. lower-/.f64 71.2

         \[\leadsto \mathsf{fma}\left(x - z, \frac{x}{\color{blue}{y}}, y\right) \cdot 0.5 \]

   10. Applied rewrites 71.2%

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

Alternative 4: 75.4% accurate, 1.2× speedup

Math

\[\mathsf{fma}\left(\left|x\right| - \left|z\right|, \frac{\left|x\right|}{y}, y\right) \cdot 0.5 \]

FPCore

(FPCore (x y z)
 :precision binary64
 (* (fma (- (fabs x) (fabs z)) (/ (fabs x) y) y) 0.5))

C

double code(double x, double y, double z) {
	return fma((fabs(x) - fabs(z)), (fabs(x) / y), y) * 0.5;
}

Julia

function code(x, y, z)
	return Float64(fma(Float64(abs(x) - abs(z)), Float64(abs(x) / y), y) * 0.5)
end

Wolfram

code[x_, y_, z_] := N[(N[(N[(N[Abs[x], $MachinePrecision] - N[Abs[z], $MachinePrecision]), $MachinePrecision] * N[(N[Abs[x], $MachinePrecision] / y), $MachinePrecision] + y), $MachinePrecision] * 0.5), $MachinePrecision]

Derivation

1. Initial program 68.9%

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

   1. lift--.f64 N/A

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

   2. lift-+.f64 N/A

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

   3. +-commutative N/A

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

   4. associate--l+ N/A

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

   5. +-commutative N/A

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

   6. lift-*.f64 N/A

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

   7. lift-*.f64 N/A

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

   8. difference-of-squares N/A

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

   9. lower-fma.f64 N/A

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

   10. lower-+.f64 N/A

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

   11. lower--.f64 74.0

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

   12. lift-*.f64 N/A

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

   13. *-commutative N/A

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

   14. count-2-rev N/A

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

   15. lower-+.f64 74.0

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

3. Applied rewrites 74.0%

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

   1. lift-/.f64 N/A

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

   2. lift-+.f64 N/A

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

   3. count-2 N/A

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

   4. *-commutative N/A

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

   5. associate-/l/ N/A

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

   6. lift-fma.f64 N/A

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

   7. lift-*.f64 N/A

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

   8. +-commutative N/A

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

   9. lift-*.f64 N/A

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

   10. add-to-fraction N/A

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

   11. lift-/.f64 N/A

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

   12. lift-+.f64 N/A

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

   13. mult-flip N/A

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

   14. metadata-eval N/A

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

   15. lower-*.f64 89.3

       \[\leadsto \color{blue}{\left(y + \frac{\left(x + z\right) \cdot \left(x - z\right)}{y}\right) \cdot 0.5} \]

5. Applied rewrites 99.9%

   \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{x - z}{y}, z + x, y\right) \cdot 0.5} \]
6. Step-by-step derivation

   1. lift-fma.f64 N/A

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

   2. lift-/.f64 N/A

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

   3. mult-flip N/A

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

   4. lift-+.f64 N/A

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

   5. +-commutative N/A

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

   6. lift-+.f64 N/A

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

   7. associate-*l* N/A

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

   8. lower-fma.f64 N/A

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

   9. lower-*.f64 N/A

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

   10. lower-/.f64 99.8

       \[\leadsto \mathsf{fma}\left(x - z, \color{blue}{\frac{1}{y}} \cdot \left(x + z\right), y\right) \cdot 0.5 \]

   11. lift-+.f64 N/A

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

   12. +-commutative N/A

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

   13. lift-+.f64 99.8

       \[\leadsto \mathsf{fma}\left(x - z, \frac{1}{y} \cdot \color{blue}{\left(z + x\right)}, y\right) \cdot 0.5 \]

7. Applied rewrites 99.8%

   \[\leadsto \color{blue}{\mathsf{fma}\left(x - z, \frac{1}{y} \cdot \left(z + x\right), y\right)} \cdot 0.5 \]

8. Taylor expanded in x around inf

   \[\leadsto \mathsf{fma}\left(x - z, \color{blue}{\frac{x}{y}}, y\right) \cdot \frac{1}{2} \]
9. Step-by-step derivation

   1. lower-/.f64 71.2

      \[\leadsto \mathsf{fma}\left(x - z, \frac{x}{\color{blue}{y}}, y\right) \cdot 0.5 \]

10. Applied rewrites 71.2%

    \[\leadsto \mathsf{fma}\left(x - z, \color{blue}{\frac{x}{y}}, y\right) \cdot 0.5 \]
11. Add Preprocessing

Alternative 5: 34.8% accurate, 5.4× speedup

Math

\[0.5 \cdot y \]

FPCore

(FPCore (x y z) :precision binary64 (* 0.5 y))

C

double code(double x, double y, double z) {
	return 0.5 * 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 = 0.5d0 * y
end function

Java

public static double code(double x, double y, double z) {
	return 0.5 * y;
}

Python

def code(x, y, z):
	return 0.5 * y

Julia

function code(x, y, z)
	return Float64(0.5 * y)
end

MATLAB

function tmp = code(x, y, z)
	tmp = 0.5 * y;
end

Wolfram

code[x_, y_, z_] := N[(0.5 * y), $MachinePrecision]

Derivation

1. Initial program 68.9%

   \[\frac{\left(x \cdot x + y \cdot y\right) - z \cdot z}{y \cdot 2} \]

2. Taylor expanded in y around inf

   \[\leadsto \color{blue}{\frac{1}{2} \cdot y} \]
3. Step-by-step derivation

   1. lower-*.f64 34.8

      \[\leadsto 0.5 \cdot \color{blue}{y} \]

4. Applied rewrites 34.8%

   \[\leadsto \color{blue}{0.5 \cdot y} \]
5. Add Preprocessing

Reproduce

herbie shell --seed 2025172 
(FPCore (x y z)
  :name "Diagrams.TwoD.Apollonian:initialConfig from diagrams-contrib-1.3.0.5, A"
  :precision binary64
  (/ (- (+ (* x x) (* y y)) (* z z)) (* y 2.0)))