Numeric.SpecFunctions:logGamma from math-functions-0.1.5.2, D

Percentage Accurate: 58.1% → 96.6%

Time: 16.6s

Alternatives: 12

Speedup: 4.0×

Specification

Math

\[\begin{array}{l} \\ x + \frac{y \cdot \left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b\right)}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (+
  x
  (/
   (*
    y
    (+ (* (+ (* (+ (* (+ (* z 3.13060547623) 11.1667541262) z) t) z) a) z) b))
   (+
    (* (+ (* (+ (* (+ z 15.234687407) z) 31.4690115749) z) 11.9400905721) z)
    0.607771387771))))

C

double code(double x, double y, double z, double t, double a, double b) {
	return x + ((y * ((((((((z * 3.13060547623) + 11.1667541262) * z) + t) * z) + a) * z) + b)) / (((((((z + 15.234687407) * z) + 31.4690115749) * z) + 11.9400905721) * z) + 0.607771387771));
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    code = x + ((y * ((((((((z * 3.13060547623d0) + 11.1667541262d0) * z) + t) * z) + a) * z) + b)) / (((((((z + 15.234687407d0) * z) + 31.4690115749d0) * z) + 11.9400905721d0) * z) + 0.607771387771d0))
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	return x + ((y * ((((((((z * 3.13060547623) + 11.1667541262) * z) + t) * z) + a) * z) + b)) / (((((((z + 15.234687407) * z) + 31.4690115749) * z) + 11.9400905721) * z) + 0.607771387771));
}

Python

def code(x, y, z, t, a, b):
	return x + ((y * ((((((((z * 3.13060547623) + 11.1667541262) * z) + t) * z) + a) * z) + b)) / (((((((z + 15.234687407) * z) + 31.4690115749) * z) + 11.9400905721) * z) + 0.607771387771))

Julia

function code(x, y, z, t, a, b)
	return Float64(x + Float64(Float64(y * Float64(Float64(Float64(Float64(Float64(Float64(Float64(Float64(z * 3.13060547623) + 11.1667541262) * z) + t) * z) + a) * z) + b)) / Float64(Float64(Float64(Float64(Float64(Float64(Float64(z + 15.234687407) * z) + 31.4690115749) * z) + 11.9400905721) * z) + 0.607771387771)))
end

MATLAB

function tmp = code(x, y, z, t, a, b)
	tmp = x + ((y * ((((((((z * 3.13060547623) + 11.1667541262) * z) + t) * z) + a) * z) + b)) / (((((((z + 15.234687407) * z) + 31.4690115749) * z) + 11.9400905721) * z) + 0.607771387771));
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := N[(x + N[(N[(y * N[(N[(N[(N[(N[(N[(N[(N[(z * 3.13060547623), $MachinePrecision] + 11.1667541262), $MachinePrecision] * z), $MachinePrecision] + t), $MachinePrecision] * z), $MachinePrecision] + a), $MachinePrecision] * z), $MachinePrecision] + b), $MachinePrecision]), $MachinePrecision] / N[(N[(N[(N[(N[(N[(N[(z + 15.234687407), $MachinePrecision] * z), $MachinePrecision] + 31.4690115749), $MachinePrecision] * z), $MachinePrecision] + 11.9400905721), $MachinePrecision] * z), $MachinePrecision] + 0.607771387771), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Initial Program: 58.1% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ x + \frac{y \cdot \left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b\right)}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (+
  x
  (/
   (*
    y
    (+ (* (+ (* (+ (* (+ (* z 3.13060547623) 11.1667541262) z) t) z) a) z) b))
   (+
    (* (+ (* (+ (* (+ z 15.234687407) z) 31.4690115749) z) 11.9400905721) z)
    0.607771387771))))

C

double code(double x, double y, double z, double t, double a, double b) {
	return x + ((y * ((((((((z * 3.13060547623) + 11.1667541262) * z) + t) * z) + a) * z) + b)) / (((((((z + 15.234687407) * z) + 31.4690115749) * z) + 11.9400905721) * z) + 0.607771387771));
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    code = x + ((y * ((((((((z * 3.13060547623d0) + 11.1667541262d0) * z) + t) * z) + a) * z) + b)) / (((((((z + 15.234687407d0) * z) + 31.4690115749d0) * z) + 11.9400905721d0) * z) + 0.607771387771d0))
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	return x + ((y * ((((((((z * 3.13060547623) + 11.1667541262) * z) + t) * z) + a) * z) + b)) / (((((((z + 15.234687407) * z) + 31.4690115749) * z) + 11.9400905721) * z) + 0.607771387771));
}

Python

def code(x, y, z, t, a, b):
	return x + ((y * ((((((((z * 3.13060547623) + 11.1667541262) * z) + t) * z) + a) * z) + b)) / (((((((z + 15.234687407) * z) + 31.4690115749) * z) + 11.9400905721) * z) + 0.607771387771))

Julia

function code(x, y, z, t, a, b)
	return Float64(x + Float64(Float64(y * Float64(Float64(Float64(Float64(Float64(Float64(Float64(Float64(z * 3.13060547623) + 11.1667541262) * z) + t) * z) + a) * z) + b)) / Float64(Float64(Float64(Float64(Float64(Float64(Float64(z + 15.234687407) * z) + 31.4690115749) * z) + 11.9400905721) * z) + 0.607771387771)))
end

MATLAB

function tmp = code(x, y, z, t, a, b)
	tmp = x + ((y * ((((((((z * 3.13060547623) + 11.1667541262) * z) + t) * z) + a) * z) + b)) / (((((((z + 15.234687407) * z) + 31.4690115749) * z) + 11.9400905721) * z) + 0.607771387771));
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := N[(x + N[(N[(y * N[(N[(N[(N[(N[(N[(N[(N[(z * 3.13060547623), $MachinePrecision] + 11.1667541262), $MachinePrecision] * z), $MachinePrecision] + t), $MachinePrecision] * z), $MachinePrecision] + a), $MachinePrecision] * z), $MachinePrecision] + b), $MachinePrecision]), $MachinePrecision] / N[(N[(N[(N[(N[(N[(N[(z + 15.234687407), $MachinePrecision] * z), $MachinePrecision] + 31.4690115749), $MachinePrecision] * z), $MachinePrecision] + 11.9400905721), $MachinePrecision] * z), $MachinePrecision] + 0.607771387771), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Alternative 1: 96.6% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -6.8 \cdot 10^{+47} \lor \neg \left(z \leq 6.6 \cdot 10^{+35}\right):\\ \;\;\;\;x + \left(\frac{y}{\left(0.31942702700572795 + \frac{3.7269864963038164}{z}\right) - \frac{3.241970391368047}{z \cdot z}} + 0.10203362558171805 \cdot \left(9.800690647801265 \cdot \frac{y}{\frac{z \cdot z}{t}}\right)\right)\\ \mathbf{else}:\\ \;\;\;\;x + \frac{y \cdot \left(b + z \cdot \left(a + z \cdot \left(t + z \cdot 11.1667541262\right)\right)\right)}{z \cdot \left(z \cdot \left(z \cdot \left(z + 15.234687407\right) + 31.4690115749\right) + 11.9400905721\right) + 0.607771387771}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (or (<= z -6.8e+47) (not (<= z 6.6e+35)))
   (+
    x
    (+
     (/
      y
      (-
       (+ 0.31942702700572795 (/ 3.7269864963038164 z))
       (/ 3.241970391368047 (* z z))))
     (* 0.10203362558171805 (* 9.800690647801265 (/ y (/ (* z z) t))))))
   (+
    x
    (/
     (* y (+ b (* z (+ a (* z (+ t (* z 11.1667541262)))))))
     (+
      (* z (+ (* z (+ (* z (+ z 15.234687407)) 31.4690115749)) 11.9400905721))
      0.607771387771)))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((z <= -6.8e+47) || !(z <= 6.6e+35)) {
		tmp = x + ((y / ((0.31942702700572795 + (3.7269864963038164 / z)) - (3.241970391368047 / (z * z)))) + (0.10203362558171805 * (9.800690647801265 * (y / ((z * z) / t)))));
	} else {
		tmp = x + ((y * (b + (z * (a + (z * (t + (z * 11.1667541262))))))) / ((z * ((z * ((z * (z + 15.234687407)) + 31.4690115749)) + 11.9400905721)) + 0.607771387771));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if ((z <= (-6.8d+47)) .or. (.not. (z <= 6.6d+35))) then
        tmp = x + ((y / ((0.31942702700572795d0 + (3.7269864963038164d0 / z)) - (3.241970391368047d0 / (z * z)))) + (0.10203362558171805d0 * (9.800690647801265d0 * (y / ((z * z) / t)))))
    else
        tmp = x + ((y * (b + (z * (a + (z * (t + (z * 11.1667541262d0))))))) / ((z * ((z * ((z * (z + 15.234687407d0)) + 31.4690115749d0)) + 11.9400905721d0)) + 0.607771387771d0))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((z <= -6.8e+47) || !(z <= 6.6e+35)) {
		tmp = x + ((y / ((0.31942702700572795 + (3.7269864963038164 / z)) - (3.241970391368047 / (z * z)))) + (0.10203362558171805 * (9.800690647801265 * (y / ((z * z) / t)))));
	} else {
		tmp = x + ((y * (b + (z * (a + (z * (t + (z * 11.1667541262))))))) / ((z * ((z * ((z * (z + 15.234687407)) + 31.4690115749)) + 11.9400905721)) + 0.607771387771));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if (z <= -6.8e+47) or not (z <= 6.6e+35):
		tmp = x + ((y / ((0.31942702700572795 + (3.7269864963038164 / z)) - (3.241970391368047 / (z * z)))) + (0.10203362558171805 * (9.800690647801265 * (y / ((z * z) / t)))))
	else:
		tmp = x + ((y * (b + (z * (a + (z * (t + (z * 11.1667541262))))))) / ((z * ((z * ((z * (z + 15.234687407)) + 31.4690115749)) + 11.9400905721)) + 0.607771387771))
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if ((z <= -6.8e+47) || !(z <= 6.6e+35))
		tmp = Float64(x + Float64(Float64(y / Float64(Float64(0.31942702700572795 + Float64(3.7269864963038164 / z)) - Float64(3.241970391368047 / Float64(z * z)))) + Float64(0.10203362558171805 * Float64(9.800690647801265 * Float64(y / Float64(Float64(z * z) / t))))));
	else
		tmp = Float64(x + Float64(Float64(y * Float64(b + Float64(z * Float64(a + Float64(z * Float64(t + Float64(z * 11.1667541262))))))) / Float64(Float64(z * Float64(Float64(z * Float64(Float64(z * Float64(z + 15.234687407)) + 31.4690115749)) + 11.9400905721)) + 0.607771387771)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if ((z <= -6.8e+47) || ~((z <= 6.6e+35)))
		tmp = x + ((y / ((0.31942702700572795 + (3.7269864963038164 / z)) - (3.241970391368047 / (z * z)))) + (0.10203362558171805 * (9.800690647801265 * (y / ((z * z) / t)))));
	else
		tmp = x + ((y * (b + (z * (a + (z * (t + (z * 11.1667541262))))))) / ((z * ((z * ((z * (z + 15.234687407)) + 31.4690115749)) + 11.9400905721)) + 0.607771387771));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[Or[LessEqual[z, -6.8e+47], N[Not[LessEqual[z, 6.6e+35]], $MachinePrecision]], N[(x + N[(N[(y / N[(N[(0.31942702700572795 + N[(3.7269864963038164 / z), $MachinePrecision]), $MachinePrecision] - N[(3.241970391368047 / N[(z * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(0.10203362558171805 * N[(9.800690647801265 * N[(y / N[(N[(z * z), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(N[(y * N[(b + N[(z * N[(a + N[(z * N[(t + N[(z * 11.1667541262), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(N[(z * N[(N[(z * N[(N[(z * N[(z + 15.234687407), $MachinePrecision]), $MachinePrecision] + 31.4690115749), $MachinePrecision]), $MachinePrecision] + 11.9400905721), $MachinePrecision]), $MachinePrecision] + 0.607771387771), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if z < -6.7999999999999996e47 or 6.6000000000000003e35 < z

   1. Initial program 4.9%

      \[x + \frac{y \cdot \left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b\right)}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} \]
   2. Step-by-step derivation

      1. associate-/l* 12.1%

         \[\leadsto x + \color{blue}{\frac{y}{\frac{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771}{\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b}}} \]

      2. fma-def 12.1%

         \[\leadsto x + \frac{y}{\frac{\color{blue}{\mathsf{fma}\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721, z, 0.607771387771\right)}}{\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b}} \]

      3. fma-def 12.1%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749, z, 11.9400905721\right)}, z, 0.607771387771\right)}{\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b}} \]

      4. fma-def 12.1%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right)}, z, 11.9400905721\right), z, 0.607771387771\right)}{\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b}} \]

      5. fma-def 12.1%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\color{blue}{\mathsf{fma}\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a, z, b\right)}}} \]

      6. fma-def 12.1%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t, z, a\right)}, z, b\right)}} \]

      7. fma-def 12.1%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(z \cdot 3.13060547623 + 11.1667541262, z, t\right)}, z, a\right), z, b\right)}} \]

      8. fma-def 12.1%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(z, 3.13060547623, 11.1667541262\right)}, z, t\right), z, a\right), z, b\right)}} \]

   3. Simplified 12.1%

      \[\leadsto \color{blue}{x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z, 3.13060547623, 11.1667541262\right), z, t\right), z, a\right), z, b\right)}}} \]

   4. Taylor expanded in z around inf 93.7%

      \[\leadsto x + \frac{y}{\color{blue}{3.7269864963038164 \cdot \frac{1}{z} + \left(0.31942702700572795 + -1 \cdot \frac{3.241970391368047 + 0.10203362558171805 \cdot t}{{z}^{2}}\right)}} \]
   5. Step-by-step derivation

      1. associate-*r/ 93.7%

         \[\leadsto x + \frac{y}{\color{blue}{\frac{3.7269864963038164 \cdot 1}{z}} + \left(0.31942702700572795 + -1 \cdot \frac{3.241970391368047 + 0.10203362558171805 \cdot t}{{z}^{2}}\right)} \]

      2. metadata-eval 93.7%

         \[\leadsto x + \frac{y}{\frac{\color{blue}{3.7269864963038164}}{z} + \left(0.31942702700572795 + -1 \cdot \frac{3.241970391368047 + 0.10203362558171805 \cdot t}{{z}^{2}}\right)} \]

      3. mul-1-neg 93.7%

         \[\leadsto x + \frac{y}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \color{blue}{\left(-\frac{3.241970391368047 + 0.10203362558171805 \cdot t}{{z}^{2}}\right)}\right)} \]

      4. *-commutative 93.7%

         \[\leadsto x + \frac{y}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \left(-\frac{3.241970391368047 + \color{blue}{t \cdot 0.10203362558171805}}{{z}^{2}}\right)\right)} \]

      5. unpow2 93.7%

         \[\leadsto x + \frac{y}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \left(-\frac{3.241970391368047 + t \cdot 0.10203362558171805}{\color{blue}{z \cdot z}}\right)\right)} \]

   6. Simplified 93.7%

      \[\leadsto x + \frac{y}{\color{blue}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \left(-\frac{3.241970391368047 + t \cdot 0.10203362558171805}{z \cdot z}\right)\right)}} \]

   7. Taylor expanded in t around 0 94.9%

      \[\leadsto x + \color{blue}{\left(\frac{y}{\left(3.7269864963038164 \cdot \frac{1}{z} + 0.31942702700572795\right) - 3.241970391368047 \cdot \frac{1}{{z}^{2}}} + 0.10203362558171805 \cdot \frac{y \cdot t}{{z}^{2} \cdot {\left(\left(3.7269864963038164 \cdot \frac{1}{z} + 0.31942702700572795\right) - 3.241970391368047 \cdot \frac{1}{{z}^{2}}\right)}^{2}}\right)} \]
   8. Step-by-step derivation

      1. associate--l+ 94.9%

         \[\leadsto x + \left(\frac{y}{\color{blue}{3.7269864963038164 \cdot \frac{1}{z} + \left(0.31942702700572795 - 3.241970391368047 \cdot \frac{1}{{z}^{2}}\right)}} + 0.10203362558171805 \cdot \frac{y \cdot t}{{z}^{2} \cdot {\left(\left(3.7269864963038164 \cdot \frac{1}{z} + 0.31942702700572795\right) - 3.241970391368047 \cdot \frac{1}{{z}^{2}}\right)}^{2}}\right) \]

      2. associate-*r/ 94.9%

         \[\leadsto x + \left(\frac{y}{\color{blue}{\frac{3.7269864963038164 \cdot 1}{z}} + \left(0.31942702700572795 - 3.241970391368047 \cdot \frac{1}{{z}^{2}}\right)} + 0.10203362558171805 \cdot \frac{y \cdot t}{{z}^{2} \cdot {\left(\left(3.7269864963038164 \cdot \frac{1}{z} + 0.31942702700572795\right) - 3.241970391368047 \cdot \frac{1}{{z}^{2}}\right)}^{2}}\right) \]

      3. metadata-eval 94.9%

         \[\leadsto x + \left(\frac{y}{\frac{\color{blue}{3.7269864963038164}}{z} + \left(0.31942702700572795 - 3.241970391368047 \cdot \frac{1}{{z}^{2}}\right)} + 0.10203362558171805 \cdot \frac{y \cdot t}{{z}^{2} \cdot {\left(\left(3.7269864963038164 \cdot \frac{1}{z} + 0.31942702700572795\right) - 3.241970391368047 \cdot \frac{1}{{z}^{2}}\right)}^{2}}\right) \]

      4. associate-+r- 94.9%

         \[\leadsto x + \left(\frac{y}{\color{blue}{\left(\frac{3.7269864963038164}{z} + 0.31942702700572795\right) - 3.241970391368047 \cdot \frac{1}{{z}^{2}}}} + 0.10203362558171805 \cdot \frac{y \cdot t}{{z}^{2} \cdot {\left(\left(3.7269864963038164 \cdot \frac{1}{z} + 0.31942702700572795\right) - 3.241970391368047 \cdot \frac{1}{{z}^{2}}\right)}^{2}}\right) \]

      5. +-commutative 94.9%

         \[\leadsto x + \left(\frac{y}{\color{blue}{\left(0.31942702700572795 + \frac{3.7269864963038164}{z}\right)} - 3.241970391368047 \cdot \frac{1}{{z}^{2}}} + 0.10203362558171805 \cdot \frac{y \cdot t}{{z}^{2} \cdot {\left(\left(3.7269864963038164 \cdot \frac{1}{z} + 0.31942702700572795\right) - 3.241970391368047 \cdot \frac{1}{{z}^{2}}\right)}^{2}}\right) \]

      6. associate-*r/ 94.9%

         \[\leadsto x + \left(\frac{y}{\left(0.31942702700572795 + \frac{3.7269864963038164}{z}\right) - \color{blue}{\frac{3.241970391368047 \cdot 1}{{z}^{2}}}} + 0.10203362558171805 \cdot \frac{y \cdot t}{{z}^{2} \cdot {\left(\left(3.7269864963038164 \cdot \frac{1}{z} + 0.31942702700572795\right) - 3.241970391368047 \cdot \frac{1}{{z}^{2}}\right)}^{2}}\right) \]

      7. metadata-eval 94.9%

         \[\leadsto x + \left(\frac{y}{\left(0.31942702700572795 + \frac{3.7269864963038164}{z}\right) - \frac{\color{blue}{3.241970391368047}}{{z}^{2}}} + 0.10203362558171805 \cdot \frac{y \cdot t}{{z}^{2} \cdot {\left(\left(3.7269864963038164 \cdot \frac{1}{z} + 0.31942702700572795\right) - 3.241970391368047 \cdot \frac{1}{{z}^{2}}\right)}^{2}}\right) \]

      8. unpow2 94.9%

         \[\leadsto x + \left(\frac{y}{\left(0.31942702700572795 + \frac{3.7269864963038164}{z}\right) - \frac{3.241970391368047}{\color{blue}{z \cdot z}}} + 0.10203362558171805 \cdot \frac{y \cdot t}{{z}^{2} \cdot {\left(\left(3.7269864963038164 \cdot \frac{1}{z} + 0.31942702700572795\right) - 3.241970391368047 \cdot \frac{1}{{z}^{2}}\right)}^{2}}\right) \]

      9. times-frac 98.3%

         \[\leadsto x + \left(\frac{y}{\left(0.31942702700572795 + \frac{3.7269864963038164}{z}\right) - \frac{3.241970391368047}{z \cdot z}} + 0.10203362558171805 \cdot \color{blue}{\left(\frac{y}{{z}^{2}} \cdot \frac{t}{{\left(\left(3.7269864963038164 \cdot \frac{1}{z} + 0.31942702700572795\right) - 3.241970391368047 \cdot \frac{1}{{z}^{2}}\right)}^{2}}\right)}\right) \]

      10. unpow2 98.3%

          \[\leadsto x + \left(\frac{y}{\left(0.31942702700572795 + \frac{3.7269864963038164}{z}\right) - \frac{3.241970391368047}{z \cdot z}} + 0.10203362558171805 \cdot \left(\frac{y}{\color{blue}{z \cdot z}} \cdot \frac{t}{{\left(\left(3.7269864963038164 \cdot \frac{1}{z} + 0.31942702700572795\right) - 3.241970391368047 \cdot \frac{1}{{z}^{2}}\right)}^{2}}\right)\right) \]

   9. Simplified 98.3%

      \[\leadsto x + \color{blue}{\left(\frac{y}{\left(0.31942702700572795 + \frac{3.7269864963038164}{z}\right) - \frac{3.241970391368047}{z \cdot z}} + 0.10203362558171805 \cdot \left(\frac{y}{z \cdot z} \cdot \frac{t}{{\left(\left(0.31942702700572795 + \frac{3.7269864963038164}{z}\right) - \frac{3.241970391368047}{z \cdot z}\right)}^{2}}\right)\right)} \]

   10. Taylor expanded in z around inf 94.9%

       \[\leadsto x + \left(\frac{y}{\left(0.31942702700572795 + \frac{3.7269864963038164}{z}\right) - \frac{3.241970391368047}{z \cdot z}} + 0.10203362558171805 \cdot \color{blue}{\left(9.800690647801265 \cdot \frac{y \cdot t}{{z}^{2}}\right)}\right) \]
   11. Step-by-step derivation

       1. associate-/l* 99.9%

          \[\leadsto x + \left(\frac{y}{\left(0.31942702700572795 + \frac{3.7269864963038164}{z}\right) - \frac{3.241970391368047}{z \cdot z}} + 0.10203362558171805 \cdot \left(9.800690647801265 \cdot \color{blue}{\frac{y}{\frac{{z}^{2}}{t}}}\right)\right) \]

       2. unpow2 99.9%

          \[\leadsto x + \left(\frac{y}{\left(0.31942702700572795 + \frac{3.7269864963038164}{z}\right) - \frac{3.241970391368047}{z \cdot z}} + 0.10203362558171805 \cdot \left(9.800690647801265 \cdot \frac{y}{\frac{\color{blue}{z \cdot z}}{t}}\right)\right) \]

   12. Simplified 99.9%

       \[\leadsto x + \left(\frac{y}{\left(0.31942702700572795 + \frac{3.7269864963038164}{z}\right) - \frac{3.241970391368047}{z \cdot z}} + 0.10203362558171805 \cdot \color{blue}{\left(9.800690647801265 \cdot \frac{y}{\frac{z \cdot z}{t}}\right)}\right) \]

   if -6.7999999999999996e47 < z < 6.6000000000000003e35

   1. Initial program 99.8%

      \[x + \frac{y \cdot \left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b\right)}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} \]

   2. Taylor expanded in z around 0 99.8%

      \[\leadsto x + \frac{y \cdot \left(\left(\left(\color{blue}{11.1667541262 \cdot z} + t\right) \cdot z + a\right) \cdot z + b\right)}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} \]
   3. Step-by-step derivation

      1. *-commutative 99.8%

         \[\leadsto x + \frac{y \cdot \left(\left(\left(\color{blue}{z \cdot 11.1667541262} + t\right) \cdot z + a\right) \cdot z + b\right)}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} \]

   4. Simplified 99.8%

      \[\leadsto x + \frac{y \cdot \left(\left(\left(\color{blue}{z \cdot 11.1667541262} + t\right) \cdot z + a\right) \cdot z + b\right)}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} \]
3. Recombined 2 regimes into one program.

4. Final simplification 99.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -6.8 \cdot 10^{+47} \lor \neg \left(z \leq 6.6 \cdot 10^{+35}\right):\\ \;\;\;\;x + \left(\frac{y}{\left(0.31942702700572795 + \frac{3.7269864963038164}{z}\right) - \frac{3.241970391368047}{z \cdot z}} + 0.10203362558171805 \cdot \left(9.800690647801265 \cdot \frac{y}{\frac{z \cdot z}{t}}\right)\right)\\ \mathbf{else}:\\ \;\;\;\;x + \frac{y \cdot \left(b + z \cdot \left(a + z \cdot \left(t + z \cdot 11.1667541262\right)\right)\right)}{z \cdot \left(z \cdot \left(z \cdot \left(z + 15.234687407\right) + 31.4690115749\right) + 11.9400905721\right) + 0.607771387771}\\ \end{array} \]

Alternative 2: 97.9% accurate, 0.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\frac{y \cdot \left(z \cdot \left(z \cdot \left(z \cdot \left(z \cdot 3.13060547623 + 11.1667541262\right) + t\right) + a\right) + b\right)}{z \cdot \left(z \cdot \left(z \cdot \left(z + 15.234687407\right) + 31.4690115749\right) + 11.9400905721\right) + 0.607771387771} \leq \infty:\\ \;\;\;\;x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z, 3.13060547623, 11.1667541262\right), z, t\right), z, a\right), z, b\right)}}\\ \mathbf{else}:\\ \;\;\;\;x + \left(\frac{y}{\left(0.31942702700572795 + \frac{3.7269864963038164}{z}\right) - \frac{3.241970391368047}{z \cdot z}} + 0.10203362558171805 \cdot \left(9.800690647801265 \cdot \frac{y}{\frac{z \cdot z}{t}}\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<=
      (/
       (*
        y
        (+
         (* z (+ (* z (+ (* z (+ (* z 3.13060547623) 11.1667541262)) t)) a))
         b))
       (+
        (*
         z
         (+ (* z (+ (* z (+ z 15.234687407)) 31.4690115749)) 11.9400905721))
        0.607771387771))
      INFINITY)
   (+
    x
    (/
     y
     (/
      (fma
       (fma (fma (+ z 15.234687407) z 31.4690115749) z 11.9400905721)
       z
       0.607771387771)
      (fma (fma (fma (fma z 3.13060547623 11.1667541262) z t) z a) z b))))
   (+
    x
    (+
     (/
      y
      (-
       (+ 0.31942702700572795 (/ 3.7269864963038164 z))
       (/ 3.241970391368047 (* z z))))
     (* 0.10203362558171805 (* 9.800690647801265 (/ y (/ (* z z) t))))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (((y * ((z * ((z * ((z * ((z * 3.13060547623) + 11.1667541262)) + t)) + a)) + b)) / ((z * ((z * ((z * (z + 15.234687407)) + 31.4690115749)) + 11.9400905721)) + 0.607771387771)) <= ((double) INFINITY)) {
		tmp = x + (y / (fma(fma(fma((z + 15.234687407), z, 31.4690115749), z, 11.9400905721), z, 0.607771387771) / fma(fma(fma(fma(z, 3.13060547623, 11.1667541262), z, t), z, a), z, b)));
	} else {
		tmp = x + ((y / ((0.31942702700572795 + (3.7269864963038164 / z)) - (3.241970391368047 / (z * z)))) + (0.10203362558171805 * (9.800690647801265 * (y / ((z * z) / t)))));
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (Float64(Float64(y * Float64(Float64(z * Float64(Float64(z * Float64(Float64(z * Float64(Float64(z * 3.13060547623) + 11.1667541262)) + t)) + a)) + b)) / Float64(Float64(z * Float64(Float64(z * Float64(Float64(z * Float64(z + 15.234687407)) + 31.4690115749)) + 11.9400905721)) + 0.607771387771)) <= Inf)
		tmp = Float64(x + Float64(y / Float64(fma(fma(fma(Float64(z + 15.234687407), z, 31.4690115749), z, 11.9400905721), z, 0.607771387771) / fma(fma(fma(fma(z, 3.13060547623, 11.1667541262), z, t), z, a), z, b))));
	else
		tmp = Float64(x + Float64(Float64(y / Float64(Float64(0.31942702700572795 + Float64(3.7269864963038164 / z)) - Float64(3.241970391368047 / Float64(z * z)))) + Float64(0.10203362558171805 * Float64(9.800690647801265 * Float64(y / Float64(Float64(z * z) / t))))));
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[N[(N[(y * N[(N[(z * N[(N[(z * N[(N[(z * N[(N[(z * 3.13060547623), $MachinePrecision] + 11.1667541262), $MachinePrecision]), $MachinePrecision] + t), $MachinePrecision]), $MachinePrecision] + a), $MachinePrecision]), $MachinePrecision] + b), $MachinePrecision]), $MachinePrecision] / N[(N[(z * N[(N[(z * N[(N[(z * N[(z + 15.234687407), $MachinePrecision]), $MachinePrecision] + 31.4690115749), $MachinePrecision]), $MachinePrecision] + 11.9400905721), $MachinePrecision]), $MachinePrecision] + 0.607771387771), $MachinePrecision]), $MachinePrecision], Infinity], N[(x + N[(y / N[(N[(N[(N[(N[(z + 15.234687407), $MachinePrecision] * z + 31.4690115749), $MachinePrecision] * z + 11.9400905721), $MachinePrecision] * z + 0.607771387771), $MachinePrecision] / N[(N[(N[(N[(z * 3.13060547623 + 11.1667541262), $MachinePrecision] * z + t), $MachinePrecision] * z + a), $MachinePrecision] * z + b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(N[(y / N[(N[(0.31942702700572795 + N[(3.7269864963038164 / z), $MachinePrecision]), $MachinePrecision] - N[(3.241970391368047 / N[(z * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(0.10203362558171805 * N[(9.800690647801265 * N[(y / N[(N[(z * z), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 (*.f64 y (+.f64 (*.f64 (+.f64 (*.f64 (+.f64 (*.f64 (+.f64 (*.f64 z 313060547623/100000000000) 55833770631/5000000000) z) t) z) a) z) b)) (+.f64 (*.f64 (+.f64 (*.f64 (+.f64 (*.f64 (+.f64 z 15234687407/1000000000) z) 314690115749/10000000000) z) 119400905721/10000000000) z) 607771387771/1000000000000)) < +inf.0

   1. Initial program 93.2%

      \[x + \frac{y \cdot \left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b\right)}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} \]
   2. Step-by-step derivation

      1. associate-/l* 98.5%

         \[\leadsto x + \color{blue}{\frac{y}{\frac{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771}{\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b}}} \]

      2. fma-def 98.5%

         \[\leadsto x + \frac{y}{\frac{\color{blue}{\mathsf{fma}\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721, z, 0.607771387771\right)}}{\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b}} \]

      3. fma-def 98.5%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749, z, 11.9400905721\right)}, z, 0.607771387771\right)}{\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b}} \]

      4. fma-def 98.5%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right)}, z, 11.9400905721\right), z, 0.607771387771\right)}{\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b}} \]

      5. fma-def 98.5%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\color{blue}{\mathsf{fma}\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a, z, b\right)}}} \]

      6. fma-def 98.5%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t, z, a\right)}, z, b\right)}} \]

      7. fma-def 98.5%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(z \cdot 3.13060547623 + 11.1667541262, z, t\right)}, z, a\right), z, b\right)}} \]

      8. fma-def 98.5%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(z, 3.13060547623, 11.1667541262\right)}, z, t\right), z, a\right), z, b\right)}} \]

   3. Simplified 98.5%

      \[\leadsto \color{blue}{x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z, 3.13060547623, 11.1667541262\right), z, t\right), z, a\right), z, b\right)}}} \]

   if +inf.0 < (/.f64 (*.f64 y (+.f64 (*.f64 (+.f64 (*.f64 (+.f64 (*.f64 (+.f64 (*.f64 z 313060547623/100000000000) 55833770631/5000000000) z) t) z) a) z) b)) (+.f64 (*.f64 (+.f64 (*.f64 (+.f64 (*.f64 (+.f64 z 15234687407/1000000000) z) 314690115749/10000000000) z) 119400905721/10000000000) z) 607771387771/1000000000000))

   1. Initial program 0.0%

      \[x + \frac{y \cdot \left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b\right)}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} \]
   2. Step-by-step derivation

      1. associate-/l* 0.0%

         \[\leadsto x + \color{blue}{\frac{y}{\frac{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771}{\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b}}} \]

      2. fma-def 0.0%

         \[\leadsto x + \frac{y}{\frac{\color{blue}{\mathsf{fma}\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721, z, 0.607771387771\right)}}{\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b}} \]

      3. fma-def 0.0%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749, z, 11.9400905721\right)}, z, 0.607771387771\right)}{\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b}} \]

      4. fma-def 0.0%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right)}, z, 11.9400905721\right), z, 0.607771387771\right)}{\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b}} \]

      5. fma-def 0.0%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\color{blue}{\mathsf{fma}\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a, z, b\right)}}} \]

      6. fma-def 0.0%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t, z, a\right)}, z, b\right)}} \]

      7. fma-def 0.0%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(z \cdot 3.13060547623 + 11.1667541262, z, t\right)}, z, a\right), z, b\right)}} \]

      8. fma-def 0.0%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(z, 3.13060547623, 11.1667541262\right)}, z, t\right), z, a\right), z, b\right)}} \]

   3. Simplified 0.0%

      \[\leadsto \color{blue}{x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z, 3.13060547623, 11.1667541262\right), z, t\right), z, a\right), z, b\right)}}} \]

   4. Taylor expanded in z around inf 95.7%

      \[\leadsto x + \frac{y}{\color{blue}{3.7269864963038164 \cdot \frac{1}{z} + \left(0.31942702700572795 + -1 \cdot \frac{3.241970391368047 + 0.10203362558171805 \cdot t}{{z}^{2}}\right)}} \]
   5. Step-by-step derivation

      1. associate-*r/ 95.7%

         \[\leadsto x + \frac{y}{\color{blue}{\frac{3.7269864963038164 \cdot 1}{z}} + \left(0.31942702700572795 + -1 \cdot \frac{3.241970391368047 + 0.10203362558171805 \cdot t}{{z}^{2}}\right)} \]

      2. metadata-eval 95.7%

         \[\leadsto x + \frac{y}{\frac{\color{blue}{3.7269864963038164}}{z} + \left(0.31942702700572795 + -1 \cdot \frac{3.241970391368047 + 0.10203362558171805 \cdot t}{{z}^{2}}\right)} \]

      3. mul-1-neg 95.7%

         \[\leadsto x + \frac{y}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \color{blue}{\left(-\frac{3.241970391368047 + 0.10203362558171805 \cdot t}{{z}^{2}}\right)}\right)} \]

      4. *-commutative 95.7%

         \[\leadsto x + \frac{y}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \left(-\frac{3.241970391368047 + \color{blue}{t \cdot 0.10203362558171805}}{{z}^{2}}\right)\right)} \]

      5. unpow2 95.7%

         \[\leadsto x + \frac{y}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \left(-\frac{3.241970391368047 + t \cdot 0.10203362558171805}{\color{blue}{z \cdot z}}\right)\right)} \]

   6. Simplified 95.7%

      \[\leadsto x + \frac{y}{\color{blue}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \left(-\frac{3.241970391368047 + t \cdot 0.10203362558171805}{z \cdot z}\right)\right)}} \]

   7. Taylor expanded in t around 0 95.0%

      \[\leadsto x + \color{blue}{\left(\frac{y}{\left(3.7269864963038164 \cdot \frac{1}{z} + 0.31942702700572795\right) - 3.241970391368047 \cdot \frac{1}{{z}^{2}}} + 0.10203362558171805 \cdot \frac{y \cdot t}{{z}^{2} \cdot {\left(\left(3.7269864963038164 \cdot \frac{1}{z} + 0.31942702700572795\right) - 3.241970391368047 \cdot \frac{1}{{z}^{2}}\right)}^{2}}\right)} \]
   8. Step-by-step derivation

      1. associate--l+ 95.0%

         \[\leadsto x + \left(\frac{y}{\color{blue}{3.7269864963038164 \cdot \frac{1}{z} + \left(0.31942702700572795 - 3.241970391368047 \cdot \frac{1}{{z}^{2}}\right)}} + 0.10203362558171805 \cdot \frac{y \cdot t}{{z}^{2} \cdot {\left(\left(3.7269864963038164 \cdot \frac{1}{z} + 0.31942702700572795\right) - 3.241970391368047 \cdot \frac{1}{{z}^{2}}\right)}^{2}}\right) \]

      2. associate-*r/ 95.0%

         \[\leadsto x + \left(\frac{y}{\color{blue}{\frac{3.7269864963038164 \cdot 1}{z}} + \left(0.31942702700572795 - 3.241970391368047 \cdot \frac{1}{{z}^{2}}\right)} + 0.10203362558171805 \cdot \frac{y \cdot t}{{z}^{2} \cdot {\left(\left(3.7269864963038164 \cdot \frac{1}{z} + 0.31942702700572795\right) - 3.241970391368047 \cdot \frac{1}{{z}^{2}}\right)}^{2}}\right) \]

      3. metadata-eval 95.0%

         \[\leadsto x + \left(\frac{y}{\frac{\color{blue}{3.7269864963038164}}{z} + \left(0.31942702700572795 - 3.241970391368047 \cdot \frac{1}{{z}^{2}}\right)} + 0.10203362558171805 \cdot \frac{y \cdot t}{{z}^{2} \cdot {\left(\left(3.7269864963038164 \cdot \frac{1}{z} + 0.31942702700572795\right) - 3.241970391368047 \cdot \frac{1}{{z}^{2}}\right)}^{2}}\right) \]

      4. associate-+r- 95.0%

         \[\leadsto x + \left(\frac{y}{\color{blue}{\left(\frac{3.7269864963038164}{z} + 0.31942702700572795\right) - 3.241970391368047 \cdot \frac{1}{{z}^{2}}}} + 0.10203362558171805 \cdot \frac{y \cdot t}{{z}^{2} \cdot {\left(\left(3.7269864963038164 \cdot \frac{1}{z} + 0.31942702700572795\right) - 3.241970391368047 \cdot \frac{1}{{z}^{2}}\right)}^{2}}\right) \]

      5. +-commutative 95.0%

         \[\leadsto x + \left(\frac{y}{\color{blue}{\left(0.31942702700572795 + \frac{3.7269864963038164}{z}\right)} - 3.241970391368047 \cdot \frac{1}{{z}^{2}}} + 0.10203362558171805 \cdot \frac{y \cdot t}{{z}^{2} \cdot {\left(\left(3.7269864963038164 \cdot \frac{1}{z} + 0.31942702700572795\right) - 3.241970391368047 \cdot \frac{1}{{z}^{2}}\right)}^{2}}\right) \]

      6. associate-*r/ 95.0%

         \[\leadsto x + \left(\frac{y}{\left(0.31942702700572795 + \frac{3.7269864963038164}{z}\right) - \color{blue}{\frac{3.241970391368047 \cdot 1}{{z}^{2}}}} + 0.10203362558171805 \cdot \frac{y \cdot t}{{z}^{2} \cdot {\left(\left(3.7269864963038164 \cdot \frac{1}{z} + 0.31942702700572795\right) - 3.241970391368047 \cdot \frac{1}{{z}^{2}}\right)}^{2}}\right) \]

      7. metadata-eval 95.0%

         \[\leadsto x + \left(\frac{y}{\left(0.31942702700572795 + \frac{3.7269864963038164}{z}\right) - \frac{\color{blue}{3.241970391368047}}{{z}^{2}}} + 0.10203362558171805 \cdot \frac{y \cdot t}{{z}^{2} \cdot {\left(\left(3.7269864963038164 \cdot \frac{1}{z} + 0.31942702700572795\right) - 3.241970391368047 \cdot \frac{1}{{z}^{2}}\right)}^{2}}\right) \]

      8. unpow2 95.0%

         \[\leadsto x + \left(\frac{y}{\left(0.31942702700572795 + \frac{3.7269864963038164}{z}\right) - \frac{3.241970391368047}{\color{blue}{z \cdot z}}} + 0.10203362558171805 \cdot \frac{y \cdot t}{{z}^{2} \cdot {\left(\left(3.7269864963038164 \cdot \frac{1}{z} + 0.31942702700572795\right) - 3.241970391368047 \cdot \frac{1}{{z}^{2}}\right)}^{2}}\right) \]

      9. times-frac 98.0%

         \[\leadsto x + \left(\frac{y}{\left(0.31942702700572795 + \frac{3.7269864963038164}{z}\right) - \frac{3.241970391368047}{z \cdot z}} + 0.10203362558171805 \cdot \color{blue}{\left(\frac{y}{{z}^{2}} \cdot \frac{t}{{\left(\left(3.7269864963038164 \cdot \frac{1}{z} + 0.31942702700572795\right) - 3.241970391368047 \cdot \frac{1}{{z}^{2}}\right)}^{2}}\right)}\right) \]

      10. unpow2 98.0%

          \[\leadsto x + \left(\frac{y}{\left(0.31942702700572795 + \frac{3.7269864963038164}{z}\right) - \frac{3.241970391368047}{z \cdot z}} + 0.10203362558171805 \cdot \left(\frac{y}{\color{blue}{z \cdot z}} \cdot \frac{t}{{\left(\left(3.7269864963038164 \cdot \frac{1}{z} + 0.31942702700572795\right) - 3.241970391368047 \cdot \frac{1}{{z}^{2}}\right)}^{2}}\right)\right) \]

   9. Simplified 98.0%

      \[\leadsto x + \color{blue}{\left(\frac{y}{\left(0.31942702700572795 + \frac{3.7269864963038164}{z}\right) - \frac{3.241970391368047}{z \cdot z}} + 0.10203362558171805 \cdot \left(\frac{y}{z \cdot z} \cdot \frac{t}{{\left(\left(0.31942702700572795 + \frac{3.7269864963038164}{z}\right) - \frac{3.241970391368047}{z \cdot z}\right)}^{2}}\right)\right)} \]

   10. Taylor expanded in z around inf 95.0%

       \[\leadsto x + \left(\frac{y}{\left(0.31942702700572795 + \frac{3.7269864963038164}{z}\right) - \frac{3.241970391368047}{z \cdot z}} + 0.10203362558171805 \cdot \color{blue}{\left(9.800690647801265 \cdot \frac{y \cdot t}{{z}^{2}}\right)}\right) \]
   11. Step-by-step derivation

       1. associate-/l* 99.9%

          \[\leadsto x + \left(\frac{y}{\left(0.31942702700572795 + \frac{3.7269864963038164}{z}\right) - \frac{3.241970391368047}{z \cdot z}} + 0.10203362558171805 \cdot \left(9.800690647801265 \cdot \color{blue}{\frac{y}{\frac{{z}^{2}}{t}}}\right)\right) \]

       2. unpow2 99.9%

          \[\leadsto x + \left(\frac{y}{\left(0.31942702700572795 + \frac{3.7269864963038164}{z}\right) - \frac{3.241970391368047}{z \cdot z}} + 0.10203362558171805 \cdot \left(9.800690647801265 \cdot \frac{y}{\frac{\color{blue}{z \cdot z}}{t}}\right)\right) \]

   12. Simplified 99.9%

       \[\leadsto x + \left(\frac{y}{\left(0.31942702700572795 + \frac{3.7269864963038164}{z}\right) - \frac{3.241970391368047}{z \cdot z}} + 0.10203362558171805 \cdot \color{blue}{\left(9.800690647801265 \cdot \frac{y}{\frac{z \cdot z}{t}}\right)}\right) \]
3. Recombined 2 regimes into one program.

4. Final simplification 99.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{y \cdot \left(z \cdot \left(z \cdot \left(z \cdot \left(z \cdot 3.13060547623 + 11.1667541262\right) + t\right) + a\right) + b\right)}{z \cdot \left(z \cdot \left(z \cdot \left(z + 15.234687407\right) + 31.4690115749\right) + 11.9400905721\right) + 0.607771387771} \leq \infty:\\ \;\;\;\;x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z, 3.13060547623, 11.1667541262\right), z, t\right), z, a\right), z, b\right)}}\\ \mathbf{else}:\\ \;\;\;\;x + \left(\frac{y}{\left(0.31942702700572795 + \frac{3.7269864963038164}{z}\right) - \frac{3.241970391368047}{z \cdot z}} + 0.10203362558171805 \cdot \left(9.800690647801265 \cdot \frac{y}{\frac{z \cdot z}{t}}\right)\right)\\ \end{array} \]

Alternative 3: 97.4% accurate, 0.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\frac{y \cdot \left(z \cdot \left(z \cdot \left(z \cdot \left(z \cdot 3.13060547623 + 11.1667541262\right) + t\right) + a\right) + b\right)}{z \cdot \left(z \cdot \left(z \cdot \left(z + 15.234687407\right) + 31.4690115749\right) + 11.9400905721\right) + 0.607771387771} \leq \infty:\\ \;\;\;\;x + \frac{y}{\mathsf{fma}\left(z, \mathsf{fma}\left(z, \mathsf{fma}\left(z, z + 15.234687407, 31.4690115749\right), 11.9400905721\right), 0.607771387771\right)} \cdot \mathsf{fma}\left(z, \mathsf{fma}\left(z, \mathsf{fma}\left(z, \mathsf{fma}\left(z, 3.13060547623, 11.1667541262\right), t\right), a\right), b\right)\\ \mathbf{else}:\\ \;\;\;\;x + \left(\frac{y}{\left(0.31942702700572795 + \frac{3.7269864963038164}{z}\right) - \frac{3.241970391368047}{z \cdot z}} + 0.10203362558171805 \cdot \left(9.800690647801265 \cdot \frac{y}{\frac{z \cdot z}{t}}\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<=
      (/
       (*
        y
        (+
         (* z (+ (* z (+ (* z (+ (* z 3.13060547623) 11.1667541262)) t)) a))
         b))
       (+
        (*
         z
         (+ (* z (+ (* z (+ z 15.234687407)) 31.4690115749)) 11.9400905721))
        0.607771387771))
      INFINITY)
   (+
    x
    (*
     (/
      y
      (fma
       z
       (fma z (fma z (+ z 15.234687407) 31.4690115749) 11.9400905721)
       0.607771387771))
     (fma z (fma z (fma z (fma z 3.13060547623 11.1667541262) t) a) b)))
   (+
    x
    (+
     (/
      y
      (-
       (+ 0.31942702700572795 (/ 3.7269864963038164 z))
       (/ 3.241970391368047 (* z z))))
     (* 0.10203362558171805 (* 9.800690647801265 (/ y (/ (* z z) t))))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (((y * ((z * ((z * ((z * ((z * 3.13060547623) + 11.1667541262)) + t)) + a)) + b)) / ((z * ((z * ((z * (z + 15.234687407)) + 31.4690115749)) + 11.9400905721)) + 0.607771387771)) <= ((double) INFINITY)) {
		tmp = x + ((y / fma(z, fma(z, fma(z, (z + 15.234687407), 31.4690115749), 11.9400905721), 0.607771387771)) * fma(z, fma(z, fma(z, fma(z, 3.13060547623, 11.1667541262), t), a), b));
	} else {
		tmp = x + ((y / ((0.31942702700572795 + (3.7269864963038164 / z)) - (3.241970391368047 / (z * z)))) + (0.10203362558171805 * (9.800690647801265 * (y / ((z * z) / t)))));
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (Float64(Float64(y * Float64(Float64(z * Float64(Float64(z * Float64(Float64(z * Float64(Float64(z * 3.13060547623) + 11.1667541262)) + t)) + a)) + b)) / Float64(Float64(z * Float64(Float64(z * Float64(Float64(z * Float64(z + 15.234687407)) + 31.4690115749)) + 11.9400905721)) + 0.607771387771)) <= Inf)
		tmp = Float64(x + Float64(Float64(y / fma(z, fma(z, fma(z, Float64(z + 15.234687407), 31.4690115749), 11.9400905721), 0.607771387771)) * fma(z, fma(z, fma(z, fma(z, 3.13060547623, 11.1667541262), t), a), b)));
	else
		tmp = Float64(x + Float64(Float64(y / Float64(Float64(0.31942702700572795 + Float64(3.7269864963038164 / z)) - Float64(3.241970391368047 / Float64(z * z)))) + Float64(0.10203362558171805 * Float64(9.800690647801265 * Float64(y / Float64(Float64(z * z) / t))))));
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[N[(N[(y * N[(N[(z * N[(N[(z * N[(N[(z * N[(N[(z * 3.13060547623), $MachinePrecision] + 11.1667541262), $MachinePrecision]), $MachinePrecision] + t), $MachinePrecision]), $MachinePrecision] + a), $MachinePrecision]), $MachinePrecision] + b), $MachinePrecision]), $MachinePrecision] / N[(N[(z * N[(N[(z * N[(N[(z * N[(z + 15.234687407), $MachinePrecision]), $MachinePrecision] + 31.4690115749), $MachinePrecision]), $MachinePrecision] + 11.9400905721), $MachinePrecision]), $MachinePrecision] + 0.607771387771), $MachinePrecision]), $MachinePrecision], Infinity], N[(x + N[(N[(y / N[(z * N[(z * N[(z * N[(z + 15.234687407), $MachinePrecision] + 31.4690115749), $MachinePrecision] + 11.9400905721), $MachinePrecision] + 0.607771387771), $MachinePrecision]), $MachinePrecision] * N[(z * N[(z * N[(z * N[(z * 3.13060547623 + 11.1667541262), $MachinePrecision] + t), $MachinePrecision] + a), $MachinePrecision] + b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(N[(y / N[(N[(0.31942702700572795 + N[(3.7269864963038164 / z), $MachinePrecision]), $MachinePrecision] - N[(3.241970391368047 / N[(z * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(0.10203362558171805 * N[(9.800690647801265 * N[(y / N[(N[(z * z), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 (*.f64 y (+.f64 (*.f64 (+.f64 (*.f64 (+.f64 (*.f64 (+.f64 (*.f64 z 313060547623/100000000000) 55833770631/5000000000) z) t) z) a) z) b)) (+.f64 (*.f64 (+.f64 (*.f64 (+.f64 (*.f64 (+.f64 z 15234687407/1000000000) z) 314690115749/10000000000) z) 119400905721/10000000000) z) 607771387771/1000000000000)) < +inf.0

   1. Initial program 93.2%

      \[x + \frac{y \cdot \left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b\right)}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} \]
   2. Step-by-step derivation

      1. associate-*l/ 97.6%

         \[\leadsto x + \color{blue}{\frac{y}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} \cdot \left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b\right)} \]

      2. *-commutative 97.6%

         \[\leadsto x + \frac{y}{\color{blue}{z \cdot \left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right)} + 0.607771387771} \cdot \left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b\right) \]

      3. fma-def 97.6%

         \[\leadsto x + \frac{y}{\color{blue}{\mathsf{fma}\left(z, \left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721, 0.607771387771\right)}} \cdot \left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b\right) \]

      4. *-commutative 97.6%

         \[\leadsto x + \frac{y}{\mathsf{fma}\left(z, \color{blue}{z \cdot \left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right)} + 11.9400905721, 0.607771387771\right)} \cdot \left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b\right) \]

      5. fma-def 97.6%

         \[\leadsto x + \frac{y}{\mathsf{fma}\left(z, \color{blue}{\mathsf{fma}\left(z, \left(z + 15.234687407\right) \cdot z + 31.4690115749, 11.9400905721\right)}, 0.607771387771\right)} \cdot \left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b\right) \]

      6. *-commutative 97.6%

         \[\leadsto x + \frac{y}{\mathsf{fma}\left(z, \mathsf{fma}\left(z, \color{blue}{z \cdot \left(z + 15.234687407\right)} + 31.4690115749, 11.9400905721\right), 0.607771387771\right)} \cdot \left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b\right) \]

      7. fma-def 97.6%

         \[\leadsto x + \frac{y}{\mathsf{fma}\left(z, \mathsf{fma}\left(z, \color{blue}{\mathsf{fma}\left(z, z + 15.234687407, 31.4690115749\right)}, 11.9400905721\right), 0.607771387771\right)} \cdot \left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b\right) \]

      8. *-commutative 97.6%

         \[\leadsto x + \frac{y}{\mathsf{fma}\left(z, \mathsf{fma}\left(z, \mathsf{fma}\left(z, z + 15.234687407, 31.4690115749\right), 11.9400905721\right), 0.607771387771\right)} \cdot \left(\color{blue}{z \cdot \left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right)} + b\right) \]

      9. fma-def 97.6%

         \[\leadsto x + \frac{y}{\mathsf{fma}\left(z, \mathsf{fma}\left(z, \mathsf{fma}\left(z, z + 15.234687407, 31.4690115749\right), 11.9400905721\right), 0.607771387771\right)} \cdot \color{blue}{\mathsf{fma}\left(z, \left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a, b\right)} \]

   3. Simplified 97.6%

      \[\leadsto \color{blue}{x + \frac{y}{\mathsf{fma}\left(z, \mathsf{fma}\left(z, \mathsf{fma}\left(z, z + 15.234687407, 31.4690115749\right), 11.9400905721\right), 0.607771387771\right)} \cdot \mathsf{fma}\left(z, \mathsf{fma}\left(z, \mathsf{fma}\left(z, \mathsf{fma}\left(z, 3.13060547623, 11.1667541262\right), t\right), a\right), b\right)} \]

   if +inf.0 < (/.f64 (*.f64 y (+.f64 (*.f64 (+.f64 (*.f64 (+.f64 (*.f64 (+.f64 (*.f64 z 313060547623/100000000000) 55833770631/5000000000) z) t) z) a) z) b)) (+.f64 (*.f64 (+.f64 (*.f64 (+.f64 (*.f64 (+.f64 z 15234687407/1000000000) z) 314690115749/10000000000) z) 119400905721/10000000000) z) 607771387771/1000000000000))

   1. Initial program 0.0%

      \[x + \frac{y \cdot \left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b\right)}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} \]
   2. Step-by-step derivation

      1. associate-/l* 0.0%

         \[\leadsto x + \color{blue}{\frac{y}{\frac{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771}{\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b}}} \]

      2. fma-def 0.0%

         \[\leadsto x + \frac{y}{\frac{\color{blue}{\mathsf{fma}\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721, z, 0.607771387771\right)}}{\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b}} \]

      3. fma-def 0.0%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749, z, 11.9400905721\right)}, z, 0.607771387771\right)}{\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b}} \]

      4. fma-def 0.0%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right)}, z, 11.9400905721\right), z, 0.607771387771\right)}{\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b}} \]

      5. fma-def 0.0%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\color{blue}{\mathsf{fma}\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a, z, b\right)}}} \]

      6. fma-def 0.0%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t, z, a\right)}, z, b\right)}} \]

      7. fma-def 0.0%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(z \cdot 3.13060547623 + 11.1667541262, z, t\right)}, z, a\right), z, b\right)}} \]

      8. fma-def 0.0%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(z, 3.13060547623, 11.1667541262\right)}, z, t\right), z, a\right), z, b\right)}} \]

   3. Simplified 0.0%

      \[\leadsto \color{blue}{x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z, 3.13060547623, 11.1667541262\right), z, t\right), z, a\right), z, b\right)}}} \]

   4. Taylor expanded in z around inf 95.7%

      \[\leadsto x + \frac{y}{\color{blue}{3.7269864963038164 \cdot \frac{1}{z} + \left(0.31942702700572795 + -1 \cdot \frac{3.241970391368047 + 0.10203362558171805 \cdot t}{{z}^{2}}\right)}} \]
   5. Step-by-step derivation

      1. associate-*r/ 95.7%

         \[\leadsto x + \frac{y}{\color{blue}{\frac{3.7269864963038164 \cdot 1}{z}} + \left(0.31942702700572795 + -1 \cdot \frac{3.241970391368047 + 0.10203362558171805 \cdot t}{{z}^{2}}\right)} \]

      2. metadata-eval 95.7%

         \[\leadsto x + \frac{y}{\frac{\color{blue}{3.7269864963038164}}{z} + \left(0.31942702700572795 + -1 \cdot \frac{3.241970391368047 + 0.10203362558171805 \cdot t}{{z}^{2}}\right)} \]

      3. mul-1-neg 95.7%

         \[\leadsto x + \frac{y}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \color{blue}{\left(-\frac{3.241970391368047 + 0.10203362558171805 \cdot t}{{z}^{2}}\right)}\right)} \]

      4. *-commutative 95.7%

         \[\leadsto x + \frac{y}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \left(-\frac{3.241970391368047 + \color{blue}{t \cdot 0.10203362558171805}}{{z}^{2}}\right)\right)} \]

      5. unpow2 95.7%

         \[\leadsto x + \frac{y}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \left(-\frac{3.241970391368047 + t \cdot 0.10203362558171805}{\color{blue}{z \cdot z}}\right)\right)} \]

   6. Simplified 95.7%

      \[\leadsto x + \frac{y}{\color{blue}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \left(-\frac{3.241970391368047 + t \cdot 0.10203362558171805}{z \cdot z}\right)\right)}} \]

   7. Taylor expanded in t around 0 95.0%

      \[\leadsto x + \color{blue}{\left(\frac{y}{\left(3.7269864963038164 \cdot \frac{1}{z} + 0.31942702700572795\right) - 3.241970391368047 \cdot \frac{1}{{z}^{2}}} + 0.10203362558171805 \cdot \frac{y \cdot t}{{z}^{2} \cdot {\left(\left(3.7269864963038164 \cdot \frac{1}{z} + 0.31942702700572795\right) - 3.241970391368047 \cdot \frac{1}{{z}^{2}}\right)}^{2}}\right)} \]
   8. Step-by-step derivation

      1. associate--l+ 95.0%

         \[\leadsto x + \left(\frac{y}{\color{blue}{3.7269864963038164 \cdot \frac{1}{z} + \left(0.31942702700572795 - 3.241970391368047 \cdot \frac{1}{{z}^{2}}\right)}} + 0.10203362558171805 \cdot \frac{y \cdot t}{{z}^{2} \cdot {\left(\left(3.7269864963038164 \cdot \frac{1}{z} + 0.31942702700572795\right) - 3.241970391368047 \cdot \frac{1}{{z}^{2}}\right)}^{2}}\right) \]

      2. associate-*r/ 95.0%

         \[\leadsto x + \left(\frac{y}{\color{blue}{\frac{3.7269864963038164 \cdot 1}{z}} + \left(0.31942702700572795 - 3.241970391368047 \cdot \frac{1}{{z}^{2}}\right)} + 0.10203362558171805 \cdot \frac{y \cdot t}{{z}^{2} \cdot {\left(\left(3.7269864963038164 \cdot \frac{1}{z} + 0.31942702700572795\right) - 3.241970391368047 \cdot \frac{1}{{z}^{2}}\right)}^{2}}\right) \]

      3. metadata-eval 95.0%

         \[\leadsto x + \left(\frac{y}{\frac{\color{blue}{3.7269864963038164}}{z} + \left(0.31942702700572795 - 3.241970391368047 \cdot \frac{1}{{z}^{2}}\right)} + 0.10203362558171805 \cdot \frac{y \cdot t}{{z}^{2} \cdot {\left(\left(3.7269864963038164 \cdot \frac{1}{z} + 0.31942702700572795\right) - 3.241970391368047 \cdot \frac{1}{{z}^{2}}\right)}^{2}}\right) \]

      4. associate-+r- 95.0%

         \[\leadsto x + \left(\frac{y}{\color{blue}{\left(\frac{3.7269864963038164}{z} + 0.31942702700572795\right) - 3.241970391368047 \cdot \frac{1}{{z}^{2}}}} + 0.10203362558171805 \cdot \frac{y \cdot t}{{z}^{2} \cdot {\left(\left(3.7269864963038164 \cdot \frac{1}{z} + 0.31942702700572795\right) - 3.241970391368047 \cdot \frac{1}{{z}^{2}}\right)}^{2}}\right) \]

      5. +-commutative 95.0%

         \[\leadsto x + \left(\frac{y}{\color{blue}{\left(0.31942702700572795 + \frac{3.7269864963038164}{z}\right)} - 3.241970391368047 \cdot \frac{1}{{z}^{2}}} + 0.10203362558171805 \cdot \frac{y \cdot t}{{z}^{2} \cdot {\left(\left(3.7269864963038164 \cdot \frac{1}{z} + 0.31942702700572795\right) - 3.241970391368047 \cdot \frac{1}{{z}^{2}}\right)}^{2}}\right) \]

      6. associate-*r/ 95.0%

         \[\leadsto x + \left(\frac{y}{\left(0.31942702700572795 + \frac{3.7269864963038164}{z}\right) - \color{blue}{\frac{3.241970391368047 \cdot 1}{{z}^{2}}}} + 0.10203362558171805 \cdot \frac{y \cdot t}{{z}^{2} \cdot {\left(\left(3.7269864963038164 \cdot \frac{1}{z} + 0.31942702700572795\right) - 3.241970391368047 \cdot \frac{1}{{z}^{2}}\right)}^{2}}\right) \]

      7. metadata-eval 95.0%

         \[\leadsto x + \left(\frac{y}{\left(0.31942702700572795 + \frac{3.7269864963038164}{z}\right) - \frac{\color{blue}{3.241970391368047}}{{z}^{2}}} + 0.10203362558171805 \cdot \frac{y \cdot t}{{z}^{2} \cdot {\left(\left(3.7269864963038164 \cdot \frac{1}{z} + 0.31942702700572795\right) - 3.241970391368047 \cdot \frac{1}{{z}^{2}}\right)}^{2}}\right) \]

      8. unpow2 95.0%

         \[\leadsto x + \left(\frac{y}{\left(0.31942702700572795 + \frac{3.7269864963038164}{z}\right) - \frac{3.241970391368047}{\color{blue}{z \cdot z}}} + 0.10203362558171805 \cdot \frac{y \cdot t}{{z}^{2} \cdot {\left(\left(3.7269864963038164 \cdot \frac{1}{z} + 0.31942702700572795\right) - 3.241970391368047 \cdot \frac{1}{{z}^{2}}\right)}^{2}}\right) \]

      9. times-frac 98.0%

         \[\leadsto x + \left(\frac{y}{\left(0.31942702700572795 + \frac{3.7269864963038164}{z}\right) - \frac{3.241970391368047}{z \cdot z}} + 0.10203362558171805 \cdot \color{blue}{\left(\frac{y}{{z}^{2}} \cdot \frac{t}{{\left(\left(3.7269864963038164 \cdot \frac{1}{z} + 0.31942702700572795\right) - 3.241970391368047 \cdot \frac{1}{{z}^{2}}\right)}^{2}}\right)}\right) \]

      10. unpow2 98.0%

          \[\leadsto x + \left(\frac{y}{\left(0.31942702700572795 + \frac{3.7269864963038164}{z}\right) - \frac{3.241970391368047}{z \cdot z}} + 0.10203362558171805 \cdot \left(\frac{y}{\color{blue}{z \cdot z}} \cdot \frac{t}{{\left(\left(3.7269864963038164 \cdot \frac{1}{z} + 0.31942702700572795\right) - 3.241970391368047 \cdot \frac{1}{{z}^{2}}\right)}^{2}}\right)\right) \]

   9. Simplified 98.0%

      \[\leadsto x + \color{blue}{\left(\frac{y}{\left(0.31942702700572795 + \frac{3.7269864963038164}{z}\right) - \frac{3.241970391368047}{z \cdot z}} + 0.10203362558171805 \cdot \left(\frac{y}{z \cdot z} \cdot \frac{t}{{\left(\left(0.31942702700572795 + \frac{3.7269864963038164}{z}\right) - \frac{3.241970391368047}{z \cdot z}\right)}^{2}}\right)\right)} \]

   10. Taylor expanded in z around inf 95.0%

       \[\leadsto x + \left(\frac{y}{\left(0.31942702700572795 + \frac{3.7269864963038164}{z}\right) - \frac{3.241970391368047}{z \cdot z}} + 0.10203362558171805 \cdot \color{blue}{\left(9.800690647801265 \cdot \frac{y \cdot t}{{z}^{2}}\right)}\right) \]
   11. Step-by-step derivation

       1. associate-/l* 99.9%

          \[\leadsto x + \left(\frac{y}{\left(0.31942702700572795 + \frac{3.7269864963038164}{z}\right) - \frac{3.241970391368047}{z \cdot z}} + 0.10203362558171805 \cdot \left(9.800690647801265 \cdot \color{blue}{\frac{y}{\frac{{z}^{2}}{t}}}\right)\right) \]

       2. unpow2 99.9%

          \[\leadsto x + \left(\frac{y}{\left(0.31942702700572795 + \frac{3.7269864963038164}{z}\right) - \frac{3.241970391368047}{z \cdot z}} + 0.10203362558171805 \cdot \left(9.800690647801265 \cdot \frac{y}{\frac{\color{blue}{z \cdot z}}{t}}\right)\right) \]

   12. Simplified 99.9%

       \[\leadsto x + \left(\frac{y}{\left(0.31942702700572795 + \frac{3.7269864963038164}{z}\right) - \frac{3.241970391368047}{z \cdot z}} + 0.10203362558171805 \cdot \color{blue}{\left(9.800690647801265 \cdot \frac{y}{\frac{z \cdot z}{t}}\right)}\right) \]
3. Recombined 2 regimes into one program.

4. Final simplification 98.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{y \cdot \left(z \cdot \left(z \cdot \left(z \cdot \left(z \cdot 3.13060547623 + 11.1667541262\right) + t\right) + a\right) + b\right)}{z \cdot \left(z \cdot \left(z \cdot \left(z + 15.234687407\right) + 31.4690115749\right) + 11.9400905721\right) + 0.607771387771} \leq \infty:\\ \;\;\;\;x + \frac{y}{\mathsf{fma}\left(z, \mathsf{fma}\left(z, \mathsf{fma}\left(z, z + 15.234687407, 31.4690115749\right), 11.9400905721\right), 0.607771387771\right)} \cdot \mathsf{fma}\left(z, \mathsf{fma}\left(z, \mathsf{fma}\left(z, \mathsf{fma}\left(z, 3.13060547623, 11.1667541262\right), t\right), a\right), b\right)\\ \mathbf{else}:\\ \;\;\;\;x + \left(\frac{y}{\left(0.31942702700572795 + \frac{3.7269864963038164}{z}\right) - \frac{3.241970391368047}{z \cdot z}} + 0.10203362558171805 \cdot \left(9.800690647801265 \cdot \frac{y}{\frac{z \cdot z}{t}}\right)\right)\\ \end{array} \]

Alternative 4: 86.2% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -4.6 \cdot 10^{+23} \lor \neg \left(z \leq 2.6 \cdot 10^{-34}\right):\\ \;\;\;\;x + \left(\frac{y}{\left(0.31942702700572795 + \frac{3.7269864963038164}{z}\right) - \frac{3.241970391368047}{z \cdot z}} + 0.10203362558171805 \cdot \left(9.800690647801265 \cdot \frac{y}{\frac{z \cdot z}{t}}\right)\right)\\ \mathbf{else}:\\ \;\;\;\;z \cdot \left(1.6453555072203998 \cdot \left(y \cdot a\right) + -32.324150453290734 \cdot \left(y \cdot b\right)\right) + \left(x + 1.6453555072203998 \cdot \left(y \cdot b\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (or (<= z -4.6e+23) (not (<= z 2.6e-34)))
   (+
    x
    (+
     (/
      y
      (-
       (+ 0.31942702700572795 (/ 3.7269864963038164 z))
       (/ 3.241970391368047 (* z z))))
     (* 0.10203362558171805 (* 9.800690647801265 (/ y (/ (* z z) t))))))
   (+
    (* z (+ (* 1.6453555072203998 (* y a)) (* -32.324150453290734 (* y b))))
    (+ x (* 1.6453555072203998 (* y b))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((z <= -4.6e+23) || !(z <= 2.6e-34)) {
		tmp = x + ((y / ((0.31942702700572795 + (3.7269864963038164 / z)) - (3.241970391368047 / (z * z)))) + (0.10203362558171805 * (9.800690647801265 * (y / ((z * z) / t)))));
	} else {
		tmp = (z * ((1.6453555072203998 * (y * a)) + (-32.324150453290734 * (y * b)))) + (x + (1.6453555072203998 * (y * b)));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if ((z <= (-4.6d+23)) .or. (.not. (z <= 2.6d-34))) then
        tmp = x + ((y / ((0.31942702700572795d0 + (3.7269864963038164d0 / z)) - (3.241970391368047d0 / (z * z)))) + (0.10203362558171805d0 * (9.800690647801265d0 * (y / ((z * z) / t)))))
    else
        tmp = (z * ((1.6453555072203998d0 * (y * a)) + ((-32.324150453290734d0) * (y * b)))) + (x + (1.6453555072203998d0 * (y * b)))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((z <= -4.6e+23) || !(z <= 2.6e-34)) {
		tmp = x + ((y / ((0.31942702700572795 + (3.7269864963038164 / z)) - (3.241970391368047 / (z * z)))) + (0.10203362558171805 * (9.800690647801265 * (y / ((z * z) / t)))));
	} else {
		tmp = (z * ((1.6453555072203998 * (y * a)) + (-32.324150453290734 * (y * b)))) + (x + (1.6453555072203998 * (y * b)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if (z <= -4.6e+23) or not (z <= 2.6e-34):
		tmp = x + ((y / ((0.31942702700572795 + (3.7269864963038164 / z)) - (3.241970391368047 / (z * z)))) + (0.10203362558171805 * (9.800690647801265 * (y / ((z * z) / t)))))
	else:
		tmp = (z * ((1.6453555072203998 * (y * a)) + (-32.324150453290734 * (y * b)))) + (x + (1.6453555072203998 * (y * b)))
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if ((z <= -4.6e+23) || !(z <= 2.6e-34))
		tmp = Float64(x + Float64(Float64(y / Float64(Float64(0.31942702700572795 + Float64(3.7269864963038164 / z)) - Float64(3.241970391368047 / Float64(z * z)))) + Float64(0.10203362558171805 * Float64(9.800690647801265 * Float64(y / Float64(Float64(z * z) / t))))));
	else
		tmp = Float64(Float64(z * Float64(Float64(1.6453555072203998 * Float64(y * a)) + Float64(-32.324150453290734 * Float64(y * b)))) + Float64(x + Float64(1.6453555072203998 * Float64(y * b))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if ((z <= -4.6e+23) || ~((z <= 2.6e-34)))
		tmp = x + ((y / ((0.31942702700572795 + (3.7269864963038164 / z)) - (3.241970391368047 / (z * z)))) + (0.10203362558171805 * (9.800690647801265 * (y / ((z * z) / t)))));
	else
		tmp = (z * ((1.6453555072203998 * (y * a)) + (-32.324150453290734 * (y * b)))) + (x + (1.6453555072203998 * (y * b)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[Or[LessEqual[z, -4.6e+23], N[Not[LessEqual[z, 2.6e-34]], $MachinePrecision]], N[(x + N[(N[(y / N[(N[(0.31942702700572795 + N[(3.7269864963038164 / z), $MachinePrecision]), $MachinePrecision] - N[(3.241970391368047 / N[(z * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(0.10203362558171805 * N[(9.800690647801265 * N[(y / N[(N[(z * z), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(z * N[(N[(1.6453555072203998 * N[(y * a), $MachinePrecision]), $MachinePrecision] + N[(-32.324150453290734 * N[(y * b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(x + N[(1.6453555072203998 * N[(y * b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if z < -4.6000000000000001e23 or 2.5999999999999999e-34 < z

   1. Initial program 16.4%

      \[x + \frac{y \cdot \left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b\right)}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} \]
   2. Step-by-step derivation

      1. associate-/l* 22.7%

         \[\leadsto x + \color{blue}{\frac{y}{\frac{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771}{\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b}}} \]

      2. fma-def 22.7%

         \[\leadsto x + \frac{y}{\frac{\color{blue}{\mathsf{fma}\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721, z, 0.607771387771\right)}}{\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b}} \]

      3. fma-def 22.7%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749, z, 11.9400905721\right)}, z, 0.607771387771\right)}{\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b}} \]

      4. fma-def 22.7%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right)}, z, 11.9400905721\right), z, 0.607771387771\right)}{\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b}} \]

      5. fma-def 22.7%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\color{blue}{\mathsf{fma}\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a, z, b\right)}}} \]

      6. fma-def 22.7%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t, z, a\right)}, z, b\right)}} \]

      7. fma-def 22.7%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(z \cdot 3.13060547623 + 11.1667541262, z, t\right)}, z, a\right), z, b\right)}} \]

      8. fma-def 22.7%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(z, 3.13060547623, 11.1667541262\right)}, z, t\right), z, a\right), z, b\right)}} \]

   3. Simplified 22.7%

      \[\leadsto \color{blue}{x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z, 3.13060547623, 11.1667541262\right), z, t\right), z, a\right), z, b\right)}}} \]

   4. Taylor expanded in z around inf 88.6%

      \[\leadsto x + \frac{y}{\color{blue}{3.7269864963038164 \cdot \frac{1}{z} + \left(0.31942702700572795 + -1 \cdot \frac{3.241970391368047 + 0.10203362558171805 \cdot t}{{z}^{2}}\right)}} \]
   5. Step-by-step derivation

      1. associate-*r/ 88.6%

         \[\leadsto x + \frac{y}{\color{blue}{\frac{3.7269864963038164 \cdot 1}{z}} + \left(0.31942702700572795 + -1 \cdot \frac{3.241970391368047 + 0.10203362558171805 \cdot t}{{z}^{2}}\right)} \]

      2. metadata-eval 88.6%

         \[\leadsto x + \frac{y}{\frac{\color{blue}{3.7269864963038164}}{z} + \left(0.31942702700572795 + -1 \cdot \frac{3.241970391368047 + 0.10203362558171805 \cdot t}{{z}^{2}}\right)} \]

      3. mul-1-neg 88.6%

         \[\leadsto x + \frac{y}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \color{blue}{\left(-\frac{3.241970391368047 + 0.10203362558171805 \cdot t}{{z}^{2}}\right)}\right)} \]

      4. *-commutative 88.6%

         \[\leadsto x + \frac{y}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \left(-\frac{3.241970391368047 + \color{blue}{t \cdot 0.10203362558171805}}{{z}^{2}}\right)\right)} \]

      5. unpow2 88.6%

         \[\leadsto x + \frac{y}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \left(-\frac{3.241970391368047 + t \cdot 0.10203362558171805}{\color{blue}{z \cdot z}}\right)\right)} \]

   6. Simplified 88.6%

      \[\leadsto x + \frac{y}{\color{blue}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \left(-\frac{3.241970391368047 + t \cdot 0.10203362558171805}{z \cdot z}\right)\right)}} \]

   7. Taylor expanded in t around 0 92.1%

      \[\leadsto x + \color{blue}{\left(\frac{y}{\left(3.7269864963038164 \cdot \frac{1}{z} + 0.31942702700572795\right) - 3.241970391368047 \cdot \frac{1}{{z}^{2}}} + 0.10203362558171805 \cdot \frac{y \cdot t}{{z}^{2} \cdot {\left(\left(3.7269864963038164 \cdot \frac{1}{z} + 0.31942702700572795\right) - 3.241970391368047 \cdot \frac{1}{{z}^{2}}\right)}^{2}}\right)} \]
   8. Step-by-step derivation

      1. associate--l+ 92.1%

         \[\leadsto x + \left(\frac{y}{\color{blue}{3.7269864963038164 \cdot \frac{1}{z} + \left(0.31942702700572795 - 3.241970391368047 \cdot \frac{1}{{z}^{2}}\right)}} + 0.10203362558171805 \cdot \frac{y \cdot t}{{z}^{2} \cdot {\left(\left(3.7269864963038164 \cdot \frac{1}{z} + 0.31942702700572795\right) - 3.241970391368047 \cdot \frac{1}{{z}^{2}}\right)}^{2}}\right) \]

      2. associate-*r/ 92.1%

         \[\leadsto x + \left(\frac{y}{\color{blue}{\frac{3.7269864963038164 \cdot 1}{z}} + \left(0.31942702700572795 - 3.241970391368047 \cdot \frac{1}{{z}^{2}}\right)} + 0.10203362558171805 \cdot \frac{y \cdot t}{{z}^{2} \cdot {\left(\left(3.7269864963038164 \cdot \frac{1}{z} + 0.31942702700572795\right) - 3.241970391368047 \cdot \frac{1}{{z}^{2}}\right)}^{2}}\right) \]

      3. metadata-eval 92.1%

         \[\leadsto x + \left(\frac{y}{\frac{\color{blue}{3.7269864963038164}}{z} + \left(0.31942702700572795 - 3.241970391368047 \cdot \frac{1}{{z}^{2}}\right)} + 0.10203362558171805 \cdot \frac{y \cdot t}{{z}^{2} \cdot {\left(\left(3.7269864963038164 \cdot \frac{1}{z} + 0.31942702700572795\right) - 3.241970391368047 \cdot \frac{1}{{z}^{2}}\right)}^{2}}\right) \]

      4. associate-+r- 92.1%

         \[\leadsto x + \left(\frac{y}{\color{blue}{\left(\frac{3.7269864963038164}{z} + 0.31942702700572795\right) - 3.241970391368047 \cdot \frac{1}{{z}^{2}}}} + 0.10203362558171805 \cdot \frac{y \cdot t}{{z}^{2} \cdot {\left(\left(3.7269864963038164 \cdot \frac{1}{z} + 0.31942702700572795\right) - 3.241970391368047 \cdot \frac{1}{{z}^{2}}\right)}^{2}}\right) \]

      5. +-commutative 92.1%

         \[\leadsto x + \left(\frac{y}{\color{blue}{\left(0.31942702700572795 + \frac{3.7269864963038164}{z}\right)} - 3.241970391368047 \cdot \frac{1}{{z}^{2}}} + 0.10203362558171805 \cdot \frac{y \cdot t}{{z}^{2} \cdot {\left(\left(3.7269864963038164 \cdot \frac{1}{z} + 0.31942702700572795\right) - 3.241970391368047 \cdot \frac{1}{{z}^{2}}\right)}^{2}}\right) \]

      6. associate-*r/ 92.1%

         \[\leadsto x + \left(\frac{y}{\left(0.31942702700572795 + \frac{3.7269864963038164}{z}\right) - \color{blue}{\frac{3.241970391368047 \cdot 1}{{z}^{2}}}} + 0.10203362558171805 \cdot \frac{y \cdot t}{{z}^{2} \cdot {\left(\left(3.7269864963038164 \cdot \frac{1}{z} + 0.31942702700572795\right) - 3.241970391368047 \cdot \frac{1}{{z}^{2}}\right)}^{2}}\right) \]

      7. metadata-eval 92.1%

         \[\leadsto x + \left(\frac{y}{\left(0.31942702700572795 + \frac{3.7269864963038164}{z}\right) - \frac{\color{blue}{3.241970391368047}}{{z}^{2}}} + 0.10203362558171805 \cdot \frac{y \cdot t}{{z}^{2} \cdot {\left(\left(3.7269864963038164 \cdot \frac{1}{z} + 0.31942702700572795\right) - 3.241970391368047 \cdot \frac{1}{{z}^{2}}\right)}^{2}}\right) \]

      8. unpow2 92.1%

         \[\leadsto x + \left(\frac{y}{\left(0.31942702700572795 + \frac{3.7269864963038164}{z}\right) - \frac{3.241970391368047}{\color{blue}{z \cdot z}}} + 0.10203362558171805 \cdot \frac{y \cdot t}{{z}^{2} \cdot {\left(\left(3.7269864963038164 \cdot \frac{1}{z} + 0.31942702700572795\right) - 3.241970391368047 \cdot \frac{1}{{z}^{2}}\right)}^{2}}\right) \]

      9. times-frac 95.1%

         \[\leadsto x + \left(\frac{y}{\left(0.31942702700572795 + \frac{3.7269864963038164}{z}\right) - \frac{3.241970391368047}{z \cdot z}} + 0.10203362558171805 \cdot \color{blue}{\left(\frac{y}{{z}^{2}} \cdot \frac{t}{{\left(\left(3.7269864963038164 \cdot \frac{1}{z} + 0.31942702700572795\right) - 3.241970391368047 \cdot \frac{1}{{z}^{2}}\right)}^{2}}\right)}\right) \]

      10. unpow2 95.1%

          \[\leadsto x + \left(\frac{y}{\left(0.31942702700572795 + \frac{3.7269864963038164}{z}\right) - \frac{3.241970391368047}{z \cdot z}} + 0.10203362558171805 \cdot \left(\frac{y}{\color{blue}{z \cdot z}} \cdot \frac{t}{{\left(\left(3.7269864963038164 \cdot \frac{1}{z} + 0.31942702700572795\right) - 3.241970391368047 \cdot \frac{1}{{z}^{2}}\right)}^{2}}\right)\right) \]

   9. Simplified 95.1%

      \[\leadsto x + \color{blue}{\left(\frac{y}{\left(0.31942702700572795 + \frac{3.7269864963038164}{z}\right) - \frac{3.241970391368047}{z \cdot z}} + 0.10203362558171805 \cdot \left(\frac{y}{z \cdot z} \cdot \frac{t}{{\left(\left(0.31942702700572795 + \frac{3.7269864963038164}{z}\right) - \frac{3.241970391368047}{z \cdot z}\right)}^{2}}\right)\right)} \]

   10. Taylor expanded in z around inf 92.1%

       \[\leadsto x + \left(\frac{y}{\left(0.31942702700572795 + \frac{3.7269864963038164}{z}\right) - \frac{3.241970391368047}{z \cdot z}} + 0.10203362558171805 \cdot \color{blue}{\left(9.800690647801265 \cdot \frac{y \cdot t}{{z}^{2}}\right)}\right) \]
   11. Step-by-step derivation

       1. associate-/l* 96.5%

          \[\leadsto x + \left(\frac{y}{\left(0.31942702700572795 + \frac{3.7269864963038164}{z}\right) - \frac{3.241970391368047}{z \cdot z}} + 0.10203362558171805 \cdot \left(9.800690647801265 \cdot \color{blue}{\frac{y}{\frac{{z}^{2}}{t}}}\right)\right) \]

       2. unpow2 96.5%

          \[\leadsto x + \left(\frac{y}{\left(0.31942702700572795 + \frac{3.7269864963038164}{z}\right) - \frac{3.241970391368047}{z \cdot z}} + 0.10203362558171805 \cdot \left(9.800690647801265 \cdot \frac{y}{\frac{\color{blue}{z \cdot z}}{t}}\right)\right) \]

   12. Simplified 96.5%

       \[\leadsto x + \left(\frac{y}{\left(0.31942702700572795 + \frac{3.7269864963038164}{z}\right) - \frac{3.241970391368047}{z \cdot z}} + 0.10203362558171805 \cdot \color{blue}{\left(9.800690647801265 \cdot \frac{y}{\frac{z \cdot z}{t}}\right)}\right) \]

   if -4.6000000000000001e23 < z < 2.5999999999999999e-34

   1. Initial program 99.8%

      \[x + \frac{y \cdot \left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b\right)}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} \]
   2. Step-by-step derivation

      1. +-commutative 99.8%

         \[\leadsto \color{blue}{\frac{y \cdot \left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b\right)}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} + x} \]

      2. associate-*l/ 99.8%

         \[\leadsto \color{blue}{\frac{y}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} \cdot \left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b\right)} + x \]

      3. *-commutative 99.8%

         \[\leadsto \color{blue}{\left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b\right) \cdot \frac{y}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771}} + x \]

      4. fma-def 99.8%

         \[\leadsto \color{blue}{\mathsf{fma}\left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b, \frac{y}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771}, x\right)} \]

   3. Simplified 99.8%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(z, \mathsf{fma}\left(z, \mathsf{fma}\left(z, \mathsf{fma}\left(z, 3.13060547623, 11.1667541262\right), t\right), a\right), b\right), \frac{y}{\mathsf{fma}\left(z, \mathsf{fma}\left(z, \mathsf{fma}\left(z, z + 15.234687407, 31.4690115749\right), 11.9400905721\right), 0.607771387771\right)}, x\right)} \]

   4. Taylor expanded in z around 0 85.5%

      \[\leadsto \color{blue}{\left(1.6453555072203998 \cdot \left(y \cdot a\right) + -32.324150453290734 \cdot \left(y \cdot b\right)\right) \cdot z + \left(x + 1.6453555072203998 \cdot \left(y \cdot b\right)\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 91.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -4.6 \cdot 10^{+23} \lor \neg \left(z \leq 2.6 \cdot 10^{-34}\right):\\ \;\;\;\;x + \left(\frac{y}{\left(0.31942702700572795 + \frac{3.7269864963038164}{z}\right) - \frac{3.241970391368047}{z \cdot z}} + 0.10203362558171805 \cdot \left(9.800690647801265 \cdot \frac{y}{\frac{z \cdot z}{t}}\right)\right)\\ \mathbf{else}:\\ \;\;\;\;z \cdot \left(1.6453555072203998 \cdot \left(y \cdot a\right) + -32.324150453290734 \cdot \left(y \cdot b\right)\right) + \left(x + 1.6453555072203998 \cdot \left(y \cdot b\right)\right)\\ \end{array} \]

Alternative 5: 95.8% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -12.6 \lor \neg \left(z \leq 1.05 \cdot 10^{+20}\right):\\ \;\;\;\;x + \left(\frac{y}{\left(0.31942702700572795 + \frac{3.7269864963038164}{z}\right) - \frac{3.241970391368047}{z \cdot z}} + 0.10203362558171805 \cdot \left(9.800690647801265 \cdot \frac{y}{\frac{z \cdot z}{t}}\right)\right)\\ \mathbf{else}:\\ \;\;\;\;x + \frac{y \cdot \left(z \cdot \left(z \cdot \left(z \cdot \left(z \cdot 3.13060547623 + 11.1667541262\right) + t\right) + a\right) + b\right)}{0.607771387771 + z \cdot 11.9400905721}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (or (<= z -12.6) (not (<= z 1.05e+20)))
   (+
    x
    (+
     (/
      y
      (-
       (+ 0.31942702700572795 (/ 3.7269864963038164 z))
       (/ 3.241970391368047 (* z z))))
     (* 0.10203362558171805 (* 9.800690647801265 (/ y (/ (* z z) t))))))
   (+
    x
    (/
     (*
      y
      (+
       (* z (+ (* z (+ (* z (+ (* z 3.13060547623) 11.1667541262)) t)) a))
       b))
     (+ 0.607771387771 (* z 11.9400905721))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((z <= -12.6) || !(z <= 1.05e+20)) {
		tmp = x + ((y / ((0.31942702700572795 + (3.7269864963038164 / z)) - (3.241970391368047 / (z * z)))) + (0.10203362558171805 * (9.800690647801265 * (y / ((z * z) / t)))));
	} else {
		tmp = x + ((y * ((z * ((z * ((z * ((z * 3.13060547623) + 11.1667541262)) + t)) + a)) + b)) / (0.607771387771 + (z * 11.9400905721)));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if ((z <= (-12.6d0)) .or. (.not. (z <= 1.05d+20))) then
        tmp = x + ((y / ((0.31942702700572795d0 + (3.7269864963038164d0 / z)) - (3.241970391368047d0 / (z * z)))) + (0.10203362558171805d0 * (9.800690647801265d0 * (y / ((z * z) / t)))))
    else
        tmp = x + ((y * ((z * ((z * ((z * ((z * 3.13060547623d0) + 11.1667541262d0)) + t)) + a)) + b)) / (0.607771387771d0 + (z * 11.9400905721d0)))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((z <= -12.6) || !(z <= 1.05e+20)) {
		tmp = x + ((y / ((0.31942702700572795 + (3.7269864963038164 / z)) - (3.241970391368047 / (z * z)))) + (0.10203362558171805 * (9.800690647801265 * (y / ((z * z) / t)))));
	} else {
		tmp = x + ((y * ((z * ((z * ((z * ((z * 3.13060547623) + 11.1667541262)) + t)) + a)) + b)) / (0.607771387771 + (z * 11.9400905721)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if (z <= -12.6) or not (z <= 1.05e+20):
		tmp = x + ((y / ((0.31942702700572795 + (3.7269864963038164 / z)) - (3.241970391368047 / (z * z)))) + (0.10203362558171805 * (9.800690647801265 * (y / ((z * z) / t)))))
	else:
		tmp = x + ((y * ((z * ((z * ((z * ((z * 3.13060547623) + 11.1667541262)) + t)) + a)) + b)) / (0.607771387771 + (z * 11.9400905721)))
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if ((z <= -12.6) || !(z <= 1.05e+20))
		tmp = Float64(x + Float64(Float64(y / Float64(Float64(0.31942702700572795 + Float64(3.7269864963038164 / z)) - Float64(3.241970391368047 / Float64(z * z)))) + Float64(0.10203362558171805 * Float64(9.800690647801265 * Float64(y / Float64(Float64(z * z) / t))))));
	else
		tmp = Float64(x + Float64(Float64(y * Float64(Float64(z * Float64(Float64(z * Float64(Float64(z * Float64(Float64(z * 3.13060547623) + 11.1667541262)) + t)) + a)) + b)) / Float64(0.607771387771 + Float64(z * 11.9400905721))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if ((z <= -12.6) || ~((z <= 1.05e+20)))
		tmp = x + ((y / ((0.31942702700572795 + (3.7269864963038164 / z)) - (3.241970391368047 / (z * z)))) + (0.10203362558171805 * (9.800690647801265 * (y / ((z * z) / t)))));
	else
		tmp = x + ((y * ((z * ((z * ((z * ((z * 3.13060547623) + 11.1667541262)) + t)) + a)) + b)) / (0.607771387771 + (z * 11.9400905721)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[Or[LessEqual[z, -12.6], N[Not[LessEqual[z, 1.05e+20]], $MachinePrecision]], N[(x + N[(N[(y / N[(N[(0.31942702700572795 + N[(3.7269864963038164 / z), $MachinePrecision]), $MachinePrecision] - N[(3.241970391368047 / N[(z * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(0.10203362558171805 * N[(9.800690647801265 * N[(y / N[(N[(z * z), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(N[(y * N[(N[(z * N[(N[(z * N[(N[(z * N[(N[(z * 3.13060547623), $MachinePrecision] + 11.1667541262), $MachinePrecision]), $MachinePrecision] + t), $MachinePrecision]), $MachinePrecision] + a), $MachinePrecision]), $MachinePrecision] + b), $MachinePrecision]), $MachinePrecision] / N[(0.607771387771 + N[(z * 11.9400905721), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if z < -12.5999999999999996 or 1.05e20 < z

   1. Initial program 11.0%

      \[x + \frac{y \cdot \left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b\right)}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} \]
   2. Step-by-step derivation

      1. associate-/l* 17.8%

         \[\leadsto x + \color{blue}{\frac{y}{\frac{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771}{\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b}}} \]

      2. fma-def 17.8%

         \[\leadsto x + \frac{y}{\frac{\color{blue}{\mathsf{fma}\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721, z, 0.607771387771\right)}}{\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b}} \]

      3. fma-def 17.8%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749, z, 11.9400905721\right)}, z, 0.607771387771\right)}{\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b}} \]

      4. fma-def 17.8%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right)}, z, 11.9400905721\right), z, 0.607771387771\right)}{\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b}} \]

      5. fma-def 17.8%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\color{blue}{\mathsf{fma}\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a, z, b\right)}}} \]

      6. fma-def 17.8%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t, z, a\right)}, z, b\right)}} \]

      7. fma-def 17.8%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(z \cdot 3.13060547623 + 11.1667541262, z, t\right)}, z, a\right), z, b\right)}} \]

      8. fma-def 17.8%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(z, 3.13060547623, 11.1667541262\right)}, z, t\right), z, a\right), z, b\right)}} \]

   3. Simplified 17.8%

      \[\leadsto \color{blue}{x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z, 3.13060547623, 11.1667541262\right), z, t\right), z, a\right), z, b\right)}}} \]

   4. Taylor expanded in z around inf 92.5%

      \[\leadsto x + \frac{y}{\color{blue}{3.7269864963038164 \cdot \frac{1}{z} + \left(0.31942702700572795 + -1 \cdot \frac{3.241970391368047 + 0.10203362558171805 \cdot t}{{z}^{2}}\right)}} \]
   5. Step-by-step derivation

      1. associate-*r/ 92.5%

         \[\leadsto x + \frac{y}{\color{blue}{\frac{3.7269864963038164 \cdot 1}{z}} + \left(0.31942702700572795 + -1 \cdot \frac{3.241970391368047 + 0.10203362558171805 \cdot t}{{z}^{2}}\right)} \]

      2. metadata-eval 92.5%

         \[\leadsto x + \frac{y}{\frac{\color{blue}{3.7269864963038164}}{z} + \left(0.31942702700572795 + -1 \cdot \frac{3.241970391368047 + 0.10203362558171805 \cdot t}{{z}^{2}}\right)} \]

      3. mul-1-neg 92.5%

         \[\leadsto x + \frac{y}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \color{blue}{\left(-\frac{3.241970391368047 + 0.10203362558171805 \cdot t}{{z}^{2}}\right)}\right)} \]

      4. *-commutative 92.5%

         \[\leadsto x + \frac{y}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \left(-\frac{3.241970391368047 + \color{blue}{t \cdot 0.10203362558171805}}{{z}^{2}}\right)\right)} \]

      5. unpow2 92.5%

         \[\leadsto x + \frac{y}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \left(-\frac{3.241970391368047 + t \cdot 0.10203362558171805}{\color{blue}{z \cdot z}}\right)\right)} \]

   6. Simplified 92.5%

      \[\leadsto x + \frac{y}{\color{blue}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \left(-\frac{3.241970391368047 + t \cdot 0.10203362558171805}{z \cdot z}\right)\right)}} \]

   7. Taylor expanded in t around 0 94.4%

      \[\leadsto x + \color{blue}{\left(\frac{y}{\left(3.7269864963038164 \cdot \frac{1}{z} + 0.31942702700572795\right) - 3.241970391368047 \cdot \frac{1}{{z}^{2}}} + 0.10203362558171805 \cdot \frac{y \cdot t}{{z}^{2} \cdot {\left(\left(3.7269864963038164 \cdot \frac{1}{z} + 0.31942702700572795\right) - 3.241970391368047 \cdot \frac{1}{{z}^{2}}\right)}^{2}}\right)} \]
   8. Step-by-step derivation

      1. associate--l+ 94.4%

         \[\leadsto x + \left(\frac{y}{\color{blue}{3.7269864963038164 \cdot \frac{1}{z} + \left(0.31942702700572795 - 3.241970391368047 \cdot \frac{1}{{z}^{2}}\right)}} + 0.10203362558171805 \cdot \frac{y \cdot t}{{z}^{2} \cdot {\left(\left(3.7269864963038164 \cdot \frac{1}{z} + 0.31942702700572795\right) - 3.241970391368047 \cdot \frac{1}{{z}^{2}}\right)}^{2}}\right) \]

      2. associate-*r/ 94.4%

         \[\leadsto x + \left(\frac{y}{\color{blue}{\frac{3.7269864963038164 \cdot 1}{z}} + \left(0.31942702700572795 - 3.241970391368047 \cdot \frac{1}{{z}^{2}}\right)} + 0.10203362558171805 \cdot \frac{y \cdot t}{{z}^{2} \cdot {\left(\left(3.7269864963038164 \cdot \frac{1}{z} + 0.31942702700572795\right) - 3.241970391368047 \cdot \frac{1}{{z}^{2}}\right)}^{2}}\right) \]

      3. metadata-eval 94.4%

         \[\leadsto x + \left(\frac{y}{\frac{\color{blue}{3.7269864963038164}}{z} + \left(0.31942702700572795 - 3.241970391368047 \cdot \frac{1}{{z}^{2}}\right)} + 0.10203362558171805 \cdot \frac{y \cdot t}{{z}^{2} \cdot {\left(\left(3.7269864963038164 \cdot \frac{1}{z} + 0.31942702700572795\right) - 3.241970391368047 \cdot \frac{1}{{z}^{2}}\right)}^{2}}\right) \]

      4. associate-+r- 94.4%

         \[\leadsto x + \left(\frac{y}{\color{blue}{\left(\frac{3.7269864963038164}{z} + 0.31942702700572795\right) - 3.241970391368047 \cdot \frac{1}{{z}^{2}}}} + 0.10203362558171805 \cdot \frac{y \cdot t}{{z}^{2} \cdot {\left(\left(3.7269864963038164 \cdot \frac{1}{z} + 0.31942702700572795\right) - 3.241970391368047 \cdot \frac{1}{{z}^{2}}\right)}^{2}}\right) \]

      5. +-commutative 94.4%

         \[\leadsto x + \left(\frac{y}{\color{blue}{\left(0.31942702700572795 + \frac{3.7269864963038164}{z}\right)} - 3.241970391368047 \cdot \frac{1}{{z}^{2}}} + 0.10203362558171805 \cdot \frac{y \cdot t}{{z}^{2} \cdot {\left(\left(3.7269864963038164 \cdot \frac{1}{z} + 0.31942702700572795\right) - 3.241970391368047 \cdot \frac{1}{{z}^{2}}\right)}^{2}}\right) \]

      6. associate-*r/ 94.4%

         \[\leadsto x + \left(\frac{y}{\left(0.31942702700572795 + \frac{3.7269864963038164}{z}\right) - \color{blue}{\frac{3.241970391368047 \cdot 1}{{z}^{2}}}} + 0.10203362558171805 \cdot \frac{y \cdot t}{{z}^{2} \cdot {\left(\left(3.7269864963038164 \cdot \frac{1}{z} + 0.31942702700572795\right) - 3.241970391368047 \cdot \frac{1}{{z}^{2}}\right)}^{2}}\right) \]

      7. metadata-eval 94.4%

         \[\leadsto x + \left(\frac{y}{\left(0.31942702700572795 + \frac{3.7269864963038164}{z}\right) - \frac{\color{blue}{3.241970391368047}}{{z}^{2}}} + 0.10203362558171805 \cdot \frac{y \cdot t}{{z}^{2} \cdot {\left(\left(3.7269864963038164 \cdot \frac{1}{z} + 0.31942702700572795\right) - 3.241970391368047 \cdot \frac{1}{{z}^{2}}\right)}^{2}}\right) \]

      8. unpow2 94.4%

         \[\leadsto x + \left(\frac{y}{\left(0.31942702700572795 + \frac{3.7269864963038164}{z}\right) - \frac{3.241970391368047}{\color{blue}{z \cdot z}}} + 0.10203362558171805 \cdot \frac{y \cdot t}{{z}^{2} \cdot {\left(\left(3.7269864963038164 \cdot \frac{1}{z} + 0.31942702700572795\right) - 3.241970391368047 \cdot \frac{1}{{z}^{2}}\right)}^{2}}\right) \]

      9. times-frac 97.6%

         \[\leadsto x + \left(\frac{y}{\left(0.31942702700572795 + \frac{3.7269864963038164}{z}\right) - \frac{3.241970391368047}{z \cdot z}} + 0.10203362558171805 \cdot \color{blue}{\left(\frac{y}{{z}^{2}} \cdot \frac{t}{{\left(\left(3.7269864963038164 \cdot \frac{1}{z} + 0.31942702700572795\right) - 3.241970391368047 \cdot \frac{1}{{z}^{2}}\right)}^{2}}\right)}\right) \]

      10. unpow2 97.6%

          \[\leadsto x + \left(\frac{y}{\left(0.31942702700572795 + \frac{3.7269864963038164}{z}\right) - \frac{3.241970391368047}{z \cdot z}} + 0.10203362558171805 \cdot \left(\frac{y}{\color{blue}{z \cdot z}} \cdot \frac{t}{{\left(\left(3.7269864963038164 \cdot \frac{1}{z} + 0.31942702700572795\right) - 3.241970391368047 \cdot \frac{1}{{z}^{2}}\right)}^{2}}\right)\right) \]

   9. Simplified 97.6%

      \[\leadsto x + \color{blue}{\left(\frac{y}{\left(0.31942702700572795 + \frac{3.7269864963038164}{z}\right) - \frac{3.241970391368047}{z \cdot z}} + 0.10203362558171805 \cdot \left(\frac{y}{z \cdot z} \cdot \frac{t}{{\left(\left(0.31942702700572795 + \frac{3.7269864963038164}{z}\right) - \frac{3.241970391368047}{z \cdot z}\right)}^{2}}\right)\right)} \]

   10. Taylor expanded in z around inf 94.4%

       \[\leadsto x + \left(\frac{y}{\left(0.31942702700572795 + \frac{3.7269864963038164}{z}\right) - \frac{3.241970391368047}{z \cdot z}} + 0.10203362558171805 \cdot \color{blue}{\left(9.800690647801265 \cdot \frac{y \cdot t}{{z}^{2}}\right)}\right) \]
   11. Step-by-step derivation

       1. associate-/l* 99.1%

          \[\leadsto x + \left(\frac{y}{\left(0.31942702700572795 + \frac{3.7269864963038164}{z}\right) - \frac{3.241970391368047}{z \cdot z}} + 0.10203362558171805 \cdot \left(9.800690647801265 \cdot \color{blue}{\frac{y}{\frac{{z}^{2}}{t}}}\right)\right) \]

       2. unpow2 99.1%

          \[\leadsto x + \left(\frac{y}{\left(0.31942702700572795 + \frac{3.7269864963038164}{z}\right) - \frac{3.241970391368047}{z \cdot z}} + 0.10203362558171805 \cdot \left(9.800690647801265 \cdot \frac{y}{\frac{\color{blue}{z \cdot z}}{t}}\right)\right) \]

   12. Simplified 99.1%

       \[\leadsto x + \left(\frac{y}{\left(0.31942702700572795 + \frac{3.7269864963038164}{z}\right) - \frac{3.241970391368047}{z \cdot z}} + 0.10203362558171805 \cdot \color{blue}{\left(9.800690647801265 \cdot \frac{y}{\frac{z \cdot z}{t}}\right)}\right) \]

   if -12.5999999999999996 < z < 1.05e20

   1. Initial program 99.8%

      \[x + \frac{y \cdot \left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b\right)}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} \]

   2. Taylor expanded in z around 0 97.0%

      \[\leadsto x + \frac{y \cdot \left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b\right)}{\color{blue}{11.9400905721 \cdot z} + 0.607771387771} \]
   3. Step-by-step derivation

      1. *-commutative 97.0%

         \[\leadsto x + \frac{y \cdot \left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b\right)}{\color{blue}{z \cdot 11.9400905721} + 0.607771387771} \]

   4. Simplified 97.0%

      \[\leadsto x + \frac{y \cdot \left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b\right)}{\color{blue}{z \cdot 11.9400905721} + 0.607771387771} \]
3. Recombined 2 regimes into one program.

4. Final simplification 98.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -12.6 \lor \neg \left(z \leq 1.05 \cdot 10^{+20}\right):\\ \;\;\;\;x + \left(\frac{y}{\left(0.31942702700572795 + \frac{3.7269864963038164}{z}\right) - \frac{3.241970391368047}{z \cdot z}} + 0.10203362558171805 \cdot \left(9.800690647801265 \cdot \frac{y}{\frac{z \cdot z}{t}}\right)\right)\\ \mathbf{else}:\\ \;\;\;\;x + \frac{y \cdot \left(z \cdot \left(z \cdot \left(z \cdot \left(z \cdot 3.13060547623 + 11.1667541262\right) + t\right) + a\right) + b\right)}{0.607771387771 + z \cdot 11.9400905721}\\ \end{array} \]

Alternative 6: 83.1% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -1.75 \cdot 10^{+26}:\\ \;\;\;\;x + \frac{y}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \frac{t \cdot -0.10203362558171805}{z \cdot z}\right)}\\ \mathbf{elif}\;z \leq 5.5 \cdot 10^{-48}:\\ \;\;\;\;x + y \cdot \left(b \cdot 1.6453555072203998\right)\\ \mathbf{elif}\;z \leq 1.46 \cdot 10^{+27}:\\ \;\;\;\;x + \frac{a \cdot \left(y \cdot z\right)}{0.607771387771 + z \cdot \left(11.9400905721 + z \cdot \left(31.4690115749 + z \cdot z\right)\right)}\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot 3.13060547623\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= z -1.75e+26)
   (+
    x
    (/
     y
     (+
      (/ 3.7269864963038164 z)
      (+ 0.31942702700572795 (/ (* t -0.10203362558171805) (* z z))))))
   (if (<= z 5.5e-48)
     (+ x (* y (* b 1.6453555072203998)))
     (if (<= z 1.46e+27)
       (+
        x
        (/
         (* a (* y z))
         (+
          0.607771387771
          (* z (+ 11.9400905721 (* z (+ 31.4690115749 (* z z))))))))
       (+ x (* y 3.13060547623))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (z <= -1.75e+26) {
		tmp = x + (y / ((3.7269864963038164 / z) + (0.31942702700572795 + ((t * -0.10203362558171805) / (z * z)))));
	} else if (z <= 5.5e-48) {
		tmp = x + (y * (b * 1.6453555072203998));
	} else if (z <= 1.46e+27) {
		tmp = x + ((a * (y * z)) / (0.607771387771 + (z * (11.9400905721 + (z * (31.4690115749 + (z * z)))))));
	} else {
		tmp = x + (y * 3.13060547623);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if (z <= (-1.75d+26)) then
        tmp = x + (y / ((3.7269864963038164d0 / z) + (0.31942702700572795d0 + ((t * (-0.10203362558171805d0)) / (z * z)))))
    else if (z <= 5.5d-48) then
        tmp = x + (y * (b * 1.6453555072203998d0))
    else if (z <= 1.46d+27) then
        tmp = x + ((a * (y * z)) / (0.607771387771d0 + (z * (11.9400905721d0 + (z * (31.4690115749d0 + (z * z)))))))
    else
        tmp = x + (y * 3.13060547623d0)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (z <= -1.75e+26) {
		tmp = x + (y / ((3.7269864963038164 / z) + (0.31942702700572795 + ((t * -0.10203362558171805) / (z * z)))));
	} else if (z <= 5.5e-48) {
		tmp = x + (y * (b * 1.6453555072203998));
	} else if (z <= 1.46e+27) {
		tmp = x + ((a * (y * z)) / (0.607771387771 + (z * (11.9400905721 + (z * (31.4690115749 + (z * z)))))));
	} else {
		tmp = x + (y * 3.13060547623);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if z <= -1.75e+26:
		tmp = x + (y / ((3.7269864963038164 / z) + (0.31942702700572795 + ((t * -0.10203362558171805) / (z * z)))))
	elif z <= 5.5e-48:
		tmp = x + (y * (b * 1.6453555072203998))
	elif z <= 1.46e+27:
		tmp = x + ((a * (y * z)) / (0.607771387771 + (z * (11.9400905721 + (z * (31.4690115749 + (z * z)))))))
	else:
		tmp = x + (y * 3.13060547623)
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (z <= -1.75e+26)
		tmp = Float64(x + Float64(y / Float64(Float64(3.7269864963038164 / z) + Float64(0.31942702700572795 + Float64(Float64(t * -0.10203362558171805) / Float64(z * z))))));
	elseif (z <= 5.5e-48)
		tmp = Float64(x + Float64(y * Float64(b * 1.6453555072203998)));
	elseif (z <= 1.46e+27)
		tmp = Float64(x + Float64(Float64(a * Float64(y * z)) / Float64(0.607771387771 + Float64(z * Float64(11.9400905721 + Float64(z * Float64(31.4690115749 + Float64(z * z))))))));
	else
		tmp = Float64(x + Float64(y * 3.13060547623));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if (z <= -1.75e+26)
		tmp = x + (y / ((3.7269864963038164 / z) + (0.31942702700572795 + ((t * -0.10203362558171805) / (z * z)))));
	elseif (z <= 5.5e-48)
		tmp = x + (y * (b * 1.6453555072203998));
	elseif (z <= 1.46e+27)
		tmp = x + ((a * (y * z)) / (0.607771387771 + (z * (11.9400905721 + (z * (31.4690115749 + (z * z)))))));
	else
		tmp = x + (y * 3.13060547623);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[z, -1.75e+26], N[(x + N[(y / N[(N[(3.7269864963038164 / z), $MachinePrecision] + N[(0.31942702700572795 + N[(N[(t * -0.10203362558171805), $MachinePrecision] / N[(z * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 5.5e-48], N[(x + N[(y * N[(b * 1.6453555072203998), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 1.46e+27], N[(x + N[(N[(a * N[(y * z), $MachinePrecision]), $MachinePrecision] / N[(0.607771387771 + N[(z * N[(11.9400905721 + N[(z * N[(31.4690115749 + N[(z * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(y * 3.13060547623), $MachinePrecision]), $MachinePrecision]]]]

Derivation

1. Split input into 4 regimes

2. if z < -1.75e26

   1. Initial program 9.3%

      \[x + \frac{y \cdot \left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b\right)}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} \]
   2. Step-by-step derivation

      1. associate-/l* 13.5%

         \[\leadsto x + \color{blue}{\frac{y}{\frac{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771}{\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b}}} \]

      2. fma-def 13.5%

         \[\leadsto x + \frac{y}{\frac{\color{blue}{\mathsf{fma}\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721, z, 0.607771387771\right)}}{\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b}} \]

      3. fma-def 13.5%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749, z, 11.9400905721\right)}, z, 0.607771387771\right)}{\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b}} \]

      4. fma-def 13.5%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right)}, z, 11.9400905721\right), z, 0.607771387771\right)}{\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b}} \]

      5. fma-def 13.5%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\color{blue}{\mathsf{fma}\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a, z, b\right)}}} \]

      6. fma-def 13.5%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t, z, a\right)}, z, b\right)}} \]

      7. fma-def 13.5%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(z \cdot 3.13060547623 + 11.1667541262, z, t\right)}, z, a\right), z, b\right)}} \]

      8. fma-def 13.5%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(z, 3.13060547623, 11.1667541262\right)}, z, t\right), z, a\right), z, b\right)}} \]

   3. Simplified 13.5%

      \[\leadsto \color{blue}{x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z, 3.13060547623, 11.1667541262\right), z, t\right), z, a\right), z, b\right)}}} \]

   4. Taylor expanded in z around inf 95.0%

      \[\leadsto x + \frac{y}{\color{blue}{3.7269864963038164 \cdot \frac{1}{z} + \left(0.31942702700572795 + -1 \cdot \frac{3.241970391368047 + 0.10203362558171805 \cdot t}{{z}^{2}}\right)}} \]
   5. Step-by-step derivation

      1. associate-*r/ 95.0%

         \[\leadsto x + \frac{y}{\color{blue}{\frac{3.7269864963038164 \cdot 1}{z}} + \left(0.31942702700572795 + -1 \cdot \frac{3.241970391368047 + 0.10203362558171805 \cdot t}{{z}^{2}}\right)} \]

      2. metadata-eval 95.0%

         \[\leadsto x + \frac{y}{\frac{\color{blue}{3.7269864963038164}}{z} + \left(0.31942702700572795 + -1 \cdot \frac{3.241970391368047 + 0.10203362558171805 \cdot t}{{z}^{2}}\right)} \]

      3. mul-1-neg 95.0%

         \[\leadsto x + \frac{y}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \color{blue}{\left(-\frac{3.241970391368047 + 0.10203362558171805 \cdot t}{{z}^{2}}\right)}\right)} \]

      4. *-commutative 95.0%

         \[\leadsto x + \frac{y}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \left(-\frac{3.241970391368047 + \color{blue}{t \cdot 0.10203362558171805}}{{z}^{2}}\right)\right)} \]

      5. unpow2 95.0%

         \[\leadsto x + \frac{y}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \left(-\frac{3.241970391368047 + t \cdot 0.10203362558171805}{\color{blue}{z \cdot z}}\right)\right)} \]

   6. Simplified 95.0%

      \[\leadsto x + \frac{y}{\color{blue}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \left(-\frac{3.241970391368047 + t \cdot 0.10203362558171805}{z \cdot z}\right)\right)}} \]

   7. Taylor expanded in t around inf 95.0%

      \[\leadsto x + \frac{y}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \left(-\color{blue}{0.10203362558171805 \cdot \frac{t}{{z}^{2}}}\right)\right)} \]
   8. Step-by-step derivation

      1. associate-*r/ 95.0%

         \[\leadsto x + \frac{y}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \left(-\color{blue}{\frac{0.10203362558171805 \cdot t}{{z}^{2}}}\right)\right)} \]

      2. unpow2 95.0%

         \[\leadsto x + \frac{y}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \left(-\frac{0.10203362558171805 \cdot t}{\color{blue}{z \cdot z}}\right)\right)} \]

   9. Simplified 95.0%

      \[\leadsto x + \frac{y}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \left(-\color{blue}{\frac{0.10203362558171805 \cdot t}{z \cdot z}}\right)\right)} \]

   10. Taylor expanded in y around 0 95.0%

       \[\leadsto x + \color{blue}{\frac{y}{\left(3.7269864963038164 \cdot \frac{1}{z} + 0.31942702700572795\right) - 0.10203362558171805 \cdot \frac{t}{{z}^{2}}}} \]
   11. Step-by-step derivation

       1. associate--l+ 95.0%

          \[\leadsto x + \frac{y}{\color{blue}{3.7269864963038164 \cdot \frac{1}{z} + \left(0.31942702700572795 - 0.10203362558171805 \cdot \frac{t}{{z}^{2}}\right)}} \]

       2. associate-*r/ 95.0%

          \[\leadsto x + \frac{y}{\color{blue}{\frac{3.7269864963038164 \cdot 1}{z}} + \left(0.31942702700572795 - 0.10203362558171805 \cdot \frac{t}{{z}^{2}}\right)} \]

       3. metadata-eval 95.0%

          \[\leadsto x + \frac{y}{\frac{\color{blue}{3.7269864963038164}}{z} + \left(0.31942702700572795 - 0.10203362558171805 \cdot \frac{t}{{z}^{2}}\right)} \]

       4. cancel-sign-sub-inv 95.0%

          \[\leadsto x + \frac{y}{\frac{3.7269864963038164}{z} + \color{blue}{\left(0.31942702700572795 + \left(-0.10203362558171805\right) \cdot \frac{t}{{z}^{2}}\right)}} \]

       5. associate-*r/ 95.0%

          \[\leadsto x + \frac{y}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \color{blue}{\frac{\left(-0.10203362558171805\right) \cdot t}{{z}^{2}}}\right)} \]

       6. metadata-eval 95.0%

          \[\leadsto x + \frac{y}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \frac{\color{blue}{-0.10203362558171805} \cdot t}{{z}^{2}}\right)} \]

       7. unpow2 95.0%

          \[\leadsto x + \frac{y}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \frac{-0.10203362558171805 \cdot t}{\color{blue}{z \cdot z}}\right)} \]

   12. Simplified 95.0%

       \[\leadsto x + \color{blue}{\frac{y}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \frac{-0.10203362558171805 \cdot t}{z \cdot z}\right)}} \]

   if -1.75e26 < z < 5.50000000000000047e-48

   1. Initial program 99.8%

      \[x + \frac{y \cdot \left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b\right)}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} \]
   2. Step-by-step derivation

      1. associate-/l* 99.8%

         \[\leadsto x + \color{blue}{\frac{y}{\frac{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771}{\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b}}} \]

      2. fma-def 99.8%

         \[\leadsto x + \frac{y}{\frac{\color{blue}{\mathsf{fma}\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721, z, 0.607771387771\right)}}{\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b}} \]

      3. fma-def 99.8%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749, z, 11.9400905721\right)}, z, 0.607771387771\right)}{\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b}} \]

      4. fma-def 99.8%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right)}, z, 11.9400905721\right), z, 0.607771387771\right)}{\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b}} \]

      5. fma-def 99.8%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\color{blue}{\mathsf{fma}\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a, z, b\right)}}} \]

      6. fma-def 99.8%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t, z, a\right)}, z, b\right)}} \]

      7. fma-def 99.8%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(z \cdot 3.13060547623 + 11.1667541262, z, t\right)}, z, a\right), z, b\right)}} \]

      8. fma-def 99.8%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(z, 3.13060547623, 11.1667541262\right)}, z, t\right), z, a\right), z, b\right)}} \]

   3. Simplified 99.8%

      \[\leadsto \color{blue}{x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z, 3.13060547623, 11.1667541262\right), z, t\right), z, a\right), z, b\right)}}} \]

   4. Taylor expanded in z around 0 83.1%

      \[\leadsto x + \frac{y}{\color{blue}{\frac{0.607771387771}{b}}} \]

   5. Taylor expanded in y around 0 83.2%

      \[\leadsto x + \color{blue}{1.6453555072203998 \cdot \left(y \cdot b\right)} \]
   6. Step-by-step derivation

      1. *-commutative 83.2%

         \[\leadsto x + \color{blue}{\left(y \cdot b\right) \cdot 1.6453555072203998} \]

      2. associate-*l* 83.2%

         \[\leadsto x + \color{blue}{y \cdot \left(b \cdot 1.6453555072203998\right)} \]

      3. *-commutative 83.2%

         \[\leadsto x + y \cdot \color{blue}{\left(1.6453555072203998 \cdot b\right)} \]

   7. Simplified 83.2%

      \[\leadsto x + \color{blue}{y \cdot \left(1.6453555072203998 \cdot b\right)} \]

   if 5.50000000000000047e-48 < z < 1.46000000000000002e27

   1. Initial program 99.6%

      \[x + \frac{y \cdot \left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b\right)}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} \]

   2. Taylor expanded in a around inf 82.9%

      \[\leadsto x + \frac{\color{blue}{y \cdot \left(a \cdot z\right)}}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} \]
   3. Step-by-step derivation

      1. associate-*r* 75.5%

         \[\leadsto x + \frac{\color{blue}{\left(y \cdot a\right) \cdot z}}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} \]

      2. *-commutative 75.5%

         \[\leadsto x + \frac{\color{blue}{\left(a \cdot y\right)} \cdot z}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} \]

      3. associate-*r* 82.6%

         \[\leadsto x + \frac{\color{blue}{a \cdot \left(y \cdot z\right)}}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} \]

   4. Simplified 82.6%

      \[\leadsto x + \frac{\color{blue}{a \cdot \left(y \cdot z\right)}}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} \]

   5. Taylor expanded in z around inf 82.5%

      \[\leadsto x + \frac{a \cdot \left(y \cdot z\right)}{\left(\left(\color{blue}{{z}^{2}} + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} \]
   6. Step-by-step derivation

      1. unpow2 82.5%

         \[\leadsto x + \frac{a \cdot \left(y \cdot z\right)}{\left(\left(\color{blue}{z \cdot z} + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} \]

   7. Simplified 82.5%

      \[\leadsto x + \frac{a \cdot \left(y \cdot z\right)}{\left(\left(\color{blue}{z \cdot z} + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} \]

   if 1.46000000000000002e27 < z

   1. Initial program 9.9%

      \[x + \frac{y \cdot \left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b\right)}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} \]
   2. Step-by-step derivation

      1. +-commutative 9.9%

         \[\leadsto \color{blue}{\frac{y \cdot \left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b\right)}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} + x} \]

      2. associate-*l/ 19.1%

         \[\leadsto \color{blue}{\frac{y}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} \cdot \left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b\right)} + x \]

      3. *-commutative 19.1%

         \[\leadsto \color{blue}{\left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b\right) \cdot \frac{y}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771}} + x \]

      4. fma-def 19.1%

         \[\leadsto \color{blue}{\mathsf{fma}\left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b, \frac{y}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771}, x\right)} \]

   3. Simplified 19.1%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(z, \mathsf{fma}\left(z, \mathsf{fma}\left(z, \mathsf{fma}\left(z, 3.13060547623, 11.1667541262\right), t\right), a\right), b\right), \frac{y}{\mathsf{fma}\left(z, \mathsf{fma}\left(z, \mathsf{fma}\left(z, z + 15.234687407, 31.4690115749\right), 11.9400905721\right), 0.607771387771\right)}, x\right)} \]

   4. Taylor expanded in z around inf 91.3%

      \[\leadsto \color{blue}{3.13060547623 \cdot y + x} \]
3. Recombined 4 regimes into one program.

4. Final simplification 88.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -1.75 \cdot 10^{+26}:\\ \;\;\;\;x + \frac{y}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \frac{t \cdot -0.10203362558171805}{z \cdot z}\right)}\\ \mathbf{elif}\;z \leq 5.5 \cdot 10^{-48}:\\ \;\;\;\;x + y \cdot \left(b \cdot 1.6453555072203998\right)\\ \mathbf{elif}\;z \leq 1.46 \cdot 10^{+27}:\\ \;\;\;\;x + \frac{a \cdot \left(y \cdot z\right)}{0.607771387771 + z \cdot \left(11.9400905721 + z \cdot \left(31.4690115749 + z \cdot z\right)\right)}\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot 3.13060547623\\ \end{array} \]

Alternative 7: 83.0% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -1.3 \cdot 10^{+26}:\\ \;\;\;\;x + \frac{y}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \frac{t \cdot -0.10203362558171805}{z \cdot z}\right)}\\ \mathbf{elif}\;z \leq 1.95 \cdot 10^{-50}:\\ \;\;\;\;x + y \cdot \left(b \cdot 1.6453555072203998\right)\\ \mathbf{elif}\;z \leq 7.6 \cdot 10^{+21}:\\ \;\;\;\;x + \frac{y \cdot \left(z \cdot a\right)}{0.607771387771 + z \cdot \left(11.9400905721 + z \cdot \left(31.4690115749 + z \cdot z\right)\right)}\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot 3.13060547623\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= z -1.3e+26)
   (+
    x
    (/
     y
     (+
      (/ 3.7269864963038164 z)
      (+ 0.31942702700572795 (/ (* t -0.10203362558171805) (* z z))))))
   (if (<= z 1.95e-50)
     (+ x (* y (* b 1.6453555072203998)))
     (if (<= z 7.6e+21)
       (+
        x
        (/
         (* y (* z a))
         (+
          0.607771387771
          (* z (+ 11.9400905721 (* z (+ 31.4690115749 (* z z))))))))
       (+ x (* y 3.13060547623))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (z <= -1.3e+26) {
		tmp = x + (y / ((3.7269864963038164 / z) + (0.31942702700572795 + ((t * -0.10203362558171805) / (z * z)))));
	} else if (z <= 1.95e-50) {
		tmp = x + (y * (b * 1.6453555072203998));
	} else if (z <= 7.6e+21) {
		tmp = x + ((y * (z * a)) / (0.607771387771 + (z * (11.9400905721 + (z * (31.4690115749 + (z * z)))))));
	} else {
		tmp = x + (y * 3.13060547623);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if (z <= (-1.3d+26)) then
        tmp = x + (y / ((3.7269864963038164d0 / z) + (0.31942702700572795d0 + ((t * (-0.10203362558171805d0)) / (z * z)))))
    else if (z <= 1.95d-50) then
        tmp = x + (y * (b * 1.6453555072203998d0))
    else if (z <= 7.6d+21) then
        tmp = x + ((y * (z * a)) / (0.607771387771d0 + (z * (11.9400905721d0 + (z * (31.4690115749d0 + (z * z)))))))
    else
        tmp = x + (y * 3.13060547623d0)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (z <= -1.3e+26) {
		tmp = x + (y / ((3.7269864963038164 / z) + (0.31942702700572795 + ((t * -0.10203362558171805) / (z * z)))));
	} else if (z <= 1.95e-50) {
		tmp = x + (y * (b * 1.6453555072203998));
	} else if (z <= 7.6e+21) {
		tmp = x + ((y * (z * a)) / (0.607771387771 + (z * (11.9400905721 + (z * (31.4690115749 + (z * z)))))));
	} else {
		tmp = x + (y * 3.13060547623);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if z <= -1.3e+26:
		tmp = x + (y / ((3.7269864963038164 / z) + (0.31942702700572795 + ((t * -0.10203362558171805) / (z * z)))))
	elif z <= 1.95e-50:
		tmp = x + (y * (b * 1.6453555072203998))
	elif z <= 7.6e+21:
		tmp = x + ((y * (z * a)) / (0.607771387771 + (z * (11.9400905721 + (z * (31.4690115749 + (z * z)))))))
	else:
		tmp = x + (y * 3.13060547623)
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (z <= -1.3e+26)
		tmp = Float64(x + Float64(y / Float64(Float64(3.7269864963038164 / z) + Float64(0.31942702700572795 + Float64(Float64(t * -0.10203362558171805) / Float64(z * z))))));
	elseif (z <= 1.95e-50)
		tmp = Float64(x + Float64(y * Float64(b * 1.6453555072203998)));
	elseif (z <= 7.6e+21)
		tmp = Float64(x + Float64(Float64(y * Float64(z * a)) / Float64(0.607771387771 + Float64(z * Float64(11.9400905721 + Float64(z * Float64(31.4690115749 + Float64(z * z))))))));
	else
		tmp = Float64(x + Float64(y * 3.13060547623));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if (z <= -1.3e+26)
		tmp = x + (y / ((3.7269864963038164 / z) + (0.31942702700572795 + ((t * -0.10203362558171805) / (z * z)))));
	elseif (z <= 1.95e-50)
		tmp = x + (y * (b * 1.6453555072203998));
	elseif (z <= 7.6e+21)
		tmp = x + ((y * (z * a)) / (0.607771387771 + (z * (11.9400905721 + (z * (31.4690115749 + (z * z)))))));
	else
		tmp = x + (y * 3.13060547623);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[z, -1.3e+26], N[(x + N[(y / N[(N[(3.7269864963038164 / z), $MachinePrecision] + N[(0.31942702700572795 + N[(N[(t * -0.10203362558171805), $MachinePrecision] / N[(z * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 1.95e-50], N[(x + N[(y * N[(b * 1.6453555072203998), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 7.6e+21], N[(x + N[(N[(y * N[(z * a), $MachinePrecision]), $MachinePrecision] / N[(0.607771387771 + N[(z * N[(11.9400905721 + N[(z * N[(31.4690115749 + N[(z * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(y * 3.13060547623), $MachinePrecision]), $MachinePrecision]]]]

Derivation

1. Split input into 4 regimes

2. if z < -1.30000000000000001e26

   1. Initial program 9.3%

      \[x + \frac{y \cdot \left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b\right)}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} \]
   2. Step-by-step derivation

      1. associate-/l* 13.5%

         \[\leadsto x + \color{blue}{\frac{y}{\frac{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771}{\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b}}} \]

      2. fma-def 13.5%

         \[\leadsto x + \frac{y}{\frac{\color{blue}{\mathsf{fma}\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721, z, 0.607771387771\right)}}{\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b}} \]

      3. fma-def 13.5%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749, z, 11.9400905721\right)}, z, 0.607771387771\right)}{\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b}} \]

      4. fma-def 13.5%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right)}, z, 11.9400905721\right), z, 0.607771387771\right)}{\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b}} \]

      5. fma-def 13.5%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\color{blue}{\mathsf{fma}\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a, z, b\right)}}} \]

      6. fma-def 13.5%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t, z, a\right)}, z, b\right)}} \]

      7. fma-def 13.5%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(z \cdot 3.13060547623 + 11.1667541262, z, t\right)}, z, a\right), z, b\right)}} \]

      8. fma-def 13.5%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(z, 3.13060547623, 11.1667541262\right)}, z, t\right), z, a\right), z, b\right)}} \]

   3. Simplified 13.5%

      \[\leadsto \color{blue}{x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z, 3.13060547623, 11.1667541262\right), z, t\right), z, a\right), z, b\right)}}} \]

   4. Taylor expanded in z around inf 95.0%

      \[\leadsto x + \frac{y}{\color{blue}{3.7269864963038164 \cdot \frac{1}{z} + \left(0.31942702700572795 + -1 \cdot \frac{3.241970391368047 + 0.10203362558171805 \cdot t}{{z}^{2}}\right)}} \]
   5. Step-by-step derivation

      1. associate-*r/ 95.0%

         \[\leadsto x + \frac{y}{\color{blue}{\frac{3.7269864963038164 \cdot 1}{z}} + \left(0.31942702700572795 + -1 \cdot \frac{3.241970391368047 + 0.10203362558171805 \cdot t}{{z}^{2}}\right)} \]

      2. metadata-eval 95.0%

         \[\leadsto x + \frac{y}{\frac{\color{blue}{3.7269864963038164}}{z} + \left(0.31942702700572795 + -1 \cdot \frac{3.241970391368047 + 0.10203362558171805 \cdot t}{{z}^{2}}\right)} \]

      3. mul-1-neg 95.0%

         \[\leadsto x + \frac{y}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \color{blue}{\left(-\frac{3.241970391368047 + 0.10203362558171805 \cdot t}{{z}^{2}}\right)}\right)} \]

      4. *-commutative 95.0%

         \[\leadsto x + \frac{y}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \left(-\frac{3.241970391368047 + \color{blue}{t \cdot 0.10203362558171805}}{{z}^{2}}\right)\right)} \]

      5. unpow2 95.0%

         \[\leadsto x + \frac{y}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \left(-\frac{3.241970391368047 + t \cdot 0.10203362558171805}{\color{blue}{z \cdot z}}\right)\right)} \]

   6. Simplified 95.0%

      \[\leadsto x + \frac{y}{\color{blue}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \left(-\frac{3.241970391368047 + t \cdot 0.10203362558171805}{z \cdot z}\right)\right)}} \]

   7. Taylor expanded in t around inf 95.0%

      \[\leadsto x + \frac{y}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \left(-\color{blue}{0.10203362558171805 \cdot \frac{t}{{z}^{2}}}\right)\right)} \]
   8. Step-by-step derivation

      1. associate-*r/ 95.0%

         \[\leadsto x + \frac{y}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \left(-\color{blue}{\frac{0.10203362558171805 \cdot t}{{z}^{2}}}\right)\right)} \]

      2. unpow2 95.0%

         \[\leadsto x + \frac{y}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \left(-\frac{0.10203362558171805 \cdot t}{\color{blue}{z \cdot z}}\right)\right)} \]

   9. Simplified 95.0%

      \[\leadsto x + \frac{y}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \left(-\color{blue}{\frac{0.10203362558171805 \cdot t}{z \cdot z}}\right)\right)} \]

   10. Taylor expanded in y around 0 95.0%

       \[\leadsto x + \color{blue}{\frac{y}{\left(3.7269864963038164 \cdot \frac{1}{z} + 0.31942702700572795\right) - 0.10203362558171805 \cdot \frac{t}{{z}^{2}}}} \]
   11. Step-by-step derivation

       1. associate--l+ 95.0%

          \[\leadsto x + \frac{y}{\color{blue}{3.7269864963038164 \cdot \frac{1}{z} + \left(0.31942702700572795 - 0.10203362558171805 \cdot \frac{t}{{z}^{2}}\right)}} \]

       2. associate-*r/ 95.0%

          \[\leadsto x + \frac{y}{\color{blue}{\frac{3.7269864963038164 \cdot 1}{z}} + \left(0.31942702700572795 - 0.10203362558171805 \cdot \frac{t}{{z}^{2}}\right)} \]

       3. metadata-eval 95.0%

          \[\leadsto x + \frac{y}{\frac{\color{blue}{3.7269864963038164}}{z} + \left(0.31942702700572795 - 0.10203362558171805 \cdot \frac{t}{{z}^{2}}\right)} \]

       4. cancel-sign-sub-inv 95.0%

          \[\leadsto x + \frac{y}{\frac{3.7269864963038164}{z} + \color{blue}{\left(0.31942702700572795 + \left(-0.10203362558171805\right) \cdot \frac{t}{{z}^{2}}\right)}} \]

       5. associate-*r/ 95.0%

          \[\leadsto x + \frac{y}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \color{blue}{\frac{\left(-0.10203362558171805\right) \cdot t}{{z}^{2}}}\right)} \]

       6. metadata-eval 95.0%

          \[\leadsto x + \frac{y}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \frac{\color{blue}{-0.10203362558171805} \cdot t}{{z}^{2}}\right)} \]

       7. unpow2 95.0%

          \[\leadsto x + \frac{y}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \frac{-0.10203362558171805 \cdot t}{\color{blue}{z \cdot z}}\right)} \]

   12. Simplified 95.0%

       \[\leadsto x + \color{blue}{\frac{y}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \frac{-0.10203362558171805 \cdot t}{z \cdot z}\right)}} \]

   if -1.30000000000000001e26 < z < 1.9500000000000001e-50

   1. Initial program 99.8%

      \[x + \frac{y \cdot \left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b\right)}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} \]
   2. Step-by-step derivation

      1. associate-/l* 99.8%

         \[\leadsto x + \color{blue}{\frac{y}{\frac{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771}{\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b}}} \]

      2. fma-def 99.8%

         \[\leadsto x + \frac{y}{\frac{\color{blue}{\mathsf{fma}\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721, z, 0.607771387771\right)}}{\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b}} \]

      3. fma-def 99.8%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749, z, 11.9400905721\right)}, z, 0.607771387771\right)}{\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b}} \]

      4. fma-def 99.8%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right)}, z, 11.9400905721\right), z, 0.607771387771\right)}{\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b}} \]

      5. fma-def 99.8%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\color{blue}{\mathsf{fma}\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a, z, b\right)}}} \]

      6. fma-def 99.8%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t, z, a\right)}, z, b\right)}} \]

      7. fma-def 99.8%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(z \cdot 3.13060547623 + 11.1667541262, z, t\right)}, z, a\right), z, b\right)}} \]

      8. fma-def 99.8%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(z, 3.13060547623, 11.1667541262\right)}, z, t\right), z, a\right), z, b\right)}} \]

   3. Simplified 99.8%

      \[\leadsto \color{blue}{x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z, 3.13060547623, 11.1667541262\right), z, t\right), z, a\right), z, b\right)}}} \]

   4. Taylor expanded in z around 0 83.1%

      \[\leadsto x + \frac{y}{\color{blue}{\frac{0.607771387771}{b}}} \]

   5. Taylor expanded in y around 0 83.2%

      \[\leadsto x + \color{blue}{1.6453555072203998 \cdot \left(y \cdot b\right)} \]
   6. Step-by-step derivation

      1. *-commutative 83.2%

         \[\leadsto x + \color{blue}{\left(y \cdot b\right) \cdot 1.6453555072203998} \]

      2. associate-*l* 83.2%

         \[\leadsto x + \color{blue}{y \cdot \left(b \cdot 1.6453555072203998\right)} \]

      3. *-commutative 83.2%

         \[\leadsto x + y \cdot \color{blue}{\left(1.6453555072203998 \cdot b\right)} \]

   7. Simplified 83.2%

      \[\leadsto x + \color{blue}{y \cdot \left(1.6453555072203998 \cdot b\right)} \]

   if 1.9500000000000001e-50 < z < 7.6e21

   1. Initial program 99.6%

      \[x + \frac{y \cdot \left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b\right)}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} \]

   2. Taylor expanded in a around inf 82.9%

      \[\leadsto x + \frac{\color{blue}{y \cdot \left(a \cdot z\right)}}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} \]
   3. Step-by-step derivation

      1. associate-*r* 75.5%

         \[\leadsto x + \frac{\color{blue}{\left(y \cdot a\right) \cdot z}}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} \]

      2. *-commutative 75.5%

         \[\leadsto x + \frac{\color{blue}{\left(a \cdot y\right)} \cdot z}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} \]

      3. associate-*r* 82.6%

         \[\leadsto x + \frac{\color{blue}{a \cdot \left(y \cdot z\right)}}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} \]

   4. Simplified 82.6%

      \[\leadsto x + \frac{\color{blue}{a \cdot \left(y \cdot z\right)}}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} \]

   5. Taylor expanded in z around inf 82.5%

      \[\leadsto x + \frac{a \cdot \left(y \cdot z\right)}{\left(\left(\color{blue}{{z}^{2}} + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} \]
   6. Step-by-step derivation

      1. unpow2 82.5%

         \[\leadsto x + \frac{a \cdot \left(y \cdot z\right)}{\left(\left(\color{blue}{z \cdot z} + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} \]

   7. Simplified 82.5%

      \[\leadsto x + \frac{a \cdot \left(y \cdot z\right)}{\left(\left(\color{blue}{z \cdot z} + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} \]

   8. Taylor expanded in a around 0 82.9%

      \[\leadsto x + \frac{\color{blue}{y \cdot \left(a \cdot z\right)}}{\left(\left(z \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} \]
   9. Step-by-step derivation

      1. *-commutative 82.9%

         \[\leadsto x + \frac{y \cdot \color{blue}{\left(z \cdot a\right)}}{\left(\left(z \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} \]

   10. Simplified 82.9%

       \[\leadsto x + \frac{\color{blue}{y \cdot \left(z \cdot a\right)}}{\left(\left(z \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} \]

   if 7.6e21 < z

   1. Initial program 9.9%

      \[x + \frac{y \cdot \left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b\right)}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} \]
   2. Step-by-step derivation

      1. +-commutative 9.9%

         \[\leadsto \color{blue}{\frac{y \cdot \left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b\right)}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} + x} \]

      2. associate-*l/ 19.1%

         \[\leadsto \color{blue}{\frac{y}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} \cdot \left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b\right)} + x \]

      3. *-commutative 19.1%

         \[\leadsto \color{blue}{\left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b\right) \cdot \frac{y}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771}} + x \]

      4. fma-def 19.1%

         \[\leadsto \color{blue}{\mathsf{fma}\left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b, \frac{y}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771}, x\right)} \]

   3. Simplified 19.1%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(z, \mathsf{fma}\left(z, \mathsf{fma}\left(z, \mathsf{fma}\left(z, 3.13060547623, 11.1667541262\right), t\right), a\right), b\right), \frac{y}{\mathsf{fma}\left(z, \mathsf{fma}\left(z, \mathsf{fma}\left(z, z + 15.234687407, 31.4690115749\right), 11.9400905721\right), 0.607771387771\right)}, x\right)} \]

   4. Taylor expanded in z around inf 91.3%

      \[\leadsto \color{blue}{3.13060547623 \cdot y + x} \]
3. Recombined 4 regimes into one program.

4. Final simplification 88.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -1.3 \cdot 10^{+26}:\\ \;\;\;\;x + \frac{y}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \frac{t \cdot -0.10203362558171805}{z \cdot z}\right)}\\ \mathbf{elif}\;z \leq 1.95 \cdot 10^{-50}:\\ \;\;\;\;x + y \cdot \left(b \cdot 1.6453555072203998\right)\\ \mathbf{elif}\;z \leq 7.6 \cdot 10^{+21}:\\ \;\;\;\;x + \frac{y \cdot \left(z \cdot a\right)}{0.607771387771 + z \cdot \left(11.9400905721 + z \cdot \left(31.4690115749 + z \cdot z\right)\right)}\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot 3.13060547623\\ \end{array} \]

Alternative 8: 84.0% accurate, 1.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -1.15 \cdot 10^{+24}:\\ \;\;\;\;x + \frac{y}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \frac{t \cdot -0.10203362558171805}{z \cdot z}\right)}\\ \mathbf{elif}\;z \leq 1.12 \cdot 10^{+18}:\\ \;\;\;\;z \cdot \left(1.6453555072203998 \cdot \left(y \cdot a\right) + -32.324150453290734 \cdot \left(y \cdot b\right)\right) + \left(x + 1.6453555072203998 \cdot \left(y \cdot b\right)\right)\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot 3.13060547623\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= z -1.15e+24)
   (+
    x
    (/
     y
     (+
      (/ 3.7269864963038164 z)
      (+ 0.31942702700572795 (/ (* t -0.10203362558171805) (* z z))))))
   (if (<= z 1.12e+18)
     (+
      (* z (+ (* 1.6453555072203998 (* y a)) (* -32.324150453290734 (* y b))))
      (+ x (* 1.6453555072203998 (* y b))))
     (+ x (* y 3.13060547623)))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (z <= -1.15e+24) {
		tmp = x + (y / ((3.7269864963038164 / z) + (0.31942702700572795 + ((t * -0.10203362558171805) / (z * z)))));
	} else if (z <= 1.12e+18) {
		tmp = (z * ((1.6453555072203998 * (y * a)) + (-32.324150453290734 * (y * b)))) + (x + (1.6453555072203998 * (y * b)));
	} else {
		tmp = x + (y * 3.13060547623);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if (z <= (-1.15d+24)) then
        tmp = x + (y / ((3.7269864963038164d0 / z) + (0.31942702700572795d0 + ((t * (-0.10203362558171805d0)) / (z * z)))))
    else if (z <= 1.12d+18) then
        tmp = (z * ((1.6453555072203998d0 * (y * a)) + ((-32.324150453290734d0) * (y * b)))) + (x + (1.6453555072203998d0 * (y * b)))
    else
        tmp = x + (y * 3.13060547623d0)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (z <= -1.15e+24) {
		tmp = x + (y / ((3.7269864963038164 / z) + (0.31942702700572795 + ((t * -0.10203362558171805) / (z * z)))));
	} else if (z <= 1.12e+18) {
		tmp = (z * ((1.6453555072203998 * (y * a)) + (-32.324150453290734 * (y * b)))) + (x + (1.6453555072203998 * (y * b)));
	} else {
		tmp = x + (y * 3.13060547623);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if z <= -1.15e+24:
		tmp = x + (y / ((3.7269864963038164 / z) + (0.31942702700572795 + ((t * -0.10203362558171805) / (z * z)))))
	elif z <= 1.12e+18:
		tmp = (z * ((1.6453555072203998 * (y * a)) + (-32.324150453290734 * (y * b)))) + (x + (1.6453555072203998 * (y * b)))
	else:
		tmp = x + (y * 3.13060547623)
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (z <= -1.15e+24)
		tmp = Float64(x + Float64(y / Float64(Float64(3.7269864963038164 / z) + Float64(0.31942702700572795 + Float64(Float64(t * -0.10203362558171805) / Float64(z * z))))));
	elseif (z <= 1.12e+18)
		tmp = Float64(Float64(z * Float64(Float64(1.6453555072203998 * Float64(y * a)) + Float64(-32.324150453290734 * Float64(y * b)))) + Float64(x + Float64(1.6453555072203998 * Float64(y * b))));
	else
		tmp = Float64(x + Float64(y * 3.13060547623));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if (z <= -1.15e+24)
		tmp = x + (y / ((3.7269864963038164 / z) + (0.31942702700572795 + ((t * -0.10203362558171805) / (z * z)))));
	elseif (z <= 1.12e+18)
		tmp = (z * ((1.6453555072203998 * (y * a)) + (-32.324150453290734 * (y * b)))) + (x + (1.6453555072203998 * (y * b)));
	else
		tmp = x + (y * 3.13060547623);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[z, -1.15e+24], N[(x + N[(y / N[(N[(3.7269864963038164 / z), $MachinePrecision] + N[(0.31942702700572795 + N[(N[(t * -0.10203362558171805), $MachinePrecision] / N[(z * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 1.12e+18], N[(N[(z * N[(N[(1.6453555072203998 * N[(y * a), $MachinePrecision]), $MachinePrecision] + N[(-32.324150453290734 * N[(y * b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(x + N[(1.6453555072203998 * N[(y * b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(y * 3.13060547623), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if z < -1.15e24

   1. Initial program 9.3%

      \[x + \frac{y \cdot \left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b\right)}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} \]
   2. Step-by-step derivation

      1. associate-/l* 13.5%

         \[\leadsto x + \color{blue}{\frac{y}{\frac{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771}{\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b}}} \]

      2. fma-def 13.5%

         \[\leadsto x + \frac{y}{\frac{\color{blue}{\mathsf{fma}\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721, z, 0.607771387771\right)}}{\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b}} \]

      3. fma-def 13.5%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749, z, 11.9400905721\right)}, z, 0.607771387771\right)}{\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b}} \]

      4. fma-def 13.5%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right)}, z, 11.9400905721\right), z, 0.607771387771\right)}{\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b}} \]

      5. fma-def 13.5%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\color{blue}{\mathsf{fma}\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a, z, b\right)}}} \]

      6. fma-def 13.5%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t, z, a\right)}, z, b\right)}} \]

      7. fma-def 13.5%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(z \cdot 3.13060547623 + 11.1667541262, z, t\right)}, z, a\right), z, b\right)}} \]

      8. fma-def 13.5%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(z, 3.13060547623, 11.1667541262\right)}, z, t\right), z, a\right), z, b\right)}} \]

   3. Simplified 13.5%

      \[\leadsto \color{blue}{x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z, 3.13060547623, 11.1667541262\right), z, t\right), z, a\right), z, b\right)}}} \]

   4. Taylor expanded in z around inf 95.0%

      \[\leadsto x + \frac{y}{\color{blue}{3.7269864963038164 \cdot \frac{1}{z} + \left(0.31942702700572795 + -1 \cdot \frac{3.241970391368047 + 0.10203362558171805 \cdot t}{{z}^{2}}\right)}} \]
   5. Step-by-step derivation

      1. associate-*r/ 95.0%

         \[\leadsto x + \frac{y}{\color{blue}{\frac{3.7269864963038164 \cdot 1}{z}} + \left(0.31942702700572795 + -1 \cdot \frac{3.241970391368047 + 0.10203362558171805 \cdot t}{{z}^{2}}\right)} \]

      2. metadata-eval 95.0%

         \[\leadsto x + \frac{y}{\frac{\color{blue}{3.7269864963038164}}{z} + \left(0.31942702700572795 + -1 \cdot \frac{3.241970391368047 + 0.10203362558171805 \cdot t}{{z}^{2}}\right)} \]

      3. mul-1-neg 95.0%

         \[\leadsto x + \frac{y}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \color{blue}{\left(-\frac{3.241970391368047 + 0.10203362558171805 \cdot t}{{z}^{2}}\right)}\right)} \]

      4. *-commutative 95.0%

         \[\leadsto x + \frac{y}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \left(-\frac{3.241970391368047 + \color{blue}{t \cdot 0.10203362558171805}}{{z}^{2}}\right)\right)} \]

      5. unpow2 95.0%

         \[\leadsto x + \frac{y}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \left(-\frac{3.241970391368047 + t \cdot 0.10203362558171805}{\color{blue}{z \cdot z}}\right)\right)} \]

   6. Simplified 95.0%

      \[\leadsto x + \frac{y}{\color{blue}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \left(-\frac{3.241970391368047 + t \cdot 0.10203362558171805}{z \cdot z}\right)\right)}} \]

   7. Taylor expanded in t around inf 95.0%

      \[\leadsto x + \frac{y}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \left(-\color{blue}{0.10203362558171805 \cdot \frac{t}{{z}^{2}}}\right)\right)} \]
   8. Step-by-step derivation

      1. associate-*r/ 95.0%

         \[\leadsto x + \frac{y}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \left(-\color{blue}{\frac{0.10203362558171805 \cdot t}{{z}^{2}}}\right)\right)} \]

      2. unpow2 95.0%

         \[\leadsto x + \frac{y}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \left(-\frac{0.10203362558171805 \cdot t}{\color{blue}{z \cdot z}}\right)\right)} \]

   9. Simplified 95.0%

      \[\leadsto x + \frac{y}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \left(-\color{blue}{\frac{0.10203362558171805 \cdot t}{z \cdot z}}\right)\right)} \]

   10. Taylor expanded in y around 0 95.0%

       \[\leadsto x + \color{blue}{\frac{y}{\left(3.7269864963038164 \cdot \frac{1}{z} + 0.31942702700572795\right) - 0.10203362558171805 \cdot \frac{t}{{z}^{2}}}} \]
   11. Step-by-step derivation

       1. associate--l+ 95.0%

          \[\leadsto x + \frac{y}{\color{blue}{3.7269864963038164 \cdot \frac{1}{z} + \left(0.31942702700572795 - 0.10203362558171805 \cdot \frac{t}{{z}^{2}}\right)}} \]

       2. associate-*r/ 95.0%

          \[\leadsto x + \frac{y}{\color{blue}{\frac{3.7269864963038164 \cdot 1}{z}} + \left(0.31942702700572795 - 0.10203362558171805 \cdot \frac{t}{{z}^{2}}\right)} \]

       3. metadata-eval 95.0%

          \[\leadsto x + \frac{y}{\frac{\color{blue}{3.7269864963038164}}{z} + \left(0.31942702700572795 - 0.10203362558171805 \cdot \frac{t}{{z}^{2}}\right)} \]

       4. cancel-sign-sub-inv 95.0%

          \[\leadsto x + \frac{y}{\frac{3.7269864963038164}{z} + \color{blue}{\left(0.31942702700572795 + \left(-0.10203362558171805\right) \cdot \frac{t}{{z}^{2}}\right)}} \]

       5. associate-*r/ 95.0%

          \[\leadsto x + \frac{y}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \color{blue}{\frac{\left(-0.10203362558171805\right) \cdot t}{{z}^{2}}}\right)} \]

       6. metadata-eval 95.0%

          \[\leadsto x + \frac{y}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \frac{\color{blue}{-0.10203362558171805} \cdot t}{{z}^{2}}\right)} \]

       7. unpow2 95.0%

          \[\leadsto x + \frac{y}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \frac{-0.10203362558171805 \cdot t}{\color{blue}{z \cdot z}}\right)} \]

   12. Simplified 95.0%

       \[\leadsto x + \color{blue}{\frac{y}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \frac{-0.10203362558171805 \cdot t}{z \cdot z}\right)}} \]

   if -1.15e24 < z < 1.12e18

   1. Initial program 99.8%

      \[x + \frac{y \cdot \left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b\right)}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} \]
   2. Step-by-step derivation

      1. +-commutative 99.8%

         \[\leadsto \color{blue}{\frac{y \cdot \left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b\right)}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} + x} \]

      2. associate-*l/ 99.0%

         \[\leadsto \color{blue}{\frac{y}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} \cdot \left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b\right)} + x \]

      3. *-commutative 99.0%

         \[\leadsto \color{blue}{\left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b\right) \cdot \frac{y}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771}} + x \]

      4. fma-def 99.1%

         \[\leadsto \color{blue}{\mathsf{fma}\left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b, \frac{y}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771}, x\right)} \]

   3. Simplified 99.1%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(z, \mathsf{fma}\left(z, \mathsf{fma}\left(z, \mathsf{fma}\left(z, 3.13060547623, 11.1667541262\right), t\right), a\right), b\right), \frac{y}{\mathsf{fma}\left(z, \mathsf{fma}\left(z, \mathsf{fma}\left(z, z + 15.234687407, 31.4690115749\right), 11.9400905721\right), 0.607771387771\right)}, x\right)} \]

   4. Taylor expanded in z around 0 83.6%

      \[\leadsto \color{blue}{\left(1.6453555072203998 \cdot \left(y \cdot a\right) + -32.324150453290734 \cdot \left(y \cdot b\right)\right) \cdot z + \left(x + 1.6453555072203998 \cdot \left(y \cdot b\right)\right)} \]

   if 1.12e18 < z

   1. Initial program 11.5%

      \[x + \frac{y \cdot \left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b\right)}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} \]
   2. Step-by-step derivation

      1. +-commutative 11.5%

         \[\leadsto \color{blue}{\frac{y \cdot \left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b\right)}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} + x} \]

      2. associate-*l/ 20.6%

         \[\leadsto \color{blue}{\frac{y}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} \cdot \left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b\right)} + x \]

      3. *-commutative 20.6%

         \[\leadsto \color{blue}{\left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b\right) \cdot \frac{y}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771}} + x \]

      4. fma-def 20.6%

         \[\leadsto \color{blue}{\mathsf{fma}\left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b, \frac{y}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771}, x\right)} \]

   3. Simplified 20.6%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(z, \mathsf{fma}\left(z, \mathsf{fma}\left(z, \mathsf{fma}\left(z, 3.13060547623, 11.1667541262\right), t\right), a\right), b\right), \frac{y}{\mathsf{fma}\left(z, \mathsf{fma}\left(z, \mathsf{fma}\left(z, z + 15.234687407, 31.4690115749\right), 11.9400905721\right), 0.607771387771\right)}, x\right)} \]

   4. Taylor expanded in z around inf 89.8%

      \[\leadsto \color{blue}{3.13060547623 \cdot y + x} \]
3. Recombined 3 regimes into one program.

4. Final simplification 87.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -1.15 \cdot 10^{+24}:\\ \;\;\;\;x + \frac{y}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \frac{t \cdot -0.10203362558171805}{z \cdot z}\right)}\\ \mathbf{elif}\;z \leq 1.12 \cdot 10^{+18}:\\ \;\;\;\;z \cdot \left(1.6453555072203998 \cdot \left(y \cdot a\right) + -32.324150453290734 \cdot \left(y \cdot b\right)\right) + \left(x + 1.6453555072203998 \cdot \left(y \cdot b\right)\right)\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot 3.13060547623\\ \end{array} \]

Alternative 9: 82.7% accurate, 1.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -2.3 \cdot 10^{+25}:\\ \;\;\;\;x + \frac{y}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \frac{t \cdot -0.10203362558171805}{z \cdot z}\right)}\\ \mathbf{elif}\;z \leq 6 \cdot 10^{-30}:\\ \;\;\;\;x + y \cdot \left(b \cdot 1.6453555072203998\right)\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot 3.13060547623\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= z -2.3e+25)
   (+
    x
    (/
     y
     (+
      (/ 3.7269864963038164 z)
      (+ 0.31942702700572795 (/ (* t -0.10203362558171805) (* z z))))))
   (if (<= z 6e-30)
     (+ x (* y (* b 1.6453555072203998)))
     (+ x (* y 3.13060547623)))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (z <= -2.3e+25) {
		tmp = x + (y / ((3.7269864963038164 / z) + (0.31942702700572795 + ((t * -0.10203362558171805) / (z * z)))));
	} else if (z <= 6e-30) {
		tmp = x + (y * (b * 1.6453555072203998));
	} else {
		tmp = x + (y * 3.13060547623);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if (z <= (-2.3d+25)) then
        tmp = x + (y / ((3.7269864963038164d0 / z) + (0.31942702700572795d0 + ((t * (-0.10203362558171805d0)) / (z * z)))))
    else if (z <= 6d-30) then
        tmp = x + (y * (b * 1.6453555072203998d0))
    else
        tmp = x + (y * 3.13060547623d0)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (z <= -2.3e+25) {
		tmp = x + (y / ((3.7269864963038164 / z) + (0.31942702700572795 + ((t * -0.10203362558171805) / (z * z)))));
	} else if (z <= 6e-30) {
		tmp = x + (y * (b * 1.6453555072203998));
	} else {
		tmp = x + (y * 3.13060547623);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if z <= -2.3e+25:
		tmp = x + (y / ((3.7269864963038164 / z) + (0.31942702700572795 + ((t * -0.10203362558171805) / (z * z)))))
	elif z <= 6e-30:
		tmp = x + (y * (b * 1.6453555072203998))
	else:
		tmp = x + (y * 3.13060547623)
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (z <= -2.3e+25)
		tmp = Float64(x + Float64(y / Float64(Float64(3.7269864963038164 / z) + Float64(0.31942702700572795 + Float64(Float64(t * -0.10203362558171805) / Float64(z * z))))));
	elseif (z <= 6e-30)
		tmp = Float64(x + Float64(y * Float64(b * 1.6453555072203998)));
	else
		tmp = Float64(x + Float64(y * 3.13060547623));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if (z <= -2.3e+25)
		tmp = x + (y / ((3.7269864963038164 / z) + (0.31942702700572795 + ((t * -0.10203362558171805) / (z * z)))));
	elseif (z <= 6e-30)
		tmp = x + (y * (b * 1.6453555072203998));
	else
		tmp = x + (y * 3.13060547623);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[z, -2.3e+25], N[(x + N[(y / N[(N[(3.7269864963038164 / z), $MachinePrecision] + N[(0.31942702700572795 + N[(N[(t * -0.10203362558171805), $MachinePrecision] / N[(z * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 6e-30], N[(x + N[(y * N[(b * 1.6453555072203998), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(y * 3.13060547623), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if z < -2.2999999999999998e25

   1. Initial program 9.3%

      \[x + \frac{y \cdot \left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b\right)}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} \]
   2. Step-by-step derivation

      1. associate-/l* 13.5%

         \[\leadsto x + \color{blue}{\frac{y}{\frac{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771}{\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b}}} \]

      2. fma-def 13.5%

         \[\leadsto x + \frac{y}{\frac{\color{blue}{\mathsf{fma}\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721, z, 0.607771387771\right)}}{\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b}} \]

      3. fma-def 13.5%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749, z, 11.9400905721\right)}, z, 0.607771387771\right)}{\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b}} \]

      4. fma-def 13.5%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right)}, z, 11.9400905721\right), z, 0.607771387771\right)}{\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b}} \]

      5. fma-def 13.5%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\color{blue}{\mathsf{fma}\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a, z, b\right)}}} \]

      6. fma-def 13.5%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t, z, a\right)}, z, b\right)}} \]

      7. fma-def 13.5%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(z \cdot 3.13060547623 + 11.1667541262, z, t\right)}, z, a\right), z, b\right)}} \]

      8. fma-def 13.5%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(z, 3.13060547623, 11.1667541262\right)}, z, t\right), z, a\right), z, b\right)}} \]

   3. Simplified 13.5%

      \[\leadsto \color{blue}{x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z, 3.13060547623, 11.1667541262\right), z, t\right), z, a\right), z, b\right)}}} \]

   4. Taylor expanded in z around inf 95.0%

      \[\leadsto x + \frac{y}{\color{blue}{3.7269864963038164 \cdot \frac{1}{z} + \left(0.31942702700572795 + -1 \cdot \frac{3.241970391368047 + 0.10203362558171805 \cdot t}{{z}^{2}}\right)}} \]
   5. Step-by-step derivation

      1. associate-*r/ 95.0%

         \[\leadsto x + \frac{y}{\color{blue}{\frac{3.7269864963038164 \cdot 1}{z}} + \left(0.31942702700572795 + -1 \cdot \frac{3.241970391368047 + 0.10203362558171805 \cdot t}{{z}^{2}}\right)} \]

      2. metadata-eval 95.0%

         \[\leadsto x + \frac{y}{\frac{\color{blue}{3.7269864963038164}}{z} + \left(0.31942702700572795 + -1 \cdot \frac{3.241970391368047 + 0.10203362558171805 \cdot t}{{z}^{2}}\right)} \]

      3. mul-1-neg 95.0%

         \[\leadsto x + \frac{y}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \color{blue}{\left(-\frac{3.241970391368047 + 0.10203362558171805 \cdot t}{{z}^{2}}\right)}\right)} \]

      4. *-commutative 95.0%

         \[\leadsto x + \frac{y}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \left(-\frac{3.241970391368047 + \color{blue}{t \cdot 0.10203362558171805}}{{z}^{2}}\right)\right)} \]

      5. unpow2 95.0%

         \[\leadsto x + \frac{y}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \left(-\frac{3.241970391368047 + t \cdot 0.10203362558171805}{\color{blue}{z \cdot z}}\right)\right)} \]

   6. Simplified 95.0%

      \[\leadsto x + \frac{y}{\color{blue}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \left(-\frac{3.241970391368047 + t \cdot 0.10203362558171805}{z \cdot z}\right)\right)}} \]

   7. Taylor expanded in t around inf 95.0%

      \[\leadsto x + \frac{y}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \left(-\color{blue}{0.10203362558171805 \cdot \frac{t}{{z}^{2}}}\right)\right)} \]
   8. Step-by-step derivation

      1. associate-*r/ 95.0%

         \[\leadsto x + \frac{y}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \left(-\color{blue}{\frac{0.10203362558171805 \cdot t}{{z}^{2}}}\right)\right)} \]

      2. unpow2 95.0%

         \[\leadsto x + \frac{y}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \left(-\frac{0.10203362558171805 \cdot t}{\color{blue}{z \cdot z}}\right)\right)} \]

   9. Simplified 95.0%

      \[\leadsto x + \frac{y}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \left(-\color{blue}{\frac{0.10203362558171805 \cdot t}{z \cdot z}}\right)\right)} \]

   10. Taylor expanded in y around 0 95.0%

       \[\leadsto x + \color{blue}{\frac{y}{\left(3.7269864963038164 \cdot \frac{1}{z} + 0.31942702700572795\right) - 0.10203362558171805 \cdot \frac{t}{{z}^{2}}}} \]
   11. Step-by-step derivation

       1. associate--l+ 95.0%

          \[\leadsto x + \frac{y}{\color{blue}{3.7269864963038164 \cdot \frac{1}{z} + \left(0.31942702700572795 - 0.10203362558171805 \cdot \frac{t}{{z}^{2}}\right)}} \]

       2. associate-*r/ 95.0%

          \[\leadsto x + \frac{y}{\color{blue}{\frac{3.7269864963038164 \cdot 1}{z}} + \left(0.31942702700572795 - 0.10203362558171805 \cdot \frac{t}{{z}^{2}}\right)} \]

       3. metadata-eval 95.0%

          \[\leadsto x + \frac{y}{\frac{\color{blue}{3.7269864963038164}}{z} + \left(0.31942702700572795 - 0.10203362558171805 \cdot \frac{t}{{z}^{2}}\right)} \]

       4. cancel-sign-sub-inv 95.0%

          \[\leadsto x + \frac{y}{\frac{3.7269864963038164}{z} + \color{blue}{\left(0.31942702700572795 + \left(-0.10203362558171805\right) \cdot \frac{t}{{z}^{2}}\right)}} \]

       5. associate-*r/ 95.0%

          \[\leadsto x + \frac{y}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \color{blue}{\frac{\left(-0.10203362558171805\right) \cdot t}{{z}^{2}}}\right)} \]

       6. metadata-eval 95.0%

          \[\leadsto x + \frac{y}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \frac{\color{blue}{-0.10203362558171805} \cdot t}{{z}^{2}}\right)} \]

       7. unpow2 95.0%

          \[\leadsto x + \frac{y}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \frac{-0.10203362558171805 \cdot t}{\color{blue}{z \cdot z}}\right)} \]

   12. Simplified 95.0%

       \[\leadsto x + \color{blue}{\frac{y}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \frac{-0.10203362558171805 \cdot t}{z \cdot z}\right)}} \]

   if -2.2999999999999998e25 < z < 5.9999999999999998e-30

   1. Initial program 99.8%

      \[x + \frac{y \cdot \left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b\right)}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} \]
   2. Step-by-step derivation

      1. associate-/l* 99.8%

         \[\leadsto x + \color{blue}{\frac{y}{\frac{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771}{\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b}}} \]

      2. fma-def 99.8%

         \[\leadsto x + \frac{y}{\frac{\color{blue}{\mathsf{fma}\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721, z, 0.607771387771\right)}}{\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b}} \]

      3. fma-def 99.8%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749, z, 11.9400905721\right)}, z, 0.607771387771\right)}{\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b}} \]

      4. fma-def 99.8%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right)}, z, 11.9400905721\right), z, 0.607771387771\right)}{\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b}} \]

      5. fma-def 99.8%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\color{blue}{\mathsf{fma}\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a, z, b\right)}}} \]

      6. fma-def 99.8%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t, z, a\right)}, z, b\right)}} \]

      7. fma-def 99.8%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(z \cdot 3.13060547623 + 11.1667541262, z, t\right)}, z, a\right), z, b\right)}} \]

      8. fma-def 99.8%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(z, 3.13060547623, 11.1667541262\right)}, z, t\right), z, a\right), z, b\right)}} \]

   3. Simplified 99.8%

      \[\leadsto \color{blue}{x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z, 3.13060547623, 11.1667541262\right), z, t\right), z, a\right), z, b\right)}}} \]

   4. Taylor expanded in z around 0 82.0%

      \[\leadsto x + \frac{y}{\color{blue}{\frac{0.607771387771}{b}}} \]

   5. Taylor expanded in y around 0 82.1%

      \[\leadsto x + \color{blue}{1.6453555072203998 \cdot \left(y \cdot b\right)} \]
   6. Step-by-step derivation

      1. *-commutative 82.1%

         \[\leadsto x + \color{blue}{\left(y \cdot b\right) \cdot 1.6453555072203998} \]

      2. associate-*l* 82.1%

         \[\leadsto x + \color{blue}{y \cdot \left(b \cdot 1.6453555072203998\right)} \]

      3. *-commutative 82.1%

         \[\leadsto x + y \cdot \color{blue}{\left(1.6453555072203998 \cdot b\right)} \]

   7. Simplified 82.1%

      \[\leadsto x + \color{blue}{y \cdot \left(1.6453555072203998 \cdot b\right)} \]

   if 5.9999999999999998e-30 < z

   1. Initial program 22.5%

      \[x + \frac{y \cdot \left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b\right)}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} \]
   2. Step-by-step derivation

      1. +-commutative 22.5%

         \[\leadsto \color{blue}{\frac{y \cdot \left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b\right)}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} + x} \]

      2. associate-*l/ 28.9%

         \[\leadsto \color{blue}{\frac{y}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} \cdot \left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b\right)} + x \]

      3. *-commutative 28.9%

         \[\leadsto \color{blue}{\left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b\right) \cdot \frac{y}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771}} + x \]

      4. fma-def 28.9%

         \[\leadsto \color{blue}{\mathsf{fma}\left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b, \frac{y}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771}, x\right)} \]

   3. Simplified 28.9%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(z, \mathsf{fma}\left(z, \mathsf{fma}\left(z, \mathsf{fma}\left(z, 3.13060547623, 11.1667541262\right), t\right), a\right), b\right), \frac{y}{\mathsf{fma}\left(z, \mathsf{fma}\left(z, \mathsf{fma}\left(z, z + 15.234687407, 31.4690115749\right), 11.9400905721\right), 0.607771387771\right)}, x\right)} \]

   4. Taylor expanded in z around inf 83.6%

      \[\leadsto \color{blue}{3.13060547623 \cdot y + x} \]
3. Recombined 3 regimes into one program.

4. Final simplification 85.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -2.3 \cdot 10^{+25}:\\ \;\;\;\;x + \frac{y}{\frac{3.7269864963038164}{z} + \left(0.31942702700572795 + \frac{t \cdot -0.10203362558171805}{z \cdot z}\right)}\\ \mathbf{elif}\;z \leq 6 \cdot 10^{-30}:\\ \;\;\;\;x + y \cdot \left(b \cdot 1.6453555072203998\right)\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot 3.13060547623\\ \end{array} \]

Alternative 10: 82.7% accurate, 3.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -6 \cdot 10^{+23} \lor \neg \left(z \leq 6 \cdot 10^{-30}\right):\\ \;\;\;\;x + y \cdot 3.13060547623\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot \left(b \cdot 1.6453555072203998\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (or (<= z -6e+23) (not (<= z 6e-30)))
   (+ x (* y 3.13060547623))
   (+ x (* y (* b 1.6453555072203998)))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((z <= -6e+23) || !(z <= 6e-30)) {
		tmp = x + (y * 3.13060547623);
	} else {
		tmp = x + (y * (b * 1.6453555072203998));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if ((z <= (-6d+23)) .or. (.not. (z <= 6d-30))) then
        tmp = x + (y * 3.13060547623d0)
    else
        tmp = x + (y * (b * 1.6453555072203998d0))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((z <= -6e+23) || !(z <= 6e-30)) {
		tmp = x + (y * 3.13060547623);
	} else {
		tmp = x + (y * (b * 1.6453555072203998));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if (z <= -6e+23) or not (z <= 6e-30):
		tmp = x + (y * 3.13060547623)
	else:
		tmp = x + (y * (b * 1.6453555072203998))
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if ((z <= -6e+23) || !(z <= 6e-30))
		tmp = Float64(x + Float64(y * 3.13060547623));
	else
		tmp = Float64(x + Float64(y * Float64(b * 1.6453555072203998)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if ((z <= -6e+23) || ~((z <= 6e-30)))
		tmp = x + (y * 3.13060547623);
	else
		tmp = x + (y * (b * 1.6453555072203998));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[Or[LessEqual[z, -6e+23], N[Not[LessEqual[z, 6e-30]], $MachinePrecision]], N[(x + N[(y * 3.13060547623), $MachinePrecision]), $MachinePrecision], N[(x + N[(y * N[(b * 1.6453555072203998), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if z < -6.0000000000000002e23 or 5.9999999999999998e-30 < z

   1. Initial program 15.8%

      \[x + \frac{y \cdot \left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b\right)}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} \]
   2. Step-by-step derivation

      1. +-commutative 15.8%

         \[\leadsto \color{blue}{\frac{y \cdot \left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b\right)}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} + x} \]

      2. associate-*l/ 21.0%

         \[\leadsto \color{blue}{\frac{y}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} \cdot \left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b\right)} + x \]

      3. *-commutative 21.0%

         \[\leadsto \color{blue}{\left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b\right) \cdot \frac{y}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771}} + x \]

      4. fma-def 21.0%

         \[\leadsto \color{blue}{\mathsf{fma}\left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b, \frac{y}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771}, x\right)} \]

   3. Simplified 21.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(z, \mathsf{fma}\left(z, \mathsf{fma}\left(z, \mathsf{fma}\left(z, 3.13060547623, 11.1667541262\right), t\right), a\right), b\right), \frac{y}{\mathsf{fma}\left(z, \mathsf{fma}\left(z, \mathsf{fma}\left(z, z + 15.234687407, 31.4690115749\right), 11.9400905721\right), 0.607771387771\right)}, x\right)} \]

   4. Taylor expanded in z around inf 89.3%

      \[\leadsto \color{blue}{3.13060547623 \cdot y + x} \]

   if -6.0000000000000002e23 < z < 5.9999999999999998e-30

   1. Initial program 99.8%

      \[x + \frac{y \cdot \left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b\right)}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} \]
   2. Step-by-step derivation

      1. associate-/l* 99.8%

         \[\leadsto x + \color{blue}{\frac{y}{\frac{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771}{\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b}}} \]

      2. fma-def 99.8%

         \[\leadsto x + \frac{y}{\frac{\color{blue}{\mathsf{fma}\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721, z, 0.607771387771\right)}}{\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b}} \]

      3. fma-def 99.8%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749, z, 11.9400905721\right)}, z, 0.607771387771\right)}{\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b}} \]

      4. fma-def 99.8%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right)}, z, 11.9400905721\right), z, 0.607771387771\right)}{\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b}} \]

      5. fma-def 99.8%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\color{blue}{\mathsf{fma}\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a, z, b\right)}}} \]

      6. fma-def 99.8%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t, z, a\right)}, z, b\right)}} \]

      7. fma-def 99.8%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(z \cdot 3.13060547623 + 11.1667541262, z, t\right)}, z, a\right), z, b\right)}} \]

      8. fma-def 99.8%

         \[\leadsto x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(z, 3.13060547623, 11.1667541262\right)}, z, t\right), z, a\right), z, b\right)}} \]

   3. Simplified 99.8%

      \[\leadsto \color{blue}{x + \frac{y}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z + 15.234687407, z, 31.4690115749\right), z, 11.9400905721\right), z, 0.607771387771\right)}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(z, 3.13060547623, 11.1667541262\right), z, t\right), z, a\right), z, b\right)}}} \]

   4. Taylor expanded in z around 0 82.0%

      \[\leadsto x + \frac{y}{\color{blue}{\frac{0.607771387771}{b}}} \]

   5. Taylor expanded in y around 0 82.1%

      \[\leadsto x + \color{blue}{1.6453555072203998 \cdot \left(y \cdot b\right)} \]
   6. Step-by-step derivation

      1. *-commutative 82.1%

         \[\leadsto x + \color{blue}{\left(y \cdot b\right) \cdot 1.6453555072203998} \]

      2. associate-*l* 82.1%

         \[\leadsto x + \color{blue}{y \cdot \left(b \cdot 1.6453555072203998\right)} \]

      3. *-commutative 82.1%

         \[\leadsto x + y \cdot \color{blue}{\left(1.6453555072203998 \cdot b\right)} \]

   7. Simplified 82.1%

      \[\leadsto x + \color{blue}{y \cdot \left(1.6453555072203998 \cdot b\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 85.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -6 \cdot 10^{+23} \lor \neg \left(z \leq 6 \cdot 10^{-30}\right):\\ \;\;\;\;x + y \cdot 3.13060547623\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot \left(b \cdot 1.6453555072203998\right)\\ \end{array} \]

Alternative 11: 63.6% accurate, 4.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -9.5 \cdot 10^{-113} \lor \neg \left(z \leq 5 \cdot 10^{-204}\right):\\ \;\;\;\;x + y \cdot 3.13060547623\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (or (<= z -9.5e-113) (not (<= z 5e-204))) (+ x (* y 3.13060547623)) x))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((z <= -9.5e-113) || !(z <= 5e-204)) {
		tmp = x + (y * 3.13060547623);
	} else {
		tmp = x;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if ((z <= (-9.5d-113)) .or. (.not. (z <= 5d-204))) then
        tmp = x + (y * 3.13060547623d0)
    else
        tmp = x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((z <= -9.5e-113) || !(z <= 5e-204)) {
		tmp = x + (y * 3.13060547623);
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if (z <= -9.5e-113) or not (z <= 5e-204):
		tmp = x + (y * 3.13060547623)
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if ((z <= -9.5e-113) || !(z <= 5e-204))
		tmp = Float64(x + Float64(y * 3.13060547623));
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if ((z <= -9.5e-113) || ~((z <= 5e-204)))
		tmp = x + (y * 3.13060547623);
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[Or[LessEqual[z, -9.5e-113], N[Not[LessEqual[z, 5e-204]], $MachinePrecision]], N[(x + N[(y * 3.13060547623), $MachinePrecision]), $MachinePrecision], x]

Derivation

1. Split input into 2 regimes

2. if z < -9.49999999999999987e-113 or 5.0000000000000002e-204 < z

   1. Initial program 45.5%

      \[x + \frac{y \cdot \left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b\right)}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} \]
   2. Step-by-step derivation

      1. +-commutative 45.5%

         \[\leadsto \color{blue}{\frac{y \cdot \left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b\right)}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} + x} \]

      2. associate-*l/ 48.9%

         \[\leadsto \color{blue}{\frac{y}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} \cdot \left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b\right)} + x \]

      3. *-commutative 48.9%

         \[\leadsto \color{blue}{\left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b\right) \cdot \frac{y}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771}} + x \]

      4. fma-def 48.9%

         \[\leadsto \color{blue}{\mathsf{fma}\left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b, \frac{y}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771}, x\right)} \]

   3. Simplified 48.9%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(z, \mathsf{fma}\left(z, \mathsf{fma}\left(z, \mathsf{fma}\left(z, 3.13060547623, 11.1667541262\right), t\right), a\right), b\right), \frac{y}{\mathsf{fma}\left(z, \mathsf{fma}\left(z, \mathsf{fma}\left(z, z + 15.234687407, 31.4690115749\right), 11.9400905721\right), 0.607771387771\right)}, x\right)} \]

   4. Taylor expanded in z around inf 73.2%

      \[\leadsto \color{blue}{3.13060547623 \cdot y + x} \]

   if -9.49999999999999987e-113 < z < 5.0000000000000002e-204

   1. Initial program 99.9%

      \[x + \frac{y \cdot \left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b\right)}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} \]
   2. Step-by-step derivation

      1. +-commutative 99.9%

         \[\leadsto \color{blue}{\frac{y \cdot \left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b\right)}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} + x} \]

      2. associate-*l/ 99.9%

         \[\leadsto \color{blue}{\frac{y}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} \cdot \left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b\right)} + x \]

      3. *-commutative 99.9%

         \[\leadsto \color{blue}{\left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b\right) \cdot \frac{y}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771}} + x \]

      4. fma-def 99.9%

         \[\leadsto \color{blue}{\mathsf{fma}\left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b, \frac{y}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771}, x\right)} \]

   3. Simplified 99.9%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(z, \mathsf{fma}\left(z, \mathsf{fma}\left(z, \mathsf{fma}\left(z, 3.13060547623, 11.1667541262\right), t\right), a\right), b\right), \frac{y}{\mathsf{fma}\left(z, \mathsf{fma}\left(z, \mathsf{fma}\left(z, z + 15.234687407, 31.4690115749\right), 11.9400905721\right), 0.607771387771\right)}, x\right)} \]

   4. Taylor expanded in y around 0 52.4%

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

4. Final simplification 68.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -9.5 \cdot 10^{-113} \lor \neg \left(z \leq 5 \cdot 10^{-204}\right):\\ \;\;\;\;x + y \cdot 3.13060547623\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]

Alternative 12: 44.6% accurate, 37.0× speedup

Math

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

FPCore

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

C

double code(double x, double y, double z, double t, double a, double b) {
	return x;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    code = x
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 56.8%

   \[x + \frac{y \cdot \left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b\right)}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} \]
2. Step-by-step derivation

   1. +-commutative 56.8%

      \[\leadsto \color{blue}{\frac{y \cdot \left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b\right)}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} + x} \]

   2. associate-*l/ 59.5%

      \[\leadsto \color{blue}{\frac{y}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771} \cdot \left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b\right)} + x \]

   3. *-commutative 59.5%

      \[\leadsto \color{blue}{\left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b\right) \cdot \frac{y}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771}} + x \]

   4. fma-def 59.5%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b, \frac{y}{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771}, x\right)} \]

3. Simplified 59.5%

   \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(z, \mathsf{fma}\left(z, \mathsf{fma}\left(z, \mathsf{fma}\left(z, 3.13060547623, 11.1667541262\right), t\right), a\right), b\right), \frac{y}{\mathsf{fma}\left(z, \mathsf{fma}\left(z, \mathsf{fma}\left(z, z + 15.234687407, 31.4690115749\right), 11.9400905721\right), 0.607771387771\right)}, x\right)} \]

4. Taylor expanded in y around 0 45.8%

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

5. Final simplification 45.8%

   \[\leadsto x \]

Developer target: 98.3% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + \left(\left(3.13060547623 - \frac{36.527041698806414}{z}\right) + \frac{t}{z \cdot z}\right) \cdot \frac{y}{1}\\ \mathbf{if}\;z < -6.499344996252632 \cdot 10^{+53}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z < 7.066965436914287 \cdot 10^{+59}:\\ \;\;\;\;x + \frac{y}{\frac{\left(\left(\left(z + 15.234687407\right) \cdot z + 31.4690115749\right) \cdot z + 11.9400905721\right) \cdot z + 0.607771387771}{\left(\left(\left(z \cdot 3.13060547623 + 11.1667541262\right) \cdot z + t\right) \cdot z + a\right) \cdot z + b}}\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1
         (+
          x
          (*
           (+ (- 3.13060547623 (/ 36.527041698806414 z)) (/ t (* z z)))
           (/ y 1.0)))))
   (if (< z -6.499344996252632e+53)
     t_1
     (if (< z 7.066965436914287e+59)
       (+
        x
        (/
         y
         (/
          (+
           (*
            (+ (* (+ (* (+ z 15.234687407) z) 31.4690115749) z) 11.9400905721)
            z)
           0.607771387771)
          (+
           (* (+ (* (+ (* (+ (* z 3.13060547623) 11.1667541262) z) t) z) a) z)
           b))))
       t_1))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = x + (((3.13060547623 - (36.527041698806414 / z)) + (t / (z * z))) * (y / 1.0));
	double tmp;
	if (z < -6.499344996252632e+53) {
		tmp = t_1;
	} else if (z < 7.066965436914287e+59) {
		tmp = x + (y / ((((((((z + 15.234687407) * z) + 31.4690115749) * z) + 11.9400905721) * z) + 0.607771387771) / ((((((((z * 3.13060547623) + 11.1667541262) * z) + t) * z) + a) * z) + b)));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: tmp
    t_1 = x + (((3.13060547623d0 - (36.527041698806414d0 / z)) + (t / (z * z))) * (y / 1.0d0))
    if (z < (-6.499344996252632d+53)) then
        tmp = t_1
    else if (z < 7.066965436914287d+59) then
        tmp = x + (y / ((((((((z + 15.234687407d0) * z) + 31.4690115749d0) * z) + 11.9400905721d0) * z) + 0.607771387771d0) / ((((((((z * 3.13060547623d0) + 11.1667541262d0) * z) + t) * z) + a) * z) + b)))
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = x + (((3.13060547623 - (36.527041698806414 / z)) + (t / (z * z))) * (y / 1.0));
	double tmp;
	if (z < -6.499344996252632e+53) {
		tmp = t_1;
	} else if (z < 7.066965436914287e+59) {
		tmp = x + (y / ((((((((z + 15.234687407) * z) + 31.4690115749) * z) + 11.9400905721) * z) + 0.607771387771) / ((((((((z * 3.13060547623) + 11.1667541262) * z) + t) * z) + a) * z) + b)));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = x + (((3.13060547623 - (36.527041698806414 / z)) + (t / (z * z))) * (y / 1.0))
	tmp = 0
	if z < -6.499344996252632e+53:
		tmp = t_1
	elif z < 7.066965436914287e+59:
		tmp = x + (y / ((((((((z + 15.234687407) * z) + 31.4690115749) * z) + 11.9400905721) * z) + 0.607771387771) / ((((((((z * 3.13060547623) + 11.1667541262) * z) + t) * z) + a) * z) + b)))
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(x + Float64(Float64(Float64(3.13060547623 - Float64(36.527041698806414 / z)) + Float64(t / Float64(z * z))) * Float64(y / 1.0)))
	tmp = 0.0
	if (z < -6.499344996252632e+53)
		tmp = t_1;
	elseif (z < 7.066965436914287e+59)
		tmp = Float64(x + Float64(y / Float64(Float64(Float64(Float64(Float64(Float64(Float64(Float64(z + 15.234687407) * z) + 31.4690115749) * z) + 11.9400905721) * z) + 0.607771387771) / Float64(Float64(Float64(Float64(Float64(Float64(Float64(Float64(z * 3.13060547623) + 11.1667541262) * z) + t) * z) + a) * z) + b))));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = x + (((3.13060547623 - (36.527041698806414 / z)) + (t / (z * z))) * (y / 1.0));
	tmp = 0.0;
	if (z < -6.499344996252632e+53)
		tmp = t_1;
	elseif (z < 7.066965436914287e+59)
		tmp = x + (y / ((((((((z + 15.234687407) * z) + 31.4690115749) * z) + 11.9400905721) * z) + 0.607771387771) / ((((((((z * 3.13060547623) + 11.1667541262) * z) + t) * z) + a) * z) + b)));
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(x + N[(N[(N[(3.13060547623 - N[(36.527041698806414 / z), $MachinePrecision]), $MachinePrecision] + N[(t / N[(z * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * N[(y / 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[Less[z, -6.499344996252632e+53], t$95$1, If[Less[z, 7.066965436914287e+59], N[(x + N[(y / N[(N[(N[(N[(N[(N[(N[(N[(z + 15.234687407), $MachinePrecision] * z), $MachinePrecision] + 31.4690115749), $MachinePrecision] * z), $MachinePrecision] + 11.9400905721), $MachinePrecision] * z), $MachinePrecision] + 0.607771387771), $MachinePrecision] / N[(N[(N[(N[(N[(N[(N[(N[(z * 3.13060547623), $MachinePrecision] + 11.1667541262), $MachinePrecision] * z), $MachinePrecision] + t), $MachinePrecision] * z), $MachinePrecision] + a), $MachinePrecision] * z), $MachinePrecision] + b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]

Reproduce

herbie shell --seed 2023200 
(FPCore (x y z t a b)
  :name "Numeric.SpecFunctions:logGamma from math-functions-0.1.5.2, D"
  :precision binary64

  :herbie-target
  (if (< z -6.499344996252632e+53) (+ x (* (+ (- 3.13060547623 (/ 36.527041698806414 z)) (/ t (* z z))) (/ y 1.0))) (if (< z 7.066965436914287e+59) (+ x (/ y (/ (+ (* (+ (* (+ (* (+ z 15.234687407) z) 31.4690115749) z) 11.9400905721) z) 0.607771387771) (+ (* (+ (* (+ (* (+ (* z 3.13060547623) 11.1667541262) z) t) z) a) z) b)))) (+ x (* (+ (- 3.13060547623 (/ 36.527041698806414 z)) (/ t (* z z))) (/ y 1.0)))))

  (+ x (/ (* y (+ (* (+ (* (+ (* (+ (* z 3.13060547623) 11.1667541262) z) t) z) a) z) b)) (+ (* (+ (* (+ (* (+ z 15.234687407) z) 31.4690115749) z) 11.9400905721) z) 0.607771387771))))