Diagrams.Solve.Polynomial:quartForm from diagrams-solve-0.1, B

Percentage Accurate: 100.0% → 100.0%

Time: 5.6s

Alternatives: 8

Speedup: 1.2×

Specification

Math

\[\begin{array}{l} \\ \left(\frac{1}{8} \cdot x - \frac{y \cdot z}{2}\right) + t \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (+ (- (* (/ 1.0 8.0) x) (/ (* y z) 2.0)) t))

C

double code(double x, double y, double z, double t) {
	return (((1.0 / 8.0) * x) - ((y * z) / 2.0)) + t;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    code = (((1.0d0 / 8.0d0) * x) - ((y * z) / 2.0d0)) + t
end function

Java

public static double code(double x, double y, double z, double t) {
	return (((1.0 / 8.0) * x) - ((y * z) / 2.0)) + t;
}

Python

def code(x, y, z, t):
	return (((1.0 / 8.0) * x) - ((y * z) / 2.0)) + t

Julia

function code(x, y, z, t)
	return Float64(Float64(Float64(Float64(1.0 / 8.0) * x) - Float64(Float64(y * z) / 2.0)) + t)
end

MATLAB

function tmp = code(x, y, z, t)
	tmp = (((1.0 / 8.0) * x) - ((y * z) / 2.0)) + t;
end

Wolfram

code[x_, y_, z_, t_] := N[(N[(N[(N[(1.0 / 8.0), $MachinePrecision] * x), $MachinePrecision] - N[(N[(y * z), $MachinePrecision] / 2.0), $MachinePrecision]), $MachinePrecision] + t), $MachinePrecision]

Initial Program: 100.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \left(\frac{1}{8} \cdot x - \frac{y \cdot z}{2}\right) + t \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (+ (- (* (/ 1.0 8.0) x) (/ (* y z) 2.0)) t))

C

double code(double x, double y, double z, double t) {
	return (((1.0 / 8.0) * x) - ((y * z) / 2.0)) + t;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    code = (((1.0d0 / 8.0d0) * x) - ((y * z) / 2.0d0)) + t
end function

Java

public static double code(double x, double y, double z, double t) {
	return (((1.0 / 8.0) * x) - ((y * z) / 2.0)) + t;
}

Python

def code(x, y, z, t):
	return (((1.0 / 8.0) * x) - ((y * z) / 2.0)) + t

Julia

function code(x, y, z, t)
	return Float64(Float64(Float64(Float64(1.0 / 8.0) * x) - Float64(Float64(y * z) / 2.0)) + t)
end

MATLAB

function tmp = code(x, y, z, t)
	tmp = (((1.0 / 8.0) * x) - ((y * z) / 2.0)) + t;
end

Wolfram

code[x_, y_, z_, t_] := N[(N[(N[(N[(1.0 / 8.0), $MachinePrecision] * x), $MachinePrecision] - N[(N[(y * z), $MachinePrecision] / 2.0), $MachinePrecision]), $MachinePrecision] + t), $MachinePrecision]

Alternative 1: 100.0% accurate, 0.1× speedup

Math

\[\begin{array}{l} \\ 0.125 \cdot x - \mathsf{fma}\left(y, z \cdot 0.5, -t\right) \end{array} \]

FPCore

(FPCore (x y z t) :precision binary64 (- (* 0.125 x) (fma y (* z 0.5) (- t))))

C

double code(double x, double y, double z, double t) {
	return (0.125 * x) - fma(y, (z * 0.5), -t);
}

Julia

function code(x, y, z, t)
	return Float64(Float64(0.125 * x) - fma(y, Float64(z * 0.5), Float64(-t)))
end

Wolfram

code[x_, y_, z_, t_] := N[(N[(0.125 * x), $MachinePrecision] - N[(y * N[(z * 0.5), $MachinePrecision] + (-t)), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 100.0%

   \[\left(\frac{1}{8} \cdot x - \frac{y \cdot z}{2}\right) + t \]
2. Step-by-step derivation

   1. add0 100.0%

      \[\leadsto \color{blue}{\left(\left(\frac{1}{8} \cdot x - \frac{y \cdot z}{2}\right) + 0\right)} + t \]

   2. add0 100.0%

      \[\leadsto \color{blue}{\left(\frac{1}{8} \cdot x - \frac{y \cdot z}{2}\right)} + t \]

   3. metadata-eval 100.0%

      \[\leadsto \left(\color{blue}{0.125} \cdot x - \frac{y \cdot z}{2}\right) + t \]

   4. associate-/l* 100.0%

      \[\leadsto \left(0.125 \cdot x - \color{blue}{y \cdot \frac{z}{2}}\right) + t \]

3. Simplified 100.0%

   \[\leadsto \color{blue}{\left(0.125 \cdot x - y \cdot \frac{z}{2}\right) + t} \]
4. Add Preprocessing
5. Step-by-step derivation

   1. associate-+l- 100.0%

      \[\leadsto \color{blue}{0.125 \cdot x - \left(y \cdot \frac{z}{2} - t\right)} \]

   2. fma-neg 100.0%

      \[\leadsto 0.125 \cdot x - \color{blue}{\mathsf{fma}\left(y, \frac{z}{2}, -t\right)} \]

   3. div-inv 100.0%

      \[\leadsto 0.125 \cdot x - \mathsf{fma}\left(y, \color{blue}{z \cdot \frac{1}{2}}, -t\right) \]

   4. metadata-eval 100.0%

      \[\leadsto 0.125 \cdot x - \mathsf{fma}\left(y, z \cdot \color{blue}{0.5}, -t\right) \]

6. Applied egg-rr 100.0%

   \[\leadsto \color{blue}{0.125 \cdot x - \mathsf{fma}\left(y, z \cdot 0.5, -t\right)} \]

7. Final simplification 100.0%

   \[\leadsto 0.125 \cdot x - \mathsf{fma}\left(y, z \cdot 0.5, -t\right) \]
8. Add Preprocessing

Alternative 2: 87.5% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := t - y \cdot \left(z \cdot 0.5\right)\\ \mathbf{if}\;y \cdot z \leq -2 \cdot 10^{+91}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;y \cdot z \leq -2 \cdot 10^{+42}:\\ \;\;\;\;0.125 \cdot x - 0.5 \cdot \left(y \cdot z\right)\\ \mathbf{elif}\;y \cdot z \leq -1 \cdot 10^{-13} \lor \neg \left(y \cdot z \leq 10^{+24}\right):\\ \;\;\;\;t\_1\\ \mathbf{else}:\\ \;\;\;\;0.125 \cdot x + t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (- t (* y (* z 0.5)))))
   (if (<= (* y z) -2e+91)
     t_1
     (if (<= (* y z) -2e+42)
       (- (* 0.125 x) (* 0.5 (* y z)))
       (if (or (<= (* y z) -1e-13) (not (<= (* y z) 1e+24)))
         t_1
         (+ (* 0.125 x) t))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = t - (y * (z * 0.5));
	double tmp;
	if ((y * z) <= -2e+91) {
		tmp = t_1;
	} else if ((y * z) <= -2e+42) {
		tmp = (0.125 * x) - (0.5 * (y * z));
	} else if (((y * z) <= -1e-13) || !((y * z) <= 1e+24)) {
		tmp = t_1;
	} else {
		tmp = (0.125 * x) + t;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: t_1
    real(8) :: tmp
    t_1 = t - (y * (z * 0.5d0))
    if ((y * z) <= (-2d+91)) then
        tmp = t_1
    else if ((y * z) <= (-2d+42)) then
        tmp = (0.125d0 * x) - (0.5d0 * (y * z))
    else if (((y * z) <= (-1d-13)) .or. (.not. ((y * z) <= 1d+24))) then
        tmp = t_1
    else
        tmp = (0.125d0 * x) + t
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = t - (y * (z * 0.5));
	double tmp;
	if ((y * z) <= -2e+91) {
		tmp = t_1;
	} else if ((y * z) <= -2e+42) {
		tmp = (0.125 * x) - (0.5 * (y * z));
	} else if (((y * z) <= -1e-13) || !((y * z) <= 1e+24)) {
		tmp = t_1;
	} else {
		tmp = (0.125 * x) + t;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = t - (y * (z * 0.5))
	tmp = 0
	if (y * z) <= -2e+91:
		tmp = t_1
	elif (y * z) <= -2e+42:
		tmp = (0.125 * x) - (0.5 * (y * z))
	elif ((y * z) <= -1e-13) or not ((y * z) <= 1e+24):
		tmp = t_1
	else:
		tmp = (0.125 * x) + t
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(t - Float64(y * Float64(z * 0.5)))
	tmp = 0.0
	if (Float64(y * z) <= -2e+91)
		tmp = t_1;
	elseif (Float64(y * z) <= -2e+42)
		tmp = Float64(Float64(0.125 * x) - Float64(0.5 * Float64(y * z)));
	elseif ((Float64(y * z) <= -1e-13) || !(Float64(y * z) <= 1e+24))
		tmp = t_1;
	else
		tmp = Float64(Float64(0.125 * x) + t);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = t - (y * (z * 0.5));
	tmp = 0.0;
	if ((y * z) <= -2e+91)
		tmp = t_1;
	elseif ((y * z) <= -2e+42)
		tmp = (0.125 * x) - (0.5 * (y * z));
	elseif (((y * z) <= -1e-13) || ~(((y * z) <= 1e+24)))
		tmp = t_1;
	else
		tmp = (0.125 * x) + t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(t - N[(y * N[(z * 0.5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[(y * z), $MachinePrecision], -2e+91], t$95$1, If[LessEqual[N[(y * z), $MachinePrecision], -2e+42], N[(N[(0.125 * x), $MachinePrecision] - N[(0.5 * N[(y * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[Or[LessEqual[N[(y * z), $MachinePrecision], -1e-13], N[Not[LessEqual[N[(y * z), $MachinePrecision], 1e+24]], $MachinePrecision]], t$95$1, N[(N[(0.125 * x), $MachinePrecision] + t), $MachinePrecision]]]]]

Derivation

1. Split input into 3 regimes

2. if (*.f64 y z) < -2.00000000000000016e91 or -2.00000000000000009e42 < (*.f64 y z) < -1e-13 or 9.9999999999999998e23 < (*.f64 y z)

   1. Initial program 100.0%

      \[\left(\frac{1}{8} \cdot x - \frac{y \cdot z}{2}\right) + t \]
   2. Step-by-step derivation

      1. add0 100.0%

         \[\leadsto \color{blue}{\left(\left(\frac{1}{8} \cdot x - \frac{y \cdot z}{2}\right) + 0\right)} + t \]

      2. add0 100.0%

         \[\leadsto \color{blue}{\left(\frac{1}{8} \cdot x - \frac{y \cdot z}{2}\right)} + t \]

      3. metadata-eval 100.0%

         \[\leadsto \left(\color{blue}{0.125} \cdot x - \frac{y \cdot z}{2}\right) + t \]

      4. associate-/l* 100.0%

         \[\leadsto \left(0.125 \cdot x - \color{blue}{y \cdot \frac{z}{2}}\right) + t \]

   3. Simplified 100.0%

      \[\leadsto \color{blue}{\left(0.125 \cdot x - y \cdot \frac{z}{2}\right) + t} \]
   4. Add Preprocessing

   5. Taylor expanded in x around 0 94.0%

      \[\leadsto \color{blue}{t - 0.5 \cdot \left(y \cdot z\right)} \]
   6. Step-by-step derivation

      1. *-commutative 94.0%

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

      2. associate-*r* 94.0%

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

      3. *-commutative 94.0%

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

   7. Simplified 94.0%

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

   if -2.00000000000000016e91 < (*.f64 y z) < -2.00000000000000009e42

   1. Initial program 99.8%

      \[\left(\frac{1}{8} \cdot x - \frac{y \cdot z}{2}\right) + t \]
   2. Step-by-step derivation

      1. add0 99.8%

         \[\leadsto \color{blue}{\left(\left(\frac{1}{8} \cdot x - \frac{y \cdot z}{2}\right) + 0\right)} + t \]

      2. add0 99.8%

         \[\leadsto \color{blue}{\left(\frac{1}{8} \cdot x - \frac{y \cdot z}{2}\right)} + t \]

      3. metadata-eval 99.8%

         \[\leadsto \left(\color{blue}{0.125} \cdot x - \frac{y \cdot z}{2}\right) + t \]

      4. associate-/l* 99.8%

         \[\leadsto \left(0.125 \cdot x - \color{blue}{y \cdot \frac{z}{2}}\right) + t \]

   3. Simplified 99.8%

      \[\leadsto \color{blue}{\left(0.125 \cdot x - y \cdot \frac{z}{2}\right) + t} \]
   4. Add Preprocessing

   5. Taylor expanded in t around 0 97.9%

      \[\leadsto \color{blue}{0.125 \cdot x - 0.5 \cdot \left(y \cdot z\right)} \]

   if -1e-13 < (*.f64 y z) < 9.9999999999999998e23

   1. Initial program 100.0%

      \[\left(\frac{1}{8} \cdot x - \frac{y \cdot z}{2}\right) + t \]
   2. Step-by-step derivation

      1. add0 100.0%

         \[\leadsto \color{blue}{\left(\left(\frac{1}{8} \cdot x - \frac{y \cdot z}{2}\right) + 0\right)} + t \]

      2. add0 100.0%

         \[\leadsto \color{blue}{\left(\frac{1}{8} \cdot x - \frac{y \cdot z}{2}\right)} + t \]

      3. metadata-eval 100.0%

         \[\leadsto \left(\color{blue}{0.125} \cdot x - \frac{y \cdot z}{2}\right) + t \]

      4. associate-/l* 100.0%

         \[\leadsto \left(0.125 \cdot x - \color{blue}{y \cdot \frac{z}{2}}\right) + t \]

   3. Simplified 100.0%

      \[\leadsto \color{blue}{\left(0.125 \cdot x - y \cdot \frac{z}{2}\right) + t} \]
   4. Add Preprocessing

   5. Taylor expanded in y around 0 94.6%

      \[\leadsto \color{blue}{t + 0.125 \cdot x} \]
3. Recombined 3 regimes into one program.

4. Final simplification 94.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \cdot z \leq -2 \cdot 10^{+91}:\\ \;\;\;\;t - y \cdot \left(z \cdot 0.5\right)\\ \mathbf{elif}\;y \cdot z \leq -2 \cdot 10^{+42}:\\ \;\;\;\;0.125 \cdot x - 0.5 \cdot \left(y \cdot z\right)\\ \mathbf{elif}\;y \cdot z \leq -1 \cdot 10^{-13} \lor \neg \left(y \cdot z \leq 10^{+24}\right):\\ \;\;\;\;t - y \cdot \left(z \cdot 0.5\right)\\ \mathbf{else}:\\ \;\;\;\;0.125 \cdot x + t\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 49.4% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := z \cdot \left(y \cdot -0.5\right)\\ \mathbf{if}\;y \leq -3.6 \cdot 10^{+37}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;y \leq 3.15 \cdot 10^{-254}:\\ \;\;\;\;t\\ \mathbf{elif}\;y \leq 1.5 \cdot 10^{-170}:\\ \;\;\;\;0.125 \cdot x\\ \mathbf{elif}\;y \leq 3 \cdot 10^{-92}:\\ \;\;\;\;t\\ \mathbf{elif}\;y \leq 4.5 \cdot 10^{-13}:\\ \;\;\;\;0.125 \cdot x\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (* z (* y -0.5))))
   (if (<= y -3.6e+37)
     t_1
     (if (<= y 3.15e-254)
       t
       (if (<= y 1.5e-170)
         (* 0.125 x)
         (if (<= y 3e-92) t (if (<= y 4.5e-13) (* 0.125 x) t_1)))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = z * (y * -0.5);
	double tmp;
	if (y <= -3.6e+37) {
		tmp = t_1;
	} else if (y <= 3.15e-254) {
		tmp = t;
	} else if (y <= 1.5e-170) {
		tmp = 0.125 * x;
	} else if (y <= 3e-92) {
		tmp = t;
	} else if (y <= 4.5e-13) {
		tmp = 0.125 * x;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: t_1
    real(8) :: tmp
    t_1 = z * (y * (-0.5d0))
    if (y <= (-3.6d+37)) then
        tmp = t_1
    else if (y <= 3.15d-254) then
        tmp = t
    else if (y <= 1.5d-170) then
        tmp = 0.125d0 * x
    else if (y <= 3d-92) then
        tmp = t
    else if (y <= 4.5d-13) then
        tmp = 0.125d0 * x
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = z * (y * -0.5);
	double tmp;
	if (y <= -3.6e+37) {
		tmp = t_1;
	} else if (y <= 3.15e-254) {
		tmp = t;
	} else if (y <= 1.5e-170) {
		tmp = 0.125 * x;
	} else if (y <= 3e-92) {
		tmp = t;
	} else if (y <= 4.5e-13) {
		tmp = 0.125 * x;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = z * (y * -0.5)
	tmp = 0
	if y <= -3.6e+37:
		tmp = t_1
	elif y <= 3.15e-254:
		tmp = t
	elif y <= 1.5e-170:
		tmp = 0.125 * x
	elif y <= 3e-92:
		tmp = t
	elif y <= 4.5e-13:
		tmp = 0.125 * x
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(z * Float64(y * -0.5))
	tmp = 0.0
	if (y <= -3.6e+37)
		tmp = t_1;
	elseif (y <= 3.15e-254)
		tmp = t;
	elseif (y <= 1.5e-170)
		tmp = Float64(0.125 * x);
	elseif (y <= 3e-92)
		tmp = t;
	elseif (y <= 4.5e-13)
		tmp = Float64(0.125 * x);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = z * (y * -0.5);
	tmp = 0.0;
	if (y <= -3.6e+37)
		tmp = t_1;
	elseif (y <= 3.15e-254)
		tmp = t;
	elseif (y <= 1.5e-170)
		tmp = 0.125 * x;
	elseif (y <= 3e-92)
		tmp = t;
	elseif (y <= 4.5e-13)
		tmp = 0.125 * x;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(z * N[(y * -0.5), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -3.6e+37], t$95$1, If[LessEqual[y, 3.15e-254], t, If[LessEqual[y, 1.5e-170], N[(0.125 * x), $MachinePrecision], If[LessEqual[y, 3e-92], t, If[LessEqual[y, 4.5e-13], N[(0.125 * x), $MachinePrecision], t$95$1]]]]]]

Derivation

1. Split input into 3 regimes

2. if y < -3.59999999999999998e37 or 4.5e-13 < y

   1. Initial program 100.0%

      \[\left(\frac{1}{8} \cdot x - \frac{y \cdot z}{2}\right) + t \]
   2. Step-by-step derivation

      1. add0 100.0%

         \[\leadsto \color{blue}{\left(\left(\frac{1}{8} \cdot x - \frac{y \cdot z}{2}\right) + 0\right)} + t \]

      2. add0 100.0%

         \[\leadsto \color{blue}{\left(\frac{1}{8} \cdot x - \frac{y \cdot z}{2}\right)} + t \]

      3. metadata-eval 100.0%

         \[\leadsto \left(\color{blue}{0.125} \cdot x - \frac{y \cdot z}{2}\right) + t \]

      4. associate-/l* 100.0%

         \[\leadsto \left(0.125 \cdot x - \color{blue}{y \cdot \frac{z}{2}}\right) + t \]

   3. Simplified 100.0%

      \[\leadsto \color{blue}{\left(0.125 \cdot x - y \cdot \frac{z}{2}\right) + t} \]
   4. Add Preprocessing

   5. Taylor expanded in y around inf 62.7%

      \[\leadsto \color{blue}{-0.5 \cdot \left(y \cdot z\right)} \]
   6. Step-by-step derivation

      1. associate-*r* 62.7%

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

   7. Simplified 62.7%

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

   if -3.59999999999999998e37 < y < 3.1500000000000001e-254 or 1.50000000000000007e-170 < y < 3.00000000000000013e-92

   1. Initial program 100.0%

      \[\left(\frac{1}{8} \cdot x - \frac{y \cdot z}{2}\right) + t \]
   2. Step-by-step derivation

      1. add0 100.0%

         \[\leadsto \color{blue}{\left(\left(\frac{1}{8} \cdot x - \frac{y \cdot z}{2}\right) + 0\right)} + t \]

      2. add0 100.0%

         \[\leadsto \color{blue}{\left(\frac{1}{8} \cdot x - \frac{y \cdot z}{2}\right)} + t \]

      3. metadata-eval 100.0%

         \[\leadsto \left(\color{blue}{0.125} \cdot x - \frac{y \cdot z}{2}\right) + t \]

      4. associate-/l* 100.0%

         \[\leadsto \left(0.125 \cdot x - \color{blue}{y \cdot \frac{z}{2}}\right) + t \]

   3. Simplified 100.0%

      \[\leadsto \color{blue}{\left(0.125 \cdot x - y \cdot \frac{z}{2}\right) + t} \]
   4. Add Preprocessing

   5. Taylor expanded in t around inf 43.9%

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

   if 3.1500000000000001e-254 < y < 1.50000000000000007e-170 or 3.00000000000000013e-92 < y < 4.5e-13

   1. Initial program 100.0%

      \[\left(\frac{1}{8} \cdot x - \frac{y \cdot z}{2}\right) + t \]
   2. Step-by-step derivation

      1. add0 100.0%

         \[\leadsto \color{blue}{\left(\left(\frac{1}{8} \cdot x - \frac{y \cdot z}{2}\right) + 0\right)} + t \]

      2. add0 100.0%

         \[\leadsto \color{blue}{\left(\frac{1}{8} \cdot x - \frac{y \cdot z}{2}\right)} + t \]

      3. metadata-eval 100.0%

         \[\leadsto \left(\color{blue}{0.125} \cdot x - \frac{y \cdot z}{2}\right) + t \]

      4. associate-/l* 100.0%

         \[\leadsto \left(0.125 \cdot x - \color{blue}{y \cdot \frac{z}{2}}\right) + t \]

   3. Simplified 100.0%

      \[\leadsto \color{blue}{\left(0.125 \cdot x - y \cdot \frac{z}{2}\right) + t} \]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 37.5%

      \[\leadsto \color{blue}{0.125 \cdot x} \]
3. Recombined 3 regimes into one program.

4. Final simplification 52.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -3.6 \cdot 10^{+37}:\\ \;\;\;\;z \cdot \left(y \cdot -0.5\right)\\ \mathbf{elif}\;y \leq 3.15 \cdot 10^{-254}:\\ \;\;\;\;t\\ \mathbf{elif}\;y \leq 1.5 \cdot 10^{-170}:\\ \;\;\;\;0.125 \cdot x\\ \mathbf{elif}\;y \leq 3 \cdot 10^{-92}:\\ \;\;\;\;t\\ \mathbf{elif}\;y \leq 4.5 \cdot 10^{-13}:\\ \;\;\;\;0.125 \cdot x\\ \mathbf{else}:\\ \;\;\;\;z \cdot \left(y \cdot -0.5\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 80.4% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -3.4 \cdot 10^{+46} \lor \neg \left(y \leq 7.4 \cdot 10^{-13}\right):\\ \;\;\;\;t - y \cdot \left(z \cdot 0.5\right)\\ \mathbf{else}:\\ \;\;\;\;0.125 \cdot x + t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= y -3.4e+46) (not (<= y 7.4e-13)))
   (- t (* y (* z 0.5)))
   (+ (* 0.125 x) t)))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((y <= -3.4e+46) || !(y <= 7.4e-13)) {
		tmp = t - (y * (z * 0.5));
	} else {
		tmp = (0.125 * x) + t;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: tmp
    if ((y <= (-3.4d+46)) .or. (.not. (y <= 7.4d-13))) then
        tmp = t - (y * (z * 0.5d0))
    else
        tmp = (0.125d0 * x) + t
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if ((y <= -3.4e+46) || !(y <= 7.4e-13)) {
		tmp = t - (y * (z * 0.5));
	} else {
		tmp = (0.125 * x) + t;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (y <= -3.4e+46) or not (y <= 7.4e-13):
		tmp = t - (y * (z * 0.5))
	else:
		tmp = (0.125 * x) + t
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((y <= -3.4e+46) || !(y <= 7.4e-13))
		tmp = Float64(t - Float64(y * Float64(z * 0.5)));
	else
		tmp = Float64(Float64(0.125 * x) + t);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((y <= -3.4e+46) || ~((y <= 7.4e-13)))
		tmp = t - (y * (z * 0.5));
	else
		tmp = (0.125 * x) + t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[y, -3.4e+46], N[Not[LessEqual[y, 7.4e-13]], $MachinePrecision]], N[(t - N[(y * N[(z * 0.5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(0.125 * x), $MachinePrecision] + t), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < -3.3999999999999998e46 or 7.39999999999999977e-13 < y

   1. Initial program 100.0%

      \[\left(\frac{1}{8} \cdot x - \frac{y \cdot z}{2}\right) + t \]
   2. Step-by-step derivation

      1. add0 100.0%

         \[\leadsto \color{blue}{\left(\left(\frac{1}{8} \cdot x - \frac{y \cdot z}{2}\right) + 0\right)} + t \]

      2. add0 100.0%

         \[\leadsto \color{blue}{\left(\frac{1}{8} \cdot x - \frac{y \cdot z}{2}\right)} + t \]

      3. metadata-eval 100.0%

         \[\leadsto \left(\color{blue}{0.125} \cdot x - \frac{y \cdot z}{2}\right) + t \]

      4. associate-/l* 100.0%

         \[\leadsto \left(0.125 \cdot x - \color{blue}{y \cdot \frac{z}{2}}\right) + t \]

   3. Simplified 100.0%

      \[\leadsto \color{blue}{\left(0.125 \cdot x - y \cdot \frac{z}{2}\right) + t} \]
   4. Add Preprocessing

   5. Taylor expanded in x around 0 84.4%

      \[\leadsto \color{blue}{t - 0.5 \cdot \left(y \cdot z\right)} \]
   6. Step-by-step derivation

      1. *-commutative 84.4%

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

      2. associate-*r* 84.4%

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

      3. *-commutative 84.4%

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

   7. Simplified 84.4%

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

   if -3.3999999999999998e46 < y < 7.39999999999999977e-13

   1. Initial program 100.0%

      \[\left(\frac{1}{8} \cdot x - \frac{y \cdot z}{2}\right) + t \]
   2. Step-by-step derivation

      1. add0 100.0%

         \[\leadsto \color{blue}{\left(\left(\frac{1}{8} \cdot x - \frac{y \cdot z}{2}\right) + 0\right)} + t \]

      2. add0 100.0%

         \[\leadsto \color{blue}{\left(\frac{1}{8} \cdot x - \frac{y \cdot z}{2}\right)} + t \]

      3. metadata-eval 100.0%

         \[\leadsto \left(\color{blue}{0.125} \cdot x - \frac{y \cdot z}{2}\right) + t \]

      4. associate-/l* 100.0%

         \[\leadsto \left(0.125 \cdot x - \color{blue}{y \cdot \frac{z}{2}}\right) + t \]

   3. Simplified 100.0%

      \[\leadsto \color{blue}{\left(0.125 \cdot x - y \cdot \frac{z}{2}\right) + t} \]
   4. Add Preprocessing

   5. Taylor expanded in y around 0 82.3%

      \[\leadsto \color{blue}{t + 0.125 \cdot x} \]
3. Recombined 2 regimes into one program.

4. Final simplification 83.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -3.4 \cdot 10^{+46} \lor \neg \left(y \leq 7.4 \cdot 10^{-13}\right):\\ \;\;\;\;t - y \cdot \left(z \cdot 0.5\right)\\ \mathbf{else}:\\ \;\;\;\;0.125 \cdot x + t\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 71.1% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.4 \cdot 10^{+74} \lor \neg \left(y \leq 2.6 \cdot 10^{-11}\right):\\ \;\;\;\;z \cdot \left(y \cdot -0.5\right)\\ \mathbf{else}:\\ \;\;\;\;0.125 \cdot x + t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= y -1.4e+74) (not (<= y 2.6e-11)))
   (* z (* y -0.5))
   (+ (* 0.125 x) t)))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((y <= -1.4e+74) || !(y <= 2.6e-11)) {
		tmp = z * (y * -0.5);
	} else {
		tmp = (0.125 * x) + t;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: tmp
    if ((y <= (-1.4d+74)) .or. (.not. (y <= 2.6d-11))) then
        tmp = z * (y * (-0.5d0))
    else
        tmp = (0.125d0 * x) + t
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if ((y <= -1.4e+74) || !(y <= 2.6e-11)) {
		tmp = z * (y * -0.5);
	} else {
		tmp = (0.125 * x) + t;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (y <= -1.4e+74) or not (y <= 2.6e-11):
		tmp = z * (y * -0.5)
	else:
		tmp = (0.125 * x) + t
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((y <= -1.4e+74) || !(y <= 2.6e-11))
		tmp = Float64(z * Float64(y * -0.5));
	else
		tmp = Float64(Float64(0.125 * x) + t);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((y <= -1.4e+74) || ~((y <= 2.6e-11)))
		tmp = z * (y * -0.5);
	else
		tmp = (0.125 * x) + t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[y, -1.4e+74], N[Not[LessEqual[y, 2.6e-11]], $MachinePrecision]], N[(z * N[(y * -0.5), $MachinePrecision]), $MachinePrecision], N[(N[(0.125 * x), $MachinePrecision] + t), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < -1.40000000000000001e74 or 2.6000000000000001e-11 < y

   1. Initial program 100.0%

      \[\left(\frac{1}{8} \cdot x - \frac{y \cdot z}{2}\right) + t \]
   2. Step-by-step derivation

      1. add0 100.0%

         \[\leadsto \color{blue}{\left(\left(\frac{1}{8} \cdot x - \frac{y \cdot z}{2}\right) + 0\right)} + t \]

      2. add0 100.0%

         \[\leadsto \color{blue}{\left(\frac{1}{8} \cdot x - \frac{y \cdot z}{2}\right)} + t \]

      3. metadata-eval 100.0%

         \[\leadsto \left(\color{blue}{0.125} \cdot x - \frac{y \cdot z}{2}\right) + t \]

      4. associate-/l* 100.0%

         \[\leadsto \left(0.125 \cdot x - \color{blue}{y \cdot \frac{z}{2}}\right) + t \]

   3. Simplified 100.0%

      \[\leadsto \color{blue}{\left(0.125 \cdot x - y \cdot \frac{z}{2}\right) + t} \]
   4. Add Preprocessing

   5. Taylor expanded in y around inf 63.6%

      \[\leadsto \color{blue}{-0.5 \cdot \left(y \cdot z\right)} \]
   6. Step-by-step derivation

      1. associate-*r* 63.6%

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

   7. Simplified 63.6%

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

   if -1.40000000000000001e74 < y < 2.6000000000000001e-11

   1. Initial program 100.0%

      \[\left(\frac{1}{8} \cdot x - \frac{y \cdot z}{2}\right) + t \]
   2. Step-by-step derivation

      1. add0 100.0%

         \[\leadsto \color{blue}{\left(\left(\frac{1}{8} \cdot x - \frac{y \cdot z}{2}\right) + 0\right)} + t \]

      2. add0 100.0%

         \[\leadsto \color{blue}{\left(\frac{1}{8} \cdot x - \frac{y \cdot z}{2}\right)} + t \]

      3. metadata-eval 100.0%

         \[\leadsto \left(\color{blue}{0.125} \cdot x - \frac{y \cdot z}{2}\right) + t \]

      4. associate-/l* 100.0%

         \[\leadsto \left(0.125 \cdot x - \color{blue}{y \cdot \frac{z}{2}}\right) + t \]

   3. Simplified 100.0%

      \[\leadsto \color{blue}{\left(0.125 \cdot x - y \cdot \frac{z}{2}\right) + t} \]
   4. Add Preprocessing

   5. Taylor expanded in y around 0 81.5%

      \[\leadsto \color{blue}{t + 0.125 \cdot x} \]
3. Recombined 2 regimes into one program.

4. Final simplification 72.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -1.4 \cdot 10^{+74} \lor \neg \left(y \leq 2.6 \cdot 10^{-11}\right):\\ \;\;\;\;z \cdot \left(y \cdot -0.5\right)\\ \mathbf{else}:\\ \;\;\;\;0.125 \cdot x + t\\ \end{array} \]
5. Add Preprocessing

Alternative 6: 50.1% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -1.35 \cdot 10^{+22}:\\ \;\;\;\;t\\ \mathbf{elif}\;t \leq 7.5 \cdot 10^{+24}:\\ \;\;\;\;0.125 \cdot x\\ \mathbf{else}:\\ \;\;\;\;t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= t -1.35e+22) t (if (<= t 7.5e+24) (* 0.125 x) t)))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (t <= -1.35e+22) {
		tmp = t;
	} else if (t <= 7.5e+24) {
		tmp = 0.125 * x;
	} else {
		tmp = t;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: tmp
    if (t <= (-1.35d+22)) then
        tmp = t
    else if (t <= 7.5d+24) then
        tmp = 0.125d0 * x
    else
        tmp = t
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if (t <= -1.35e+22) {
		tmp = t;
	} else if (t <= 7.5e+24) {
		tmp = 0.125 * x;
	} else {
		tmp = t;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if t <= -1.35e+22:
		tmp = t
	elif t <= 7.5e+24:
		tmp = 0.125 * x
	else:
		tmp = t
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (t <= -1.35e+22)
		tmp = t;
	elseif (t <= 7.5e+24)
		tmp = Float64(0.125 * x);
	else
		tmp = t;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (t <= -1.35e+22)
		tmp = t;
	elseif (t <= 7.5e+24)
		tmp = 0.125 * x;
	else
		tmp = t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[t, -1.35e+22], t, If[LessEqual[t, 7.5e+24], N[(0.125 * x), $MachinePrecision], t]]

Derivation

1. Split input into 2 regimes

2. if t < -1.3500000000000001e22 or 7.50000000000000014e24 < t

   1. Initial program 100.0%

      \[\left(\frac{1}{8} \cdot x - \frac{y \cdot z}{2}\right) + t \]
   2. Step-by-step derivation

      1. add0 100.0%

         \[\leadsto \color{blue}{\left(\left(\frac{1}{8} \cdot x - \frac{y \cdot z}{2}\right) + 0\right)} + t \]

      2. add0 100.0%

         \[\leadsto \color{blue}{\left(\frac{1}{8} \cdot x - \frac{y \cdot z}{2}\right)} + t \]

      3. metadata-eval 100.0%

         \[\leadsto \left(\color{blue}{0.125} \cdot x - \frac{y \cdot z}{2}\right) + t \]

      4. associate-/l* 100.0%

         \[\leadsto \left(0.125 \cdot x - \color{blue}{y \cdot \frac{z}{2}}\right) + t \]

   3. Simplified 100.0%

      \[\leadsto \color{blue}{\left(0.125 \cdot x - y \cdot \frac{z}{2}\right) + t} \]
   4. Add Preprocessing

   5. Taylor expanded in t around inf 56.3%

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

   if -1.3500000000000001e22 < t < 7.50000000000000014e24

   1. Initial program 100.0%

      \[\left(\frac{1}{8} \cdot x - \frac{y \cdot z}{2}\right) + t \]
   2. Step-by-step derivation

      1. add0 100.0%

         \[\leadsto \color{blue}{\left(\left(\frac{1}{8} \cdot x - \frac{y \cdot z}{2}\right) + 0\right)} + t \]

      2. add0 100.0%

         \[\leadsto \color{blue}{\left(\frac{1}{8} \cdot x - \frac{y \cdot z}{2}\right)} + t \]

      3. metadata-eval 100.0%

         \[\leadsto \left(\color{blue}{0.125} \cdot x - \frac{y \cdot z}{2}\right) + t \]

      4. associate-/l* 100.0%

         \[\leadsto \left(0.125 \cdot x - \color{blue}{y \cdot \frac{z}{2}}\right) + t \]

   3. Simplified 100.0%

      \[\leadsto \color{blue}{\left(0.125 \cdot x - y \cdot \frac{z}{2}\right) + t} \]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 45.1%

      \[\leadsto \color{blue}{0.125 \cdot x} \]
3. Recombined 2 regimes into one program.

4. Final simplification 50.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;t \leq -1.35 \cdot 10^{+22}:\\ \;\;\;\;t\\ \mathbf{elif}\;t \leq 7.5 \cdot 10^{+24}:\\ \;\;\;\;0.125 \cdot x\\ \mathbf{else}:\\ \;\;\;\;t\\ \end{array} \]
5. Add Preprocessing

Alternative 7: 100.0% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ t + \left(0.125 \cdot x - y \cdot \frac{z}{2}\right) \end{array} \]

FPCore

(FPCore (x y z t) :precision binary64 (+ t (- (* 0.125 x) (* y (/ z 2.0)))))

C

double code(double x, double y, double z, double t) {
	return t + ((0.125 * x) - (y * (z / 2.0)));
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    code = t + ((0.125d0 * x) - (y * (z / 2.0d0)))
end function

Java

public static double code(double x, double y, double z, double t) {
	return t + ((0.125 * x) - (y * (z / 2.0)));
}

Python

def code(x, y, z, t):
	return t + ((0.125 * x) - (y * (z / 2.0)))

Julia

function code(x, y, z, t)
	return Float64(t + Float64(Float64(0.125 * x) - Float64(y * Float64(z / 2.0))))
end

MATLAB

function tmp = code(x, y, z, t)
	tmp = t + ((0.125 * x) - (y * (z / 2.0)));
end

Wolfram

code[x_, y_, z_, t_] := N[(t + N[(N[(0.125 * x), $MachinePrecision] - N[(y * N[(z / 2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 100.0%

   \[\left(\frac{1}{8} \cdot x - \frac{y \cdot z}{2}\right) + t \]
2. Step-by-step derivation

   1. add0 100.0%

      \[\leadsto \color{blue}{\left(\left(\frac{1}{8} \cdot x - \frac{y \cdot z}{2}\right) + 0\right)} + t \]

   2. add0 100.0%

      \[\leadsto \color{blue}{\left(\frac{1}{8} \cdot x - \frac{y \cdot z}{2}\right)} + t \]

   3. metadata-eval 100.0%

      \[\leadsto \left(\color{blue}{0.125} \cdot x - \frac{y \cdot z}{2}\right) + t \]

   4. associate-/l* 100.0%

      \[\leadsto \left(0.125 \cdot x - \color{blue}{y \cdot \frac{z}{2}}\right) + t \]

3. Simplified 100.0%

   \[\leadsto \color{blue}{\left(0.125 \cdot x - y \cdot \frac{z}{2}\right) + t} \]
4. Add Preprocessing

5. Final simplification 100.0%

   \[\leadsto t + \left(0.125 \cdot x - y \cdot \frac{z}{2}\right) \]
6. Add Preprocessing

Alternative 8: 32.2% accurate, 13.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 100.0%

   \[\left(\frac{1}{8} \cdot x - \frac{y \cdot z}{2}\right) + t \]
2. Step-by-step derivation

   1. add0 100.0%

      \[\leadsto \color{blue}{\left(\left(\frac{1}{8} \cdot x - \frac{y \cdot z}{2}\right) + 0\right)} + t \]

   2. add0 100.0%

      \[\leadsto \color{blue}{\left(\frac{1}{8} \cdot x - \frac{y \cdot z}{2}\right)} + t \]

   3. metadata-eval 100.0%

      \[\leadsto \left(\color{blue}{0.125} \cdot x - \frac{y \cdot z}{2}\right) + t \]

   4. associate-/l* 100.0%

      \[\leadsto \left(0.125 \cdot x - \color{blue}{y \cdot \frac{z}{2}}\right) + t \]

3. Simplified 100.0%

   \[\leadsto \color{blue}{\left(0.125 \cdot x - y \cdot \frac{z}{2}\right) + t} \]
4. Add Preprocessing

5. Taylor expanded in t around inf 33.5%

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

6. Final simplification 33.5%

   \[\leadsto t \]
7. Add Preprocessing

Developer target: 100.0% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \left(\frac{x}{8} + t\right) - \frac{z}{2} \cdot y \end{array} \]

FPCore

(FPCore (x y z t) :precision binary64 (- (+ (/ x 8.0) t) (* (/ z 2.0) y)))

C

double code(double x, double y, double z, double t) {
	return ((x / 8.0) + t) - ((z / 2.0) * y);
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    code = ((x / 8.0d0) + t) - ((z / 2.0d0) * y)
end function

Java

public static double code(double x, double y, double z, double t) {
	return ((x / 8.0) + t) - ((z / 2.0) * y);
}

Python

def code(x, y, z, t):
	return ((x / 8.0) + t) - ((z / 2.0) * y)

Julia

function code(x, y, z, t)
	return Float64(Float64(Float64(x / 8.0) + t) - Float64(Float64(z / 2.0) * y))
end

MATLAB

function tmp = code(x, y, z, t)
	tmp = ((x / 8.0) + t) - ((z / 2.0) * y);
end

Wolfram

code[x_, y_, z_, t_] := N[(N[(N[(x / 8.0), $MachinePrecision] + t), $MachinePrecision] - N[(N[(z / 2.0), $MachinePrecision] * y), $MachinePrecision]), $MachinePrecision]

Reproduce

herbie shell --seed 2024046 
(FPCore (x y z t)
  :name "Diagrams.Solve.Polynomial:quartForm  from diagrams-solve-0.1, B"
  :precision binary64

  :alt
  (- (+ (/ x 8.0) t) (* (/ z 2.0) y))

  (+ (- (* (/ 1.0 8.0) x) (/ (* y z) 2.0)) t))