Diagrams.ThreeD.Shapes:frustum from diagrams-lib-1.3.0.3, A

Percentage Accurate: 89.7% → 96.5%

Time: 19.2s

Alternatives: 15

Speedup: 0.5×

• 47.8% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

\[\begin{array}{l} \\ 2 \cdot \left(\left(x \cdot y + z \cdot t\right) - \left(\left(a + b \cdot c\right) \cdot c\right) \cdot i\right) \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (* 2.0 (- (+ (* x y) (* z t)) (* (* (+ a (* b c)) c) i))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	return 2.0 * (((x * y) + (z * t)) - (((a + (b * c)) * c) * i));
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i)
    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), intent (in) :: c
    real(8), intent (in) :: i
    code = 2.0d0 * (((x * y) + (z * t)) - (((a + (b * c)) * c) * i))
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	return 2.0 * (((x * y) + (z * t)) - (((a + (b * c)) * c) * i));
}

Python

def code(x, y, z, t, a, b, c, i):
	return 2.0 * (((x * y) + (z * t)) - (((a + (b * c)) * c) * i))

Julia

function code(x, y, z, t, a, b, c, i)
	return Float64(2.0 * Float64(Float64(Float64(x * y) + Float64(z * t)) - Float64(Float64(Float64(a + Float64(b * c)) * c) * i)))
end

MATLAB

function tmp = code(x, y, z, t, a, b, c, i)
	tmp = 2.0 * (((x * y) + (z * t)) - (((a + (b * c)) * c) * i));
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := N[(2.0 * N[(N[(N[(x * y), $MachinePrecision] + N[(z * t), $MachinePrecision]), $MachinePrecision] - N[(N[(N[(a + N[(b * c), $MachinePrecision]), $MachinePrecision] * c), $MachinePrecision] * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Initial Program: 89.7% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ 2 \cdot \left(\left(x \cdot y + z \cdot t\right) - \left(\left(a + b \cdot c\right) \cdot c\right) \cdot i\right) \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (* 2.0 (- (+ (* x y) (* z t)) (* (* (+ a (* b c)) c) i))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	return 2.0 * (((x * y) + (z * t)) - (((a + (b * c)) * c) * i));
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i)
    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), intent (in) :: c
    real(8), intent (in) :: i
    code = 2.0d0 * (((x * y) + (z * t)) - (((a + (b * c)) * c) * i))
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	return 2.0 * (((x * y) + (z * t)) - (((a + (b * c)) * c) * i));
}

Python

def code(x, y, z, t, a, b, c, i):
	return 2.0 * (((x * y) + (z * t)) - (((a + (b * c)) * c) * i))

Julia

function code(x, y, z, t, a, b, c, i)
	return Float64(2.0 * Float64(Float64(Float64(x * y) + Float64(z * t)) - Float64(Float64(Float64(a + Float64(b * c)) * c) * i)))
end

MATLAB

function tmp = code(x, y, z, t, a, b, c, i)
	tmp = 2.0 * (((x * y) + (z * t)) - (((a + (b * c)) * c) * i));
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := N[(2.0 * N[(N[(N[(x * y), $MachinePrecision] + N[(z * t), $MachinePrecision]), $MachinePrecision] - N[(N[(N[(a + N[(b * c), $MachinePrecision]), $MachinePrecision] * c), $MachinePrecision] * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Alternative 1: 96.5% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := a + b \cdot c\\ \mathbf{if}\;\left(x \cdot y + z \cdot t\right) - \left(t\_1 \cdot c\right) \cdot i \leq \infty:\\ \;\;\;\;\left(x \cdot y + \left(z \cdot t - t\_1 \cdot \left(c \cdot i\right)\right)\right) \cdot 2\\ \mathbf{else}:\\ \;\;\;\;\left(t - \frac{c \cdot \left(i \cdot \left(a + c \cdot b\right)\right)}{z}\right) \cdot \left(z \cdot 2\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (let* ((t_1 (+ a (* b c))))
   (if (<= (- (+ (* x y) (* z t)) (* (* t_1 c) i)) INFINITY)
     (* (+ (* x y) (- (* z t) (* t_1 (* c i)))) 2.0)
     (* (- t (/ (* c (* i (+ a (* c b)))) z)) (* z 2.0)))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double t_1 = a + (b * c);
	double tmp;
	if ((((x * y) + (z * t)) - ((t_1 * c) * i)) <= ((double) INFINITY)) {
		tmp = ((x * y) + ((z * t) - (t_1 * (c * i)))) * 2.0;
	} else {
		tmp = (t - ((c * (i * (a + (c * b)))) / z)) * (z * 2.0);
	}
	return tmp;
}

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double t_1 = a + (b * c);
	double tmp;
	if ((((x * y) + (z * t)) - ((t_1 * c) * i)) <= Double.POSITIVE_INFINITY) {
		tmp = ((x * y) + ((z * t) - (t_1 * (c * i)))) * 2.0;
	} else {
		tmp = (t - ((c * (i * (a + (c * b)))) / z)) * (z * 2.0);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i):
	t_1 = a + (b * c)
	tmp = 0
	if (((x * y) + (z * t)) - ((t_1 * c) * i)) <= math.inf:
		tmp = ((x * y) + ((z * t) - (t_1 * (c * i)))) * 2.0
	else:
		tmp = (t - ((c * (i * (a + (c * b)))) / z)) * (z * 2.0)
	return tmp

Julia

function code(x, y, z, t, a, b, c, i)
	t_1 = Float64(a + Float64(b * c))
	tmp = 0.0
	if (Float64(Float64(Float64(x * y) + Float64(z * t)) - Float64(Float64(t_1 * c) * i)) <= Inf)
		tmp = Float64(Float64(Float64(x * y) + Float64(Float64(z * t) - Float64(t_1 * Float64(c * i)))) * 2.0);
	else
		tmp = Float64(Float64(t - Float64(Float64(c * Float64(i * Float64(a + Float64(c * b)))) / z)) * Float64(z * 2.0));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i)
	t_1 = a + (b * c);
	tmp = 0.0;
	if ((((x * y) + (z * t)) - ((t_1 * c) * i)) <= Inf)
		tmp = ((x * y) + ((z * t) - (t_1 * (c * i)))) * 2.0;
	else
		tmp = (t - ((c * (i * (a + (c * b)))) / z)) * (z * 2.0);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := Block[{t$95$1 = N[(a + N[(b * c), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[(N[(N[(x * y), $MachinePrecision] + N[(z * t), $MachinePrecision]), $MachinePrecision] - N[(N[(t$95$1 * c), $MachinePrecision] * i), $MachinePrecision]), $MachinePrecision], Infinity], N[(N[(N[(x * y), $MachinePrecision] + N[(N[(z * t), $MachinePrecision] - N[(t$95$1 * N[(c * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * 2.0), $MachinePrecision], N[(N[(t - N[(N[(c * N[(i * N[(a + N[(c * b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision] * N[(z * 2.0), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if (-.f64 (+.f64 (*.f64 x y) (*.f64 z t)) (*.f64 (*.f64 (+.f64 a (*.f64 b c)) c) i)) < +inf.0

   1. Initial program 93.0%

      \[2 \cdot \left(\left(x \cdot y + z \cdot t\right) - \left(\left(a + b \cdot c\right) \cdot c\right) \cdot i\right) \]
   2. Add Preprocessing

   4. Applied egg-rr 0

      \[\leadsto expr\]

   if +inf.0 < (-.f64 (+.f64 (*.f64 x y) (*.f64 z t)) (*.f64 (*.f64 (+.f64 a (*.f64 b c)) c) i))

   1. Initial program 0.0%

      \[2 \cdot \left(\left(x \cdot y + z \cdot t\right) - \left(\left(a + b \cdot c\right) \cdot c\right) \cdot i\right) \]
   2. Add Preprocessing

   4. Applied egg-rr 0

      \[\leadsto expr\]

   5. Taylor expanded in z around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   8. Taylor expanded in x around 0 0

      \[\leadsto expr\]

   10. Simplified 0

       \[\leadsto expr\]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 2: 86.1% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := a + b \cdot c\\ t_2 := 2 \cdot \left(x \cdot y - c \cdot \left(i \cdot t\_1\right)\right)\\ t_3 := \left(t\_1 \cdot c\right) \cdot i\\ t_4 := 2 \cdot \left(t \cdot z - t\_3\right)\\ \mathbf{if}\;t\_3 \leq -\infty:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;t\_3 \leq -2 \cdot 10^{+94}:\\ \;\;\;\;t\_4\\ \mathbf{elif}\;t\_3 \leq 10^{+115}:\\ \;\;\;\;2 \cdot \left(t \cdot z + x \cdot y\right)\\ \mathbf{elif}\;t\_3 \leq 2 \cdot 10^{+284}:\\ \;\;\;\;t\_4\\ \mathbf{else}:\\ \;\;\;\;t\_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (let* ((t_1 (+ a (* b c)))
        (t_2 (* 2.0 (- (* x y) (* c (* i t_1)))))
        (t_3 (* (* t_1 c) i))
        (t_4 (* 2.0 (- (* t z) t_3))))
   (if (<= t_3 (- INFINITY))
     t_2
     (if (<= t_3 -2e+94)
       t_4
       (if (<= t_3 1e+115)
         (* 2.0 (+ (* t z) (* x y)))
         (if (<= t_3 2e+284) t_4 t_2))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double t_1 = a + (b * c);
	double t_2 = 2.0 * ((x * y) - (c * (i * t_1)));
	double t_3 = (t_1 * c) * i;
	double t_4 = 2.0 * ((t * z) - t_3);
	double tmp;
	if (t_3 <= -((double) INFINITY)) {
		tmp = t_2;
	} else if (t_3 <= -2e+94) {
		tmp = t_4;
	} else if (t_3 <= 1e+115) {
		tmp = 2.0 * ((t * z) + (x * y));
	} else if (t_3 <= 2e+284) {
		tmp = t_4;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double t_1 = a + (b * c);
	double t_2 = 2.0 * ((x * y) - (c * (i * t_1)));
	double t_3 = (t_1 * c) * i;
	double t_4 = 2.0 * ((t * z) - t_3);
	double tmp;
	if (t_3 <= -Double.POSITIVE_INFINITY) {
		tmp = t_2;
	} else if (t_3 <= -2e+94) {
		tmp = t_4;
	} else if (t_3 <= 1e+115) {
		tmp = 2.0 * ((t * z) + (x * y));
	} else if (t_3 <= 2e+284) {
		tmp = t_4;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i):
	t_1 = a + (b * c)
	t_2 = 2.0 * ((x * y) - (c * (i * t_1)))
	t_3 = (t_1 * c) * i
	t_4 = 2.0 * ((t * z) - t_3)
	tmp = 0
	if t_3 <= -math.inf:
		tmp = t_2
	elif t_3 <= -2e+94:
		tmp = t_4
	elif t_3 <= 1e+115:
		tmp = 2.0 * ((t * z) + (x * y))
	elif t_3 <= 2e+284:
		tmp = t_4
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a, b, c, i)
	t_1 = Float64(a + Float64(b * c))
	t_2 = Float64(2.0 * Float64(Float64(x * y) - Float64(c * Float64(i * t_1))))
	t_3 = Float64(Float64(t_1 * c) * i)
	t_4 = Float64(2.0 * Float64(Float64(t * z) - t_3))
	tmp = 0.0
	if (t_3 <= Float64(-Inf))
		tmp = t_2;
	elseif (t_3 <= -2e+94)
		tmp = t_4;
	elseif (t_3 <= 1e+115)
		tmp = Float64(2.0 * Float64(Float64(t * z) + Float64(x * y)));
	elseif (t_3 <= 2e+284)
		tmp = t_4;
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i)
	t_1 = a + (b * c);
	t_2 = 2.0 * ((x * y) - (c * (i * t_1)));
	t_3 = (t_1 * c) * i;
	t_4 = 2.0 * ((t * z) - t_3);
	tmp = 0.0;
	if (t_3 <= -Inf)
		tmp = t_2;
	elseif (t_3 <= -2e+94)
		tmp = t_4;
	elseif (t_3 <= 1e+115)
		tmp = 2.0 * ((t * z) + (x * y));
	elseif (t_3 <= 2e+284)
		tmp = t_4;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := Block[{t$95$1 = N[(a + N[(b * c), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(2.0 * N[(N[(x * y), $MachinePrecision] - N[(c * N[(i * t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(N[(t$95$1 * c), $MachinePrecision] * i), $MachinePrecision]}, Block[{t$95$4 = N[(2.0 * N[(N[(t * z), $MachinePrecision] - t$95$3), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$3, (-Infinity)], t$95$2, If[LessEqual[t$95$3, -2e+94], t$95$4, If[LessEqual[t$95$3, 1e+115], N[(2.0 * N[(N[(t * z), $MachinePrecision] + N[(x * y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$3, 2e+284], t$95$4, t$95$2]]]]]]]]

Derivation

1. Split input into 3 regimes

2. if (*.f64 (*.f64 (+.f64 a (*.f64 b c)) c) i) < -inf.0 or 2.00000000000000016e284 < (*.f64 (*.f64 (+.f64 a (*.f64 b c)) c) i)

   1. Initial program 73.2%

      \[2 \cdot \left(\left(x \cdot y + z \cdot t\right) - \left(\left(a + b \cdot c\right) \cdot c\right) \cdot i\right) \]
   2. Add Preprocessing

   3. Taylor expanded in z around 0 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]

   if -inf.0 < (*.f64 (*.f64 (+.f64 a (*.f64 b c)) c) i) < -2e94 or 1e115 < (*.f64 (*.f64 (+.f64 a (*.f64 b c)) c) i) < 2.00000000000000016e284

   1. Initial program 99.8%

      \[2 \cdot \left(\left(x \cdot y + z \cdot t\right) - \left(\left(a + b \cdot c\right) \cdot c\right) \cdot i\right) \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]

   if -2e94 < (*.f64 (*.f64 (+.f64 a (*.f64 b c)) c) i) < 1e115

   1. Initial program 98.3%

      \[2 \cdot \left(\left(x \cdot y + z \cdot t\right) - \left(\left(a + b \cdot c\right) \cdot c\right) \cdot i\right) \]
   2. Add Preprocessing

   3. Taylor expanded in c around 0 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 3: 86.2% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := a + b \cdot c\\ t_2 := \left(i \cdot t\_1\right) \cdot \left(c \cdot -2\right)\\ t_3 := \left(t\_1 \cdot c\right) \cdot i\\ t_4 := 2 \cdot \left(t \cdot z - t\_3\right)\\ \mathbf{if}\;t\_3 \leq -\infty:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;t\_3 \leq -2 \cdot 10^{+94}:\\ \;\;\;\;t\_4\\ \mathbf{elif}\;t\_3 \leq 10^{+115}:\\ \;\;\;\;2 \cdot \left(t \cdot z + x \cdot y\right)\\ \mathbf{elif}\;t\_3 \leq 2 \cdot 10^{+284}:\\ \;\;\;\;t\_4\\ \mathbf{else}:\\ \;\;\;\;t\_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (let* ((t_1 (+ a (* b c)))
        (t_2 (* (* i t_1) (* c -2.0)))
        (t_3 (* (* t_1 c) i))
        (t_4 (* 2.0 (- (* t z) t_3))))
   (if (<= t_3 (- INFINITY))
     t_2
     (if (<= t_3 -2e+94)
       t_4
       (if (<= t_3 1e+115)
         (* 2.0 (+ (* t z) (* x y)))
         (if (<= t_3 2e+284) t_4 t_2))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double t_1 = a + (b * c);
	double t_2 = (i * t_1) * (c * -2.0);
	double t_3 = (t_1 * c) * i;
	double t_4 = 2.0 * ((t * z) - t_3);
	double tmp;
	if (t_3 <= -((double) INFINITY)) {
		tmp = t_2;
	} else if (t_3 <= -2e+94) {
		tmp = t_4;
	} else if (t_3 <= 1e+115) {
		tmp = 2.0 * ((t * z) + (x * y));
	} else if (t_3 <= 2e+284) {
		tmp = t_4;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double t_1 = a + (b * c);
	double t_2 = (i * t_1) * (c * -2.0);
	double t_3 = (t_1 * c) * i;
	double t_4 = 2.0 * ((t * z) - t_3);
	double tmp;
	if (t_3 <= -Double.POSITIVE_INFINITY) {
		tmp = t_2;
	} else if (t_3 <= -2e+94) {
		tmp = t_4;
	} else if (t_3 <= 1e+115) {
		tmp = 2.0 * ((t * z) + (x * y));
	} else if (t_3 <= 2e+284) {
		tmp = t_4;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i):
	t_1 = a + (b * c)
	t_2 = (i * t_1) * (c * -2.0)
	t_3 = (t_1 * c) * i
	t_4 = 2.0 * ((t * z) - t_3)
	tmp = 0
	if t_3 <= -math.inf:
		tmp = t_2
	elif t_3 <= -2e+94:
		tmp = t_4
	elif t_3 <= 1e+115:
		tmp = 2.0 * ((t * z) + (x * y))
	elif t_3 <= 2e+284:
		tmp = t_4
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a, b, c, i)
	t_1 = Float64(a + Float64(b * c))
	t_2 = Float64(Float64(i * t_1) * Float64(c * -2.0))
	t_3 = Float64(Float64(t_1 * c) * i)
	t_4 = Float64(2.0 * Float64(Float64(t * z) - t_3))
	tmp = 0.0
	if (t_3 <= Float64(-Inf))
		tmp = t_2;
	elseif (t_3 <= -2e+94)
		tmp = t_4;
	elseif (t_3 <= 1e+115)
		tmp = Float64(2.0 * Float64(Float64(t * z) + Float64(x * y)));
	elseif (t_3 <= 2e+284)
		tmp = t_4;
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i)
	t_1 = a + (b * c);
	t_2 = (i * t_1) * (c * -2.0);
	t_3 = (t_1 * c) * i;
	t_4 = 2.0 * ((t * z) - t_3);
	tmp = 0.0;
	if (t_3 <= -Inf)
		tmp = t_2;
	elseif (t_3 <= -2e+94)
		tmp = t_4;
	elseif (t_3 <= 1e+115)
		tmp = 2.0 * ((t * z) + (x * y));
	elseif (t_3 <= 2e+284)
		tmp = t_4;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := Block[{t$95$1 = N[(a + N[(b * c), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(i * t$95$1), $MachinePrecision] * N[(c * -2.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(N[(t$95$1 * c), $MachinePrecision] * i), $MachinePrecision]}, Block[{t$95$4 = N[(2.0 * N[(N[(t * z), $MachinePrecision] - t$95$3), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$3, (-Infinity)], t$95$2, If[LessEqual[t$95$3, -2e+94], t$95$4, If[LessEqual[t$95$3, 1e+115], N[(2.0 * N[(N[(t * z), $MachinePrecision] + N[(x * y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$3, 2e+284], t$95$4, t$95$2]]]]]]]]

Derivation

1. Split input into 3 regimes

2. if (*.f64 (*.f64 (+.f64 a (*.f64 b c)) c) i) < -inf.0 or 2.00000000000000016e284 < (*.f64 (*.f64 (+.f64 a (*.f64 b c)) c) i)

   1. Initial program 73.2%

      \[2 \cdot \left(\left(x \cdot y + z \cdot t\right) - \left(\left(a + b \cdot c\right) \cdot c\right) \cdot i\right) \]
   2. Add Preprocessing

   3. Taylor expanded in i around inf 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]

   if -inf.0 < (*.f64 (*.f64 (+.f64 a (*.f64 b c)) c) i) < -2e94 or 1e115 < (*.f64 (*.f64 (+.f64 a (*.f64 b c)) c) i) < 2.00000000000000016e284

   1. Initial program 99.8%

      \[2 \cdot \left(\left(x \cdot y + z \cdot t\right) - \left(\left(a + b \cdot c\right) \cdot c\right) \cdot i\right) \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]

   if -2e94 < (*.f64 (*.f64 (+.f64 a (*.f64 b c)) c) i) < 1e115

   1. Initial program 98.3%

      \[2 \cdot \left(\left(x \cdot y + z \cdot t\right) - \left(\left(a + b \cdot c\right) \cdot c\right) \cdot i\right) \]
   2. Add Preprocessing

   3. Taylor expanded in c around 0 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 4: 94.4% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := a + b \cdot c\\ t_2 := 2 \cdot \left(x \cdot y - c \cdot \left(i \cdot t\_1\right)\right)\\ t_3 := t\_1 \cdot c\\ \mathbf{if}\;t\_3 \leq -\infty:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;t\_3 \leq 10^{+267}:\\ \;\;\;\;2 \cdot \left(\left(x \cdot y + z \cdot t\right) - t\_3 \cdot i\right)\\ \mathbf{else}:\\ \;\;\;\;t\_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (let* ((t_1 (+ a (* b c)))
        (t_2 (* 2.0 (- (* x y) (* c (* i t_1)))))
        (t_3 (* t_1 c)))
   (if (<= t_3 (- INFINITY))
     t_2
     (if (<= t_3 1e+267) (* 2.0 (- (+ (* x y) (* z t)) (* t_3 i))) t_2))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double t_1 = a + (b * c);
	double t_2 = 2.0 * ((x * y) - (c * (i * t_1)));
	double t_3 = t_1 * c;
	double tmp;
	if (t_3 <= -((double) INFINITY)) {
		tmp = t_2;
	} else if (t_3 <= 1e+267) {
		tmp = 2.0 * (((x * y) + (z * t)) - (t_3 * i));
	} else {
		tmp = t_2;
	}
	return tmp;
}

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double t_1 = a + (b * c);
	double t_2 = 2.0 * ((x * y) - (c * (i * t_1)));
	double t_3 = t_1 * c;
	double tmp;
	if (t_3 <= -Double.POSITIVE_INFINITY) {
		tmp = t_2;
	} else if (t_3 <= 1e+267) {
		tmp = 2.0 * (((x * y) + (z * t)) - (t_3 * i));
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i):
	t_1 = a + (b * c)
	t_2 = 2.0 * ((x * y) - (c * (i * t_1)))
	t_3 = t_1 * c
	tmp = 0
	if t_3 <= -math.inf:
		tmp = t_2
	elif t_3 <= 1e+267:
		tmp = 2.0 * (((x * y) + (z * t)) - (t_3 * i))
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a, b, c, i)
	t_1 = Float64(a + Float64(b * c))
	t_2 = Float64(2.0 * Float64(Float64(x * y) - Float64(c * Float64(i * t_1))))
	t_3 = Float64(t_1 * c)
	tmp = 0.0
	if (t_3 <= Float64(-Inf))
		tmp = t_2;
	elseif (t_3 <= 1e+267)
		tmp = Float64(2.0 * Float64(Float64(Float64(x * y) + Float64(z * t)) - Float64(t_3 * i)));
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i)
	t_1 = a + (b * c);
	t_2 = 2.0 * ((x * y) - (c * (i * t_1)));
	t_3 = t_1 * c;
	tmp = 0.0;
	if (t_3 <= -Inf)
		tmp = t_2;
	elseif (t_3 <= 1e+267)
		tmp = 2.0 * (((x * y) + (z * t)) - (t_3 * i));
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := Block[{t$95$1 = N[(a + N[(b * c), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(2.0 * N[(N[(x * y), $MachinePrecision] - N[(c * N[(i * t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(t$95$1 * c), $MachinePrecision]}, If[LessEqual[t$95$3, (-Infinity)], t$95$2, If[LessEqual[t$95$3, 1e+267], N[(2.0 * N[(N[(N[(x * y), $MachinePrecision] + N[(z * t), $MachinePrecision]), $MachinePrecision] - N[(t$95$3 * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$2]]]]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (+.f64 a (*.f64 b c)) c) < -inf.0 or 9.9999999999999997e266 < (*.f64 (+.f64 a (*.f64 b c)) c)

   1. Initial program 67.5%

      \[2 \cdot \left(\left(x \cdot y + z \cdot t\right) - \left(\left(a + b \cdot c\right) \cdot c\right) \cdot i\right) \]
   2. Add Preprocessing

   3. Taylor expanded in z around 0 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]

   if -inf.0 < (*.f64 (+.f64 a (*.f64 b c)) c) < 9.9999999999999997e266

   1. Initial program 98.3%

      \[2 \cdot \left(\left(x \cdot y + z \cdot t\right) - \left(\left(a + b \cdot c\right) \cdot c\right) \cdot i\right) \]
   2. Add Preprocessing
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 5: 73.8% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := 2 \cdot \left(t \cdot z + x \cdot y\right)\\ t_2 := \left(i \cdot \left(a + b \cdot c\right)\right) \cdot \left(c \cdot -2\right)\\ \mathbf{if}\;c \leq -3.5 \cdot 10^{+159}:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;c \leq -4.1 \cdot 10^{+114}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;c \leq -100000000:\\ \;\;\;\;\left(\left(c \cdot -2\right) \cdot c\right) \cdot \left(i \cdot \left(b + \frac{a}{c}\right)\right)\\ \mathbf{elif}\;c \leq 5 \cdot 10^{+39}:\\ \;\;\;\;t\_1\\ \mathbf{else}:\\ \;\;\;\;t\_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (let* ((t_1 (* 2.0 (+ (* t z) (* x y))))
        (t_2 (* (* i (+ a (* b c))) (* c -2.0))))
   (if (<= c -3.5e+159)
     t_2
     (if (<= c -4.1e+114)
       t_1
       (if (<= c -100000000.0)
         (* (* (* c -2.0) c) (* i (+ b (/ a c))))
         (if (<= c 5e+39) t_1 t_2))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double t_1 = 2.0 * ((t * z) + (x * y));
	double t_2 = (i * (a + (b * c))) * (c * -2.0);
	double tmp;
	if (c <= -3.5e+159) {
		tmp = t_2;
	} else if (c <= -4.1e+114) {
		tmp = t_1;
	} else if (c <= -100000000.0) {
		tmp = ((c * -2.0) * c) * (i * (b + (a / c)));
	} else if (c <= 5e+39) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i)
    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), intent (in) :: c
    real(8), intent (in) :: i
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = 2.0d0 * ((t * z) + (x * y))
    t_2 = (i * (a + (b * c))) * (c * (-2.0d0))
    if (c <= (-3.5d+159)) then
        tmp = t_2
    else if (c <= (-4.1d+114)) then
        tmp = t_1
    else if (c <= (-100000000.0d0)) then
        tmp = ((c * (-2.0d0)) * c) * (i * (b + (a / c)))
    else if (c <= 5d+39) then
        tmp = t_1
    else
        tmp = t_2
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double t_1 = 2.0 * ((t * z) + (x * y));
	double t_2 = (i * (a + (b * c))) * (c * -2.0);
	double tmp;
	if (c <= -3.5e+159) {
		tmp = t_2;
	} else if (c <= -4.1e+114) {
		tmp = t_1;
	} else if (c <= -100000000.0) {
		tmp = ((c * -2.0) * c) * (i * (b + (a / c)));
	} else if (c <= 5e+39) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i):
	t_1 = 2.0 * ((t * z) + (x * y))
	t_2 = (i * (a + (b * c))) * (c * -2.0)
	tmp = 0
	if c <= -3.5e+159:
		tmp = t_2
	elif c <= -4.1e+114:
		tmp = t_1
	elif c <= -100000000.0:
		tmp = ((c * -2.0) * c) * (i * (b + (a / c)))
	elif c <= 5e+39:
		tmp = t_1
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a, b, c, i)
	t_1 = Float64(2.0 * Float64(Float64(t * z) + Float64(x * y)))
	t_2 = Float64(Float64(i * Float64(a + Float64(b * c))) * Float64(c * -2.0))
	tmp = 0.0
	if (c <= -3.5e+159)
		tmp = t_2;
	elseif (c <= -4.1e+114)
		tmp = t_1;
	elseif (c <= -100000000.0)
		tmp = Float64(Float64(Float64(c * -2.0) * c) * Float64(i * Float64(b + Float64(a / c))));
	elseif (c <= 5e+39)
		tmp = t_1;
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i)
	t_1 = 2.0 * ((t * z) + (x * y));
	t_2 = (i * (a + (b * c))) * (c * -2.0);
	tmp = 0.0;
	if (c <= -3.5e+159)
		tmp = t_2;
	elseif (c <= -4.1e+114)
		tmp = t_1;
	elseif (c <= -100000000.0)
		tmp = ((c * -2.0) * c) * (i * (b + (a / c)));
	elseif (c <= 5e+39)
		tmp = t_1;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := Block[{t$95$1 = N[(2.0 * N[(N[(t * z), $MachinePrecision] + N[(x * y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(i * N[(a + N[(b * c), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * N[(c * -2.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[c, -3.5e+159], t$95$2, If[LessEqual[c, -4.1e+114], t$95$1, If[LessEqual[c, -100000000.0], N[(N[(N[(c * -2.0), $MachinePrecision] * c), $MachinePrecision] * N[(i * N[(b + N[(a / c), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[c, 5e+39], t$95$1, t$95$2]]]]]]

Derivation

1. Split input into 3 regimes

2. if c < -3.4999999999999999e159 or 5.00000000000000015e39 < c

   1. Initial program 79.4%

      \[2 \cdot \left(\left(x \cdot y + z \cdot t\right) - \left(\left(a + b \cdot c\right) \cdot c\right) \cdot i\right) \]
   2. Add Preprocessing

   3. Taylor expanded in i around inf 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]

   if -3.4999999999999999e159 < c < -4.1000000000000001e114 or -1e8 < c < 5.00000000000000015e39

   1. Initial program 96.2%

      \[2 \cdot \left(\left(x \cdot y + z \cdot t\right) - \left(\left(a + b \cdot c\right) \cdot c\right) \cdot i\right) \]
   2. Add Preprocessing

   3. Taylor expanded in c around 0 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]

   if -4.1000000000000001e114 < c < -1e8

   1. Initial program 81.6%

      \[2 \cdot \left(\left(x \cdot y + z \cdot t\right) - \left(\left(a + b \cdot c\right) \cdot c\right) \cdot i\right) \]
   2. Add Preprocessing

   3. Taylor expanded in c around inf 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 6: 73.7% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := 2 \cdot \left(t \cdot z + x \cdot y\right)\\ t_2 := \left(i \cdot \left(a + b \cdot c\right)\right) \cdot \left(c \cdot -2\right)\\ \mathbf{if}\;c \leq -3.5 \cdot 10^{+159}:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;c \leq -4.7 \cdot 10^{+114}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;c \leq -120000:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;c \leq 4.8 \cdot 10^{+38}:\\ \;\;\;\;t\_1\\ \mathbf{else}:\\ \;\;\;\;t\_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (let* ((t_1 (* 2.0 (+ (* t z) (* x y))))
        (t_2 (* (* i (+ a (* b c))) (* c -2.0))))
   (if (<= c -3.5e+159)
     t_2
     (if (<= c -4.7e+114)
       t_1
       (if (<= c -120000.0) t_2 (if (<= c 4.8e+38) t_1 t_2))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double t_1 = 2.0 * ((t * z) + (x * y));
	double t_2 = (i * (a + (b * c))) * (c * -2.0);
	double tmp;
	if (c <= -3.5e+159) {
		tmp = t_2;
	} else if (c <= -4.7e+114) {
		tmp = t_1;
	} else if (c <= -120000.0) {
		tmp = t_2;
	} else if (c <= 4.8e+38) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i)
    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), intent (in) :: c
    real(8), intent (in) :: i
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = 2.0d0 * ((t * z) + (x * y))
    t_2 = (i * (a + (b * c))) * (c * (-2.0d0))
    if (c <= (-3.5d+159)) then
        tmp = t_2
    else if (c <= (-4.7d+114)) then
        tmp = t_1
    else if (c <= (-120000.0d0)) then
        tmp = t_2
    else if (c <= 4.8d+38) then
        tmp = t_1
    else
        tmp = t_2
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double t_1 = 2.0 * ((t * z) + (x * y));
	double t_2 = (i * (a + (b * c))) * (c * -2.0);
	double tmp;
	if (c <= -3.5e+159) {
		tmp = t_2;
	} else if (c <= -4.7e+114) {
		tmp = t_1;
	} else if (c <= -120000.0) {
		tmp = t_2;
	} else if (c <= 4.8e+38) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i):
	t_1 = 2.0 * ((t * z) + (x * y))
	t_2 = (i * (a + (b * c))) * (c * -2.0)
	tmp = 0
	if c <= -3.5e+159:
		tmp = t_2
	elif c <= -4.7e+114:
		tmp = t_1
	elif c <= -120000.0:
		tmp = t_2
	elif c <= 4.8e+38:
		tmp = t_1
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a, b, c, i)
	t_1 = Float64(2.0 * Float64(Float64(t * z) + Float64(x * y)))
	t_2 = Float64(Float64(i * Float64(a + Float64(b * c))) * Float64(c * -2.0))
	tmp = 0.0
	if (c <= -3.5e+159)
		tmp = t_2;
	elseif (c <= -4.7e+114)
		tmp = t_1;
	elseif (c <= -120000.0)
		tmp = t_2;
	elseif (c <= 4.8e+38)
		tmp = t_1;
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i)
	t_1 = 2.0 * ((t * z) + (x * y));
	t_2 = (i * (a + (b * c))) * (c * -2.0);
	tmp = 0.0;
	if (c <= -3.5e+159)
		tmp = t_2;
	elseif (c <= -4.7e+114)
		tmp = t_1;
	elseif (c <= -120000.0)
		tmp = t_2;
	elseif (c <= 4.8e+38)
		tmp = t_1;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := Block[{t$95$1 = N[(2.0 * N[(N[(t * z), $MachinePrecision] + N[(x * y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(i * N[(a + N[(b * c), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * N[(c * -2.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[c, -3.5e+159], t$95$2, If[LessEqual[c, -4.7e+114], t$95$1, If[LessEqual[c, -120000.0], t$95$2, If[LessEqual[c, 4.8e+38], t$95$1, t$95$2]]]]]]

Derivation

1. Split input into 2 regimes

2. if c < -3.4999999999999999e159 or -4.7000000000000001e114 < c < -1.2e5 or 4.80000000000000035e38 < c

   1. Initial program 79.9%

      \[2 \cdot \left(\left(x \cdot y + z \cdot t\right) - \left(\left(a + b \cdot c\right) \cdot c\right) \cdot i\right) \]
   2. Add Preprocessing

   3. Taylor expanded in i around inf 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]

   if -3.4999999999999999e159 < c < -4.7000000000000001e114 or -1.2e5 < c < 4.80000000000000035e38

   1. Initial program 96.2%

      \[2 \cdot \left(\left(x \cdot y + z \cdot t\right) - \left(\left(a + b \cdot c\right) \cdot c\right) \cdot i\right) \]
   2. Add Preprocessing

   3. Taylor expanded in c around 0 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 7: 68.3% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := 2 \cdot \left(t \cdot z + x \cdot y\right)\\ \mathbf{if}\;c \leq -3.5 \cdot 10^{+159}:\\ \;\;\;\;\left(c \cdot \left(b \cdot \left(i \cdot \left(-c\right)\right)\right)\right) \cdot 2\\ \mathbf{elif}\;c \leq -3.2 \cdot 10^{+90}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;c \leq -4.2 \cdot 10^{+69}:\\ \;\;\;\;c \cdot \left(\left(c \cdot \left(i \cdot b\right)\right) \cdot -2\right)\\ \mathbf{elif}\;c \leq 7.8 \cdot 10^{+105}:\\ \;\;\;\;t\_1\\ \mathbf{else}:\\ \;\;\;\;\left(b \cdot \left(c \cdot \left(-2 \cdot i\right)\right)\right) \cdot c\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (let* ((t_1 (* 2.0 (+ (* t z) (* x y)))))
   (if (<= c -3.5e+159)
     (* (* c (* b (* i (- c)))) 2.0)
     (if (<= c -3.2e+90)
       t_1
       (if (<= c -4.2e+69)
         (* c (* (* c (* i b)) -2.0))
         (if (<= c 7.8e+105) t_1 (* (* b (* c (* -2.0 i))) c)))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double t_1 = 2.0 * ((t * z) + (x * y));
	double tmp;
	if (c <= -3.5e+159) {
		tmp = (c * (b * (i * -c))) * 2.0;
	} else if (c <= -3.2e+90) {
		tmp = t_1;
	} else if (c <= -4.2e+69) {
		tmp = c * ((c * (i * b)) * -2.0);
	} else if (c <= 7.8e+105) {
		tmp = t_1;
	} else {
		tmp = (b * (c * (-2.0 * i))) * c;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i)
    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), intent (in) :: c
    real(8), intent (in) :: i
    real(8) :: t_1
    real(8) :: tmp
    t_1 = 2.0d0 * ((t * z) + (x * y))
    if (c <= (-3.5d+159)) then
        tmp = (c * (b * (i * -c))) * 2.0d0
    else if (c <= (-3.2d+90)) then
        tmp = t_1
    else if (c <= (-4.2d+69)) then
        tmp = c * ((c * (i * b)) * (-2.0d0))
    else if (c <= 7.8d+105) then
        tmp = t_1
    else
        tmp = (b * (c * ((-2.0d0) * i))) * c
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double t_1 = 2.0 * ((t * z) + (x * y));
	double tmp;
	if (c <= -3.5e+159) {
		tmp = (c * (b * (i * -c))) * 2.0;
	} else if (c <= -3.2e+90) {
		tmp = t_1;
	} else if (c <= -4.2e+69) {
		tmp = c * ((c * (i * b)) * -2.0);
	} else if (c <= 7.8e+105) {
		tmp = t_1;
	} else {
		tmp = (b * (c * (-2.0 * i))) * c;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i):
	t_1 = 2.0 * ((t * z) + (x * y))
	tmp = 0
	if c <= -3.5e+159:
		tmp = (c * (b * (i * -c))) * 2.0
	elif c <= -3.2e+90:
		tmp = t_1
	elif c <= -4.2e+69:
		tmp = c * ((c * (i * b)) * -2.0)
	elif c <= 7.8e+105:
		tmp = t_1
	else:
		tmp = (b * (c * (-2.0 * i))) * c
	return tmp

Julia

function code(x, y, z, t, a, b, c, i)
	t_1 = Float64(2.0 * Float64(Float64(t * z) + Float64(x * y)))
	tmp = 0.0
	if (c <= -3.5e+159)
		tmp = Float64(Float64(c * Float64(b * Float64(i * Float64(-c)))) * 2.0);
	elseif (c <= -3.2e+90)
		tmp = t_1;
	elseif (c <= -4.2e+69)
		tmp = Float64(c * Float64(Float64(c * Float64(i * b)) * -2.0));
	elseif (c <= 7.8e+105)
		tmp = t_1;
	else
		tmp = Float64(Float64(b * Float64(c * Float64(-2.0 * i))) * c);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i)
	t_1 = 2.0 * ((t * z) + (x * y));
	tmp = 0.0;
	if (c <= -3.5e+159)
		tmp = (c * (b * (i * -c))) * 2.0;
	elseif (c <= -3.2e+90)
		tmp = t_1;
	elseif (c <= -4.2e+69)
		tmp = c * ((c * (i * b)) * -2.0);
	elseif (c <= 7.8e+105)
		tmp = t_1;
	else
		tmp = (b * (c * (-2.0 * i))) * c;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := Block[{t$95$1 = N[(2.0 * N[(N[(t * z), $MachinePrecision] + N[(x * y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[c, -3.5e+159], N[(N[(c * N[(b * N[(i * (-c)), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * 2.0), $MachinePrecision], If[LessEqual[c, -3.2e+90], t$95$1, If[LessEqual[c, -4.2e+69], N[(c * N[(N[(c * N[(i * b), $MachinePrecision]), $MachinePrecision] * -2.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[c, 7.8e+105], t$95$1, N[(N[(b * N[(c * N[(-2.0 * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * c), $MachinePrecision]]]]]]

Derivation

1. Split input into 4 regimes

2. if c < -3.4999999999999999e159

   1. Initial program 73.6%

      \[2 \cdot \left(\left(x \cdot y + z \cdot t\right) - \left(\left(a + b \cdot c\right) \cdot c\right) \cdot i\right) \]
   2. Add Preprocessing

   4. Applied egg-rr 0

      \[\leadsto expr\]

   5. Taylor expanded in b around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if -3.4999999999999999e159 < c < -3.19999999999999998e90 or -4.2000000000000003e69 < c < 7.79999999999999957e105

   1. Initial program 95.1%

      \[2 \cdot \left(\left(x \cdot y + z \cdot t\right) - \left(\left(a + b \cdot c\right) \cdot c\right) \cdot i\right) \]
   2. Add Preprocessing

   3. Taylor expanded in c around 0 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]

   if -3.19999999999999998e90 < c < -4.2000000000000003e69

   1. Initial program 68.4%

      \[2 \cdot \left(\left(x \cdot y + z \cdot t\right) - \left(\left(a + b \cdot c\right) \cdot c\right) \cdot i\right) \]
   2. Add Preprocessing

   4. Applied egg-rr 0

      \[\leadsto expr\]

   5. Taylor expanded in b around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   8. Taylor expanded in c around 0 0

      \[\leadsto expr\]

   10. Simplified 0

       \[\leadsto expr\]

   if 7.79999999999999957e105 < c

   1. Initial program 80.9%

      \[2 \cdot \left(\left(x \cdot y + z \cdot t\right) - \left(\left(a + b \cdot c\right) \cdot c\right) \cdot i\right) \]
   2. Add Preprocessing

   4. Applied egg-rr 0

      \[\leadsto expr\]

   5. Taylor expanded in b around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   8. Taylor expanded in c around 0 0

      \[\leadsto expr\]

   10. Simplified 0

       \[\leadsto expr\]

   12. Applied egg-rr 0

       \[\leadsto expr\]
3. Recombined 4 regimes into one program.
4. Add Preprocessing

Alternative 8: 68.3% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := 2 \cdot \left(t \cdot z + x \cdot y\right)\\ \mathbf{if}\;c \leq -6.4 \cdot 10^{+159}:\\ \;\;\;\;\left(\left(c \cdot i\right) \cdot \left(b \cdot c\right)\right) \cdot -2\\ \mathbf{elif}\;c \leq -5.5 \cdot 10^{+90}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;c \leq -3.6 \cdot 10^{+67}:\\ \;\;\;\;c \cdot \left(\left(c \cdot \left(i \cdot b\right)\right) \cdot -2\right)\\ \mathbf{elif}\;c \leq 1.35 \cdot 10^{+102}:\\ \;\;\;\;t\_1\\ \mathbf{else}:\\ \;\;\;\;\left(b \cdot \left(c \cdot \left(-2 \cdot i\right)\right)\right) \cdot c\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (let* ((t_1 (* 2.0 (+ (* t z) (* x y)))))
   (if (<= c -6.4e+159)
     (* (* (* c i) (* b c)) -2.0)
     (if (<= c -5.5e+90)
       t_1
       (if (<= c -3.6e+67)
         (* c (* (* c (* i b)) -2.0))
         (if (<= c 1.35e+102) t_1 (* (* b (* c (* -2.0 i))) c)))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double t_1 = 2.0 * ((t * z) + (x * y));
	double tmp;
	if (c <= -6.4e+159) {
		tmp = ((c * i) * (b * c)) * -2.0;
	} else if (c <= -5.5e+90) {
		tmp = t_1;
	} else if (c <= -3.6e+67) {
		tmp = c * ((c * (i * b)) * -2.0);
	} else if (c <= 1.35e+102) {
		tmp = t_1;
	} else {
		tmp = (b * (c * (-2.0 * i))) * c;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i)
    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), intent (in) :: c
    real(8), intent (in) :: i
    real(8) :: t_1
    real(8) :: tmp
    t_1 = 2.0d0 * ((t * z) + (x * y))
    if (c <= (-6.4d+159)) then
        tmp = ((c * i) * (b * c)) * (-2.0d0)
    else if (c <= (-5.5d+90)) then
        tmp = t_1
    else if (c <= (-3.6d+67)) then
        tmp = c * ((c * (i * b)) * (-2.0d0))
    else if (c <= 1.35d+102) then
        tmp = t_1
    else
        tmp = (b * (c * ((-2.0d0) * i))) * c
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double t_1 = 2.0 * ((t * z) + (x * y));
	double tmp;
	if (c <= -6.4e+159) {
		tmp = ((c * i) * (b * c)) * -2.0;
	} else if (c <= -5.5e+90) {
		tmp = t_1;
	} else if (c <= -3.6e+67) {
		tmp = c * ((c * (i * b)) * -2.0);
	} else if (c <= 1.35e+102) {
		tmp = t_1;
	} else {
		tmp = (b * (c * (-2.0 * i))) * c;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i):
	t_1 = 2.0 * ((t * z) + (x * y))
	tmp = 0
	if c <= -6.4e+159:
		tmp = ((c * i) * (b * c)) * -2.0
	elif c <= -5.5e+90:
		tmp = t_1
	elif c <= -3.6e+67:
		tmp = c * ((c * (i * b)) * -2.0)
	elif c <= 1.35e+102:
		tmp = t_1
	else:
		tmp = (b * (c * (-2.0 * i))) * c
	return tmp

Julia

function code(x, y, z, t, a, b, c, i)
	t_1 = Float64(2.0 * Float64(Float64(t * z) + Float64(x * y)))
	tmp = 0.0
	if (c <= -6.4e+159)
		tmp = Float64(Float64(Float64(c * i) * Float64(b * c)) * -2.0);
	elseif (c <= -5.5e+90)
		tmp = t_1;
	elseif (c <= -3.6e+67)
		tmp = Float64(c * Float64(Float64(c * Float64(i * b)) * -2.0));
	elseif (c <= 1.35e+102)
		tmp = t_1;
	else
		tmp = Float64(Float64(b * Float64(c * Float64(-2.0 * i))) * c);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i)
	t_1 = 2.0 * ((t * z) + (x * y));
	tmp = 0.0;
	if (c <= -6.4e+159)
		tmp = ((c * i) * (b * c)) * -2.0;
	elseif (c <= -5.5e+90)
		tmp = t_1;
	elseif (c <= -3.6e+67)
		tmp = c * ((c * (i * b)) * -2.0);
	elseif (c <= 1.35e+102)
		tmp = t_1;
	else
		tmp = (b * (c * (-2.0 * i))) * c;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := Block[{t$95$1 = N[(2.0 * N[(N[(t * z), $MachinePrecision] + N[(x * y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[c, -6.4e+159], N[(N[(N[(c * i), $MachinePrecision] * N[(b * c), $MachinePrecision]), $MachinePrecision] * -2.0), $MachinePrecision], If[LessEqual[c, -5.5e+90], t$95$1, If[LessEqual[c, -3.6e+67], N[(c * N[(N[(c * N[(i * b), $MachinePrecision]), $MachinePrecision] * -2.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[c, 1.35e+102], t$95$1, N[(N[(b * N[(c * N[(-2.0 * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * c), $MachinePrecision]]]]]]

Derivation

1. Split input into 4 regimes

2. if c < -6.3999999999999997e159

   1. Initial program 73.6%

      \[2 \cdot \left(\left(x \cdot y + z \cdot t\right) - \left(\left(a + b \cdot c\right) \cdot c\right) \cdot i\right) \]
   2. Add Preprocessing

   3. Taylor expanded in b around inf 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]

   if -6.3999999999999997e159 < c < -5.49999999999999999e90 or -3.5999999999999999e67 < c < 1.3500000000000001e102

   1. Initial program 95.1%

      \[2 \cdot \left(\left(x \cdot y + z \cdot t\right) - \left(\left(a + b \cdot c\right) \cdot c\right) \cdot i\right) \]
   2. Add Preprocessing

   3. Taylor expanded in c around 0 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]

   if -5.49999999999999999e90 < c < -3.5999999999999999e67

   1. Initial program 68.4%

      \[2 \cdot \left(\left(x \cdot y + z \cdot t\right) - \left(\left(a + b \cdot c\right) \cdot c\right) \cdot i\right) \]
   2. Add Preprocessing

   4. Applied egg-rr 0

      \[\leadsto expr\]

   5. Taylor expanded in b around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   8. Taylor expanded in c around 0 0

      \[\leadsto expr\]

   10. Simplified 0

       \[\leadsto expr\]

   if 1.3500000000000001e102 < c

   1. Initial program 80.9%

      \[2 \cdot \left(\left(x \cdot y + z \cdot t\right) - \left(\left(a + b \cdot c\right) \cdot c\right) \cdot i\right) \]
   2. Add Preprocessing

   4. Applied egg-rr 0

      \[\leadsto expr\]

   5. Taylor expanded in b around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   8. Taylor expanded in c around 0 0

      \[\leadsto expr\]

   10. Simplified 0

       \[\leadsto expr\]

   12. Applied egg-rr 0

       \[\leadsto expr\]
3. Recombined 4 regimes into one program.
4. Add Preprocessing

Alternative 9: 68.4% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := 2 \cdot \left(t \cdot z + x \cdot y\right)\\ t_2 := \left(b \cdot \left(c \cdot \left(-2 \cdot i\right)\right)\right) \cdot c\\ \mathbf{if}\;c \leq -3.5 \cdot 10^{+159}:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;c \leq -1.9 \cdot 10^{+92}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;c \leq -4.4 \cdot 10^{+66}:\\ \;\;\;\;c \cdot \left(\left(c \cdot \left(i \cdot b\right)\right) \cdot -2\right)\\ \mathbf{elif}\;c \leq 3.9 \cdot 10^{+101}:\\ \;\;\;\;t\_1\\ \mathbf{else}:\\ \;\;\;\;t\_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (let* ((t_1 (* 2.0 (+ (* t z) (* x y)))) (t_2 (* (* b (* c (* -2.0 i))) c)))
   (if (<= c -3.5e+159)
     t_2
     (if (<= c -1.9e+92)
       t_1
       (if (<= c -4.4e+66)
         (* c (* (* c (* i b)) -2.0))
         (if (<= c 3.9e+101) t_1 t_2))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double t_1 = 2.0 * ((t * z) + (x * y));
	double t_2 = (b * (c * (-2.0 * i))) * c;
	double tmp;
	if (c <= -3.5e+159) {
		tmp = t_2;
	} else if (c <= -1.9e+92) {
		tmp = t_1;
	} else if (c <= -4.4e+66) {
		tmp = c * ((c * (i * b)) * -2.0);
	} else if (c <= 3.9e+101) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i)
    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), intent (in) :: c
    real(8), intent (in) :: i
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = 2.0d0 * ((t * z) + (x * y))
    t_2 = (b * (c * ((-2.0d0) * i))) * c
    if (c <= (-3.5d+159)) then
        tmp = t_2
    else if (c <= (-1.9d+92)) then
        tmp = t_1
    else if (c <= (-4.4d+66)) then
        tmp = c * ((c * (i * b)) * (-2.0d0))
    else if (c <= 3.9d+101) then
        tmp = t_1
    else
        tmp = t_2
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double t_1 = 2.0 * ((t * z) + (x * y));
	double t_2 = (b * (c * (-2.0 * i))) * c;
	double tmp;
	if (c <= -3.5e+159) {
		tmp = t_2;
	} else if (c <= -1.9e+92) {
		tmp = t_1;
	} else if (c <= -4.4e+66) {
		tmp = c * ((c * (i * b)) * -2.0);
	} else if (c <= 3.9e+101) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i):
	t_1 = 2.0 * ((t * z) + (x * y))
	t_2 = (b * (c * (-2.0 * i))) * c
	tmp = 0
	if c <= -3.5e+159:
		tmp = t_2
	elif c <= -1.9e+92:
		tmp = t_1
	elif c <= -4.4e+66:
		tmp = c * ((c * (i * b)) * -2.0)
	elif c <= 3.9e+101:
		tmp = t_1
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a, b, c, i)
	t_1 = Float64(2.0 * Float64(Float64(t * z) + Float64(x * y)))
	t_2 = Float64(Float64(b * Float64(c * Float64(-2.0 * i))) * c)
	tmp = 0.0
	if (c <= -3.5e+159)
		tmp = t_2;
	elseif (c <= -1.9e+92)
		tmp = t_1;
	elseif (c <= -4.4e+66)
		tmp = Float64(c * Float64(Float64(c * Float64(i * b)) * -2.0));
	elseif (c <= 3.9e+101)
		tmp = t_1;
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i)
	t_1 = 2.0 * ((t * z) + (x * y));
	t_2 = (b * (c * (-2.0 * i))) * c;
	tmp = 0.0;
	if (c <= -3.5e+159)
		tmp = t_2;
	elseif (c <= -1.9e+92)
		tmp = t_1;
	elseif (c <= -4.4e+66)
		tmp = c * ((c * (i * b)) * -2.0);
	elseif (c <= 3.9e+101)
		tmp = t_1;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := Block[{t$95$1 = N[(2.0 * N[(N[(t * z), $MachinePrecision] + N[(x * y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(b * N[(c * N[(-2.0 * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * c), $MachinePrecision]}, If[LessEqual[c, -3.5e+159], t$95$2, If[LessEqual[c, -1.9e+92], t$95$1, If[LessEqual[c, -4.4e+66], N[(c * N[(N[(c * N[(i * b), $MachinePrecision]), $MachinePrecision] * -2.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[c, 3.9e+101], t$95$1, t$95$2]]]]]]

Derivation

1. Split input into 3 regimes

2. if c < -3.4999999999999999e159 or 3.9e101 < c

   1. Initial program 77.9%

      \[2 \cdot \left(\left(x \cdot y + z \cdot t\right) - \left(\left(a + b \cdot c\right) \cdot c\right) \cdot i\right) \]
   2. Add Preprocessing

   4. Applied egg-rr 0

      \[\leadsto expr\]

   5. Taylor expanded in b around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   8. Taylor expanded in c around 0 0

      \[\leadsto expr\]

   10. Simplified 0

       \[\leadsto expr\]

   12. Applied egg-rr 0

       \[\leadsto expr\]

   if -3.4999999999999999e159 < c < -1.9e92 or -4.3999999999999997e66 < c < 3.9e101

   1. Initial program 95.1%

      \[2 \cdot \left(\left(x \cdot y + z \cdot t\right) - \left(\left(a + b \cdot c\right) \cdot c\right) \cdot i\right) \]
   2. Add Preprocessing

   3. Taylor expanded in c around 0 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]

   if -1.9e92 < c < -4.3999999999999997e66

   1. Initial program 68.4%

      \[2 \cdot \left(\left(x \cdot y + z \cdot t\right) - \left(\left(a + b \cdot c\right) \cdot c\right) \cdot i\right) \]
   2. Add Preprocessing

   4. Applied egg-rr 0

      \[\leadsto expr\]

   5. Taylor expanded in b around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   8. Taylor expanded in c around 0 0

      \[\leadsto expr\]

   10. Simplified 0

       \[\leadsto expr\]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 10: 67.8% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := 2 \cdot \left(t \cdot z + x \cdot y\right)\\ t_2 := c \cdot \left(\left(c \cdot \left(i \cdot b\right)\right) \cdot -2\right)\\ \mathbf{if}\;c \leq -3.5 \cdot 10^{+159}:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;c \leq -6.2 \cdot 10^{+91}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;c \leq -1.65 \cdot 10^{+69}:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;c \leq 3 \cdot 10^{+107}:\\ \;\;\;\;t\_1\\ \mathbf{else}:\\ \;\;\;\;t\_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (let* ((t_1 (* 2.0 (+ (* t z) (* x y)))) (t_2 (* c (* (* c (* i b)) -2.0))))
   (if (<= c -3.5e+159)
     t_2
     (if (<= c -6.2e+91)
       t_1
       (if (<= c -1.65e+69) t_2 (if (<= c 3e+107) t_1 t_2))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double t_1 = 2.0 * ((t * z) + (x * y));
	double t_2 = c * ((c * (i * b)) * -2.0);
	double tmp;
	if (c <= -3.5e+159) {
		tmp = t_2;
	} else if (c <= -6.2e+91) {
		tmp = t_1;
	} else if (c <= -1.65e+69) {
		tmp = t_2;
	} else if (c <= 3e+107) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i)
    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), intent (in) :: c
    real(8), intent (in) :: i
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = 2.0d0 * ((t * z) + (x * y))
    t_2 = c * ((c * (i * b)) * (-2.0d0))
    if (c <= (-3.5d+159)) then
        tmp = t_2
    else if (c <= (-6.2d+91)) then
        tmp = t_1
    else if (c <= (-1.65d+69)) then
        tmp = t_2
    else if (c <= 3d+107) then
        tmp = t_1
    else
        tmp = t_2
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double t_1 = 2.0 * ((t * z) + (x * y));
	double t_2 = c * ((c * (i * b)) * -2.0);
	double tmp;
	if (c <= -3.5e+159) {
		tmp = t_2;
	} else if (c <= -6.2e+91) {
		tmp = t_1;
	} else if (c <= -1.65e+69) {
		tmp = t_2;
	} else if (c <= 3e+107) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i):
	t_1 = 2.0 * ((t * z) + (x * y))
	t_2 = c * ((c * (i * b)) * -2.0)
	tmp = 0
	if c <= -3.5e+159:
		tmp = t_2
	elif c <= -6.2e+91:
		tmp = t_1
	elif c <= -1.65e+69:
		tmp = t_2
	elif c <= 3e+107:
		tmp = t_1
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a, b, c, i)
	t_1 = Float64(2.0 * Float64(Float64(t * z) + Float64(x * y)))
	t_2 = Float64(c * Float64(Float64(c * Float64(i * b)) * -2.0))
	tmp = 0.0
	if (c <= -3.5e+159)
		tmp = t_2;
	elseif (c <= -6.2e+91)
		tmp = t_1;
	elseif (c <= -1.65e+69)
		tmp = t_2;
	elseif (c <= 3e+107)
		tmp = t_1;
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i)
	t_1 = 2.0 * ((t * z) + (x * y));
	t_2 = c * ((c * (i * b)) * -2.0);
	tmp = 0.0;
	if (c <= -3.5e+159)
		tmp = t_2;
	elseif (c <= -6.2e+91)
		tmp = t_1;
	elseif (c <= -1.65e+69)
		tmp = t_2;
	elseif (c <= 3e+107)
		tmp = t_1;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := Block[{t$95$1 = N[(2.0 * N[(N[(t * z), $MachinePrecision] + N[(x * y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(c * N[(N[(c * N[(i * b), $MachinePrecision]), $MachinePrecision] * -2.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[c, -3.5e+159], t$95$2, If[LessEqual[c, -6.2e+91], t$95$1, If[LessEqual[c, -1.65e+69], t$95$2, If[LessEqual[c, 3e+107], t$95$1, t$95$2]]]]]]

Derivation

1. Split input into 2 regimes

2. if c < -3.4999999999999999e159 or -6.19999999999999995e91 < c < -1.6499999999999999e69 or 3.00000000000000023e107 < c

   1. Initial program 77.1%

      \[2 \cdot \left(\left(x \cdot y + z \cdot t\right) - \left(\left(a + b \cdot c\right) \cdot c\right) \cdot i\right) \]
   2. Add Preprocessing

   4. Applied egg-rr 0

      \[\leadsto expr\]

   5. Taylor expanded in b around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   8. Taylor expanded in c around 0 0

      \[\leadsto expr\]

   10. Simplified 0

       \[\leadsto expr\]

   if -3.4999999999999999e159 < c < -6.19999999999999995e91 or -1.6499999999999999e69 < c < 3.00000000000000023e107

   1. Initial program 95.1%

      \[2 \cdot \left(\left(x \cdot y + z \cdot t\right) - \left(\left(a + b \cdot c\right) \cdot c\right) \cdot i\right) \]
   2. Add Preprocessing

   3. Taylor expanded in c around 0 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 11: 37.7% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x \cdot \left(2 \cdot y\right)\\ \mathbf{if}\;t \leq -7.6:\\ \;\;\;\;2 \cdot \left(x \cdot \left(t \cdot \frac{z}{x}\right)\right)\\ \mathbf{elif}\;t \leq 2.3 \cdot 10^{-276}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;t \leq 2.4 \cdot 10^{-212}:\\ \;\;\;\;a \cdot \left(i \cdot \left(c \cdot -2\right)\right)\\ \mathbf{elif}\;t \leq 1.22 \cdot 10^{+42}:\\ \;\;\;\;t\_1\\ \mathbf{else}:\\ \;\;\;\;2 \cdot \left(t \cdot z\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (let* ((t_1 (* x (* 2.0 y))))
   (if (<= t -7.6)
     (* 2.0 (* x (* t (/ z x))))
     (if (<= t 2.3e-276)
       t_1
       (if (<= t 2.4e-212)
         (* a (* i (* c -2.0)))
         (if (<= t 1.22e+42) t_1 (* 2.0 (* t z))))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double t_1 = x * (2.0 * y);
	double tmp;
	if (t <= -7.6) {
		tmp = 2.0 * (x * (t * (z / x)));
	} else if (t <= 2.3e-276) {
		tmp = t_1;
	} else if (t <= 2.4e-212) {
		tmp = a * (i * (c * -2.0));
	} else if (t <= 1.22e+42) {
		tmp = t_1;
	} else {
		tmp = 2.0 * (t * z);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i)
    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), intent (in) :: c
    real(8), intent (in) :: i
    real(8) :: t_1
    real(8) :: tmp
    t_1 = x * (2.0d0 * y)
    if (t <= (-7.6d0)) then
        tmp = 2.0d0 * (x * (t * (z / x)))
    else if (t <= 2.3d-276) then
        tmp = t_1
    else if (t <= 2.4d-212) then
        tmp = a * (i * (c * (-2.0d0)))
    else if (t <= 1.22d+42) then
        tmp = t_1
    else
        tmp = 2.0d0 * (t * z)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double t_1 = x * (2.0 * y);
	double tmp;
	if (t <= -7.6) {
		tmp = 2.0 * (x * (t * (z / x)));
	} else if (t <= 2.3e-276) {
		tmp = t_1;
	} else if (t <= 2.4e-212) {
		tmp = a * (i * (c * -2.0));
	} else if (t <= 1.22e+42) {
		tmp = t_1;
	} else {
		tmp = 2.0 * (t * z);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i):
	t_1 = x * (2.0 * y)
	tmp = 0
	if t <= -7.6:
		tmp = 2.0 * (x * (t * (z / x)))
	elif t <= 2.3e-276:
		tmp = t_1
	elif t <= 2.4e-212:
		tmp = a * (i * (c * -2.0))
	elif t <= 1.22e+42:
		tmp = t_1
	else:
		tmp = 2.0 * (t * z)
	return tmp

Julia

function code(x, y, z, t, a, b, c, i)
	t_1 = Float64(x * Float64(2.0 * y))
	tmp = 0.0
	if (t <= -7.6)
		tmp = Float64(2.0 * Float64(x * Float64(t * Float64(z / x))));
	elseif (t <= 2.3e-276)
		tmp = t_1;
	elseif (t <= 2.4e-212)
		tmp = Float64(a * Float64(i * Float64(c * -2.0)));
	elseif (t <= 1.22e+42)
		tmp = t_1;
	else
		tmp = Float64(2.0 * Float64(t * z));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i)
	t_1 = x * (2.0 * y);
	tmp = 0.0;
	if (t <= -7.6)
		tmp = 2.0 * (x * (t * (z / x)));
	elseif (t <= 2.3e-276)
		tmp = t_1;
	elseif (t <= 2.4e-212)
		tmp = a * (i * (c * -2.0));
	elseif (t <= 1.22e+42)
		tmp = t_1;
	else
		tmp = 2.0 * (t * z);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := Block[{t$95$1 = N[(x * N[(2.0 * y), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -7.6], N[(2.0 * N[(x * N[(t * N[(z / x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 2.3e-276], t$95$1, If[LessEqual[t, 2.4e-212], N[(a * N[(i * N[(c * -2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 1.22e+42], t$95$1, N[(2.0 * N[(t * z), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

1. Split input into 4 regimes

2. if t < -7.5999999999999996

   1. Initial program 87.5%

      \[2 \cdot \left(\left(x \cdot y + z \cdot t\right) - \left(\left(a + b \cdot c\right) \cdot c\right) \cdot i\right) \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]

   6. Taylor expanded in t around inf 0

      \[\leadsto expr\]

   8. Simplified 0

      \[\leadsto expr\]

   if -7.5999999999999996 < t < 2.29999999999999982e-276 or 2.39999999999999989e-212 < t < 1.22e42

   1. Initial program 91.3%

      \[2 \cdot \left(\left(x \cdot y + z \cdot t\right) - \left(\left(a + b \cdot c\right) \cdot c\right) \cdot i\right) \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]

   if 2.29999999999999982e-276 < t < 2.39999999999999989e-212

   1. Initial program 92.4%

      \[2 \cdot \left(\left(x \cdot y + z \cdot t\right) - \left(\left(a + b \cdot c\right) \cdot c\right) \cdot i\right) \]
   2. Add Preprocessing

   3. Taylor expanded in c around inf 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]

   7. Applied egg-rr 0

      \[\leadsto expr\]

   8. Taylor expanded in c around 0 0

      \[\leadsto expr\]

   10. Simplified 0

       \[\leadsto expr\]

   if 1.22e42 < t

   1. Initial program 88.3%

      \[2 \cdot \left(\left(x \cdot y + z \cdot t\right) - \left(\left(a + b \cdot c\right) \cdot c\right) \cdot i\right) \]
   2. Add Preprocessing

   3. Taylor expanded in z around inf 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]
3. Recombined 4 regimes into one program.
4. Add Preprocessing

Alternative 12: 39.0% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x \cdot \left(2 \cdot y\right)\\ t_2 := 2 \cdot \left(t \cdot z\right)\\ \mathbf{if}\;t \leq -7.6:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;t \leq 5.8 \cdot 10^{-274}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;t \leq 1.65 \cdot 10^{-212}:\\ \;\;\;\;a \cdot \left(i \cdot \left(c \cdot -2\right)\right)\\ \mathbf{elif}\;t \leq 1.8 \cdot 10^{+42}:\\ \;\;\;\;t\_1\\ \mathbf{else}:\\ \;\;\;\;t\_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (let* ((t_1 (* x (* 2.0 y))) (t_2 (* 2.0 (* t z))))
   (if (<= t -7.6)
     t_2
     (if (<= t 5.8e-274)
       t_1
       (if (<= t 1.65e-212)
         (* a (* i (* c -2.0)))
         (if (<= t 1.8e+42) t_1 t_2))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double t_1 = x * (2.0 * y);
	double t_2 = 2.0 * (t * z);
	double tmp;
	if (t <= -7.6) {
		tmp = t_2;
	} else if (t <= 5.8e-274) {
		tmp = t_1;
	} else if (t <= 1.65e-212) {
		tmp = a * (i * (c * -2.0));
	} else if (t <= 1.8e+42) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i)
    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), intent (in) :: c
    real(8), intent (in) :: i
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = x * (2.0d0 * y)
    t_2 = 2.0d0 * (t * z)
    if (t <= (-7.6d0)) then
        tmp = t_2
    else if (t <= 5.8d-274) then
        tmp = t_1
    else if (t <= 1.65d-212) then
        tmp = a * (i * (c * (-2.0d0)))
    else if (t <= 1.8d+42) then
        tmp = t_1
    else
        tmp = t_2
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double t_1 = x * (2.0 * y);
	double t_2 = 2.0 * (t * z);
	double tmp;
	if (t <= -7.6) {
		tmp = t_2;
	} else if (t <= 5.8e-274) {
		tmp = t_1;
	} else if (t <= 1.65e-212) {
		tmp = a * (i * (c * -2.0));
	} else if (t <= 1.8e+42) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i):
	t_1 = x * (2.0 * y)
	t_2 = 2.0 * (t * z)
	tmp = 0
	if t <= -7.6:
		tmp = t_2
	elif t <= 5.8e-274:
		tmp = t_1
	elif t <= 1.65e-212:
		tmp = a * (i * (c * -2.0))
	elif t <= 1.8e+42:
		tmp = t_1
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a, b, c, i)
	t_1 = Float64(x * Float64(2.0 * y))
	t_2 = Float64(2.0 * Float64(t * z))
	tmp = 0.0
	if (t <= -7.6)
		tmp = t_2;
	elseif (t <= 5.8e-274)
		tmp = t_1;
	elseif (t <= 1.65e-212)
		tmp = Float64(a * Float64(i * Float64(c * -2.0)));
	elseif (t <= 1.8e+42)
		tmp = t_1;
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i)
	t_1 = x * (2.0 * y);
	t_2 = 2.0 * (t * z);
	tmp = 0.0;
	if (t <= -7.6)
		tmp = t_2;
	elseif (t <= 5.8e-274)
		tmp = t_1;
	elseif (t <= 1.65e-212)
		tmp = a * (i * (c * -2.0));
	elseif (t <= 1.8e+42)
		tmp = t_1;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := Block[{t$95$1 = N[(x * N[(2.0 * y), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(2.0 * N[(t * z), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -7.6], t$95$2, If[LessEqual[t, 5.8e-274], t$95$1, If[LessEqual[t, 1.65e-212], N[(a * N[(i * N[(c * -2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 1.8e+42], t$95$1, t$95$2]]]]]]

Derivation

1. Split input into 3 regimes

2. if t < -7.5999999999999996 or 1.8e42 < t

   1. Initial program 87.8%

      \[2 \cdot \left(\left(x \cdot y + z \cdot t\right) - \left(\left(a + b \cdot c\right) \cdot c\right) \cdot i\right) \]
   2. Add Preprocessing

   3. Taylor expanded in z around inf 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]

   if -7.5999999999999996 < t < 5.79999999999999952e-274 or 1.6500000000000001e-212 < t < 1.8e42

   1. Initial program 91.3%

      \[2 \cdot \left(\left(x \cdot y + z \cdot t\right) - \left(\left(a + b \cdot c\right) \cdot c\right) \cdot i\right) \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]

   if 5.79999999999999952e-274 < t < 1.6500000000000001e-212

   1. Initial program 92.4%

      \[2 \cdot \left(\left(x \cdot y + z \cdot t\right) - \left(\left(a + b \cdot c\right) \cdot c\right) \cdot i\right) \]
   2. Add Preprocessing

   3. Taylor expanded in c around inf 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]

   7. Applied egg-rr 0

      \[\leadsto expr\]

   8. Taylor expanded in c around 0 0

      \[\leadsto expr\]

   10. Simplified 0

       \[\leadsto expr\]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 13: 39.6% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := 2 \cdot \left(t \cdot z\right)\\ \mathbf{if}\;t \leq -2.9 \cdot 10^{-19}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;t \leq 3 \cdot 10^{+41}:\\ \;\;\;\;x \cdot \left(2 \cdot y\right)\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (let* ((t_1 (* 2.0 (* t z))))
   (if (<= t -2.9e-19) t_1 (if (<= t 3e+41) (* x (* 2.0 y)) t_1))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double t_1 = 2.0 * (t * z);
	double tmp;
	if (t <= -2.9e-19) {
		tmp = t_1;
	} else if (t <= 3e+41) {
		tmp = x * (2.0 * y);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i)
    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), intent (in) :: c
    real(8), intent (in) :: i
    real(8) :: t_1
    real(8) :: tmp
    t_1 = 2.0d0 * (t * z)
    if (t <= (-2.9d-19)) then
        tmp = t_1
    else if (t <= 3d+41) then
        tmp = x * (2.0d0 * y)
    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 c, double i) {
	double t_1 = 2.0 * (t * z);
	double tmp;
	if (t <= -2.9e-19) {
		tmp = t_1;
	} else if (t <= 3e+41) {
		tmp = x * (2.0 * y);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i):
	t_1 = 2.0 * (t * z)
	tmp = 0
	if t <= -2.9e-19:
		tmp = t_1
	elif t <= 3e+41:
		tmp = x * (2.0 * y)
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b, c, i)
	t_1 = Float64(2.0 * Float64(t * z))
	tmp = 0.0
	if (t <= -2.9e-19)
		tmp = t_1;
	elseif (t <= 3e+41)
		tmp = Float64(x * Float64(2.0 * y));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i)
	t_1 = 2.0 * (t * z);
	tmp = 0.0;
	if (t <= -2.9e-19)
		tmp = t_1;
	elseif (t <= 3e+41)
		tmp = x * (2.0 * y);
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := Block[{t$95$1 = N[(2.0 * N[(t * z), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -2.9e-19], t$95$1, If[LessEqual[t, 3e+41], N[(x * N[(2.0 * y), $MachinePrecision]), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if t < -2.9e-19 or 2.9999999999999998e41 < t

   1. Initial program 87.8%

      \[2 \cdot \left(\left(x \cdot y + z \cdot t\right) - \left(\left(a + b \cdot c\right) \cdot c\right) \cdot i\right) \]
   2. Add Preprocessing

   3. Taylor expanded in z around inf 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]

   if -2.9e-19 < t < 2.9999999999999998e41

   1. Initial program 91.4%

      \[2 \cdot \left(\left(x \cdot y + z \cdot t\right) - \left(\left(a + b \cdot c\right) \cdot c\right) \cdot i\right) \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 14: 55.2% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;c \leq 1.08 \cdot 10^{+119}:\\ \;\;\;\;2 \cdot \left(t \cdot z + x \cdot y\right)\\ \mathbf{else}:\\ \;\;\;\;a \cdot \left(i \cdot \left(c \cdot -2\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (if (<= c 1.08e+119) (* 2.0 (+ (* t z) (* x y))) (* a (* i (* c -2.0)))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double tmp;
	if (c <= 1.08e+119) {
		tmp = 2.0 * ((t * z) + (x * y));
	} else {
		tmp = a * (i * (c * -2.0));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i)
    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), intent (in) :: c
    real(8), intent (in) :: i
    real(8) :: tmp
    if (c <= 1.08d+119) then
        tmp = 2.0d0 * ((t * z) + (x * y))
    else
        tmp = a * (i * (c * (-2.0d0)))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double tmp;
	if (c <= 1.08e+119) {
		tmp = 2.0 * ((t * z) + (x * y));
	} else {
		tmp = a * (i * (c * -2.0));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i):
	tmp = 0
	if c <= 1.08e+119:
		tmp = 2.0 * ((t * z) + (x * y))
	else:
		tmp = a * (i * (c * -2.0))
	return tmp

Julia

function code(x, y, z, t, a, b, c, i)
	tmp = 0.0
	if (c <= 1.08e+119)
		tmp = Float64(2.0 * Float64(Float64(t * z) + Float64(x * y)));
	else
		tmp = Float64(a * Float64(i * Float64(c * -2.0)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i)
	tmp = 0.0;
	if (c <= 1.08e+119)
		tmp = 2.0 * ((t * z) + (x * y));
	else
		tmp = a * (i * (c * -2.0));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := If[LessEqual[c, 1.08e+119], N[(2.0 * N[(N[(t * z), $MachinePrecision] + N[(x * y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(a * N[(i * N[(c * -2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if c < 1.08e119

   1. Initial program 91.5%

      \[2 \cdot \left(\left(x \cdot y + z \cdot t\right) - \left(\left(a + b \cdot c\right) \cdot c\right) \cdot i\right) \]
   2. Add Preprocessing

   3. Taylor expanded in c around 0 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]

   if 1.08e119 < c

   1. Initial program 79.4%

      \[2 \cdot \left(\left(x \cdot y + z \cdot t\right) - \left(\left(a + b \cdot c\right) \cdot c\right) \cdot i\right) \]
   2. Add Preprocessing

   3. Taylor expanded in c around inf 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]

   7. Applied egg-rr 0

      \[\leadsto expr\]

   8. Taylor expanded in c around 0 0

      \[\leadsto expr\]

   10. Simplified 0

       \[\leadsto expr\]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 15: 29.3% accurate, 3.8× speedup

Math

\[\begin{array}{l} \\ 2 \cdot \left(t \cdot z\right) \end{array} \]

FPCore

(FPCore (x y z t a b c i) :precision binary64 (* 2.0 (* t z)))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	return 2.0 * (t * z);
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i)
    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), intent (in) :: c
    real(8), intent (in) :: i
    code = 2.0d0 * (t * z)
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	return 2.0 * (t * z);
}

Python

def code(x, y, z, t, a, b, c, i):
	return 2.0 * (t * z)

Julia

function code(x, y, z, t, a, b, c, i)
	return Float64(2.0 * Float64(t * z))
end

MATLAB

function tmp = code(x, y, z, t, a, b, c, i)
	tmp = 2.0 * (t * z);
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := N[(2.0 * N[(t * z), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 89.7%

   \[2 \cdot \left(\left(x \cdot y + z \cdot t\right) - \left(\left(a + b \cdot c\right) \cdot c\right) \cdot i\right) \]
2. Add Preprocessing

3. Taylor expanded in z around inf 0

   \[\leadsto expr\]

5. Simplified 0

   \[\leadsto expr\]
6. Add Preprocessing

Developer target: 93.6% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ 2 \cdot \left(\left(x \cdot y + z \cdot t\right) - \left(a + b \cdot c\right) \cdot \left(c \cdot i\right)\right) \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (* 2.0 (- (+ (* x y) (* z t)) (* (+ a (* b c)) (* c i)))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	return 2.0 * (((x * y) + (z * t)) - ((a + (b * c)) * (c * i)));
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i)
    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), intent (in) :: c
    real(8), intent (in) :: i
    code = 2.0d0 * (((x * y) + (z * t)) - ((a + (b * c)) * (c * i)))
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	return 2.0 * (((x * y) + (z * t)) - ((a + (b * c)) * (c * i)));
}

Python

def code(x, y, z, t, a, b, c, i):
	return 2.0 * (((x * y) + (z * t)) - ((a + (b * c)) * (c * i)))

Julia

function code(x, y, z, t, a, b, c, i)
	return Float64(2.0 * Float64(Float64(Float64(x * y) + Float64(z * t)) - Float64(Float64(a + Float64(b * c)) * Float64(c * i))))
end

MATLAB

function tmp = code(x, y, z, t, a, b, c, i)
	tmp = 2.0 * (((x * y) + (z * t)) - ((a + (b * c)) * (c * i)));
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := N[(2.0 * N[(N[(N[(x * y), $MachinePrecision] + N[(z * t), $MachinePrecision]), $MachinePrecision] - N[(N[(a + N[(b * c), $MachinePrecision]), $MachinePrecision] * N[(c * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Reproduce

herbie shell --seed 2024110 
(FPCore (x y z t a b c i)
  :name "Diagrams.ThreeD.Shapes:frustum from diagrams-lib-1.3.0.3, A"
  :precision binary64

  :alt
  (* 2.0 (- (+ (* x y) (* z t)) (* (+ a (* b c)) (* c i))))

  (* 2.0 (- (+ (* x y) (* z t)) (* (* (+ a (* b c)) c) i))))