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

Percentage Accurate: 97.5% → 98.9%

Time: 10.4s

Alternatives: 14

Speedup: 1.0×

• 33.5% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

\[\begin{array}{l} \\ \left(\left(x \cdot y + \frac{z \cdot t}{16}\right) - \frac{a \cdot b}{4}\right) + c \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (+ (- (+ (* x y) (/ (* z t) 16.0)) (/ (* a b) 4.0)) c))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	return (((x * y) + ((z * t) / 16.0)) - ((a * b) / 4.0)) + c;
}

Fortran

real(8) function code(x, y, z, t, a, b, c)
    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
    code = (((x * y) + ((z * t) / 16.0d0)) - ((a * b) / 4.0d0)) + c
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c) {
	return (((x * y) + ((z * t) / 16.0)) - ((a * b) / 4.0)) + c;
}

Python

def code(x, y, z, t, a, b, c):
	return (((x * y) + ((z * t) / 16.0)) - ((a * b) / 4.0)) + c

Julia

function code(x, y, z, t, a, b, c)
	return Float64(Float64(Float64(Float64(x * y) + Float64(Float64(z * t) / 16.0)) - Float64(Float64(a * b) / 4.0)) + c)
end

MATLAB

function tmp = code(x, y, z, t, a, b, c)
	tmp = (((x * y) + ((z * t) / 16.0)) - ((a * b) / 4.0)) + c;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := N[(N[(N[(N[(x * y), $MachinePrecision] + N[(N[(z * t), $MachinePrecision] / 16.0), $MachinePrecision]), $MachinePrecision] - N[(N[(a * b), $MachinePrecision] / 4.0), $MachinePrecision]), $MachinePrecision] + c), $MachinePrecision]

Initial Program: 97.5% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \left(\left(x \cdot y + \frac{z \cdot t}{16}\right) - \frac{a \cdot b}{4}\right) + c \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (+ (- (+ (* x y) (/ (* z t) 16.0)) (/ (* a b) 4.0)) c))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	return (((x * y) + ((z * t) / 16.0)) - ((a * b) / 4.0)) + c;
}

Fortran

real(8) function code(x, y, z, t, a, b, c)
    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
    code = (((x * y) + ((z * t) / 16.0d0)) - ((a * b) / 4.0d0)) + c
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c) {
	return (((x * y) + ((z * t) / 16.0)) - ((a * b) / 4.0)) + c;
}

Python

def code(x, y, z, t, a, b, c):
	return (((x * y) + ((z * t) / 16.0)) - ((a * b) / 4.0)) + c

Julia

function code(x, y, z, t, a, b, c)
	return Float64(Float64(Float64(Float64(x * y) + Float64(Float64(z * t) / 16.0)) - Float64(Float64(a * b) / 4.0)) + c)
end

MATLAB

function tmp = code(x, y, z, t, a, b, c)
	tmp = (((x * y) + ((z * t) / 16.0)) - ((a * b) / 4.0)) + c;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := N[(N[(N[(N[(x * y), $MachinePrecision] + N[(N[(z * t), $MachinePrecision] / 16.0), $MachinePrecision]), $MachinePrecision] - N[(N[(a * b), $MachinePrecision] / 4.0), $MachinePrecision]), $MachinePrecision] + c), $MachinePrecision]

Alternative 1: 98.9% accurate, 0.1× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(x, y, \mathsf{fma}\left(\frac{z}{16}, t, \mathsf{fma}\left(\frac{a}{-4}, b, c\right)\right)\right) \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (fma x y (fma (/ z 16.0) t (fma (/ a -4.0) b c))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	return fma(x, y, fma((z / 16.0), t, fma((a / -4.0), b, c)));
}

Julia

function code(x, y, z, t, a, b, c)
	return fma(x, y, fma(Float64(z / 16.0), t, fma(Float64(a / -4.0), b, c)))
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := N[(x * y + N[(N[(z / 16.0), $MachinePrecision] * t + N[(N[(a / -4.0), $MachinePrecision] * b + c), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 2: 98.1% accurate, 0.1× speedup

Math

\[\begin{array}{l} \\ c + \mathsf{fma}\left(b, a \cdot -0.25, \mathsf{fma}\left(0.0625, z \cdot t, x \cdot y\right)\right) \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (+ c (fma b (* a -0.25) (fma 0.0625 (* z t) (* x y)))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	return c + fma(b, (a * -0.25), fma(0.0625, (z * t), (x * y)));
}

Julia

function code(x, y, z, t, a, b, c)
	return Float64(c + fma(b, Float64(a * -0.25), fma(0.0625, Float64(z * t), Float64(x * y))))
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := N[(c + N[(b * N[(a * -0.25), $MachinePrecision] + N[(0.0625 * N[(z * t), $MachinePrecision] + N[(x * y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 3: 87.7% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := 0.0625 \cdot \left(z \cdot t\right)\\ \mathbf{if}\;x \cdot y \leq -5.3 \cdot 10^{+153} \lor \neg \left(x \cdot y \leq 5.8 \cdot 10^{+208}\right):\\ \;\;\;\;c + \left(x \cdot y + t_1\right)\\ \mathbf{else}:\\ \;\;\;\;\left(c + t_1\right) - \left(a \cdot b\right) \cdot 0.25\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (let* ((t_1 (* 0.0625 (* z t))))
   (if (or (<= (* x y) -5.3e+153) (not (<= (* x y) 5.8e+208)))
     (+ c (+ (* x y) t_1))
     (- (+ c t_1) (* (* a b) 0.25)))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = 0.0625 * (z * t);
	double tmp;
	if (((x * y) <= -5.3e+153) || !((x * y) <= 5.8e+208)) {
		tmp = c + ((x * y) + t_1);
	} else {
		tmp = (c + t_1) - ((a * b) * 0.25);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c)
    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) :: t_1
    real(8) :: tmp
    t_1 = 0.0625d0 * (z * t)
    if (((x * y) <= (-5.3d+153)) .or. (.not. ((x * y) <= 5.8d+208))) then
        tmp = c + ((x * y) + t_1)
    else
        tmp = (c + t_1) - ((a * b) * 0.25d0)
    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 t_1 = 0.0625 * (z * t);
	double tmp;
	if (((x * y) <= -5.3e+153) || !((x * y) <= 5.8e+208)) {
		tmp = c + ((x * y) + t_1);
	} else {
		tmp = (c + t_1) - ((a * b) * 0.25);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	t_1 = 0.0625 * (z * t)
	tmp = 0
	if ((x * y) <= -5.3e+153) or not ((x * y) <= 5.8e+208):
		tmp = c + ((x * y) + t_1)
	else:
		tmp = (c + t_1) - ((a * b) * 0.25)
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	t_1 = Float64(0.0625 * Float64(z * t))
	tmp = 0.0
	if ((Float64(x * y) <= -5.3e+153) || !(Float64(x * y) <= 5.8e+208))
		tmp = Float64(c + Float64(Float64(x * y) + t_1));
	else
		tmp = Float64(Float64(c + t_1) - Float64(Float64(a * b) * 0.25));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	t_1 = 0.0625 * (z * t);
	tmp = 0.0;
	if (((x * y) <= -5.3e+153) || ~(((x * y) <= 5.8e+208)))
		tmp = c + ((x * y) + t_1);
	else
		tmp = (c + t_1) - ((a * b) * 0.25);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := Block[{t$95$1 = N[(0.0625 * N[(z * t), $MachinePrecision]), $MachinePrecision]}, If[Or[LessEqual[N[(x * y), $MachinePrecision], -5.3e+153], N[Not[LessEqual[N[(x * y), $MachinePrecision], 5.8e+208]], $MachinePrecision]], N[(c + N[(N[(x * y), $MachinePrecision] + t$95$1), $MachinePrecision]), $MachinePrecision], N[(N[(c + t$95$1), $MachinePrecision] - N[(N[(a * b), $MachinePrecision] * 0.25), $MachinePrecision]), $MachinePrecision]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 4: 51.8% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := c + b \cdot \left(a \cdot -0.25\right)\\ t_2 := 0.0625 \cdot \left(z \cdot t\right)\\ t_3 := c + x \cdot y\\ \mathbf{if}\;z \leq -1.1 \cdot 10^{+218}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;z \leq -5.5 \cdot 10^{+139}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq -7.5 \cdot 10^{+77}:\\ \;\;\;\;t_3\\ \mathbf{elif}\;z \leq -1.7 \cdot 10^{+28}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq -9 \cdot 10^{-101}:\\ \;\;\;\;t_3\\ \mathbf{elif}\;z \leq 1.75 \cdot 10^{-63}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (let* ((t_1 (+ c (* b (* a -0.25))))
        (t_2 (* 0.0625 (* z t)))
        (t_3 (+ c (* x y))))
   (if (<= z -1.1e+218)
     t_2
     (if (<= z -5.5e+139)
       t_1
       (if (<= z -7.5e+77)
         t_3
         (if (<= z -1.7e+28)
           t_1
           (if (<= z -9e-101) t_3 (if (<= z 1.75e-63) t_1 t_2))))))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = c + (b * (a * -0.25));
	double t_2 = 0.0625 * (z * t);
	double t_3 = c + (x * y);
	double tmp;
	if (z <= -1.1e+218) {
		tmp = t_2;
	} else if (z <= -5.5e+139) {
		tmp = t_1;
	} else if (z <= -7.5e+77) {
		tmp = t_3;
	} else if (z <= -1.7e+28) {
		tmp = t_1;
	} else if (z <= -9e-101) {
		tmp = t_3;
	} else if (z <= 1.75e-63) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c)
    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) :: t_1
    real(8) :: t_2
    real(8) :: t_3
    real(8) :: tmp
    t_1 = c + (b * (a * (-0.25d0)))
    t_2 = 0.0625d0 * (z * t)
    t_3 = c + (x * y)
    if (z <= (-1.1d+218)) then
        tmp = t_2
    else if (z <= (-5.5d+139)) then
        tmp = t_1
    else if (z <= (-7.5d+77)) then
        tmp = t_3
    else if (z <= (-1.7d+28)) then
        tmp = t_1
    else if (z <= (-9d-101)) then
        tmp = t_3
    else if (z <= 1.75d-63) 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 t_1 = c + (b * (a * -0.25));
	double t_2 = 0.0625 * (z * t);
	double t_3 = c + (x * y);
	double tmp;
	if (z <= -1.1e+218) {
		tmp = t_2;
	} else if (z <= -5.5e+139) {
		tmp = t_1;
	} else if (z <= -7.5e+77) {
		tmp = t_3;
	} else if (z <= -1.7e+28) {
		tmp = t_1;
	} else if (z <= -9e-101) {
		tmp = t_3;
	} else if (z <= 1.75e-63) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	t_1 = c + (b * (a * -0.25))
	t_2 = 0.0625 * (z * t)
	t_3 = c + (x * y)
	tmp = 0
	if z <= -1.1e+218:
		tmp = t_2
	elif z <= -5.5e+139:
		tmp = t_1
	elif z <= -7.5e+77:
		tmp = t_3
	elif z <= -1.7e+28:
		tmp = t_1
	elif z <= -9e-101:
		tmp = t_3
	elif z <= 1.75e-63:
		tmp = t_1
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	t_1 = Float64(c + Float64(b * Float64(a * -0.25)))
	t_2 = Float64(0.0625 * Float64(z * t))
	t_3 = Float64(c + Float64(x * y))
	tmp = 0.0
	if (z <= -1.1e+218)
		tmp = t_2;
	elseif (z <= -5.5e+139)
		tmp = t_1;
	elseif (z <= -7.5e+77)
		tmp = t_3;
	elseif (z <= -1.7e+28)
		tmp = t_1;
	elseif (z <= -9e-101)
		tmp = t_3;
	elseif (z <= 1.75e-63)
		tmp = t_1;
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	t_1 = c + (b * (a * -0.25));
	t_2 = 0.0625 * (z * t);
	t_3 = c + (x * y);
	tmp = 0.0;
	if (z <= -1.1e+218)
		tmp = t_2;
	elseif (z <= -5.5e+139)
		tmp = t_1;
	elseif (z <= -7.5e+77)
		tmp = t_3;
	elseif (z <= -1.7e+28)
		tmp = t_1;
	elseif (z <= -9e-101)
		tmp = t_3;
	elseif (z <= 1.75e-63)
		tmp = t_1;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := Block[{t$95$1 = N[(c + N[(b * N[(a * -0.25), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(0.0625 * N[(z * t), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(c + N[(x * y), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -1.1e+218], t$95$2, If[LessEqual[z, -5.5e+139], t$95$1, If[LessEqual[z, -7.5e+77], t$95$3, If[LessEqual[z, -1.7e+28], t$95$1, If[LessEqual[z, -9e-101], t$95$3, If[LessEqual[z, 1.75e-63], t$95$1, t$95$2]]]]]]]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 5: 86.2% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \cdot b \leq -1 \cdot 10^{+151} \lor \neg \left(a \cdot b \leq 10^{+204}\right):\\ \;\;\;\;c + b \cdot \left(a \cdot -0.25\right)\\ \mathbf{else}:\\ \;\;\;\;c + \left(x \cdot y + 0.0625 \cdot \left(z \cdot t\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (if (or (<= (* a b) -1e+151) (not (<= (* a b) 1e+204)))
   (+ c (* b (* a -0.25)))
   (+ c (+ (* x y) (* 0.0625 (* z t))))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double tmp;
	if (((a * b) <= -1e+151) || !((a * b) <= 1e+204)) {
		tmp = c + (b * (a * -0.25));
	} else {
		tmp = c + ((x * y) + (0.0625 * (z * t)));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c)
    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) :: tmp
    if (((a * b) <= (-1d+151)) .or. (.not. ((a * b) <= 1d+204))) then
        tmp = c + (b * (a * (-0.25d0)))
    else
        tmp = c + ((x * y) + (0.0625d0 * (z * t)))
    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 tmp;
	if (((a * b) <= -1e+151) || !((a * b) <= 1e+204)) {
		tmp = c + (b * (a * -0.25));
	} else {
		tmp = c + ((x * y) + (0.0625 * (z * t)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	tmp = 0
	if ((a * b) <= -1e+151) or not ((a * b) <= 1e+204):
		tmp = c + (b * (a * -0.25))
	else:
		tmp = c + ((x * y) + (0.0625 * (z * t)))
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	tmp = 0.0
	if ((Float64(a * b) <= -1e+151) || !(Float64(a * b) <= 1e+204))
		tmp = Float64(c + Float64(b * Float64(a * -0.25)));
	else
		tmp = Float64(c + Float64(Float64(x * y) + Float64(0.0625 * Float64(z * t))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	tmp = 0.0;
	if (((a * b) <= -1e+151) || ~(((a * b) <= 1e+204)))
		tmp = c + (b * (a * -0.25));
	else
		tmp = c + ((x * y) + (0.0625 * (z * t)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := If[Or[LessEqual[N[(a * b), $MachinePrecision], -1e+151], N[Not[LessEqual[N[(a * b), $MachinePrecision], 1e+204]], $MachinePrecision]], N[(c + N[(b * N[(a * -0.25), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(c + N[(N[(x * y), $MachinePrecision] + N[(0.0625 * N[(z * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 6: 86.4% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := 0.0625 \cdot \left(z \cdot t\right)\\ \mathbf{if}\;a \cdot b \leq -1 \cdot 10^{+151}:\\ \;\;\;\;c + b \cdot \left(a \cdot -0.25\right)\\ \mathbf{elif}\;a \cdot b \leq 10^{+204}:\\ \;\;\;\;c + \left(x \cdot y + t_1\right)\\ \mathbf{else}:\\ \;\;\;\;t_1 - \left(a \cdot b\right) \cdot 0.25\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (let* ((t_1 (* 0.0625 (* z t))))
   (if (<= (* a b) -1e+151)
     (+ c (* b (* a -0.25)))
     (if (<= (* a b) 1e+204) (+ c (+ (* x y) t_1)) (- t_1 (* (* a b) 0.25))))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = 0.0625 * (z * t);
	double tmp;
	if ((a * b) <= -1e+151) {
		tmp = c + (b * (a * -0.25));
	} else if ((a * b) <= 1e+204) {
		tmp = c + ((x * y) + t_1);
	} else {
		tmp = t_1 - ((a * b) * 0.25);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c)
    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) :: t_1
    real(8) :: tmp
    t_1 = 0.0625d0 * (z * t)
    if ((a * b) <= (-1d+151)) then
        tmp = c + (b * (a * (-0.25d0)))
    else if ((a * b) <= 1d+204) then
        tmp = c + ((x * y) + t_1)
    else
        tmp = t_1 - ((a * b) * 0.25d0)
    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 t_1 = 0.0625 * (z * t);
	double tmp;
	if ((a * b) <= -1e+151) {
		tmp = c + (b * (a * -0.25));
	} else if ((a * b) <= 1e+204) {
		tmp = c + ((x * y) + t_1);
	} else {
		tmp = t_1 - ((a * b) * 0.25);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	t_1 = 0.0625 * (z * t)
	tmp = 0
	if (a * b) <= -1e+151:
		tmp = c + (b * (a * -0.25))
	elif (a * b) <= 1e+204:
		tmp = c + ((x * y) + t_1)
	else:
		tmp = t_1 - ((a * b) * 0.25)
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	t_1 = Float64(0.0625 * Float64(z * t))
	tmp = 0.0
	if (Float64(a * b) <= -1e+151)
		tmp = Float64(c + Float64(b * Float64(a * -0.25)));
	elseif (Float64(a * b) <= 1e+204)
		tmp = Float64(c + Float64(Float64(x * y) + t_1));
	else
		tmp = Float64(t_1 - Float64(Float64(a * b) * 0.25));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	t_1 = 0.0625 * (z * t);
	tmp = 0.0;
	if ((a * b) <= -1e+151)
		tmp = c + (b * (a * -0.25));
	elseif ((a * b) <= 1e+204)
		tmp = c + ((x * y) + t_1);
	else
		tmp = t_1 - ((a * b) * 0.25);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := Block[{t$95$1 = N[(0.0625 * N[(z * t), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[(a * b), $MachinePrecision], -1e+151], N[(c + N[(b * N[(a * -0.25), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[N[(a * b), $MachinePrecision], 1e+204], N[(c + N[(N[(x * y), $MachinePrecision] + t$95$1), $MachinePrecision]), $MachinePrecision], N[(t$95$1 - N[(N[(a * b), $MachinePrecision] * 0.25), $MachinePrecision]), $MachinePrecision]]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 7: 97.5% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ c + \left(\left(x \cdot y + \frac{z \cdot t}{16}\right) - \frac{a \cdot b}{4}\right) \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (+ c (- (+ (* x y) (/ (* z t) 16.0)) (/ (* a b) 4.0))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	return c + (((x * y) + ((z * t) / 16.0)) - ((a * b) / 4.0));
}

Fortran

real(8) function code(x, y, z, t, a, b, c)
    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
    code = c + (((x * y) + ((z * t) / 16.0d0)) - ((a * b) / 4.0d0))
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c) {
	return c + (((x * y) + ((z * t) / 16.0)) - ((a * b) / 4.0));
}

Python

def code(x, y, z, t, a, b, c):
	return c + (((x * y) + ((z * t) / 16.0)) - ((a * b) / 4.0))

Julia

function code(x, y, z, t, a, b, c)
	return Float64(c + Float64(Float64(Float64(x * y) + Float64(Float64(z * t) / 16.0)) - Float64(Float64(a * b) / 4.0)))
end

MATLAB

function tmp = code(x, y, z, t, a, b, c)
	tmp = c + (((x * y) + ((z * t) / 16.0)) - ((a * b) / 4.0));
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := N[(c + N[(N[(N[(x * y), $MachinePrecision] + N[(N[(z * t), $MachinePrecision] / 16.0), $MachinePrecision]), $MachinePrecision] - N[(N[(a * b), $MachinePrecision] / 4.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 8: 37.7% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := a \cdot \left(b \cdot -0.25\right)\\ t_2 := 0.0625 \cdot \left(z \cdot t\right)\\ \mathbf{if}\;z \leq -2.4 \cdot 10^{+160}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;z \leq -5.5 \cdot 10^{-101}:\\ \;\;\;\;x \cdot y\\ \mathbf{elif}\;z \leq -1.25 \cdot 10^{-247}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq 2.7 \cdot 10^{-264}:\\ \;\;\;\;c\\ \mathbf{elif}\;z \leq 1.12 \cdot 10^{-68}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (let* ((t_1 (* a (* b -0.25))) (t_2 (* 0.0625 (* z t))))
   (if (<= z -2.4e+160)
     t_2
     (if (<= z -5.5e-101)
       (* x y)
       (if (<= z -1.25e-247)
         t_1
         (if (<= z 2.7e-264) c (if (<= z 1.12e-68) t_1 t_2)))))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = a * (b * -0.25);
	double t_2 = 0.0625 * (z * t);
	double tmp;
	if (z <= -2.4e+160) {
		tmp = t_2;
	} else if (z <= -5.5e-101) {
		tmp = x * y;
	} else if (z <= -1.25e-247) {
		tmp = t_1;
	} else if (z <= 2.7e-264) {
		tmp = c;
	} else if (z <= 1.12e-68) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c)
    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) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = a * (b * (-0.25d0))
    t_2 = 0.0625d0 * (z * t)
    if (z <= (-2.4d+160)) then
        tmp = t_2
    else if (z <= (-5.5d-101)) then
        tmp = x * y
    else if (z <= (-1.25d-247)) then
        tmp = t_1
    else if (z <= 2.7d-264) then
        tmp = c
    else if (z <= 1.12d-68) 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 t_1 = a * (b * -0.25);
	double t_2 = 0.0625 * (z * t);
	double tmp;
	if (z <= -2.4e+160) {
		tmp = t_2;
	} else if (z <= -5.5e-101) {
		tmp = x * y;
	} else if (z <= -1.25e-247) {
		tmp = t_1;
	} else if (z <= 2.7e-264) {
		tmp = c;
	} else if (z <= 1.12e-68) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	t_1 = a * (b * -0.25)
	t_2 = 0.0625 * (z * t)
	tmp = 0
	if z <= -2.4e+160:
		tmp = t_2
	elif z <= -5.5e-101:
		tmp = x * y
	elif z <= -1.25e-247:
		tmp = t_1
	elif z <= 2.7e-264:
		tmp = c
	elif z <= 1.12e-68:
		tmp = t_1
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	t_1 = Float64(a * Float64(b * -0.25))
	t_2 = Float64(0.0625 * Float64(z * t))
	tmp = 0.0
	if (z <= -2.4e+160)
		tmp = t_2;
	elseif (z <= -5.5e-101)
		tmp = Float64(x * y);
	elseif (z <= -1.25e-247)
		tmp = t_1;
	elseif (z <= 2.7e-264)
		tmp = c;
	elseif (z <= 1.12e-68)
		tmp = t_1;
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	t_1 = a * (b * -0.25);
	t_2 = 0.0625 * (z * t);
	tmp = 0.0;
	if (z <= -2.4e+160)
		tmp = t_2;
	elseif (z <= -5.5e-101)
		tmp = x * y;
	elseif (z <= -1.25e-247)
		tmp = t_1;
	elseif (z <= 2.7e-264)
		tmp = c;
	elseif (z <= 1.12e-68)
		tmp = t_1;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := Block[{t$95$1 = N[(a * N[(b * -0.25), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(0.0625 * N[(z * t), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -2.4e+160], t$95$2, If[LessEqual[z, -5.5e-101], N[(x * y), $MachinePrecision], If[LessEqual[z, -1.25e-247], t$95$1, If[LessEqual[z, 2.7e-264], c, If[LessEqual[z, 1.12e-68], t$95$1, t$95$2]]]]]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 9: 59.1% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -2.4 \cdot 10^{+160}:\\ \;\;\;\;c + z \cdot \left(t \cdot 0.0625\right)\\ \mathbf{elif}\;z \leq -9 \cdot 10^{-101}:\\ \;\;\;\;c + x \cdot y\\ \mathbf{elif}\;z \leq 2.4 \cdot 10^{-125}:\\ \;\;\;\;c + b \cdot \left(a \cdot -0.25\right)\\ \mathbf{else}:\\ \;\;\;\;x \cdot y + 0.0625 \cdot \left(z \cdot t\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (if (<= z -2.4e+160)
   (+ c (* z (* t 0.0625)))
   (if (<= z -9e-101)
     (+ c (* x y))
     (if (<= z 2.4e-125)
       (+ c (* b (* a -0.25)))
       (+ (* x y) (* 0.0625 (* z t)))))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double tmp;
	if (z <= -2.4e+160) {
		tmp = c + (z * (t * 0.0625));
	} else if (z <= -9e-101) {
		tmp = c + (x * y);
	} else if (z <= 2.4e-125) {
		tmp = c + (b * (a * -0.25));
	} else {
		tmp = (x * y) + (0.0625 * (z * t));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c)
    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) :: tmp
    if (z <= (-2.4d+160)) then
        tmp = c + (z * (t * 0.0625d0))
    else if (z <= (-9d-101)) then
        tmp = c + (x * y)
    else if (z <= 2.4d-125) then
        tmp = c + (b * (a * (-0.25d0)))
    else
        tmp = (x * y) + (0.0625d0 * (z * t))
    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 tmp;
	if (z <= -2.4e+160) {
		tmp = c + (z * (t * 0.0625));
	} else if (z <= -9e-101) {
		tmp = c + (x * y);
	} else if (z <= 2.4e-125) {
		tmp = c + (b * (a * -0.25));
	} else {
		tmp = (x * y) + (0.0625 * (z * t));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	tmp = 0
	if z <= -2.4e+160:
		tmp = c + (z * (t * 0.0625))
	elif z <= -9e-101:
		tmp = c + (x * y)
	elif z <= 2.4e-125:
		tmp = c + (b * (a * -0.25))
	else:
		tmp = (x * y) + (0.0625 * (z * t))
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	tmp = 0.0
	if (z <= -2.4e+160)
		tmp = Float64(c + Float64(z * Float64(t * 0.0625)));
	elseif (z <= -9e-101)
		tmp = Float64(c + Float64(x * y));
	elseif (z <= 2.4e-125)
		tmp = Float64(c + Float64(b * Float64(a * -0.25)));
	else
		tmp = Float64(Float64(x * y) + Float64(0.0625 * Float64(z * t)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	tmp = 0.0;
	if (z <= -2.4e+160)
		tmp = c + (z * (t * 0.0625));
	elseif (z <= -9e-101)
		tmp = c + (x * y);
	elseif (z <= 2.4e-125)
		tmp = c + (b * (a * -0.25));
	else
		tmp = (x * y) + (0.0625 * (z * t));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := If[LessEqual[z, -2.4e+160], N[(c + N[(z * N[(t * 0.0625), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, -9e-101], N[(c + N[(x * y), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 2.4e-125], N[(c + N[(b * N[(a * -0.25), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(x * y), $MachinePrecision] + N[(0.0625 * N[(z * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 10: 52.1% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := c + x \cdot y\\ t_2 := a \cdot \left(b \cdot -0.25\right)\\ \mathbf{if}\;b \leq -4.4 \cdot 10^{+17}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;b \leq -1.35 \cdot 10^{-114}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;b \leq -1.3 \cdot 10^{-165}:\\ \;\;\;\;0.0625 \cdot \left(z \cdot t\right)\\ \mathbf{elif}\;b \leq 2 \cdot 10^{+169}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (let* ((t_1 (+ c (* x y))) (t_2 (* a (* b -0.25))))
   (if (<= b -4.4e+17)
     t_2
     (if (<= b -1.35e-114)
       t_1
       (if (<= b -1.3e-165) (* 0.0625 (* z t)) (if (<= b 2e+169) t_1 t_2))))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = c + (x * y);
	double t_2 = a * (b * -0.25);
	double tmp;
	if (b <= -4.4e+17) {
		tmp = t_2;
	} else if (b <= -1.35e-114) {
		tmp = t_1;
	} else if (b <= -1.3e-165) {
		tmp = 0.0625 * (z * t);
	} else if (b <= 2e+169) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c)
    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) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = c + (x * y)
    t_2 = a * (b * (-0.25d0))
    if (b <= (-4.4d+17)) then
        tmp = t_2
    else if (b <= (-1.35d-114)) then
        tmp = t_1
    else if (b <= (-1.3d-165)) then
        tmp = 0.0625d0 * (z * t)
    else if (b <= 2d+169) 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 t_1 = c + (x * y);
	double t_2 = a * (b * -0.25);
	double tmp;
	if (b <= -4.4e+17) {
		tmp = t_2;
	} else if (b <= -1.35e-114) {
		tmp = t_1;
	} else if (b <= -1.3e-165) {
		tmp = 0.0625 * (z * t);
	} else if (b <= 2e+169) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	t_1 = c + (x * y)
	t_2 = a * (b * -0.25)
	tmp = 0
	if b <= -4.4e+17:
		tmp = t_2
	elif b <= -1.35e-114:
		tmp = t_1
	elif b <= -1.3e-165:
		tmp = 0.0625 * (z * t)
	elif b <= 2e+169:
		tmp = t_1
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	t_1 = Float64(c + Float64(x * y))
	t_2 = Float64(a * Float64(b * -0.25))
	tmp = 0.0
	if (b <= -4.4e+17)
		tmp = t_2;
	elseif (b <= -1.35e-114)
		tmp = t_1;
	elseif (b <= -1.3e-165)
		tmp = Float64(0.0625 * Float64(z * t));
	elseif (b <= 2e+169)
		tmp = t_1;
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	t_1 = c + (x * y);
	t_2 = a * (b * -0.25);
	tmp = 0.0;
	if (b <= -4.4e+17)
		tmp = t_2;
	elseif (b <= -1.35e-114)
		tmp = t_1;
	elseif (b <= -1.3e-165)
		tmp = 0.0625 * (z * t);
	elseif (b <= 2e+169)
		tmp = t_1;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := Block[{t$95$1 = N[(c + N[(x * y), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(a * N[(b * -0.25), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[b, -4.4e+17], t$95$2, If[LessEqual[b, -1.35e-114], t$95$1, If[LessEqual[b, -1.3e-165], N[(0.0625 * N[(z * t), $MachinePrecision]), $MachinePrecision], If[LessEqual[b, 2e+169], t$95$1, t$95$2]]]]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 11: 58.4% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := c + z \cdot \left(t \cdot 0.0625\right)\\ \mathbf{if}\;z \leq -2.4 \cdot 10^{+160}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq -6.5 \cdot 10^{-101}:\\ \;\;\;\;c + x \cdot y\\ \mathbf{elif}\;z \leq 4.3 \cdot 10^{-66}:\\ \;\;\;\;c + b \cdot \left(a \cdot -0.25\right)\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (let* ((t_1 (+ c (* z (* t 0.0625)))))
   (if (<= z -2.4e+160)
     t_1
     (if (<= z -6.5e-101)
       (+ c (* x y))
       (if (<= z 4.3e-66) (+ c (* b (* a -0.25))) t_1)))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = c + (z * (t * 0.0625));
	double tmp;
	if (z <= -2.4e+160) {
		tmp = t_1;
	} else if (z <= -6.5e-101) {
		tmp = c + (x * y);
	} else if (z <= 4.3e-66) {
		tmp = c + (b * (a * -0.25));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c)
    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) :: t_1
    real(8) :: tmp
    t_1 = c + (z * (t * 0.0625d0))
    if (z <= (-2.4d+160)) then
        tmp = t_1
    else if (z <= (-6.5d-101)) then
        tmp = c + (x * y)
    else if (z <= 4.3d-66) then
        tmp = c + (b * (a * (-0.25d0)))
    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 t_1 = c + (z * (t * 0.0625));
	double tmp;
	if (z <= -2.4e+160) {
		tmp = t_1;
	} else if (z <= -6.5e-101) {
		tmp = c + (x * y);
	} else if (z <= 4.3e-66) {
		tmp = c + (b * (a * -0.25));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	t_1 = c + (z * (t * 0.0625))
	tmp = 0
	if z <= -2.4e+160:
		tmp = t_1
	elif z <= -6.5e-101:
		tmp = c + (x * y)
	elif z <= 4.3e-66:
		tmp = c + (b * (a * -0.25))
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	t_1 = Float64(c + Float64(z * Float64(t * 0.0625)))
	tmp = 0.0
	if (z <= -2.4e+160)
		tmp = t_1;
	elseif (z <= -6.5e-101)
		tmp = Float64(c + Float64(x * y));
	elseif (z <= 4.3e-66)
		tmp = Float64(c + Float64(b * Float64(a * -0.25)));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	t_1 = c + (z * (t * 0.0625));
	tmp = 0.0;
	if (z <= -2.4e+160)
		tmp = t_1;
	elseif (z <= -6.5e-101)
		tmp = c + (x * y);
	elseif (z <= 4.3e-66)
		tmp = c + (b * (a * -0.25));
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := Block[{t$95$1 = N[(c + N[(z * N[(t * 0.0625), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -2.4e+160], t$95$1, If[LessEqual[z, -6.5e-101], N[(c + N[(x * y), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 4.3e-66], N[(c + N[(b * N[(a * -0.25), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 12: 36.1% accurate, 1.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := 0.0625 \cdot \left(z \cdot t\right)\\ \mathbf{if}\;z \leq -2.4 \cdot 10^{+160}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq -6 \cdot 10^{-101}:\\ \;\;\;\;x \cdot y\\ \mathbf{elif}\;z \leq 2.6 \cdot 10^{-134}:\\ \;\;\;\;c\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (let* ((t_1 (* 0.0625 (* z t))))
   (if (<= z -2.4e+160)
     t_1
     (if (<= z -6e-101) (* x y) (if (<= z 2.6e-134) c t_1)))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = 0.0625 * (z * t);
	double tmp;
	if (z <= -2.4e+160) {
		tmp = t_1;
	} else if (z <= -6e-101) {
		tmp = x * y;
	} else if (z <= 2.6e-134) {
		tmp = c;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c)
    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) :: t_1
    real(8) :: tmp
    t_1 = 0.0625d0 * (z * t)
    if (z <= (-2.4d+160)) then
        tmp = t_1
    else if (z <= (-6d-101)) then
        tmp = x * y
    else if (z <= 2.6d-134) then
        tmp = c
    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 t_1 = 0.0625 * (z * t);
	double tmp;
	if (z <= -2.4e+160) {
		tmp = t_1;
	} else if (z <= -6e-101) {
		tmp = x * y;
	} else if (z <= 2.6e-134) {
		tmp = c;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	t_1 = 0.0625 * (z * t)
	tmp = 0
	if z <= -2.4e+160:
		tmp = t_1
	elif z <= -6e-101:
		tmp = x * y
	elif z <= 2.6e-134:
		tmp = c
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	t_1 = Float64(0.0625 * Float64(z * t))
	tmp = 0.0
	if (z <= -2.4e+160)
		tmp = t_1;
	elseif (z <= -6e-101)
		tmp = Float64(x * y);
	elseif (z <= 2.6e-134)
		tmp = c;
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	t_1 = 0.0625 * (z * t);
	tmp = 0.0;
	if (z <= -2.4e+160)
		tmp = t_1;
	elseif (z <= -6e-101)
		tmp = x * y;
	elseif (z <= 2.6e-134)
		tmp = c;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := Block[{t$95$1 = N[(0.0625 * N[(z * t), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -2.4e+160], t$95$1, If[LessEqual[z, -6e-101], N[(x * y), $MachinePrecision], If[LessEqual[z, 2.6e-134], c, t$95$1]]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 13: 42.3% accurate, 1.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \cdot y \leq -2.45 \cdot 10^{+58} \lor \neg \left(x \cdot y \leq 1.1 \cdot 10^{+66}\right):\\ \;\;\;\;x \cdot y\\ \mathbf{else}:\\ \;\;\;\;c\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (if (or (<= (* x y) -2.45e+58) (not (<= (* x y) 1.1e+66))) (* x y) c))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double tmp;
	if (((x * y) <= -2.45e+58) || !((x * y) <= 1.1e+66)) {
		tmp = x * y;
	} else {
		tmp = c;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c)
    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) :: tmp
    if (((x * y) <= (-2.45d+58)) .or. (.not. ((x * y) <= 1.1d+66))) then
        tmp = x * y
    else
        tmp = 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 tmp;
	if (((x * y) <= -2.45e+58) || !((x * y) <= 1.1e+66)) {
		tmp = x * y;
	} else {
		tmp = c;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	tmp = 0
	if ((x * y) <= -2.45e+58) or not ((x * y) <= 1.1e+66):
		tmp = x * y
	else:
		tmp = c
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	tmp = 0.0
	if ((Float64(x * y) <= -2.45e+58) || !(Float64(x * y) <= 1.1e+66))
		tmp = Float64(x * y);
	else
		tmp = c;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	tmp = 0.0;
	if (((x * y) <= -2.45e+58) || ~(((x * y) <= 1.1e+66)))
		tmp = x * y;
	else
		tmp = c;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := If[Or[LessEqual[N[(x * y), $MachinePrecision], -2.45e+58], N[Not[LessEqual[N[(x * y), $MachinePrecision], 1.1e+66]], $MachinePrecision]], N[(x * y), $MachinePrecision], c]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 14: 22.4% accurate, 17.0× speedup

Math

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

FPCore

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

C

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

Fortran

real(8) function code(x, y, z, t, a, b, c)
    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
    code = c
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Reproduce

herbie shell --seed 2023347 
(FPCore (x y z t a b c)
  :name "Diagrams.Solve.Polynomial:quartForm  from diagrams-solve-0.1, C"
  :precision binary64
  (+ (- (+ (* x y) (/ (* z t) 16.0)) (/ (* a b) 4.0)) c))