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

Percentage Accurate: 97.7% → 98.9%

Time: 10.4s

Alternatives: 13

Speedup: 1.0×

• 33.0% 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: 97.7% 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: 98.0% accurate, 0.1× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

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

Alternative 3: 42.8% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := a \cdot \left(b \cdot -0.25\right)\\ t_2 := 0.0625 \cdot \left(z \cdot t\right)\\ \mathbf{if}\;x \cdot y \leq -9 \cdot 10^{+18}:\\ \;\;\;\;x \cdot y\\ \mathbf{elif}\;x \cdot y \leq -7.7 \cdot 10^{-31}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \cdot y \leq -7.2 \cdot 10^{-62}:\\ \;\;\;\;x \cdot y\\ \mathbf{elif}\;x \cdot y \leq -1.4 \cdot 10^{-79}:\\ \;\;\;\;c\\ \mathbf{elif}\;x \cdot y \leq -2.7 \cdot 10^{-257}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \cdot y \leq 2 \cdot 10^{-323}:\\ \;\;\;\;c\\ \mathbf{elif}\;x \cdot y \leq 4 \cdot 10^{-160}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;x \cdot y \leq 3.5 \cdot 10^{+36}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \cdot y \leq 5.4 \cdot 10^{+125}:\\ \;\;\;\;t_2\\ \mathbf{else}:\\ \;\;\;\;x \cdot y\\ \end{array} \end{array} \]

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 (<= (* x y) -9e+18)
     (* x y)
     (if (<= (* x y) -7.7e-31)
       t_1
       (if (<= (* x y) -7.2e-62)
         (* x y)
         (if (<= (* x y) -1.4e-79)
           c
           (if (<= (* x y) -2.7e-257)
             t_1
             (if (<= (* x y) 2e-323)
               c
               (if (<= (* x y) 4e-160)
                 t_2
                 (if (<= (* x y) 3.5e+36)
                   t_1
                   (if (<= (* x y) 5.4e+125) t_2 (* x y))))))))))))

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 ((x * y) <= -9e+18) {
		tmp = x * y;
	} else if ((x * y) <= -7.7e-31) {
		tmp = t_1;
	} else if ((x * y) <= -7.2e-62) {
		tmp = x * y;
	} else if ((x * y) <= -1.4e-79) {
		tmp = c;
	} else if ((x * y) <= -2.7e-257) {
		tmp = t_1;
	} else if ((x * y) <= 2e-323) {
		tmp = c;
	} else if ((x * y) <= 4e-160) {
		tmp = t_2;
	} else if ((x * y) <= 3.5e+36) {
		tmp = t_1;
	} else if ((x * y) <= 5.4e+125) {
		tmp = t_2;
	} else {
		tmp = x * y;
	}
	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 ((x * y) <= (-9d+18)) then
        tmp = x * y
    else if ((x * y) <= (-7.7d-31)) then
        tmp = t_1
    else if ((x * y) <= (-7.2d-62)) then
        tmp = x * y
    else if ((x * y) <= (-1.4d-79)) then
        tmp = c
    else if ((x * y) <= (-2.7d-257)) then
        tmp = t_1
    else if ((x * y) <= 2d-323) then
        tmp = c
    else if ((x * y) <= 4d-160) then
        tmp = t_2
    else if ((x * y) <= 3.5d+36) then
        tmp = t_1
    else if ((x * y) <= 5.4d+125) then
        tmp = t_2
    else
        tmp = x * y
    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 ((x * y) <= -9e+18) {
		tmp = x * y;
	} else if ((x * y) <= -7.7e-31) {
		tmp = t_1;
	} else if ((x * y) <= -7.2e-62) {
		tmp = x * y;
	} else if ((x * y) <= -1.4e-79) {
		tmp = c;
	} else if ((x * y) <= -2.7e-257) {
		tmp = t_1;
	} else if ((x * y) <= 2e-323) {
		tmp = c;
	} else if ((x * y) <= 4e-160) {
		tmp = t_2;
	} else if ((x * y) <= 3.5e+36) {
		tmp = t_1;
	} else if ((x * y) <= 5.4e+125) {
		tmp = t_2;
	} else {
		tmp = x * y;
	}
	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 (x * y) <= -9e+18:
		tmp = x * y
	elif (x * y) <= -7.7e-31:
		tmp = t_1
	elif (x * y) <= -7.2e-62:
		tmp = x * y
	elif (x * y) <= -1.4e-79:
		tmp = c
	elif (x * y) <= -2.7e-257:
		tmp = t_1
	elif (x * y) <= 2e-323:
		tmp = c
	elif (x * y) <= 4e-160:
		tmp = t_2
	elif (x * y) <= 3.5e+36:
		tmp = t_1
	elif (x * y) <= 5.4e+125:
		tmp = t_2
	else:
		tmp = x * y
	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 (Float64(x * y) <= -9e+18)
		tmp = Float64(x * y);
	elseif (Float64(x * y) <= -7.7e-31)
		tmp = t_1;
	elseif (Float64(x * y) <= -7.2e-62)
		tmp = Float64(x * y);
	elseif (Float64(x * y) <= -1.4e-79)
		tmp = c;
	elseif (Float64(x * y) <= -2.7e-257)
		tmp = t_1;
	elseif (Float64(x * y) <= 2e-323)
		tmp = c;
	elseif (Float64(x * y) <= 4e-160)
		tmp = t_2;
	elseif (Float64(x * y) <= 3.5e+36)
		tmp = t_1;
	elseif (Float64(x * y) <= 5.4e+125)
		tmp = t_2;
	else
		tmp = Float64(x * y);
	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 ((x * y) <= -9e+18)
		tmp = x * y;
	elseif ((x * y) <= -7.7e-31)
		tmp = t_1;
	elseif ((x * y) <= -7.2e-62)
		tmp = x * y;
	elseif ((x * y) <= -1.4e-79)
		tmp = c;
	elseif ((x * y) <= -2.7e-257)
		tmp = t_1;
	elseif ((x * y) <= 2e-323)
		tmp = c;
	elseif ((x * y) <= 4e-160)
		tmp = t_2;
	elseif ((x * y) <= 3.5e+36)
		tmp = t_1;
	elseif ((x * y) <= 5.4e+125)
		tmp = t_2;
	else
		tmp = x * y;
	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[N[(x * y), $MachinePrecision], -9e+18], N[(x * y), $MachinePrecision], If[LessEqual[N[(x * y), $MachinePrecision], -7.7e-31], t$95$1, If[LessEqual[N[(x * y), $MachinePrecision], -7.2e-62], N[(x * y), $MachinePrecision], If[LessEqual[N[(x * y), $MachinePrecision], -1.4e-79], c, If[LessEqual[N[(x * y), $MachinePrecision], -2.7e-257], t$95$1, If[LessEqual[N[(x * y), $MachinePrecision], 2e-323], c, If[LessEqual[N[(x * y), $MachinePrecision], 4e-160], t$95$2, If[LessEqual[N[(x * y), $MachinePrecision], 3.5e+36], t$95$1, If[LessEqual[N[(x * y), $MachinePrecision], 5.4e+125], t$95$2, N[(x * y), $MachinePrecision]]]]]]]]]]]]

Derivation

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

Alternative 4: 66.5% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \cdot y \leq -1.5 \cdot 10^{+95} \lor \neg \left(x \cdot y \leq -8.5 \cdot 10^{-31}\right) \land \left(x \cdot y \leq -2.1 \cdot 10^{-63} \lor \neg \left(x \cdot y \leq 2.05 \cdot 10^{+37}\right)\right):\\ \;\;\;\;x \cdot y + 0.0625 \cdot \left(z \cdot t\right)\\ \mathbf{else}:\\ \;\;\;\;c + a \cdot \left(b \cdot -0.25\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (if (or (<= (* x y) -1.5e+95)
         (and (not (<= (* x y) -8.5e-31))
              (or (<= (* x y) -2.1e-63) (not (<= (* x y) 2.05e+37)))))
   (+ (* x y) (* 0.0625 (* z t)))
   (+ c (* a (* b -0.25)))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double tmp;
	if (((x * y) <= -1.5e+95) || (!((x * y) <= -8.5e-31) && (((x * y) <= -2.1e-63) || !((x * y) <= 2.05e+37)))) {
		tmp = (x * y) + (0.0625 * (z * t));
	} else {
		tmp = c + (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) :: tmp
    if (((x * y) <= (-1.5d+95)) .or. (.not. ((x * y) <= (-8.5d-31))) .and. ((x * y) <= (-2.1d-63)) .or. (.not. ((x * y) <= 2.05d+37))) then
        tmp = (x * y) + (0.0625d0 * (z * t))
    else
        tmp = c + (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 tmp;
	if (((x * y) <= -1.5e+95) || (!((x * y) <= -8.5e-31) && (((x * y) <= -2.1e-63) || !((x * y) <= 2.05e+37)))) {
		tmp = (x * y) + (0.0625 * (z * t));
	} else {
		tmp = c + (a * (b * -0.25));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	tmp = 0
	if ((x * y) <= -1.5e+95) or (not ((x * y) <= -8.5e-31) and (((x * y) <= -2.1e-63) or not ((x * y) <= 2.05e+37))):
		tmp = (x * y) + (0.0625 * (z * t))
	else:
		tmp = c + (a * (b * -0.25))
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	tmp = 0.0
	if ((Float64(x * y) <= -1.5e+95) || (!(Float64(x * y) <= -8.5e-31) && ((Float64(x * y) <= -2.1e-63) || !(Float64(x * y) <= 2.05e+37))))
		tmp = Float64(Float64(x * y) + Float64(0.0625 * Float64(z * t)));
	else
		tmp = Float64(c + Float64(a * Float64(b * -0.25)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	tmp = 0.0;
	if (((x * y) <= -1.5e+95) || (~(((x * y) <= -8.5e-31)) && (((x * y) <= -2.1e-63) || ~(((x * y) <= 2.05e+37)))))
		tmp = (x * y) + (0.0625 * (z * t));
	else
		tmp = c + (a * (b * -0.25));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := If[Or[LessEqual[N[(x * y), $MachinePrecision], -1.5e+95], And[N[Not[LessEqual[N[(x * y), $MachinePrecision], -8.5e-31]], $MachinePrecision], Or[LessEqual[N[(x * y), $MachinePrecision], -2.1e-63], N[Not[LessEqual[N[(x * y), $MachinePrecision], 2.05e+37]], $MachinePrecision]]]], N[(N[(x * y), $MachinePrecision] + N[(0.0625 * N[(z * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(c + N[(a * N[(b * -0.25), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 5: 65.0% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := c + 0.0625 \cdot \left(z \cdot t\right)\\ t_2 := c + x \cdot y\\ \mathbf{if}\;x \cdot y \leq -3.7 \cdot 10^{+19}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;x \cdot y \leq 1.7 \cdot 10^{-146}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \cdot y \leq 5.6 \cdot 10^{+30}:\\ \;\;\;\;c + a \cdot \left(b \cdot -0.25\right)\\ \mathbf{elif}\;x \cdot y \leq 1.15 \cdot 10^{+126}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (let* ((t_1 (+ c (* 0.0625 (* z t)))) (t_2 (+ c (* x y))))
   (if (<= (* x y) -3.7e+19)
     t_2
     (if (<= (* x y) 1.7e-146)
       t_1
       (if (<= (* x y) 5.6e+30)
         (+ c (* a (* b -0.25)))
         (if (<= (* x y) 1.15e+126) 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 + (0.0625 * (z * t));
	double t_2 = c + (x * y);
	double tmp;
	if ((x * y) <= -3.7e+19) {
		tmp = t_2;
	} else if ((x * y) <= 1.7e-146) {
		tmp = t_1;
	} else if ((x * y) <= 5.6e+30) {
		tmp = c + (a * (b * -0.25));
	} else if ((x * y) <= 1.15e+126) {
		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 + (0.0625d0 * (z * t))
    t_2 = c + (x * y)
    if ((x * y) <= (-3.7d+19)) then
        tmp = t_2
    else if ((x * y) <= 1.7d-146) then
        tmp = t_1
    else if ((x * y) <= 5.6d+30) then
        tmp = c + (a * (b * (-0.25d0)))
    else if ((x * y) <= 1.15d+126) 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 + (0.0625 * (z * t));
	double t_2 = c + (x * y);
	double tmp;
	if ((x * y) <= -3.7e+19) {
		tmp = t_2;
	} else if ((x * y) <= 1.7e-146) {
		tmp = t_1;
	} else if ((x * y) <= 5.6e+30) {
		tmp = c + (a * (b * -0.25));
	} else if ((x * y) <= 1.15e+126) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	t_1 = c + (0.0625 * (z * t))
	t_2 = c + (x * y)
	tmp = 0
	if (x * y) <= -3.7e+19:
		tmp = t_2
	elif (x * y) <= 1.7e-146:
		tmp = t_1
	elif (x * y) <= 5.6e+30:
		tmp = c + (a * (b * -0.25))
	elif (x * y) <= 1.15e+126:
		tmp = t_1
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	t_1 = Float64(c + Float64(0.0625 * Float64(z * t)))
	t_2 = Float64(c + Float64(x * y))
	tmp = 0.0
	if (Float64(x * y) <= -3.7e+19)
		tmp = t_2;
	elseif (Float64(x * y) <= 1.7e-146)
		tmp = t_1;
	elseif (Float64(x * y) <= 5.6e+30)
		tmp = Float64(c + Float64(a * Float64(b * -0.25)));
	elseif (Float64(x * y) <= 1.15e+126)
		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 + (0.0625 * (z * t));
	t_2 = c + (x * y);
	tmp = 0.0;
	if ((x * y) <= -3.7e+19)
		tmp = t_2;
	elseif ((x * y) <= 1.7e-146)
		tmp = t_1;
	elseif ((x * y) <= 5.6e+30)
		tmp = c + (a * (b * -0.25));
	elseif ((x * y) <= 1.15e+126)
		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[(0.0625 * N[(z * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(c + N[(x * y), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[(x * y), $MachinePrecision], -3.7e+19], t$95$2, If[LessEqual[N[(x * y), $MachinePrecision], 1.7e-146], t$95$1, If[LessEqual[N[(x * y), $MachinePrecision], 5.6e+30], N[(c + N[(a * N[(b * -0.25), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[N[(x * y), $MachinePrecision], 1.15e+126], t$95$1, t$95$2]]]]]]

Derivation

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

Alternative 6: 38.5% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := 0.0625 \cdot \left(z \cdot t\right)\\ \mathbf{if}\;z \leq -2.6 \cdot 10^{+85}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq -5 \cdot 10^{-105}:\\ \;\;\;\;x \cdot y\\ \mathbf{elif}\;z \leq -3 \cdot 10^{-125}:\\ \;\;\;\;c\\ \mathbf{elif}\;z \leq -1.7 \cdot 10^{-205}:\\ \;\;\;\;x \cdot y\\ \mathbf{elif}\;z \leq -8.2 \cdot 10^{-269}:\\ \;\;\;\;c\\ \mathbf{elif}\;z \leq 8.6 \cdot 10^{-15}:\\ \;\;\;\;x \cdot y\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (let* ((t_1 (* 0.0625 (* z t))))
   (if (<= z -2.6e+85)
     t_1
     (if (<= z -5e-105)
       (* x y)
       (if (<= z -3e-125)
         c
         (if (<= z -1.7e-205)
           (* x y)
           (if (<= z -8.2e-269) c (if (<= z 8.6e-15) (* x y) 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 <= -2.6e+85) {
		tmp = t_1;
	} else if (z <= -5e-105) {
		tmp = x * y;
	} else if (z <= -3e-125) {
		tmp = c;
	} else if (z <= -1.7e-205) {
		tmp = x * y;
	} else if (z <= -8.2e-269) {
		tmp = c;
	} else if (z <= 8.6e-15) {
		tmp = x * y;
	} 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 <= (-2.6d+85)) then
        tmp = t_1
    else if (z <= (-5d-105)) then
        tmp = x * y
    else if (z <= (-3d-125)) then
        tmp = c
    else if (z <= (-1.7d-205)) then
        tmp = x * y
    else if (z <= (-8.2d-269)) then
        tmp = c
    else if (z <= 8.6d-15) then
        tmp = x * 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 t_1 = 0.0625 * (z * t);
	double tmp;
	if (z <= -2.6e+85) {
		tmp = t_1;
	} else if (z <= -5e-105) {
		tmp = x * y;
	} else if (z <= -3e-125) {
		tmp = c;
	} else if (z <= -1.7e-205) {
		tmp = x * y;
	} else if (z <= -8.2e-269) {
		tmp = c;
	} else if (z <= 8.6e-15) {
		tmp = x * y;
	} 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 <= -2.6e+85:
		tmp = t_1
	elif z <= -5e-105:
		tmp = x * y
	elif z <= -3e-125:
		tmp = c
	elif z <= -1.7e-205:
		tmp = x * y
	elif z <= -8.2e-269:
		tmp = c
	elif z <= 8.6e-15:
		tmp = x * y
	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 <= -2.6e+85)
		tmp = t_1;
	elseif (z <= -5e-105)
		tmp = Float64(x * y);
	elseif (z <= -3e-125)
		tmp = c;
	elseif (z <= -1.7e-205)
		tmp = Float64(x * y);
	elseif (z <= -8.2e-269)
		tmp = c;
	elseif (z <= 8.6e-15)
		tmp = Float64(x * y);
	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 <= -2.6e+85)
		tmp = t_1;
	elseif (z <= -5e-105)
		tmp = x * y;
	elseif (z <= -3e-125)
		tmp = c;
	elseif (z <= -1.7e-205)
		tmp = x * y;
	elseif (z <= -8.2e-269)
		tmp = c;
	elseif (z <= 8.6e-15)
		tmp = x * y;
	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, -2.6e+85], t$95$1, If[LessEqual[z, -5e-105], N[(x * y), $MachinePrecision], If[LessEqual[z, -3e-125], c, If[LessEqual[z, -1.7e-205], N[(x * y), $MachinePrecision], If[LessEqual[z, -8.2e-269], c, If[LessEqual[z, 8.6e-15], N[(x * y), $MachinePrecision], t$95$1]]]]]]]

Derivation

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

Alternative 7: 97.7% accurate, 1.0× speedup

Math

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

FPCore

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

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	return c + (((z / (16.0 / t)) + (x * y)) - ((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 + (((z / (16.0d0 / t)) + (x * y)) - ((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 + (((z / (16.0 / t)) + (x * y)) - ((a * b) / 4.0));
}

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

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

Alternative 8: 97.7% 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 9: 64.6% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \cdot y \leq -6.2 \cdot 10^{+15} \lor \neg \left(x \cdot y \leq 7 \cdot 10^{+125}\right):\\ \;\;\;\;c + x \cdot y\\ \mathbf{else}:\\ \;\;\;\;c + 0.0625 \cdot \left(z \cdot t\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (if (or (<= (* x y) -6.2e+15) (not (<= (* x y) 7e+125)))
   (+ c (* x y))
   (+ c (* 0.0625 (* z t)))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double tmp;
	if (((x * y) <= -6.2e+15) || !((x * y) <= 7e+125)) {
		tmp = c + (x * y);
	} else {
		tmp = c + (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 (((x * y) <= (-6.2d+15)) .or. (.not. ((x * y) <= 7d+125))) then
        tmp = c + (x * y)
    else
        tmp = c + (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 (((x * y) <= -6.2e+15) || !((x * y) <= 7e+125)) {
		tmp = c + (x * y);
	} else {
		tmp = c + (0.0625 * (z * t));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	tmp = 0
	if ((x * y) <= -6.2e+15) or not ((x * y) <= 7e+125):
		tmp = c + (x * y)
	else:
		tmp = c + (0.0625 * (z * t))
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	tmp = 0.0
	if ((Float64(x * y) <= -6.2e+15) || !(Float64(x * y) <= 7e+125))
		tmp = Float64(c + Float64(x * y));
	else
		tmp = Float64(c + 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 (((x * y) <= -6.2e+15) || ~(((x * y) <= 7e+125)))
		tmp = c + (x * y);
	else
		tmp = c + (0.0625 * (z * t));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := If[Or[LessEqual[N[(x * y), $MachinePrecision], -6.2e+15], N[Not[LessEqual[N[(x * y), $MachinePrecision], 7e+125]], $MachinePrecision]], N[(c + N[(x * y), $MachinePrecision]), $MachinePrecision], N[(c + N[(0.0625 * N[(z * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 10: 77.7% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;b \leq -11000000000 \lor \neg \left(b \leq 3.7 \cdot 10^{+211}\right):\\ \;\;\;\;c + a \cdot \left(b \cdot -0.25\right)\\ \mathbf{else}:\\ \;\;\;\;c + \left(x \cdot y + 0.0625 \cdot \left(z \cdot t\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (if (or (<= b -11000000000.0) (not (<= b 3.7e+211)))
   (+ c (* a (* b -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 ((b <= -11000000000.0) || !(b <= 3.7e+211)) {
		tmp = c + (a * (b * -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 ((b <= (-11000000000.0d0)) .or. (.not. (b <= 3.7d+211))) then
        tmp = c + (a * (b * (-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 ((b <= -11000000000.0) || !(b <= 3.7e+211)) {
		tmp = c + (a * (b * -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 (b <= -11000000000.0) or not (b <= 3.7e+211):
		tmp = c + (a * (b * -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 ((b <= -11000000000.0) || !(b <= 3.7e+211))
		tmp = Float64(c + Float64(a * Float64(b * -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 ((b <= -11000000000.0) || ~((b <= 3.7e+211)))
		tmp = c + (a * (b * -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[b, -11000000000.0], N[Not[LessEqual[b, 3.7e+211]], $MachinePrecision]], N[(c + N[(a * N[(b * -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 11: 40.6% accurate, 1.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \cdot y \leq -7.2 \cdot 10^{-62} \lor \neg \left(x \cdot y \leq 3.7 \cdot 10^{+37}\right):\\ \;\;\;\;x \cdot y\\ \mathbf{else}:\\ \;\;\;\;c\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (if (or (<= (* x y) -7.2e-62) (not (<= (* x y) 3.7e+37))) (* 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) <= -7.2e-62) || !((x * y) <= 3.7e+37)) {
		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) <= (-7.2d-62)) .or. (.not. ((x * y) <= 3.7d+37))) 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) <= -7.2e-62) || !((x * y) <= 3.7e+37)) {
		tmp = x * y;
	} else {
		tmp = c;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	tmp = 0
	if ((x * y) <= -7.2e-62) or not ((x * y) <= 3.7e+37):
		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) <= -7.2e-62) || !(Float64(x * y) <= 3.7e+37))
		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) <= -7.2e-62) || ~(((x * y) <= 3.7e+37)))
		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], -7.2e-62], N[Not[LessEqual[N[(x * y), $MachinePrecision], 3.7e+37]], $MachinePrecision]], N[(x * y), $MachinePrecision], c]

Derivation

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1. Add Preprocessing

Alternative 12: 54.3% accurate, 1.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -5.6 \cdot 10^{+20} \lor \neg \left(t \leq 5.5 \cdot 10^{+199}\right):\\ \;\;\;\;0.0625 \cdot \left(z \cdot t\right)\\ \mathbf{else}:\\ \;\;\;\;c + x \cdot y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (if (or (<= t -5.6e+20) (not (<= t 5.5e+199)))
   (* 0.0625 (* z t))
   (+ c (* x y))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double tmp;
	if ((t <= -5.6e+20) || !(t <= 5.5e+199)) {
		tmp = 0.0625 * (z * t);
	} else {
		tmp = c + (x * y);
	}
	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 ((t <= (-5.6d+20)) .or. (.not. (t <= 5.5d+199))) then
        tmp = 0.0625d0 * (z * t)
    else
        tmp = c + (x * y)
    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 ((t <= -5.6e+20) || !(t <= 5.5e+199)) {
		tmp = 0.0625 * (z * t);
	} else {
		tmp = c + (x * y);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	tmp = 0
	if (t <= -5.6e+20) or not (t <= 5.5e+199):
		tmp = 0.0625 * (z * t)
	else:
		tmp = c + (x * y)
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	tmp = 0.0
	if ((t <= -5.6e+20) || !(t <= 5.5e+199))
		tmp = Float64(0.0625 * Float64(z * t));
	else
		tmp = Float64(c + Float64(x * y));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	tmp = 0.0;
	if ((t <= -5.6e+20) || ~((t <= 5.5e+199)))
		tmp = 0.0625 * (z * t);
	else
		tmp = c + (x * y);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := If[Or[LessEqual[t, -5.6e+20], N[Not[LessEqual[t, 5.5e+199]], $MachinePrecision]], N[(0.0625 * N[(z * t), $MachinePrecision]), $MachinePrecision], N[(c + N[(x * y), $MachinePrecision]), $MachinePrecision]]

Derivation

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1. Add Preprocessing

Alternative 13: 23.1% 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

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1. Add Preprocessing

Reproduce

herbie shell --seed 2024008 
(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))