Diagrams.Solve.Tridiagonal:solveCyclicTriDiagonal from diagrams-solve-0.1, B

Percentage Accurate: 75.5% → 88.6%

Time: 18.6s

Alternatives: 20

Speedup: 0.8×

Specification

Math

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

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (/ (+ x (/ (* y z) t)) (+ (+ a 1.0) (/ (* y b) t))))

C

double code(double x, double y, double z, double t, double a, double b) {
	return (x + ((y * z) / t)) / ((a + 1.0) + ((y * b) / t));
}

Fortran

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

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	return (x + ((y * z) / t)) / ((a + 1.0) + ((y * b) / t));
}

Python

def code(x, y, z, t, a, b):
	return (x + ((y * z) / t)) / ((a + 1.0) + ((y * b) / t))

Julia

function code(x, y, z, t, a, b)
	return Float64(Float64(x + Float64(Float64(y * z) / t)) / Float64(Float64(a + 1.0) + Float64(Float64(y * b) / t)))
end

MATLAB

function tmp = code(x, y, z, t, a, b)
	tmp = (x + ((y * z) / t)) / ((a + 1.0) + ((y * b) / t));
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := N[(N[(x + N[(N[(y * z), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision] / N[(N[(a + 1.0), $MachinePrecision] + N[(N[(y * b), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Initial Program: 75.5% accurate, 1.0× speedup

Math

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

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (/ (+ x (/ (* y z) t)) (+ (+ a 1.0) (/ (* y b) t))))

C

double code(double x, double y, double z, double t, double a, double b) {
	return (x + ((y * z) / t)) / ((a + 1.0) + ((y * b) / t));
}

Fortran

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

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	return (x + ((y * z) / t)) / ((a + 1.0) + ((y * b) / t));
}

Python

def code(x, y, z, t, a, b):
	return (x + ((y * z) / t)) / ((a + 1.0) + ((y * b) / t))

Julia

function code(x, y, z, t, a, b)
	return Float64(Float64(x + Float64(Float64(y * z) / t)) / Float64(Float64(a + 1.0) + Float64(Float64(y * b) / t)))
end

MATLAB

function tmp = code(x, y, z, t, a, b)
	tmp = (x + ((y * z) / t)) / ((a + 1.0) + ((y * b) / t));
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := N[(N[(x + N[(N[(y * z), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision] / N[(N[(a + 1.0), $MachinePrecision] + N[(N[(y * b), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Alternative 1: 88.6% accurate, 0.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x + \frac{y \cdot z}{t}}{\left(a + 1\right) + \frac{y \cdot b}{t}}\\ \mathbf{if}\;t_1 \leq -\infty:\\ \;\;\;\;\frac{y}{t} \cdot \frac{z}{1 + \left(a + y \cdot \frac{b}{t}\right)}\\ \mathbf{elif}\;t_1 \leq -1 \cdot 10^{-276}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t_1 \leq 2 \cdot 10^{+266}:\\ \;\;\;\;\frac{x + \frac{y}{\frac{t}{z}}}{a + \left(1 + b \cdot \frac{y}{t}\right)}\\ \mathbf{elif}\;t_1 \leq \infty:\\ \;\;\;\;\frac{y \cdot \frac{z}{1 + \mathsf{fma}\left(y, \frac{b}{t}, a\right)}}{t}\\ \mathbf{else}:\\ \;\;\;\;\frac{z}{b}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (/ (+ x (/ (* y z) t)) (+ (+ a 1.0) (/ (* y b) t)))))
   (if (<= t_1 (- INFINITY))
     (* (/ y t) (/ z (+ 1.0 (+ a (* y (/ b t))))))
     (if (<= t_1 -1e-276)
       t_1
       (if (<= t_1 2e+266)
         (/ (+ x (/ y (/ t z))) (+ a (+ 1.0 (* b (/ y t)))))
         (if (<= t_1 INFINITY)
           (/ (* y (/ z (+ 1.0 (fma y (/ b t) a)))) t)
           (/ z b)))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (x + ((y * z) / t)) / ((a + 1.0) + ((y * b) / t));
	double tmp;
	if (t_1 <= -((double) INFINITY)) {
		tmp = (y / t) * (z / (1.0 + (a + (y * (b / t)))));
	} else if (t_1 <= -1e-276) {
		tmp = t_1;
	} else if (t_1 <= 2e+266) {
		tmp = (x + (y / (t / z))) / (a + (1.0 + (b * (y / t))));
	} else if (t_1 <= ((double) INFINITY)) {
		tmp = (y * (z / (1.0 + fma(y, (b / t), a)))) / t;
	} else {
		tmp = z / b;
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(x + Float64(Float64(y * z) / t)) / Float64(Float64(a + 1.0) + Float64(Float64(y * b) / t)))
	tmp = 0.0
	if (t_1 <= Float64(-Inf))
		tmp = Float64(Float64(y / t) * Float64(z / Float64(1.0 + Float64(a + Float64(y * Float64(b / t))))));
	elseif (t_1 <= -1e-276)
		tmp = t_1;
	elseif (t_1 <= 2e+266)
		tmp = Float64(Float64(x + Float64(y / Float64(t / z))) / Float64(a + Float64(1.0 + Float64(b * Float64(y / t)))));
	elseif (t_1 <= Inf)
		tmp = Float64(Float64(y * Float64(z / Float64(1.0 + fma(y, Float64(b / t), a)))) / t);
	else
		tmp = Float64(z / b);
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(N[(x + N[(N[(y * z), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision] / N[(N[(a + 1.0), $MachinePrecision] + N[(N[(y * b), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, (-Infinity)], N[(N[(y / t), $MachinePrecision] * N[(z / N[(1.0 + N[(a + N[(y * N[(b / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$1, -1e-276], t$95$1, If[LessEqual[t$95$1, 2e+266], N[(N[(x + N[(y / N[(t / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(a + N[(1.0 + N[(b * N[(y / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$1, Infinity], N[(N[(y * N[(z / N[(1.0 + N[(y * N[(b / t), $MachinePrecision] + a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / t), $MachinePrecision], N[(z / b), $MachinePrecision]]]]]]

Derivation

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

Alternative 2: 87.6% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x + \frac{y \cdot z}{t}}{\left(a + 1\right) + \frac{y \cdot b}{t}}\\ \mathbf{if}\;t_1 \leq -\infty:\\ \;\;\;\;\frac{y}{t} \cdot \frac{z}{1 + \left(a + y \cdot \frac{b}{t}\right)}\\ \mathbf{elif}\;t_1 \leq -1 \cdot 10^{-276}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t_1 \leq \infty:\\ \;\;\;\;\frac{x + \frac{y}{\frac{t}{z}}}{a + \left(1 + b \cdot \frac{y}{t}\right)}\\ \mathbf{else}:\\ \;\;\;\;\frac{z}{b}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (/ (+ x (/ (* y z) t)) (+ (+ a 1.0) (/ (* y b) t)))))
   (if (<= t_1 (- INFINITY))
     (* (/ y t) (/ z (+ 1.0 (+ a (* y (/ b t))))))
     (if (<= t_1 -1e-276)
       t_1
       (if (<= t_1 INFINITY)
         (/ (+ x (/ y (/ t z))) (+ a (+ 1.0 (* b (/ y t)))))
         (/ z b))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (x + ((y * z) / t)) / ((a + 1.0) + ((y * b) / t));
	double tmp;
	if (t_1 <= -((double) INFINITY)) {
		tmp = (y / t) * (z / (1.0 + (a + (y * (b / t)))));
	} else if (t_1 <= -1e-276) {
		tmp = t_1;
	} else if (t_1 <= ((double) INFINITY)) {
		tmp = (x + (y / (t / z))) / (a + (1.0 + (b * (y / t))));
	} else {
		tmp = z / b;
	}
	return tmp;
}

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (x + ((y * z) / t)) / ((a + 1.0) + ((y * b) / t));
	double tmp;
	if (t_1 <= -Double.POSITIVE_INFINITY) {
		tmp = (y / t) * (z / (1.0 + (a + (y * (b / t)))));
	} else if (t_1 <= -1e-276) {
		tmp = t_1;
	} else if (t_1 <= Double.POSITIVE_INFINITY) {
		tmp = (x + (y / (t / z))) / (a + (1.0 + (b * (y / t))));
	} else {
		tmp = z / b;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = (x + ((y * z) / t)) / ((a + 1.0) + ((y * b) / t))
	tmp = 0
	if t_1 <= -math.inf:
		tmp = (y / t) * (z / (1.0 + (a + (y * (b / t)))))
	elif t_1 <= -1e-276:
		tmp = t_1
	elif t_1 <= math.inf:
		tmp = (x + (y / (t / z))) / (a + (1.0 + (b * (y / t))))
	else:
		tmp = z / b
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(x + Float64(Float64(y * z) / t)) / Float64(Float64(a + 1.0) + Float64(Float64(y * b) / t)))
	tmp = 0.0
	if (t_1 <= Float64(-Inf))
		tmp = Float64(Float64(y / t) * Float64(z / Float64(1.0 + Float64(a + Float64(y * Float64(b / t))))));
	elseif (t_1 <= -1e-276)
		tmp = t_1;
	elseif (t_1 <= Inf)
		tmp = Float64(Float64(x + Float64(y / Float64(t / z))) / Float64(a + Float64(1.0 + Float64(b * Float64(y / t)))));
	else
		tmp = Float64(z / b);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = (x + ((y * z) / t)) / ((a + 1.0) + ((y * b) / t));
	tmp = 0.0;
	if (t_1 <= -Inf)
		tmp = (y / t) * (z / (1.0 + (a + (y * (b / t)))));
	elseif (t_1 <= -1e-276)
		tmp = t_1;
	elseif (t_1 <= Inf)
		tmp = (x + (y / (t / z))) / (a + (1.0 + (b * (y / t))));
	else
		tmp = z / b;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(N[(x + N[(N[(y * z), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision] / N[(N[(a + 1.0), $MachinePrecision] + N[(N[(y * b), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, (-Infinity)], N[(N[(y / t), $MachinePrecision] * N[(z / N[(1.0 + N[(a + N[(y * N[(b / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$1, -1e-276], t$95$1, If[LessEqual[t$95$1, Infinity], N[(N[(x + N[(y / N[(t / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(a + N[(1.0 + N[(b * N[(y / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(z / b), $MachinePrecision]]]]]

Derivation

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

Alternative 3: 65.9% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x}{1 + \left(a + \frac{y \cdot b}{t}\right)}\\ \mathbf{if}\;t \leq -1.9 \cdot 10^{-98}:\\ \;\;\;\;\frac{x + \frac{z}{\frac{t}{y}}}{a + 1}\\ \mathbf{elif}\;t \leq 6.3 \cdot 10^{-152}:\\ \;\;\;\;\frac{y \cdot z}{y \cdot b + t \cdot \left(a + 1\right)}\\ \mathbf{elif}\;t \leq 2.25 \cdot 10^{-27}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq 1.5 \cdot 10^{+29}:\\ \;\;\;\;\frac{y}{t} \cdot \frac{z}{1 + \left(a + y \cdot \frac{b}{t}\right)}\\ \mathbf{elif}\;t \leq 1.6 \cdot 10^{+120}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;\frac{x + y \cdot \frac{z}{t}}{a + 1}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (/ x (+ 1.0 (+ a (/ (* y b) t))))))
   (if (<= t -1.9e-98)
     (/ (+ x (/ z (/ t y))) (+ a 1.0))
     (if (<= t 6.3e-152)
       (/ (* y z) (+ (* y b) (* t (+ a 1.0))))
       (if (<= t 2.25e-27)
         t_1
         (if (<= t 1.5e+29)
           (* (/ y t) (/ z (+ 1.0 (+ a (* y (/ b t))))))
           (if (<= t 1.6e+120) t_1 (/ (+ x (* y (/ z t))) (+ a 1.0)))))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = x / (1.0 + (a + ((y * b) / t)));
	double tmp;
	if (t <= -1.9e-98) {
		tmp = (x + (z / (t / y))) / (a + 1.0);
	} else if (t <= 6.3e-152) {
		tmp = (y * z) / ((y * b) + (t * (a + 1.0)));
	} else if (t <= 2.25e-27) {
		tmp = t_1;
	} else if (t <= 1.5e+29) {
		tmp = (y / t) * (z / (1.0 + (a + (y * (b / t)))));
	} else if (t <= 1.6e+120) {
		tmp = t_1;
	} else {
		tmp = (x + (y * (z / t))) / (a + 1.0);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: tmp
    t_1 = x / (1.0d0 + (a + ((y * b) / t)))
    if (t <= (-1.9d-98)) then
        tmp = (x + (z / (t / y))) / (a + 1.0d0)
    else if (t <= 6.3d-152) then
        tmp = (y * z) / ((y * b) + (t * (a + 1.0d0)))
    else if (t <= 2.25d-27) then
        tmp = t_1
    else if (t <= 1.5d+29) then
        tmp = (y / t) * (z / (1.0d0 + (a + (y * (b / t)))))
    else if (t <= 1.6d+120) then
        tmp = t_1
    else
        tmp = (x + (y * (z / t))) / (a + 1.0d0)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = x / (1.0 + (a + ((y * b) / t)));
	double tmp;
	if (t <= -1.9e-98) {
		tmp = (x + (z / (t / y))) / (a + 1.0);
	} else if (t <= 6.3e-152) {
		tmp = (y * z) / ((y * b) + (t * (a + 1.0)));
	} else if (t <= 2.25e-27) {
		tmp = t_1;
	} else if (t <= 1.5e+29) {
		tmp = (y / t) * (z / (1.0 + (a + (y * (b / t)))));
	} else if (t <= 1.6e+120) {
		tmp = t_1;
	} else {
		tmp = (x + (y * (z / t))) / (a + 1.0);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = x / (1.0 + (a + ((y * b) / t)))
	tmp = 0
	if t <= -1.9e-98:
		tmp = (x + (z / (t / y))) / (a + 1.0)
	elif t <= 6.3e-152:
		tmp = (y * z) / ((y * b) + (t * (a + 1.0)))
	elif t <= 2.25e-27:
		tmp = t_1
	elif t <= 1.5e+29:
		tmp = (y / t) * (z / (1.0 + (a + (y * (b / t)))))
	elif t <= 1.6e+120:
		tmp = t_1
	else:
		tmp = (x + (y * (z / t))) / (a + 1.0)
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(x / Float64(1.0 + Float64(a + Float64(Float64(y * b) / t))))
	tmp = 0.0
	if (t <= -1.9e-98)
		tmp = Float64(Float64(x + Float64(z / Float64(t / y))) / Float64(a + 1.0));
	elseif (t <= 6.3e-152)
		tmp = Float64(Float64(y * z) / Float64(Float64(y * b) + Float64(t * Float64(a + 1.0))));
	elseif (t <= 2.25e-27)
		tmp = t_1;
	elseif (t <= 1.5e+29)
		tmp = Float64(Float64(y / t) * Float64(z / Float64(1.0 + Float64(a + Float64(y * Float64(b / t))))));
	elseif (t <= 1.6e+120)
		tmp = t_1;
	else
		tmp = Float64(Float64(x + Float64(y * Float64(z / t))) / Float64(a + 1.0));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = x / (1.0 + (a + ((y * b) / t)));
	tmp = 0.0;
	if (t <= -1.9e-98)
		tmp = (x + (z / (t / y))) / (a + 1.0);
	elseif (t <= 6.3e-152)
		tmp = (y * z) / ((y * b) + (t * (a + 1.0)));
	elseif (t <= 2.25e-27)
		tmp = t_1;
	elseif (t <= 1.5e+29)
		tmp = (y / t) * (z / (1.0 + (a + (y * (b / t)))));
	elseif (t <= 1.6e+120)
		tmp = t_1;
	else
		tmp = (x + (y * (z / t))) / (a + 1.0);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(x / N[(1.0 + N[(a + N[(N[(y * b), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -1.9e-98], N[(N[(x + N[(z / N[(t / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(a + 1.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 6.3e-152], N[(N[(y * z), $MachinePrecision] / N[(N[(y * b), $MachinePrecision] + N[(t * N[(a + 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 2.25e-27], t$95$1, If[LessEqual[t, 1.5e+29], N[(N[(y / t), $MachinePrecision] * N[(z / N[(1.0 + N[(a + N[(y * N[(b / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 1.6e+120], t$95$1, N[(N[(x + N[(y * N[(z / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(a + 1.0), $MachinePrecision]), $MachinePrecision]]]]]]]

Derivation

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

Alternative 4: 65.8% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x}{1 + \left(a + \frac{y \cdot b}{t}\right)}\\ \mathbf{if}\;t \leq -2.5 \cdot 10^{-98}:\\ \;\;\;\;\frac{x + \frac{z}{\frac{t}{y}}}{a + 1}\\ \mathbf{elif}\;t \leq 1.35 \cdot 10^{-151}:\\ \;\;\;\;\frac{y \cdot z}{y \cdot b + t \cdot \left(a + 1\right)}\\ \mathbf{elif}\;t \leq 2 \cdot 10^{-27}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq 1.52 \cdot 10^{+29}:\\ \;\;\;\;\frac{y}{t} \cdot \frac{z}{\left(a + 1\right) + \frac{b}{\frac{t}{y}}}\\ \mathbf{elif}\;t \leq 1.5 \cdot 10^{+117}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;\frac{x + y \cdot \frac{z}{t}}{a + 1}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (/ x (+ 1.0 (+ a (/ (* y b) t))))))
   (if (<= t -2.5e-98)
     (/ (+ x (/ z (/ t y))) (+ a 1.0))
     (if (<= t 1.35e-151)
       (/ (* y z) (+ (* y b) (* t (+ a 1.0))))
       (if (<= t 2e-27)
         t_1
         (if (<= t 1.52e+29)
           (* (/ y t) (/ z (+ (+ a 1.0) (/ b (/ t y)))))
           (if (<= t 1.5e+117) t_1 (/ (+ x (* y (/ z t))) (+ a 1.0)))))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = x / (1.0 + (a + ((y * b) / t)));
	double tmp;
	if (t <= -2.5e-98) {
		tmp = (x + (z / (t / y))) / (a + 1.0);
	} else if (t <= 1.35e-151) {
		tmp = (y * z) / ((y * b) + (t * (a + 1.0)));
	} else if (t <= 2e-27) {
		tmp = t_1;
	} else if (t <= 1.52e+29) {
		tmp = (y / t) * (z / ((a + 1.0) + (b / (t / y))));
	} else if (t <= 1.5e+117) {
		tmp = t_1;
	} else {
		tmp = (x + (y * (z / t))) / (a + 1.0);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: tmp
    t_1 = x / (1.0d0 + (a + ((y * b) / t)))
    if (t <= (-2.5d-98)) then
        tmp = (x + (z / (t / y))) / (a + 1.0d0)
    else if (t <= 1.35d-151) then
        tmp = (y * z) / ((y * b) + (t * (a + 1.0d0)))
    else if (t <= 2d-27) then
        tmp = t_1
    else if (t <= 1.52d+29) then
        tmp = (y / t) * (z / ((a + 1.0d0) + (b / (t / y))))
    else if (t <= 1.5d+117) then
        tmp = t_1
    else
        tmp = (x + (y * (z / t))) / (a + 1.0d0)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = x / (1.0 + (a + ((y * b) / t)));
	double tmp;
	if (t <= -2.5e-98) {
		tmp = (x + (z / (t / y))) / (a + 1.0);
	} else if (t <= 1.35e-151) {
		tmp = (y * z) / ((y * b) + (t * (a + 1.0)));
	} else if (t <= 2e-27) {
		tmp = t_1;
	} else if (t <= 1.52e+29) {
		tmp = (y / t) * (z / ((a + 1.0) + (b / (t / y))));
	} else if (t <= 1.5e+117) {
		tmp = t_1;
	} else {
		tmp = (x + (y * (z / t))) / (a + 1.0);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = x / (1.0 + (a + ((y * b) / t)))
	tmp = 0
	if t <= -2.5e-98:
		tmp = (x + (z / (t / y))) / (a + 1.0)
	elif t <= 1.35e-151:
		tmp = (y * z) / ((y * b) + (t * (a + 1.0)))
	elif t <= 2e-27:
		tmp = t_1
	elif t <= 1.52e+29:
		tmp = (y / t) * (z / ((a + 1.0) + (b / (t / y))))
	elif t <= 1.5e+117:
		tmp = t_1
	else:
		tmp = (x + (y * (z / t))) / (a + 1.0)
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(x / Float64(1.0 + Float64(a + Float64(Float64(y * b) / t))))
	tmp = 0.0
	if (t <= -2.5e-98)
		tmp = Float64(Float64(x + Float64(z / Float64(t / y))) / Float64(a + 1.0));
	elseif (t <= 1.35e-151)
		tmp = Float64(Float64(y * z) / Float64(Float64(y * b) + Float64(t * Float64(a + 1.0))));
	elseif (t <= 2e-27)
		tmp = t_1;
	elseif (t <= 1.52e+29)
		tmp = Float64(Float64(y / t) * Float64(z / Float64(Float64(a + 1.0) + Float64(b / Float64(t / y)))));
	elseif (t <= 1.5e+117)
		tmp = t_1;
	else
		tmp = Float64(Float64(x + Float64(y * Float64(z / t))) / Float64(a + 1.0));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = x / (1.0 + (a + ((y * b) / t)));
	tmp = 0.0;
	if (t <= -2.5e-98)
		tmp = (x + (z / (t / y))) / (a + 1.0);
	elseif (t <= 1.35e-151)
		tmp = (y * z) / ((y * b) + (t * (a + 1.0)));
	elseif (t <= 2e-27)
		tmp = t_1;
	elseif (t <= 1.52e+29)
		tmp = (y / t) * (z / ((a + 1.0) + (b / (t / y))));
	elseif (t <= 1.5e+117)
		tmp = t_1;
	else
		tmp = (x + (y * (z / t))) / (a + 1.0);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(x / N[(1.0 + N[(a + N[(N[(y * b), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -2.5e-98], N[(N[(x + N[(z / N[(t / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(a + 1.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 1.35e-151], N[(N[(y * z), $MachinePrecision] / N[(N[(y * b), $MachinePrecision] + N[(t * N[(a + 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 2e-27], t$95$1, If[LessEqual[t, 1.52e+29], N[(N[(y / t), $MachinePrecision] * N[(z / N[(N[(a + 1.0), $MachinePrecision] + N[(b / N[(t / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 1.5e+117], t$95$1, N[(N[(x + N[(y * N[(z / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(a + 1.0), $MachinePrecision]), $MachinePrecision]]]]]]]

Derivation

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

Alternative 5: 65.7% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x}{1 + \left(a + \frac{y \cdot b}{t}\right)}\\ t_2 := \frac{y \cdot z}{y \cdot b + t \cdot \left(a + 1\right)}\\ \mathbf{if}\;t \leq -1.6 \cdot 10^{-97}:\\ \;\;\;\;\frac{x + \frac{z}{\frac{t}{y}}}{a + 1}\\ \mathbf{elif}\;t \leq 6.5 \cdot 10^{-152}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;t \leq 2 \cdot 10^{-27}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq 1.5 \cdot 10^{+29}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;t \leq 3.1 \cdot 10^{+121}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;\frac{x + y \cdot \frac{z}{t}}{a + 1}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (/ x (+ 1.0 (+ a (/ (* y b) t)))))
        (t_2 (/ (* y z) (+ (* y b) (* t (+ a 1.0))))))
   (if (<= t -1.6e-97)
     (/ (+ x (/ z (/ t y))) (+ a 1.0))
     (if (<= t 6.5e-152)
       t_2
       (if (<= t 2e-27)
         t_1
         (if (<= t 1.5e+29)
           t_2
           (if (<= t 3.1e+121) t_1 (/ (+ x (* y (/ z t))) (+ a 1.0)))))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = x / (1.0 + (a + ((y * b) / t)));
	double t_2 = (y * z) / ((y * b) + (t * (a + 1.0)));
	double tmp;
	if (t <= -1.6e-97) {
		tmp = (x + (z / (t / y))) / (a + 1.0);
	} else if (t <= 6.5e-152) {
		tmp = t_2;
	} else if (t <= 2e-27) {
		tmp = t_1;
	} else if (t <= 1.5e+29) {
		tmp = t_2;
	} else if (t <= 3.1e+121) {
		tmp = t_1;
	} else {
		tmp = (x + (y * (z / t))) / (a + 1.0);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = x / (1.0d0 + (a + ((y * b) / t)))
    t_2 = (y * z) / ((y * b) + (t * (a + 1.0d0)))
    if (t <= (-1.6d-97)) then
        tmp = (x + (z / (t / y))) / (a + 1.0d0)
    else if (t <= 6.5d-152) then
        tmp = t_2
    else if (t <= 2d-27) then
        tmp = t_1
    else if (t <= 1.5d+29) then
        tmp = t_2
    else if (t <= 3.1d+121) then
        tmp = t_1
    else
        tmp = (x + (y * (z / t))) / (a + 1.0d0)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = x / (1.0 + (a + ((y * b) / t)));
	double t_2 = (y * z) / ((y * b) + (t * (a + 1.0)));
	double tmp;
	if (t <= -1.6e-97) {
		tmp = (x + (z / (t / y))) / (a + 1.0);
	} else if (t <= 6.5e-152) {
		tmp = t_2;
	} else if (t <= 2e-27) {
		tmp = t_1;
	} else if (t <= 1.5e+29) {
		tmp = t_2;
	} else if (t <= 3.1e+121) {
		tmp = t_1;
	} else {
		tmp = (x + (y * (z / t))) / (a + 1.0);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = x / (1.0 + (a + ((y * b) / t)))
	t_2 = (y * z) / ((y * b) + (t * (a + 1.0)))
	tmp = 0
	if t <= -1.6e-97:
		tmp = (x + (z / (t / y))) / (a + 1.0)
	elif t <= 6.5e-152:
		tmp = t_2
	elif t <= 2e-27:
		tmp = t_1
	elif t <= 1.5e+29:
		tmp = t_2
	elif t <= 3.1e+121:
		tmp = t_1
	else:
		tmp = (x + (y * (z / t))) / (a + 1.0)
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(x / Float64(1.0 + Float64(a + Float64(Float64(y * b) / t))))
	t_2 = Float64(Float64(y * z) / Float64(Float64(y * b) + Float64(t * Float64(a + 1.0))))
	tmp = 0.0
	if (t <= -1.6e-97)
		tmp = Float64(Float64(x + Float64(z / Float64(t / y))) / Float64(a + 1.0));
	elseif (t <= 6.5e-152)
		tmp = t_2;
	elseif (t <= 2e-27)
		tmp = t_1;
	elseif (t <= 1.5e+29)
		tmp = t_2;
	elseif (t <= 3.1e+121)
		tmp = t_1;
	else
		tmp = Float64(Float64(x + Float64(y * Float64(z / t))) / Float64(a + 1.0));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = x / (1.0 + (a + ((y * b) / t)));
	t_2 = (y * z) / ((y * b) + (t * (a + 1.0)));
	tmp = 0.0;
	if (t <= -1.6e-97)
		tmp = (x + (z / (t / y))) / (a + 1.0);
	elseif (t <= 6.5e-152)
		tmp = t_2;
	elseif (t <= 2e-27)
		tmp = t_1;
	elseif (t <= 1.5e+29)
		tmp = t_2;
	elseif (t <= 3.1e+121)
		tmp = t_1;
	else
		tmp = (x + (y * (z / t))) / (a + 1.0);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(x / N[(1.0 + N[(a + N[(N[(y * b), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(y * z), $MachinePrecision] / N[(N[(y * b), $MachinePrecision] + N[(t * N[(a + 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -1.6e-97], N[(N[(x + N[(z / N[(t / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(a + 1.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 6.5e-152], t$95$2, If[LessEqual[t, 2e-27], t$95$1, If[LessEqual[t, 1.5e+29], t$95$2, If[LessEqual[t, 3.1e+121], t$95$1, N[(N[(x + N[(y * N[(z / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(a + 1.0), $MachinePrecision]), $MachinePrecision]]]]]]]]

Derivation

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

Alternative 6: 79.9% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -4.8 \cdot 10^{-184} \lor \neg \left(t \leq 3.4 \cdot 10^{-152}\right):\\ \;\;\;\;\frac{x + y \cdot \frac{z}{t}}{1 + \left(a + y \cdot \frac{b}{t}\right)}\\ \mathbf{else}:\\ \;\;\;\;\frac{y \cdot z}{y \cdot b + t \cdot \left(a + 1\right)}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (or (<= t -4.8e-184) (not (<= t 3.4e-152)))
   (/ (+ x (* y (/ z t))) (+ 1.0 (+ a (* y (/ b t)))))
   (/ (* y z) (+ (* y b) (* t (+ a 1.0))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((t <= -4.8e-184) || !(t <= 3.4e-152)) {
		tmp = (x + (y * (z / t))) / (1.0 + (a + (y * (b / t))));
	} else {
		tmp = (y * z) / ((y * b) + (t * (a + 1.0)));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if ((t <= (-4.8d-184)) .or. (.not. (t <= 3.4d-152))) then
        tmp = (x + (y * (z / t))) / (1.0d0 + (a + (y * (b / t))))
    else
        tmp = (y * z) / ((y * b) + (t * (a + 1.0d0)))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((t <= -4.8e-184) || !(t <= 3.4e-152)) {
		tmp = (x + (y * (z / t))) / (1.0 + (a + (y * (b / t))));
	} else {
		tmp = (y * z) / ((y * b) + (t * (a + 1.0)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if (t <= -4.8e-184) or not (t <= 3.4e-152):
		tmp = (x + (y * (z / t))) / (1.0 + (a + (y * (b / t))))
	else:
		tmp = (y * z) / ((y * b) + (t * (a + 1.0)))
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if ((t <= -4.8e-184) || !(t <= 3.4e-152))
		tmp = Float64(Float64(x + Float64(y * Float64(z / t))) / Float64(1.0 + Float64(a + Float64(y * Float64(b / t)))));
	else
		tmp = Float64(Float64(y * z) / Float64(Float64(y * b) + Float64(t * Float64(a + 1.0))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if ((t <= -4.8e-184) || ~((t <= 3.4e-152)))
		tmp = (x + (y * (z / t))) / (1.0 + (a + (y * (b / t))));
	else
		tmp = (y * z) / ((y * b) + (t * (a + 1.0)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[Or[LessEqual[t, -4.8e-184], N[Not[LessEqual[t, 3.4e-152]], $MachinePrecision]], N[(N[(x + N[(y * N[(z / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(1.0 + N[(a + N[(y * N[(b / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(y * z), $MachinePrecision] / N[(N[(y * b), $MachinePrecision] + N[(t * N[(a + 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 7: 80.0% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -5.2 \cdot 10^{-184}:\\ \;\;\;\;\frac{x + \frac{y}{\frac{t}{z}}}{a + \left(1 + \frac{y}{\frac{t}{b}}\right)}\\ \mathbf{elif}\;t \leq 1.4 \cdot 10^{-151}:\\ \;\;\;\;\frac{y \cdot z}{y \cdot b + t \cdot \left(a + 1\right)}\\ \mathbf{else}:\\ \;\;\;\;\frac{x + y \cdot \frac{z}{t}}{1 + \left(a + y \cdot \frac{b}{t}\right)}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= t -5.2e-184)
   (/ (+ x (/ y (/ t z))) (+ a (+ 1.0 (/ y (/ t b)))))
   (if (<= t 1.4e-151)
     (/ (* y z) (+ (* y b) (* t (+ a 1.0))))
     (/ (+ x (* y (/ z t))) (+ 1.0 (+ a (* y (/ b t))))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (t <= -5.2e-184) {
		tmp = (x + (y / (t / z))) / (a + (1.0 + (y / (t / b))));
	} else if (t <= 1.4e-151) {
		tmp = (y * z) / ((y * b) + (t * (a + 1.0)));
	} else {
		tmp = (x + (y * (z / t))) / (1.0 + (a + (y * (b / t))));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if (t <= (-5.2d-184)) then
        tmp = (x + (y / (t / z))) / (a + (1.0d0 + (y / (t / b))))
    else if (t <= 1.4d-151) then
        tmp = (y * z) / ((y * b) + (t * (a + 1.0d0)))
    else
        tmp = (x + (y * (z / t))) / (1.0d0 + (a + (y * (b / 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 tmp;
	if (t <= -5.2e-184) {
		tmp = (x + (y / (t / z))) / (a + (1.0 + (y / (t / b))));
	} else if (t <= 1.4e-151) {
		tmp = (y * z) / ((y * b) + (t * (a + 1.0)));
	} else {
		tmp = (x + (y * (z / t))) / (1.0 + (a + (y * (b / t))));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if t <= -5.2e-184:
		tmp = (x + (y / (t / z))) / (a + (1.0 + (y / (t / b))))
	elif t <= 1.4e-151:
		tmp = (y * z) / ((y * b) + (t * (a + 1.0)))
	else:
		tmp = (x + (y * (z / t))) / (1.0 + (a + (y * (b / t))))
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (t <= -5.2e-184)
		tmp = Float64(Float64(x + Float64(y / Float64(t / z))) / Float64(a + Float64(1.0 + Float64(y / Float64(t / b)))));
	elseif (t <= 1.4e-151)
		tmp = Float64(Float64(y * z) / Float64(Float64(y * b) + Float64(t * Float64(a + 1.0))));
	else
		tmp = Float64(Float64(x + Float64(y * Float64(z / t))) / Float64(1.0 + Float64(a + Float64(y * Float64(b / t)))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if (t <= -5.2e-184)
		tmp = (x + (y / (t / z))) / (a + (1.0 + (y / (t / b))));
	elseif (t <= 1.4e-151)
		tmp = (y * z) / ((y * b) + (t * (a + 1.0)));
	else
		tmp = (x + (y * (z / t))) / (1.0 + (a + (y * (b / t))));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[t, -5.2e-184], N[(N[(x + N[(y / N[(t / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(a + N[(1.0 + N[(y / N[(t / b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 1.4e-151], N[(N[(y * z), $MachinePrecision] / N[(N[(y * b), $MachinePrecision] + N[(t * N[(a + 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(x + N[(y * N[(z / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(1.0 + N[(a + N[(y * N[(b / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

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

Alternative 8: 61.3% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -3.05 \cdot 10^{+124}:\\ \;\;\;\;\frac{z}{b}\\ \mathbf{elif}\;y \leq -7.6 \cdot 10^{+43}:\\ \;\;\;\;\frac{x}{a + \left(1 + b \cdot \frac{y}{t}\right)}\\ \mathbf{elif}\;y \leq -3.3 \cdot 10^{+16}:\\ \;\;\;\;\frac{y}{\frac{t + y \cdot b}{z}}\\ \mathbf{elif}\;y \leq 1.56 \cdot 10^{+105}:\\ \;\;\;\;\frac{x}{1 + \left(a + \frac{y \cdot b}{t}\right)}\\ \mathbf{else}:\\ \;\;\;\;\frac{z}{b}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= y -3.05e+124)
   (/ z b)
   (if (<= y -7.6e+43)
     (/ x (+ a (+ 1.0 (* b (/ y t)))))
     (if (<= y -3.3e+16)
       (/ y (/ (+ t (* y b)) z))
       (if (<= y 1.56e+105) (/ x (+ 1.0 (+ a (/ (* y b) t)))) (/ z b))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (y <= -3.05e+124) {
		tmp = z / b;
	} else if (y <= -7.6e+43) {
		tmp = x / (a + (1.0 + (b * (y / t))));
	} else if (y <= -3.3e+16) {
		tmp = y / ((t + (y * b)) / z);
	} else if (y <= 1.56e+105) {
		tmp = x / (1.0 + (a + ((y * b) / t)));
	} else {
		tmp = z / b;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if (y <= (-3.05d+124)) then
        tmp = z / b
    else if (y <= (-7.6d+43)) then
        tmp = x / (a + (1.0d0 + (b * (y / t))))
    else if (y <= (-3.3d+16)) then
        tmp = y / ((t + (y * b)) / z)
    else if (y <= 1.56d+105) then
        tmp = x / (1.0d0 + (a + ((y * b) / t)))
    else
        tmp = z / b
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (y <= -3.05e+124) {
		tmp = z / b;
	} else if (y <= -7.6e+43) {
		tmp = x / (a + (1.0 + (b * (y / t))));
	} else if (y <= -3.3e+16) {
		tmp = y / ((t + (y * b)) / z);
	} else if (y <= 1.56e+105) {
		tmp = x / (1.0 + (a + ((y * b) / t)));
	} else {
		tmp = z / b;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if y <= -3.05e+124:
		tmp = z / b
	elif y <= -7.6e+43:
		tmp = x / (a + (1.0 + (b * (y / t))))
	elif y <= -3.3e+16:
		tmp = y / ((t + (y * b)) / z)
	elif y <= 1.56e+105:
		tmp = x / (1.0 + (a + ((y * b) / t)))
	else:
		tmp = z / b
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (y <= -3.05e+124)
		tmp = Float64(z / b);
	elseif (y <= -7.6e+43)
		tmp = Float64(x / Float64(a + Float64(1.0 + Float64(b * Float64(y / t)))));
	elseif (y <= -3.3e+16)
		tmp = Float64(y / Float64(Float64(t + Float64(y * b)) / z));
	elseif (y <= 1.56e+105)
		tmp = Float64(x / Float64(1.0 + Float64(a + Float64(Float64(y * b) / t))));
	else
		tmp = Float64(z / b);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if (y <= -3.05e+124)
		tmp = z / b;
	elseif (y <= -7.6e+43)
		tmp = x / (a + (1.0 + (b * (y / t))));
	elseif (y <= -3.3e+16)
		tmp = y / ((t + (y * b)) / z);
	elseif (y <= 1.56e+105)
		tmp = x / (1.0 + (a + ((y * b) / t)));
	else
		tmp = z / b;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[y, -3.05e+124], N[(z / b), $MachinePrecision], If[LessEqual[y, -7.6e+43], N[(x / N[(a + N[(1.0 + N[(b * N[(y / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, -3.3e+16], N[(y / N[(N[(t + N[(y * b), $MachinePrecision]), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 1.56e+105], N[(x / N[(1.0 + N[(a + N[(N[(y * b), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(z / b), $MachinePrecision]]]]]

Derivation

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

Alternative 9: 61.3% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.8 \cdot 10^{+127}:\\ \;\;\;\;\frac{z}{b}\\ \mathbf{elif}\;y \leq -9.5 \cdot 10^{+44}:\\ \;\;\;\;\frac{x}{a + \left(1 + \frac{y}{\frac{t}{b}}\right)}\\ \mathbf{elif}\;y \leq -6.8 \cdot 10^{+15}:\\ \;\;\;\;\frac{y}{\frac{t + y \cdot b}{z}}\\ \mathbf{elif}\;y \leq 1.25 \cdot 10^{+102}:\\ \;\;\;\;\frac{x}{1 + \left(a + \frac{y \cdot b}{t}\right)}\\ \mathbf{else}:\\ \;\;\;\;\frac{z}{b}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= y -1.8e+127)
   (/ z b)
   (if (<= y -9.5e+44)
     (/ x (+ a (+ 1.0 (/ y (/ t b)))))
     (if (<= y -6.8e+15)
       (/ y (/ (+ t (* y b)) z))
       (if (<= y 1.25e+102) (/ x (+ 1.0 (+ a (/ (* y b) t)))) (/ z b))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (y <= -1.8e+127) {
		tmp = z / b;
	} else if (y <= -9.5e+44) {
		tmp = x / (a + (1.0 + (y / (t / b))));
	} else if (y <= -6.8e+15) {
		tmp = y / ((t + (y * b)) / z);
	} else if (y <= 1.25e+102) {
		tmp = x / (1.0 + (a + ((y * b) / t)));
	} else {
		tmp = z / b;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if (y <= (-1.8d+127)) then
        tmp = z / b
    else if (y <= (-9.5d+44)) then
        tmp = x / (a + (1.0d0 + (y / (t / b))))
    else if (y <= (-6.8d+15)) then
        tmp = y / ((t + (y * b)) / z)
    else if (y <= 1.25d+102) then
        tmp = x / (1.0d0 + (a + ((y * b) / t)))
    else
        tmp = z / b
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (y <= -1.8e+127) {
		tmp = z / b;
	} else if (y <= -9.5e+44) {
		tmp = x / (a + (1.0 + (y / (t / b))));
	} else if (y <= -6.8e+15) {
		tmp = y / ((t + (y * b)) / z);
	} else if (y <= 1.25e+102) {
		tmp = x / (1.0 + (a + ((y * b) / t)));
	} else {
		tmp = z / b;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if y <= -1.8e+127:
		tmp = z / b
	elif y <= -9.5e+44:
		tmp = x / (a + (1.0 + (y / (t / b))))
	elif y <= -6.8e+15:
		tmp = y / ((t + (y * b)) / z)
	elif y <= 1.25e+102:
		tmp = x / (1.0 + (a + ((y * b) / t)))
	else:
		tmp = z / b
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (y <= -1.8e+127)
		tmp = Float64(z / b);
	elseif (y <= -9.5e+44)
		tmp = Float64(x / Float64(a + Float64(1.0 + Float64(y / Float64(t / b)))));
	elseif (y <= -6.8e+15)
		tmp = Float64(y / Float64(Float64(t + Float64(y * b)) / z));
	elseif (y <= 1.25e+102)
		tmp = Float64(x / Float64(1.0 + Float64(a + Float64(Float64(y * b) / t))));
	else
		tmp = Float64(z / b);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if (y <= -1.8e+127)
		tmp = z / b;
	elseif (y <= -9.5e+44)
		tmp = x / (a + (1.0 + (y / (t / b))));
	elseif (y <= -6.8e+15)
		tmp = y / ((t + (y * b)) / z);
	elseif (y <= 1.25e+102)
		tmp = x / (1.0 + (a + ((y * b) / t)));
	else
		tmp = z / b;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[y, -1.8e+127], N[(z / b), $MachinePrecision], If[LessEqual[y, -9.5e+44], N[(x / N[(a + N[(1.0 + N[(y / N[(t / b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, -6.8e+15], N[(y / N[(N[(t + N[(y * b), $MachinePrecision]), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 1.25e+102], N[(x / N[(1.0 + N[(a + N[(N[(y * b), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(z / b), $MachinePrecision]]]]]

Derivation

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

Alternative 10: 68.9% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -4600000:\\ \;\;\;\;\frac{x + y \cdot \frac{z}{t}}{a + 1}\\ \mathbf{elif}\;a \leq 7.8 \cdot 10^{+24}:\\ \;\;\;\;\frac{x + \frac{y \cdot z}{t}}{1 + \frac{y \cdot b}{t}}\\ \mathbf{else}:\\ \;\;\;\;\frac{x + \frac{z}{\frac{t}{y}}}{a + 1}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= a -4600000.0)
   (/ (+ x (* y (/ z t))) (+ a 1.0))
   (if (<= a 7.8e+24)
     (/ (+ x (/ (* y z) t)) (+ 1.0 (/ (* y b) t)))
     (/ (+ x (/ z (/ t y))) (+ a 1.0)))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (a <= -4600000.0) {
		tmp = (x + (y * (z / t))) / (a + 1.0);
	} else if (a <= 7.8e+24) {
		tmp = (x + ((y * z) / t)) / (1.0 + ((y * b) / t));
	} else {
		tmp = (x + (z / (t / y))) / (a + 1.0);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if (a <= (-4600000.0d0)) then
        tmp = (x + (y * (z / t))) / (a + 1.0d0)
    else if (a <= 7.8d+24) then
        tmp = (x + ((y * z) / t)) / (1.0d0 + ((y * b) / t))
    else
        tmp = (x + (z / (t / y))) / (a + 1.0d0)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (a <= -4600000.0) {
		tmp = (x + (y * (z / t))) / (a + 1.0);
	} else if (a <= 7.8e+24) {
		tmp = (x + ((y * z) / t)) / (1.0 + ((y * b) / t));
	} else {
		tmp = (x + (z / (t / y))) / (a + 1.0);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if a <= -4600000.0:
		tmp = (x + (y * (z / t))) / (a + 1.0)
	elif a <= 7.8e+24:
		tmp = (x + ((y * z) / t)) / (1.0 + ((y * b) / t))
	else:
		tmp = (x + (z / (t / y))) / (a + 1.0)
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (a <= -4600000.0)
		tmp = Float64(Float64(x + Float64(y * Float64(z / t))) / Float64(a + 1.0));
	elseif (a <= 7.8e+24)
		tmp = Float64(Float64(x + Float64(Float64(y * z) / t)) / Float64(1.0 + Float64(Float64(y * b) / t)));
	else
		tmp = Float64(Float64(x + Float64(z / Float64(t / y))) / Float64(a + 1.0));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if (a <= -4600000.0)
		tmp = (x + (y * (z / t))) / (a + 1.0);
	elseif (a <= 7.8e+24)
		tmp = (x + ((y * z) / t)) / (1.0 + ((y * b) / t));
	else
		tmp = (x + (z / (t / y))) / (a + 1.0);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[a, -4600000.0], N[(N[(x + N[(y * N[(z / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(a + 1.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 7.8e+24], N[(N[(x + N[(N[(y * z), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision] / N[(1.0 + N[(N[(y * b), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(x + N[(z / N[(t / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(a + 1.0), $MachinePrecision]), $MachinePrecision]]]

Derivation

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

Alternative 11: 63.2% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x}{a + \left(1 + b \cdot \frac{y}{t}\right)}\\ \mathbf{if}\;b \leq -1.6 \cdot 10^{+188}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;b \leq -9.6 \cdot 10^{+88}:\\ \;\;\;\;\frac{z}{b}\\ \mathbf{elif}\;b \leq 1.4 \cdot 10^{+31}:\\ \;\;\;\;\frac{x + y \cdot \frac{z}{t}}{a + 1}\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (/ x (+ a (+ 1.0 (* b (/ y t)))))))
   (if (<= b -1.6e+188)
     t_1
     (if (<= b -9.6e+88)
       (/ z b)
       (if (<= b 1.4e+31) (/ (+ x (* y (/ z t))) (+ a 1.0)) t_1)))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = x / (a + (1.0 + (b * (y / t))));
	double tmp;
	if (b <= -1.6e+188) {
		tmp = t_1;
	} else if (b <= -9.6e+88) {
		tmp = z / b;
	} else if (b <= 1.4e+31) {
		tmp = (x + (y * (z / t))) / (a + 1.0);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: tmp
    t_1 = x / (a + (1.0d0 + (b * (y / t))))
    if (b <= (-1.6d+188)) then
        tmp = t_1
    else if (b <= (-9.6d+88)) then
        tmp = z / b
    else if (b <= 1.4d+31) then
        tmp = (x + (y * (z / t))) / (a + 1.0d0)
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = x / (a + (1.0 + (b * (y / t))));
	double tmp;
	if (b <= -1.6e+188) {
		tmp = t_1;
	} else if (b <= -9.6e+88) {
		tmp = z / b;
	} else if (b <= 1.4e+31) {
		tmp = (x + (y * (z / t))) / (a + 1.0);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = x / (a + (1.0 + (b * (y / t))))
	tmp = 0
	if b <= -1.6e+188:
		tmp = t_1
	elif b <= -9.6e+88:
		tmp = z / b
	elif b <= 1.4e+31:
		tmp = (x + (y * (z / t))) / (a + 1.0)
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(x / Float64(a + Float64(1.0 + Float64(b * Float64(y / t)))))
	tmp = 0.0
	if (b <= -1.6e+188)
		tmp = t_1;
	elseif (b <= -9.6e+88)
		tmp = Float64(z / b);
	elseif (b <= 1.4e+31)
		tmp = Float64(Float64(x + Float64(y * Float64(z / t))) / Float64(a + 1.0));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = x / (a + (1.0 + (b * (y / t))));
	tmp = 0.0;
	if (b <= -1.6e+188)
		tmp = t_1;
	elseif (b <= -9.6e+88)
		tmp = z / b;
	elseif (b <= 1.4e+31)
		tmp = (x + (y * (z / t))) / (a + 1.0);
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(x / N[(a + N[(1.0 + N[(b * N[(y / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[b, -1.6e+188], t$95$1, If[LessEqual[b, -9.6e+88], N[(z / b), $MachinePrecision], If[LessEqual[b, 1.4e+31], N[(N[(x + N[(y * N[(z / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(a + 1.0), $MachinePrecision]), $MachinePrecision], t$95$1]]]]

Derivation

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

Alternative 12: 55.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a + 1 \leq -4 \cdot 10^{+44} \lor \neg \left(a + 1 \leq 10^{+25}\right):\\ \;\;\;\;\frac{x + \frac{y \cdot z}{t}}{a}\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{1 + b \cdot \frac{y}{t}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (or (<= (+ a 1.0) -4e+44) (not (<= (+ a 1.0) 1e+25)))
   (/ (+ x (/ (* y z) t)) a)
   (/ x (+ 1.0 (* b (/ y t))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (((a + 1.0) <= -4e+44) || !((a + 1.0) <= 1e+25)) {
		tmp = (x + ((y * z) / t)) / a;
	} else {
		tmp = x / (1.0 + (b * (y / t)));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if (((a + 1.0d0) <= (-4d+44)) .or. (.not. ((a + 1.0d0) <= 1d+25))) then
        tmp = (x + ((y * z) / t)) / a
    else
        tmp = x / (1.0d0 + (b * (y / 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 tmp;
	if (((a + 1.0) <= -4e+44) || !((a + 1.0) <= 1e+25)) {
		tmp = (x + ((y * z) / t)) / a;
	} else {
		tmp = x / (1.0 + (b * (y / t)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if ((a + 1.0) <= -4e+44) or not ((a + 1.0) <= 1e+25):
		tmp = (x + ((y * z) / t)) / a
	else:
		tmp = x / (1.0 + (b * (y / t)))
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if ((Float64(a + 1.0) <= -4e+44) || !(Float64(a + 1.0) <= 1e+25))
		tmp = Float64(Float64(x + Float64(Float64(y * z) / t)) / a);
	else
		tmp = Float64(x / Float64(1.0 + Float64(b * Float64(y / t))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if (((a + 1.0) <= -4e+44) || ~(((a + 1.0) <= 1e+25)))
		tmp = (x + ((y * z) / t)) / a;
	else
		tmp = x / (1.0 + (b * (y / t)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[Or[LessEqual[N[(a + 1.0), $MachinePrecision], -4e+44], N[Not[LessEqual[N[(a + 1.0), $MachinePrecision], 1e+25]], $MachinePrecision]], N[(N[(x + N[(N[(y * z), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision] / a), $MachinePrecision], N[(x / N[(1.0 + N[(b * N[(y / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 13: 42.2% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -3.9 \cdot 10^{+40}:\\ \;\;\;\;\frac{x}{a}\\ \mathbf{elif}\;a \leq -1.9 \cdot 10^{-131}:\\ \;\;\;\;\frac{z}{b}\\ \mathbf{elif}\;a \leq 3.2 \cdot 10^{-196}:\\ \;\;\;\;x\\ \mathbf{elif}\;a \leq 3.15 \cdot 10^{+25} \lor \neg \left(a \leq 4.2 \cdot 10^{+62}\right) \land a \leq 2.3 \cdot 10^{+105}:\\ \;\;\;\;\frac{z}{b}\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{a}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= a -3.9e+40)
   (/ x a)
   (if (<= a -1.9e-131)
     (/ z b)
     (if (<= a 3.2e-196)
       x
       (if (or (<= a 3.15e+25) (and (not (<= a 4.2e+62)) (<= a 2.3e+105)))
         (/ z b)
         (/ x a))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (a <= -3.9e+40) {
		tmp = x / a;
	} else if (a <= -1.9e-131) {
		tmp = z / b;
	} else if (a <= 3.2e-196) {
		tmp = x;
	} else if ((a <= 3.15e+25) || (!(a <= 4.2e+62) && (a <= 2.3e+105))) {
		tmp = z / b;
	} else {
		tmp = x / a;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if (a <= (-3.9d+40)) then
        tmp = x / a
    else if (a <= (-1.9d-131)) then
        tmp = z / b
    else if (a <= 3.2d-196) then
        tmp = x
    else if ((a <= 3.15d+25) .or. (.not. (a <= 4.2d+62)) .and. (a <= 2.3d+105)) then
        tmp = z / b
    else
        tmp = x / a
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (a <= -3.9e+40) {
		tmp = x / a;
	} else if (a <= -1.9e-131) {
		tmp = z / b;
	} else if (a <= 3.2e-196) {
		tmp = x;
	} else if ((a <= 3.15e+25) || (!(a <= 4.2e+62) && (a <= 2.3e+105))) {
		tmp = z / b;
	} else {
		tmp = x / a;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if a <= -3.9e+40:
		tmp = x / a
	elif a <= -1.9e-131:
		tmp = z / b
	elif a <= 3.2e-196:
		tmp = x
	elif (a <= 3.15e+25) or (not (a <= 4.2e+62) and (a <= 2.3e+105)):
		tmp = z / b
	else:
		tmp = x / a
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (a <= -3.9e+40)
		tmp = Float64(x / a);
	elseif (a <= -1.9e-131)
		tmp = Float64(z / b);
	elseif (a <= 3.2e-196)
		tmp = x;
	elseif ((a <= 3.15e+25) || (!(a <= 4.2e+62) && (a <= 2.3e+105)))
		tmp = Float64(z / b);
	else
		tmp = Float64(x / a);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if (a <= -3.9e+40)
		tmp = x / a;
	elseif (a <= -1.9e-131)
		tmp = z / b;
	elseif (a <= 3.2e-196)
		tmp = x;
	elseif ((a <= 3.15e+25) || (~((a <= 4.2e+62)) && (a <= 2.3e+105)))
		tmp = z / b;
	else
		tmp = x / a;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[a, -3.9e+40], N[(x / a), $MachinePrecision], If[LessEqual[a, -1.9e-131], N[(z / b), $MachinePrecision], If[LessEqual[a, 3.2e-196], x, If[Or[LessEqual[a, 3.15e+25], And[N[Not[LessEqual[a, 4.2e+62]], $MachinePrecision], LessEqual[a, 2.3e+105]]], N[(z / b), $MachinePrecision], N[(x / a), $MachinePrecision]]]]]

Derivation

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

Alternative 14: 54.5% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -2.7 \cdot 10^{+22}:\\ \;\;\;\;\frac{z}{b}\\ \mathbf{elif}\;y \leq -5.9 \cdot 10^{-21}:\\ \;\;\;\;\frac{z}{\left(a + 1\right) \cdot \frac{t}{y}}\\ \mathbf{elif}\;y \leq -7 \cdot 10^{-44}:\\ \;\;\;\;\frac{y}{\frac{t + y \cdot b}{z}}\\ \mathbf{elif}\;y \leq 5 \cdot 10^{+97}:\\ \;\;\;\;\frac{x}{a + 1}\\ \mathbf{else}:\\ \;\;\;\;\frac{z}{b}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= y -2.7e+22)
   (/ z b)
   (if (<= y -5.9e-21)
     (/ z (* (+ a 1.0) (/ t y)))
     (if (<= y -7e-44)
       (/ y (/ (+ t (* y b)) z))
       (if (<= y 5e+97) (/ x (+ a 1.0)) (/ z b))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (y <= -2.7e+22) {
		tmp = z / b;
	} else if (y <= -5.9e-21) {
		tmp = z / ((a + 1.0) * (t / y));
	} else if (y <= -7e-44) {
		tmp = y / ((t + (y * b)) / z);
	} else if (y <= 5e+97) {
		tmp = x / (a + 1.0);
	} else {
		tmp = z / b;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if (y <= (-2.7d+22)) then
        tmp = z / b
    else if (y <= (-5.9d-21)) then
        tmp = z / ((a + 1.0d0) * (t / y))
    else if (y <= (-7d-44)) then
        tmp = y / ((t + (y * b)) / z)
    else if (y <= 5d+97) then
        tmp = x / (a + 1.0d0)
    else
        tmp = z / b
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (y <= -2.7e+22) {
		tmp = z / b;
	} else if (y <= -5.9e-21) {
		tmp = z / ((a + 1.0) * (t / y));
	} else if (y <= -7e-44) {
		tmp = y / ((t + (y * b)) / z);
	} else if (y <= 5e+97) {
		tmp = x / (a + 1.0);
	} else {
		tmp = z / b;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if y <= -2.7e+22:
		tmp = z / b
	elif y <= -5.9e-21:
		tmp = z / ((a + 1.0) * (t / y))
	elif y <= -7e-44:
		tmp = y / ((t + (y * b)) / z)
	elif y <= 5e+97:
		tmp = x / (a + 1.0)
	else:
		tmp = z / b
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (y <= -2.7e+22)
		tmp = Float64(z / b);
	elseif (y <= -5.9e-21)
		tmp = Float64(z / Float64(Float64(a + 1.0) * Float64(t / y)));
	elseif (y <= -7e-44)
		tmp = Float64(y / Float64(Float64(t + Float64(y * b)) / z));
	elseif (y <= 5e+97)
		tmp = Float64(x / Float64(a + 1.0));
	else
		tmp = Float64(z / b);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if (y <= -2.7e+22)
		tmp = z / b;
	elseif (y <= -5.9e-21)
		tmp = z / ((a + 1.0) * (t / y));
	elseif (y <= -7e-44)
		tmp = y / ((t + (y * b)) / z);
	elseif (y <= 5e+97)
		tmp = x / (a + 1.0);
	else
		tmp = z / b;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[y, -2.7e+22], N[(z / b), $MachinePrecision], If[LessEqual[y, -5.9e-21], N[(z / N[(N[(a + 1.0), $MachinePrecision] * N[(t / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, -7e-44], N[(y / N[(N[(t + N[(y * b), $MachinePrecision]), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 5e+97], N[(x / N[(a + 1.0), $MachinePrecision]), $MachinePrecision], N[(z / b), $MachinePrecision]]]]]

Derivation

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

Alternative 15: 54.5% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.9 \cdot 10^{+23}:\\ \;\;\;\;\frac{z}{b}\\ \mathbf{elif}\;y \leq -1.26 \cdot 10^{-20}:\\ \;\;\;\;\frac{y \cdot z}{t \cdot \left(a + 1\right)}\\ \mathbf{elif}\;y \leq -6.6 \cdot 10^{-43}:\\ \;\;\;\;\frac{y}{\frac{t + y \cdot b}{z}}\\ \mathbf{elif}\;y \leq 4.5 \cdot 10^{+97}:\\ \;\;\;\;\frac{x}{a + 1}\\ \mathbf{else}:\\ \;\;\;\;\frac{z}{b}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= y -1.9e+23)
   (/ z b)
   (if (<= y -1.26e-20)
     (/ (* y z) (* t (+ a 1.0)))
     (if (<= y -6.6e-43)
       (/ y (/ (+ t (* y b)) z))
       (if (<= y 4.5e+97) (/ x (+ a 1.0)) (/ z b))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (y <= -1.9e+23) {
		tmp = z / b;
	} else if (y <= -1.26e-20) {
		tmp = (y * z) / (t * (a + 1.0));
	} else if (y <= -6.6e-43) {
		tmp = y / ((t + (y * b)) / z);
	} else if (y <= 4.5e+97) {
		tmp = x / (a + 1.0);
	} else {
		tmp = z / b;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if (y <= (-1.9d+23)) then
        tmp = z / b
    else if (y <= (-1.26d-20)) then
        tmp = (y * z) / (t * (a + 1.0d0))
    else if (y <= (-6.6d-43)) then
        tmp = y / ((t + (y * b)) / z)
    else if (y <= 4.5d+97) then
        tmp = x / (a + 1.0d0)
    else
        tmp = z / b
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (y <= -1.9e+23) {
		tmp = z / b;
	} else if (y <= -1.26e-20) {
		tmp = (y * z) / (t * (a + 1.0));
	} else if (y <= -6.6e-43) {
		tmp = y / ((t + (y * b)) / z);
	} else if (y <= 4.5e+97) {
		tmp = x / (a + 1.0);
	} else {
		tmp = z / b;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if y <= -1.9e+23:
		tmp = z / b
	elif y <= -1.26e-20:
		tmp = (y * z) / (t * (a + 1.0))
	elif y <= -6.6e-43:
		tmp = y / ((t + (y * b)) / z)
	elif y <= 4.5e+97:
		tmp = x / (a + 1.0)
	else:
		tmp = z / b
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (y <= -1.9e+23)
		tmp = Float64(z / b);
	elseif (y <= -1.26e-20)
		tmp = Float64(Float64(y * z) / Float64(t * Float64(a + 1.0)));
	elseif (y <= -6.6e-43)
		tmp = Float64(y / Float64(Float64(t + Float64(y * b)) / z));
	elseif (y <= 4.5e+97)
		tmp = Float64(x / Float64(a + 1.0));
	else
		tmp = Float64(z / b);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if (y <= -1.9e+23)
		tmp = z / b;
	elseif (y <= -1.26e-20)
		tmp = (y * z) / (t * (a + 1.0));
	elseif (y <= -6.6e-43)
		tmp = y / ((t + (y * b)) / z);
	elseif (y <= 4.5e+97)
		tmp = x / (a + 1.0);
	else
		tmp = z / b;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[y, -1.9e+23], N[(z / b), $MachinePrecision], If[LessEqual[y, -1.26e-20], N[(N[(y * z), $MachinePrecision] / N[(t * N[(a + 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, -6.6e-43], N[(y / N[(N[(t + N[(y * b), $MachinePrecision]), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 4.5e+97], N[(x / N[(a + 1.0), $MachinePrecision]), $MachinePrecision], N[(z / b), $MachinePrecision]]]]]

Derivation

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

Alternative 16: 61.2% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -4.2 \cdot 10^{+23} \lor \neg \left(y \leq 2.1 \cdot 10^{+104}\right):\\ \;\;\;\;\frac{z}{b}\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{1 + \left(a + \frac{y \cdot b}{t}\right)}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (or (<= y -4.2e+23) (not (<= y 2.1e+104)))
   (/ z b)
   (/ x (+ 1.0 (+ a (/ (* y b) t))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((y <= -4.2e+23) || !(y <= 2.1e+104)) {
		tmp = z / b;
	} else {
		tmp = x / (1.0 + (a + ((y * b) / t)));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if ((y <= (-4.2d+23)) .or. (.not. (y <= 2.1d+104))) then
        tmp = z / b
    else
        tmp = x / (1.0d0 + (a + ((y * b) / 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 tmp;
	if ((y <= -4.2e+23) || !(y <= 2.1e+104)) {
		tmp = z / b;
	} else {
		tmp = x / (1.0 + (a + ((y * b) / t)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if (y <= -4.2e+23) or not (y <= 2.1e+104):
		tmp = z / b
	else:
		tmp = x / (1.0 + (a + ((y * b) / t)))
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if ((y <= -4.2e+23) || !(y <= 2.1e+104))
		tmp = Float64(z / b);
	else
		tmp = Float64(x / Float64(1.0 + Float64(a + Float64(Float64(y * b) / t))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if ((y <= -4.2e+23) || ~((y <= 2.1e+104)))
		tmp = z / b;
	else
		tmp = x / (1.0 + (a + ((y * b) / t)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[Or[LessEqual[y, -4.2e+23], N[Not[LessEqual[y, 2.1e+104]], $MachinePrecision]], N[(z / b), $MachinePrecision], N[(x / N[(1.0 + N[(a + N[(N[(y * b), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 17: 64.3% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -9 \cdot 10^{+237} \lor \neg \left(y \leq 9.2 \cdot 10^{+108}\right):\\ \;\;\;\;\frac{z}{b}\\ \mathbf{else}:\\ \;\;\;\;\frac{x + \frac{z}{\frac{t}{y}}}{a + 1}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (or (<= y -9e+237) (not (<= y 9.2e+108)))
   (/ z b)
   (/ (+ x (/ z (/ t y))) (+ a 1.0))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((y <= -9e+237) || !(y <= 9.2e+108)) {
		tmp = z / b;
	} else {
		tmp = (x + (z / (t / y))) / (a + 1.0);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if ((y <= (-9d+237)) .or. (.not. (y <= 9.2d+108))) then
        tmp = z / b
    else
        tmp = (x + (z / (t / y))) / (a + 1.0d0)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((y <= -9e+237) || !(y <= 9.2e+108)) {
		tmp = z / b;
	} else {
		tmp = (x + (z / (t / y))) / (a + 1.0);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if (y <= -9e+237) or not (y <= 9.2e+108):
		tmp = z / b
	else:
		tmp = (x + (z / (t / y))) / (a + 1.0)
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if ((y <= -9e+237) || !(y <= 9.2e+108))
		tmp = Float64(z / b);
	else
		tmp = Float64(Float64(x + Float64(z / Float64(t / y))) / Float64(a + 1.0));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if ((y <= -9e+237) || ~((y <= 9.2e+108)))
		tmp = z / b;
	else
		tmp = (x + (z / (t / y))) / (a + 1.0);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[Or[LessEqual[y, -9e+237], N[Not[LessEqual[y, 9.2e+108]], $MachinePrecision]], N[(z / b), $MachinePrecision], N[(N[(x + N[(z / N[(t / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(a + 1.0), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 18: 55.5% accurate, 1.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.05 \cdot 10^{+24} \lor \neg \left(y \leq 2.3 \cdot 10^{+98}\right):\\ \;\;\;\;\frac{z}{b}\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{a + 1}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (or (<= y -1.05e+24) (not (<= y 2.3e+98))) (/ z b) (/ x (+ a 1.0))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((y <= -1.05e+24) || !(y <= 2.3e+98)) {
		tmp = z / b;
	} else {
		tmp = x / (a + 1.0);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if ((y <= (-1.05d+24)) .or. (.not. (y <= 2.3d+98))) then
        tmp = z / b
    else
        tmp = x / (a + 1.0d0)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((y <= -1.05e+24) || !(y <= 2.3e+98)) {
		tmp = z / b;
	} else {
		tmp = x / (a + 1.0);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if (y <= -1.05e+24) or not (y <= 2.3e+98):
		tmp = z / b
	else:
		tmp = x / (a + 1.0)
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if ((y <= -1.05e+24) || !(y <= 2.3e+98))
		tmp = Float64(z / b);
	else
		tmp = Float64(x / Float64(a + 1.0));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if ((y <= -1.05e+24) || ~((y <= 2.3e+98)))
		tmp = z / b;
	else
		tmp = x / (a + 1.0);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[Or[LessEqual[y, -1.05e+24], N[Not[LessEqual[y, 2.3e+98]], $MachinePrecision]], N[(z / b), $MachinePrecision], N[(x / N[(a + 1.0), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 19: 41.7% accurate, 2.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -1 \lor \neg \left(a \leq 1\right):\\ \;\;\;\;\frac{x}{a}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (or (<= a -1.0) (not (<= a 1.0))) (/ x a) x))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((a <= -1.0) || !(a <= 1.0)) {
		tmp = x / a;
	} else {
		tmp = x;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if ((a <= (-1.0d0)) .or. (.not. (a <= 1.0d0))) then
        tmp = x / a
    else
        tmp = x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((a <= -1.0) || !(a <= 1.0)) {
		tmp = x / a;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if (a <= -1.0) or not (a <= 1.0):
		tmp = x / a
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if ((a <= -1.0) || !(a <= 1.0))
		tmp = Float64(x / a);
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if ((a <= -1.0) || ~((a <= 1.0)))
		tmp = x / a;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[Or[LessEqual[a, -1.0], N[Not[LessEqual[a, 1.0]], $MachinePrecision]], N[(x / a), $MachinePrecision], x]

Derivation

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

Alternative 20: 19.8% accurate, 17.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

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

Developer target: 79.0% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := 1 \cdot \left(\left(x + \frac{y}{t} \cdot z\right) \cdot \frac{1}{\left(a + 1\right) + \frac{y}{t} \cdot b}\right)\\ \mathbf{if}\;t < -1.3659085366310088 \cdot 10^{-271}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t < 3.036967103737246 \cdot 10^{-130}:\\ \;\;\;\;\frac{z}{b}\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1
         (* 1.0 (* (+ x (* (/ y t) z)) (/ 1.0 (+ (+ a 1.0) (* (/ y t) b)))))))
   (if (< t -1.3659085366310088e-271)
     t_1
     (if (< t 3.036967103737246e-130) (/ z b) t_1))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = 1.0 * ((x + ((y / t) * z)) * (1.0 / ((a + 1.0) + ((y / t) * b))));
	double tmp;
	if (t < -1.3659085366310088e-271) {
		tmp = t_1;
	} else if (t < 3.036967103737246e-130) {
		tmp = z / b;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: tmp
    t_1 = 1.0d0 * ((x + ((y / t) * z)) * (1.0d0 / ((a + 1.0d0) + ((y / t) * b))))
    if (t < (-1.3659085366310088d-271)) then
        tmp = t_1
    else if (t < 3.036967103737246d-130) then
        tmp = z / b
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = 1.0 * ((x + ((y / t) * z)) * (1.0 / ((a + 1.0) + ((y / t) * b))));
	double tmp;
	if (t < -1.3659085366310088e-271) {
		tmp = t_1;
	} else if (t < 3.036967103737246e-130) {
		tmp = z / b;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = 1.0 * ((x + ((y / t) * z)) * (1.0 / ((a + 1.0) + ((y / t) * b))))
	tmp = 0
	if t < -1.3659085366310088e-271:
		tmp = t_1
	elif t < 3.036967103737246e-130:
		tmp = z / b
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(1.0 * Float64(Float64(x + Float64(Float64(y / t) * z)) * Float64(1.0 / Float64(Float64(a + 1.0) + Float64(Float64(y / t) * b)))))
	tmp = 0.0
	if (t < -1.3659085366310088e-271)
		tmp = t_1;
	elseif (t < 3.036967103737246e-130)
		tmp = Float64(z / b);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = 1.0 * ((x + ((y / t) * z)) * (1.0 / ((a + 1.0) + ((y / t) * b))));
	tmp = 0.0;
	if (t < -1.3659085366310088e-271)
		tmp = t_1;
	elseif (t < 3.036967103737246e-130)
		tmp = z / b;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(1.0 * N[(N[(x + N[(N[(y / t), $MachinePrecision] * z), $MachinePrecision]), $MachinePrecision] * N[(1.0 / N[(N[(a + 1.0), $MachinePrecision] + N[(N[(y / t), $MachinePrecision] * b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[Less[t, -1.3659085366310088e-271], t$95$1, If[Less[t, 3.036967103737246e-130], N[(z / b), $MachinePrecision], t$95$1]]]

Reproduce

herbie shell --seed 2023343 
(FPCore (x y z t a b)
  :name "Diagrams.Solve.Tridiagonal:solveCyclicTriDiagonal from diagrams-solve-0.1, B"
  :precision binary64

  :herbie-target
  (if (< t -1.3659085366310088e-271) (* 1.0 (* (+ x (* (/ y t) z)) (/ 1.0 (+ (+ a 1.0) (* (/ y t) b))))) (if (< t 3.036967103737246e-130) (/ z b) (* 1.0 (* (+ x (* (/ y t) z)) (/ 1.0 (+ (+ a 1.0) (* (/ y t) b)))))))

  (/ (+ x (/ (* y z) t)) (+ (+ a 1.0) (/ (* y b) t))))