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

Percentage Accurate: 75.2% → 90.9%

Time: 17.8s

Alternatives: 21

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: 75.2% 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: 90.9% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x + \frac{y \cdot z}{t}}{\frac{y \cdot b}{t} + \left(a + 1\right)}\\ \mathbf{if}\;t_1 \leq -\infty:\\ \;\;\;\;\frac{y}{t} \cdot \frac{z}{\left(a + 1\right) + y \cdot \frac{b}{t}}\\ \mathbf{elif}\;t_1 \leq -5 \cdot 10^{-290}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t_1 \leq 0:\\ \;\;\;\;\frac{x + \frac{y}{\frac{t}{z}}}{a + \left(1 + \frac{y}{\frac{t}{b}}\right)}\\ \mathbf{elif}\;t_1 \leq 10^{+302}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;\frac{z}{b}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (/ (+ x (/ (* y z) t)) (+ (/ (* y b) t) (+ a 1.0)))))
   (if (<= t_1 (- INFINITY))
     (* (/ y t) (/ z (+ (+ a 1.0) (* y (/ b t)))))
     (if (<= t_1 -5e-290)
       t_1
       (if (<= t_1 0.0)
         (/ (+ x (/ y (/ t z))) (+ a (+ 1.0 (/ y (/ t b)))))
         (if (<= t_1 1e+302) t_1 (/ z b)))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (x + ((y * z) / t)) / (((y * b) / t) + (a + 1.0));
	double tmp;
	if (t_1 <= -((double) INFINITY)) {
		tmp = (y / t) * (z / ((a + 1.0) + (y * (b / t))));
	} else if (t_1 <= -5e-290) {
		tmp = t_1;
	} else if (t_1 <= 0.0) {
		tmp = (x + (y / (t / z))) / (a + (1.0 + (y / (t / b))));
	} else if (t_1 <= 1e+302) {
		tmp = t_1;
	} 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)) / (((y * b) / t) + (a + 1.0));
	double tmp;
	if (t_1 <= -Double.POSITIVE_INFINITY) {
		tmp = (y / t) * (z / ((a + 1.0) + (y * (b / t))));
	} else if (t_1 <= -5e-290) {
		tmp = t_1;
	} else if (t_1 <= 0.0) {
		tmp = (x + (y / (t / z))) / (a + (1.0 + (y / (t / b))));
	} else if (t_1 <= 1e+302) {
		tmp = t_1;
	} else {
		tmp = z / b;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = (x + ((y * z) / t)) / (((y * b) / t) + (a + 1.0))
	tmp = 0
	if t_1 <= -math.inf:
		tmp = (y / t) * (z / ((a + 1.0) + (y * (b / t))))
	elif t_1 <= -5e-290:
		tmp = t_1
	elif t_1 <= 0.0:
		tmp = (x + (y / (t / z))) / (a + (1.0 + (y / (t / b))))
	elif t_1 <= 1e+302:
		tmp = t_1
	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(Float64(y * b) / t) + Float64(a + 1.0)))
	tmp = 0.0
	if (t_1 <= Float64(-Inf))
		tmp = Float64(Float64(y / t) * Float64(z / Float64(Float64(a + 1.0) + Float64(y * Float64(b / t)))));
	elseif (t_1 <= -5e-290)
		tmp = t_1;
	elseif (t_1 <= 0.0)
		tmp = Float64(Float64(x + Float64(y / Float64(t / z))) / Float64(a + Float64(1.0 + Float64(y / Float64(t / b)))));
	elseif (t_1 <= 1e+302)
		tmp = t_1;
	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)) / (((y * b) / t) + (a + 1.0));
	tmp = 0.0;
	if (t_1 <= -Inf)
		tmp = (y / t) * (z / ((a + 1.0) + (y * (b / t))));
	elseif (t_1 <= -5e-290)
		tmp = t_1;
	elseif (t_1 <= 0.0)
		tmp = (x + (y / (t / z))) / (a + (1.0 + (y / (t / b))));
	elseif (t_1 <= 1e+302)
		tmp = t_1;
	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[(N[(y * b), $MachinePrecision] / t), $MachinePrecision] + N[(a + 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, (-Infinity)], N[(N[(y / t), $MachinePrecision] * N[(z / N[(N[(a + 1.0), $MachinePrecision] + N[(y * N[(b / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$1, -5e-290], t$95$1, If[LessEqual[t$95$1, 0.0], 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$95$1, 1e+302], t$95$1, N[(z / b), $MachinePrecision]]]]]]

Derivation

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

Alternative 2: 89.9% accurate, 0.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{y \cdot b}{t}\\ t_2 := \frac{x + \frac{y \cdot z}{t}}{t_1 + \left(a + 1\right)}\\ \mathbf{if}\;t_2 \leq -4 \cdot 10^{-161}:\\ \;\;\;\;\frac{x}{1 + \left(a + t_1\right)} + \frac{y}{\frac{y \cdot b + t \cdot \left(a + 1\right)}{z}}\\ \mathbf{elif}\;t_2 \leq 0:\\ \;\;\;\;\frac{\mathsf{fma}\left(y, \frac{z}{t}, x\right)}{\mathsf{fma}\left(b, \frac{y}{t}, a + 1\right)}\\ \mathbf{elif}\;t_2 \leq 10^{+302}:\\ \;\;\;\;t_2\\ \mathbf{else}:\\ \;\;\;\;\frac{z}{b}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (/ (* y b) t)) (t_2 (/ (+ x (/ (* y z) t)) (+ t_1 (+ a 1.0)))))
   (if (<= t_2 -4e-161)
     (+ (/ x (+ 1.0 (+ a t_1))) (/ y (/ (+ (* y b) (* t (+ a 1.0))) z)))
     (if (<= t_2 0.0)
       (/ (fma y (/ z t) x) (fma b (/ y t) (+ a 1.0)))
       (if (<= t_2 1e+302) t_2 (/ z b))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (y * b) / t;
	double t_2 = (x + ((y * z) / t)) / (t_1 + (a + 1.0));
	double tmp;
	if (t_2 <= -4e-161) {
		tmp = (x / (1.0 + (a + t_1))) + (y / (((y * b) + (t * (a + 1.0))) / z));
	} else if (t_2 <= 0.0) {
		tmp = fma(y, (z / t), x) / fma(b, (y / t), (a + 1.0));
	} else if (t_2 <= 1e+302) {
		tmp = t_2;
	} else {
		tmp = z / b;
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(y * b) / t)
	t_2 = Float64(Float64(x + Float64(Float64(y * z) / t)) / Float64(t_1 + Float64(a + 1.0)))
	tmp = 0.0
	if (t_2 <= -4e-161)
		tmp = Float64(Float64(x / Float64(1.0 + Float64(a + t_1))) + Float64(y / Float64(Float64(Float64(y * b) + Float64(t * Float64(a + 1.0))) / z)));
	elseif (t_2 <= 0.0)
		tmp = Float64(fma(y, Float64(z / t), x) / fma(b, Float64(y / t), Float64(a + 1.0)));
	elseif (t_2 <= 1e+302)
		tmp = t_2;
	else
		tmp = Float64(z / b);
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(N[(y * b), $MachinePrecision] / t), $MachinePrecision]}, Block[{t$95$2 = N[(N[(x + N[(N[(y * z), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision] / N[(t$95$1 + N[(a + 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$2, -4e-161], N[(N[(x / N[(1.0 + N[(a + t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(y / N[(N[(N[(y * b), $MachinePrecision] + N[(t * N[(a + 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$2, 0.0], N[(N[(y * N[(z / t), $MachinePrecision] + x), $MachinePrecision] / N[(b * N[(y / t), $MachinePrecision] + N[(a + 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$2, 1e+302], t$95$2, N[(z / b), $MachinePrecision]]]]]]

Derivation

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

Alternative 3: 89.9% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{y \cdot b}{t}\\ t_2 := \frac{x + \frac{y \cdot z}{t}}{t_1 + \left(a + 1\right)}\\ \mathbf{if}\;t_2 \leq -4 \cdot 10^{-161}:\\ \;\;\;\;\frac{x}{1 + \left(a + t_1\right)} + \frac{y}{\frac{y \cdot b + t \cdot \left(a + 1\right)}{z}}\\ \mathbf{elif}\;t_2 \leq 0:\\ \;\;\;\;\frac{x + \frac{y}{\frac{t}{z}}}{a + \left(1 + \frac{b}{\frac{t}{y}}\right)}\\ \mathbf{elif}\;t_2 \leq 10^{+302}:\\ \;\;\;\;t_2\\ \mathbf{else}:\\ \;\;\;\;\frac{z}{b}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (/ (* y b) t)) (t_2 (/ (+ x (/ (* y z) t)) (+ t_1 (+ a 1.0)))))
   (if (<= t_2 -4e-161)
     (+ (/ x (+ 1.0 (+ a t_1))) (/ y (/ (+ (* y b) (* t (+ a 1.0))) z)))
     (if (<= t_2 0.0)
       (/ (+ x (/ y (/ t z))) (+ a (+ 1.0 (/ b (/ t y)))))
       (if (<= t_2 1e+302) t_2 (/ z b))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (y * b) / t;
	double t_2 = (x + ((y * z) / t)) / (t_1 + (a + 1.0));
	double tmp;
	if (t_2 <= -4e-161) {
		tmp = (x / (1.0 + (a + t_1))) + (y / (((y * b) + (t * (a + 1.0))) / z));
	} else if (t_2 <= 0.0) {
		tmp = (x + (y / (t / z))) / (a + (1.0 + (b / (t / y))));
	} else if (t_2 <= 1e+302) {
		tmp = t_2;
	} 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) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = (y * b) / t
    t_2 = (x + ((y * z) / t)) / (t_1 + (a + 1.0d0))
    if (t_2 <= (-4d-161)) then
        tmp = (x / (1.0d0 + (a + t_1))) + (y / (((y * b) + (t * (a + 1.0d0))) / z))
    else if (t_2 <= 0.0d0) then
        tmp = (x + (y / (t / z))) / (a + (1.0d0 + (b / (t / y))))
    else if (t_2 <= 1d+302) then
        tmp = t_2
    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 t_1 = (y * b) / t;
	double t_2 = (x + ((y * z) / t)) / (t_1 + (a + 1.0));
	double tmp;
	if (t_2 <= -4e-161) {
		tmp = (x / (1.0 + (a + t_1))) + (y / (((y * b) + (t * (a + 1.0))) / z));
	} else if (t_2 <= 0.0) {
		tmp = (x + (y / (t / z))) / (a + (1.0 + (b / (t / y))));
	} else if (t_2 <= 1e+302) {
		tmp = t_2;
	} else {
		tmp = z / b;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = (y * b) / t
	t_2 = (x + ((y * z) / t)) / (t_1 + (a + 1.0))
	tmp = 0
	if t_2 <= -4e-161:
		tmp = (x / (1.0 + (a + t_1))) + (y / (((y * b) + (t * (a + 1.0))) / z))
	elif t_2 <= 0.0:
		tmp = (x + (y / (t / z))) / (a + (1.0 + (b / (t / y))))
	elif t_2 <= 1e+302:
		tmp = t_2
	else:
		tmp = z / b
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(y * b) / t)
	t_2 = Float64(Float64(x + Float64(Float64(y * z) / t)) / Float64(t_1 + Float64(a + 1.0)))
	tmp = 0.0
	if (t_2 <= -4e-161)
		tmp = Float64(Float64(x / Float64(1.0 + Float64(a + t_1))) + Float64(y / Float64(Float64(Float64(y * b) + Float64(t * Float64(a + 1.0))) / z)));
	elseif (t_2 <= 0.0)
		tmp = Float64(Float64(x + Float64(y / Float64(t / z))) / Float64(a + Float64(1.0 + Float64(b / Float64(t / y)))));
	elseif (t_2 <= 1e+302)
		tmp = t_2;
	else
		tmp = Float64(z / b);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = (y * b) / t;
	t_2 = (x + ((y * z) / t)) / (t_1 + (a + 1.0));
	tmp = 0.0;
	if (t_2 <= -4e-161)
		tmp = (x / (1.0 + (a + t_1))) + (y / (((y * b) + (t * (a + 1.0))) / z));
	elseif (t_2 <= 0.0)
		tmp = (x + (y / (t / z))) / (a + (1.0 + (b / (t / y))));
	elseif (t_2 <= 1e+302)
		tmp = t_2;
	else
		tmp = z / b;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(N[(y * b), $MachinePrecision] / t), $MachinePrecision]}, Block[{t$95$2 = N[(N[(x + N[(N[(y * z), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision] / N[(t$95$1 + N[(a + 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$2, -4e-161], N[(N[(x / N[(1.0 + N[(a + t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(y / N[(N[(N[(y * b), $MachinePrecision] + N[(t * N[(a + 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$2, 0.0], N[(N[(x + N[(y / N[(t / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(a + N[(1.0 + N[(b / N[(t / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$2, 1e+302], t$95$2, N[(z / b), $MachinePrecision]]]]]]

Derivation

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

Alternative 4: 66.4% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x + \frac{y}{\frac{t}{z}}}{a + 1}\\ \mathbf{if}\;t \leq -6.5 \cdot 10^{-41}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq -1.45 \cdot 10^{-100}:\\ \;\;\;\;\frac{z}{b} + \frac{t}{b} \cdot \frac{x}{y}\\ \mathbf{elif}\;t \leq -8 \cdot 10^{-117}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq -9.2 \cdot 10^{-147}:\\ \;\;\;\;\frac{y}{t} \cdot \frac{z}{\left(a + 1\right) + y \cdot \frac{b}{t}}\\ \mathbf{elif}\;t \leq 7.2 \cdot 10^{-154}:\\ \;\;\;\;\frac{z}{b}\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (/ (+ x (/ y (/ t z))) (+ a 1.0))))
   (if (<= t -6.5e-41)
     t_1
     (if (<= t -1.45e-100)
       (+ (/ z b) (* (/ t b) (/ x y)))
       (if (<= t -8e-117)
         t_1
         (if (<= t -9.2e-147)
           (* (/ y t) (/ z (+ (+ a 1.0) (* y (/ b t)))))
           (if (<= t 7.2e-154) (/ z b) t_1)))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (x + (y / (t / z))) / (a + 1.0);
	double tmp;
	if (t <= -6.5e-41) {
		tmp = t_1;
	} else if (t <= -1.45e-100) {
		tmp = (z / b) + ((t / b) * (x / y));
	} else if (t <= -8e-117) {
		tmp = t_1;
	} else if (t <= -9.2e-147) {
		tmp = (y / t) * (z / ((a + 1.0) + (y * (b / t))));
	} else if (t <= 7.2e-154) {
		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 = (x + (y / (t / z))) / (a + 1.0d0)
    if (t <= (-6.5d-41)) then
        tmp = t_1
    else if (t <= (-1.45d-100)) then
        tmp = (z / b) + ((t / b) * (x / y))
    else if (t <= (-8d-117)) then
        tmp = t_1
    else if (t <= (-9.2d-147)) then
        tmp = (y / t) * (z / ((a + 1.0d0) + (y * (b / t))))
    else if (t <= 7.2d-154) 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 = (x + (y / (t / z))) / (a + 1.0);
	double tmp;
	if (t <= -6.5e-41) {
		tmp = t_1;
	} else if (t <= -1.45e-100) {
		tmp = (z / b) + ((t / b) * (x / y));
	} else if (t <= -8e-117) {
		tmp = t_1;
	} else if (t <= -9.2e-147) {
		tmp = (y / t) * (z / ((a + 1.0) + (y * (b / t))));
	} else if (t <= 7.2e-154) {
		tmp = z / b;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = (x + (y / (t / z))) / (a + 1.0)
	tmp = 0
	if t <= -6.5e-41:
		tmp = t_1
	elif t <= -1.45e-100:
		tmp = (z / b) + ((t / b) * (x / y))
	elif t <= -8e-117:
		tmp = t_1
	elif t <= -9.2e-147:
		tmp = (y / t) * (z / ((a + 1.0) + (y * (b / t))))
	elif t <= 7.2e-154:
		tmp = z / b
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(x + Float64(y / Float64(t / z))) / Float64(a + 1.0))
	tmp = 0.0
	if (t <= -6.5e-41)
		tmp = t_1;
	elseif (t <= -1.45e-100)
		tmp = Float64(Float64(z / b) + Float64(Float64(t / b) * Float64(x / y)));
	elseif (t <= -8e-117)
		tmp = t_1;
	elseif (t <= -9.2e-147)
		tmp = Float64(Float64(y / t) * Float64(z / Float64(Float64(a + 1.0) + Float64(y * Float64(b / t)))));
	elseif (t <= 7.2e-154)
		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 = (x + (y / (t / z))) / (a + 1.0);
	tmp = 0.0;
	if (t <= -6.5e-41)
		tmp = t_1;
	elseif (t <= -1.45e-100)
		tmp = (z / b) + ((t / b) * (x / y));
	elseif (t <= -8e-117)
		tmp = t_1;
	elseif (t <= -9.2e-147)
		tmp = (y / t) * (z / ((a + 1.0) + (y * (b / t))));
	elseif (t <= 7.2e-154)
		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[(N[(x + N[(y / N[(t / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(a + 1.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -6.5e-41], t$95$1, If[LessEqual[t, -1.45e-100], N[(N[(z / b), $MachinePrecision] + N[(N[(t / b), $MachinePrecision] * N[(x / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, -8e-117], t$95$1, If[LessEqual[t, -9.2e-147], N[(N[(y / t), $MachinePrecision] * N[(z / N[(N[(a + 1.0), $MachinePrecision] + N[(y * N[(b / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 7.2e-154], N[(z / b), $MachinePrecision], t$95$1]]]]]]

Derivation

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

Alternative 5: 66.6% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x + \frac{y}{\frac{t}{z}}}{a + 1}\\ \mathbf{if}\;t \leq -3.05 \cdot 10^{-41}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq -2.4 \cdot 10^{-100}:\\ \;\;\;\;\frac{z}{b} + \frac{t}{b} \cdot \frac{x}{y}\\ \mathbf{elif}\;t \leq -1.35 \cdot 10^{-115}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq -2.2 \cdot 10^{-188}:\\ \;\;\;\;\frac{y \cdot z}{t \cdot \left(1 + \left(a + \frac{y \cdot b}{t}\right)\right)}\\ \mathbf{elif}\;t \leq 1.45 \cdot 10^{-154}:\\ \;\;\;\;\frac{z}{b}\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (/ (+ x (/ y (/ t z))) (+ a 1.0))))
   (if (<= t -3.05e-41)
     t_1
     (if (<= t -2.4e-100)
       (+ (/ z b) (* (/ t b) (/ x y)))
       (if (<= t -1.35e-115)
         t_1
         (if (<= t -2.2e-188)
           (/ (* y z) (* t (+ 1.0 (+ a (/ (* y b) t)))))
           (if (<= t 1.45e-154) (/ z b) t_1)))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (x + (y / (t / z))) / (a + 1.0);
	double tmp;
	if (t <= -3.05e-41) {
		tmp = t_1;
	} else if (t <= -2.4e-100) {
		tmp = (z / b) + ((t / b) * (x / y));
	} else if (t <= -1.35e-115) {
		tmp = t_1;
	} else if (t <= -2.2e-188) {
		tmp = (y * z) / (t * (1.0 + (a + ((y * b) / t))));
	} else if (t <= 1.45e-154) {
		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 = (x + (y / (t / z))) / (a + 1.0d0)
    if (t <= (-3.05d-41)) then
        tmp = t_1
    else if (t <= (-2.4d-100)) then
        tmp = (z / b) + ((t / b) * (x / y))
    else if (t <= (-1.35d-115)) then
        tmp = t_1
    else if (t <= (-2.2d-188)) then
        tmp = (y * z) / (t * (1.0d0 + (a + ((y * b) / t))))
    else if (t <= 1.45d-154) 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 = (x + (y / (t / z))) / (a + 1.0);
	double tmp;
	if (t <= -3.05e-41) {
		tmp = t_1;
	} else if (t <= -2.4e-100) {
		tmp = (z / b) + ((t / b) * (x / y));
	} else if (t <= -1.35e-115) {
		tmp = t_1;
	} else if (t <= -2.2e-188) {
		tmp = (y * z) / (t * (1.0 + (a + ((y * b) / t))));
	} else if (t <= 1.45e-154) {
		tmp = z / b;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = (x + (y / (t / z))) / (a + 1.0)
	tmp = 0
	if t <= -3.05e-41:
		tmp = t_1
	elif t <= -2.4e-100:
		tmp = (z / b) + ((t / b) * (x / y))
	elif t <= -1.35e-115:
		tmp = t_1
	elif t <= -2.2e-188:
		tmp = (y * z) / (t * (1.0 + (a + ((y * b) / t))))
	elif t <= 1.45e-154:
		tmp = z / b
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(x + Float64(y / Float64(t / z))) / Float64(a + 1.0))
	tmp = 0.0
	if (t <= -3.05e-41)
		tmp = t_1;
	elseif (t <= -2.4e-100)
		tmp = Float64(Float64(z / b) + Float64(Float64(t / b) * Float64(x / y)));
	elseif (t <= -1.35e-115)
		tmp = t_1;
	elseif (t <= -2.2e-188)
		tmp = Float64(Float64(y * z) / Float64(t * Float64(1.0 + Float64(a + Float64(Float64(y * b) / t)))));
	elseif (t <= 1.45e-154)
		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 = (x + (y / (t / z))) / (a + 1.0);
	tmp = 0.0;
	if (t <= -3.05e-41)
		tmp = t_1;
	elseif (t <= -2.4e-100)
		tmp = (z / b) + ((t / b) * (x / y));
	elseif (t <= -1.35e-115)
		tmp = t_1;
	elseif (t <= -2.2e-188)
		tmp = (y * z) / (t * (1.0 + (a + ((y * b) / t))));
	elseif (t <= 1.45e-154)
		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[(N[(x + N[(y / N[(t / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(a + 1.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -3.05e-41], t$95$1, If[LessEqual[t, -2.4e-100], N[(N[(z / b), $MachinePrecision] + N[(N[(t / b), $MachinePrecision] * N[(x / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, -1.35e-115], t$95$1, If[LessEqual[t, -2.2e-188], N[(N[(y * z), $MachinePrecision] / N[(t * N[(1.0 + N[(a + N[(N[(y * b), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 1.45e-154], N[(z / b), $MachinePrecision], t$95$1]]]]]]

Derivation

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

Alternative 6: 66.1% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x + \frac{y}{\frac{t}{z}}}{a + 1}\\ \mathbf{if}\;t \leq -2.7 \cdot 10^{-41}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq -2.5 \cdot 10^{-100}:\\ \;\;\;\;\frac{z}{b} + \frac{t}{b} \cdot \frac{x}{y}\\ \mathbf{elif}\;t \leq -9.5 \cdot 10^{-117}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq -3.1 \cdot 10^{-151}:\\ \;\;\;\;\frac{y}{t} \cdot \frac{z}{1 + \frac{y \cdot b}{t}}\\ \mathbf{elif}\;t \leq 6.4 \cdot 10^{-155}:\\ \;\;\;\;\frac{z}{b}\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (/ (+ x (/ y (/ t z))) (+ a 1.0))))
   (if (<= t -2.7e-41)
     t_1
     (if (<= t -2.5e-100)
       (+ (/ z b) (* (/ t b) (/ x y)))
       (if (<= t -9.5e-117)
         t_1
         (if (<= t -3.1e-151)
           (* (/ y t) (/ z (+ 1.0 (/ (* y b) t))))
           (if (<= t 6.4e-155) (/ z b) t_1)))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (x + (y / (t / z))) / (a + 1.0);
	double tmp;
	if (t <= -2.7e-41) {
		tmp = t_1;
	} else if (t <= -2.5e-100) {
		tmp = (z / b) + ((t / b) * (x / y));
	} else if (t <= -9.5e-117) {
		tmp = t_1;
	} else if (t <= -3.1e-151) {
		tmp = (y / t) * (z / (1.0 + ((y * b) / t)));
	} else if (t <= 6.4e-155) {
		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 = (x + (y / (t / z))) / (a + 1.0d0)
    if (t <= (-2.7d-41)) then
        tmp = t_1
    else if (t <= (-2.5d-100)) then
        tmp = (z / b) + ((t / b) * (x / y))
    else if (t <= (-9.5d-117)) then
        tmp = t_1
    else if (t <= (-3.1d-151)) then
        tmp = (y / t) * (z / (1.0d0 + ((y * b) / t)))
    else if (t <= 6.4d-155) 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 = (x + (y / (t / z))) / (a + 1.0);
	double tmp;
	if (t <= -2.7e-41) {
		tmp = t_1;
	} else if (t <= -2.5e-100) {
		tmp = (z / b) + ((t / b) * (x / y));
	} else if (t <= -9.5e-117) {
		tmp = t_1;
	} else if (t <= -3.1e-151) {
		tmp = (y / t) * (z / (1.0 + ((y * b) / t)));
	} else if (t <= 6.4e-155) {
		tmp = z / b;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = (x + (y / (t / z))) / (a + 1.0)
	tmp = 0
	if t <= -2.7e-41:
		tmp = t_1
	elif t <= -2.5e-100:
		tmp = (z / b) + ((t / b) * (x / y))
	elif t <= -9.5e-117:
		tmp = t_1
	elif t <= -3.1e-151:
		tmp = (y / t) * (z / (1.0 + ((y * b) / t)))
	elif t <= 6.4e-155:
		tmp = z / b
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(x + Float64(y / Float64(t / z))) / Float64(a + 1.0))
	tmp = 0.0
	if (t <= -2.7e-41)
		tmp = t_1;
	elseif (t <= -2.5e-100)
		tmp = Float64(Float64(z / b) + Float64(Float64(t / b) * Float64(x / y)));
	elseif (t <= -9.5e-117)
		tmp = t_1;
	elseif (t <= -3.1e-151)
		tmp = Float64(Float64(y / t) * Float64(z / Float64(1.0 + Float64(Float64(y * b) / t))));
	elseif (t <= 6.4e-155)
		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 = (x + (y / (t / z))) / (a + 1.0);
	tmp = 0.0;
	if (t <= -2.7e-41)
		tmp = t_1;
	elseif (t <= -2.5e-100)
		tmp = (z / b) + ((t / b) * (x / y));
	elseif (t <= -9.5e-117)
		tmp = t_1;
	elseif (t <= -3.1e-151)
		tmp = (y / t) * (z / (1.0 + ((y * b) / t)));
	elseif (t <= 6.4e-155)
		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[(N[(x + N[(y / N[(t / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(a + 1.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -2.7e-41], t$95$1, If[LessEqual[t, -2.5e-100], N[(N[(z / b), $MachinePrecision] + N[(N[(t / b), $MachinePrecision] * N[(x / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, -9.5e-117], t$95$1, If[LessEqual[t, -3.1e-151], N[(N[(y / t), $MachinePrecision] * N[(z / N[(1.0 + N[(N[(y * b), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 6.4e-155], N[(z / b), $MachinePrecision], t$95$1]]]]]]

Derivation

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

Alternative 7: 66.3% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x + \frac{y}{\frac{t}{z}}}{a + 1}\\ \mathbf{if}\;t \leq -1.85 \cdot 10^{-40}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq -4.4 \cdot 10^{-100}:\\ \;\;\;\;\frac{z}{b} + \frac{t}{b} \cdot \frac{x}{y}\\ \mathbf{elif}\;t \leq -3.5 \cdot 10^{-115}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq -2.65 \cdot 10^{-148}:\\ \;\;\;\;\frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{y \cdot b}\\ \mathbf{elif}\;t \leq 2.9 \cdot 10^{-155}:\\ \;\;\;\;\frac{z}{b}\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (/ (+ x (/ y (/ t z))) (+ a 1.0))))
   (if (<= t -1.85e-40)
     t_1
     (if (<= t -4.4e-100)
       (+ (/ z b) (* (/ t b) (/ x y)))
       (if (<= t -3.5e-115)
         t_1
         (if (<= t -2.65e-148)
           (/ (* t (+ x (/ (* y z) t))) (* y b))
           (if (<= t 2.9e-155) (/ z b) t_1)))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (x + (y / (t / z))) / (a + 1.0);
	double tmp;
	if (t <= -1.85e-40) {
		tmp = t_1;
	} else if (t <= -4.4e-100) {
		tmp = (z / b) + ((t / b) * (x / y));
	} else if (t <= -3.5e-115) {
		tmp = t_1;
	} else if (t <= -2.65e-148) {
		tmp = (t * (x + ((y * z) / t))) / (y * b);
	} else if (t <= 2.9e-155) {
		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 = (x + (y / (t / z))) / (a + 1.0d0)
    if (t <= (-1.85d-40)) then
        tmp = t_1
    else if (t <= (-4.4d-100)) then
        tmp = (z / b) + ((t / b) * (x / y))
    else if (t <= (-3.5d-115)) then
        tmp = t_1
    else if (t <= (-2.65d-148)) then
        tmp = (t * (x + ((y * z) / t))) / (y * b)
    else if (t <= 2.9d-155) 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 = (x + (y / (t / z))) / (a + 1.0);
	double tmp;
	if (t <= -1.85e-40) {
		tmp = t_1;
	} else if (t <= -4.4e-100) {
		tmp = (z / b) + ((t / b) * (x / y));
	} else if (t <= -3.5e-115) {
		tmp = t_1;
	} else if (t <= -2.65e-148) {
		tmp = (t * (x + ((y * z) / t))) / (y * b);
	} else if (t <= 2.9e-155) {
		tmp = z / b;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = (x + (y / (t / z))) / (a + 1.0)
	tmp = 0
	if t <= -1.85e-40:
		tmp = t_1
	elif t <= -4.4e-100:
		tmp = (z / b) + ((t / b) * (x / y))
	elif t <= -3.5e-115:
		tmp = t_1
	elif t <= -2.65e-148:
		tmp = (t * (x + ((y * z) / t))) / (y * b)
	elif t <= 2.9e-155:
		tmp = z / b
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(x + Float64(y / Float64(t / z))) / Float64(a + 1.0))
	tmp = 0.0
	if (t <= -1.85e-40)
		tmp = t_1;
	elseif (t <= -4.4e-100)
		tmp = Float64(Float64(z / b) + Float64(Float64(t / b) * Float64(x / y)));
	elseif (t <= -3.5e-115)
		tmp = t_1;
	elseif (t <= -2.65e-148)
		tmp = Float64(Float64(t * Float64(x + Float64(Float64(y * z) / t))) / Float64(y * b));
	elseif (t <= 2.9e-155)
		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 = (x + (y / (t / z))) / (a + 1.0);
	tmp = 0.0;
	if (t <= -1.85e-40)
		tmp = t_1;
	elseif (t <= -4.4e-100)
		tmp = (z / b) + ((t / b) * (x / y));
	elseif (t <= -3.5e-115)
		tmp = t_1;
	elseif (t <= -2.65e-148)
		tmp = (t * (x + ((y * z) / t))) / (y * b);
	elseif (t <= 2.9e-155)
		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[(N[(x + N[(y / N[(t / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(a + 1.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -1.85e-40], t$95$1, If[LessEqual[t, -4.4e-100], N[(N[(z / b), $MachinePrecision] + N[(N[(t / b), $MachinePrecision] * N[(x / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, -3.5e-115], t$95$1, If[LessEqual[t, -2.65e-148], N[(N[(t * N[(x + N[(N[(y * z), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(y * b), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 2.9e-155], N[(z / b), $MachinePrecision], t$95$1]]]]]]

Derivation

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

Alternative 8: 80.1% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -5.5 \cdot 10^{-188} \lor \neg \left(t \leq 4.9 \cdot 10^{-148}\right):\\ \;\;\;\;\frac{x + \frac{y}{\frac{t}{z}}}{a + \left(1 + \frac{b}{\frac{t}{y}}\right)}\\ \mathbf{else}:\\ \;\;\;\;\frac{z}{b}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (or (<= t -5.5e-188) (not (<= t 4.9e-148)))
   (/ (+ x (/ y (/ t z))) (+ a (+ 1.0 (/ b (/ t y)))))
   (/ z b)))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((t <= -5.5e-188) || !(t <= 4.9e-148)) {
		tmp = (x + (y / (t / z))) / (a + (1.0 + (b / (t / y))));
	} 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 ((t <= (-5.5d-188)) .or. (.not. (t <= 4.9d-148))) then
        tmp = (x + (y / (t / z))) / (a + (1.0d0 + (b / (t / y))))
    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 ((t <= -5.5e-188) || !(t <= 4.9e-148)) {
		tmp = (x + (y / (t / z))) / (a + (1.0 + (b / (t / y))));
	} else {
		tmp = z / b;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if (t <= -5.5e-188) or not (t <= 4.9e-148):
		tmp = (x + (y / (t / z))) / (a + (1.0 + (b / (t / y))))
	else:
		tmp = z / b
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if ((t <= -5.5e-188) || !(t <= 4.9e-148))
		tmp = Float64(Float64(x + Float64(y / Float64(t / z))) / Float64(a + Float64(1.0 + Float64(b / Float64(t / y)))));
	else
		tmp = Float64(z / b);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if ((t <= -5.5e-188) || ~((t <= 4.9e-148)))
		tmp = (x + (y / (t / z))) / (a + (1.0 + (b / (t / y))));
	else
		tmp = z / b;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[Or[LessEqual[t, -5.5e-188], N[Not[LessEqual[t, 4.9e-148]], $MachinePrecision]], N[(N[(x + N[(y / N[(t / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(a + N[(1.0 + N[(b / N[(t / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(z / b), $MachinePrecision]]

Derivation

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

Alternative 9: 79.6% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + \frac{y}{\frac{t}{z}}\\ \mathbf{if}\;t \leq -3.7 \cdot 10^{-186}:\\ \;\;\;\;\frac{t_1}{a + \left(1 + \frac{b}{\frac{t}{y}}\right)}\\ \mathbf{elif}\;t \leq 2.7 \cdot 10^{-193}:\\ \;\;\;\;\frac{z}{b}\\ \mathbf{else}:\\ \;\;\;\;\frac{t_1}{a + \left(1 + \frac{y}{\frac{t}{b}}\right)}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (+ x (/ y (/ t z)))))
   (if (<= t -3.7e-186)
     (/ t_1 (+ a (+ 1.0 (/ b (/ t y)))))
     (if (<= t 2.7e-193) (/ z b) (/ t_1 (+ a (+ 1.0 (/ y (/ t b)))))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = x + (y / (t / z));
	double tmp;
	if (t <= -3.7e-186) {
		tmp = t_1 / (a + (1.0 + (b / (t / y))));
	} else if (t <= 2.7e-193) {
		tmp = z / b;
	} else {
		tmp = t_1 / (a + (1.0 + (y / (t / 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) :: t_1
    real(8) :: tmp
    t_1 = x + (y / (t / z))
    if (t <= (-3.7d-186)) then
        tmp = t_1 / (a + (1.0d0 + (b / (t / y))))
    else if (t <= 2.7d-193) then
        tmp = z / b
    else
        tmp = t_1 / (a + (1.0d0 + (y / (t / 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 t_1 = x + (y / (t / z));
	double tmp;
	if (t <= -3.7e-186) {
		tmp = t_1 / (a + (1.0 + (b / (t / y))));
	} else if (t <= 2.7e-193) {
		tmp = z / b;
	} else {
		tmp = t_1 / (a + (1.0 + (y / (t / b))));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = x + (y / (t / z))
	tmp = 0
	if t <= -3.7e-186:
		tmp = t_1 / (a + (1.0 + (b / (t / y))))
	elif t <= 2.7e-193:
		tmp = z / b
	else:
		tmp = t_1 / (a + (1.0 + (y / (t / b))))
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(x + Float64(y / Float64(t / z)))
	tmp = 0.0
	if (t <= -3.7e-186)
		tmp = Float64(t_1 / Float64(a + Float64(1.0 + Float64(b / Float64(t / y)))));
	elseif (t <= 2.7e-193)
		tmp = Float64(z / b);
	else
		tmp = Float64(t_1 / Float64(a + Float64(1.0 + Float64(y / Float64(t / b)))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = x + (y / (t / z));
	tmp = 0.0;
	if (t <= -3.7e-186)
		tmp = t_1 / (a + (1.0 + (b / (t / y))));
	elseif (t <= 2.7e-193)
		tmp = z / b;
	else
		tmp = t_1 / (a + (1.0 + (y / (t / b))));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(x + N[(y / N[(t / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -3.7e-186], N[(t$95$1 / N[(a + N[(1.0 + N[(b / N[(t / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 2.7e-193], N[(z / b), $MachinePrecision], N[(t$95$1 / N[(a + N[(1.0 + N[(y / N[(t / b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]

Derivation

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

Alternative 10: 79.8% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -2.3 \cdot 10^{-187}:\\ \;\;\;\;\frac{x + \frac{y}{\frac{t}{z}}}{a + \left(1 + \frac{b}{\frac{t}{y}}\right)}\\ \mathbf{elif}\;t \leq 1.15 \cdot 10^{-154}:\\ \;\;\;\;\frac{z}{b}\\ \mathbf{else}:\\ \;\;\;\;\frac{x + \frac{z}{\frac{t}{y}}}{1 + \left(a + y \cdot \frac{b}{t}\right)}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= t -2.3e-187)
   (/ (+ x (/ y (/ t z))) (+ a (+ 1.0 (/ b (/ t y)))))
   (if (<= t 1.15e-154)
     (/ z b)
     (/ (+ x (/ z (/ t y))) (+ 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 <= -2.3e-187) {
		tmp = (x + (y / (t / z))) / (a + (1.0 + (b / (t / y))));
	} else if (t <= 1.15e-154) {
		tmp = z / b;
	} else {
		tmp = (x + (z / (t / y))) / (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 <= (-2.3d-187)) then
        tmp = (x + (y / (t / z))) / (a + (1.0d0 + (b / (t / y))))
    else if (t <= 1.15d-154) then
        tmp = z / b
    else
        tmp = (x + (z / (t / y))) / (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 <= -2.3e-187) {
		tmp = (x + (y / (t / z))) / (a + (1.0 + (b / (t / y))));
	} else if (t <= 1.15e-154) {
		tmp = z / b;
	} else {
		tmp = (x + (z / (t / y))) / (1.0 + (a + (y * (b / t))));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if t <= -2.3e-187:
		tmp = (x + (y / (t / z))) / (a + (1.0 + (b / (t / y))))
	elif t <= 1.15e-154:
		tmp = z / b
	else:
		tmp = (x + (z / (t / y))) / (1.0 + (a + (y * (b / t))))
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (t <= -2.3e-187)
		tmp = Float64(Float64(x + Float64(y / Float64(t / z))) / Float64(a + Float64(1.0 + Float64(b / Float64(t / y)))));
	elseif (t <= 1.15e-154)
		tmp = Float64(z / b);
	else
		tmp = Float64(Float64(x + Float64(z / Float64(t / y))) / 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 <= -2.3e-187)
		tmp = (x + (y / (t / z))) / (a + (1.0 + (b / (t / y))));
	elseif (t <= 1.15e-154)
		tmp = z / b;
	else
		tmp = (x + (z / (t / y))) / (1.0 + (a + (y * (b / t))));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[t, -2.3e-187], N[(N[(x + N[(y / N[(t / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(a + N[(1.0 + N[(b / N[(t / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 1.15e-154], N[(z / b), $MachinePrecision], N[(N[(x + N[(z / N[(t / y), $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 11: 72.7% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -0.95 \lor \neg \left(a \leq 410\right):\\ \;\;\;\;\frac{x}{a} + \frac{y \cdot z}{y \cdot b + t \cdot a}\\ \mathbf{else}:\\ \;\;\;\;\frac{x + \frac{y \cdot z}{t}}{1 + \frac{y \cdot b}{t}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (or (<= a -0.95) (not (<= a 410.0)))
   (+ (/ x a) (/ (* y z) (+ (* y b) (* t a))))
   (/ (+ x (/ (* y z) t)) (+ 1.0 (/ (* y b) t)))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((a <= -0.95) || !(a <= 410.0)) {
		tmp = (x / a) + ((y * z) / ((y * b) + (t * a)));
	} else {
		tmp = (x + ((y * z) / t)) / (1.0 + ((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 ((a <= (-0.95d0)) .or. (.not. (a <= 410.0d0))) then
        tmp = (x / a) + ((y * z) / ((y * b) + (t * a)))
    else
        tmp = (x + ((y * z) / t)) / (1.0d0 + ((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 ((a <= -0.95) || !(a <= 410.0)) {
		tmp = (x / a) + ((y * z) / ((y * b) + (t * a)));
	} else {
		tmp = (x + ((y * z) / t)) / (1.0 + ((y * b) / t));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if (a <= -0.95) or not (a <= 410.0):
		tmp = (x / a) + ((y * z) / ((y * b) + (t * a)))
	else:
		tmp = (x + ((y * z) / t)) / (1.0 + ((y * b) / t))
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if ((a <= -0.95) || !(a <= 410.0))
		tmp = Float64(Float64(x / a) + Float64(Float64(y * z) / Float64(Float64(y * b) + Float64(t * a))));
	else
		tmp = Float64(Float64(x + Float64(Float64(y * z) / t)) / Float64(1.0 + Float64(Float64(y * b) / t)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if ((a <= -0.95) || ~((a <= 410.0)))
		tmp = (x / a) + ((y * z) / ((y * b) + (t * a)));
	else
		tmp = (x + ((y * z) / t)) / (1.0 + ((y * b) / t));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[Or[LessEqual[a, -0.95], N[Not[LessEqual[a, 410.0]], $MachinePrecision]], N[(N[(x / a), $MachinePrecision] + N[(N[(y * z), $MachinePrecision] / N[(N[(y * b), $MachinePrecision] + N[(t * a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(x + N[(N[(y * z), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision] / N[(1.0 + N[(N[(y * b), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 12: 55.2% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x}{a + 1}\\ \mathbf{if}\;t \leq -7.6 \cdot 10^{+14}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq 1.46 \cdot 10^{-154}:\\ \;\;\;\;\frac{z}{b}\\ \mathbf{elif}\;t \leq 0.00038:\\ \;\;\;\;\frac{x}{a} + \frac{y \cdot z}{t \cdot a}\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (/ x (+ a 1.0))))
   (if (<= t -7.6e+14)
     t_1
     (if (<= t 1.46e-154)
       (/ z b)
       (if (<= t 0.00038) (+ (/ x a) (/ (* y z) (* t a))) t_1)))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = x / (a + 1.0);
	double tmp;
	if (t <= -7.6e+14) {
		tmp = t_1;
	} else if (t <= 1.46e-154) {
		tmp = z / b;
	} else if (t <= 0.00038) {
		tmp = (x / a) + ((y * z) / (t * a));
	} 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)
    if (t <= (-7.6d+14)) then
        tmp = t_1
    else if (t <= 1.46d-154) then
        tmp = z / b
    else if (t <= 0.00038d0) then
        tmp = (x / a) + ((y * z) / (t * a))
    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);
	double tmp;
	if (t <= -7.6e+14) {
		tmp = t_1;
	} else if (t <= 1.46e-154) {
		tmp = z / b;
	} else if (t <= 0.00038) {
		tmp = (x / a) + ((y * z) / (t * a));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = x / (a + 1.0)
	tmp = 0
	if t <= -7.6e+14:
		tmp = t_1
	elif t <= 1.46e-154:
		tmp = z / b
	elif t <= 0.00038:
		tmp = (x / a) + ((y * z) / (t * a))
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(x / Float64(a + 1.0))
	tmp = 0.0
	if (t <= -7.6e+14)
		tmp = t_1;
	elseif (t <= 1.46e-154)
		tmp = Float64(z / b);
	elseif (t <= 0.00038)
		tmp = Float64(Float64(x / a) + Float64(Float64(y * z) / Float64(t * a)));
	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);
	tmp = 0.0;
	if (t <= -7.6e+14)
		tmp = t_1;
	elseif (t <= 1.46e-154)
		tmp = z / b;
	elseif (t <= 0.00038)
		tmp = (x / a) + ((y * z) / (t * a));
	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 + 1.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -7.6e+14], t$95$1, If[LessEqual[t, 1.46e-154], N[(z / b), $MachinePrecision], If[LessEqual[t, 0.00038], N[(N[(x / a), $MachinePrecision] + N[(N[(y * z), $MachinePrecision] / N[(t * a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]]

Derivation

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

Alternative 13: 60.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x}{1 + \left(a + y \cdot \frac{b}{t}\right)}\\ \mathbf{if}\;t \leq -2.4 \cdot 10^{-61}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq 6 \cdot 10^{-154}:\\ \;\;\;\;\frac{z}{b}\\ \mathbf{elif}\;t \leq 0.85:\\ \;\;\;\;\frac{x}{a} + \frac{y \cdot z}{t \cdot a}\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (/ x (+ 1.0 (+ a (* y (/ b t)))))))
   (if (<= t -2.4e-61)
     t_1
     (if (<= t 6e-154)
       (/ z b)
       (if (<= t 0.85) (+ (/ x a) (/ (* y z) (* t a))) t_1)))))

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 <= -2.4e-61) {
		tmp = t_1;
	} else if (t <= 6e-154) {
		tmp = z / b;
	} else if (t <= 0.85) {
		tmp = (x / a) + ((y * z) / (t * a));
	} 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 / (1.0d0 + (a + (y * (b / t))))
    if (t <= (-2.4d-61)) then
        tmp = t_1
    else if (t <= 6d-154) then
        tmp = z / b
    else if (t <= 0.85d0) then
        tmp = (x / a) + ((y * z) / (t * a))
    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 / (1.0 + (a + (y * (b / t))));
	double tmp;
	if (t <= -2.4e-61) {
		tmp = t_1;
	} else if (t <= 6e-154) {
		tmp = z / b;
	} else if (t <= 0.85) {
		tmp = (x / a) + ((y * z) / (t * a));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = x / (1.0 + (a + (y * (b / t))))
	tmp = 0
	if t <= -2.4e-61:
		tmp = t_1
	elif t <= 6e-154:
		tmp = z / b
	elif t <= 0.85:
		tmp = (x / a) + ((y * z) / (t * a))
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(x / Float64(1.0 + Float64(a + Float64(y * Float64(b / t)))))
	tmp = 0.0
	if (t <= -2.4e-61)
		tmp = t_1;
	elseif (t <= 6e-154)
		tmp = Float64(z / b);
	elseif (t <= 0.85)
		tmp = Float64(Float64(x / a) + Float64(Float64(y * z) / Float64(t * a)));
	else
		tmp = t_1;
	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 <= -2.4e-61)
		tmp = t_1;
	elseif (t <= 6e-154)
		tmp = z / b;
	elseif (t <= 0.85)
		tmp = (x / a) + ((y * z) / (t * a));
	else
		tmp = t_1;
	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[(y * N[(b / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -2.4e-61], t$95$1, If[LessEqual[t, 6e-154], N[(z / b), $MachinePrecision], If[LessEqual[t, 0.85], N[(N[(x / a), $MachinePrecision] + N[(N[(y * z), $MachinePrecision] / N[(t * a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]]

Derivation

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

Alternative 14: 55.0% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x}{a + 1}\\ \mathbf{if}\;t \leq -2.95 \cdot 10^{+15}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq 10^{-153}:\\ \;\;\;\;\frac{z}{b}\\ \mathbf{elif}\;t \leq 30000:\\ \;\;\;\;\frac{x + \frac{y}{\frac{t}{z}}}{a}\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (/ x (+ a 1.0))))
   (if (<= t -2.95e+15)
     t_1
     (if (<= t 1e-153)
       (/ z b)
       (if (<= t 30000.0) (/ (+ x (/ y (/ t z))) a) t_1)))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = x / (a + 1.0);
	double tmp;
	if (t <= -2.95e+15) {
		tmp = t_1;
	} else if (t <= 1e-153) {
		tmp = z / b;
	} else if (t <= 30000.0) {
		tmp = (x + (y / (t / z))) / a;
	} 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)
    if (t <= (-2.95d+15)) then
        tmp = t_1
    else if (t <= 1d-153) then
        tmp = z / b
    else if (t <= 30000.0d0) then
        tmp = (x + (y / (t / z))) / a
    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);
	double tmp;
	if (t <= -2.95e+15) {
		tmp = t_1;
	} else if (t <= 1e-153) {
		tmp = z / b;
	} else if (t <= 30000.0) {
		tmp = (x + (y / (t / z))) / a;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = x / (a + 1.0)
	tmp = 0
	if t <= -2.95e+15:
		tmp = t_1
	elif t <= 1e-153:
		tmp = z / b
	elif t <= 30000.0:
		tmp = (x + (y / (t / z))) / a
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(x / Float64(a + 1.0))
	tmp = 0.0
	if (t <= -2.95e+15)
		tmp = t_1;
	elseif (t <= 1e-153)
		tmp = Float64(z / b);
	elseif (t <= 30000.0)
		tmp = Float64(Float64(x + Float64(y / Float64(t / z))) / a);
	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);
	tmp = 0.0;
	if (t <= -2.95e+15)
		tmp = t_1;
	elseif (t <= 1e-153)
		tmp = z / b;
	elseif (t <= 30000.0)
		tmp = (x + (y / (t / z))) / a;
	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 + 1.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -2.95e+15], t$95$1, If[LessEqual[t, 1e-153], N[(z / b), $MachinePrecision], If[LessEqual[t, 30000.0], N[(N[(x + N[(y / N[(t / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / a), $MachinePrecision], t$95$1]]]]

Derivation

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

Alternative 15: 55.2% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x}{a + 1}\\ \mathbf{if}\;t \leq -4.6 \cdot 10^{+14}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq 5.5 \cdot 10^{-154}:\\ \;\;\;\;\frac{z}{b}\\ \mathbf{elif}\;t \leq 320:\\ \;\;\;\;\frac{x + \frac{y \cdot z}{t}}{a}\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (/ x (+ a 1.0))))
   (if (<= t -4.6e+14)
     t_1
     (if (<= t 5.5e-154)
       (/ z b)
       (if (<= t 320.0) (/ (+ x (/ (* y z) t)) a) t_1)))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = x / (a + 1.0);
	double tmp;
	if (t <= -4.6e+14) {
		tmp = t_1;
	} else if (t <= 5.5e-154) {
		tmp = z / b;
	} else if (t <= 320.0) {
		tmp = (x + ((y * z) / t)) / a;
	} 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)
    if (t <= (-4.6d+14)) then
        tmp = t_1
    else if (t <= 5.5d-154) then
        tmp = z / b
    else if (t <= 320.0d0) then
        tmp = (x + ((y * z) / t)) / a
    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);
	double tmp;
	if (t <= -4.6e+14) {
		tmp = t_1;
	} else if (t <= 5.5e-154) {
		tmp = z / b;
	} else if (t <= 320.0) {
		tmp = (x + ((y * z) / t)) / a;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = x / (a + 1.0)
	tmp = 0
	if t <= -4.6e+14:
		tmp = t_1
	elif t <= 5.5e-154:
		tmp = z / b
	elif t <= 320.0:
		tmp = (x + ((y * z) / t)) / a
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(x / Float64(a + 1.0))
	tmp = 0.0
	if (t <= -4.6e+14)
		tmp = t_1;
	elseif (t <= 5.5e-154)
		tmp = Float64(z / b);
	elseif (t <= 320.0)
		tmp = Float64(Float64(x + Float64(Float64(y * z) / t)) / a);
	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);
	tmp = 0.0;
	if (t <= -4.6e+14)
		tmp = t_1;
	elseif (t <= 5.5e-154)
		tmp = z / b;
	elseif (t <= 320.0)
		tmp = (x + ((y * z) / t)) / a;
	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 + 1.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -4.6e+14], t$95$1, If[LessEqual[t, 5.5e-154], N[(z / b), $MachinePrecision], If[LessEqual[t, 320.0], N[(N[(x + N[(N[(y * z), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision] / a), $MachinePrecision], t$95$1]]]]

Derivation

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

Alternative 16: 66.1% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -1.42 \cdot 10^{-64} \lor \neg \left(t \leq 10^{-153}\right):\\ \;\;\;\;\frac{x + \frac{y}{\frac{t}{z}}}{a + 1}\\ \mathbf{else}:\\ \;\;\;\;\frac{z}{b}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (or (<= t -1.42e-64) (not (<= t 1e-153)))
   (/ (+ x (/ y (/ t z))) (+ a 1.0))
   (/ z b)))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((t <= -1.42e-64) || !(t <= 1e-153)) {
		tmp = (x + (y / (t / z))) / (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 ((t <= (-1.42d-64)) .or. (.not. (t <= 1d-153))) then
        tmp = (x + (y / (t / z))) / (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 ((t <= -1.42e-64) || !(t <= 1e-153)) {
		tmp = (x + (y / (t / z))) / (a + 1.0);
	} else {
		tmp = z / b;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if (t <= -1.42e-64) or not (t <= 1e-153):
		tmp = (x + (y / (t / z))) / (a + 1.0)
	else:
		tmp = z / b
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if ((t <= -1.42e-64) || !(t <= 1e-153))
		tmp = Float64(Float64(x + Float64(y / Float64(t / z))) / 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 ((t <= -1.42e-64) || ~((t <= 1e-153)))
		tmp = (x + (y / (t / z))) / (a + 1.0);
	else
		tmp = z / b;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[Or[LessEqual[t, -1.42e-64], N[Not[LessEqual[t, 1e-153]], $MachinePrecision]], N[(N[(x + N[(y / N[(t / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(a + 1.0), $MachinePrecision]), $MachinePrecision], N[(z / b), $MachinePrecision]]

Derivation

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

Alternative 17: 42.5% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -1:\\ \;\;\;\;\frac{x}{a}\\ \mathbf{elif}\;a \leq -1.8 \cdot 10^{-121}:\\ \;\;\;\;x\\ \mathbf{elif}\;a \leq -3.05 \cdot 10^{-208}:\\ \;\;\;\;\frac{z}{b}\\ \mathbf{elif}\;a \leq 2 \cdot 10^{-176}:\\ \;\;\;\;x\\ \mathbf{elif}\;a \leq 2.75 \cdot 10^{+25}:\\ \;\;\;\;\frac{z}{b}\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{a}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= a -1.0)
   (/ x a)
   (if (<= a -1.8e-121)
     x
     (if (<= a -3.05e-208)
       (/ z b)
       (if (<= a 2e-176) x (if (<= a 2.75e+25) (/ z b) (/ x a)))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (a <= -1.0) {
		tmp = x / a;
	} else if (a <= -1.8e-121) {
		tmp = x;
	} else if (a <= -3.05e-208) {
		tmp = z / b;
	} else if (a <= 2e-176) {
		tmp = x;
	} else if (a <= 2.75e+25) {
		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 <= (-1.0d0)) then
        tmp = x / a
    else if (a <= (-1.8d-121)) then
        tmp = x
    else if (a <= (-3.05d-208)) then
        tmp = z / b
    else if (a <= 2d-176) then
        tmp = x
    else if (a <= 2.75d+25) 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 <= -1.0) {
		tmp = x / a;
	} else if (a <= -1.8e-121) {
		tmp = x;
	} else if (a <= -3.05e-208) {
		tmp = z / b;
	} else if (a <= 2e-176) {
		tmp = x;
	} else if (a <= 2.75e+25) {
		tmp = z / b;
	} else {
		tmp = x / a;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if a <= -1.0:
		tmp = x / a
	elif a <= -1.8e-121:
		tmp = x
	elif a <= -3.05e-208:
		tmp = z / b
	elif a <= 2e-176:
		tmp = x
	elif a <= 2.75e+25:
		tmp = z / b
	else:
		tmp = x / a
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (a <= -1.0)
		tmp = Float64(x / a);
	elseif (a <= -1.8e-121)
		tmp = x;
	elseif (a <= -3.05e-208)
		tmp = Float64(z / b);
	elseif (a <= 2e-176)
		tmp = x;
	elseif (a <= 2.75e+25)
		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 <= -1.0)
		tmp = x / a;
	elseif (a <= -1.8e-121)
		tmp = x;
	elseif (a <= -3.05e-208)
		tmp = z / b;
	elseif (a <= 2e-176)
		tmp = x;
	elseif (a <= 2.75e+25)
		tmp = z / b;
	else
		tmp = x / a;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[a, -1.0], N[(x / a), $MachinePrecision], If[LessEqual[a, -1.8e-121], x, If[LessEqual[a, -3.05e-208], N[(z / b), $MachinePrecision], If[LessEqual[a, 2e-176], x, If[LessEqual[a, 2.75e+25], N[(z / b), $MachinePrecision], N[(x / a), $MachinePrecision]]]]]]

Derivation

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

Alternative 18: 42.7% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -1:\\ \;\;\;\;\frac{x}{a}\\ \mathbf{elif}\;a \leq -9.5 \cdot 10^{-123}:\\ \;\;\;\;x - x \cdot a\\ \mathbf{elif}\;a \leq -7.4 \cdot 10^{-208}:\\ \;\;\;\;\frac{z}{b}\\ \mathbf{elif}\;a \leq 2.45 \cdot 10^{-177}:\\ \;\;\;\;x\\ \mathbf{elif}\;a \leq 5.2 \cdot 10^{+24}:\\ \;\;\;\;\frac{z}{b}\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{a}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= a -1.0)
   (/ x a)
   (if (<= a -9.5e-123)
     (- x (* x a))
     (if (<= a -7.4e-208)
       (/ z b)
       (if (<= a 2.45e-177) x (if (<= a 5.2e+24) (/ z b) (/ x a)))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (a <= -1.0) {
		tmp = x / a;
	} else if (a <= -9.5e-123) {
		tmp = x - (x * a);
	} else if (a <= -7.4e-208) {
		tmp = z / b;
	} else if (a <= 2.45e-177) {
		tmp = x;
	} else if (a <= 5.2e+24) {
		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 <= (-1.0d0)) then
        tmp = x / a
    else if (a <= (-9.5d-123)) then
        tmp = x - (x * a)
    else if (a <= (-7.4d-208)) then
        tmp = z / b
    else if (a <= 2.45d-177) then
        tmp = x
    else if (a <= 5.2d+24) 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 <= -1.0) {
		tmp = x / a;
	} else if (a <= -9.5e-123) {
		tmp = x - (x * a);
	} else if (a <= -7.4e-208) {
		tmp = z / b;
	} else if (a <= 2.45e-177) {
		tmp = x;
	} else if (a <= 5.2e+24) {
		tmp = z / b;
	} else {
		tmp = x / a;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if a <= -1.0:
		tmp = x / a
	elif a <= -9.5e-123:
		tmp = x - (x * a)
	elif a <= -7.4e-208:
		tmp = z / b
	elif a <= 2.45e-177:
		tmp = x
	elif a <= 5.2e+24:
		tmp = z / b
	else:
		tmp = x / a
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (a <= -1.0)
		tmp = Float64(x / a);
	elseif (a <= -9.5e-123)
		tmp = Float64(x - Float64(x * a));
	elseif (a <= -7.4e-208)
		tmp = Float64(z / b);
	elseif (a <= 2.45e-177)
		tmp = x;
	elseif (a <= 5.2e+24)
		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 <= -1.0)
		tmp = x / a;
	elseif (a <= -9.5e-123)
		tmp = x - (x * a);
	elseif (a <= -7.4e-208)
		tmp = z / b;
	elseif (a <= 2.45e-177)
		tmp = x;
	elseif (a <= 5.2e+24)
		tmp = z / b;
	else
		tmp = x / a;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[a, -1.0], N[(x / a), $MachinePrecision], If[LessEqual[a, -9.5e-123], N[(x - N[(x * a), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, -7.4e-208], N[(z / b), $MachinePrecision], If[LessEqual[a, 2.45e-177], x, If[LessEqual[a, 5.2e+24], N[(z / b), $MachinePrecision], N[(x / a), $MachinePrecision]]]]]]

Derivation

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

Alternative 19: 56.3% accurate, 1.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -1.76 \cdot 10^{+16} \lor \neg \left(t \leq 2.85 \cdot 10^{-117}\right):\\ \;\;\;\;\frac{x}{a + 1}\\ \mathbf{else}:\\ \;\;\;\;\frac{z}{b}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (or (<= t -1.76e+16) (not (<= t 2.85e-117))) (/ x (+ a 1.0)) (/ z b)))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((t <= -1.76e+16) || !(t <= 2.85e-117)) {
		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 ((t <= (-1.76d+16)) .or. (.not. (t <= 2.85d-117))) 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 ((t <= -1.76e+16) || !(t <= 2.85e-117)) {
		tmp = x / (a + 1.0);
	} else {
		tmp = z / b;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if (t <= -1.76e+16) or not (t <= 2.85e-117):
		tmp = x / (a + 1.0)
	else:
		tmp = z / b
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if ((t <= -1.76e+16) || !(t <= 2.85e-117))
		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 ((t <= -1.76e+16) || ~((t <= 2.85e-117)))
		tmp = x / (a + 1.0);
	else
		tmp = z / b;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[Or[LessEqual[t, -1.76e+16], N[Not[LessEqual[t, 2.85e-117]], $MachinePrecision]], N[(x / N[(a + 1.0), $MachinePrecision]), $MachinePrecision], N[(z / b), $MachinePrecision]]

Derivation

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

Alternative 20: 41.7% accurate, 2.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -1 \lor \neg \left(a \leq 1.05 \cdot 10^{+19}\right):\\ \;\;\;\;\frac{x}{a}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (or (<= a -1.0) (not (<= a 1.05e+19))) (/ 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 <= 1.05e+19)) {
		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 <= 1.05d+19))) 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 <= 1.05e+19)) {
		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 <= 1.05e+19):
		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 <= 1.05e+19))
		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 <= 1.05e+19)))
		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, 1.05e+19]], $MachinePrecision]], N[(x / a), $MachinePrecision], x]

Derivation

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

Alternative 21: 20.2% 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: 79.7% 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 2023364 
(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))))