Diagrams.Solve.Tridiagonal:solveTriDiagonal from diagrams-solve-0.1, A

Percentage Accurate: 84.5% → 94.9%

Time: 12.0s

Alternatives: 11

Speedup: 0.7×

Specification

Math

\[\begin{array}{l} \\ \frac{x - y \cdot z}{t - a \cdot z} \end{array} \]

FPCore

(FPCore (x y z t a) :precision binary64 (/ (- x (* y z)) (- t (* a z))))

C

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

Fortran

real(8) function code(x, y, z, t, a)
    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
    code = (x - (y * z)) / (t - (a * z))
end function

Java

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

Python

def code(x, y, z, t, a):
	return (x - (y * z)) / (t - (a * z))

Julia

function code(x, y, z, t, a)
	return Float64(Float64(x - Float64(y * z)) / Float64(t - Float64(a * z)))
end

MATLAB

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

Wolfram

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

Initial Program: 84.5% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{x - y \cdot z}{t - a \cdot z} \end{array} \]

FPCore

(FPCore (x y z t a) :precision binary64 (/ (- x (* y z)) (- t (* a z))))

C

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

Fortran

real(8) function code(x, y, z, t, a)
    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
    code = (x - (y * z)) / (t - (a * z))
end function

Java

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

Python

def code(x, y, z, t, a):
	return (x - (y * z)) / (t - (a * z))

Julia

function code(x, y, z, t, a)
	return Float64(Float64(x - Float64(y * z)) / Float64(t - Float64(a * z)))
end

MATLAB

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

Wolfram

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

Alternative 1: 94.9% accurate, 0.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := t - z \cdot a\\ t_2 := \frac{x - y \cdot z}{t_1}\\ \mathbf{if}\;t_2 \leq -\infty:\\ \;\;\;\;{\left(\frac{\sqrt[3]{x}}{\sqrt[3]{t_1}}\right)}^{3} - y \cdot \frac{z}{t_1}\\ \mathbf{elif}\;t_2 \leq -4 \cdot 10^{-315} \lor \neg \left(t_2 \leq 0\right) \land t_2 \leq 5 \cdot 10^{+281}:\\ \;\;\;\;t_2\\ \mathbf{else}:\\ \;\;\;\;\frac{-y}{\frac{t}{z} - a}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (- t (* z a))) (t_2 (/ (- x (* y z)) t_1)))
   (if (<= t_2 (- INFINITY))
     (- (pow (/ (cbrt x) (cbrt t_1)) 3.0) (* y (/ z t_1)))
     (if (or (<= t_2 -4e-315) (and (not (<= t_2 0.0)) (<= t_2 5e+281)))
       t_2
       (/ (- y) (- (/ t z) a))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = t - (z * a);
	double t_2 = (x - (y * z)) / t_1;
	double tmp;
	if (t_2 <= -((double) INFINITY)) {
		tmp = pow((cbrt(x) / cbrt(t_1)), 3.0) - (y * (z / t_1));
	} else if ((t_2 <= -4e-315) || (!(t_2 <= 0.0) && (t_2 <= 5e+281))) {
		tmp = t_2;
	} else {
		tmp = -y / ((t / z) - a);
	}
	return tmp;
}

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = t - (z * a);
	double t_2 = (x - (y * z)) / t_1;
	double tmp;
	if (t_2 <= -Double.POSITIVE_INFINITY) {
		tmp = Math.pow((Math.cbrt(x) / Math.cbrt(t_1)), 3.0) - (y * (z / t_1));
	} else if ((t_2 <= -4e-315) || (!(t_2 <= 0.0) && (t_2 <= 5e+281))) {
		tmp = t_2;
	} else {
		tmp = -y / ((t / z) - a);
	}
	return tmp;
}

Julia

function code(x, y, z, t, a)
	t_1 = Float64(t - Float64(z * a))
	t_2 = Float64(Float64(x - Float64(y * z)) / t_1)
	tmp = 0.0
	if (t_2 <= Float64(-Inf))
		tmp = Float64((Float64(cbrt(x) / cbrt(t_1)) ^ 3.0) - Float64(y * Float64(z / t_1)));
	elseif ((t_2 <= -4e-315) || (!(t_2 <= 0.0) && (t_2 <= 5e+281)))
		tmp = t_2;
	else
		tmp = Float64(Float64(-y) / Float64(Float64(t / z) - a));
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(t - N[(z * a), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(x - N[(y * z), $MachinePrecision]), $MachinePrecision] / t$95$1), $MachinePrecision]}, If[LessEqual[t$95$2, (-Infinity)], N[(N[Power[N[(N[Power[x, 1/3], $MachinePrecision] / N[Power[t$95$1, 1/3], $MachinePrecision]), $MachinePrecision], 3.0], $MachinePrecision] - N[(y * N[(z / t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[Or[LessEqual[t$95$2, -4e-315], And[N[Not[LessEqual[t$95$2, 0.0]], $MachinePrecision], LessEqual[t$95$2, 5e+281]]], t$95$2, N[((-y) / N[(N[(t / z), $MachinePrecision] - a), $MachinePrecision]), $MachinePrecision]]]]]

Derivation

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

Alternative 2: 71.4% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x}{t - z \cdot a}\\ t_2 := \frac{-y}{\frac{t}{z} - a}\\ t_3 := \frac{x - y \cdot z}{t}\\ \mathbf{if}\;z \leq -1.3 \cdot 10^{+76}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;z \leq -4.6 \cdot 10^{-12}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq -3.9 \cdot 10^{-95}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;z \leq 6.5 \cdot 10^{-247}:\\ \;\;\;\;t_3\\ \mathbf{elif}\;z \leq 2.15 \cdot 10^{-139}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq 1.45 \cdot 10^{+59}:\\ \;\;\;\;t_3\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (/ x (- t (* z a))))
        (t_2 (/ (- y) (- (/ t z) a)))
        (t_3 (/ (- x (* y z)) t)))
   (if (<= z -1.3e+76)
     t_2
     (if (<= z -4.6e-12)
       t_1
       (if (<= z -3.9e-95)
         t_2
         (if (<= z 6.5e-247)
           t_3
           (if (<= z 2.15e-139) t_1 (if (<= z 1.45e+59) t_3 t_2))))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x / (t - (z * a));
	double t_2 = -y / ((t / z) - a);
	double t_3 = (x - (y * z)) / t;
	double tmp;
	if (z <= -1.3e+76) {
		tmp = t_2;
	} else if (z <= -4.6e-12) {
		tmp = t_1;
	} else if (z <= -3.9e-95) {
		tmp = t_2;
	} else if (z <= 6.5e-247) {
		tmp = t_3;
	} else if (z <= 2.15e-139) {
		tmp = t_1;
	} else if (z <= 1.45e+59) {
		tmp = t_3;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    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) :: t_1
    real(8) :: t_2
    real(8) :: t_3
    real(8) :: tmp
    t_1 = x / (t - (z * a))
    t_2 = -y / ((t / z) - a)
    t_3 = (x - (y * z)) / t
    if (z <= (-1.3d+76)) then
        tmp = t_2
    else if (z <= (-4.6d-12)) then
        tmp = t_1
    else if (z <= (-3.9d-95)) then
        tmp = t_2
    else if (z <= 6.5d-247) then
        tmp = t_3
    else if (z <= 2.15d-139) then
        tmp = t_1
    else if (z <= 1.45d+59) then
        tmp = t_3
    else
        tmp = t_2
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = x / (t - (z * a));
	double t_2 = -y / ((t / z) - a);
	double t_3 = (x - (y * z)) / t;
	double tmp;
	if (z <= -1.3e+76) {
		tmp = t_2;
	} else if (z <= -4.6e-12) {
		tmp = t_1;
	} else if (z <= -3.9e-95) {
		tmp = t_2;
	} else if (z <= 6.5e-247) {
		tmp = t_3;
	} else if (z <= 2.15e-139) {
		tmp = t_1;
	} else if (z <= 1.45e+59) {
		tmp = t_3;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x / (t - (z * a))
	t_2 = -y / ((t / z) - a)
	t_3 = (x - (y * z)) / t
	tmp = 0
	if z <= -1.3e+76:
		tmp = t_2
	elif z <= -4.6e-12:
		tmp = t_1
	elif z <= -3.9e-95:
		tmp = t_2
	elif z <= 6.5e-247:
		tmp = t_3
	elif z <= 2.15e-139:
		tmp = t_1
	elif z <= 1.45e+59:
		tmp = t_3
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x / Float64(t - Float64(z * a)))
	t_2 = Float64(Float64(-y) / Float64(Float64(t / z) - a))
	t_3 = Float64(Float64(x - Float64(y * z)) / t)
	tmp = 0.0
	if (z <= -1.3e+76)
		tmp = t_2;
	elseif (z <= -4.6e-12)
		tmp = t_1;
	elseif (z <= -3.9e-95)
		tmp = t_2;
	elseif (z <= 6.5e-247)
		tmp = t_3;
	elseif (z <= 2.15e-139)
		tmp = t_1;
	elseif (z <= 1.45e+59)
		tmp = t_3;
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x / (t - (z * a));
	t_2 = -y / ((t / z) - a);
	t_3 = (x - (y * z)) / t;
	tmp = 0.0;
	if (z <= -1.3e+76)
		tmp = t_2;
	elseif (z <= -4.6e-12)
		tmp = t_1;
	elseif (z <= -3.9e-95)
		tmp = t_2;
	elseif (z <= 6.5e-247)
		tmp = t_3;
	elseif (z <= 2.15e-139)
		tmp = t_1;
	elseif (z <= 1.45e+59)
		tmp = t_3;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x / N[(t - N[(z * a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[((-y) / N[(N[(t / z), $MachinePrecision] - a), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(N[(x - N[(y * z), $MachinePrecision]), $MachinePrecision] / t), $MachinePrecision]}, If[LessEqual[z, -1.3e+76], t$95$2, If[LessEqual[z, -4.6e-12], t$95$1, If[LessEqual[z, -3.9e-95], t$95$2, If[LessEqual[z, 6.5e-247], t$95$3, If[LessEqual[z, 2.15e-139], t$95$1, If[LessEqual[z, 1.45e+59], t$95$3, t$95$2]]]]]]]]]

Derivation

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

Alternative 3: 65.5% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x - y \cdot z}{t}\\ \mathbf{if}\;z \leq -7.5 \cdot 10^{+93}:\\ \;\;\;\;\frac{y}{a}\\ \mathbf{elif}\;z \leq 4.6 \cdot 10^{-241}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq 1.15 \cdot 10^{-137}:\\ \;\;\;\;\frac{x}{t - z \cdot a}\\ \mathbf{elif}\;z \leq 1.75 \cdot 10^{+84}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;\frac{y}{a}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (/ (- x (* y z)) t)))
   (if (<= z -7.5e+93)
     (/ y a)
     (if (<= z 4.6e-241)
       t_1
       (if (<= z 1.15e-137)
         (/ x (- t (* z a)))
         (if (<= z 1.75e+84) t_1 (/ y a)))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = (x - (y * z)) / t;
	double tmp;
	if (z <= -7.5e+93) {
		tmp = y / a;
	} else if (z <= 4.6e-241) {
		tmp = t_1;
	} else if (z <= 1.15e-137) {
		tmp = x / (t - (z * a));
	} else if (z <= 1.75e+84) {
		tmp = t_1;
	} else {
		tmp = y / a;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    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) :: t_1
    real(8) :: tmp
    t_1 = (x - (y * z)) / t
    if (z <= (-7.5d+93)) then
        tmp = y / a
    else if (z <= 4.6d-241) then
        tmp = t_1
    else if (z <= 1.15d-137) then
        tmp = x / (t - (z * a))
    else if (z <= 1.75d+84) then
        tmp = t_1
    else
        tmp = y / a
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = (x - (y * z)) / t;
	double tmp;
	if (z <= -7.5e+93) {
		tmp = y / a;
	} else if (z <= 4.6e-241) {
		tmp = t_1;
	} else if (z <= 1.15e-137) {
		tmp = x / (t - (z * a));
	} else if (z <= 1.75e+84) {
		tmp = t_1;
	} else {
		tmp = y / a;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = (x - (y * z)) / t
	tmp = 0
	if z <= -7.5e+93:
		tmp = y / a
	elif z <= 4.6e-241:
		tmp = t_1
	elif z <= 1.15e-137:
		tmp = x / (t - (z * a))
	elif z <= 1.75e+84:
		tmp = t_1
	else:
		tmp = y / a
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(Float64(x - Float64(y * z)) / t)
	tmp = 0.0
	if (z <= -7.5e+93)
		tmp = Float64(y / a);
	elseif (z <= 4.6e-241)
		tmp = t_1;
	elseif (z <= 1.15e-137)
		tmp = Float64(x / Float64(t - Float64(z * a)));
	elseif (z <= 1.75e+84)
		tmp = t_1;
	else
		tmp = Float64(y / a);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = (x - (y * z)) / t;
	tmp = 0.0;
	if (z <= -7.5e+93)
		tmp = y / a;
	elseif (z <= 4.6e-241)
		tmp = t_1;
	elseif (z <= 1.15e-137)
		tmp = x / (t - (z * a));
	elseif (z <= 1.75e+84)
		tmp = t_1;
	else
		tmp = y / a;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(N[(x - N[(y * z), $MachinePrecision]), $MachinePrecision] / t), $MachinePrecision]}, If[LessEqual[z, -7.5e+93], N[(y / a), $MachinePrecision], If[LessEqual[z, 4.6e-241], t$95$1, If[LessEqual[z, 1.15e-137], N[(x / N[(t - N[(z * a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 1.75e+84], t$95$1, N[(y / a), $MachinePrecision]]]]]]

Derivation

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

Alternative 4: 91.2% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -2.05 \cdot 10^{+96} \lor \neg \left(z \leq 5.5 \cdot 10^{+118}\right):\\ \;\;\;\;\frac{-y}{\frac{t}{z} - a}\\ \mathbf{else}:\\ \;\;\;\;\frac{x - y \cdot z}{t - z \cdot a}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= z -2.05e+96) (not (<= z 5.5e+118)))
   (/ (- y) (- (/ t z) a))
   (/ (- x (* y z)) (- t (* z a)))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((z <= -2.05e+96) || !(z <= 5.5e+118)) {
		tmp = -y / ((t / z) - a);
	} else {
		tmp = (x - (y * z)) / (t - (z * a));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    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) :: tmp
    if ((z <= (-2.05d+96)) .or. (.not. (z <= 5.5d+118))) then
        tmp = -y / ((t / z) - a)
    else
        tmp = (x - (y * z)) / (t - (z * a))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((z <= -2.05e+96) || !(z <= 5.5e+118)) {
		tmp = -y / ((t / z) - a);
	} else {
		tmp = (x - (y * z)) / (t - (z * a));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (z <= -2.05e+96) or not (z <= 5.5e+118):
		tmp = -y / ((t / z) - a)
	else:
		tmp = (x - (y * z)) / (t - (z * a))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((z <= -2.05e+96) || !(z <= 5.5e+118))
		tmp = Float64(Float64(-y) / Float64(Float64(t / z) - a));
	else
		tmp = Float64(Float64(x - Float64(y * z)) / Float64(t - Float64(z * a)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((z <= -2.05e+96) || ~((z <= 5.5e+118)))
		tmp = -y / ((t / z) - a);
	else
		tmp = (x - (y * z)) / (t - (z * a));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[z, -2.05e+96], N[Not[LessEqual[z, 5.5e+118]], $MachinePrecision]], N[((-y) / N[(N[(t / z), $MachinePrecision] - a), $MachinePrecision]), $MachinePrecision], N[(N[(x - N[(y * z), $MachinePrecision]), $MachinePrecision] / N[(t - N[(z * a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 5: 44.4% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{y}{t} \cdot \left(-z\right)\\ \mathbf{if}\;t \leq -4.8 \cdot 10^{+158}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq -0.15:\\ \;\;\;\;\frac{x}{t}\\ \mathbf{elif}\;t \leq 6.2 \cdot 10^{-10}:\\ \;\;\;\;\frac{y}{a}\\ \mathbf{elif}\;t \leq 3.8 \cdot 10^{+244}:\\ \;\;\;\;\frac{x}{t}\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* (/ y t) (- z))))
   (if (<= t -4.8e+158)
     t_1
     (if (<= t -0.15)
       (/ x t)
       (if (<= t 6.2e-10) (/ y a) (if (<= t 3.8e+244) (/ x t) t_1))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = (y / t) * -z;
	double tmp;
	if (t <= -4.8e+158) {
		tmp = t_1;
	} else if (t <= -0.15) {
		tmp = x / t;
	} else if (t <= 6.2e-10) {
		tmp = y / a;
	} else if (t <= 3.8e+244) {
		tmp = x / t;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    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) :: t_1
    real(8) :: tmp
    t_1 = (y / t) * -z
    if (t <= (-4.8d+158)) then
        tmp = t_1
    else if (t <= (-0.15d0)) then
        tmp = x / t
    else if (t <= 6.2d-10) then
        tmp = y / a
    else if (t <= 3.8d+244) then
        tmp = x / t
    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 t_1 = (y / t) * -z;
	double tmp;
	if (t <= -4.8e+158) {
		tmp = t_1;
	} else if (t <= -0.15) {
		tmp = x / t;
	} else if (t <= 6.2e-10) {
		tmp = y / a;
	} else if (t <= 3.8e+244) {
		tmp = x / t;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = (y / t) * -z
	tmp = 0
	if t <= -4.8e+158:
		tmp = t_1
	elif t <= -0.15:
		tmp = x / t
	elif t <= 6.2e-10:
		tmp = y / a
	elif t <= 3.8e+244:
		tmp = x / t
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(Float64(y / t) * Float64(-z))
	tmp = 0.0
	if (t <= -4.8e+158)
		tmp = t_1;
	elseif (t <= -0.15)
		tmp = Float64(x / t);
	elseif (t <= 6.2e-10)
		tmp = Float64(y / a);
	elseif (t <= 3.8e+244)
		tmp = Float64(x / t);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = (y / t) * -z;
	tmp = 0.0;
	if (t <= -4.8e+158)
		tmp = t_1;
	elseif (t <= -0.15)
		tmp = x / t;
	elseif (t <= 6.2e-10)
		tmp = y / a;
	elseif (t <= 3.8e+244)
		tmp = x / t;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(N[(y / t), $MachinePrecision] * (-z)), $MachinePrecision]}, If[LessEqual[t, -4.8e+158], t$95$1, If[LessEqual[t, -0.15], N[(x / t), $MachinePrecision], If[LessEqual[t, 6.2e-10], N[(y / a), $MachinePrecision], If[LessEqual[t, 3.8e+244], N[(x / t), $MachinePrecision], t$95$1]]]]]

Derivation

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

Alternative 6: 44.4% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -1.02 \cdot 10^{+160}:\\ \;\;\;\;\frac{y}{t} \cdot \left(-z\right)\\ \mathbf{elif}\;t \leq -205000:\\ \;\;\;\;\frac{x}{t}\\ \mathbf{elif}\;t \leq 8.2 \cdot 10^{-13}:\\ \;\;\;\;\frac{y}{a}\\ \mathbf{elif}\;t \leq 3.7 \cdot 10^{+244}:\\ \;\;\;\;\frac{x}{t}\\ \mathbf{else}:\\ \;\;\;\;\frac{z}{-\frac{t}{y}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= t -1.02e+160)
   (* (/ y t) (- z))
   (if (<= t -205000.0)
     (/ x t)
     (if (<= t 8.2e-13)
       (/ y a)
       (if (<= t 3.7e+244) (/ x t) (/ z (- (/ t y))))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (t <= -1.02e+160) {
		tmp = (y / t) * -z;
	} else if (t <= -205000.0) {
		tmp = x / t;
	} else if (t <= 8.2e-13) {
		tmp = y / a;
	} else if (t <= 3.7e+244) {
		tmp = x / t;
	} else {
		tmp = z / -(t / y);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    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) :: tmp
    if (t <= (-1.02d+160)) then
        tmp = (y / t) * -z
    else if (t <= (-205000.0d0)) then
        tmp = x / t
    else if (t <= 8.2d-13) then
        tmp = y / a
    else if (t <= 3.7d+244) then
        tmp = x / t
    else
        tmp = z / -(t / y)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (t <= -1.02e+160) {
		tmp = (y / t) * -z;
	} else if (t <= -205000.0) {
		tmp = x / t;
	} else if (t <= 8.2e-13) {
		tmp = y / a;
	} else if (t <= 3.7e+244) {
		tmp = x / t;
	} else {
		tmp = z / -(t / y);
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if t <= -1.02e+160:
		tmp = (y / t) * -z
	elif t <= -205000.0:
		tmp = x / t
	elif t <= 8.2e-13:
		tmp = y / a
	elif t <= 3.7e+244:
		tmp = x / t
	else:
		tmp = z / -(t / y)
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (t <= -1.02e+160)
		tmp = Float64(Float64(y / t) * Float64(-z));
	elseif (t <= -205000.0)
		tmp = Float64(x / t);
	elseif (t <= 8.2e-13)
		tmp = Float64(y / a);
	elseif (t <= 3.7e+244)
		tmp = Float64(x / t);
	else
		tmp = Float64(z / Float64(-Float64(t / y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (t <= -1.02e+160)
		tmp = (y / t) * -z;
	elseif (t <= -205000.0)
		tmp = x / t;
	elseif (t <= 8.2e-13)
		tmp = y / a;
	elseif (t <= 3.7e+244)
		tmp = x / t;
	else
		tmp = z / -(t / y);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[t, -1.02e+160], N[(N[(y / t), $MachinePrecision] * (-z)), $MachinePrecision], If[LessEqual[t, -205000.0], N[(x / t), $MachinePrecision], If[LessEqual[t, 8.2e-13], N[(y / a), $MachinePrecision], If[LessEqual[t, 3.7e+244], N[(x / t), $MachinePrecision], N[(z / (-N[(t / y), $MachinePrecision])), $MachinePrecision]]]]]

Derivation

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

Alternative 7: 44.2% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -9.2 \cdot 10^{+160}:\\ \;\;\;\;\frac{y}{t} \cdot \left(-z\right)\\ \mathbf{elif}\;t \leq -1050:\\ \;\;\;\;\frac{x}{t}\\ \mathbf{elif}\;t \leq 1.15 \cdot 10^{-10}:\\ \;\;\;\;\frac{y}{a}\\ \mathbf{elif}\;t \leq 1.65 \cdot 10^{+246}:\\ \;\;\;\;\frac{x}{t}\\ \mathbf{else}:\\ \;\;\;\;-\frac{y \cdot z}{t}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= t -9.2e+160)
   (* (/ y t) (- z))
   (if (<= t -1050.0)
     (/ x t)
     (if (<= t 1.15e-10)
       (/ y a)
       (if (<= t 1.65e+246) (/ x t) (- (/ (* y z) t)))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (t <= -9.2e+160) {
		tmp = (y / t) * -z;
	} else if (t <= -1050.0) {
		tmp = x / t;
	} else if (t <= 1.15e-10) {
		tmp = y / a;
	} else if (t <= 1.65e+246) {
		tmp = x / t;
	} else {
		tmp = -((y * z) / t);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    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) :: tmp
    if (t <= (-9.2d+160)) then
        tmp = (y / t) * -z
    else if (t <= (-1050.0d0)) then
        tmp = x / t
    else if (t <= 1.15d-10) then
        tmp = y / a
    else if (t <= 1.65d+246) then
        tmp = x / t
    else
        tmp = -((y * z) / t)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (t <= -9.2e+160) {
		tmp = (y / t) * -z;
	} else if (t <= -1050.0) {
		tmp = x / t;
	} else if (t <= 1.15e-10) {
		tmp = y / a;
	} else if (t <= 1.65e+246) {
		tmp = x / t;
	} else {
		tmp = -((y * z) / t);
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if t <= -9.2e+160:
		tmp = (y / t) * -z
	elif t <= -1050.0:
		tmp = x / t
	elif t <= 1.15e-10:
		tmp = y / a
	elif t <= 1.65e+246:
		tmp = x / t
	else:
		tmp = -((y * z) / t)
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (t <= -9.2e+160)
		tmp = Float64(Float64(y / t) * Float64(-z));
	elseif (t <= -1050.0)
		tmp = Float64(x / t);
	elseif (t <= 1.15e-10)
		tmp = Float64(y / a);
	elseif (t <= 1.65e+246)
		tmp = Float64(x / t);
	else
		tmp = Float64(-Float64(Float64(y * z) / t));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (t <= -9.2e+160)
		tmp = (y / t) * -z;
	elseif (t <= -1050.0)
		tmp = x / t;
	elseif (t <= 1.15e-10)
		tmp = y / a;
	elseif (t <= 1.65e+246)
		tmp = x / t;
	else
		tmp = -((y * z) / t);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[t, -9.2e+160], N[(N[(y / t), $MachinePrecision] * (-z)), $MachinePrecision], If[LessEqual[t, -1050.0], N[(x / t), $MachinePrecision], If[LessEqual[t, 1.15e-10], N[(y / a), $MachinePrecision], If[LessEqual[t, 1.65e+246], N[(x / t), $MachinePrecision], (-N[(N[(y * z), $MachinePrecision] / t), $MachinePrecision])]]]]

Derivation

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

Alternative 8: 54.6% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -3.15 \cdot 10^{+72}:\\ \;\;\;\;\frac{y}{a}\\ \mathbf{elif}\;z \leq 82000000000000:\\ \;\;\;\;\frac{x}{t}\\ \mathbf{elif}\;z \leq 6.5 \cdot 10^{+39}:\\ \;\;\;\;\frac{-y}{\frac{t}{z}}\\ \mathbf{elif}\;z \leq 1.05 \cdot 10^{+84}:\\ \;\;\;\;\frac{x}{t}\\ \mathbf{else}:\\ \;\;\;\;\frac{y}{a}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= z -3.15e+72)
   (/ y a)
   (if (<= z 82000000000000.0)
     (/ x t)
     (if (<= z 6.5e+39)
       (/ (- y) (/ t z))
       (if (<= z 1.05e+84) (/ x t) (/ y a))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -3.15e+72) {
		tmp = y / a;
	} else if (z <= 82000000000000.0) {
		tmp = x / t;
	} else if (z <= 6.5e+39) {
		tmp = -y / (t / z);
	} else if (z <= 1.05e+84) {
		tmp = x / t;
	} else {
		tmp = y / a;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    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) :: tmp
    if (z <= (-3.15d+72)) then
        tmp = y / a
    else if (z <= 82000000000000.0d0) then
        tmp = x / t
    else if (z <= 6.5d+39) then
        tmp = -y / (t / z)
    else if (z <= 1.05d+84) then
        tmp = x / t
    else
        tmp = y / a
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -3.15e+72) {
		tmp = y / a;
	} else if (z <= 82000000000000.0) {
		tmp = x / t;
	} else if (z <= 6.5e+39) {
		tmp = -y / (t / z);
	} else if (z <= 1.05e+84) {
		tmp = x / t;
	} else {
		tmp = y / a;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if z <= -3.15e+72:
		tmp = y / a
	elif z <= 82000000000000.0:
		tmp = x / t
	elif z <= 6.5e+39:
		tmp = -y / (t / z)
	elif z <= 1.05e+84:
		tmp = x / t
	else:
		tmp = y / a
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (z <= -3.15e+72)
		tmp = Float64(y / a);
	elseif (z <= 82000000000000.0)
		tmp = Float64(x / t);
	elseif (z <= 6.5e+39)
		tmp = Float64(Float64(-y) / Float64(t / z));
	elseif (z <= 1.05e+84)
		tmp = Float64(x / t);
	else
		tmp = Float64(y / a);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (z <= -3.15e+72)
		tmp = y / a;
	elseif (z <= 82000000000000.0)
		tmp = x / t;
	elseif (z <= 6.5e+39)
		tmp = -y / (t / z);
	elseif (z <= 1.05e+84)
		tmp = x / t;
	else
		tmp = y / a;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[z, -3.15e+72], N[(y / a), $MachinePrecision], If[LessEqual[z, 82000000000000.0], N[(x / t), $MachinePrecision], If[LessEqual[z, 6.5e+39], N[((-y) / N[(t / z), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 1.05e+84], N[(x / t), $MachinePrecision], N[(y / a), $MachinePrecision]]]]]

Derivation

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

Alternative 9: 66.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -2.1 \cdot 10^{+76} \lor \neg \left(z \leq 1.35 \cdot 10^{+84}\right):\\ \;\;\;\;\frac{y}{a}\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{t - z \cdot a}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= z -2.1e+76) (not (<= z 1.35e+84))) (/ y a) (/ x (- t (* z a)))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((z <= -2.1e+76) || !(z <= 1.35e+84)) {
		tmp = y / a;
	} else {
		tmp = x / (t - (z * a));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    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) :: tmp
    if ((z <= (-2.1d+76)) .or. (.not. (z <= 1.35d+84))) then
        tmp = y / a
    else
        tmp = x / (t - (z * a))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((z <= -2.1e+76) || !(z <= 1.35e+84)) {
		tmp = y / a;
	} else {
		tmp = x / (t - (z * a));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (z <= -2.1e+76) or not (z <= 1.35e+84):
		tmp = y / a
	else:
		tmp = x / (t - (z * a))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((z <= -2.1e+76) || !(z <= 1.35e+84))
		tmp = Float64(y / a);
	else
		tmp = Float64(x / Float64(t - Float64(z * a)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((z <= -2.1e+76) || ~((z <= 1.35e+84)))
		tmp = y / a;
	else
		tmp = x / (t - (z * a));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[z, -2.1e+76], N[Not[LessEqual[z, 1.35e+84]], $MachinePrecision]], N[(y / a), $MachinePrecision], N[(x / N[(t - N[(z * a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 10: 54.5% accurate, 1.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -1.12 \cdot 10^{+72} \lor \neg \left(z \leq 9.5 \cdot 10^{+83}\right):\\ \;\;\;\;\frac{y}{a}\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{t}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= z -1.12e+72) (not (<= z 9.5e+83))) (/ y a) (/ x t)))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((z <= -1.12e+72) || !(z <= 9.5e+83)) {
		tmp = y / a;
	} else {
		tmp = x / t;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    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) :: tmp
    if ((z <= (-1.12d+72)) .or. (.not. (z <= 9.5d+83))) then
        tmp = y / a
    else
        tmp = x / t
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((z <= -1.12e+72) || !(z <= 9.5e+83)) {
		tmp = y / a;
	} else {
		tmp = x / t;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (z <= -1.12e+72) or not (z <= 9.5e+83):
		tmp = y / a
	else:
		tmp = x / t
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((z <= -1.12e+72) || !(z <= 9.5e+83))
		tmp = Float64(y / a);
	else
		tmp = Float64(x / t);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((z <= -1.12e+72) || ~((z <= 9.5e+83)))
		tmp = y / a;
	else
		tmp = x / t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[z, -1.12e+72], N[Not[LessEqual[z, 9.5e+83]], $MachinePrecision]], N[(y / a), $MachinePrecision], N[(x / t), $MachinePrecision]]

Derivation

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

Alternative 11: 35.3% accurate, 3.7× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

function code(x, y, z, t, a)
	return Float64(x / t)
end

MATLAB

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

Wolfram

code[x_, y_, z_, t_, a_] := N[(x / t), $MachinePrecision]

Derivation

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

Developer target: 97.4% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := t - a \cdot z\\ t_2 := \frac{x}{t_1} - \frac{y}{\frac{t}{z} - a}\\ \mathbf{if}\;z < -32113435955957344:\\ \;\;\;\;t_2\\ \mathbf{elif}\;z < 3.5139522372978296 \cdot 10^{-86}:\\ \;\;\;\;\left(x - y \cdot z\right) \cdot \frac{1}{t_1}\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (- t (* a z))) (t_2 (- (/ x t_1) (/ y (- (/ t z) a)))))
   (if (< z -32113435955957344.0)
     t_2
     (if (< z 3.5139522372978296e-86) (* (- x (* y z)) (/ 1.0 t_1)) t_2))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = t - (a * z);
	double t_2 = (x / t_1) - (y / ((t / z) - a));
	double tmp;
	if (z < -32113435955957344.0) {
		tmp = t_2;
	} else if (z < 3.5139522372978296e-86) {
		tmp = (x - (y * z)) * (1.0 / t_1);
	} else {
		tmp = t_2;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    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) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = t - (a * z)
    t_2 = (x / t_1) - (y / ((t / z) - a))
    if (z < (-32113435955957344.0d0)) then
        tmp = t_2
    else if (z < 3.5139522372978296d-86) then
        tmp = (x - (y * z)) * (1.0d0 / t_1)
    else
        tmp = t_2
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = t - (a * z);
	double t_2 = (x / t_1) - (y / ((t / z) - a));
	double tmp;
	if (z < -32113435955957344.0) {
		tmp = t_2;
	} else if (z < 3.5139522372978296e-86) {
		tmp = (x - (y * z)) * (1.0 / t_1);
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = t - (a * z)
	t_2 = (x / t_1) - (y / ((t / z) - a))
	tmp = 0
	if z < -32113435955957344.0:
		tmp = t_2
	elif z < 3.5139522372978296e-86:
		tmp = (x - (y * z)) * (1.0 / t_1)
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(t - Float64(a * z))
	t_2 = Float64(Float64(x / t_1) - Float64(y / Float64(Float64(t / z) - a)))
	tmp = 0.0
	if (z < -32113435955957344.0)
		tmp = t_2;
	elseif (z < 3.5139522372978296e-86)
		tmp = Float64(Float64(x - Float64(y * z)) * Float64(1.0 / t_1));
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = t - (a * z);
	t_2 = (x / t_1) - (y / ((t / z) - a));
	tmp = 0.0;
	if (z < -32113435955957344.0)
		tmp = t_2;
	elseif (z < 3.5139522372978296e-86)
		tmp = (x - (y * z)) * (1.0 / t_1);
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(t - N[(a * z), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(x / t$95$1), $MachinePrecision] - N[(y / N[(N[(t / z), $MachinePrecision] - a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[Less[z, -32113435955957344.0], t$95$2, If[Less[z, 3.5139522372978296e-86], N[(N[(x - N[(y * z), $MachinePrecision]), $MachinePrecision] * N[(1.0 / t$95$1), $MachinePrecision]), $MachinePrecision], t$95$2]]]]

Reproduce

herbie shell --seed 2023348 
(FPCore (x y z t a)
  :name "Diagrams.Solve.Tridiagonal:solveTriDiagonal from diagrams-solve-0.1, A"
  :precision binary64

  :herbie-target
  (if (< z -32113435955957344.0) (- (/ x (- t (* a z))) (/ y (- (/ t z) a))) (if (< z 3.5139522372978296e-86) (* (- x (* y z)) (/ 1.0 (- t (* a z)))) (- (/ x (- t (* a z))) (/ y (- (/ t z) a)))))

  (/ (- x (* y z)) (- t (* a z))))