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

Percentage Accurate: 96.1% → 98.0%

Time: 7.9s

Alternatives: 4

Speedup: 1.0×

Specification

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 96.1% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 98.0% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \cdot z \leq 5 \cdot 10^{+301}:\\ \;\;\;\;\frac{x}{y - t \cdot z}\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{x}{t}}{-z}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= (* t z) 5e+301) (/ x (- y (* t z))) (/ (/ x t) (- z))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((t * z) <= 5e+301) {
		tmp = x / (y - (t * z));
	} else {
		tmp = (x / t) / -z;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: tmp
    if ((t * z) <= 5d+301) then
        tmp = x / (y - (t * z))
    else
        tmp = (x / t) / -z
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if ((t * z) <= 5e+301) {
		tmp = x / (y - (t * z));
	} else {
		tmp = (x / t) / -z;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (t * z) <= 5e+301:
		tmp = x / (y - (t * z))
	else:
		tmp = (x / t) / -z
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (Float64(t * z) <= 5e+301)
		tmp = Float64(x / Float64(y - Float64(t * z)));
	else
		tmp = Float64(Float64(x / t) / Float64(-z));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((t * z) <= 5e+301)
		tmp = x / (y - (t * z));
	else
		tmp = (x / t) / -z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[N[(t * z), $MachinePrecision], 5e+301], N[(x / N[(y - N[(t * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(x / t), $MachinePrecision] / (-z)), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 z t) < 5.0000000000000004e301

   1. Initial program 98.3%

      \[\frac{x}{y - z \cdot t} \]
   2. Add Preprocessing

   if 5.0000000000000004e301 < (*.f64 z t)

   1. Initial program 72.3%

      \[\frac{x}{y - z \cdot t} \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift--.f64 N/A

         \[\leadsto \frac{x}{\color{blue}{y - z \cdot t}} \]

      2. sub-neg N/A

         \[\leadsto \frac{x}{\color{blue}{y + \left(\mathsf{neg}\left(z \cdot t\right)\right)}} \]

      3. +-commutative N/A

         \[\leadsto \frac{x}{\color{blue}{\left(\mathsf{neg}\left(z \cdot t\right)\right) + y}} \]

      4. flip-+ N/A

         \[\leadsto \frac{x}{\color{blue}{\frac{\left(\mathsf{neg}\left(z \cdot t\right)\right) \cdot \left(\mathsf{neg}\left(z \cdot t\right)\right) - y \cdot y}{\left(\mathsf{neg}\left(z \cdot t\right)\right) - y}}} \]

      5. lower-/.f64 N/A

         \[\leadsto \frac{x}{\color{blue}{\frac{\left(\mathsf{neg}\left(z \cdot t\right)\right) \cdot \left(\mathsf{neg}\left(z \cdot t\right)\right) - y \cdot y}{\left(\mathsf{neg}\left(z \cdot t\right)\right) - y}}} \]

      6. sqr-neg N/A

         \[\leadsto \frac{x}{\frac{\color{blue}{\left(z \cdot t\right) \cdot \left(z \cdot t\right)} - y \cdot y}{\left(\mathsf{neg}\left(z \cdot t\right)\right) - y}} \]

      7. lower--.f64 N/A

         \[\leadsto \frac{x}{\frac{\color{blue}{\left(z \cdot t\right) \cdot \left(z \cdot t\right) - y \cdot y}}{\left(\mathsf{neg}\left(z \cdot t\right)\right) - y}} \]

      8. lift-*.f64 N/A

         \[\leadsto \frac{x}{\frac{\left(z \cdot t\right) \cdot \color{blue}{\left(z \cdot t\right)} - y \cdot y}{\left(\mathsf{neg}\left(z \cdot t\right)\right) - y}} \]

      9. *-commutative N/A

         \[\leadsto \frac{x}{\frac{\left(z \cdot t\right) \cdot \color{blue}{\left(t \cdot z\right)} - y \cdot y}{\left(\mathsf{neg}\left(z \cdot t\right)\right) - y}} \]

      10. associate-*r* N/A

          \[\leadsto \frac{x}{\frac{\color{blue}{\left(\left(z \cdot t\right) \cdot t\right) \cdot z} - y \cdot y}{\left(\mathsf{neg}\left(z \cdot t\right)\right) - y}} \]

      11. lower-*.f64 N/A

          \[\leadsto \frac{x}{\frac{\color{blue}{\left(\left(z \cdot t\right) \cdot t\right) \cdot z} - y \cdot y}{\left(\mathsf{neg}\left(z \cdot t\right)\right) - y}} \]

      12. *-commutative N/A

          \[\leadsto \frac{x}{\frac{\color{blue}{\left(t \cdot \left(z \cdot t\right)\right)} \cdot z - y \cdot y}{\left(\mathsf{neg}\left(z \cdot t\right)\right) - y}} \]

      13. lift-*.f64 N/A

          \[\leadsto \frac{x}{\frac{\left(t \cdot \color{blue}{\left(z \cdot t\right)}\right) \cdot z - y \cdot y}{\left(\mathsf{neg}\left(z \cdot t\right)\right) - y}} \]

      14. *-commutative N/A

          \[\leadsto \frac{x}{\frac{\left(t \cdot \color{blue}{\left(t \cdot z\right)}\right) \cdot z - y \cdot y}{\left(\mathsf{neg}\left(z \cdot t\right)\right) - y}} \]

      15. associate-*r* N/A

          \[\leadsto \frac{x}{\frac{\color{blue}{\left(\left(t \cdot t\right) \cdot z\right)} \cdot z - y \cdot y}{\left(\mathsf{neg}\left(z \cdot t\right)\right) - y}} \]

      16. lower-*.f64 N/A

          \[\leadsto \frac{x}{\frac{\color{blue}{\left(\left(t \cdot t\right) \cdot z\right)} \cdot z - y \cdot y}{\left(\mathsf{neg}\left(z \cdot t\right)\right) - y}} \]

      17. lower-*.f64 N/A

          \[\leadsto \frac{x}{\frac{\left(\color{blue}{\left(t \cdot t\right)} \cdot z\right) \cdot z - y \cdot y}{\left(\mathsf{neg}\left(z \cdot t\right)\right) - y}} \]

      18. lower-*.f64 N/A

          \[\leadsto \frac{x}{\frac{\left(\left(t \cdot t\right) \cdot z\right) \cdot z - \color{blue}{y \cdot y}}{\left(\mathsf{neg}\left(z \cdot t\right)\right) - y}} \]

      19. lower--.f64 N/A

          \[\leadsto \frac{x}{\frac{\left(\left(t \cdot t\right) \cdot z\right) \cdot z - y \cdot y}{\color{blue}{\left(\mathsf{neg}\left(z \cdot t\right)\right) - y}}} \]

      20. lift-*.f64 N/A

          \[\leadsto \frac{x}{\frac{\left(\left(t \cdot t\right) \cdot z\right) \cdot z - y \cdot y}{\left(\mathsf{neg}\left(\color{blue}{z \cdot t}\right)\right) - y}} \]

      21. *-commutative N/A

          \[\leadsto \frac{x}{\frac{\left(\left(t \cdot t\right) \cdot z\right) \cdot z - y \cdot y}{\left(\mathsf{neg}\left(\color{blue}{t \cdot z}\right)\right) - y}} \]

      22. distribute-lft-neg-in N/A

          \[\leadsto \frac{x}{\frac{\left(\left(t \cdot t\right) \cdot z\right) \cdot z - y \cdot y}{\color{blue}{\left(\mathsf{neg}\left(t\right)\right) \cdot z} - y}} \]

      23. lower-*.f64 N/A

          \[\leadsto \frac{x}{\frac{\left(\left(t \cdot t\right) \cdot z\right) \cdot z - y \cdot y}{\color{blue}{\left(\mathsf{neg}\left(t\right)\right) \cdot z} - y}} \]

      24. lower-neg.f64 0.0

          \[\leadsto \frac{x}{\frac{\left(\left(t \cdot t\right) \cdot z\right) \cdot z - y \cdot y}{\color{blue}{\left(-t\right)} \cdot z - y}} \]

   4. Applied rewrites 0.0%

      \[\leadsto \frac{x}{\color{blue}{\frac{\left(\left(t \cdot t\right) \cdot z\right) \cdot z - y \cdot y}{\left(-t\right) \cdot z - y}}} \]

   5. Taylor expanded in z around inf

      \[\leadsto \color{blue}{\frac{-1 \cdot \frac{x}{t} + -1 \cdot \frac{x \cdot y}{{t}^{2} \cdot z}}{z}} \]
   6. Step-by-step derivation

      1. distribute-lft-out N/A

         \[\leadsto \frac{\color{blue}{-1 \cdot \left(\frac{x}{t} + \frac{x \cdot y}{{t}^{2} \cdot z}\right)}}{z} \]

      2. associate-*r/ N/A

         \[\leadsto \color{blue}{-1 \cdot \frac{\frac{x}{t} + \frac{x \cdot y}{{t}^{2} \cdot z}}{z}} \]

      3. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(\frac{\frac{x}{t} + \frac{x \cdot y}{{t}^{2} \cdot z}}{z}\right)} \]

      4. distribute-neg-frac2 N/A

         \[\leadsto \color{blue}{\frac{\frac{x}{t} + \frac{x \cdot y}{{t}^{2} \cdot z}}{\mathsf{neg}\left(z\right)}} \]

      5. mul-1-neg N/A

         \[\leadsto \frac{\frac{x}{t} + \frac{x \cdot y}{{t}^{2} \cdot z}}{\color{blue}{-1 \cdot z}} \]

      6. lower-/.f64 N/A

         \[\leadsto \color{blue}{\frac{\frac{x}{t} + \frac{x \cdot y}{{t}^{2} \cdot z}}{-1 \cdot z}} \]

      7. +-commutative N/A

         \[\leadsto \frac{\color{blue}{\frac{x \cdot y}{{t}^{2} \cdot z} + \frac{x}{t}}}{-1 \cdot z} \]

      8. associate-/l* N/A

         \[\leadsto \frac{\color{blue}{x \cdot \frac{y}{{t}^{2} \cdot z}} + \frac{x}{t}}{-1 \cdot z} \]

      9. lower-fma.f64 N/A

         \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(x, \frac{y}{{t}^{2} \cdot z}, \frac{x}{t}\right)}}{-1 \cdot z} \]

      10. lower-/.f64 N/A

          \[\leadsto \frac{\mathsf{fma}\left(x, \color{blue}{\frac{y}{{t}^{2} \cdot z}}, \frac{x}{t}\right)}{-1 \cdot z} \]

      11. lower-*.f64 N/A

          \[\leadsto \frac{\mathsf{fma}\left(x, \frac{y}{\color{blue}{{t}^{2} \cdot z}}, \frac{x}{t}\right)}{-1 \cdot z} \]

      12. unpow2 N/A

          \[\leadsto \frac{\mathsf{fma}\left(x, \frac{y}{\color{blue}{\left(t \cdot t\right)} \cdot z}, \frac{x}{t}\right)}{-1 \cdot z} \]

      13. lower-*.f64 N/A

          \[\leadsto \frac{\mathsf{fma}\left(x, \frac{y}{\color{blue}{\left(t \cdot t\right)} \cdot z}, \frac{x}{t}\right)}{-1 \cdot z} \]

      14. lower-/.f64 N/A

          \[\leadsto \frac{\mathsf{fma}\left(x, \frac{y}{\left(t \cdot t\right) \cdot z}, \color{blue}{\frac{x}{t}}\right)}{-1 \cdot z} \]

      15. mul-1-neg N/A

          \[\leadsto \frac{\mathsf{fma}\left(x, \frac{y}{\left(t \cdot t\right) \cdot z}, \frac{x}{t}\right)}{\color{blue}{\mathsf{neg}\left(z\right)}} \]

      16. lower-neg.f64 99.8

          \[\leadsto \frac{\mathsf{fma}\left(x, \frac{y}{\left(t \cdot t\right) \cdot z}, \frac{x}{t}\right)}{\color{blue}{-z}} \]

   7. Applied rewrites 99.8%

      \[\leadsto \color{blue}{\frac{\mathsf{fma}\left(x, \frac{y}{\left(t \cdot t\right) \cdot z}, \frac{x}{t}\right)}{-z}} \]

   8. Taylor expanded in t around inf

      \[\leadsto \frac{\frac{x}{t}}{\mathsf{neg}\left(\color{blue}{z}\right)} \]
   9. Step-by-step derivation

   10. Applied rewrites 99.8%

       \[\leadsto \frac{\frac{x}{t}}{-\color{blue}{z}} \]
3. Recombined 2 regimes into one program.

4. Final simplification 98.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;t \cdot z \leq 5 \cdot 10^{+301}:\\ \;\;\;\;\frac{x}{y - t \cdot z}\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{x}{t}}{-z}\\ \end{array} \]
5. Add Preprocessing

Alternative 2: 74.1% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -6 \cdot 10^{+23}:\\ \;\;\;\;\frac{x}{y}\\ \mathbf{elif}\;y \leq 6.2 \cdot 10^{-5}:\\ \;\;\;\;\frac{x}{\left(-t\right) \cdot z}\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= y -6e+23) (/ x y) (if (<= y 6.2e-5) (/ x (* (- t) z)) (/ x y))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (y <= -6e+23) {
		tmp = x / y;
	} else if (y <= 6.2e-5) {
		tmp = x / (-t * z);
	} else {
		tmp = x / y;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: tmp
    if (y <= (-6d+23)) then
        tmp = x / y
    else if (y <= 6.2d-5) then
        tmp = x / (-t * z)
    else
        tmp = x / y
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if (y <= -6e+23) {
		tmp = x / y;
	} else if (y <= 6.2e-5) {
		tmp = x / (-t * z);
	} else {
		tmp = x / y;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if y <= -6e+23:
		tmp = x / y
	elif y <= 6.2e-5:
		tmp = x / (-t * z)
	else:
		tmp = x / y
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (y <= -6e+23)
		tmp = Float64(x / y);
	elseif (y <= 6.2e-5)
		tmp = Float64(x / Float64(Float64(-t) * z));
	else
		tmp = Float64(x / y);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (y <= -6e+23)
		tmp = x / y;
	elseif (y <= 6.2e-5)
		tmp = x / (-t * z);
	else
		tmp = x / y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[y, -6e+23], N[(x / y), $MachinePrecision], If[LessEqual[y, 6.2e-5], N[(x / N[((-t) * z), $MachinePrecision]), $MachinePrecision], N[(x / y), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if y < -6.0000000000000002e23 or 6.20000000000000027e-5 < y

   1. Initial program 98.5%

      \[\frac{x}{y - z \cdot t} \]
   2. Add Preprocessing

   3. Taylor expanded in t around 0

      \[\leadsto \color{blue}{\frac{x}{y}} \]
   4. Step-by-step derivation

      1. lower-/.f64 81.5

         \[\leadsto \color{blue}{\frac{x}{y}} \]

   5. Applied rewrites 81.5%

      \[\leadsto \color{blue}{\frac{x}{y}} \]

   if -6.0000000000000002e23 < y < 6.20000000000000027e-5

   1. Initial program 94.6%

      \[\frac{x}{y - z \cdot t} \]
   2. Add Preprocessing

   3. Taylor expanded in t around inf

      \[\leadsto \frac{x}{\color{blue}{-1 \cdot \left(t \cdot z\right)}} \]
   4. Step-by-step derivation

      1. associate-*r* N/A

         \[\leadsto \frac{x}{\color{blue}{\left(-1 \cdot t\right) \cdot z}} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{x}{\color{blue}{\left(-1 \cdot t\right) \cdot z}} \]

      3. mul-1-neg N/A

         \[\leadsto \frac{x}{\color{blue}{\left(\mathsf{neg}\left(t\right)\right)} \cdot z} \]

      4. lower-neg.f64 77.6

         \[\leadsto \frac{x}{\color{blue}{\left(-t\right)} \cdot z} \]

   5. Applied rewrites 77.6%

      \[\leadsto \frac{x}{\color{blue}{\left(-t\right) \cdot z}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 3: 96.1% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 96.6%

   \[\frac{x}{y - z \cdot t} \]
2. Add Preprocessing

3. Final simplification 96.6%

   \[\leadsto \frac{x}{y - t \cdot z} \]
4. Add Preprocessing

Alternative 4: 54.3% accurate, 1.7× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 96.6%

   \[\frac{x}{y - z \cdot t} \]
2. Add Preprocessing

3. Taylor expanded in t around 0

   \[\leadsto \color{blue}{\frac{x}{y}} \]
4. Step-by-step derivation

   1. lower-/.f64 52.4

      \[\leadsto \color{blue}{\frac{x}{y}} \]

5. Applied rewrites 52.4%

   \[\leadsto \color{blue}{\frac{x}{y}} \]
6. Add Preprocessing

Developer Target 1: 96.8% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{1}{\frac{y}{x} - \frac{z}{x} \cdot t}\\ \mathbf{if}\;x < -1.618195973607049 \cdot 10^{+50}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;x < 2.1378306434876444 \cdot 10^{+131}:\\ \;\;\;\;\frac{x}{y - z \cdot t}\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (/ 1.0 (- (/ y x) (* (/ z x) t)))))
   (if (< x -1.618195973607049e+50)
     t_1
     (if (< x 2.1378306434876444e+131) (/ x (- y (* z t))) t_1))))

C

double code(double x, double y, double z, double t) {
	double t_1 = 1.0 / ((y / x) - ((z / x) * t));
	double tmp;
	if (x < -1.618195973607049e+50) {
		tmp = t_1;
	} else if (x < 2.1378306434876444e+131) {
		tmp = x / (y - (z * t));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: t_1
    real(8) :: tmp
    t_1 = 1.0d0 / ((y / x) - ((z / x) * t))
    if (x < (-1.618195973607049d+50)) then
        tmp = t_1
    else if (x < 2.1378306434876444d+131) then
        tmp = x / (y - (z * 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 t_1 = 1.0 / ((y / x) - ((z / x) * t));
	double tmp;
	if (x < -1.618195973607049e+50) {
		tmp = t_1;
	} else if (x < 2.1378306434876444e+131) {
		tmp = x / (y - (z * t));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = 1.0 / ((y / x) - ((z / x) * t))
	tmp = 0
	if x < -1.618195973607049e+50:
		tmp = t_1
	elif x < 2.1378306434876444e+131:
		tmp = x / (y - (z * t))
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(1.0 / Float64(Float64(y / x) - Float64(Float64(z / x) * t)))
	tmp = 0.0
	if (x < -1.618195973607049e+50)
		tmp = t_1;
	elseif (x < 2.1378306434876444e+131)
		tmp = Float64(x / Float64(y - Float64(z * t)));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = 1.0 / ((y / x) - ((z / x) * t));
	tmp = 0.0;
	if (x < -1.618195973607049e+50)
		tmp = t_1;
	elseif (x < 2.1378306434876444e+131)
		tmp = x / (y - (z * t));
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(1.0 / N[(N[(y / x), $MachinePrecision] - N[(N[(z / x), $MachinePrecision] * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[Less[x, -1.618195973607049e+50], t$95$1, If[Less[x, 2.1378306434876444e+131], N[(x / N[(y - N[(z * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]

Reproduce

herbie shell --seed 2024235 
(FPCore (x y z t)
  :name "Diagrams.Solve.Tridiagonal:solveTriDiagonal from diagrams-solve-0.1, B"
  :precision binary64

  :alt
  (! :herbie-platform default (if (< x -161819597360704900000000000000000000000000000000000) (/ 1 (- (/ y x) (* (/ z x) t))) (if (< x 213783064348764440000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000) (/ x (- y (* z t))) (/ 1 (- (/ y x) (* (/ z x) t))))))

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