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

Percentage Accurate: 95.6% → 95.6%

Time: 5.9s

Alternatives: 5

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: 95.6% 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: 95.6% 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]

Derivation

1. Initial program 97.7%

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

Alternative 2: 76.8% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \cdot t \leq -1 \cdot 10^{+63} \lor \neg \left(z \cdot t \leq 200\right):\\ \;\;\;\;\frac{x}{\left(-z\right) \cdot t}\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= (* z t) -1e+63) (not (<= (* z t) 200.0)))
   (/ x (* (- z) t))
   (/ x y)))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (((z * t) <= -1e+63) || !((z * t) <= 200.0)) {
		tmp = x / (-z * t);
	} 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 (((z * t) <= (-1d+63)) .or. (.not. ((z * t) <= 200.0d0))) then
        tmp = x / (-z * t)
    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 (((z * t) <= -1e+63) || !((z * t) <= 200.0)) {
		tmp = x / (-z * t);
	} else {
		tmp = x / y;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if ((z * t) <= -1e+63) or not ((z * t) <= 200.0):
		tmp = x / (-z * t)
	else:
		tmp = x / y
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((Float64(z * t) <= -1e+63) || !(Float64(z * t) <= 200.0))
		tmp = Float64(x / Float64(Float64(-z) * t));
	else
		tmp = Float64(x / y);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (((z * t) <= -1e+63) || ~(((z * t) <= 200.0)))
		tmp = x / (-z * t);
	else
		tmp = x / y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[N[(z * t), $MachinePrecision], -1e+63], N[Not[LessEqual[N[(z * t), $MachinePrecision], 200.0]], $MachinePrecision]], N[(x / N[((-z) * t), $MachinePrecision]), $MachinePrecision], N[(x / y), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 z t) < -1.00000000000000006e63 or 200 < (*.f64 z t)

   1. Initial program 94.7%

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

   3. Taylor expanded in y around 0

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

      1. *-commutative N/A

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

      2. associate-*r* N/A

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

      3. lower-*.f64 N/A

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

      4. mul-1-neg N/A

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

      5. lower-neg.f64 86.0

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

   5. Applied rewrites 86.0%

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

   if -1.00000000000000006e63 < (*.f64 z t) < 200

   1. Initial program 99.9%

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

   3. Taylor expanded in y around inf

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

      1. lower-/.f64 79.1

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

   5. Applied rewrites 79.1%

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

4. Final simplification 82.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \cdot t \leq -1 \cdot 10^{+63} \lor \neg \left(z \cdot t \leq 200\right):\\ \;\;\;\;\frac{x}{\left(-z\right) \cdot t}\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{y}\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 61.5% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \cdot t \leq -1 \cdot 10^{+63} \lor \neg \left(z \cdot t \leq 10^{+232}\right):\\ \;\;\;\;\frac{x}{t \cdot z}\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= (* z t) -1e+63) (not (<= (* z t) 1e+232))) (/ x (* t z)) (/ x y)))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (((z * t) <= -1e+63) || !((z * t) <= 1e+232)) {
		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 (((z * t) <= (-1d+63)) .or. (.not. ((z * t) <= 1d+232))) 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 (((z * t) <= -1e+63) || !((z * t) <= 1e+232)) {
		tmp = x / (t * z);
	} else {
		tmp = x / y;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if ((z * t) <= -1e+63) or not ((z * t) <= 1e+232):
		tmp = x / (t * z)
	else:
		tmp = x / y
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((Float64(z * t) <= -1e+63) || !(Float64(z * t) <= 1e+232))
		tmp = Float64(x / 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 (((z * t) <= -1e+63) || ~(((z * t) <= 1e+232)))
		tmp = x / (t * z);
	else
		tmp = x / y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[N[(z * t), $MachinePrecision], -1e+63], N[Not[LessEqual[N[(z * t), $MachinePrecision], 1e+232]], $MachinePrecision]], N[(x / N[(t * z), $MachinePrecision]), $MachinePrecision], N[(x / y), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 z t) < -1.00000000000000006e63 or 1.00000000000000006e232 < (*.f64 z t)

   1. Initial program 92.4%

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

   3. Taylor expanded in y around 0

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

      1. associate-*r/ N/A

         \[\leadsto \color{blue}{\frac{-1 \cdot x}{t \cdot z}} \]

      2. associate-/l/ N/A

         \[\leadsto \color{blue}{\frac{\frac{-1 \cdot x}{z}}{t}} \]

      3. associate-*r/ N/A

         \[\leadsto \frac{\color{blue}{-1 \cdot \frac{x}{z}}}{t} \]

      4. lower-/.f64 N/A

         \[\leadsto \color{blue}{\frac{-1 \cdot \frac{x}{z}}{t}} \]

      5. associate-*r/ N/A

         \[\leadsto \frac{\color{blue}{\frac{-1 \cdot x}{z}}}{t} \]

      6. lower-/.f64 N/A

         \[\leadsto \frac{\color{blue}{\frac{-1 \cdot x}{z}}}{t} \]

      7. mul-1-neg N/A

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

      8. lower-neg.f64 95.0

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

   5. Applied rewrites 95.0%

      \[\leadsto \color{blue}{\frac{\frac{-x}{z}}{t}} \]
   6. Step-by-step derivation

   7. Applied rewrites 98.3%

      \[\leadsto \frac{\frac{-x}{t}}{\color{blue}{z}} \]
   8. Step-by-step derivation

   9. Applied rewrites 58.8%

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

   if -1.00000000000000006e63 < (*.f64 z t) < 1.00000000000000006e232

   1. Initial program 99.9%

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

   3. Taylor expanded in y around inf

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

      1. lower-/.f64 71.9

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

   5. Applied rewrites 71.9%

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

4. Final simplification 68.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \cdot t \leq -1 \cdot 10^{+63} \lor \neg \left(z \cdot t \leq 10^{+232}\right):\\ \;\;\;\;\frac{x}{t \cdot z}\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{y}\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 64.2% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \frac{x}{\mathsf{fma}\left(t, z, y\right)} \end{array} \]

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 97.7%

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

   1. lift-/.f64 N/A

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

   2. clear-num N/A

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

   3. lower-/.f64 N/A

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

   4. lower-/.f64 96.9

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

   5. lift-*.f64 N/A

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

   6. *-commutative N/A

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

   7. lower-*.f64 96.9

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

4. Applied rewrites 96.9%

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

   1. lift-/.f64 N/A

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

   2. lift-/.f64 N/A

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

   3. clear-num N/A

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

   4. frac-2neg N/A

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

   5. neg-mul-1 N/A

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

   6. *-commutative N/A

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

   7. neg-mul-1 N/A

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

   8. times-frac N/A

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

   9. div-inv N/A

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

   10. metadata-eval N/A

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

   11. *-commutative N/A

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

   12. neg-mul-1 N/A

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

   13. lift-neg.f64 N/A

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

   14. lower-*.f64 N/A

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

   15. lower-/.f64 97.5

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

   16. lift-*.f64 N/A

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

   17. *-commutative N/A

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

   18. lower-*.f64 97.5

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

6. Applied rewrites 97.5%

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

   1. lift-*.f64 N/A

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

   2. lift-/.f64 N/A

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

   3. frac-2neg N/A

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

   4. metadata-eval N/A

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

   5. div-inv N/A

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

   6. lift-neg.f64 N/A

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

   7. frac-2neg N/A

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

   8. lower-/.f64 97.7

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

   9. /-rgt-identity N/A

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

   10. lift--.f64 N/A

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

   11. div-sub N/A

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

   12. /-rgt-identity N/A

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

   13. clear-num N/A

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

   14. inv-pow N/A

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

   15. metadata-eval N/A

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

   16. metadata-eval N/A

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

   17. pow-pow N/A

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

8. Applied rewrites 67.8%

   \[\leadsto \color{blue}{\frac{x}{\mathsf{fma}\left(t, z, y\right)}} \]
9. Add Preprocessing

Alternative 5: 53.8% 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 97.7%

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

3. Taylor expanded in y around inf

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

   1. lower-/.f64 55.6

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

5. Applied rewrites 55.6%

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

Developer Target 1: 96.6% 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 2024315 
(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))))