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

Percentage Accurate: 96.0% → 97.6%

Time: 3.3s

Alternatives: 9

Speedup: N/A×

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

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x, y, z, t)
use fmin_fmax_functions
    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: 96.0% 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

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x, y, z, t)
use fmin_fmax_functions
    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: 97.6% accurate, N/A× speedup

Math

\[\begin{array}{l} [x, y, z, t] = \mathsf{sort}([x, y, z, t])\\ \\ \begin{array}{l} \mathbf{if}\;z \cdot t \leq -5 \cdot 10^{+230}:\\ \;\;\;\;\frac{\frac{x}{t} \cdot -1}{z}\\ \mathbf{else}:\\ \;\;\;\;-1 \cdot \frac{x}{-1 \cdot \mathsf{fma}\left(-1 \cdot t, z, y\right)}\\ \end{array} \end{array} \]

FPCore

NOTE: x, y, z, and t should be sorted in increasing order before calling this function.
(FPCore (x y z t)
 :precision binary64
 (if (<= (* z t) -5e+230)
   (/ (* (/ x t) -1.0) z)
   (* -1.0 (/ x (* -1.0 (fma (* -1.0 t) z y))))))

C

assert(x < y && y < z && z < t);
double code(double x, double y, double z, double t) {
	double tmp;
	if ((z * t) <= -5e+230) {
		tmp = ((x / t) * -1.0) / z;
	} else {
		tmp = -1.0 * (x / (-1.0 * fma((-1.0 * t), z, y)));
	}
	return tmp;
}

Julia

x, y, z, t = sort([x, y, z, t])
function code(x, y, z, t)
	tmp = 0.0
	if (Float64(z * t) <= -5e+230)
		tmp = Float64(Float64(Float64(x / t) * -1.0) / z);
	else
		tmp = Float64(-1.0 * Float64(x / Float64(-1.0 * fma(Float64(-1.0 * t), z, y))));
	end
	return tmp
end

Wolfram

NOTE: x, y, z, and t should be sorted in increasing order before calling this function.
code[x_, y_, z_, t_] := If[LessEqual[N[(z * t), $MachinePrecision], -5e+230], N[(N[(N[(x / t), $MachinePrecision] * -1.0), $MachinePrecision] / z), $MachinePrecision], N[(-1.0 * N[(x / N[(-1.0 * N[(N[(-1.0 * t), $MachinePrecision] * z + y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 z t) < -5.0000000000000003e230

   1. Initial program 77.5%

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

      1. lift-/.f64 N/A

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

      2. lift--.f64 N/A

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

      3. lift-*.f64 N/A

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

      4. frac-2neg N/A

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

      5. mul-1-neg N/A

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

      6. lower-/.f64 N/A

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

      7. lower-*.f64 N/A

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

      8. lower-neg.f64 N/A

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

      9. *-commutative N/A

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

      10. fp-cancel-sub-sign-inv N/A

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

      11. mul-1-neg N/A

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

      12. associate-*r* N/A

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

      13. +-commutative N/A

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

      14. associate-*r* N/A

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

      15. mul-1-neg N/A

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

      16. lower-fma.f64 N/A

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

      17. mul-1-neg N/A

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

      18. lower-*.f64 77.5

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

   4. Applied rewrites 77.5%

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

   5. Taylor expanded in y around 0

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

      1. associate-*r/ N/A

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

      2. *-commutative N/A

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

      3. frac-times N/A

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

      4. associate-*l/ N/A

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

      5. lower-/.f64 N/A

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

      6. *-commutative N/A

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

      7. lift-/.f64 N/A

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

      8. lift-*.f64 99.8

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

   7. Applied rewrites 99.8%

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

   if -5.0000000000000003e230 < (*.f64 z t)

   1. Initial program 98.3%

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

      1. lift-/.f64 N/A

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

      2. lift--.f64 N/A

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

      3. lift-*.f64 N/A

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

      4. frac-2neg N/A

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

      5. mul-1-neg N/A

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

      6. lower-/.f64 N/A

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

      7. lower-*.f64 N/A

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

      8. lower-neg.f64 N/A

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

      9. *-commutative N/A

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

      10. fp-cancel-sub-sign-inv N/A

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

      11. mul-1-neg N/A

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

      12. associate-*r* N/A

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

      13. +-commutative N/A

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

      14. associate-*r* N/A

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

      15. mul-1-neg N/A

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

      16. lower-fma.f64 N/A

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

      17. mul-1-neg N/A

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

      18. lower-*.f64 98.3

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

   4. Applied rewrites 98.3%

      \[\leadsto \color{blue}{\frac{-1 \cdot x}{-\mathsf{fma}\left(-1 \cdot t, z, y\right)}} \]
3. Recombined 2 regimes into one program.

4. Final simplification 98.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \cdot t \leq -5 \cdot 10^{+230}:\\ \;\;\;\;\frac{\frac{x}{t} \cdot -1}{z}\\ \mathbf{else}:\\ \;\;\;\;-1 \cdot \frac{x}{-1 \cdot \mathsf{fma}\left(-1 \cdot t, z, y\right)}\\ \end{array} \]
5. Add Preprocessing

Alternative 2: 97.6% accurate, N/A× speedup

Math

\[\begin{array}{l} [x, y, z, t] = \mathsf{sort}([x, y, z, t])\\ \\ \begin{array}{l} \mathbf{if}\;z \cdot t \leq -5 \cdot 10^{+230}:\\ \;\;\;\;\frac{\frac{x}{t} \cdot -1}{z}\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{y - z \cdot t}\\ \end{array} \end{array} \]

FPCore

NOTE: x, y, z, and t should be sorted in increasing order before calling this function.
(FPCore (x y z t)
 :precision binary64
 (if (<= (* z t) -5e+230) (/ (* (/ x t) -1.0) z) (/ x (- y (* z t)))))

C

assert(x < y && y < z && z < t);
double code(double x, double y, double z, double t) {
	double tmp;
	if ((z * t) <= -5e+230) {
		tmp = ((x / t) * -1.0) / z;
	} else {
		tmp = x / (y - (z * t));
	}
	return tmp;
}

Fortran

NOTE: x, y, z, and t should be sorted in increasing order before calling this function.
module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x, y, z, t)
use fmin_fmax_functions
    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) <= (-5d+230)) then
        tmp = ((x / t) * (-1.0d0)) / z
    else
        tmp = x / (y - (z * t))
    end if
    code = tmp
end function

Java

assert x < y && y < z && z < t;
public static double code(double x, double y, double z, double t) {
	double tmp;
	if ((z * t) <= -5e+230) {
		tmp = ((x / t) * -1.0) / z;
	} else {
		tmp = x / (y - (z * t));
	}
	return tmp;
}

Python

[x, y, z, t] = sort([x, y, z, t])
def code(x, y, z, t):
	tmp = 0
	if (z * t) <= -5e+230:
		tmp = ((x / t) * -1.0) / z
	else:
		tmp = x / (y - (z * t))
	return tmp

Julia

x, y, z, t = sort([x, y, z, t])
function code(x, y, z, t)
	tmp = 0.0
	if (Float64(z * t) <= -5e+230)
		tmp = Float64(Float64(Float64(x / t) * -1.0) / z);
	else
		tmp = Float64(x / Float64(y - Float64(z * t)));
	end
	return tmp
end

MATLAB

x, y, z, t = num2cell(sort([x, y, z, t])){:}
function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((z * t) <= -5e+230)
		tmp = ((x / t) * -1.0) / z;
	else
		tmp = x / (y - (z * t));
	end
	tmp_2 = tmp;
end

Wolfram

NOTE: x, y, z, and t should be sorted in increasing order before calling this function.
code[x_, y_, z_, t_] := If[LessEqual[N[(z * t), $MachinePrecision], -5e+230], N[(N[(N[(x / t), $MachinePrecision] * -1.0), $MachinePrecision] / z), $MachinePrecision], N[(x / N[(y - N[(z * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 z t) < -5.0000000000000003e230

   1. Initial program 77.5%

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

      1. lift-/.f64 N/A

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

      2. lift--.f64 N/A

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

      3. lift-*.f64 N/A

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

      4. frac-2neg N/A

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

      5. mul-1-neg N/A

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

      6. lower-/.f64 N/A

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

      7. lower-*.f64 N/A

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

      8. lower-neg.f64 N/A

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

      9. *-commutative N/A

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

      10. fp-cancel-sub-sign-inv N/A

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

      11. mul-1-neg N/A

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

      12. associate-*r* N/A

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

      13. +-commutative N/A

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

      14. associate-*r* N/A

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

      15. mul-1-neg N/A

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

      16. lower-fma.f64 N/A

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

      17. mul-1-neg N/A

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

      18. lower-*.f64 77.5

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

   4. Applied rewrites 77.5%

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

   5. Taylor expanded in y around 0

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

      1. associate-*r/ N/A

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

      2. *-commutative N/A

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

      3. frac-times N/A

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

      4. associate-*l/ N/A

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

      5. lower-/.f64 N/A

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

      6. *-commutative N/A

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

      7. lift-/.f64 N/A

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

      8. lift-*.f64 99.8

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

   7. Applied rewrites 99.8%

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

   if -5.0000000000000003e230 < (*.f64 z t)

   1. Initial program 98.3%

      \[\frac{x}{y - z \cdot t} \]
   2. Add Preprocessing
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 3: 74.6% accurate, N/A× speedup

Math

\[\begin{array}{l} [x, y, z, t] = \mathsf{sort}([x, y, z, t])\\ \\ \begin{array}{l} t_1 := \frac{\frac{x}{t} \cdot -1}{z}\\ \mathbf{if}\;z \cdot t \leq -2 \cdot 10^{-136}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;z \cdot t \leq 5 \cdot 10^{-46}:\\ \;\;\;\;\frac{\frac{\left(z \cdot x\right) \cdot t}{y}}{y} - -1 \cdot \frac{x}{y}\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

NOTE: x, y, z, and t should be sorted in increasing order before calling this function.
(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (/ (* (/ x t) -1.0) z)))
   (if (<= (* z t) -2e-136)
     t_1
     (if (<= (* z t) 5e-46)
       (- (/ (/ (* (* z x) t) y) y) (* -1.0 (/ x y)))
       t_1))))

C

assert(x < y && y < z && z < t);
double code(double x, double y, double z, double t) {
	double t_1 = ((x / t) * -1.0) / z;
	double tmp;
	if ((z * t) <= -2e-136) {
		tmp = t_1;
	} else if ((z * t) <= 5e-46) {
		tmp = ((((z * x) * t) / y) / y) - (-1.0 * (x / y));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

NOTE: x, y, z, and t should be sorted in increasing order before calling this function.
module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x, y, z, t)
use fmin_fmax_functions
    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 = ((x / t) * (-1.0d0)) / z
    if ((z * t) <= (-2d-136)) then
        tmp = t_1
    else if ((z * t) <= 5d-46) then
        tmp = ((((z * x) * t) / y) / y) - ((-1.0d0) * (x / y))
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

assert x < y && y < z && z < t;
public static double code(double x, double y, double z, double t) {
	double t_1 = ((x / t) * -1.0) / z;
	double tmp;
	if ((z * t) <= -2e-136) {
		tmp = t_1;
	} else if ((z * t) <= 5e-46) {
		tmp = ((((z * x) * t) / y) / y) - (-1.0 * (x / y));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

[x, y, z, t] = sort([x, y, z, t])
def code(x, y, z, t):
	t_1 = ((x / t) * -1.0) / z
	tmp = 0
	if (z * t) <= -2e-136:
		tmp = t_1
	elif (z * t) <= 5e-46:
		tmp = ((((z * x) * t) / y) / y) - (-1.0 * (x / y))
	else:
		tmp = t_1
	return tmp

Julia

x, y, z, t = sort([x, y, z, t])
function code(x, y, z, t)
	t_1 = Float64(Float64(Float64(x / t) * -1.0) / z)
	tmp = 0.0
	if (Float64(z * t) <= -2e-136)
		tmp = t_1;
	elseif (Float64(z * t) <= 5e-46)
		tmp = Float64(Float64(Float64(Float64(Float64(z * x) * t) / y) / y) - Float64(-1.0 * Float64(x / y)));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

x, y, z, t = num2cell(sort([x, y, z, t])){:}
function tmp_2 = code(x, y, z, t)
	t_1 = ((x / t) * -1.0) / z;
	tmp = 0.0;
	if ((z * t) <= -2e-136)
		tmp = t_1;
	elseif ((z * t) <= 5e-46)
		tmp = ((((z * x) * t) / y) / y) - (-1.0 * (x / y));
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

NOTE: x, y, z, and t should be sorted in increasing order before calling this function.
code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(N[(x / t), $MachinePrecision] * -1.0), $MachinePrecision] / z), $MachinePrecision]}, If[LessEqual[N[(z * t), $MachinePrecision], -2e-136], t$95$1, If[LessEqual[N[(z * t), $MachinePrecision], 5e-46], N[(N[(N[(N[(N[(z * x), $MachinePrecision] * t), $MachinePrecision] / y), $MachinePrecision] / y), $MachinePrecision] - N[(-1.0 * N[(x / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 z t) < -2e-136 or 4.99999999999999992e-46 < (*.f64 z t)

   1. Initial program 94.6%

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

      1. lift-/.f64 N/A

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

      2. lift--.f64 N/A

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

      3. lift-*.f64 N/A

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

      4. frac-2neg N/A

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

      5. mul-1-neg N/A

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

      6. lower-/.f64 N/A

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

      7. lower-*.f64 N/A

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

      8. lower-neg.f64 N/A

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

      9. *-commutative N/A

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

      10. fp-cancel-sub-sign-inv N/A

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

      11. mul-1-neg N/A

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

      12. associate-*r* N/A

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

      13. +-commutative N/A

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

      14. associate-*r* N/A

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

      15. mul-1-neg N/A

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

      16. lower-fma.f64 N/A

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

      17. mul-1-neg N/A

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

      18. lower-*.f64 94.6

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

   4. Applied rewrites 94.6%

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

   5. Taylor expanded in y around 0

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

      1. associate-*r/ N/A

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

      2. *-commutative N/A

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

      3. frac-times N/A

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

      4. associate-*l/ N/A

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

      5. lower-/.f64 N/A

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

      6. *-commutative N/A

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

      7. lift-/.f64 N/A

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

      8. lift-*.f64 72.1

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

   7. Applied rewrites 72.1%

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

   if -2e-136 < (*.f64 z t) < 4.99999999999999992e-46

   1. Initial program 99.9%

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

   3. Taylor expanded in y around inf

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

      1. lower-/.f64 N/A

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

      2. +-commutative N/A

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

      3. lower-+.f64 N/A

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

      4. lower-/.f64 N/A

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

      5. *-commutative N/A

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

      6. lower-*.f64 N/A

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

      7. *-commutative N/A

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

      8. lower-*.f64 88.5

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

   5. Applied rewrites 88.5%

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

      1. lift-/.f64 N/A

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

      2. lift-+.f64 N/A

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

      3. div-add N/A

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

      4. lift-/.f64 N/A

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

      5. lift-*.f64 N/A

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

      6. lift-*.f64 N/A

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

      7. *-commutative N/A

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

      8. *-commutative N/A

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

      9. lower-+.f64 N/A

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

      10. lower-/.f64 N/A

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

      11. *-commutative N/A

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

      12. *-commutative N/A

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

      13. lift-*.f64 N/A

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

      14. lift-*.f64 N/A

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

      15. lift-/.f64 N/A

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

      16. lower-/.f64 88.5

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

   7. Applied rewrites 88.5%

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

4. Final simplification 78.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \cdot t \leq -2 \cdot 10^{-136}:\\ \;\;\;\;\frac{\frac{x}{t} \cdot -1}{z}\\ \mathbf{elif}\;z \cdot t \leq 5 \cdot 10^{-46}:\\ \;\;\;\;\frac{\frac{\left(z \cdot x\right) \cdot t}{y}}{y} - -1 \cdot \frac{x}{y}\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{x}{t} \cdot -1}{z}\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 72.9% accurate, N/A× speedup

Math

\[\begin{array}{l} [x, y, z, t] = \mathsf{sort}([x, y, z, t])\\ \\ \begin{array}{l} t_1 := \mathsf{fma}\left(\frac{-1}{z}, \frac{x}{t}, \frac{y \cdot \left(-1 \cdot x\right)}{{\left(t \cdot z\right)}^{2}}\right)\\ \mathbf{if}\;z \cdot t \leq -5 \cdot 10^{-34}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;z \cdot t \leq 5 \cdot 10^{+18}:\\ \;\;\;\;\frac{\frac{\left(z \cdot x\right) \cdot t}{y}}{y} - -1 \cdot \frac{x}{y}\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

NOTE: x, y, z, and t should be sorted in increasing order before calling this function.
(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (fma (/ -1.0 z) (/ x t) (/ (* y (* -1.0 x)) (pow (* t z) 2.0)))))
   (if (<= (* z t) -5e-34)
     t_1
     (if (<= (* z t) 5e+18)
       (- (/ (/ (* (* z x) t) y) y) (* -1.0 (/ x y)))
       t_1))))

C

assert(x < y && y < z && z < t);
double code(double x, double y, double z, double t) {
	double t_1 = fma((-1.0 / z), (x / t), ((y * (-1.0 * x)) / pow((t * z), 2.0)));
	double tmp;
	if ((z * t) <= -5e-34) {
		tmp = t_1;
	} else if ((z * t) <= 5e+18) {
		tmp = ((((z * x) * t) / y) / y) - (-1.0 * (x / y));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Julia

x, y, z, t = sort([x, y, z, t])
function code(x, y, z, t)
	t_1 = fma(Float64(-1.0 / z), Float64(x / t), Float64(Float64(y * Float64(-1.0 * x)) / (Float64(t * z) ^ 2.0)))
	tmp = 0.0
	if (Float64(z * t) <= -5e-34)
		tmp = t_1;
	elseif (Float64(z * t) <= 5e+18)
		tmp = Float64(Float64(Float64(Float64(Float64(z * x) * t) / y) / y) - Float64(-1.0 * Float64(x / y)));
	else
		tmp = t_1;
	end
	return tmp
end

Wolfram

NOTE: x, y, z, and t should be sorted in increasing order before calling this function.
code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(-1.0 / z), $MachinePrecision] * N[(x / t), $MachinePrecision] + N[(N[(y * N[(-1.0 * x), $MachinePrecision]), $MachinePrecision] / N[Power[N[(t * z), $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[(z * t), $MachinePrecision], -5e-34], t$95$1, If[LessEqual[N[(z * t), $MachinePrecision], 5e+18], N[(N[(N[(N[(N[(z * x), $MachinePrecision] * t), $MachinePrecision] / y), $MachinePrecision] / y), $MachinePrecision] - N[(-1.0 * N[(x / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 z t) < -5.0000000000000003e-34 or 5e18 < (*.f64 z t)

   1. Initial program 92.7%

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

   3. Taylor expanded in y around 0

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

      1. associate-*r/ N/A

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

      2. *-commutative N/A

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

      3. times-frac N/A

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

      4. lower-fma.f64 N/A

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

      5. lower-/.f64 N/A

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

      6. lower-/.f64 N/A

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

      7. associate-*r/ N/A

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

      8. lower-/.f64 N/A

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

      9. lower-*.f64 N/A

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

      10. *-commutative N/A

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

      11. lower-*.f64 N/A

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

      12. pow-prod-down N/A

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

      13. lower-pow.f64 N/A

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

      14. lower-*.f64 75.1

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

   5. Applied rewrites 75.1%

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

   if -5.0000000000000003e-34 < (*.f64 z t) < 5e18

   1. Initial program 99.9%

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

   3. Taylor expanded in y around inf

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

      1. lower-/.f64 N/A

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

      2. +-commutative N/A

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

      3. lower-+.f64 N/A

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

      4. lower-/.f64 N/A

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

      5. *-commutative N/A

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

      6. lower-*.f64 N/A

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

      7. *-commutative N/A

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

      8. lower-*.f64 74.8

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

   5. Applied rewrites 74.8%

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

      1. lift-/.f64 N/A

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

      2. lift-+.f64 N/A

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

      3. div-add N/A

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

      4. lift-/.f64 N/A

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

      5. lift-*.f64 N/A

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

      6. lift-*.f64 N/A

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

      7. *-commutative N/A

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

      8. *-commutative N/A

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

      9. lower-+.f64 N/A

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

      10. lower-/.f64 N/A

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

      11. *-commutative N/A

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

      12. *-commutative N/A

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

      13. lift-*.f64 N/A

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

      14. lift-*.f64 N/A

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

      15. lift-/.f64 N/A

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

      16. lower-/.f64 74.8

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

   7. Applied rewrites 74.8%

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

4. Final simplification 74.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \cdot t \leq -5 \cdot 10^{-34}:\\ \;\;\;\;\mathsf{fma}\left(\frac{-1}{z}, \frac{x}{t}, \frac{y \cdot \left(-1 \cdot x\right)}{{\left(t \cdot z\right)}^{2}}\right)\\ \mathbf{elif}\;z \cdot t \leq 5 \cdot 10^{+18}:\\ \;\;\;\;\frac{\frac{\left(z \cdot x\right) \cdot t}{y}}{y} - -1 \cdot \frac{x}{y}\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(\frac{-1}{z}, \frac{x}{t}, \frac{y \cdot \left(-1 \cdot x\right)}{{\left(t \cdot z\right)}^{2}}\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 71.2% accurate, N/A× speedup

Math

\[\begin{array}{l} [x, y, z, t] = \mathsf{sort}([x, y, z, t])\\ \\ \begin{array}{l} t_1 := {\left(\left(z \cdot t\right) \cdot -1\right)}^{2}\\ t_2 := \frac{x}{t} \cdot -1\\ \mathbf{if}\;z \cdot t \leq -1 \cdot 10^{+118}:\\ \;\;\;\;\frac{\mathsf{fma}\left(\left(-1 \cdot \frac{x}{\left(z \cdot z\right) \cdot z}\right) \cdot \left(-1 \cdot {\left(\frac{y}{t} \cdot -1\right)}^{2}\right), -1, \frac{x}{z} \cdot -1\right) - \left(-1 \cdot \frac{x}{z \cdot z}\right) \cdot \left(-1 \cdot \frac{y}{t}\right)}{t}\\ \mathbf{elif}\;z \cdot t \leq -5 \cdot 10^{-34}:\\ \;\;\;\;\frac{\mathsf{fma}\left(\left(y \cdot x\right) \cdot -1, z, t\_1 \cdot t\_2\right)}{t\_1 \cdot z}\\ \mathbf{elif}\;z \cdot t \leq 5 \cdot 10^{+18}:\\ \;\;\;\;\frac{\frac{\left(z \cdot x\right) \cdot t}{y}}{y} - -1 \cdot \frac{x}{y}\\ \mathbf{else}:\\ \;\;\;\;\frac{\mathsf{fma}\left(\left(-1 \cdot {\left(\frac{y}{z} \cdot -1\right)}^{2}\right) \cdot \left(-1 \cdot \frac{x}{\left(t \cdot t\right) \cdot t}\right), -1, t\_2\right) - \left(-1 \cdot \frac{y}{t \cdot t}\right) \cdot \left(-1 \cdot \frac{x}{z}\right)}{z}\\ \end{array} \end{array} \]

FPCore

NOTE: x, y, z, and t should be sorted in increasing order before calling this function.
(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (pow (* (* z t) -1.0) 2.0)) (t_2 (* (/ x t) -1.0)))
   (if (<= (* z t) -1e+118)
     (/
      (-
       (fma
        (* (* -1.0 (/ x (* (* z z) z))) (* -1.0 (pow (* (/ y t) -1.0) 2.0)))
        -1.0
        (* (/ x z) -1.0))
       (* (* -1.0 (/ x (* z z))) (* -1.0 (/ y t))))
      t)
     (if (<= (* z t) -5e-34)
       (/ (fma (* (* y x) -1.0) z (* t_1 t_2)) (* t_1 z))
       (if (<= (* z t) 5e+18)
         (- (/ (/ (* (* z x) t) y) y) (* -1.0 (/ x y)))
         (/
          (-
           (fma
            (*
             (* -1.0 (pow (* (/ y z) -1.0) 2.0))
             (* -1.0 (/ x (* (* t t) t))))
            -1.0
            t_2)
           (* (* -1.0 (/ y (* t t))) (* -1.0 (/ x z))))
          z))))))

C

assert(x < y && y < z && z < t);
double code(double x, double y, double z, double t) {
	double t_1 = pow(((z * t) * -1.0), 2.0);
	double t_2 = (x / t) * -1.0;
	double tmp;
	if ((z * t) <= -1e+118) {
		tmp = (fma(((-1.0 * (x / ((z * z) * z))) * (-1.0 * pow(((y / t) * -1.0), 2.0))), -1.0, ((x / z) * -1.0)) - ((-1.0 * (x / (z * z))) * (-1.0 * (y / t)))) / t;
	} else if ((z * t) <= -5e-34) {
		tmp = fma(((y * x) * -1.0), z, (t_1 * t_2)) / (t_1 * z);
	} else if ((z * t) <= 5e+18) {
		tmp = ((((z * x) * t) / y) / y) - (-1.0 * (x / y));
	} else {
		tmp = (fma(((-1.0 * pow(((y / z) * -1.0), 2.0)) * (-1.0 * (x / ((t * t) * t)))), -1.0, t_2) - ((-1.0 * (y / (t * t))) * (-1.0 * (x / z)))) / z;
	}
	return tmp;
}

Julia

x, y, z, t = sort([x, y, z, t])
function code(x, y, z, t)
	t_1 = Float64(Float64(z * t) * -1.0) ^ 2.0
	t_2 = Float64(Float64(x / t) * -1.0)
	tmp = 0.0
	if (Float64(z * t) <= -1e+118)
		tmp = Float64(Float64(fma(Float64(Float64(-1.0 * Float64(x / Float64(Float64(z * z) * z))) * Float64(-1.0 * (Float64(Float64(y / t) * -1.0) ^ 2.0))), -1.0, Float64(Float64(x / z) * -1.0)) - Float64(Float64(-1.0 * Float64(x / Float64(z * z))) * Float64(-1.0 * Float64(y / t)))) / t);
	elseif (Float64(z * t) <= -5e-34)
		tmp = Float64(fma(Float64(Float64(y * x) * -1.0), z, Float64(t_1 * t_2)) / Float64(t_1 * z));
	elseif (Float64(z * t) <= 5e+18)
		tmp = Float64(Float64(Float64(Float64(Float64(z * x) * t) / y) / y) - Float64(-1.0 * Float64(x / y)));
	else
		tmp = Float64(Float64(fma(Float64(Float64(-1.0 * (Float64(Float64(y / z) * -1.0) ^ 2.0)) * Float64(-1.0 * Float64(x / Float64(Float64(t * t) * t)))), -1.0, t_2) - Float64(Float64(-1.0 * Float64(y / Float64(t * t))) * Float64(-1.0 * Float64(x / z)))) / z);
	end
	return tmp
end

Wolfram

NOTE: x, y, z, and t should be sorted in increasing order before calling this function.
code[x_, y_, z_, t_] := Block[{t$95$1 = N[Power[N[(N[(z * t), $MachinePrecision] * -1.0), $MachinePrecision], 2.0], $MachinePrecision]}, Block[{t$95$2 = N[(N[(x / t), $MachinePrecision] * -1.0), $MachinePrecision]}, If[LessEqual[N[(z * t), $MachinePrecision], -1e+118], N[(N[(N[(N[(N[(-1.0 * N[(x / N[(N[(z * z), $MachinePrecision] * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * N[(-1.0 * N[Power[N[(N[(y / t), $MachinePrecision] * -1.0), $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * -1.0 + N[(N[(x / z), $MachinePrecision] * -1.0), $MachinePrecision]), $MachinePrecision] - N[(N[(-1.0 * N[(x / N[(z * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * N[(-1.0 * N[(y / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / t), $MachinePrecision], If[LessEqual[N[(z * t), $MachinePrecision], -5e-34], N[(N[(N[(N[(y * x), $MachinePrecision] * -1.0), $MachinePrecision] * z + N[(t$95$1 * t$95$2), $MachinePrecision]), $MachinePrecision] / N[(t$95$1 * z), $MachinePrecision]), $MachinePrecision], If[LessEqual[N[(z * t), $MachinePrecision], 5e+18], N[(N[(N[(N[(N[(z * x), $MachinePrecision] * t), $MachinePrecision] / y), $MachinePrecision] / y), $MachinePrecision] - N[(-1.0 * N[(x / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(N[(N[(N[(-1.0 * N[Power[N[(N[(y / z), $MachinePrecision] * -1.0), $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision] * N[(-1.0 * N[(x / N[(N[(t * t), $MachinePrecision] * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * -1.0 + t$95$2), $MachinePrecision] - N[(N[(-1.0 * N[(y / N[(t * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * N[(-1.0 * N[(x / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / z), $MachinePrecision]]]]]]

Derivation

1. Split input into 4 regimes

2. if (*.f64 z t) < -9.99999999999999967e117

   1. Initial program 86.0%

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

   3. Taylor expanded in t around inf

      \[\leadsto \color{blue}{\frac{\left(-1 \cdot \frac{x}{z} + -1 \cdot \frac{x \cdot {y}^{2}}{{t}^{2} \cdot {z}^{3}}\right) - \frac{x \cdot y}{t \cdot {z}^{2}}}{t}} \]

   5. Applied rewrites 85.2%

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

   if -9.99999999999999967e117 < (*.f64 z t) < -5.0000000000000003e-34

   1. Initial program 99.7%

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

   3. Taylor expanded in y around 0

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

      1. associate-*r/ N/A

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

      2. *-commutative N/A

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

      3. times-frac N/A

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

      4. lower-fma.f64 N/A

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

      5. lower-/.f64 N/A

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

      6. lower-/.f64 N/A

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

      7. associate-*r/ N/A

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

      8. lower-/.f64 N/A

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

      9. lower-*.f64 N/A

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

      10. *-commutative N/A

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

      11. lower-*.f64 N/A

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

      12. pow-prod-down N/A

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

      13. lower-pow.f64 N/A

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

      14. lower-*.f64 62.7

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

   5. Applied rewrites 62.7%

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

      1. lift-/.f64 N/A

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

      2. lift-/.f64 N/A

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

      3. lift-fma.f64 N/A

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

      4. +-commutative N/A

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

      5. lift-/.f64 N/A

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

      6. lift-*.f64 N/A

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

      7. lift-pow.f64 N/A

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

      8. unpow-prod-down N/A

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

      9. lift-*.f64 N/A

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

      10. lift-*.f64 N/A

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

      11. *-commutative N/A

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

      12. mul-1-neg N/A

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

      13. associate-*l/ N/A

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

      14. frac-add N/A

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

      15. lower-/.f64 N/A

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

   7. Applied rewrites 59.4%

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

   if -5.0000000000000003e-34 < (*.f64 z t) < 5e18

   1. Initial program 99.9%

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

   3. Taylor expanded in y around inf

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

      1. lower-/.f64 N/A

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

      2. +-commutative N/A

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

      3. lower-+.f64 N/A

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

      4. lower-/.f64 N/A

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

      5. *-commutative N/A

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

      6. lower-*.f64 N/A

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

      7. *-commutative N/A

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

      8. lower-*.f64 74.8

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

   5. Applied rewrites 74.8%

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

      1. lift-/.f64 N/A

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

      2. lift-+.f64 N/A

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

      3. div-add N/A

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

      4. lift-/.f64 N/A

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

      5. lift-*.f64 N/A

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

      6. lift-*.f64 N/A

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

      7. *-commutative N/A

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

      8. *-commutative N/A

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

      9. lower-+.f64 N/A

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

      10. lower-/.f64 N/A

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

      11. *-commutative N/A

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

      12. *-commutative N/A

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

      13. lift-*.f64 N/A

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

      14. lift-*.f64 N/A

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

      15. lift-/.f64 N/A

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

      16. lower-/.f64 74.8

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

   7. Applied rewrites 74.8%

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

   if 5e18 < (*.f64 z t)

   1. Initial program 93.1%

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

   3. Taylor expanded in z around inf

      \[\leadsto \color{blue}{\frac{\left(-1 \cdot \frac{x}{t} + -1 \cdot \frac{x \cdot {y}^{2}}{{t}^{3} \cdot {z}^{2}}\right) - \frac{x \cdot y}{{t}^{2} \cdot z}}{z}} \]

   5. Applied rewrites 69.4%

      \[\leadsto \color{blue}{\frac{\mathsf{fma}\left({\left(\frac{y}{z} \cdot -1\right)}^{2} \cdot \frac{x}{\left(t \cdot t\right) \cdot t}, -1, \frac{x}{t} \cdot -1\right) - \frac{y}{t \cdot t} \cdot \frac{x}{z}}{z}} \]
3. Recombined 4 regimes into one program.

4. Final simplification 73.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \cdot t \leq -1 \cdot 10^{+118}:\\ \;\;\;\;\frac{\mathsf{fma}\left(\left(-1 \cdot \frac{x}{\left(z \cdot z\right) \cdot z}\right) \cdot \left(-1 \cdot {\left(\frac{y}{t} \cdot -1\right)}^{2}\right), -1, \frac{x}{z} \cdot -1\right) - \left(-1 \cdot \frac{x}{z \cdot z}\right) \cdot \left(-1 \cdot \frac{y}{t}\right)}{t}\\ \mathbf{elif}\;z \cdot t \leq -5 \cdot 10^{-34}:\\ \;\;\;\;\frac{\mathsf{fma}\left(\left(y \cdot x\right) \cdot -1, z, {\left(\left(z \cdot t\right) \cdot -1\right)}^{2} \cdot \left(\frac{x}{t} \cdot -1\right)\right)}{{\left(\left(z \cdot t\right) \cdot -1\right)}^{2} \cdot z}\\ \mathbf{elif}\;z \cdot t \leq 5 \cdot 10^{+18}:\\ \;\;\;\;\frac{\frac{\left(z \cdot x\right) \cdot t}{y}}{y} - -1 \cdot \frac{x}{y}\\ \mathbf{else}:\\ \;\;\;\;\frac{\mathsf{fma}\left(\left(-1 \cdot {\left(\frac{y}{z} \cdot -1\right)}^{2}\right) \cdot \left(-1 \cdot \frac{x}{\left(t \cdot t\right) \cdot t}\right), -1, \frac{x}{t} \cdot -1\right) - \left(-1 \cdot \frac{y}{t \cdot t}\right) \cdot \left(-1 \cdot \frac{x}{z}\right)}{z}\\ \end{array} \]
5. Add Preprocessing

Alternative 6: 70.4% accurate, N/A× speedup

Math

\[\begin{array}{l} [x, y, z, t] = \mathsf{sort}([x, y, z, t])\\ \\ \begin{array}{l} \mathbf{if}\;z \cdot t \leq -1 \cdot 10^{+68}:\\ \;\;\;\;\frac{\mathsf{fma}\left(\left(-1 \cdot \frac{x}{\left(z \cdot z\right) \cdot z}\right) \cdot \left(-1 \cdot {\left(\frac{y}{t} \cdot -1\right)}^{2}\right), -1, \frac{x}{z} \cdot -1\right) - \left(-1 \cdot \frac{x}{z \cdot z}\right) \cdot \left(-1 \cdot \frac{y}{t}\right)}{t}\\ \mathbf{elif}\;z \cdot t \leq 5 \cdot 10^{+18}:\\ \;\;\;\;\frac{\frac{\left(z \cdot x\right) \cdot t}{y}}{y} - -1 \cdot \frac{x}{y}\\ \mathbf{else}:\\ \;\;\;\;\frac{\mathsf{fma}\left(\left(-1 \cdot {\left(\frac{y}{z} \cdot -1\right)}^{2}\right) \cdot \left(-1 \cdot \frac{x}{\left(t \cdot t\right) \cdot t}\right), -1, \frac{x}{t} \cdot -1\right) - \left(-1 \cdot \frac{y}{t \cdot t}\right) \cdot \left(-1 \cdot \frac{x}{z}\right)}{z}\\ \end{array} \end{array} \]

FPCore

NOTE: x, y, z, and t should be sorted in increasing order before calling this function.
(FPCore (x y z t)
 :precision binary64
 (if (<= (* z t) -1e+68)
   (/
    (-
     (fma
      (* (* -1.0 (/ x (* (* z z) z))) (* -1.0 (pow (* (/ y t) -1.0) 2.0)))
      -1.0
      (* (/ x z) -1.0))
     (* (* -1.0 (/ x (* z z))) (* -1.0 (/ y t))))
    t)
   (if (<= (* z t) 5e+18)
     (- (/ (/ (* (* z x) t) y) y) (* -1.0 (/ x y)))
     (/
      (-
       (fma
        (* (* -1.0 (pow (* (/ y z) -1.0) 2.0)) (* -1.0 (/ x (* (* t t) t))))
        -1.0
        (* (/ x t) -1.0))
       (* (* -1.0 (/ y (* t t))) (* -1.0 (/ x z))))
      z))))

C

assert(x < y && y < z && z < t);
double code(double x, double y, double z, double t) {
	double tmp;
	if ((z * t) <= -1e+68) {
		tmp = (fma(((-1.0 * (x / ((z * z) * z))) * (-1.0 * pow(((y / t) * -1.0), 2.0))), -1.0, ((x / z) * -1.0)) - ((-1.0 * (x / (z * z))) * (-1.0 * (y / t)))) / t;
	} else if ((z * t) <= 5e+18) {
		tmp = ((((z * x) * t) / y) / y) - (-1.0 * (x / y));
	} else {
		tmp = (fma(((-1.0 * pow(((y / z) * -1.0), 2.0)) * (-1.0 * (x / ((t * t) * t)))), -1.0, ((x / t) * -1.0)) - ((-1.0 * (y / (t * t))) * (-1.0 * (x / z)))) / z;
	}
	return tmp;
}

Julia

x, y, z, t = sort([x, y, z, t])
function code(x, y, z, t)
	tmp = 0.0
	if (Float64(z * t) <= -1e+68)
		tmp = Float64(Float64(fma(Float64(Float64(-1.0 * Float64(x / Float64(Float64(z * z) * z))) * Float64(-1.0 * (Float64(Float64(y / t) * -1.0) ^ 2.0))), -1.0, Float64(Float64(x / z) * -1.0)) - Float64(Float64(-1.0 * Float64(x / Float64(z * z))) * Float64(-1.0 * Float64(y / t)))) / t);
	elseif (Float64(z * t) <= 5e+18)
		tmp = Float64(Float64(Float64(Float64(Float64(z * x) * t) / y) / y) - Float64(-1.0 * Float64(x / y)));
	else
		tmp = Float64(Float64(fma(Float64(Float64(-1.0 * (Float64(Float64(y / z) * -1.0) ^ 2.0)) * Float64(-1.0 * Float64(x / Float64(Float64(t * t) * t)))), -1.0, Float64(Float64(x / t) * -1.0)) - Float64(Float64(-1.0 * Float64(y / Float64(t * t))) * Float64(-1.0 * Float64(x / z)))) / z);
	end
	return tmp
end

Wolfram

NOTE: x, y, z, and t should be sorted in increasing order before calling this function.
code[x_, y_, z_, t_] := If[LessEqual[N[(z * t), $MachinePrecision], -1e+68], N[(N[(N[(N[(N[(-1.0 * N[(x / N[(N[(z * z), $MachinePrecision] * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * N[(-1.0 * N[Power[N[(N[(y / t), $MachinePrecision] * -1.0), $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * -1.0 + N[(N[(x / z), $MachinePrecision] * -1.0), $MachinePrecision]), $MachinePrecision] - N[(N[(-1.0 * N[(x / N[(z * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * N[(-1.0 * N[(y / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / t), $MachinePrecision], If[LessEqual[N[(z * t), $MachinePrecision], 5e+18], N[(N[(N[(N[(N[(z * x), $MachinePrecision] * t), $MachinePrecision] / y), $MachinePrecision] / y), $MachinePrecision] - N[(-1.0 * N[(x / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(N[(N[(N[(-1.0 * N[Power[N[(N[(y / z), $MachinePrecision] * -1.0), $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision] * N[(-1.0 * N[(x / N[(N[(t * t), $MachinePrecision] * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * -1.0 + N[(N[(x / t), $MachinePrecision] * -1.0), $MachinePrecision]), $MachinePrecision] - N[(N[(-1.0 * N[(y / N[(t * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * N[(-1.0 * N[(x / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / z), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if (*.f64 z t) < -9.99999999999999953e67

   1. Initial program 89.4%

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

   3. Taylor expanded in t around inf

      \[\leadsto \color{blue}{\frac{\left(-1 \cdot \frac{x}{z} + -1 \cdot \frac{x \cdot {y}^{2}}{{t}^{2} \cdot {z}^{3}}\right) - \frac{x \cdot y}{t \cdot {z}^{2}}}{t}} \]

   5. Applied rewrites 75.6%

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

   if -9.99999999999999953e67 < (*.f64 z t) < 5e18

   1. Initial program 99.9%

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

   3. Taylor expanded in y around inf

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

      1. lower-/.f64 N/A

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

      2. +-commutative N/A

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

      3. lower-+.f64 N/A

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

      4. lower-/.f64 N/A

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

      5. *-commutative N/A

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

      6. lower-*.f64 N/A

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

      7. *-commutative N/A

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

      8. lower-*.f64 70.0

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

   5. Applied rewrites 70.0%

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

      1. lift-/.f64 N/A

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

      2. lift-+.f64 N/A

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

      3. div-add N/A

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

      4. lift-/.f64 N/A

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

      5. lift-*.f64 N/A

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

      6. lift-*.f64 N/A

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

      7. *-commutative N/A

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

      8. *-commutative N/A

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

      9. lower-+.f64 N/A

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

      10. lower-/.f64 N/A

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

      11. *-commutative N/A

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

      12. *-commutative N/A

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

      13. lift-*.f64 N/A

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

      14. lift-*.f64 N/A

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

      15. lift-/.f64 N/A

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

      16. lower-/.f64 70.0

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

   7. Applied rewrites 70.0%

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

   if 5e18 < (*.f64 z t)

   1. Initial program 93.1%

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

   3. Taylor expanded in z around inf

      \[\leadsto \color{blue}{\frac{\left(-1 \cdot \frac{x}{t} + -1 \cdot \frac{x \cdot {y}^{2}}{{t}^{3} \cdot {z}^{2}}\right) - \frac{x \cdot y}{{t}^{2} \cdot z}}{z}} \]

   5. Applied rewrites 69.4%

      \[\leadsto \color{blue}{\frac{\mathsf{fma}\left({\left(\frac{y}{z} \cdot -1\right)}^{2} \cdot \frac{x}{\left(t \cdot t\right) \cdot t}, -1, \frac{x}{t} \cdot -1\right) - \frac{y}{t \cdot t} \cdot \frac{x}{z}}{z}} \]
3. Recombined 3 regimes into one program.

4. Final simplification 70.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \cdot t \leq -1 \cdot 10^{+68}:\\ \;\;\;\;\frac{\mathsf{fma}\left(\left(-1 \cdot \frac{x}{\left(z \cdot z\right) \cdot z}\right) \cdot \left(-1 \cdot {\left(\frac{y}{t} \cdot -1\right)}^{2}\right), -1, \frac{x}{z} \cdot -1\right) - \left(-1 \cdot \frac{x}{z \cdot z}\right) \cdot \left(-1 \cdot \frac{y}{t}\right)}{t}\\ \mathbf{elif}\;z \cdot t \leq 5 \cdot 10^{+18}:\\ \;\;\;\;\frac{\frac{\left(z \cdot x\right) \cdot t}{y}}{y} - -1 \cdot \frac{x}{y}\\ \mathbf{else}:\\ \;\;\;\;\frac{\mathsf{fma}\left(\left(-1 \cdot {\left(\frac{y}{z} \cdot -1\right)}^{2}\right) \cdot \left(-1 \cdot \frac{x}{\left(t \cdot t\right) \cdot t}\right), -1, \frac{x}{t} \cdot -1\right) - \left(-1 \cdot \frac{y}{t \cdot t}\right) \cdot \left(-1 \cdot \frac{x}{z}\right)}{z}\\ \end{array} \]
5. Add Preprocessing

Alternative 7: 70.3% accurate, N/A× speedup

Math

\[\begin{array}{l} [x, y, z, t] = \mathsf{sort}([x, y, z, t])\\ \\ \begin{array}{l} \mathbf{if}\;z \cdot t \leq -1 \cdot 10^{+68} \lor \neg \left(z \cdot t \leq 5 \cdot 10^{+18}\right):\\ \;\;\;\;\frac{\mathsf{fma}\left(\left(-1 \cdot \frac{x}{\left(z \cdot z\right) \cdot z}\right) \cdot \left(-1 \cdot {\left(\frac{y}{t} \cdot -1\right)}^{2}\right), -1, \frac{x}{z} \cdot -1\right) - \left(-1 \cdot \frac{x}{z \cdot z}\right) \cdot \left(-1 \cdot \frac{y}{t}\right)}{t}\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{\left(z \cdot x\right) \cdot t}{y}}{y} - -1 \cdot \frac{x}{y}\\ \end{array} \end{array} \]

FPCore

NOTE: x, y, z, and t should be sorted in increasing order before calling this function.
(FPCore (x y z t)
 :precision binary64
 (if (or (<= (* z t) -1e+68) (not (<= (* z t) 5e+18)))
   (/
    (-
     (fma
      (* (* -1.0 (/ x (* (* z z) z))) (* -1.0 (pow (* (/ y t) -1.0) 2.0)))
      -1.0
      (* (/ x z) -1.0))
     (* (* -1.0 (/ x (* z z))) (* -1.0 (/ y t))))
    t)
   (- (/ (/ (* (* z x) t) y) y) (* -1.0 (/ x y)))))

C

assert(x < y && y < z && z < t);
double code(double x, double y, double z, double t) {
	double tmp;
	if (((z * t) <= -1e+68) || !((z * t) <= 5e+18)) {
		tmp = (fma(((-1.0 * (x / ((z * z) * z))) * (-1.0 * pow(((y / t) * -1.0), 2.0))), -1.0, ((x / z) * -1.0)) - ((-1.0 * (x / (z * z))) * (-1.0 * (y / t)))) / t;
	} else {
		tmp = ((((z * x) * t) / y) / y) - (-1.0 * (x / y));
	}
	return tmp;
}

Julia

x, y, z, t = sort([x, y, z, t])
function code(x, y, z, t)
	tmp = 0.0
	if ((Float64(z * t) <= -1e+68) || !(Float64(z * t) <= 5e+18))
		tmp = Float64(Float64(fma(Float64(Float64(-1.0 * Float64(x / Float64(Float64(z * z) * z))) * Float64(-1.0 * (Float64(Float64(y / t) * -1.0) ^ 2.0))), -1.0, Float64(Float64(x / z) * -1.0)) - Float64(Float64(-1.0 * Float64(x / Float64(z * z))) * Float64(-1.0 * Float64(y / t)))) / t);
	else
		tmp = Float64(Float64(Float64(Float64(Float64(z * x) * t) / y) / y) - Float64(-1.0 * Float64(x / y)));
	end
	return tmp
end

Wolfram

NOTE: x, y, z, and t should be sorted in increasing order before calling this function.
code[x_, y_, z_, t_] := If[Or[LessEqual[N[(z * t), $MachinePrecision], -1e+68], N[Not[LessEqual[N[(z * t), $MachinePrecision], 5e+18]], $MachinePrecision]], N[(N[(N[(N[(N[(-1.0 * N[(x / N[(N[(z * z), $MachinePrecision] * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * N[(-1.0 * N[Power[N[(N[(y / t), $MachinePrecision] * -1.0), $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * -1.0 + N[(N[(x / z), $MachinePrecision] * -1.0), $MachinePrecision]), $MachinePrecision] - N[(N[(-1.0 * N[(x / N[(z * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * N[(-1.0 * N[(y / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / t), $MachinePrecision], N[(N[(N[(N[(N[(z * x), $MachinePrecision] * t), $MachinePrecision] / y), $MachinePrecision] / y), $MachinePrecision] - N[(-1.0 * N[(x / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 z t) < -9.99999999999999953e67 or 5e18 < (*.f64 z t)

   1. Initial program 91.4%

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

   3. Taylor expanded in t around inf

      \[\leadsto \color{blue}{\frac{\left(-1 \cdot \frac{x}{z} + -1 \cdot \frac{x \cdot {y}^{2}}{{t}^{2} \cdot {z}^{3}}\right) - \frac{x \cdot y}{t \cdot {z}^{2}}}{t}} \]

   5. Applied rewrites 74.6%

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

   if -9.99999999999999953e67 < (*.f64 z t) < 5e18

   1. Initial program 99.9%

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

   3. Taylor expanded in y around inf

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

      1. lower-/.f64 N/A

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

      2. +-commutative N/A

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

      3. lower-+.f64 N/A

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

      4. lower-/.f64 N/A

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

      5. *-commutative N/A

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

      6. lower-*.f64 N/A

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

      7. *-commutative N/A

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

      8. lower-*.f64 70.0

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

   5. Applied rewrites 70.0%

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

      1. lift-/.f64 N/A

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

      2. lift-+.f64 N/A

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

      3. div-add N/A

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

      4. lift-/.f64 N/A

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

      5. lift-*.f64 N/A

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

      6. lift-*.f64 N/A

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

      7. *-commutative N/A

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

      8. *-commutative N/A

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

      9. lower-+.f64 N/A

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

      10. lower-/.f64 N/A

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

      11. *-commutative N/A

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

      12. *-commutative N/A

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

      13. lift-*.f64 N/A

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

      14. lift-*.f64 N/A

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

      15. lift-/.f64 N/A

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

      16. lower-/.f64 70.0

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

   7. Applied rewrites 70.0%

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

4. Final simplification 71.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \cdot t \leq -1 \cdot 10^{+68} \lor \neg \left(z \cdot t \leq 5 \cdot 10^{+18}\right):\\ \;\;\;\;\frac{\mathsf{fma}\left(\left(-1 \cdot \frac{x}{\left(z \cdot z\right) \cdot z}\right) \cdot \left(-1 \cdot {\left(\frac{y}{t} \cdot -1\right)}^{2}\right), -1, \frac{x}{z} \cdot -1\right) - \left(-1 \cdot \frac{x}{z \cdot z}\right) \cdot \left(-1 \cdot \frac{y}{t}\right)}{t}\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{\left(z \cdot x\right) \cdot t}{y}}{y} - -1 \cdot \frac{x}{y}\\ \end{array} \]
5. Add Preprocessing

Alternative 8: 47.7% accurate, N/A× speedup

Math

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

FPCore

NOTE: x, y, z, and t should be sorted in increasing order before calling this function.
(FPCore (x y z t) :precision binary64 (/ (+ (/ (* (* z x) t) y) x) y))

C

assert(x < y && y < z && z < t);
double code(double x, double y, double z, double t) {
	return ((((z * x) * t) / y) + x) / y;
}

Fortran

NOTE: x, y, z, and t should be sorted in increasing order before calling this function.
module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

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

Java

assert x < y && y < z && z < t;
public static double code(double x, double y, double z, double t) {
	return ((((z * x) * t) / y) + x) / y;
}

Python

[x, y, z, t] = sort([x, y, z, t])
def code(x, y, z, t):
	return ((((z * x) * t) / y) + x) / y

Julia

x, y, z, t = sort([x, y, z, t])
function code(x, y, z, t)
	return Float64(Float64(Float64(Float64(Float64(z * x) * t) / y) + x) / y)
end

MATLAB

x, y, z, t = num2cell(sort([x, y, z, t])){:}
function tmp = code(x, y, z, t)
	tmp = ((((z * x) * t) / y) + x) / y;
end

Wolfram

NOTE: x, y, z, and t should be sorted in increasing order before calling this function.
code[x_, y_, z_, t_] := N[(N[(N[(N[(N[(z * x), $MachinePrecision] * t), $MachinePrecision] / y), $MachinePrecision] + x), $MachinePrecision] / y), $MachinePrecision]

Derivation

1. Initial program 96.6%

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

3. Taylor expanded in y around inf

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

   1. lower-/.f64 N/A

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

   2. +-commutative N/A

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

   3. lower-+.f64 N/A

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

   4. lower-/.f64 N/A

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

   5. *-commutative N/A

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

   6. lower-*.f64 N/A

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

   7. *-commutative N/A

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

   8. lower-*.f64 46.3

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

5. Applied rewrites 46.3%

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

6. Final simplification 46.3%

   \[\leadsto \frac{\frac{\left(z \cdot x\right) \cdot t}{y} + x}{y} \]
7. Add Preprocessing

Alternative 9: 47.2% accurate, N/A× speedup

Math

\[\begin{array}{l} [x, y, z, t] = \mathsf{sort}([x, y, z, t])\\ \\ \frac{\frac{\left(z \cdot x\right) \cdot t}{y}}{y} - -1 \cdot \frac{x}{y} \end{array} \]

FPCore

NOTE: x, y, z, and t should be sorted in increasing order before calling this function.
(FPCore (x y z t)
 :precision binary64
 (- (/ (/ (* (* z x) t) y) y) (* -1.0 (/ x y))))

C

assert(x < y && y < z && z < t);
double code(double x, double y, double z, double t) {
	return ((((z * x) * t) / y) / y) - (-1.0 * (x / y));
}

Fortran

NOTE: x, y, z, and t should be sorted in increasing order before calling this function.
module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x, y, z, t)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    code = ((((z * x) * t) / y) / y) - ((-1.0d0) * (x / y))
end function

Java

assert x < y && y < z && z < t;
public static double code(double x, double y, double z, double t) {
	return ((((z * x) * t) / y) / y) - (-1.0 * (x / y));
}

Python

[x, y, z, t] = sort([x, y, z, t])
def code(x, y, z, t):
	return ((((z * x) * t) / y) / y) - (-1.0 * (x / y))

Julia

x, y, z, t = sort([x, y, z, t])
function code(x, y, z, t)
	return Float64(Float64(Float64(Float64(Float64(z * x) * t) / y) / y) - Float64(-1.0 * Float64(x / y)))
end

MATLAB

x, y, z, t = num2cell(sort([x, y, z, t])){:}
function tmp = code(x, y, z, t)
	tmp = ((((z * x) * t) / y) / y) - (-1.0 * (x / y));
end

Wolfram

NOTE: x, y, z, and t should be sorted in increasing order before calling this function.
code[x_, y_, z_, t_] := N[(N[(N[(N[(N[(z * x), $MachinePrecision] * t), $MachinePrecision] / y), $MachinePrecision] / y), $MachinePrecision] - N[(-1.0 * N[(x / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 96.6%

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

3. Taylor expanded in y around inf

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

   1. lower-/.f64 N/A

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

   2. +-commutative N/A

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

   3. lower-+.f64 N/A

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

   4. lower-/.f64 N/A

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

   5. *-commutative N/A

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

   6. lower-*.f64 N/A

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

   7. *-commutative N/A

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

   8. lower-*.f64 46.3

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

5. Applied rewrites 46.3%

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

   1. lift-/.f64 N/A

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

   2. lift-+.f64 N/A

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

   3. div-add N/A

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

   4. lift-/.f64 N/A

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

   5. lift-*.f64 N/A

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

   6. lift-*.f64 N/A

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

   7. *-commutative N/A

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

   8. *-commutative N/A

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

   9. lower-+.f64 N/A

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

   10. lower-/.f64 N/A

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

   11. *-commutative N/A

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

   12. *-commutative N/A

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

   13. lift-*.f64 N/A

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

   14. lift-*.f64 N/A

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

   15. lift-/.f64 N/A

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

   16. lower-/.f64 46.3

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

7. Applied rewrites 46.3%

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

8. Final simplification 46.3%

   \[\leadsto \frac{\frac{\left(z \cdot x\right) \cdot t}{y}}{y} - -1 \cdot \frac{x}{y} \]
9. Add Preprocessing

Reproduce

herbie shell --seed 2025065 
(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))))