Graphics.Rendering.Plot.Render.Plot.Axis:renderAxisLine from plot-0.2.3.4, A

Percentage Accurate: 98.3% → 98.3%

Time: 5.9s

Alternatives: 13

Speedup: 1.0×

Specification

Math

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

FPCore

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

C

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

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 98.3% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 98.3% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 98.1%

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

Alternative 2: 80.5% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{z - t}{z - a}\\ \mathbf{if}\;t\_1 \leq -2000000:\\ \;\;\;\;\mathsf{fma}\left(\frac{-t}{z}, y, x\right)\\ \mathbf{elif}\;t\_1 \leq 4 \cdot 10^{-11} \lor \neg \left(t\_1 \leq 10^{+40}\right):\\ \;\;\;\;\mathsf{fma}\left(\frac{y}{a}, t, x\right)\\ \mathbf{else}:\\ \;\;\;\;x + y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (/ (- z t) (- z a))))
   (if (<= t_1 -2000000.0)
     (fma (/ (- t) z) y x)
     (if (or (<= t_1 4e-11) (not (<= t_1 1e+40))) (fma (/ y a) t x) (+ x y)))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = (z - t) / (z - a);
	double tmp;
	if (t_1 <= -2000000.0) {
		tmp = fma((-t / z), y, x);
	} else if ((t_1 <= 4e-11) || !(t_1 <= 1e+40)) {
		tmp = fma((y / a), t, x);
	} else {
		tmp = x + y;
	}
	return tmp;
}

Julia

function code(x, y, z, t, a)
	t_1 = Float64(Float64(z - t) / Float64(z - a))
	tmp = 0.0
	if (t_1 <= -2000000.0)
		tmp = fma(Float64(Float64(-t) / z), y, x);
	elseif ((t_1 <= 4e-11) || !(t_1 <= 1e+40))
		tmp = fma(Float64(y / a), t, x);
	else
		tmp = Float64(x + y);
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(N[(z - t), $MachinePrecision] / N[(z - a), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, -2000000.0], N[(N[((-t) / z), $MachinePrecision] * y + x), $MachinePrecision], If[Or[LessEqual[t$95$1, 4e-11], N[Not[LessEqual[t$95$1, 1e+40]], $MachinePrecision]], N[(N[(y / a), $MachinePrecision] * t + x), $MachinePrecision], N[(x + y), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if (/.f64 (-.f64 z t) (-.f64 z a)) < -2e6

   1. Initial program 94.3%

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

   3. Taylor expanded in a around 0

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

      1. +-commutative N/A

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

      2. associate-/l* N/A

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

      3. *-commutative N/A

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

      4. div-sub N/A

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

      5. *-inverses N/A

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

      6. *-lft-identity N/A

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

      7. metadata-eval N/A

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

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

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

      9. lower-fma.f64 N/A

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

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

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

      11. *-inverses N/A

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

      12. metadata-eval N/A

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

      13. *-lft-identity N/A

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

      14. div-sub N/A

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

      15. lower-/.f64 N/A

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

      16. lower--.f64 63.9

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

   5. Applied rewrites 63.9%

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

   6. Taylor expanded in z around 0

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

      1. mul-1-neg N/A

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

      2. lower-neg.f64 62.5

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

   8. Applied rewrites 62.5%

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

   if -2e6 < (/.f64 (-.f64 z t) (-.f64 z a)) < 3.99999999999999976e-11 or 1.00000000000000003e40 < (/.f64 (-.f64 z t) (-.f64 z a))

   1. Initial program 97.4%

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

   3. Taylor expanded in z around 0

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

      1. +-commutative N/A

         \[\leadsto \frac{t \cdot y}{a} + \color{blue}{x} \]

      2. associate-/l* N/A

         \[\leadsto t \cdot \frac{y}{a} + x \]

      3. *-commutative N/A

         \[\leadsto \frac{y}{a} \cdot t + x \]

      4. lower-fma.f64 N/A

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

      5. lower-/.f64 78.0

         \[\leadsto \mathsf{fma}\left(\frac{y}{a}, t, x\right) \]

   5. Applied rewrites 78.0%

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

   if 3.99999999999999976e-11 < (/.f64 (-.f64 z t) (-.f64 z a)) < 1.00000000000000003e40

   1. Initial program 100.0%

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

   3. Taylor expanded in z around inf

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

   5. Applied rewrites 93.8%

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

4. Final simplification 82.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{z - t}{z - a} \leq -2000000:\\ \;\;\;\;\mathsf{fma}\left(\frac{-t}{z}, y, x\right)\\ \mathbf{elif}\;\frac{z - t}{z - a} \leq 4 \cdot 10^{-11} \lor \neg \left(\frac{z - t}{z - a} \leq 10^{+40}\right):\\ \;\;\;\;\mathsf{fma}\left(\frac{y}{a}, t, x\right)\\ \mathbf{else}:\\ \;\;\;\;x + y\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 80.6% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{z - t}{z - a}\\ \mathbf{if}\;t\_1 \leq -2000000:\\ \;\;\;\;\mathsf{fma}\left(\frac{-t}{z}, y, x\right)\\ \mathbf{elif}\;t\_1 \leq 5 \cdot 10^{-19}:\\ \;\;\;\;\mathsf{fma}\left(y, \frac{z}{-a}, x\right)\\ \mathbf{elif}\;t\_1 \leq 10^{+40}:\\ \;\;\;\;x + y\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(\frac{y}{a}, t, x\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (/ (- z t) (- z a))))
   (if (<= t_1 -2000000.0)
     (fma (/ (- t) z) y x)
     (if (<= t_1 5e-19)
       (fma y (/ z (- a)) x)
       (if (<= t_1 1e+40) (+ x y) (fma (/ y a) t x))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = (z - t) / (z - a);
	double tmp;
	if (t_1 <= -2000000.0) {
		tmp = fma((-t / z), y, x);
	} else if (t_1 <= 5e-19) {
		tmp = fma(y, (z / -a), x);
	} else if (t_1 <= 1e+40) {
		tmp = x + y;
	} else {
		tmp = fma((y / a), t, x);
	}
	return tmp;
}

Julia

function code(x, y, z, t, a)
	t_1 = Float64(Float64(z - t) / Float64(z - a))
	tmp = 0.0
	if (t_1 <= -2000000.0)
		tmp = fma(Float64(Float64(-t) / z), y, x);
	elseif (t_1 <= 5e-19)
		tmp = fma(y, Float64(z / Float64(-a)), x);
	elseif (t_1 <= 1e+40)
		tmp = Float64(x + y);
	else
		tmp = fma(Float64(y / a), t, x);
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(N[(z - t), $MachinePrecision] / N[(z - a), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, -2000000.0], N[(N[((-t) / z), $MachinePrecision] * y + x), $MachinePrecision], If[LessEqual[t$95$1, 5e-19], N[(y * N[(z / (-a)), $MachinePrecision] + x), $MachinePrecision], If[LessEqual[t$95$1, 1e+40], N[(x + y), $MachinePrecision], N[(N[(y / a), $MachinePrecision] * t + x), $MachinePrecision]]]]]

Derivation

1. Split input into 4 regimes

2. if (/.f64 (-.f64 z t) (-.f64 z a)) < -2e6

   1. Initial program 94.3%

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

   3. Taylor expanded in a around 0

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

      1. +-commutative N/A

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

      2. associate-/l* N/A

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

      3. *-commutative N/A

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

      4. div-sub N/A

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

      5. *-inverses N/A

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

      6. *-lft-identity N/A

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

      7. metadata-eval N/A

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

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

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

      9. lower-fma.f64 N/A

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

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

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

      11. *-inverses N/A

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

      12. metadata-eval N/A

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

      13. *-lft-identity N/A

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

      14. div-sub N/A

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

      15. lower-/.f64 N/A

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

      16. lower--.f64 63.9

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

   5. Applied rewrites 63.9%

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

   6. Taylor expanded in z around 0

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

      1. mul-1-neg N/A

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

      2. lower-neg.f64 62.5

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

   8. Applied rewrites 62.5%

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

   if -2e6 < (/.f64 (-.f64 z t) (-.f64 z a)) < 5.0000000000000004e-19

   1. Initial program 100.0%

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

   3. Taylor expanded in x around -inf

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

      1. associate-*r* N/A

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

      2. lower-*.f64 N/A

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

      3. mul-1-neg N/A

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

      4. lower-neg.f64 N/A

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

      5. metadata-eval N/A

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

      6. metadata-eval N/A

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

      7. fp-cancel-sign-sub N/A

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

      8. associate-*r/ N/A

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

      9. associate-*r* N/A

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

      10. times-frac N/A

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

      11. metadata-eval N/A

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

      12. lower-fma.f64 N/A

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

      13. mul-1-neg N/A

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

      14. lower-/.f64 N/A

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

      15. lower-neg.f64 N/A

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

      16. lower-/.f64 N/A

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

      17. lower--.f64 N/A

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

      18. lower--.f64 90.2

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

   5. Applied rewrites 90.2%

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

   6. Taylor expanded in t around 0

      \[\leadsto \color{blue}{x + \frac{y \cdot z}{z - a}} \]
   7. Step-by-step derivation

      1. associate-*r/ N/A

         \[\leadsto x + \frac{y \cdot z}{z - a} \]

      2. *-commutative N/A

         \[\leadsto x + \frac{y \cdot z}{z - a} \]

      3. +-commutative N/A

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

      4. associate-/l* N/A

         \[\leadsto y \cdot \frac{z}{z - a} + x \]

      5. lower-fma.f64 N/A

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

      6. lower-/.f64 N/A

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

      7. lower--.f64 87.4

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

   8. Applied rewrites 87.4%

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

   9. Taylor expanded in z around 0

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

       1. mul-1-neg N/A

          \[\leadsto \mathsf{fma}\left(y, \frac{z}{\mathsf{neg}\left(a\right)}, x\right) \]

       2. lower-neg.f64 86.2

          \[\leadsto \mathsf{fma}\left(y, \frac{z}{-a}, x\right) \]

   11. Applied rewrites 86.2%

       \[\leadsto \mathsf{fma}\left(y, \frac{z}{-a}, x\right) \]

   if 5.0000000000000004e-19 < (/.f64 (-.f64 z t) (-.f64 z a)) < 1.00000000000000003e40

   1. Initial program 100.0%

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

   3. Taylor expanded in z around inf

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

   5. Applied rewrites 92.3%

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

   if 1.00000000000000003e40 < (/.f64 (-.f64 z t) (-.f64 z a))

   1. Initial program 89.8%

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

   3. Taylor expanded in z around 0

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

      1. +-commutative N/A

         \[\leadsto \frac{t \cdot y}{a} + \color{blue}{x} \]

      2. associate-/l* N/A

         \[\leadsto t \cdot \frac{y}{a} + x \]

      3. *-commutative N/A

         \[\leadsto \frac{y}{a} \cdot t + x \]

      4. lower-fma.f64 N/A

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

      5. lower-/.f64 67.4

         \[\leadsto \mathsf{fma}\left(\frac{y}{a}, t, x\right) \]

   5. Applied rewrites 67.4%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{y}{a}, t, x\right)} \]
3. Recombined 4 regimes into one program.

4. Final simplification 83.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{z - t}{z - a} \leq -2000000:\\ \;\;\;\;\mathsf{fma}\left(\frac{-t}{z}, y, x\right)\\ \mathbf{elif}\;\frac{z - t}{z - a} \leq 5 \cdot 10^{-19}:\\ \;\;\;\;\mathsf{fma}\left(y, \frac{z}{-a}, x\right)\\ \mathbf{elif}\;\frac{z - t}{z - a} \leq 10^{+40}:\\ \;\;\;\;x + y\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(\frac{y}{a}, t, x\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 80.3% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{z - t}{z - a}\\ \mathbf{if}\;t\_1 \leq -2000000:\\ \;\;\;\;\mathsf{fma}\left(\frac{-t}{z}, y, x\right)\\ \mathbf{elif}\;t\_1 \leq 5 \cdot 10^{-19}:\\ \;\;\;\;x - \frac{y}{a} \cdot z\\ \mathbf{elif}\;t\_1 \leq 10^{+40}:\\ \;\;\;\;x + y\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(\frac{y}{a}, t, x\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (/ (- z t) (- z a))))
   (if (<= t_1 -2000000.0)
     (fma (/ (- t) z) y x)
     (if (<= t_1 5e-19)
       (- x (* (/ y a) z))
       (if (<= t_1 1e+40) (+ x y) (fma (/ y a) t x))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = (z - t) / (z - a);
	double tmp;
	if (t_1 <= -2000000.0) {
		tmp = fma((-t / z), y, x);
	} else if (t_1 <= 5e-19) {
		tmp = x - ((y / a) * z);
	} else if (t_1 <= 1e+40) {
		tmp = x + y;
	} else {
		tmp = fma((y / a), t, x);
	}
	return tmp;
}

Julia

function code(x, y, z, t, a)
	t_1 = Float64(Float64(z - t) / Float64(z - a))
	tmp = 0.0
	if (t_1 <= -2000000.0)
		tmp = fma(Float64(Float64(-t) / z), y, x);
	elseif (t_1 <= 5e-19)
		tmp = Float64(x - Float64(Float64(y / a) * z));
	elseif (t_1 <= 1e+40)
		tmp = Float64(x + y);
	else
		tmp = fma(Float64(y / a), t, x);
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(N[(z - t), $MachinePrecision] / N[(z - a), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, -2000000.0], N[(N[((-t) / z), $MachinePrecision] * y + x), $MachinePrecision], If[LessEqual[t$95$1, 5e-19], N[(x - N[(N[(y / a), $MachinePrecision] * z), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$1, 1e+40], N[(x + y), $MachinePrecision], N[(N[(y / a), $MachinePrecision] * t + x), $MachinePrecision]]]]]

Derivation

1. Split input into 4 regimes

2. if (/.f64 (-.f64 z t) (-.f64 z a)) < -2e6

   1. Initial program 94.3%

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

   3. Taylor expanded in a around 0

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

      1. +-commutative N/A

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

      2. associate-/l* N/A

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

      3. *-commutative N/A

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

      4. div-sub N/A

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

      5. *-inverses N/A

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

      6. *-lft-identity N/A

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

      7. metadata-eval N/A

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

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

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

      9. lower-fma.f64 N/A

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

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

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

      11. *-inverses N/A

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

      12. metadata-eval N/A

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

      13. *-lft-identity N/A

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

      14. div-sub N/A

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

      15. lower-/.f64 N/A

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

      16. lower--.f64 63.9

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

   5. Applied rewrites 63.9%

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

   6. Taylor expanded in z around 0

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

      1. mul-1-neg N/A

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

      2. lower-neg.f64 62.5

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

   8. Applied rewrites 62.5%

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

   if -2e6 < (/.f64 (-.f64 z t) (-.f64 z a)) < 5.0000000000000004e-19

   1. Initial program 100.0%

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

   3. Taylor expanded in x around -inf

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

      1. associate-*r* N/A

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

      2. lower-*.f64 N/A

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

      3. mul-1-neg N/A

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

      4. lower-neg.f64 N/A

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

      5. metadata-eval N/A

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

      6. metadata-eval N/A

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

      7. fp-cancel-sign-sub N/A

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

      8. associate-*r/ N/A

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

      9. associate-*r* N/A

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

      10. times-frac N/A

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

      11. metadata-eval N/A

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

      12. lower-fma.f64 N/A

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

      13. mul-1-neg N/A

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

      14. lower-/.f64 N/A

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

      15. lower-neg.f64 N/A

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

      16. lower-/.f64 N/A

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

      17. lower--.f64 N/A

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

      18. lower--.f64 90.2

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

   5. Applied rewrites 90.2%

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

   6. Taylor expanded in t around 0

      \[\leadsto \color{blue}{x + \frac{y \cdot z}{z - a}} \]
   7. Step-by-step derivation

      1. associate-*r/ N/A

         \[\leadsto x + \frac{y \cdot z}{z - a} \]

      2. *-commutative N/A

         \[\leadsto x + \frac{y \cdot z}{z - a} \]

      3. +-commutative N/A

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

      4. associate-/l* N/A

         \[\leadsto y \cdot \frac{z}{z - a} + x \]

      5. lower-fma.f64 N/A

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

      6. lower-/.f64 N/A

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

      7. lower--.f64 87.4

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

   8. Applied rewrites 87.4%

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

   9. Taylor expanded in z around 0

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

       1. mul-1-neg N/A

          \[\leadsto \mathsf{fma}\left(y, \frac{z}{\mathsf{neg}\left(a\right)}, x\right) \]

       2. lower-neg.f64 86.2

          \[\leadsto \mathsf{fma}\left(y, \frac{z}{-a}, x\right) \]

   11. Applied rewrites 86.2%

       \[\leadsto \mathsf{fma}\left(y, \frac{z}{-a}, x\right) \]

   12. Taylor expanded in z around 0

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

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

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

       2. metadata-eval N/A

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

       3. *-lft-identity N/A

          \[\leadsto x - \frac{y \cdot z}{a} \]

       4. lower--.f64 N/A

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

       5. *-lft-identity N/A

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

       6. metadata-eval N/A

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

       7. distribute-lft-neg-out N/A

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

       8. mul-1-neg N/A

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

       9. distribute-neg-frac N/A

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

       10. distribute-lft-neg-out N/A

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

       11. mul-1-neg N/A

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

       12. associate-*l/ N/A

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

       13. associate-*r/ N/A

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

       14. *-commutative N/A

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

       15. associate-*l* N/A

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

       16. distribute-rgt-neg-in N/A

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

       17. distribute-lft-neg-out N/A

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

       18. metadata-eval N/A

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

       19. *-lft-identity N/A

           \[\leadsto x - \frac{y}{a} \cdot z \]

       20. lower-*.f64 N/A

           \[\leadsto x - \frac{y}{a} \cdot z \]

       21. lower-/.f64 85.8

           \[\leadsto x - \frac{y}{a} \cdot z \]

   14. Applied rewrites 85.8%

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

   if 5.0000000000000004e-19 < (/.f64 (-.f64 z t) (-.f64 z a)) < 1.00000000000000003e40

   1. Initial program 100.0%

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

   3. Taylor expanded in z around inf

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

   5. Applied rewrites 92.3%

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

   if 1.00000000000000003e40 < (/.f64 (-.f64 z t) (-.f64 z a))

   1. Initial program 89.8%

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

   3. Taylor expanded in z around 0

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

      1. +-commutative N/A

         \[\leadsto \frac{t \cdot y}{a} + \color{blue}{x} \]

      2. associate-/l* N/A

         \[\leadsto t \cdot \frac{y}{a} + x \]

      3. *-commutative N/A

         \[\leadsto \frac{y}{a} \cdot t + x \]

      4. lower-fma.f64 N/A

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

      5. lower-/.f64 67.4

         \[\leadsto \mathsf{fma}\left(\frac{y}{a}, t, x\right) \]

   5. Applied rewrites 67.4%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{y}{a}, t, x\right)} \]
3. Recombined 4 regimes into one program.

4. Final simplification 83.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{z - t}{z - a} \leq -2000000:\\ \;\;\;\;\mathsf{fma}\left(\frac{-t}{z}, y, x\right)\\ \mathbf{elif}\;\frac{z - t}{z - a} \leq 5 \cdot 10^{-19}:\\ \;\;\;\;x - \frac{y}{a} \cdot z\\ \mathbf{elif}\;\frac{z - t}{z - a} \leq 10^{+40}:\\ \;\;\;\;x + y\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(\frac{y}{a}, t, x\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 70.8% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{z - t}{z - a}\\ t_2 := \frac{y \cdot t}{a}\\ \mathbf{if}\;t\_1 \leq -2 \cdot 10^{+37}:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;t\_1 \leq 10^{-105}:\\ \;\;\;\;x\\ \mathbf{elif}\;t\_1 \leq 2 \cdot 10^{+116}:\\ \;\;\;\;x + y\\ \mathbf{else}:\\ \;\;\;\;t\_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (/ (- z t) (- z a))) (t_2 (/ (* y t) a)))
   (if (<= t_1 -2e+37)
     t_2
     (if (<= t_1 1e-105) x (if (<= t_1 2e+116) (+ x y) t_2)))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = (z - t) / (z - a);
	double t_2 = (y * t) / a;
	double tmp;
	if (t_1 <= -2e+37) {
		tmp = t_2;
	} else if (t_1 <= 1e-105) {
		tmp = x;
	} else if (t_1 <= 2e+116) {
		tmp = x + y;
	} else {
		tmp = t_2;
	}
	return tmp;
}

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, a)
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), intent (in) :: a
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = (z - t) / (z - a)
    t_2 = (y * t) / a
    if (t_1 <= (-2d+37)) then
        tmp = t_2
    else if (t_1 <= 1d-105) then
        tmp = x
    else if (t_1 <= 2d+116) then
        tmp = x + y
    else
        tmp = t_2
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = (z - t) / (z - a);
	double t_2 = (y * t) / a;
	double tmp;
	if (t_1 <= -2e+37) {
		tmp = t_2;
	} else if (t_1 <= 1e-105) {
		tmp = x;
	} else if (t_1 <= 2e+116) {
		tmp = x + y;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = (z - t) / (z - a)
	t_2 = (y * t) / a
	tmp = 0
	if t_1 <= -2e+37:
		tmp = t_2
	elif t_1 <= 1e-105:
		tmp = x
	elif t_1 <= 2e+116:
		tmp = x + y
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(Float64(z - t) / Float64(z - a))
	t_2 = Float64(Float64(y * t) / a)
	tmp = 0.0
	if (t_1 <= -2e+37)
		tmp = t_2;
	elseif (t_1 <= 1e-105)
		tmp = x;
	elseif (t_1 <= 2e+116)
		tmp = Float64(x + y);
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = (z - t) / (z - a);
	t_2 = (y * t) / a;
	tmp = 0.0;
	if (t_1 <= -2e+37)
		tmp = t_2;
	elseif (t_1 <= 1e-105)
		tmp = x;
	elseif (t_1 <= 2e+116)
		tmp = x + y;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(N[(z - t), $MachinePrecision] / N[(z - a), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(y * t), $MachinePrecision] / a), $MachinePrecision]}, If[LessEqual[t$95$1, -2e+37], t$95$2, If[LessEqual[t$95$1, 1e-105], x, If[LessEqual[t$95$1, 2e+116], N[(x + y), $MachinePrecision], t$95$2]]]]]

Derivation

1. Split input into 3 regimes

2. if (/.f64 (-.f64 z t) (-.f64 z a)) < -1.99999999999999991e37 or 2.00000000000000003e116 < (/.f64 (-.f64 z t) (-.f64 z a))

   1. Initial program 90.2%

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

   3. Taylor expanded in x around -inf

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

      1. associate-*r* N/A

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

      2. lower-*.f64 N/A

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

      3. mul-1-neg N/A

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

      4. lower-neg.f64 N/A

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

      5. metadata-eval N/A

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

      6. metadata-eval N/A

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

      7. fp-cancel-sign-sub N/A

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

      8. associate-*r/ N/A

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

      9. associate-*r* N/A

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

      10. times-frac N/A

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

      11. metadata-eval N/A

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

      12. lower-fma.f64 N/A

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

      13. mul-1-neg N/A

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

      14. lower-/.f64 N/A

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

      15. lower-neg.f64 N/A

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

      16. lower-/.f64 N/A

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

      17. lower--.f64 N/A

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

      18. lower--.f64 78.6

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

   5. Applied rewrites 78.6%

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

   6. Taylor expanded in t around inf

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

      1. associate-*r/ N/A

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

      2. *-commutative N/A

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

      3. associate-/l* N/A

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

      4. associate-*r* N/A

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

      5. lower-*.f64 N/A

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

      6. mul-1-neg N/A

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

      7. lower-neg.f64 N/A

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

      8. lower-/.f64 N/A

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

      9. lower--.f64 84.3

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

   8. Applied rewrites 84.3%

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

   9. Taylor expanded in z around 0

      \[\leadsto \frac{t \cdot y}{\color{blue}{a}} \]
   10. Step-by-step derivation

       1. lower-/.f64 N/A

          \[\leadsto \frac{t \cdot y}{a} \]

       2. *-commutative N/A

          \[\leadsto \frac{y \cdot t}{a} \]

       3. lower-*.f64 46.3

          \[\leadsto \frac{y \cdot t}{a} \]

   11. Applied rewrites 46.3%

       \[\leadsto \frac{y \cdot t}{\color{blue}{a}} \]

   if -1.99999999999999991e37 < (/.f64 (-.f64 z t) (-.f64 z a)) < 9.99999999999999965e-106

   1. Initial program 100.0%

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

   3. Taylor expanded in x around inf

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

   5. Applied rewrites 73.3%

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

   if 9.99999999999999965e-106 < (/.f64 (-.f64 z t) (-.f64 z a)) < 2.00000000000000003e116

   1. Initial program 100.0%

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

   3. Taylor expanded in z around inf

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

   5. Applied rewrites 86.5%

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

4. Final simplification 75.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{z - t}{z - a} \leq -2 \cdot 10^{+37}:\\ \;\;\;\;\frac{y \cdot t}{a}\\ \mathbf{elif}\;\frac{z - t}{z - a} \leq 10^{-105}:\\ \;\;\;\;x\\ \mathbf{elif}\;\frac{z - t}{z - a} \leq 2 \cdot 10^{+116}:\\ \;\;\;\;x + y\\ \mathbf{else}:\\ \;\;\;\;\frac{y \cdot t}{a}\\ \end{array} \]
5. Add Preprocessing

Alternative 6: 83.6% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{z - t}{z - a}\\ \mathbf{if}\;t\_1 \leq -2000000 \lor \neg \left(t\_1 \leq 10^{+17}\right):\\ \;\;\;\;\left(-y\right) \cdot \frac{t}{z - a}\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(\frac{z}{z - a}, y, x\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (/ (- z t) (- z a))))
   (if (or (<= t_1 -2000000.0) (not (<= t_1 1e+17)))
     (* (- y) (/ t (- z a)))
     (fma (/ z (- z a)) y x))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = (z - t) / (z - a);
	double tmp;
	if ((t_1 <= -2000000.0) || !(t_1 <= 1e+17)) {
		tmp = -y * (t / (z - a));
	} else {
		tmp = fma((z / (z - a)), y, x);
	}
	return tmp;
}

Julia

function code(x, y, z, t, a)
	t_1 = Float64(Float64(z - t) / Float64(z - a))
	tmp = 0.0
	if ((t_1 <= -2000000.0) || !(t_1 <= 1e+17))
		tmp = Float64(Float64(-y) * Float64(t / Float64(z - a)));
	else
		tmp = fma(Float64(z / Float64(z - a)), y, x);
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(N[(z - t), $MachinePrecision] / N[(z - a), $MachinePrecision]), $MachinePrecision]}, If[Or[LessEqual[t$95$1, -2000000.0], N[Not[LessEqual[t$95$1, 1e+17]], $MachinePrecision]], N[((-y) * N[(t / N[(z - a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(z / N[(z - a), $MachinePrecision]), $MachinePrecision] * y + x), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 (-.f64 z t) (-.f64 z a)) < -2e6 or 1e17 < (/.f64 (-.f64 z t) (-.f64 z a))

   1. Initial program 92.9%

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

   3. Taylor expanded in t around inf

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

      1. mul-1-neg N/A

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

      2. *-commutative N/A

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

      3. associate-/l* N/A

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

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

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

      5. lower-*.f64 N/A

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

      6. lower-neg.f64 N/A

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

      7. lower-/.f64 N/A

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

      8. lower--.f64 71.0

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

   5. Applied rewrites 71.0%

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

   if -2e6 < (/.f64 (-.f64 z t) (-.f64 z a)) < 1e17

   1. Initial program 100.0%

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

   3. Taylor expanded in t around 0

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

      1. +-commutative N/A

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

      2. associate-/l* N/A

         \[\leadsto y \cdot \frac{z}{z - a} + x \]

      3. *-commutative N/A

         \[\leadsto \frac{z}{z - a} \cdot y + x \]

      4. lower-fma.f64 N/A

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

      5. lower-/.f64 N/A

         \[\leadsto \mathsf{fma}\left(\frac{z}{z - a}, y, x\right) \]

      6. lower--.f64 93.1

         \[\leadsto \mathsf{fma}\left(\frac{z}{z - a}, y, x\right) \]

   5. Applied rewrites 93.1%

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

4. Final simplification 87.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{z - t}{z - a} \leq -2000000 \lor \neg \left(\frac{z - t}{z - a} \leq 10^{+17}\right):\\ \;\;\;\;\left(-y\right) \cdot \frac{t}{z - a}\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(\frac{z}{z - a}, y, x\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 7: 83.1% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{z - t}{z - a}\\ \mathbf{if}\;t\_1 \leq -2000000:\\ \;\;\;\;\left(-y\right) \cdot \frac{t}{z - a}\\ \mathbf{elif}\;t\_1 \leq 2 \cdot 10^{+116}:\\ \;\;\;\;\mathsf{fma}\left(\frac{z}{z - a}, y, x\right)\\ \mathbf{else}:\\ \;\;\;\;\left(-t\right) \cdot \frac{y}{z - a}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (/ (- z t) (- z a))))
   (if (<= t_1 -2000000.0)
     (* (- y) (/ t (- z a)))
     (if (<= t_1 2e+116) (fma (/ z (- z a)) y x) (* (- t) (/ y (- z a)))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = (z - t) / (z - a);
	double tmp;
	if (t_1 <= -2000000.0) {
		tmp = -y * (t / (z - a));
	} else if (t_1 <= 2e+116) {
		tmp = fma((z / (z - a)), y, x);
	} else {
		tmp = -t * (y / (z - a));
	}
	return tmp;
}

Julia

function code(x, y, z, t, a)
	t_1 = Float64(Float64(z - t) / Float64(z - a))
	tmp = 0.0
	if (t_1 <= -2000000.0)
		tmp = Float64(Float64(-y) * Float64(t / Float64(z - a)));
	elseif (t_1 <= 2e+116)
		tmp = fma(Float64(z / Float64(z - a)), y, x);
	else
		tmp = Float64(Float64(-t) * Float64(y / Float64(z - a)));
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(N[(z - t), $MachinePrecision] / N[(z - a), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, -2000000.0], N[((-y) * N[(t / N[(z - a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$1, 2e+116], N[(N[(z / N[(z - a), $MachinePrecision]), $MachinePrecision] * y + x), $MachinePrecision], N[((-t) * N[(y / N[(z - a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if (/.f64 (-.f64 z t) (-.f64 z a)) < -2e6

   1. Initial program 94.3%

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

   3. Taylor expanded in t around inf

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

      1. mul-1-neg N/A

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

      2. *-commutative N/A

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

      3. associate-/l* N/A

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

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

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

      5. lower-*.f64 N/A

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

      6. lower-neg.f64 N/A

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

      7. lower-/.f64 N/A

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

      8. lower--.f64 78.9

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

   5. Applied rewrites 78.9%

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

   if -2e6 < (/.f64 (-.f64 z t) (-.f64 z a)) < 2.00000000000000003e116

   1. Initial program 100.0%

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

   3. Taylor expanded in t around 0

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

      1. +-commutative N/A

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

      2. associate-/l* N/A

         \[\leadsto y \cdot \frac{z}{z - a} + x \]

      3. *-commutative N/A

         \[\leadsto \frac{z}{z - a} \cdot y + x \]

      4. lower-fma.f64 N/A

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

      5. lower-/.f64 N/A

         \[\leadsto \mathsf{fma}\left(\frac{z}{z - a}, y, x\right) \]

      6. lower--.f64 89.7

         \[\leadsto \mathsf{fma}\left(\frac{z}{z - a}, y, x\right) \]

   5. Applied rewrites 89.7%

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

   if 2.00000000000000003e116 < (/.f64 (-.f64 z t) (-.f64 z a))

   1. Initial program 83.3%

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

   3. Taylor expanded in x around -inf

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

      1. associate-*r* N/A

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

      2. lower-*.f64 N/A

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

      3. mul-1-neg N/A

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

      4. lower-neg.f64 N/A

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

      5. metadata-eval N/A

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

      6. metadata-eval N/A

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

      7. fp-cancel-sign-sub N/A

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

      8. associate-*r/ N/A

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

      9. associate-*r* N/A

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

      10. times-frac N/A

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

      11. metadata-eval N/A

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

      12. lower-fma.f64 N/A

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

      13. mul-1-neg N/A

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

      14. lower-/.f64 N/A

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

      15. lower-neg.f64 N/A

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

      16. lower-/.f64 N/A

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

      17. lower--.f64 N/A

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

      18. lower--.f64 72.7

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

   5. Applied rewrites 72.7%

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

   6. Taylor expanded in t around inf

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

      1. associate-*r/ N/A

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

      2. *-commutative N/A

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

      3. associate-/l* N/A

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

      4. associate-*r* N/A

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

      5. lower-*.f64 N/A

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

      6. mul-1-neg N/A

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

      7. lower-neg.f64 N/A

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

      8. lower-/.f64 N/A

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

      9. lower--.f64 89.1

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

   8. Applied rewrites 89.1%

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

4. Final simplification 88.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{z - t}{z - a} \leq -2000000:\\ \;\;\;\;\left(-y\right) \cdot \frac{t}{z - a}\\ \mathbf{elif}\;\frac{z - t}{z - a} \leq 2 \cdot 10^{+116}:\\ \;\;\;\;\mathsf{fma}\left(\frac{z}{z - a}, y, x\right)\\ \mathbf{else}:\\ \;\;\;\;\left(-t\right) \cdot \frac{y}{z - a}\\ \end{array} \]
5. Add Preprocessing

Alternative 8: 81.2% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{z - t}{z - a}\\ \mathbf{if}\;t\_1 \leq -2000000:\\ \;\;\;\;\mathsf{fma}\left(\frac{-t}{z}, y, x\right)\\ \mathbf{elif}\;t\_1 \leq 10^{+40}:\\ \;\;\;\;\mathsf{fma}\left(\frac{z}{z - a}, y, x\right)\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(\frac{y}{a}, t, x\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (/ (- z t) (- z a))))
   (if (<= t_1 -2000000.0)
     (fma (/ (- t) z) y x)
     (if (<= t_1 1e+40) (fma (/ z (- z a)) y x) (fma (/ y a) t x)))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = (z - t) / (z - a);
	double tmp;
	if (t_1 <= -2000000.0) {
		tmp = fma((-t / z), y, x);
	} else if (t_1 <= 1e+40) {
		tmp = fma((z / (z - a)), y, x);
	} else {
		tmp = fma((y / a), t, x);
	}
	return tmp;
}

Julia

function code(x, y, z, t, a)
	t_1 = Float64(Float64(z - t) / Float64(z - a))
	tmp = 0.0
	if (t_1 <= -2000000.0)
		tmp = fma(Float64(Float64(-t) / z), y, x);
	elseif (t_1 <= 1e+40)
		tmp = fma(Float64(z / Float64(z - a)), y, x);
	else
		tmp = fma(Float64(y / a), t, x);
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(N[(z - t), $MachinePrecision] / N[(z - a), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, -2000000.0], N[(N[((-t) / z), $MachinePrecision] * y + x), $MachinePrecision], If[LessEqual[t$95$1, 1e+40], N[(N[(z / N[(z - a), $MachinePrecision]), $MachinePrecision] * y + x), $MachinePrecision], N[(N[(y / a), $MachinePrecision] * t + x), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if (/.f64 (-.f64 z t) (-.f64 z a)) < -2e6

   1. Initial program 94.3%

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

   3. Taylor expanded in a around 0

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

      1. +-commutative N/A

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

      2. associate-/l* N/A

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

      3. *-commutative N/A

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

      4. div-sub N/A

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

      5. *-inverses N/A

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

      6. *-lft-identity N/A

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

      7. metadata-eval N/A

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

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

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

      9. lower-fma.f64 N/A

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

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

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

      11. *-inverses N/A

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

      12. metadata-eval N/A

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

      13. *-lft-identity N/A

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

      14. div-sub N/A

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

      15. lower-/.f64 N/A

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

      16. lower--.f64 63.9

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

   5. Applied rewrites 63.9%

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

   6. Taylor expanded in z around 0

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

      1. mul-1-neg N/A

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

      2. lower-neg.f64 62.5

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

   8. Applied rewrites 62.5%

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

   if -2e6 < (/.f64 (-.f64 z t) (-.f64 z a)) < 1.00000000000000003e40

   1. Initial program 100.0%

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

   3. Taylor expanded in t around 0

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

      1. +-commutative N/A

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

      2. associate-/l* N/A

         \[\leadsto y \cdot \frac{z}{z - a} + x \]

      3. *-commutative N/A

         \[\leadsto \frac{z}{z - a} \cdot y + x \]

      4. lower-fma.f64 N/A

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

      5. lower-/.f64 N/A

         \[\leadsto \mathsf{fma}\left(\frac{z}{z - a}, y, x\right) \]

      6. lower--.f64 91.3

         \[\leadsto \mathsf{fma}\left(\frac{z}{z - a}, y, x\right) \]

   5. Applied rewrites 91.3%

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

   if 1.00000000000000003e40 < (/.f64 (-.f64 z t) (-.f64 z a))

   1. Initial program 89.8%

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

   3. Taylor expanded in z around 0

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

      1. +-commutative N/A

         \[\leadsto \frac{t \cdot y}{a} + \color{blue}{x} \]

      2. associate-/l* N/A

         \[\leadsto t \cdot \frac{y}{a} + x \]

      3. *-commutative N/A

         \[\leadsto \frac{y}{a} \cdot t + x \]

      4. lower-fma.f64 N/A

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

      5. lower-/.f64 67.4

         \[\leadsto \mathsf{fma}\left(\frac{y}{a}, t, x\right) \]

   5. Applied rewrites 67.4%

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

4. Final simplification 84.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{z - t}{z - a} \leq -2000000:\\ \;\;\;\;\mathsf{fma}\left(\frac{-t}{z}, y, x\right)\\ \mathbf{elif}\;\frac{z - t}{z - a} \leq 10^{+40}:\\ \;\;\;\;\mathsf{fma}\left(\frac{z}{z - a}, y, x\right)\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(\frac{y}{a}, t, x\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 9: 80.7% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{z - t}{z - a}\\ \mathbf{if}\;t\_1 \leq 4 \cdot 10^{-11} \lor \neg \left(t\_1 \leq 10^{+40}\right):\\ \;\;\;\;\mathsf{fma}\left(\frac{y}{a}, t, x\right)\\ \mathbf{else}:\\ \;\;\;\;x + y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (/ (- z t) (- z a))))
   (if (or (<= t_1 4e-11) (not (<= t_1 1e+40))) (fma (/ y a) t x) (+ x y))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = (z - t) / (z - a);
	double tmp;
	if ((t_1 <= 4e-11) || !(t_1 <= 1e+40)) {
		tmp = fma((y / a), t, x);
	} else {
		tmp = x + y;
	}
	return tmp;
}

Julia

function code(x, y, z, t, a)
	t_1 = Float64(Float64(z - t) / Float64(z - a))
	tmp = 0.0
	if ((t_1 <= 4e-11) || !(t_1 <= 1e+40))
		tmp = fma(Float64(y / a), t, x);
	else
		tmp = Float64(x + y);
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(N[(z - t), $MachinePrecision] / N[(z - a), $MachinePrecision]), $MachinePrecision]}, If[Or[LessEqual[t$95$1, 4e-11], N[Not[LessEqual[t$95$1, 1e+40]], $MachinePrecision]], N[(N[(y / a), $MachinePrecision] * t + x), $MachinePrecision], N[(x + y), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 (-.f64 z t) (-.f64 z a)) < 3.99999999999999976e-11 or 1.00000000000000003e40 < (/.f64 (-.f64 z t) (-.f64 z a))

   1. Initial program 96.6%

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

   3. Taylor expanded in z around 0

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

      1. +-commutative N/A

         \[\leadsto \frac{t \cdot y}{a} + \color{blue}{x} \]

      2. associate-/l* N/A

         \[\leadsto t \cdot \frac{y}{a} + x \]

      3. *-commutative N/A

         \[\leadsto \frac{y}{a} \cdot t + x \]

      4. lower-fma.f64 N/A

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

      5. lower-/.f64 70.2

         \[\leadsto \mathsf{fma}\left(\frac{y}{a}, t, x\right) \]

   5. Applied rewrites 70.2%

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

   if 3.99999999999999976e-11 < (/.f64 (-.f64 z t) (-.f64 z a)) < 1.00000000000000003e40

   1. Initial program 100.0%

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

   3. Taylor expanded in z around inf

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

   5. Applied rewrites 93.8%

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

4. Final simplification 80.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{z - t}{z - a} \leq 4 \cdot 10^{-11} \lor \neg \left(\frac{z - t}{z - a} \leq 10^{+40}\right):\\ \;\;\;\;\mathsf{fma}\left(\frac{y}{a}, t, x\right)\\ \mathbf{else}:\\ \;\;\;\;x + y\\ \end{array} \]
5. Add Preprocessing

Alternative 10: 82.0% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -1.02 \cdot 10^{-53}:\\ \;\;\;\;\mathsf{fma}\left(\frac{z}{z - a}, y, x\right)\\ \mathbf{elif}\;z \leq 310:\\ \;\;\;\;x + y \cdot \frac{t}{a}\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(\frac{z - t}{z}, y, x\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= z -1.02e-53)
   (fma (/ z (- z a)) y x)
   (if (<= z 310.0) (+ x (* y (/ t a))) (fma (/ (- z t) z) y x))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -1.02e-53) {
		tmp = fma((z / (z - a)), y, x);
	} else if (z <= 310.0) {
		tmp = x + (y * (t / a));
	} else {
		tmp = fma(((z - t) / z), y, x);
	}
	return tmp;
}

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (z <= -1.02e-53)
		tmp = fma(Float64(z / Float64(z - a)), y, x);
	elseif (z <= 310.0)
		tmp = Float64(x + Float64(y * Float64(t / a)));
	else
		tmp = fma(Float64(Float64(z - t) / z), y, x);
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[z, -1.02e-53], N[(N[(z / N[(z - a), $MachinePrecision]), $MachinePrecision] * y + x), $MachinePrecision], If[LessEqual[z, 310.0], N[(x + N[(y * N[(t / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(N[(z - t), $MachinePrecision] / z), $MachinePrecision] * y + x), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if z < -1.02000000000000002e-53

   1. Initial program 99.9%

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

   3. Taylor expanded in t around 0

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

      1. +-commutative N/A

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

      2. associate-/l* N/A

         \[\leadsto y \cdot \frac{z}{z - a} + x \]

      3. *-commutative N/A

         \[\leadsto \frac{z}{z - a} \cdot y + x \]

      4. lower-fma.f64 N/A

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

      5. lower-/.f64 N/A

         \[\leadsto \mathsf{fma}\left(\frac{z}{z - a}, y, x\right) \]

      6. lower--.f64 90.5

         \[\leadsto \mathsf{fma}\left(\frac{z}{z - a}, y, x\right) \]

   5. Applied rewrites 90.5%

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

   if -1.02000000000000002e-53 < z < 310

   1. Initial program 95.5%

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

   3. Taylor expanded in z around 0

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

      1. lower-/.f64 78.0

         \[\leadsto x + y \cdot \frac{t}{\color{blue}{a}} \]

   5. Applied rewrites 78.0%

      \[\leadsto x + y \cdot \color{blue}{\frac{t}{a}} \]

   if 310 < z

   1. Initial program 100.0%

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

   3. Taylor expanded in a around 0

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

      1. +-commutative N/A

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

      2. associate-/l* N/A

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

      3. *-commutative N/A

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

      4. div-sub N/A

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

      5. *-inverses N/A

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

      6. *-lft-identity N/A

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

      7. metadata-eval N/A

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

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

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

      9. lower-fma.f64 N/A

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

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

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

      11. *-inverses N/A

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

      12. metadata-eval N/A

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

      13. *-lft-identity N/A

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

      14. div-sub N/A

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

      15. lower-/.f64 N/A

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

      16. lower--.f64 89.6

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

   5. Applied rewrites 89.6%

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

4. Final simplification 85.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -1.02 \cdot 10^{-53}:\\ \;\;\;\;\mathsf{fma}\left(\frac{z}{z - a}, y, x\right)\\ \mathbf{elif}\;z \leq 310:\\ \;\;\;\;x + y \cdot \frac{t}{a}\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(\frac{z - t}{z}, y, x\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 11: 66.7% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\frac{z - t}{z - a} \leq 3.6 \cdot 10^{-75}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;x + y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= (/ (- z t) (- z a)) 3.6e-75) x (+ x y)))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (((z - t) / (z - a)) <= 3.6e-75) {
		tmp = x;
	} else {
		tmp = x + y;
	}
	return tmp;
}

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, a)
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), intent (in) :: a
    real(8) :: tmp
    if (((z - t) / (z - a)) <= 3.6d-75) then
        tmp = x
    else
        tmp = x + y
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (((z - t) / (z - a)) <= 3.6e-75) {
		tmp = x;
	} else {
		tmp = x + y;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if ((z - t) / (z - a)) <= 3.6e-75:
		tmp = x
	else:
		tmp = x + y
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (Float64(Float64(z - t) / Float64(z - a)) <= 3.6e-75)
		tmp = x;
	else
		tmp = Float64(x + y);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (((z - t) / (z - a)) <= 3.6e-75)
		tmp = x;
	else
		tmp = x + y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[N[(N[(z - t), $MachinePrecision] / N[(z - a), $MachinePrecision]), $MachinePrecision], 3.6e-75], x, N[(x + y), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 (-.f64 z t) (-.f64 z a)) < 3.6e-75

   1. Initial program 98.0%

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

   3. Taylor expanded in x around inf

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

   5. Applied rewrites 56.3%

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

   if 3.6e-75 < (/.f64 (-.f64 z t) (-.f64 z a))

   1. Initial program 98.1%

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

   3. Taylor expanded in z around inf

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

   5. Applied rewrites 78.2%

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

4. Final simplification 69.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{z - t}{z - a} \leq 3.6 \cdot 10^{-75}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;x + y\\ \end{array} \]
5. Add Preprocessing

Alternative 12: 53.5% accurate, 2.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -3.2 \cdot 10^{+125} \lor \neg \left(y \leq 7.2 \cdot 10^{+201}\right):\\ \;\;\;\;y\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= y -3.2e+125) (not (<= y 7.2e+201))) y x))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((y <= -3.2e+125) || !(y <= 7.2e+201)) {
		tmp = y;
	} else {
		tmp = x;
	}
	return tmp;
}

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, a)
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), intent (in) :: a
    real(8) :: tmp
    if ((y <= (-3.2d+125)) .or. (.not. (y <= 7.2d+201))) then
        tmp = y
    else
        tmp = x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((y <= -3.2e+125) || !(y <= 7.2e+201)) {
		tmp = y;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (y <= -3.2e+125) or not (y <= 7.2e+201):
		tmp = y
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((y <= -3.2e+125) || !(y <= 7.2e+201))
		tmp = y;
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((y <= -3.2e+125) || ~((y <= 7.2e+201)))
		tmp = y;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[y, -3.2e+125], N[Not[LessEqual[y, 7.2e+201]], $MachinePrecision]], y, x]

Derivation

1. Split input into 2 regimes

2. if y < -3.19999999999999983e125 or 7.19999999999999951e201 < y

   1. Initial program 100.0%

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

   3. Taylor expanded in a around 0

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

      1. +-commutative N/A

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

      2. associate-/l* N/A

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

      3. *-commutative N/A

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

      4. div-sub N/A

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

      5. *-inverses N/A

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

      6. *-lft-identity N/A

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

      7. metadata-eval N/A

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

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

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

      9. lower-fma.f64 N/A

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

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

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

      11. *-inverses N/A

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

      12. metadata-eval N/A

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

      13. *-lft-identity N/A

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

      14. div-sub N/A

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

      15. lower-/.f64 N/A

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

      16. lower--.f64 70.8

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

   5. Applied rewrites 70.8%

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

   6. Taylor expanded in x around 0

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

      1. associate-/l* N/A

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

      2. *-commutative N/A

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

      3. lower-*.f64 N/A

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

      4. lower-/.f64 N/A

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

      5. lower--.f64 66.1

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

   8. Applied rewrites 66.1%

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

   9. Taylor expanded in z around inf

      \[\leadsto y \]
   10. Step-by-step derivation

   11. Applied rewrites 43.2%

       \[\leadsto y \]

   if -3.19999999999999983e125 < y < 7.19999999999999951e201

   1. Initial program 97.5%

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

   3. Taylor expanded in x around inf

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

   5. Applied rewrites 64.5%

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

4. Final simplification 59.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -3.2 \cdot 10^{+125} \lor \neg \left(y \leq 7.2 \cdot 10^{+201}\right):\\ \;\;\;\;y\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]
5. Add Preprocessing

Alternative 13: 50.7% accurate, 26.0× speedup

Math

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

FPCore

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

C

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

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, a)
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), intent (in) :: a
    code = x
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

code[x_, y_, z_, t_, a_] := x

Derivation

1. Initial program 98.1%

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

3. Taylor expanded in x around inf

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

5. Applied rewrites 52.0%

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

6. Final simplification 52.0%

   \[\leadsto x \]
7. Add Preprocessing

Developer Target 1: 98.4% accurate, 0.8× speedup

Math

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

FPCore

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

C

double code(double x, double y, double z, double t, double a) {
	return x + (y / ((z - a) / (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, a)
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), intent (in) :: a
    code = x + (y / ((z - a) / (z - t)))
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Reproduce

herbie shell --seed 2025026 
(FPCore (x y z t a)
  :name "Graphics.Rendering.Plot.Render.Plot.Axis:renderAxisLine from plot-0.2.3.4, A"
  :precision binary64

  :alt
  (! :herbie-platform default (+ x (/ y (/ (- z a) (- z t)))))

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