Graphics.Rendering.Plot.Render.Plot.Axis:renderAxisTick from plot-0.2.3.4, B

Percentage Accurate: 76.8% → 93.2%

Time: 4.3s

Alternatives: 13

Speedup: 0.9×

Specification

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 76.8% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 93.2% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(\left(\left(\frac{t}{a - t} + 1\right) + \frac{x}{y}\right) - \frac{z}{a - t}\right) \cdot y\\ \mathbf{if}\;y \leq -1.18 \cdot 10^{-95}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;y \leq 2.6 \cdot 10^{-32}:\\ \;\;\;\;\left(x + y\right) - \frac{\left(z - t\right) \cdot y}{a - t}\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* (- (+ (+ (/ t (- a t)) 1.0) (/ x y)) (/ z (- a t))) y)))
   (if (<= y -1.18e-95)
     t_1
     (if (<= y 2.6e-32) (- (+ x y) (/ (* (- z t) y) (- a t))) t_1))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = ((((t / (a - t)) + 1.0) + (x / y)) - (z / (a - t))) * y;
	double tmp;
	if (y <= -1.18e-95) {
		tmp = t_1;
	} else if (y <= 2.6e-32) {
		tmp = (x + y) - (((z - t) * y) / (a - t));
	} else {
		tmp = t_1;
	}
	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) :: tmp
    t_1 = ((((t / (a - t)) + 1.0d0) + (x / y)) - (z / (a - t))) * y
    if (y <= (-1.18d-95)) then
        tmp = t_1
    else if (y <= 2.6d-32) then
        tmp = (x + y) - (((z - t) * y) / (a - t))
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = ((((t / (a - t)) + 1.0) + (x / y)) - (z / (a - t))) * y;
	double tmp;
	if (y <= -1.18e-95) {
		tmp = t_1;
	} else if (y <= 2.6e-32) {
		tmp = (x + y) - (((z - t) * y) / (a - t));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = ((((t / (a - t)) + 1.0) + (x / y)) - (z / (a - t))) * y
	tmp = 0
	if y <= -1.18e-95:
		tmp = t_1
	elif y <= 2.6e-32:
		tmp = (x + y) - (((z - t) * y) / (a - t))
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(Float64(Float64(Float64(Float64(t / Float64(a - t)) + 1.0) + Float64(x / y)) - Float64(z / Float64(a - t))) * y)
	tmp = 0.0
	if (y <= -1.18e-95)
		tmp = t_1;
	elseif (y <= 2.6e-32)
		tmp = Float64(Float64(x + y) - Float64(Float64(Float64(z - t) * y) / Float64(a - t)));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = ((((t / (a - t)) + 1.0) + (x / y)) - (z / (a - t))) * y;
	tmp = 0.0;
	if (y <= -1.18e-95)
		tmp = t_1;
	elseif (y <= 2.6e-32)
		tmp = (x + y) - (((z - t) * y) / (a - t));
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(N[(N[(N[(N[(t / N[(a - t), $MachinePrecision]), $MachinePrecision] + 1.0), $MachinePrecision] + N[(x / y), $MachinePrecision]), $MachinePrecision] - N[(z / N[(a - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * y), $MachinePrecision]}, If[LessEqual[y, -1.18e-95], t$95$1, If[LessEqual[y, 2.6e-32], N[(N[(x + y), $MachinePrecision] - N[(N[(N[(z - t), $MachinePrecision] * y), $MachinePrecision] / N[(a - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if y < -1.17999999999999993e-95 or 2.5999999999999997e-32 < y

   1. Initial program 76.8%

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

   2. Taylor expanded in y around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. lower--.f64 N/A

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

      4. associate-+r+ N/A

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

      5. lower-+.f64 N/A

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

      6. +-commutative N/A

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

      7. lower-+.f64 N/A

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

      8. lower-/.f64 N/A

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

      9. lift--.f64 N/A

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

      10. lower-/.f64 N/A

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

      11. lower-/.f64 N/A

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

      12. lift--.f64 82.6

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

   4. Applied rewrites 82.6%

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

   if -1.17999999999999993e-95 < y < 2.5999999999999997e-32

   1. Initial program 76.8%

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

Alternative 2: 92.3% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \mathsf{fma}\left(1 + \frac{t - z}{a - t}, y, x\right)\\ t_2 := \left(x + y\right) - \frac{\left(z - t\right) \cdot y}{a - t}\\ \mathbf{if}\;t\_2 \leq -2 \cdot 10^{-242}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;t\_2 \leq 0:\\ \;\;\;\;x + \frac{y \cdot \left(z - a\right)}{t}\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (fma (+ 1.0 (/ (- t z) (- a t))) y x))
        (t_2 (- (+ x y) (/ (* (- z t) y) (- a t)))))
   (if (<= t_2 -2e-242) t_1 (if (<= t_2 0.0) (+ x (/ (* y (- z a)) t)) t_1))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = fma((1.0 + ((t - z) / (a - t))), y, x);
	double t_2 = (x + y) - (((z - t) * y) / (a - t));
	double tmp;
	if (t_2 <= -2e-242) {
		tmp = t_1;
	} else if (t_2 <= 0.0) {
		tmp = x + ((y * (z - a)) / t);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Julia

function code(x, y, z, t, a)
	t_1 = fma(Float64(1.0 + Float64(Float64(t - z) / Float64(a - t))), y, x)
	t_2 = Float64(Float64(x + y) - Float64(Float64(Float64(z - t) * y) / Float64(a - t)))
	tmp = 0.0
	if (t_2 <= -2e-242)
		tmp = t_1;
	elseif (t_2 <= 0.0)
		tmp = Float64(x + Float64(Float64(y * Float64(z - a)) / t));
	else
		tmp = t_1;
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(N[(1.0 + N[(N[(t - z), $MachinePrecision] / N[(a - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * y + x), $MachinePrecision]}, Block[{t$95$2 = N[(N[(x + y), $MachinePrecision] - N[(N[(N[(z - t), $MachinePrecision] * y), $MachinePrecision] / N[(a - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$2, -2e-242], t$95$1, If[LessEqual[t$95$2, 0.0], N[(x + N[(N[(y * N[(z - a), $MachinePrecision]), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision], t$95$1]]]]

Derivation

1. Split input into 2 regimes

2. if (-.f64 (+.f64 x y) (/.f64 (*.f64 (-.f64 z t) y) (-.f64 a t))) < -2e-242 or 0.0 < (-.f64 (+.f64 x y) (/.f64 (*.f64 (-.f64 z t) y) (-.f64 a t)))

   1. Initial program 76.8%

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

   2. Taylor expanded in y around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

      4. associate--l+ N/A

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

      5. lower-+.f64 N/A

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

      6. sub-div N/A

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

      7. lower-/.f64 N/A

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

      8. lower--.f64 N/A

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

      9. lift--.f64 89.4

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

   4. Applied rewrites 89.4%

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

   if -2e-242 < (-.f64 (+.f64 x y) (/.f64 (*.f64 (-.f64 z t) y) (-.f64 a t))) < 0.0

   1. Initial program 76.8%

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

   2. Taylor expanded in y around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

      4. associate--l+ N/A

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

      5. lower-+.f64 N/A

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

      6. sub-div N/A

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

      7. lower-/.f64 N/A

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

      8. lower--.f64 N/A

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

      9. lift--.f64 89.4

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

   4. Applied rewrites 89.4%

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

   5. Taylor expanded in t around -inf

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

      1. lower-+.f64 N/A

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

      2. lower-/.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower--.f64 57.1

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

   7. Applied rewrites 57.1%

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

Alternative 3: 89.2% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \mathsf{fma}\left(\frac{z - a}{t}, y, x\right)\\ \mathbf{if}\;t \leq -1.35 \cdot 10^{+130}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;t \leq 5.2 \cdot 10^{+173}:\\ \;\;\;\;\left(x + y\right) - y \cdot \frac{z}{a - t}\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (fma (/ (- z a) t) y x)))
   (if (<= t -1.35e+130)
     t_1
     (if (<= t 5.2e+173) (- (+ x y) (* y (/ z (- a t)))) t_1))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = fma(((z - a) / t), y, x);
	double tmp;
	if (t <= -1.35e+130) {
		tmp = t_1;
	} else if (t <= 5.2e+173) {
		tmp = (x + y) - (y * (z / (a - t)));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Julia

function code(x, y, z, t, a)
	t_1 = fma(Float64(Float64(z - a) / t), y, x)
	tmp = 0.0
	if (t <= -1.35e+130)
		tmp = t_1;
	elseif (t <= 5.2e+173)
		tmp = Float64(Float64(x + y) - Float64(y * Float64(z / Float64(a - t))));
	else
		tmp = t_1;
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(N[(N[(z - a), $MachinePrecision] / t), $MachinePrecision] * y + x), $MachinePrecision]}, If[LessEqual[t, -1.35e+130], t$95$1, If[LessEqual[t, 5.2e+173], N[(N[(x + y), $MachinePrecision] - N[(y * N[(z / N[(a - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if t < -1.3499999999999999e130 or 5.1999999999999997e173 < t

   1. Initial program 76.8%

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

   2. Taylor expanded in y around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

      4. associate--l+ N/A

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

      5. lower-+.f64 N/A

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

      6. sub-div N/A

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

      7. lower-/.f64 N/A

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

      8. lower--.f64 N/A

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

      9. lift--.f64 89.4

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

   4. Applied rewrites 89.4%

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

   5. Taylor expanded in t around -inf

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

      1. lower-/.f64 N/A

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

      2. lower--.f64 59.6

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

   7. Applied rewrites 59.6%

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

   if -1.3499999999999999e130 < t < 5.1999999999999997e173

   1. Initial program 76.8%

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

   2. Taylor expanded in z around inf

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

      1. associate-/l* N/A

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

      2. lower-*.f64 N/A

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

      3. lower-/.f64 N/A

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

      4. lift--.f64 81.5

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

   4. Applied rewrites 81.5%

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

Alternative 4: 83.1% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \mathsf{fma}\left(1 - \frac{z}{a}, y, x\right)\\ \mathbf{if}\;a \leq -5.8 \cdot 10^{-95}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;a \leq 8.2 \cdot 10^{+39}:\\ \;\;\;\;x + \frac{y \cdot \left(z - a\right)}{t}\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (fma (- 1.0 (/ z a)) y x)))
   (if (<= a -5.8e-95) t_1 (if (<= a 8.2e+39) (+ x (/ (* y (- z a)) t)) t_1))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = fma((1.0 - (z / a)), y, x);
	double tmp;
	if (a <= -5.8e-95) {
		tmp = t_1;
	} else if (a <= 8.2e+39) {
		tmp = x + ((y * (z - a)) / t);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Julia

function code(x, y, z, t, a)
	t_1 = fma(Float64(1.0 - Float64(z / a)), y, x)
	tmp = 0.0
	if (a <= -5.8e-95)
		tmp = t_1;
	elseif (a <= 8.2e+39)
		tmp = Float64(x + Float64(Float64(y * Float64(z - a)) / t));
	else
		tmp = t_1;
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(N[(1.0 - N[(z / a), $MachinePrecision]), $MachinePrecision] * y + x), $MachinePrecision]}, If[LessEqual[a, -5.8e-95], t$95$1, If[LessEqual[a, 8.2e+39], N[(x + N[(N[(y * N[(z - a), $MachinePrecision]), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if a < -5.80000000000000004e-95 or 8.20000000000000008e39 < a

   1. Initial program 76.8%

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

   2. Taylor expanded in y around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

      4. associate--l+ N/A

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

      5. lower-+.f64 N/A

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

      6. sub-div N/A

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

      7. lower-/.f64 N/A

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

      8. lower--.f64 N/A

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

      9. lift--.f64 89.4

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

   4. Applied rewrites 89.4%

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

   5. Taylor expanded in t around 0

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

      1. lower--.f64 N/A

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

      2. lower-/.f64 67.0

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

   7. Applied rewrites 67.0%

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

   if -5.80000000000000004e-95 < a < 8.20000000000000008e39

   1. Initial program 76.8%

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

   2. Taylor expanded in y around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

      4. associate--l+ N/A

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

      5. lower-+.f64 N/A

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

      6. sub-div N/A

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

      7. lower-/.f64 N/A

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

      8. lower--.f64 N/A

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

      9. lift--.f64 89.4

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

   4. Applied rewrites 89.4%

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

   5. Taylor expanded in t around -inf

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

      1. lower-+.f64 N/A

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

      2. lower-/.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower--.f64 57.1

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

   7. Applied rewrites 57.1%

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

Alternative 5: 82.2% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \mathsf{fma}\left(1 - \frac{z}{a}, y, x\right)\\ \mathbf{if}\;a \leq -5.8 \cdot 10^{-95}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;a \leq 6.7 \cdot 10^{-6}:\\ \;\;\;\;\mathsf{fma}\left(\frac{z - a}{t}, y, x\right)\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (fma (- 1.0 (/ z a)) y x)))
   (if (<= a -5.8e-95) t_1 (if (<= a 6.7e-6) (fma (/ (- z a) t) y x) t_1))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = fma((1.0 - (z / a)), y, x);
	double tmp;
	if (a <= -5.8e-95) {
		tmp = t_1;
	} else if (a <= 6.7e-6) {
		tmp = fma(((z - a) / t), y, x);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Julia

function code(x, y, z, t, a)
	t_1 = fma(Float64(1.0 - Float64(z / a)), y, x)
	tmp = 0.0
	if (a <= -5.8e-95)
		tmp = t_1;
	elseif (a <= 6.7e-6)
		tmp = fma(Float64(Float64(z - a) / t), y, x);
	else
		tmp = t_1;
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(N[(1.0 - N[(z / a), $MachinePrecision]), $MachinePrecision] * y + x), $MachinePrecision]}, If[LessEqual[a, -5.8e-95], t$95$1, If[LessEqual[a, 6.7e-6], N[(N[(N[(z - a), $MachinePrecision] / t), $MachinePrecision] * y + x), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if a < -5.80000000000000004e-95 or 6.7e-6 < a

   1. Initial program 76.8%

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

   2. Taylor expanded in y around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

      4. associate--l+ N/A

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

      5. lower-+.f64 N/A

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

      6. sub-div N/A

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

      7. lower-/.f64 N/A

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

      8. lower--.f64 N/A

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

      9. lift--.f64 89.4

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

   4. Applied rewrites 89.4%

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

   5. Taylor expanded in t around 0

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

      1. lower--.f64 N/A

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

      2. lower-/.f64 67.0

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

   7. Applied rewrites 67.0%

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

   if -5.80000000000000004e-95 < a < 6.7e-6

   1. Initial program 76.8%

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

   2. Taylor expanded in y around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

      4. associate--l+ N/A

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

      5. lower-+.f64 N/A

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

      6. sub-div N/A

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

      7. lower-/.f64 N/A

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

      8. lower--.f64 N/A

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

      9. lift--.f64 89.4

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

   4. Applied rewrites 89.4%

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

   5. Taylor expanded in t around -inf

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

      1. lower-/.f64 N/A

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

      2. lower--.f64 59.6

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

   7. Applied rewrites 59.6%

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

Alternative 6: 81.4% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \mathsf{fma}\left(1 - \frac{z}{a}, y, x\right)\\ \mathbf{if}\;a \leq -5.8 \cdot 10^{-95}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;a \leq 1.6 \cdot 10^{+40}:\\ \;\;\;\;\mathsf{fma}\left(\frac{z}{t}, y, x\right)\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (fma (- 1.0 (/ z a)) y x)))
   (if (<= a -5.8e-95) t_1 (if (<= a 1.6e+40) (fma (/ z t) y x) t_1))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = fma((1.0 - (z / a)), y, x);
	double tmp;
	if (a <= -5.8e-95) {
		tmp = t_1;
	} else if (a <= 1.6e+40) {
		tmp = fma((z / t), y, x);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Julia

function code(x, y, z, t, a)
	t_1 = fma(Float64(1.0 - Float64(z / a)), y, x)
	tmp = 0.0
	if (a <= -5.8e-95)
		tmp = t_1;
	elseif (a <= 1.6e+40)
		tmp = fma(Float64(z / t), y, x);
	else
		tmp = t_1;
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(N[(1.0 - N[(z / a), $MachinePrecision]), $MachinePrecision] * y + x), $MachinePrecision]}, If[LessEqual[a, -5.8e-95], t$95$1, If[LessEqual[a, 1.6e+40], N[(N[(z / t), $MachinePrecision] * y + x), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if a < -5.80000000000000004e-95 or 1.5999999999999999e40 < a

   1. Initial program 76.8%

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

   2. Taylor expanded in y around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

      4. associate--l+ N/A

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

      5. lower-+.f64 N/A

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

      6. sub-div N/A

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

      7. lower-/.f64 N/A

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

      8. lower--.f64 N/A

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

      9. lift--.f64 89.4

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

   4. Applied rewrites 89.4%

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

   5. Taylor expanded in t around 0

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

      1. lower--.f64 N/A

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

      2. lower-/.f64 67.0

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

   7. Applied rewrites 67.0%

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

   if -5.80000000000000004e-95 < a < 1.5999999999999999e40

   1. Initial program 76.8%

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

   2. Taylor expanded in y around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

      4. associate--l+ N/A

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

      5. lower-+.f64 N/A

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

      6. sub-div N/A

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

      7. lower-/.f64 N/A

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

      8. lower--.f64 N/A

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

      9. lift--.f64 89.4

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

   4. Applied rewrites 89.4%

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

   5. Taylor expanded in a around 0

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

      1. lower-/.f64 61.3

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

   7. Applied rewrites 61.3%

      \[\leadsto \mathsf{fma}\left(\frac{z}{t}, y, x\right) \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 7: 77.1% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -1.15 \cdot 10^{+110}:\\ \;\;\;\;x + y\\ \mathbf{elif}\;a \leq -27000000:\\ \;\;\;\;\mathsf{fma}\left(\frac{-z}{a}, y, x\right)\\ \mathbf{elif}\;a \leq 2.2 \cdot 10^{+58}:\\ \;\;\;\;\mathsf{fma}\left(\frac{z}{t}, y, x\right)\\ \mathbf{else}:\\ \;\;\;\;x + y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= a -1.15e+110)
   (+ x y)
   (if (<= a -27000000.0)
     (fma (/ (- z) a) y x)
     (if (<= a 2.2e+58) (fma (/ z t) y x) (+ x y)))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (a <= -1.15e+110) {
		tmp = x + y;
	} else if (a <= -27000000.0) {
		tmp = fma((-z / a), y, x);
	} else if (a <= 2.2e+58) {
		tmp = fma((z / t), y, x);
	} else {
		tmp = x + y;
	}
	return tmp;
}

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (a <= -1.15e+110)
		tmp = Float64(x + y);
	elseif (a <= -27000000.0)
		tmp = fma(Float64(Float64(-z) / a), y, x);
	elseif (a <= 2.2e+58)
		tmp = fma(Float64(z / t), y, x);
	else
		tmp = Float64(x + y);
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[a, -1.15e+110], N[(x + y), $MachinePrecision], If[LessEqual[a, -27000000.0], N[(N[((-z) / a), $MachinePrecision] * y + x), $MachinePrecision], If[LessEqual[a, 2.2e+58], N[(N[(z / t), $MachinePrecision] * y + x), $MachinePrecision], N[(x + y), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if a < -1.15e110 or 2.2000000000000001e58 < a

   1. Initial program 76.8%

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

   2. Taylor expanded in y around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

      4. associate--l+ N/A

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

      5. lower-+.f64 N/A

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

      6. sub-div N/A

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

      7. lower-/.f64 N/A

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

      8. lower--.f64 N/A

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

      9. lift--.f64 89.4

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

   4. Applied rewrites 89.4%

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

   5. Taylor expanded in t around 0

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

      1. lower--.f64 N/A

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

      2. lower-/.f64 67.0

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

   7. Applied rewrites 67.0%

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

   8. Taylor expanded in a around inf

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

      1. lower-+.f64 61.0

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

   10. Applied rewrites 61.0%

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

   if -1.15e110 < a < -2.7e7

   1. Initial program 76.8%

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

   2. Taylor expanded in y around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

      4. associate--l+ N/A

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

      5. lower-+.f64 N/A

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

      6. sub-div N/A

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

      7. lower-/.f64 N/A

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

      8. lower--.f64 N/A

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

      9. lift--.f64 89.4

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

   4. Applied rewrites 89.4%

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

   5. Taylor expanded in t around 0

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

      1. lower--.f64 N/A

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

      2. lower-/.f64 67.0

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

   7. Applied rewrites 67.0%

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

   8. Taylor expanded in z around inf

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

      1. associate-*r/ N/A

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

      2. lower-/.f64 N/A

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

      3. mul-1-neg N/A

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

      4. lower-neg.f64 52.1

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

   10. Applied rewrites 52.1%

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

   if -2.7e7 < a < 2.2000000000000001e58

   1. Initial program 76.8%

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

   2. Taylor expanded in y around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

      4. associate--l+ N/A

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

      5. lower-+.f64 N/A

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

      6. sub-div N/A

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

      7. lower-/.f64 N/A

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

      8. lower--.f64 N/A

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

      9. lift--.f64 89.4

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

   4. Applied rewrites 89.4%

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

   5. Taylor expanded in a around 0

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

      1. lower-/.f64 61.3

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

   7. Applied rewrites 61.3%

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

Alternative 8: 76.5% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -1.85 \cdot 10^{+100}:\\ \;\;\;\;x + y\\ \mathbf{elif}\;a \leq 2.2 \cdot 10^{+58}:\\ \;\;\;\;\mathsf{fma}\left(\frac{z}{t}, y, x\right)\\ \mathbf{else}:\\ \;\;\;\;x + y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= a -1.85e+100) (+ x y) (if (<= a 2.2e+58) (fma (/ z t) y x) (+ x y))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (a <= -1.85e+100) {
		tmp = x + y;
	} else if (a <= 2.2e+58) {
		tmp = fma((z / t), y, x);
	} else {
		tmp = x + y;
	}
	return tmp;
}

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (a <= -1.85e+100)
		tmp = Float64(x + y);
	elseif (a <= 2.2e+58)
		tmp = fma(Float64(z / t), y, x);
	else
		tmp = Float64(x + y);
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[a, -1.85e+100], N[(x + y), $MachinePrecision], If[LessEqual[a, 2.2e+58], N[(N[(z / t), $MachinePrecision] * y + x), $MachinePrecision], N[(x + y), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if a < -1.8500000000000001e100 or 2.2000000000000001e58 < a

   1. Initial program 76.8%

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

   2. Taylor expanded in y around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

      4. associate--l+ N/A

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

      5. lower-+.f64 N/A

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

      6. sub-div N/A

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

      7. lower-/.f64 N/A

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

      8. lower--.f64 N/A

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

      9. lift--.f64 89.4

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

   4. Applied rewrites 89.4%

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

   5. Taylor expanded in t around 0

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

      1. lower--.f64 N/A

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

      2. lower-/.f64 67.0

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

   7. Applied rewrites 67.0%

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

   8. Taylor expanded in a around inf

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

      1. lower-+.f64 61.0

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

   10. Applied rewrites 61.0%

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

   if -1.8500000000000001e100 < a < 2.2000000000000001e58

   1. Initial program 76.8%

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

   2. Taylor expanded in y around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

      4. associate--l+ N/A

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

      5. lower-+.f64 N/A

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

      6. sub-div N/A

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

      7. lower-/.f64 N/A

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

      8. lower--.f64 N/A

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

      9. lift--.f64 89.4

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

   4. Applied rewrites 89.4%

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

   5. Taylor expanded in a around 0

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

      1. lower-/.f64 61.3

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

   7. Applied rewrites 61.3%

      \[\leadsto \mathsf{fma}\left(\frac{z}{t}, y, x\right) \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 9: 64.3% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{z}{t} \cdot y\\ t_2 := \left(x + y\right) - \frac{\left(z - t\right) \cdot y}{a - t}\\ \mathbf{if}\;t\_2 \leq -\infty:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;t\_2 \leq 10^{+306}:\\ \;\;\;\;x + y\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* (/ z t) y)) (t_2 (- (+ x y) (/ (* (- z t) y) (- a t)))))
   (if (<= t_2 (- INFINITY)) t_1 (if (<= t_2 1e+306) (+ x y) t_1))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = (z / t) * y;
	double t_2 = (x + y) - (((z - t) * y) / (a - t));
	double tmp;
	if (t_2 <= -((double) INFINITY)) {
		tmp = t_1;
	} else if (t_2 <= 1e+306) {
		tmp = x + y;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = (z / t) * y;
	double t_2 = (x + y) - (((z - t) * y) / (a - t));
	double tmp;
	if (t_2 <= -Double.POSITIVE_INFINITY) {
		tmp = t_1;
	} else if (t_2 <= 1e+306) {
		tmp = x + y;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = (z / t) * y
	t_2 = (x + y) - (((z - t) * y) / (a - t))
	tmp = 0
	if t_2 <= -math.inf:
		tmp = t_1
	elif t_2 <= 1e+306:
		tmp = x + y
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(Float64(z / t) * y)
	t_2 = Float64(Float64(x + y) - Float64(Float64(Float64(z - t) * y) / Float64(a - t)))
	tmp = 0.0
	if (t_2 <= Float64(-Inf))
		tmp = t_1;
	elseif (t_2 <= 1e+306)
		tmp = Float64(x + y);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = (z / t) * y;
	t_2 = (x + y) - (((z - t) * y) / (a - t));
	tmp = 0.0;
	if (t_2 <= -Inf)
		tmp = t_1;
	elseif (t_2 <= 1e+306)
		tmp = x + y;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(N[(z / t), $MachinePrecision] * y), $MachinePrecision]}, Block[{t$95$2 = N[(N[(x + y), $MachinePrecision] - N[(N[(N[(z - t), $MachinePrecision] * y), $MachinePrecision] / N[(a - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$2, (-Infinity)], t$95$1, If[LessEqual[t$95$2, 1e+306], N[(x + y), $MachinePrecision], t$95$1]]]]

Derivation

1. Split input into 2 regimes

2. if (-.f64 (+.f64 x y) (/.f64 (*.f64 (-.f64 z t) y) (-.f64 a t))) < -inf.0 or 1.00000000000000002e306 < (-.f64 (+.f64 x y) (/.f64 (*.f64 (-.f64 z t) y) (-.f64 a t)))

   1. Initial program 76.8%

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

   2. Taylor expanded in y around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. associate--l+ N/A

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

      4. lower-+.f64 N/A

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

      5. sub-div N/A

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

      6. lower-/.f64 N/A

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

      7. lower--.f64 N/A

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

      8. lift--.f64 40.5

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

   4. Applied rewrites 40.5%

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

   5. Taylor expanded in a around 0

      \[\leadsto \frac{z}{t} \cdot y \]
   6. Step-by-step derivation

      1. lower-/.f64 19.3

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

   7. Applied rewrites 19.3%

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

   if -inf.0 < (-.f64 (+.f64 x y) (/.f64 (*.f64 (-.f64 z t) y) (-.f64 a t))) < 1.00000000000000002e306

   1. Initial program 76.8%

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

   2. Taylor expanded in y around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

      4. associate--l+ N/A

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

      5. lower-+.f64 N/A

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

      6. sub-div N/A

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

      7. lower-/.f64 N/A

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

      8. lower--.f64 N/A

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

      9. lift--.f64 89.4

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

   4. Applied rewrites 89.4%

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

   5. Taylor expanded in t around 0

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

      1. lower--.f64 N/A

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

      2. lower-/.f64 67.0

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

   7. Applied rewrites 67.0%

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

   8. Taylor expanded in a around inf

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

      1. lower-+.f64 61.0

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

   10. Applied rewrites 61.0%

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

Alternative 10: 62.5% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq 8.6 \cdot 10^{+169}:\\ \;\;\;\;x + y\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(1 + -1, y, x\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= t 8.6e+169) (+ x y) (fma (+ 1.0 -1.0) y x)))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (t <= 8.6e+169) {
		tmp = x + y;
	} else {
		tmp = fma((1.0 + -1.0), y, x);
	}
	return tmp;
}

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (t <= 8.6e+169)
		tmp = Float64(x + y);
	else
		tmp = fma(Float64(1.0 + -1.0), y, x);
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[t, 8.6e+169], N[(x + y), $MachinePrecision], N[(N[(1.0 + -1.0), $MachinePrecision] * y + x), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if t < 8.6000000000000003e169

   1. Initial program 76.8%

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

   2. Taylor expanded in y around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

      4. associate--l+ N/A

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

      5. lower-+.f64 N/A

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

      6. sub-div N/A

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

      7. lower-/.f64 N/A

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

      8. lower--.f64 N/A

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

      9. lift--.f64 89.4

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

   4. Applied rewrites 89.4%

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

   5. Taylor expanded in t around 0

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

      1. lower--.f64 N/A

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

      2. lower-/.f64 67.0

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

   7. Applied rewrites 67.0%

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

   8. Taylor expanded in a around inf

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

      1. lower-+.f64 61.0

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

   10. Applied rewrites 61.0%

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

   if 8.6000000000000003e169 < t

   1. Initial program 76.8%

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

   2. Taylor expanded in y around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

      4. associate--l+ N/A

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

      5. lower-+.f64 N/A

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

      6. sub-div N/A

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

      7. lower-/.f64 N/A

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

      8. lower--.f64 N/A

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

      9. lift--.f64 89.4

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

   4. Applied rewrites 89.4%

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

   5. Taylor expanded in t around inf

      \[\leadsto \mathsf{fma}\left(1 + -1, y, x\right) \]
   6. Step-by-step derivation

   7. Applied rewrites 51.0%

      \[\leadsto \mathsf{fma}\left(1 + -1, y, x\right) \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 11: 61.0% accurate, 1.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -5 \cdot 10^{+233}:\\ \;\;\;\;\frac{y \cdot z}{t}\\ \mathbf{else}:\\ \;\;\;\;x + y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= z -5e+233) (/ (* y z) t) (+ x y)))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -5e+233) {
		tmp = (y * z) / t;
	} 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 <= (-5d+233)) then
        tmp = (y * z) / t
    else
        tmp = x + y
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -5e+233) {
		tmp = (y * z) / t;
	} else {
		tmp = x + y;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if z <= -5e+233:
		tmp = (y * z) / t
	else:
		tmp = x + y
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (z <= -5e+233)
		tmp = Float64(Float64(y * z) / t);
	else
		tmp = Float64(x + y);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (z <= -5e+233)
		tmp = (y * z) / t;
	else
		tmp = x + y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[z, -5e+233], N[(N[(y * z), $MachinePrecision] / t), $MachinePrecision], N[(x + y), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if z < -5.00000000000000009e233

   1. Initial program 76.8%

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

   2. Taylor expanded in z around inf

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

      1. associate-*r/ N/A

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

      2. mul-1-neg N/A

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

      3. sub-negate-rev N/A

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

      4. frac-2neg-rev N/A

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

      5. lower-/.f64 N/A

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

      6. *-commutative N/A

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

      7. lower-*.f64 N/A

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

      8. lower--.f64 25.8

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

   4. Applied rewrites 25.8%

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

   5. Taylor expanded in t around inf

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

      1. lower-/.f64 N/A

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

      2. lower-*.f64 18.0

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

   7. Applied rewrites 18.0%

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

   if -5.00000000000000009e233 < z

   1. Initial program 76.8%

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

   2. Taylor expanded in y around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

      4. associate--l+ N/A

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

      5. lower-+.f64 N/A

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

      6. sub-div N/A

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

      7. lower-/.f64 N/A

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

      8. lower--.f64 N/A

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

      9. lift--.f64 89.4

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

   4. Applied rewrites 89.4%

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

   5. Taylor expanded in t around 0

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

      1. lower--.f64 N/A

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

      2. lower-/.f64 67.0

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

   7. Applied rewrites 67.0%

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

   8. Taylor expanded in a around inf

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

      1. lower-+.f64 61.0

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

   10. Applied rewrites 61.0%

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

Alternative 12: 61.0% accurate, 4.8× speedup

Math

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

FPCore

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

C

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

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
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 76.8%

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

2. Taylor expanded in y around 0

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

   1. +-commutative N/A

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

   2. *-commutative N/A

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

   3. lower-fma.f64 N/A

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

   4. associate--l+ N/A

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

   5. lower-+.f64 N/A

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

   6. sub-div N/A

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

   7. lower-/.f64 N/A

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

   8. lower--.f64 N/A

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

   9. lift--.f64 89.4

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

4. Applied rewrites 89.4%

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

5. Taylor expanded in t around 0

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

   1. lower--.f64 N/A

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

   2. lower-/.f64 67.0

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

7. Applied rewrites 67.0%

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

8. Taylor expanded in a around inf

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

   1. lower-+.f64 61.0

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

10. Applied rewrites 61.0%

    \[\leadsto \color{blue}{x + y} \]
11. Add Preprocessing

Alternative 13: 19.2% accurate, 17.9× speedup

Math

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

FPCore

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

C

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

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 = y
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 76.8%

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

2. Taylor expanded in y around 0

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

   1. +-commutative N/A

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

   2. *-commutative N/A

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

   3. lower-fma.f64 N/A

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

   4. associate--l+ N/A

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

   5. lower-+.f64 N/A

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

   6. sub-div N/A

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

   7. lower-/.f64 N/A

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

   8. lower--.f64 N/A

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

   9. lift--.f64 89.4

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

4. Applied rewrites 89.4%

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

5. Taylor expanded in t around 0

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

   1. lower--.f64 N/A

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

   2. lower-/.f64 67.0

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

7. Applied rewrites 67.0%

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

8. Taylor expanded in a around inf

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

   1. lower-+.f64 61.0

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

10. Applied rewrites 61.0%

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

11. Taylor expanded in x around 0

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

13. Applied rewrites 19.2%

    \[\leadsto y \]
14. Add Preprocessing

Reproduce

herbie shell --seed 2025136 
(FPCore (x y z t a)
  :name "Graphics.Rendering.Plot.Render.Plot.Axis:renderAxisTick from plot-0.2.3.4, B"
  :precision binary64
  (- (+ x y) (/ (* (- z t) y) (- a t))))