Linear.Quaternion:$c/ from linear-1.19.1.3, E

Percentage Accurate: 99.9% → 99.9%

Time: 3.4s

Alternatives: 14

Speedup: 1.8×

• 43.7% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

\[\begin{array}{l} \\ \left(\left(x \cdot x + y \cdot y\right) + y \cdot y\right) + y \cdot y \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (+ (+ (+ (* x x) (* y y)) (* y y)) (* y y)))

C

double code(double x, double y) {
	return (((x * x) + (y * y)) + (y * y)) + (y * 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)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = (((x * x) + (y * y)) + (y * y)) + (y * y)
end function

Java

public static double code(double x, double y) {
	return (((x * x) + (y * y)) + (y * y)) + (y * y);
}

Python

def code(x, y):
	return (((x * x) + (y * y)) + (y * y)) + (y * y)

Julia

function code(x, y)
	return Float64(Float64(Float64(Float64(x * x) + Float64(y * y)) + Float64(y * y)) + Float64(y * y))
end

MATLAB

function tmp = code(x, y)
	tmp = (((x * x) + (y * y)) + (y * y)) + (y * y);
end

Wolfram

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

Initial Program: 99.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \left(\left(x \cdot x + y \cdot y\right) + y \cdot y\right) + y \cdot y \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (+ (+ (+ (* x x) (* y y)) (* y y)) (* y y)))

C

double code(double x, double y) {
	return (((x * x) + (y * y)) + (y * y)) + (y * 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)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = (((x * x) + (y * y)) + (y * y)) + (y * y)
end function

Java

public static double code(double x, double y) {
	return (((x * x) + (y * y)) + (y * y)) + (y * y);
}

Python

def code(x, y):
	return (((x * x) + (y * y)) + (y * y)) + (y * y)

Julia

function code(x, y)
	return Float64(Float64(Float64(Float64(x * x) + Float64(y * y)) + Float64(y * y)) + Float64(y * y))
end

MATLAB

function tmp = code(x, y)
	tmp = (((x * x) + (y * y)) + (y * y)) + (y * y);
end

Wolfram

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

Alternative 1: 99.9% accurate, 1.8× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(3, y \cdot y, x \cdot x\right) \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (fma 3.0 (* y y) (* x x)))

C

double code(double x, double y) {
	return fma(3.0, (y * y), (x * x));
}

Julia

function code(x, y)
	return fma(3.0, Float64(y * y), Float64(x * x))
end

Wolfram

code[x_, y_] := N[(3.0 * N[(y * y), $MachinePrecision] + N[(x * x), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.9%

   \[\left(\left(x \cdot x + y \cdot y\right) + y \cdot y\right) + y \cdot y \]

2. Taylor expanded in y around 0

   \[\leadsto \color{blue}{3 \cdot {y}^{2} + {x}^{2}} \]

4. Applied rewrites 99.9%

   \[\leadsto \color{blue}{\mathsf{fma}\left(3, {y}^{2}, {x}^{2}\right)} \]

6. Applied rewrites 99.9%

   \[\leadsto \mathsf{fma}\left(3, y \cdot \color{blue}{y}, {x}^{2}\right) \]

8. Applied rewrites 99.9%

   \[\leadsto \mathsf{fma}\left(3, y \cdot y, x \cdot x\right) \]
9. Add Preprocessing

Alternative 2: 69.1% accurate, 1.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq 0.00125:\\ \;\;\;\;\left(3 \cdot y\right) \cdot y\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(3, y, x \cdot x\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= x 0.00125) (* (* 3.0 y) y) (fma 3.0 y (* x x))))

C

double code(double x, double y) {
	double tmp;
	if (x <= 0.00125) {
		tmp = (3.0 * y) * y;
	} else {
		tmp = fma(3.0, y, (x * x));
	}
	return tmp;
}

Julia

function code(x, y)
	tmp = 0.0
	if (x <= 0.00125)
		tmp = Float64(Float64(3.0 * y) * y);
	else
		tmp = fma(3.0, y, Float64(x * x));
	end
	return tmp
end

Wolfram

code[x_, y_] := If[LessEqual[x, 0.00125], N[(N[(3.0 * y), $MachinePrecision] * y), $MachinePrecision], N[(3.0 * y + N[(x * x), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < 0.00125000000000000003

   1. Initial program 99.9%

      \[\left(\left(x \cdot x + y \cdot y\right) + y \cdot y\right) + y \cdot y \]

   2. Taylor expanded in y around inf

      \[\leadsto \color{blue}{3 \cdot {y}^{2}} \]

   4. Applied rewrites 57.7%

      \[\leadsto \color{blue}{3 \cdot {y}^{2}} \]

   6. Applied rewrites 57.7%

      \[\leadsto \left(3 \cdot y\right) \cdot \color{blue}{y} \]

   if 0.00125000000000000003 < x

   1. Initial program 99.9%

      \[\left(\left(x \cdot x + y \cdot y\right) + y \cdot y\right) + y \cdot y \]

   2. Taylor expanded in y around 0

      \[\leadsto \color{blue}{3 \cdot {y}^{2} + {x}^{2}} \]

   4. Applied rewrites 99.9%

      \[\leadsto \color{blue}{\mathsf{fma}\left(3, {y}^{2}, {x}^{2}\right)} \]

   6. Applied rewrites 99.9%

      \[\leadsto \mathsf{fma}\left(3, y \cdot \color{blue}{y}, {x}^{2}\right) \]

   8. Applied rewrites 99.9%

      \[\leadsto \mathsf{fma}\left(3, y \cdot y, x \cdot x\right) \]

   10. Applied rewrites 48.2%

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

Alternative 3: 57.7% accurate, 3.0× speedup

Math

\[\begin{array}{l} \\ \left(3 \cdot y\right) \cdot y \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (* (* 3.0 y) y))

C

double code(double x, double y) {
	return (3.0 * y) * 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)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = (3.0d0 * y) * y
end function

Java

public static double code(double x, double y) {
	return (3.0 * y) * y;
}

Python

def code(x, y):
	return (3.0 * y) * y

Julia

function code(x, y)
	return Float64(Float64(3.0 * y) * y)
end

MATLAB

function tmp = code(x, y)
	tmp = (3.0 * y) * y;
end

Wolfram

code[x_, y_] := N[(N[(3.0 * y), $MachinePrecision] * y), $MachinePrecision]

Derivation

1. Initial program 99.9%

   \[\left(\left(x \cdot x + y \cdot y\right) + y \cdot y\right) + y \cdot y \]

2. Taylor expanded in y around inf

   \[\leadsto \color{blue}{3 \cdot {y}^{2}} \]

4. Applied rewrites 57.7%

   \[\leadsto \color{blue}{3 \cdot {y}^{2}} \]

6. Applied rewrites 57.7%

   \[\leadsto \left(3 \cdot y\right) \cdot \color{blue}{y} \]
7. Add Preprocessing

Alternative 4: 57.7% accurate, 3.0× speedup

Math

\[\begin{array}{l} \\ 3 \cdot \left(y \cdot y\right) \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (* 3.0 (* y y)))

C

double code(double x, double y) {
	return 3.0 * (y * 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)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = 3.0d0 * (y * y)
end function

Java

public static double code(double x, double y) {
	return 3.0 * (y * y);
}

Python

def code(x, y):
	return 3.0 * (y * y)

Julia

function code(x, y)
	return Float64(3.0 * Float64(y * y))
end

MATLAB

function tmp = code(x, y)
	tmp = 3.0 * (y * y);
end

Wolfram

code[x_, y_] := N[(3.0 * N[(y * y), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.9%

   \[\left(\left(x \cdot x + y \cdot y\right) + y \cdot y\right) + y \cdot y \]

2. Taylor expanded in y around inf

   \[\leadsto \color{blue}{3 \cdot {y}^{2}} \]

4. Applied rewrites 57.7%

   \[\leadsto \color{blue}{3 \cdot {y}^{2}} \]

6. Applied rewrites 57.7%

   \[\leadsto 3 \cdot \left(y \cdot \color{blue}{y}\right) \]
7. Add Preprocessing

Alternative 5: 37.3% accurate, 3.1× speedup

Math

\[\begin{array}{l} \\ 0 + y \cdot y \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (+ 0.0 (* y y)))

C

double code(double x, double y) {
	return 0.0 + (y * 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)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = 0.0d0 + (y * y)
end function

Java

public static double code(double x, double y) {
	return 0.0 + (y * y);
}

Python

def code(x, y):
	return 0.0 + (y * y)

Julia

function code(x, y)
	return Float64(0.0 + Float64(y * y))
end

MATLAB

function tmp = code(x, y)
	tmp = 0.0 + (y * y);
end

Wolfram

code[x_, y_] := N[(0.0 + N[(y * y), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.9%

   \[\left(\left(x \cdot x + y \cdot y\right) + y \cdot y\right) + y \cdot y \]

2. Taylor expanded in x around inf

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

4. Applied rewrites 66.8%

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

6. Applied rewrites 31.0%

   \[\leadsto \color{blue}{2} + y \cdot y \]

8. Applied rewrites 37.3%

   \[\leadsto 0 + y \cdot y \]
9. Add Preprocessing

Alternative 6: 10.2% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\left(\left(x \cdot x + y \cdot y\right) + y \cdot y\right) + y \cdot y \leq 2 \cdot 10^{-214}:\\ \;\;\;\;0\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(3, y, x\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= (+ (+ (+ (* x x) (* y y)) (* y y)) (* y y)) 2e-214)
   0.0
   (fma 3.0 y x)))

C

double code(double x, double y) {
	double tmp;
	if (((((x * x) + (y * y)) + (y * y)) + (y * y)) <= 2e-214) {
		tmp = 0.0;
	} else {
		tmp = fma(3.0, y, x);
	}
	return tmp;
}

Julia

function code(x, y)
	tmp = 0.0
	if (Float64(Float64(Float64(Float64(x * x) + Float64(y * y)) + Float64(y * y)) + Float64(y * y)) <= 2e-214)
		tmp = 0.0;
	else
		tmp = fma(3.0, y, x);
	end
	return tmp
end

Wolfram

code[x_, y_] := If[LessEqual[N[(N[(N[(N[(x * x), $MachinePrecision] + N[(y * y), $MachinePrecision]), $MachinePrecision] + N[(y * y), $MachinePrecision]), $MachinePrecision] + N[(y * y), $MachinePrecision]), $MachinePrecision], 2e-214], 0.0, N[(3.0 * y + x), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (+.f64 (+.f64 (+.f64 (*.f64 x x) (*.f64 y y)) (*.f64 y y)) (*.f64 y y)) < 1.99999999999999983e-214

   1. Initial program 99.9%

      \[\left(\left(x \cdot x + y \cdot y\right) + y \cdot y\right) + y \cdot y \]

   2. Taylor expanded in x around inf

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

   4. Applied rewrites 66.8%

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

   6. Applied rewrites 4.1%

      \[\leadsto \color{blue}{2 + y} \]

   7. Taylor expanded in y around 0

      \[\leadsto \color{blue}{2} \]

   9. Applied rewrites 4.3%

      \[\leadsto \color{blue}{2} \]

   11. Applied rewrites 8.2%

       \[\leadsto 0 \]

   if 1.99999999999999983e-214 < (+.f64 (+.f64 (+.f64 (*.f64 x x) (*.f64 y y)) (*.f64 y y)) (*.f64 y y))

   1. Initial program 99.9%

      \[\left(\left(x \cdot x + y \cdot y\right) + y \cdot y\right) + y \cdot y \]

   2. Taylor expanded in y around 0

      \[\leadsto \color{blue}{3 \cdot {y}^{2} + {x}^{2}} \]

   4. Applied rewrites 99.9%

      \[\leadsto \color{blue}{\mathsf{fma}\left(3, {y}^{2}, {x}^{2}\right)} \]

   6. Applied rewrites 99.9%

      \[\leadsto \mathsf{fma}\left(3, y \cdot \color{blue}{y}, {x}^{2}\right) \]

   8. Applied rewrites 4.1%

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

Alternative 7: 10.2% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\left(\left(x \cdot x + y \cdot y\right) + y \cdot y\right) + y \cdot y \leq 10^{-275}:\\ \;\;\;\;0\\ \mathbf{else}:\\ \;\;\;\;6 \cdot y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= (+ (+ (+ (* x x) (* y y)) (* y y)) (* y y)) 1e-275) 0.0 (* 6.0 y)))

C

double code(double x, double y) {
	double tmp;
	if (((((x * x) + (y * y)) + (y * y)) + (y * y)) <= 1e-275) {
		tmp = 0.0;
	} else {
		tmp = 6.0 * 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)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (((((x * x) + (y * y)) + (y * y)) + (y * y)) <= 1d-275) then
        tmp = 0.0d0
    else
        tmp = 6.0d0 * y
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (((((x * x) + (y * y)) + (y * y)) + (y * y)) <= 1e-275) {
		tmp = 0.0;
	} else {
		tmp = 6.0 * y;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if ((((x * x) + (y * y)) + (y * y)) + (y * y)) <= 1e-275:
		tmp = 0.0
	else:
		tmp = 6.0 * y
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (Float64(Float64(Float64(Float64(x * x) + Float64(y * y)) + Float64(y * y)) + Float64(y * y)) <= 1e-275)
		tmp = 0.0;
	else
		tmp = Float64(6.0 * y);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (((((x * x) + (y * y)) + (y * y)) + (y * y)) <= 1e-275)
		tmp = 0.0;
	else
		tmp = 6.0 * y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[N[(N[(N[(N[(x * x), $MachinePrecision] + N[(y * y), $MachinePrecision]), $MachinePrecision] + N[(y * y), $MachinePrecision]), $MachinePrecision] + N[(y * y), $MachinePrecision]), $MachinePrecision], 1e-275], 0.0, N[(6.0 * y), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (+.f64 (+.f64 (+.f64 (*.f64 x x) (*.f64 y y)) (*.f64 y y)) (*.f64 y y)) < 9.99999999999999934e-276

   1. Initial program 99.9%

      \[\left(\left(x \cdot x + y \cdot y\right) + y \cdot y\right) + y \cdot y \]

   2. Taylor expanded in x around inf

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

   4. Applied rewrites 66.8%

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

   6. Applied rewrites 4.1%

      \[\leadsto \color{blue}{2 + y} \]

   7. Taylor expanded in y around 0

      \[\leadsto \color{blue}{2} \]

   9. Applied rewrites 4.3%

      \[\leadsto \color{blue}{2} \]

   11. Applied rewrites 8.2%

       \[\leadsto 0 \]

   if 9.99999999999999934e-276 < (+.f64 (+.f64 (+.f64 (*.f64 x x) (*.f64 y y)) (*.f64 y y)) (*.f64 y y))

   1. Initial program 99.9%

      \[\left(\left(x \cdot x + y \cdot y\right) + y \cdot y\right) + y \cdot y \]

   2. Taylor expanded in y around 0

      \[\leadsto \color{blue}{3 \cdot {y}^{2} + {x}^{2}} \]

   4. Applied rewrites 99.9%

      \[\leadsto \color{blue}{\mathsf{fma}\left(3, {y}^{2}, {x}^{2}\right)} \]

   6. Applied rewrites 30.6%

      \[\leadsto \left(3 \cdot y\right) \cdot \color{blue}{\mathsf{fma}\left(3, y, 2\right)} \]

   7. Taylor expanded in y around 0

      \[\leadsto 6 \cdot \color{blue}{y} \]

   9. Applied rewrites 3.7%

      \[\leadsto 6 \cdot \color{blue}{y} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 8: 10.2% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\left(\left(x \cdot x + y \cdot y\right) + y \cdot y\right) + y \cdot y \leq 10^{-167}:\\ \;\;\;\;0\\ \mathbf{else}:\\ \;\;\;\;2 + y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= (+ (+ (+ (* x x) (* y y)) (* y y)) (* y y)) 1e-167) 0.0 (+ 2.0 y)))

C

double code(double x, double y) {
	double tmp;
	if (((((x * x) + (y * y)) + (y * y)) + (y * y)) <= 1e-167) {
		tmp = 0.0;
	} else {
		tmp = 2.0 + 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)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (((((x * x) + (y * y)) + (y * y)) + (y * y)) <= 1d-167) then
        tmp = 0.0d0
    else
        tmp = 2.0d0 + y
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (((((x * x) + (y * y)) + (y * y)) + (y * y)) <= 1e-167) {
		tmp = 0.0;
	} else {
		tmp = 2.0 + y;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if ((((x * x) + (y * y)) + (y * y)) + (y * y)) <= 1e-167:
		tmp = 0.0
	else:
		tmp = 2.0 + y
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (Float64(Float64(Float64(Float64(x * x) + Float64(y * y)) + Float64(y * y)) + Float64(y * y)) <= 1e-167)
		tmp = 0.0;
	else
		tmp = Float64(2.0 + y);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (((((x * x) + (y * y)) + (y * y)) + (y * y)) <= 1e-167)
		tmp = 0.0;
	else
		tmp = 2.0 + y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[N[(N[(N[(N[(x * x), $MachinePrecision] + N[(y * y), $MachinePrecision]), $MachinePrecision] + N[(y * y), $MachinePrecision]), $MachinePrecision] + N[(y * y), $MachinePrecision]), $MachinePrecision], 1e-167], 0.0, N[(2.0 + y), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (+.f64 (+.f64 (+.f64 (*.f64 x x) (*.f64 y y)) (*.f64 y y)) (*.f64 y y)) < 1e-167

   1. Initial program 99.9%

      \[\left(\left(x \cdot x + y \cdot y\right) + y \cdot y\right) + y \cdot y \]

   2. Taylor expanded in x around inf

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

   4. Applied rewrites 66.8%

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

   6. Applied rewrites 4.1%

      \[\leadsto \color{blue}{2 + y} \]

   7. Taylor expanded in y around 0

      \[\leadsto \color{blue}{2} \]

   9. Applied rewrites 4.3%

      \[\leadsto \color{blue}{2} \]

   11. Applied rewrites 8.2%

       \[\leadsto 0 \]

   if 1e-167 < (+.f64 (+.f64 (+.f64 (*.f64 x x) (*.f64 y y)) (*.f64 y y)) (*.f64 y y))

   1. Initial program 99.9%

      \[\left(\left(x \cdot x + y \cdot y\right) + y \cdot y\right) + y \cdot y \]

   2. Taylor expanded in x around inf

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

   4. Applied rewrites 66.8%

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

   6. Applied rewrites 4.1%

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

Alternative 9: 10.0% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\left(\left(x \cdot x + y \cdot y\right) + y \cdot y\right) + y \cdot y \leq 10^{-167}:\\ \;\;\;\;0\\ \mathbf{else}:\\ \;\;\;\;1 + y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= (+ (+ (+ (* x x) (* y y)) (* y y)) (* y y)) 1e-167) 0.0 (+ 1.0 y)))

C

double code(double x, double y) {
	double tmp;
	if (((((x * x) + (y * y)) + (y * y)) + (y * y)) <= 1e-167) {
		tmp = 0.0;
	} else {
		tmp = 1.0 + 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)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (((((x * x) + (y * y)) + (y * y)) + (y * y)) <= 1d-167) then
        tmp = 0.0d0
    else
        tmp = 1.0d0 + y
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (((((x * x) + (y * y)) + (y * y)) + (y * y)) <= 1e-167) {
		tmp = 0.0;
	} else {
		tmp = 1.0 + y;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if ((((x * x) + (y * y)) + (y * y)) + (y * y)) <= 1e-167:
		tmp = 0.0
	else:
		tmp = 1.0 + y
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (Float64(Float64(Float64(Float64(x * x) + Float64(y * y)) + Float64(y * y)) + Float64(y * y)) <= 1e-167)
		tmp = 0.0;
	else
		tmp = Float64(1.0 + y);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (((((x * x) + (y * y)) + (y * y)) + (y * y)) <= 1e-167)
		tmp = 0.0;
	else
		tmp = 1.0 + y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[N[(N[(N[(N[(x * x), $MachinePrecision] + N[(y * y), $MachinePrecision]), $MachinePrecision] + N[(y * y), $MachinePrecision]), $MachinePrecision] + N[(y * y), $MachinePrecision]), $MachinePrecision], 1e-167], 0.0, N[(1.0 + y), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (+.f64 (+.f64 (+.f64 (*.f64 x x) (*.f64 y y)) (*.f64 y y)) (*.f64 y y)) < 1e-167

   1. Initial program 99.9%

      \[\left(\left(x \cdot x + y \cdot y\right) + y \cdot y\right) + y \cdot y \]

   2. Taylor expanded in x around inf

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

   4. Applied rewrites 66.8%

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

   6. Applied rewrites 4.1%

      \[\leadsto \color{blue}{2 + y} \]

   7. Taylor expanded in y around 0

      \[\leadsto \color{blue}{2} \]

   9. Applied rewrites 4.3%

      \[\leadsto \color{blue}{2} \]

   11. Applied rewrites 8.2%

       \[\leadsto 0 \]

   if 1e-167 < (+.f64 (+.f64 (+.f64 (*.f64 x x) (*.f64 y y)) (*.f64 y y)) (*.f64 y y))

   1. Initial program 99.9%

      \[\left(\left(x \cdot x + y \cdot y\right) + y \cdot y\right) + y \cdot y \]

   2. Taylor expanded in x around inf

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

   4. Applied rewrites 66.8%

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

   6. Applied rewrites 4.1%

      \[\leadsto \color{blue}{2 + y} \]

   8. Applied rewrites 4.1%

      \[\leadsto 1 + y \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 10: 10.0% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\left(\left(x \cdot x + y \cdot y\right) + y \cdot y\right) + y \cdot y \leq 10^{-275}:\\ \;\;\;\;0\\ \mathbf{else}:\\ \;\;\;\;0 + y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= (+ (+ (+ (* x x) (* y y)) (* y y)) (* y y)) 1e-275) 0.0 (+ 0.0 y)))

C

double code(double x, double y) {
	double tmp;
	if (((((x * x) + (y * y)) + (y * y)) + (y * y)) <= 1e-275) {
		tmp = 0.0;
	} else {
		tmp = 0.0 + 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)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (((((x * x) + (y * y)) + (y * y)) + (y * y)) <= 1d-275) then
        tmp = 0.0d0
    else
        tmp = 0.0d0 + y
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (((((x * x) + (y * y)) + (y * y)) + (y * y)) <= 1e-275) {
		tmp = 0.0;
	} else {
		tmp = 0.0 + y;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if ((((x * x) + (y * y)) + (y * y)) + (y * y)) <= 1e-275:
		tmp = 0.0
	else:
		tmp = 0.0 + y
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (Float64(Float64(Float64(Float64(x * x) + Float64(y * y)) + Float64(y * y)) + Float64(y * y)) <= 1e-275)
		tmp = 0.0;
	else
		tmp = Float64(0.0 + y);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (((((x * x) + (y * y)) + (y * y)) + (y * y)) <= 1e-275)
		tmp = 0.0;
	else
		tmp = 0.0 + y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[N[(N[(N[(N[(x * x), $MachinePrecision] + N[(y * y), $MachinePrecision]), $MachinePrecision] + N[(y * y), $MachinePrecision]), $MachinePrecision] + N[(y * y), $MachinePrecision]), $MachinePrecision], 1e-275], 0.0, N[(0.0 + y), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (+.f64 (+.f64 (+.f64 (*.f64 x x) (*.f64 y y)) (*.f64 y y)) (*.f64 y y)) < 9.99999999999999934e-276

   1. Initial program 99.9%

      \[\left(\left(x \cdot x + y \cdot y\right) + y \cdot y\right) + y \cdot y \]

   2. Taylor expanded in x around inf

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

   4. Applied rewrites 66.8%

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

   6. Applied rewrites 4.1%

      \[\leadsto \color{blue}{2 + y} \]

   7. Taylor expanded in y around 0

      \[\leadsto \color{blue}{2} \]

   9. Applied rewrites 4.3%

      \[\leadsto \color{blue}{2} \]

   11. Applied rewrites 8.2%

       \[\leadsto 0 \]

   if 9.99999999999999934e-276 < (+.f64 (+.f64 (+.f64 (*.f64 x x) (*.f64 y y)) (*.f64 y y)) (*.f64 y y))

   1. Initial program 99.9%

      \[\left(\left(x \cdot x + y \cdot y\right) + y \cdot y\right) + y \cdot y \]

   2. Taylor expanded in x around inf

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

   4. Applied rewrites 66.8%

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

   6. Applied rewrites 4.1%

      \[\leadsto \color{blue}{2 + y} \]

   8. Applied rewrites 3.6%

      \[\leadsto 0 + y \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 11: 9.8% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\left(\left(x \cdot x + y \cdot y\right) + y \cdot y\right) + y \cdot y \leq 2.6 \cdot 10^{-154}:\\ \;\;\;\;0\\ \mathbf{else}:\\ \;\;\;\;3 + 3\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= (+ (+ (+ (* x x) (* y y)) (* y y)) (* y y)) 2.6e-154)
   0.0
   (+ 3.0 3.0)))

C

double code(double x, double y) {
	double tmp;
	if (((((x * x) + (y * y)) + (y * y)) + (y * y)) <= 2.6e-154) {
		tmp = 0.0;
	} else {
		tmp = 3.0 + 3.0;
	}
	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)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (((((x * x) + (y * y)) + (y * y)) + (y * y)) <= 2.6d-154) then
        tmp = 0.0d0
    else
        tmp = 3.0d0 + 3.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (((((x * x) + (y * y)) + (y * y)) + (y * y)) <= 2.6e-154) {
		tmp = 0.0;
	} else {
		tmp = 3.0 + 3.0;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if ((((x * x) + (y * y)) + (y * y)) + (y * y)) <= 2.6e-154:
		tmp = 0.0
	else:
		tmp = 3.0 + 3.0
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (Float64(Float64(Float64(Float64(x * x) + Float64(y * y)) + Float64(y * y)) + Float64(y * y)) <= 2.6e-154)
		tmp = 0.0;
	else
		tmp = Float64(3.0 + 3.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (((((x * x) + (y * y)) + (y * y)) + (y * y)) <= 2.6e-154)
		tmp = 0.0;
	else
		tmp = 3.0 + 3.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[N[(N[(N[(N[(x * x), $MachinePrecision] + N[(y * y), $MachinePrecision]), $MachinePrecision] + N[(y * y), $MachinePrecision]), $MachinePrecision] + N[(y * y), $MachinePrecision]), $MachinePrecision], 2.6e-154], 0.0, N[(3.0 + 3.0), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (+.f64 (+.f64 (+.f64 (*.f64 x x) (*.f64 y y)) (*.f64 y y)) (*.f64 y y)) < 2.6e-154

   1. Initial program 99.9%

      \[\left(\left(x \cdot x + y \cdot y\right) + y \cdot y\right) + y \cdot y \]

   2. Taylor expanded in x around inf

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

   4. Applied rewrites 66.8%

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

   6. Applied rewrites 4.1%

      \[\leadsto \color{blue}{2 + y} \]

   7. Taylor expanded in y around 0

      \[\leadsto \color{blue}{2} \]

   9. Applied rewrites 4.3%

      \[\leadsto \color{blue}{2} \]

   11. Applied rewrites 8.2%

       \[\leadsto 0 \]

   if 2.6e-154 < (+.f64 (+.f64 (+.f64 (*.f64 x x) (*.f64 y y)) (*.f64 y y)) (*.f64 y y))

   1. Initial program 99.9%

      \[\left(\left(x \cdot x + y \cdot y\right) + y \cdot y\right) + y \cdot y \]

   2. Taylor expanded in y around inf

      \[\leadsto \color{blue}{3 \cdot {y}^{2}} \]

   4. Applied rewrites 57.7%

      \[\leadsto \color{blue}{3 \cdot {y}^{2}} \]

   6. Applied rewrites 57.7%

      \[\leadsto \left(3 \cdot y\right) \cdot \color{blue}{y} \]

   8. Applied rewrites 4.3%

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

Alternative 12: 9.2% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\left(\left(x \cdot x + y \cdot y\right) + y \cdot y\right) + y \cdot y \leq 1.5 \cdot 10^{-154}:\\ \;\;\;\;0\\ \mathbf{else}:\\ \;\;\;\;2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= (+ (+ (+ (* x x) (* y y)) (* y y)) (* y y)) 1.5e-154) 0.0 2.0))

C

double code(double x, double y) {
	double tmp;
	if (((((x * x) + (y * y)) + (y * y)) + (y * y)) <= 1.5e-154) {
		tmp = 0.0;
	} else {
		tmp = 2.0;
	}
	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)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (((((x * x) + (y * y)) + (y * y)) + (y * y)) <= 1.5d-154) then
        tmp = 0.0d0
    else
        tmp = 2.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (((((x * x) + (y * y)) + (y * y)) + (y * y)) <= 1.5e-154) {
		tmp = 0.0;
	} else {
		tmp = 2.0;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if ((((x * x) + (y * y)) + (y * y)) + (y * y)) <= 1.5e-154:
		tmp = 0.0
	else:
		tmp = 2.0
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (Float64(Float64(Float64(Float64(x * x) + Float64(y * y)) + Float64(y * y)) + Float64(y * y)) <= 1.5e-154)
		tmp = 0.0;
	else
		tmp = 2.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (((((x * x) + (y * y)) + (y * y)) + (y * y)) <= 1.5e-154)
		tmp = 0.0;
	else
		tmp = 2.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[N[(N[(N[(N[(x * x), $MachinePrecision] + N[(y * y), $MachinePrecision]), $MachinePrecision] + N[(y * y), $MachinePrecision]), $MachinePrecision] + N[(y * y), $MachinePrecision]), $MachinePrecision], 1.5e-154], 0.0, 2.0]

Derivation

1. Split input into 2 regimes

2. if (+.f64 (+.f64 (+.f64 (*.f64 x x) (*.f64 y y)) (*.f64 y y)) (*.f64 y y)) < 1.5000000000000001e-154

   1. Initial program 99.9%

      \[\left(\left(x \cdot x + y \cdot y\right) + y \cdot y\right) + y \cdot y \]

   2. Taylor expanded in x around inf

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

   4. Applied rewrites 66.8%

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

   6. Applied rewrites 4.1%

      \[\leadsto \color{blue}{2 + y} \]

   7. Taylor expanded in y around 0

      \[\leadsto \color{blue}{2} \]

   9. Applied rewrites 4.3%

      \[\leadsto \color{blue}{2} \]

   11. Applied rewrites 8.2%

       \[\leadsto 0 \]

   if 1.5000000000000001e-154 < (+.f64 (+.f64 (+.f64 (*.f64 x x) (*.f64 y y)) (*.f64 y y)) (*.f64 y y))

   1. Initial program 99.9%

      \[\left(\left(x \cdot x + y \cdot y\right) + y \cdot y\right) + y \cdot y \]

   2. Taylor expanded in x around inf

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

   4. Applied rewrites 66.8%

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

   6. Applied rewrites 4.1%

      \[\leadsto \color{blue}{2 + y} \]

   7. Taylor expanded in y around 0

      \[\leadsto \color{blue}{2} \]

   9. Applied rewrites 4.3%

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

Alternative 13: 9.1% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\left(\left(x \cdot x + y \cdot y\right) + y \cdot y\right) + y \cdot y \leq 1.1 \cdot 10^{-154}:\\ \;\;\;\;0\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= (+ (+ (+ (* x x) (* y y)) (* y y)) (* y y)) 1.1e-154) 0.0 1.0))

C

double code(double x, double y) {
	double tmp;
	if (((((x * x) + (y * y)) + (y * y)) + (y * y)) <= 1.1e-154) {
		tmp = 0.0;
	} else {
		tmp = 1.0;
	}
	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)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (((((x * x) + (y * y)) + (y * y)) + (y * y)) <= 1.1d-154) then
        tmp = 0.0d0
    else
        tmp = 1.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (((((x * x) + (y * y)) + (y * y)) + (y * y)) <= 1.1e-154) {
		tmp = 0.0;
	} else {
		tmp = 1.0;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if ((((x * x) + (y * y)) + (y * y)) + (y * y)) <= 1.1e-154:
		tmp = 0.0
	else:
		tmp = 1.0
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (Float64(Float64(Float64(Float64(x * x) + Float64(y * y)) + Float64(y * y)) + Float64(y * y)) <= 1.1e-154)
		tmp = 0.0;
	else
		tmp = 1.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (((((x * x) + (y * y)) + (y * y)) + (y * y)) <= 1.1e-154)
		tmp = 0.0;
	else
		tmp = 1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[N[(N[(N[(N[(x * x), $MachinePrecision] + N[(y * y), $MachinePrecision]), $MachinePrecision] + N[(y * y), $MachinePrecision]), $MachinePrecision] + N[(y * y), $MachinePrecision]), $MachinePrecision], 1.1e-154], 0.0, 1.0]

Derivation

1. Split input into 2 regimes

2. if (+.f64 (+.f64 (+.f64 (*.f64 x x) (*.f64 y y)) (*.f64 y y)) (*.f64 y y)) < 1.10000000000000004e-154

   1. Initial program 99.9%

      \[\left(\left(x \cdot x + y \cdot y\right) + y \cdot y\right) + y \cdot y \]

   2. Taylor expanded in x around inf

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

   4. Applied rewrites 66.8%

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

   6. Applied rewrites 4.1%

      \[\leadsto \color{blue}{2 + y} \]

   7. Taylor expanded in y around 0

      \[\leadsto \color{blue}{2} \]

   9. Applied rewrites 4.3%

      \[\leadsto \color{blue}{2} \]

   11. Applied rewrites 8.2%

       \[\leadsto 0 \]

   if 1.10000000000000004e-154 < (+.f64 (+.f64 (+.f64 (*.f64 x x) (*.f64 y y)) (*.f64 y y)) (*.f64 y y))

   1. Initial program 99.9%

      \[\left(\left(x \cdot x + y \cdot y\right) + y \cdot y\right) + y \cdot y \]

   2. Taylor expanded in x around inf

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

   4. Applied rewrites 66.8%

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

   6. Applied rewrites 4.1%

      \[\leadsto \color{blue}{2 + y} \]

   7. Taylor expanded in y around 0

      \[\leadsto \color{blue}{2} \]

   9. Applied rewrites 4.3%

      \[\leadsto \color{blue}{2} \]

   11. Applied rewrites 4.3%

       \[\leadsto 1 \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 14: 8.2% accurate, 21.0× speedup

Math

\[\begin{array}{l} \\ 0 \end{array} \]

FPCore

(FPCore (x y) :precision binary64 0.0)

C

double code(double x, double y) {
	return 0.0;
}

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)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = 0.0d0
end function

Java

public static double code(double x, double y) {
	return 0.0;
}

Python

def code(x, y):
	return 0.0

Julia

function code(x, y)
	return 0.0
end

MATLAB

function tmp = code(x, y)
	tmp = 0.0;
end

Wolfram

code[x_, y_] := 0.0

Derivation

1. Initial program 99.9%

   \[\left(\left(x \cdot x + y \cdot y\right) + y \cdot y\right) + y \cdot y \]

2. Taylor expanded in x around inf

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

4. Applied rewrites 66.8%

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

6. Applied rewrites 4.1%

   \[\leadsto \color{blue}{2 + y} \]

7. Taylor expanded in y around 0

   \[\leadsto \color{blue}{2} \]

9. Applied rewrites 4.3%

   \[\leadsto \color{blue}{2} \]

11. Applied rewrites 8.2%

    \[\leadsto 0 \]
12. Add Preprocessing

Reproduce

herbie shell --seed 2025159 
(FPCore (x y)
  :name "Linear.Quaternion:$c/ from linear-1.19.1.3, E"
  :precision binary64
  (+ (+ (+ (* x x) (* y y)) (* y y)) (* y y)))