Graphics.Rendering.Plot.Render.Plot.Legend:renderLegendInside from plot-0.2.3.4

Percentage Accurate: 99.9% → 99.9%

Time: 2.2s

Alternatives: 7

Speedup: 1.6×

Specification

Math

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

FPCore

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

C

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

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 99.9% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 99.9% accurate, 1.6× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(y, 2, \mathsf{fma}\left(3, x, z\right)\right) \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (fma y 2.0 (fma 3.0 x z)))

C

double code(double x, double y, double z) {
	return fma(y, 2.0, fma(3.0, x, z));
}

Julia

function code(x, y, z)
	return fma(y, 2.0, fma(3.0, x, z))
end

Wolfram

code[x_, y_, z_] := N[(y * 2.0 + N[(3.0 * x + z), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.9%

   \[\left(\left(\left(\left(x + y\right) + y\right) + x\right) + z\right) + x \]
2. Step-by-step derivation

   1. lift-+.f64 N/A

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

   2. lift-+.f64 N/A

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

   3. lift-+.f64 N/A

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

   4. lift-+.f64 N/A

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

   5. lift-+.f64 N/A

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

   6. associate-+l+ N/A

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

   7. associate-+l+ N/A

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

   8. +-commutative N/A

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

   9. count-2-rev N/A

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

   10. distribute-lft-out N/A

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

   11. associate-+r+ N/A

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

   12. +-commutative N/A

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

   13. associate-+l+ N/A

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

   14. +-commutative N/A

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

   15. associate-+r+ N/A

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

   16. distribute-rgt1-in N/A

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

   17. metadata-eval N/A

       \[\leadsto z + \left(\color{blue}{3} \cdot x + 2 \cdot y\right) \]

   18. +-commutative N/A

       \[\leadsto z + \color{blue}{\left(2 \cdot y + 3 \cdot x\right)} \]

   19. +-commutative N/A

       \[\leadsto \color{blue}{\left(2 \cdot y + 3 \cdot x\right) + z} \]

   20. associate-+l+ N/A

       \[\leadsto \color{blue}{2 \cdot y + \left(3 \cdot x + z\right)} \]

   21. *-commutative N/A

       \[\leadsto \color{blue}{y \cdot 2} + \left(3 \cdot x + z\right) \]

3. Applied rewrites 99.9%

   \[\leadsto \color{blue}{\mathsf{fma}\left(y, 2, \mathsf{fma}\left(3, x, z\right)\right)} \]
4. Add Preprocessing

Alternative 2: 83.2% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -9 \cdot 10^{-37}:\\ \;\;\;\;\mathsf{fma}\left(2, y, z\right)\\ \mathbf{elif}\;y \leq 1.05 \cdot 10^{+161}:\\ \;\;\;\;\mathsf{fma}\left(3, x, z\right)\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(2, y, z\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= y -9e-37)
   (fma 2.0 y z)
   (if (<= y 1.05e+161) (fma 3.0 x z) (fma 2.0 y z))))

C

double code(double x, double y, double z) {
	double tmp;
	if (y <= -9e-37) {
		tmp = fma(2.0, y, z);
	} else if (y <= 1.05e+161) {
		tmp = fma(3.0, x, z);
	} else {
		tmp = fma(2.0, y, z);
	}
	return tmp;
}

Julia

function code(x, y, z)
	tmp = 0.0
	if (y <= -9e-37)
		tmp = fma(2.0, y, z);
	elseif (y <= 1.05e+161)
		tmp = fma(3.0, x, z);
	else
		tmp = fma(2.0, y, z);
	end
	return tmp
end

Wolfram

code[x_, y_, z_] := If[LessEqual[y, -9e-37], N[(2.0 * y + z), $MachinePrecision], If[LessEqual[y, 1.05e+161], N[(3.0 * x + z), $MachinePrecision], N[(2.0 * y + z), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if y < -9.00000000000000081e-37 or 1.05e161 < y

   1. Initial program 99.9%

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

   2. Taylor expanded in x around 0

      \[\leadsto \color{blue}{z + 2 \cdot y} \]
   3. Step-by-step derivation

      1. +-commutative N/A

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

      2. lower-fma.f64 80.6

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

   4. Applied rewrites 80.6%

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

   if -9.00000000000000081e-37 < y < 1.05e161

   1. Initial program 99.9%

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

   2. Taylor expanded in y around 0

      \[\leadsto \color{blue}{x + \left(z + 2 \cdot x\right)} \]
   3. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto x + \left(2 \cdot x + \color{blue}{z}\right) \]

      2. associate-+r+ N/A

         \[\leadsto \left(x + 2 \cdot x\right) + \color{blue}{z} \]

      3. distribute-rgt1-in N/A

         \[\leadsto \left(2 + 1\right) \cdot x + z \]

      4. metadata-eval N/A

         \[\leadsto 3 \cdot x + z \]

      5. lower-fma.f64 85.0

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

   4. Applied rewrites 85.0%

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

Alternative 3: 79.3% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -4 \cdot 10^{+190}:\\ \;\;\;\;3 \cdot x\\ \mathbf{elif}\;x \leq 2.2 \cdot 10^{+168}:\\ \;\;\;\;\mathsf{fma}\left(2, y, z\right)\\ \mathbf{else}:\\ \;\;\;\;3 \cdot x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= x -4e+190) (* 3.0 x) (if (<= x 2.2e+168) (fma 2.0 y z) (* 3.0 x))))

C

double code(double x, double y, double z) {
	double tmp;
	if (x <= -4e+190) {
		tmp = 3.0 * x;
	} else if (x <= 2.2e+168) {
		tmp = fma(2.0, y, z);
	} else {
		tmp = 3.0 * x;
	}
	return tmp;
}

Julia

function code(x, y, z)
	tmp = 0.0
	if (x <= -4e+190)
		tmp = Float64(3.0 * x);
	elseif (x <= 2.2e+168)
		tmp = fma(2.0, y, z);
	else
		tmp = Float64(3.0 * x);
	end
	return tmp
end

Wolfram

code[x_, y_, z_] := If[LessEqual[x, -4e+190], N[(3.0 * x), $MachinePrecision], If[LessEqual[x, 2.2e+168], N[(2.0 * y + z), $MachinePrecision], N[(3.0 * x), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if x < -4.0000000000000003e190 or 2.2000000000000002e168 < x

   1. Initial program 99.8%

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

   2. Taylor expanded in x around inf

      \[\leadsto \color{blue}{3 \cdot x} \]
   3. Step-by-step derivation

      1. lower-*.f64 78.5

         \[\leadsto 3 \cdot \color{blue}{x} \]

   4. Applied rewrites 78.5%

      \[\leadsto \color{blue}{3 \cdot x} \]

   if -4.0000000000000003e190 < x < 2.2000000000000002e168

   1. Initial program 99.9%

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

   2. Taylor expanded in x around 0

      \[\leadsto \color{blue}{z + 2 \cdot y} \]
   3. Step-by-step derivation

      1. +-commutative N/A

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

      2. lower-fma.f64 79.6

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

   4. Applied rewrites 79.6%

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

Alternative 4: 53.3% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.8 \cdot 10^{+36}:\\ \;\;\;\;2 \cdot y\\ \mathbf{elif}\;y \leq -5.2 \cdot 10^{-64}:\\ \;\;\;\;z + x\\ \mathbf{elif}\;y \leq 10^{-191}:\\ \;\;\;\;3 \cdot x\\ \mathbf{elif}\;y \leq 1.02 \cdot 10^{+166}:\\ \;\;\;\;z + x\\ \mathbf{else}:\\ \;\;\;\;2 \cdot y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= y -1.8e+36)
   (* 2.0 y)
   (if (<= y -5.2e-64)
     (+ z x)
     (if (<= y 1e-191) (* 3.0 x) (if (<= y 1.02e+166) (+ z x) (* 2.0 y))))))

C

double code(double x, double y, double z) {
	double tmp;
	if (y <= -1.8e+36) {
		tmp = 2.0 * y;
	} else if (y <= -5.2e-64) {
		tmp = z + x;
	} else if (y <= 1e-191) {
		tmp = 3.0 * x;
	} else if (y <= 1.02e+166) {
		tmp = z + x;
	} 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, z)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if (y <= (-1.8d+36)) then
        tmp = 2.0d0 * y
    else if (y <= (-5.2d-64)) then
        tmp = z + x
    else if (y <= 1d-191) then
        tmp = 3.0d0 * x
    else if (y <= 1.02d+166) then
        tmp = z + x
    else
        tmp = 2.0d0 * y
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (y <= -1.8e+36) {
		tmp = 2.0 * y;
	} else if (y <= -5.2e-64) {
		tmp = z + x;
	} else if (y <= 1e-191) {
		tmp = 3.0 * x;
	} else if (y <= 1.02e+166) {
		tmp = z + x;
	} else {
		tmp = 2.0 * y;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if y <= -1.8e+36:
		tmp = 2.0 * y
	elif y <= -5.2e-64:
		tmp = z + x
	elif y <= 1e-191:
		tmp = 3.0 * x
	elif y <= 1.02e+166:
		tmp = z + x
	else:
		tmp = 2.0 * y
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (y <= -1.8e+36)
		tmp = Float64(2.0 * y);
	elseif (y <= -5.2e-64)
		tmp = Float64(z + x);
	elseif (y <= 1e-191)
		tmp = Float64(3.0 * x);
	elseif (y <= 1.02e+166)
		tmp = Float64(z + x);
	else
		tmp = Float64(2.0 * y);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (y <= -1.8e+36)
		tmp = 2.0 * y;
	elseif (y <= -5.2e-64)
		tmp = z + x;
	elseif (y <= 1e-191)
		tmp = 3.0 * x;
	elseif (y <= 1.02e+166)
		tmp = z + x;
	else
		tmp = 2.0 * y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[y, -1.8e+36], N[(2.0 * y), $MachinePrecision], If[LessEqual[y, -5.2e-64], N[(z + x), $MachinePrecision], If[LessEqual[y, 1e-191], N[(3.0 * x), $MachinePrecision], If[LessEqual[y, 1.02e+166], N[(z + x), $MachinePrecision], N[(2.0 * y), $MachinePrecision]]]]]

Derivation

1. Split input into 3 regimes

2. if y < -1.7999999999999999e36 or 1.0200000000000001e166 < y

   1. Initial program 99.9%

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

   2. Taylor expanded in y around inf

      \[\leadsto \color{blue}{2 \cdot y} \]
   3. Step-by-step derivation

      1. lower-*.f64 67.0

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

   4. Applied rewrites 67.0%

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

   if -1.7999999999999999e36 < y < -5.2e-64 or 1e-191 < y < 1.0200000000000001e166

   1. Initial program 99.9%

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

   2. Taylor expanded in z around inf

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

   4. Applied rewrites 44.4%

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

   if -5.2e-64 < y < 1e-191

   1. Initial program 99.9%

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

   2. Taylor expanded in x around inf

      \[\leadsto \color{blue}{3 \cdot x} \]
   3. Step-by-step derivation

      1. lower-*.f64 48.3

         \[\leadsto 3 \cdot \color{blue}{x} \]

   4. Applied rewrites 48.3%

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

Alternative 5: 53.2% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.15 \cdot 10^{+36}:\\ \;\;\;\;2 \cdot y\\ \mathbf{elif}\;y \leq -5.2 \cdot 10^{-64}:\\ \;\;\;\;z\\ \mathbf{elif}\;y \leq 1.05 \cdot 10^{+161}:\\ \;\;\;\;3 \cdot x\\ \mathbf{else}:\\ \;\;\;\;2 \cdot y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= y -1.15e+36)
   (* 2.0 y)
   (if (<= y -5.2e-64) z (if (<= y 1.05e+161) (* 3.0 x) (* 2.0 y)))))

C

double code(double x, double y, double z) {
	double tmp;
	if (y <= -1.15e+36) {
		tmp = 2.0 * y;
	} else if (y <= -5.2e-64) {
		tmp = z;
	} else if (y <= 1.05e+161) {
		tmp = 3.0 * x;
	} 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, z)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if (y <= (-1.15d+36)) then
        tmp = 2.0d0 * y
    else if (y <= (-5.2d-64)) then
        tmp = z
    else if (y <= 1.05d+161) then
        tmp = 3.0d0 * x
    else
        tmp = 2.0d0 * y
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (y <= -1.15e+36) {
		tmp = 2.0 * y;
	} else if (y <= -5.2e-64) {
		tmp = z;
	} else if (y <= 1.05e+161) {
		tmp = 3.0 * x;
	} else {
		tmp = 2.0 * y;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if y <= -1.15e+36:
		tmp = 2.0 * y
	elif y <= -5.2e-64:
		tmp = z
	elif y <= 1.05e+161:
		tmp = 3.0 * x
	else:
		tmp = 2.0 * y
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (y <= -1.15e+36)
		tmp = Float64(2.0 * y);
	elseif (y <= -5.2e-64)
		tmp = z;
	elseif (y <= 1.05e+161)
		tmp = Float64(3.0 * x);
	else
		tmp = Float64(2.0 * y);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (y <= -1.15e+36)
		tmp = 2.0 * y;
	elseif (y <= -5.2e-64)
		tmp = z;
	elseif (y <= 1.05e+161)
		tmp = 3.0 * x;
	else
		tmp = 2.0 * y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[y, -1.15e+36], N[(2.0 * y), $MachinePrecision], If[LessEqual[y, -5.2e-64], z, If[LessEqual[y, 1.05e+161], N[(3.0 * x), $MachinePrecision], N[(2.0 * y), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if y < -1.14999999999999998e36 or 1.05e161 < y

   1. Initial program 99.9%

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

   2. Taylor expanded in y around inf

      \[\leadsto \color{blue}{2 \cdot y} \]
   3. Step-by-step derivation

      1. lower-*.f64 66.8

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

   4. Applied rewrites 66.8%

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

   if -1.14999999999999998e36 < y < -5.2e-64

   1. Initial program 99.9%

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

   2. Taylor expanded in z around inf

      \[\leadsto \color{blue}{z} \]
   3. Step-by-step derivation

   4. Applied rewrites 40.1%

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

   if -5.2e-64 < y < 1.05e161

   1. Initial program 99.9%

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

   2. Taylor expanded in x around inf

      \[\leadsto \color{blue}{3 \cdot x} \]
   3. Step-by-step derivation

      1. lower-*.f64 42.8

         \[\leadsto 3 \cdot \color{blue}{x} \]

   4. Applied rewrites 42.8%

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

Alternative 6: 51.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -44000000:\\ \;\;\;\;z\\ \mathbf{elif}\;z \leq 1.95 \cdot 10^{+95}:\\ \;\;\;\;2 \cdot y\\ \mathbf{else}:\\ \;\;\;\;z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= z -44000000.0) z (if (<= z 1.95e+95) (* 2.0 y) z)))

C

double code(double x, double y, double z) {
	double tmp;
	if (z <= -44000000.0) {
		tmp = z;
	} else if (z <= 1.95e+95) {
		tmp = 2.0 * y;
	} else {
		tmp = z;
	}
	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)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if (z <= (-44000000.0d0)) then
        tmp = z
    else if (z <= 1.95d+95) then
        tmp = 2.0d0 * y
    else
        tmp = z
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (z <= -44000000.0) {
		tmp = z;
	} else if (z <= 1.95e+95) {
		tmp = 2.0 * y;
	} else {
		tmp = z;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if z <= -44000000.0:
		tmp = z
	elif z <= 1.95e+95:
		tmp = 2.0 * y
	else:
		tmp = z
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (z <= -44000000.0)
		tmp = z;
	elseif (z <= 1.95e+95)
		tmp = Float64(2.0 * y);
	else
		tmp = z;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (z <= -44000000.0)
		tmp = z;
	elseif (z <= 1.95e+95)
		tmp = 2.0 * y;
	else
		tmp = z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[z, -44000000.0], z, If[LessEqual[z, 1.95e+95], N[(2.0 * y), $MachinePrecision], z]]

Derivation

1. Split input into 2 regimes

2. if z < -4.4e7 or 1.9499999999999999e95 < z

   1. Initial program 99.9%

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

   2. Taylor expanded in z around inf

      \[\leadsto \color{blue}{z} \]
   3. Step-by-step derivation

   4. Applied rewrites 64.1%

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

   if -4.4e7 < z < 1.9499999999999999e95

   1. Initial program 99.9%

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

   2. Taylor expanded in y around inf

      \[\leadsto \color{blue}{2 \cdot y} \]
   3. Step-by-step derivation

      1. lower-*.f64 45.4

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

   4. Applied rewrites 45.4%

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

Alternative 7: 34.6% accurate, 11.0× speedup

Math

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

FPCore

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

C

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

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

code[x_, y_, z_] := z

Derivation

1. Initial program 99.9%

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

2. Taylor expanded in z around inf

   \[\leadsto \color{blue}{z} \]
3. Step-by-step derivation

4. Applied rewrites 34.6%

   \[\leadsto \color{blue}{z} \]
5. Add Preprocessing

Reproduce

herbie shell --seed 2025101 
(FPCore (x y z)
  :name "Graphics.Rendering.Plot.Render.Plot.Legend:renderLegendInside from plot-0.2.3.4"
  :precision binary64
  (+ (+ (+ (+ (+ x y) y) x) z) x))