Graphics.Rasterific.Svg.PathConverter:segmentToBezier from rasterific-svg-0.2.3.1, B

Percentage Accurate: 99.9% → 99.9%

Time: 5.2s

Alternatives: 13

Speedup: 1.0×

Specification

Math

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

FPCore

(FPCore (x y z) :precision binary64 (- (+ x (cos y)) (* z (sin y))))

C

double code(double x, double y, double z) {
	return (x + cos(y)) - (z * sin(y));
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

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

Java

public static double code(double x, double y, double z) {
	return (x + Math.cos(y)) - (z * Math.sin(y));
}

Python

def code(x, y, z):
	return (x + math.cos(y)) - (z * math.sin(y))

Julia

function code(x, y, z)
	return Float64(Float64(x + cos(y)) - Float64(z * sin(y)))
end

MATLAB

function tmp = code(x, y, z)
	tmp = (x + cos(y)) - (z * sin(y));
end

Wolfram

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

Initial Program: 99.9% accurate, 1.0× speedup

Math

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

FPCore

(FPCore (x y z) :precision binary64 (- (+ x (cos y)) (* z (sin y))))

C

double code(double x, double y, double z) {
	return (x + cos(y)) - (z * sin(y));
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

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

Java

public static double code(double x, double y, double z) {
	return (x + Math.cos(y)) - (z * Math.sin(y));
}

Python

def code(x, y, z):
	return (x + math.cos(y)) - (z * math.sin(y))

Julia

function code(x, y, z)
	return Float64(Float64(x + cos(y)) - Float64(z * sin(y)))
end

MATLAB

function tmp = code(x, y, z)
	tmp = (x + cos(y)) - (z * sin(y));
end

Wolfram

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

Alternative 1: 99.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(\sin y, -z, \cos y + x\right) \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (fma (sin y) (- z) (+ (cos y) x)))

C

double code(double x, double y, double z) {
	return fma(sin(y), -z, (cos(y) + x));
}

Julia

function code(x, y, z)
	return fma(sin(y), Float64(-z), Float64(cos(y) + x))
end

Wolfram

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

Derivation

1. Initial program 99.9%

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

   1. lift--.f64 N/A

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

   2. lift-*.f64 N/A

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

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

      \[\leadsto \color{blue}{\left(x + \cos y\right) + \left(\mathsf{neg}\left(z\right)\right) \cdot \sin y} \]

   4. +-commutative N/A

      \[\leadsto \color{blue}{\left(\mathsf{neg}\left(z\right)\right) \cdot \sin y + \left(x + \cos y\right)} \]

   5. *-commutative N/A

      \[\leadsto \color{blue}{\sin y \cdot \left(\mathsf{neg}\left(z\right)\right)} + \left(x + \cos y\right) \]

   6. lower-fma.f64 N/A

      \[\leadsto \color{blue}{\mathsf{fma}\left(\sin y, \mathsf{neg}\left(z\right), x + \cos y\right)} \]

   7. lower-neg.f64 99.9

      \[\leadsto \mathsf{fma}\left(\sin y, \color{blue}{-z}, x + \cos y\right) \]

   8. lift-+.f64 N/A

      \[\leadsto \mathsf{fma}\left(\sin y, -z, \color{blue}{x + \cos y}\right) \]

   9. +-commutative N/A

      \[\leadsto \mathsf{fma}\left(\sin y, -z, \color{blue}{\cos y + x}\right) \]

   10. lower-+.f64 99.9

       \[\leadsto \mathsf{fma}\left(\sin y, -z, \color{blue}{\cos y + x}\right) \]

3. Applied rewrites 99.9%

   \[\leadsto \color{blue}{\mathsf{fma}\left(\sin y, -z, \cos y + x\right)} \]
4. Add Preprocessing

Alternative 2: 99.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \cos y - \left(\sin y \cdot z - x\right) \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (- (cos y) (- (* (sin y) z) x)))

C

double code(double x, double y, double z) {
	return cos(y) - ((sin(y) * 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 = cos(y) - ((sin(y) * z) - x)
end function

Java

public static double code(double x, double y, double z) {
	return Math.cos(y) - ((Math.sin(y) * z) - x);
}

Python

def code(x, y, z):
	return math.cos(y) - ((math.sin(y) * z) - x)

Julia

function code(x, y, z)
	return Float64(cos(y) - Float64(Float64(sin(y) * z) - x))
end

MATLAB

function tmp = code(x, y, z)
	tmp = cos(y) - ((sin(y) * z) - x);
end

Wolfram

code[x_, y_, z_] := N[(N[Cos[y], $MachinePrecision] - N[(N[(N[Sin[y], $MachinePrecision] * z), $MachinePrecision] - x), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.9%

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

   1. lift--.f64 N/A

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

   2. sub-negate-rev N/A

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

   3. lift-+.f64 N/A

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

   4. associate--r+ N/A

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

   5. sub-negate N/A

      \[\leadsto \color{blue}{\cos y - \left(z \cdot \sin y - x\right)} \]

   6. lower--.f64 N/A

      \[\leadsto \color{blue}{\cos y - \left(z \cdot \sin y - x\right)} \]

   7. lower--.f64 99.9

      \[\leadsto \cos y - \color{blue}{\left(z \cdot \sin y - x\right)} \]

   8. lift-*.f64 N/A

      \[\leadsto \cos y - \left(\color{blue}{z \cdot \sin y} - x\right) \]

   9. *-commutative N/A

      \[\leadsto \cos y - \left(\color{blue}{\sin y \cdot z} - x\right) \]

   10. lower-*.f64 99.9

       \[\leadsto \cos y - \left(\color{blue}{\sin y \cdot z} - x\right) \]

3. Applied rewrites 99.9%

   \[\leadsto \color{blue}{\cos y - \left(\sin y \cdot z - x\right)} \]
4. Add Preprocessing

Alternative 3: 99.3% accurate, 1.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := 1 - \left(\sin y \cdot z - x\right)\\ \mathbf{if}\;z \leq -6600000000:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;z \leq 1.15:\\ \;\;\;\;x + \cos y\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (- 1.0 (- (* (sin y) z) x))))
   (if (<= z -6600000000.0) t_0 (if (<= z 1.15) (+ x (cos y)) t_0))))

C

double code(double x, double y, double z) {
	double t_0 = 1.0 - ((sin(y) * z) - x);
	double tmp;
	if (z <= -6600000000.0) {
		tmp = t_0;
	} else if (z <= 1.15) {
		tmp = x + cos(y);
	} else {
		tmp = t_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, z)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: t_0
    real(8) :: tmp
    t_0 = 1.0d0 - ((sin(y) * z) - x)
    if (z <= (-6600000000.0d0)) then
        tmp = t_0
    else if (z <= 1.15d0) then
        tmp = x + cos(y)
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = 1.0 - ((Math.sin(y) * z) - x);
	double tmp;
	if (z <= -6600000000.0) {
		tmp = t_0;
	} else if (z <= 1.15) {
		tmp = x + Math.cos(y);
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = 1.0 - ((math.sin(y) * z) - x)
	tmp = 0
	if z <= -6600000000.0:
		tmp = t_0
	elif z <= 1.15:
		tmp = x + math.cos(y)
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(1.0 - Float64(Float64(sin(y) * z) - x))
	tmp = 0.0
	if (z <= -6600000000.0)
		tmp = t_0;
	elseif (z <= 1.15)
		tmp = Float64(x + cos(y));
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = 1.0 - ((sin(y) * z) - x);
	tmp = 0.0;
	if (z <= -6600000000.0)
		tmp = t_0;
	elseif (z <= 1.15)
		tmp = x + cos(y);
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(1.0 - N[(N[(N[Sin[y], $MachinePrecision] * z), $MachinePrecision] - x), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -6600000000.0], t$95$0, If[LessEqual[z, 1.15], N[(x + N[Cos[y], $MachinePrecision]), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if z < -6.6e9 or 1.1499999999999999 < z

   1. Initial program 99.9%

      \[\left(x + \cos y\right) - z \cdot \sin y \]

   2. Taylor expanded in y around 0

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

   4. Applied rewrites 88.4%

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

      1. lift--.f64 N/A

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

      2. sub-negate-rev N/A

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

      3. lift-+.f64 N/A

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

      4. associate--r+ N/A

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

      5. sub-negate N/A

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

      6. lower--.f64 N/A

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

      7. *-lft-identity N/A

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

      8. metadata-eval N/A

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

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

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

      10. mul-1-neg N/A

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

      11. lower--.f64 N/A

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

      12. mul-1-neg N/A

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

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

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

      14. metadata-eval N/A

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

      15. *-lft-identity 88.4

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

      16. lift-*.f64 N/A

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

      17. *-commutative N/A

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

      18. lower-*.f64 88.4

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

   6. Applied rewrites 88.4%

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

   if -6.6e9 < z < 1.1499999999999999

   1. Initial program 99.9%

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

      1. lift--.f64 N/A

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

      2. sub-negate-rev N/A

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

      3. lift-+.f64 N/A

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

      4. associate--r+ N/A

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

      5. sub-negate N/A

         \[\leadsto \color{blue}{\cos y - \left(z \cdot \sin y - x\right)} \]

      6. lower--.f64 N/A

         \[\leadsto \color{blue}{\cos y - \left(z \cdot \sin y - x\right)} \]

      7. lower--.f64 99.9

         \[\leadsto \cos y - \color{blue}{\left(z \cdot \sin y - x\right)} \]

      8. lift-*.f64 N/A

         \[\leadsto \cos y - \left(\color{blue}{z \cdot \sin y} - x\right) \]

      9. *-commutative N/A

         \[\leadsto \cos y - \left(\color{blue}{\sin y \cdot z} - x\right) \]

      10. lower-*.f64 99.9

          \[\leadsto \cos y - \left(\color{blue}{\sin y \cdot z} - x\right) \]

   3. Applied rewrites 99.9%

      \[\leadsto \color{blue}{\cos y - \left(\sin y \cdot z - x\right)} \]

   4. Taylor expanded in z around 0

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

      1. lower-+.f64 N/A

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

      2. lower-cos.f64 72.1

         \[\leadsto x + \cos y \]

   6. Applied rewrites 72.1%

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

Alternative 4: 81.8% accurate, 1.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \sin y \cdot \left(-z\right)\\ \mathbf{if}\;z \leq -4.8 \cdot 10^{+195}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;z \leq 8.8 \cdot 10^{+117}:\\ \;\;\;\;x + \cos y\\ \mathbf{elif}\;z \leq 2.2 \cdot 10^{+233}:\\ \;\;\;\;\left(x + 1\right) - z \cdot y\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* (sin y) (- z))))
   (if (<= z -4.8e+195)
     t_0
     (if (<= z 8.8e+117)
       (+ x (cos y))
       (if (<= z 2.2e+233) (- (+ x 1.0) (* z y)) t_0)))))

C

double code(double x, double y, double z) {
	double t_0 = sin(y) * -z;
	double tmp;
	if (z <= -4.8e+195) {
		tmp = t_0;
	} else if (z <= 8.8e+117) {
		tmp = x + cos(y);
	} else if (z <= 2.2e+233) {
		tmp = (x + 1.0) - (z * y);
	} else {
		tmp = t_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, z)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: t_0
    real(8) :: tmp
    t_0 = sin(y) * -z
    if (z <= (-4.8d+195)) then
        tmp = t_0
    else if (z <= 8.8d+117) then
        tmp = x + cos(y)
    else if (z <= 2.2d+233) then
        tmp = (x + 1.0d0) - (z * y)
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = Math.sin(y) * -z;
	double tmp;
	if (z <= -4.8e+195) {
		tmp = t_0;
	} else if (z <= 8.8e+117) {
		tmp = x + Math.cos(y);
	} else if (z <= 2.2e+233) {
		tmp = (x + 1.0) - (z * y);
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = math.sin(y) * -z
	tmp = 0
	if z <= -4.8e+195:
		tmp = t_0
	elif z <= 8.8e+117:
		tmp = x + math.cos(y)
	elif z <= 2.2e+233:
		tmp = (x + 1.0) - (z * y)
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(sin(y) * Float64(-z))
	tmp = 0.0
	if (z <= -4.8e+195)
		tmp = t_0;
	elseif (z <= 8.8e+117)
		tmp = Float64(x + cos(y));
	elseif (z <= 2.2e+233)
		tmp = Float64(Float64(x + 1.0) - Float64(z * y));
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = sin(y) * -z;
	tmp = 0.0;
	if (z <= -4.8e+195)
		tmp = t_0;
	elseif (z <= 8.8e+117)
		tmp = x + cos(y);
	elseif (z <= 2.2e+233)
		tmp = (x + 1.0) - (z * y);
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(N[Sin[y], $MachinePrecision] * (-z)), $MachinePrecision]}, If[LessEqual[z, -4.8e+195], t$95$0, If[LessEqual[z, 8.8e+117], N[(x + N[Cos[y], $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 2.2e+233], N[(N[(x + 1.0), $MachinePrecision] - N[(z * y), $MachinePrecision]), $MachinePrecision], t$95$0]]]]

Derivation

1. Split input into 3 regimes

2. if z < -4.8000000000000005e195 or 2.19999999999999999e233 < z

   1. Initial program 99.9%

      \[\left(x + \cos y\right) - z \cdot \sin y \]

   2. Taylor expanded in z around inf

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

      1. lower-*.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. lower-sin.f64 29.1

         \[\leadsto -1 \cdot \left(z \cdot \sin y\right) \]

   4. Applied rewrites 29.1%

      \[\leadsto \color{blue}{-1 \cdot \left(z \cdot \sin y\right)} \]
   5. Step-by-step derivation

      1. lift-*.f64 N/A

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

      2. mul-1-neg N/A

         \[\leadsto \mathsf{neg}\left(z \cdot \sin y\right) \]

      3. lift-*.f64 N/A

         \[\leadsto \mathsf{neg}\left(z \cdot \sin y\right) \]

      4. *-commutative N/A

         \[\leadsto \mathsf{neg}\left(\sin y \cdot z\right) \]

      5. distribute-rgt-neg-out N/A

         \[\leadsto \sin y \cdot \color{blue}{\left(\mathsf{neg}\left(z\right)\right)} \]

      6. lift-neg.f64 N/A

         \[\leadsto \sin y \cdot \left(-z\right) \]

      7. lower-*.f64 29.1

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

   6. Applied rewrites 29.1%

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

   if -4.8000000000000005e195 < z < 8.80000000000000056e117

   1. Initial program 99.9%

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

      1. lift--.f64 N/A

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

      2. sub-negate-rev N/A

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

      3. lift-+.f64 N/A

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

      4. associate--r+ N/A

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

      5. sub-negate N/A

         \[\leadsto \color{blue}{\cos y - \left(z \cdot \sin y - x\right)} \]

      6. lower--.f64 N/A

         \[\leadsto \color{blue}{\cos y - \left(z \cdot \sin y - x\right)} \]

      7. lower--.f64 99.9

         \[\leadsto \cos y - \color{blue}{\left(z \cdot \sin y - x\right)} \]

      8. lift-*.f64 N/A

         \[\leadsto \cos y - \left(\color{blue}{z \cdot \sin y} - x\right) \]

      9. *-commutative N/A

         \[\leadsto \cos y - \left(\color{blue}{\sin y \cdot z} - x\right) \]

      10. lower-*.f64 99.9

          \[\leadsto \cos y - \left(\color{blue}{\sin y \cdot z} - x\right) \]

   3. Applied rewrites 99.9%

      \[\leadsto \color{blue}{\cos y - \left(\sin y \cdot z - x\right)} \]

   4. Taylor expanded in z around 0

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

      1. lower-+.f64 N/A

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

      2. lower-cos.f64 72.1

         \[\leadsto x + \cos y \]

   6. Applied rewrites 72.1%

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

   if 8.80000000000000056e117 < z < 2.19999999999999999e233

   1. Initial program 99.9%

      \[\left(x + \cos y\right) - z \cdot \sin y \]

   2. Taylor expanded in y around 0

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

   4. Applied rewrites 88.4%

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

   5. Taylor expanded in y around 0

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

   7. Applied rewrites 63.8%

      \[\leadsto \left(x + 1\right) - z \cdot \color{blue}{y} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 5: 81.0% accurate, 1.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := x + \cos y\\ \mathbf{if}\;y \leq -0.38:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;y \leq 0.195:\\ \;\;\;\;1 + \left(x + y \cdot \left(y \cdot \left(0.16666666666666666 \cdot \left(y \cdot z\right) - 0.5\right) - z\right)\right)\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (+ x (cos y))))
   (if (<= y -0.38)
     t_0
     (if (<= y 0.195)
       (+ 1.0 (+ x (* y (- (* y (- (* 0.16666666666666666 (* y z)) 0.5)) z))))
       t_0))))

C

double code(double x, double y, double z) {
	double t_0 = x + cos(y);
	double tmp;
	if (y <= -0.38) {
		tmp = t_0;
	} else if (y <= 0.195) {
		tmp = 1.0 + (x + (y * ((y * ((0.16666666666666666 * (y * z)) - 0.5)) - z)));
	} else {
		tmp = t_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, z)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: t_0
    real(8) :: tmp
    t_0 = x + cos(y)
    if (y <= (-0.38d0)) then
        tmp = t_0
    else if (y <= 0.195d0) then
        tmp = 1.0d0 + (x + (y * ((y * ((0.16666666666666666d0 * (y * z)) - 0.5d0)) - z)))
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = x + Math.cos(y);
	double tmp;
	if (y <= -0.38) {
		tmp = t_0;
	} else if (y <= 0.195) {
		tmp = 1.0 + (x + (y * ((y * ((0.16666666666666666 * (y * z)) - 0.5)) - z)));
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = x + math.cos(y)
	tmp = 0
	if y <= -0.38:
		tmp = t_0
	elif y <= 0.195:
		tmp = 1.0 + (x + (y * ((y * ((0.16666666666666666 * (y * z)) - 0.5)) - z)))
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(x + cos(y))
	tmp = 0.0
	if (y <= -0.38)
		tmp = t_0;
	elseif (y <= 0.195)
		tmp = Float64(1.0 + Float64(x + Float64(y * Float64(Float64(y * Float64(Float64(0.16666666666666666 * Float64(y * z)) - 0.5)) - z))));
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = x + cos(y);
	tmp = 0.0;
	if (y <= -0.38)
		tmp = t_0;
	elseif (y <= 0.195)
		tmp = 1.0 + (x + (y * ((y * ((0.16666666666666666 * (y * z)) - 0.5)) - z)));
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(x + N[Cos[y], $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -0.38], t$95$0, If[LessEqual[y, 0.195], N[(1.0 + N[(x + N[(y * N[(N[(y * N[(N[(0.16666666666666666 * N[(y * z), $MachinePrecision]), $MachinePrecision] - 0.5), $MachinePrecision]), $MachinePrecision] - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if y < -0.38 or 0.19500000000000001 < y

   1. Initial program 99.9%

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

      1. lift--.f64 N/A

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

      2. sub-negate-rev N/A

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

      3. lift-+.f64 N/A

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

      4. associate--r+ N/A

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

      5. sub-negate N/A

         \[\leadsto \color{blue}{\cos y - \left(z \cdot \sin y - x\right)} \]

      6. lower--.f64 N/A

         \[\leadsto \color{blue}{\cos y - \left(z \cdot \sin y - x\right)} \]

      7. lower--.f64 99.9

         \[\leadsto \cos y - \color{blue}{\left(z \cdot \sin y - x\right)} \]

      8. lift-*.f64 N/A

         \[\leadsto \cos y - \left(\color{blue}{z \cdot \sin y} - x\right) \]

      9. *-commutative N/A

         \[\leadsto \cos y - \left(\color{blue}{\sin y \cdot z} - x\right) \]

      10. lower-*.f64 99.9

          \[\leadsto \cos y - \left(\color{blue}{\sin y \cdot z} - x\right) \]

   3. Applied rewrites 99.9%

      \[\leadsto \color{blue}{\cos y - \left(\sin y \cdot z - x\right)} \]

   4. Taylor expanded in z around 0

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

      1. lower-+.f64 N/A

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

      2. lower-cos.f64 72.1

         \[\leadsto x + \cos y \]

   6. Applied rewrites 72.1%

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

   if -0.38 < y < 0.19500000000000001

   1. Initial program 99.9%

      \[\left(x + \cos y\right) - z \cdot \sin y \]

   2. Taylor expanded in y around 0

      \[\leadsto \color{blue}{1 + \left(x + y \cdot \left(y \cdot \left(\frac{1}{6} \cdot \left(y \cdot z\right) - \frac{1}{2}\right) - z\right)\right)} \]
   3. Step-by-step derivation

      1. lower-+.f64 N/A

         \[\leadsto 1 + \color{blue}{\left(x + y \cdot \left(y \cdot \left(\frac{1}{6} \cdot \left(y \cdot z\right) - \frac{1}{2}\right) - z\right)\right)} \]

      2. lower-+.f64 N/A

         \[\leadsto 1 + \left(x + \color{blue}{y \cdot \left(y \cdot \left(\frac{1}{6} \cdot \left(y \cdot z\right) - \frac{1}{2}\right) - z\right)}\right) \]

      3. lower-*.f64 N/A

         \[\leadsto 1 + \left(x + y \cdot \color{blue}{\left(y \cdot \left(\frac{1}{6} \cdot \left(y \cdot z\right) - \frac{1}{2}\right) - z\right)}\right) \]

      4. lower--.f64 N/A

         \[\leadsto 1 + \left(x + y \cdot \left(y \cdot \left(\frac{1}{6} \cdot \left(y \cdot z\right) - \frac{1}{2}\right) - \color{blue}{z}\right)\right) \]

      5. lower-*.f64 N/A

         \[\leadsto 1 + \left(x + y \cdot \left(y \cdot \left(\frac{1}{6} \cdot \left(y \cdot z\right) - \frac{1}{2}\right) - z\right)\right) \]

      6. lower--.f64 N/A

         \[\leadsto 1 + \left(x + y \cdot \left(y \cdot \left(\frac{1}{6} \cdot \left(y \cdot z\right) - \frac{1}{2}\right) - z\right)\right) \]

      7. lower-*.f64 N/A

         \[\leadsto 1 + \left(x + y \cdot \left(y \cdot \left(\frac{1}{6} \cdot \left(y \cdot z\right) - \frac{1}{2}\right) - z\right)\right) \]

      8. lower-*.f64 54.8

         \[\leadsto 1 + \left(x + y \cdot \left(y \cdot \left(0.16666666666666666 \cdot \left(y \cdot z\right) - 0.5\right) - z\right)\right) \]

   4. Applied rewrites 54.8%

      \[\leadsto \color{blue}{1 + \left(x + y \cdot \left(y \cdot \left(0.16666666666666666 \cdot \left(y \cdot z\right) - 0.5\right) - z\right)\right)} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 6: 69.9% accurate, 2.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1500000000:\\ \;\;\;\;x - -1\\ \mathbf{elif}\;y \leq 3.9 \cdot 10^{+24}:\\ \;\;\;\;1 + \left(x + y \cdot \left(y \cdot \left(0.16666666666666666 \cdot \left(y \cdot z\right) - 0.5\right) - z\right)\right)\\ \mathbf{else}:\\ \;\;\;\;x - -1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= y -1500000000.0)
   (- x -1.0)
   (if (<= y 3.9e+24)
     (+ 1.0 (+ x (* y (- (* y (- (* 0.16666666666666666 (* y z)) 0.5)) z))))
     (- x -1.0))))

C

double code(double x, double y, double z) {
	double tmp;
	if (y <= -1500000000.0) {
		tmp = x - -1.0;
	} else if (y <= 3.9e+24) {
		tmp = 1.0 + (x + (y * ((y * ((0.16666666666666666 * (y * z)) - 0.5)) - z)));
	} else {
		tmp = x - -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, 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 <= (-1500000000.0d0)) then
        tmp = x - (-1.0d0)
    else if (y <= 3.9d+24) then
        tmp = 1.0d0 + (x + (y * ((y * ((0.16666666666666666d0 * (y * z)) - 0.5d0)) - z)))
    else
        tmp = x - (-1.0d0)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (y <= -1500000000.0) {
		tmp = x - -1.0;
	} else if (y <= 3.9e+24) {
		tmp = 1.0 + (x + (y * ((y * ((0.16666666666666666 * (y * z)) - 0.5)) - z)));
	} else {
		tmp = x - -1.0;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if y <= -1500000000.0:
		tmp = x - -1.0
	elif y <= 3.9e+24:
		tmp = 1.0 + (x + (y * ((y * ((0.16666666666666666 * (y * z)) - 0.5)) - z)))
	else:
		tmp = x - -1.0
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (y <= -1500000000.0)
		tmp = Float64(x - -1.0);
	elseif (y <= 3.9e+24)
		tmp = Float64(1.0 + Float64(x + Float64(y * Float64(Float64(y * Float64(Float64(0.16666666666666666 * Float64(y * z)) - 0.5)) - z))));
	else
		tmp = Float64(x - -1.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (y <= -1500000000.0)
		tmp = x - -1.0;
	elseif (y <= 3.9e+24)
		tmp = 1.0 + (x + (y * ((y * ((0.16666666666666666 * (y * z)) - 0.5)) - z)));
	else
		tmp = x - -1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[y, -1500000000.0], N[(x - -1.0), $MachinePrecision], If[LessEqual[y, 3.9e+24], N[(1.0 + N[(x + N[(y * N[(N[(y * N[(N[(0.16666666666666666 * N[(y * z), $MachinePrecision]), $MachinePrecision] - 0.5), $MachinePrecision]), $MachinePrecision] - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x - -1.0), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if y < -1.5e9 or 3.8999999999999998e24 < y

   1. Initial program 99.9%

      \[\left(x + \cos y\right) - z \cdot \sin y \]

   2. Taylor expanded in y around 0

      \[\leadsto \color{blue}{1 + x} \]
   3. Step-by-step derivation

      1. lower-+.f64 61.1

         \[\leadsto 1 + \color{blue}{x} \]

   4. Applied rewrites 61.1%

      \[\leadsto \color{blue}{1 + x} \]
   5. Step-by-step derivation

      1. lift-+.f64 N/A

         \[\leadsto 1 + \color{blue}{x} \]

      2. +-commutative N/A

         \[\leadsto x + \color{blue}{1} \]

      3. add-flip N/A

         \[\leadsto x - \color{blue}{\left(\mathsf{neg}\left(1\right)\right)} \]

      4. metadata-eval N/A

         \[\leadsto x - -1 \]

      5. lower--.f64 61.1

         \[\leadsto x - \color{blue}{-1} \]

   6. Applied rewrites 61.1%

      \[\leadsto \color{blue}{x - -1} \]

   if -1.5e9 < y < 3.8999999999999998e24

   1. Initial program 99.9%

      \[\left(x + \cos y\right) - z \cdot \sin y \]

   2. Taylor expanded in y around 0

      \[\leadsto \color{blue}{1 + \left(x + y \cdot \left(y \cdot \left(\frac{1}{6} \cdot \left(y \cdot z\right) - \frac{1}{2}\right) - z\right)\right)} \]
   3. Step-by-step derivation

      1. lower-+.f64 N/A

         \[\leadsto 1 + \color{blue}{\left(x + y \cdot \left(y \cdot \left(\frac{1}{6} \cdot \left(y \cdot z\right) - \frac{1}{2}\right) - z\right)\right)} \]

      2. lower-+.f64 N/A

         \[\leadsto 1 + \left(x + \color{blue}{y \cdot \left(y \cdot \left(\frac{1}{6} \cdot \left(y \cdot z\right) - \frac{1}{2}\right) - z\right)}\right) \]

      3. lower-*.f64 N/A

         \[\leadsto 1 + \left(x + y \cdot \color{blue}{\left(y \cdot \left(\frac{1}{6} \cdot \left(y \cdot z\right) - \frac{1}{2}\right) - z\right)}\right) \]

      4. lower--.f64 N/A

         \[\leadsto 1 + \left(x + y \cdot \left(y \cdot \left(\frac{1}{6} \cdot \left(y \cdot z\right) - \frac{1}{2}\right) - \color{blue}{z}\right)\right) \]

      5. lower-*.f64 N/A

         \[\leadsto 1 + \left(x + y \cdot \left(y \cdot \left(\frac{1}{6} \cdot \left(y \cdot z\right) - \frac{1}{2}\right) - z\right)\right) \]

      6. lower--.f64 N/A

         \[\leadsto 1 + \left(x + y \cdot \left(y \cdot \left(\frac{1}{6} \cdot \left(y \cdot z\right) - \frac{1}{2}\right) - z\right)\right) \]

      7. lower-*.f64 N/A

         \[\leadsto 1 + \left(x + y \cdot \left(y \cdot \left(\frac{1}{6} \cdot \left(y \cdot z\right) - \frac{1}{2}\right) - z\right)\right) \]

      8. lower-*.f64 54.8

         \[\leadsto 1 + \left(x + y \cdot \left(y \cdot \left(0.16666666666666666 \cdot \left(y \cdot z\right) - 0.5\right) - z\right)\right) \]

   4. Applied rewrites 54.8%

      \[\leadsto \color{blue}{1 + \left(x + y \cdot \left(y \cdot \left(0.16666666666666666 \cdot \left(y \cdot z\right) - 0.5\right) - z\right)\right)} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 7: 69.9% accurate, 3.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -7000000000:\\ \;\;\;\;x - -1\\ \mathbf{elif}\;y \leq 1.4 \cdot 10^{+20}:\\ \;\;\;\;\mathsf{fma}\left(-0.5 \cdot y - z, y, x - -1\right)\\ \mathbf{else}:\\ \;\;\;\;x - -1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= y -7000000000.0)
   (- x -1.0)
   (if (<= y 1.4e+20) (fma (- (* -0.5 y) z) y (- x -1.0)) (- x -1.0))))

C

double code(double x, double y, double z) {
	double tmp;
	if (y <= -7000000000.0) {
		tmp = x - -1.0;
	} else if (y <= 1.4e+20) {
		tmp = fma(((-0.5 * y) - z), y, (x - -1.0));
	} else {
		tmp = x - -1.0;
	}
	return tmp;
}

Julia

function code(x, y, z)
	tmp = 0.0
	if (y <= -7000000000.0)
		tmp = Float64(x - -1.0);
	elseif (y <= 1.4e+20)
		tmp = fma(Float64(Float64(-0.5 * y) - z), y, Float64(x - -1.0));
	else
		tmp = Float64(x - -1.0);
	end
	return tmp
end

Wolfram

code[x_, y_, z_] := If[LessEqual[y, -7000000000.0], N[(x - -1.0), $MachinePrecision], If[LessEqual[y, 1.4e+20], N[(N[(N[(-0.5 * y), $MachinePrecision] - z), $MachinePrecision] * y + N[(x - -1.0), $MachinePrecision]), $MachinePrecision], N[(x - -1.0), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if y < -7e9 or 1.4e20 < y

   1. Initial program 99.9%

      \[\left(x + \cos y\right) - z \cdot \sin y \]

   2. Taylor expanded in y around 0

      \[\leadsto \color{blue}{1 + x} \]
   3. Step-by-step derivation

      1. lower-+.f64 61.1

         \[\leadsto 1 + \color{blue}{x} \]

   4. Applied rewrites 61.1%

      \[\leadsto \color{blue}{1 + x} \]
   5. Step-by-step derivation

      1. lift-+.f64 N/A

         \[\leadsto 1 + \color{blue}{x} \]

      2. +-commutative N/A

         \[\leadsto x + \color{blue}{1} \]

      3. add-flip N/A

         \[\leadsto x - \color{blue}{\left(\mathsf{neg}\left(1\right)\right)} \]

      4. metadata-eval N/A

         \[\leadsto x - -1 \]

      5. lower--.f64 61.1

         \[\leadsto x - \color{blue}{-1} \]

   6. Applied rewrites 61.1%

      \[\leadsto \color{blue}{x - -1} \]

   if -7e9 < y < 1.4e20

   1. Initial program 99.9%

      \[\left(x + \cos y\right) - z \cdot \sin y \]

   2. Taylor expanded in y around 0

      \[\leadsto \color{blue}{1 + \left(x + y \cdot \left(\frac{-1}{2} \cdot y - z\right)\right)} \]
   3. Step-by-step derivation

      1. lower-+.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower--.f64 N/A

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

      5. lower-*.f64 55.8

         \[\leadsto 1 + \left(x + y \cdot \left(-0.5 \cdot y - z\right)\right) \]

   4. Applied rewrites 55.8%

      \[\leadsto \color{blue}{1 + \left(x + y \cdot \left(-0.5 \cdot y - z\right)\right)} \]
   5. Step-by-step derivation

      1. lift-+.f64 N/A

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

      2. lift-+.f64 N/A

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

      3. associate-+r+ N/A

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

      4. lift-+.f64 N/A

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

      5. +-commutative N/A

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

      6. lift-*.f64 N/A

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

      7. *-commutative N/A

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

      8. lower-fma.f64 55.8

         \[\leadsto \mathsf{fma}\left(-0.5 \cdot y - z, \color{blue}{y}, 1 + x\right) \]

      9. lift-+.f64 N/A

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

      10. +-commutative N/A

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

      11. add-flip N/A

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

      12. metadata-eval N/A

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

      13. lower--.f64 55.8

          \[\leadsto \mathsf{fma}\left(-0.5 \cdot y - z, y, x - -1\right) \]

   6. Applied rewrites 55.8%

      \[\leadsto \mathsf{fma}\left(-0.5 \cdot y - z, \color{blue}{y}, x - -1\right) \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 8: 69.9% accurate, 2.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -7000000000:\\ \;\;\;\;x - -1\\ \mathbf{elif}\;y \leq 1.4 \cdot 10^{+20}:\\ \;\;\;\;\mathsf{fma}\left(\frac{y \cdot y}{z} \cdot -0.5 - y, z, x - -1\right)\\ \mathbf{else}:\\ \;\;\;\;x - -1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= y -7000000000.0)
   (- x -1.0)
   (if (<= y 1.4e+20)
     (fma (- (* (/ (* y y) z) -0.5) y) z (- x -1.0))
     (- x -1.0))))

C

double code(double x, double y, double z) {
	double tmp;
	if (y <= -7000000000.0) {
		tmp = x - -1.0;
	} else if (y <= 1.4e+20) {
		tmp = fma(((((y * y) / z) * -0.5) - y), z, (x - -1.0));
	} else {
		tmp = x - -1.0;
	}
	return tmp;
}

Julia

function code(x, y, z)
	tmp = 0.0
	if (y <= -7000000000.0)
		tmp = Float64(x - -1.0);
	elseif (y <= 1.4e+20)
		tmp = fma(Float64(Float64(Float64(Float64(y * y) / z) * -0.5) - y), z, Float64(x - -1.0));
	else
		tmp = Float64(x - -1.0);
	end
	return tmp
end

Wolfram

code[x_, y_, z_] := If[LessEqual[y, -7000000000.0], N[(x - -1.0), $MachinePrecision], If[LessEqual[y, 1.4e+20], N[(N[(N[(N[(N[(y * y), $MachinePrecision] / z), $MachinePrecision] * -0.5), $MachinePrecision] - y), $MachinePrecision] * z + N[(x - -1.0), $MachinePrecision]), $MachinePrecision], N[(x - -1.0), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if y < -7e9 or 1.4e20 < y

   1. Initial program 99.9%

      \[\left(x + \cos y\right) - z \cdot \sin y \]

   2. Taylor expanded in y around 0

      \[\leadsto \color{blue}{1 + x} \]
   3. Step-by-step derivation

      1. lower-+.f64 61.1

         \[\leadsto 1 + \color{blue}{x} \]

   4. Applied rewrites 61.1%

      \[\leadsto \color{blue}{1 + x} \]
   5. Step-by-step derivation

      1. lift-+.f64 N/A

         \[\leadsto 1 + \color{blue}{x} \]

      2. +-commutative N/A

         \[\leadsto x + \color{blue}{1} \]

      3. add-flip N/A

         \[\leadsto x - \color{blue}{\left(\mathsf{neg}\left(1\right)\right)} \]

      4. metadata-eval N/A

         \[\leadsto x - -1 \]

      5. lower--.f64 61.1

         \[\leadsto x - \color{blue}{-1} \]

   6. Applied rewrites 61.1%

      \[\leadsto \color{blue}{x - -1} \]

   if -7e9 < y < 1.4e20

   1. Initial program 99.9%

      \[\left(x + \cos y\right) - z \cdot \sin y \]

   2. Taylor expanded in y around 0

      \[\leadsto \color{blue}{1 + \left(x + y \cdot \left(\frac{-1}{2} \cdot y - z\right)\right)} \]
   3. Step-by-step derivation

      1. lower-+.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower--.f64 N/A

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

      5. lower-*.f64 55.8

         \[\leadsto 1 + \left(x + y \cdot \left(-0.5 \cdot y - z\right)\right) \]

   4. Applied rewrites 55.8%

      \[\leadsto \color{blue}{1 + \left(x + y \cdot \left(-0.5 \cdot y - z\right)\right)} \]
   5. Step-by-step derivation

      1. lift-+.f64 N/A

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

      2. lift-+.f64 N/A

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

      3. associate-+r+ N/A

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

      4. lift-+.f64 N/A

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

      5. +-commutative N/A

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

      6. lift-*.f64 N/A

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

      7. *-commutative N/A

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

      8. lower-fma.f64 55.8

         \[\leadsto \mathsf{fma}\left(-0.5 \cdot y - z, \color{blue}{y}, 1 + x\right) \]

      9. lift-+.f64 N/A

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

      10. +-commutative N/A

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

      11. add-flip N/A

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

      12. metadata-eval N/A

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

      13. lower--.f64 55.8

          \[\leadsto \mathsf{fma}\left(-0.5 \cdot y - z, y, x - -1\right) \]

   6. Applied rewrites 55.8%

      \[\leadsto \mathsf{fma}\left(-0.5 \cdot y - z, \color{blue}{y}, x - -1\right) \]

   8. Applied rewrites 54.8%

      \[\leadsto \mathsf{fma}\left(\frac{y \cdot y}{z} \cdot -0.5 - y, \color{blue}{z}, x - -1\right) \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 9: 69.7% accurate, 3.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -2 \cdot 10^{+77}:\\ \;\;\;\;x - -1\\ \mathbf{elif}\;y \leq 66:\\ \;\;\;\;\mathsf{fma}\left(-1 \cdot z, y, x - -1\right)\\ \mathbf{else}:\\ \;\;\;\;x - -1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= y -2e+77)
   (- x -1.0)
   (if (<= y 66.0) (fma (* -1.0 z) y (- x -1.0)) (- x -1.0))))

C

double code(double x, double y, double z) {
	double tmp;
	if (y <= -2e+77) {
		tmp = x - -1.0;
	} else if (y <= 66.0) {
		tmp = fma((-1.0 * z), y, (x - -1.0));
	} else {
		tmp = x - -1.0;
	}
	return tmp;
}

Julia

function code(x, y, z)
	tmp = 0.0
	if (y <= -2e+77)
		tmp = Float64(x - -1.0);
	elseif (y <= 66.0)
		tmp = fma(Float64(-1.0 * z), y, Float64(x - -1.0));
	else
		tmp = Float64(x - -1.0);
	end
	return tmp
end

Wolfram

code[x_, y_, z_] := If[LessEqual[y, -2e+77], N[(x - -1.0), $MachinePrecision], If[LessEqual[y, 66.0], N[(N[(-1.0 * z), $MachinePrecision] * y + N[(x - -1.0), $MachinePrecision]), $MachinePrecision], N[(x - -1.0), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if y < -1.99999999999999997e77 or 66 < y

   1. Initial program 99.9%

      \[\left(x + \cos y\right) - z \cdot \sin y \]

   2. Taylor expanded in y around 0

      \[\leadsto \color{blue}{1 + x} \]
   3. Step-by-step derivation

      1. lower-+.f64 61.1

         \[\leadsto 1 + \color{blue}{x} \]

   4. Applied rewrites 61.1%

      \[\leadsto \color{blue}{1 + x} \]
   5. Step-by-step derivation

      1. lift-+.f64 N/A

         \[\leadsto 1 + \color{blue}{x} \]

      2. +-commutative N/A

         \[\leadsto x + \color{blue}{1} \]

      3. add-flip N/A

         \[\leadsto x - \color{blue}{\left(\mathsf{neg}\left(1\right)\right)} \]

      4. metadata-eval N/A

         \[\leadsto x - -1 \]

      5. lower--.f64 61.1

         \[\leadsto x - \color{blue}{-1} \]

   6. Applied rewrites 61.1%

      \[\leadsto \color{blue}{x - -1} \]

   if -1.99999999999999997e77 < y < 66

   1. Initial program 99.9%

      \[\left(x + \cos y\right) - z \cdot \sin y \]

   2. Taylor expanded in y around 0

      \[\leadsto \color{blue}{1 + \left(x + y \cdot \left(\frac{-1}{2} \cdot y - z\right)\right)} \]
   3. Step-by-step derivation

      1. lower-+.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower--.f64 N/A

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

      5. lower-*.f64 55.8

         \[\leadsto 1 + \left(x + y \cdot \left(-0.5 \cdot y - z\right)\right) \]

   4. Applied rewrites 55.8%

      \[\leadsto \color{blue}{1 + \left(x + y \cdot \left(-0.5 \cdot y - z\right)\right)} \]
   5. Step-by-step derivation

      1. lift-+.f64 N/A

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

      2. lift-+.f64 N/A

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

      3. associate-+r+ N/A

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

      4. lift-+.f64 N/A

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

      5. +-commutative N/A

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

      6. lift-*.f64 N/A

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

      7. *-commutative N/A

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

      8. lower-fma.f64 55.8

         \[\leadsto \mathsf{fma}\left(-0.5 \cdot y - z, \color{blue}{y}, 1 + x\right) \]

      9. lift-+.f64 N/A

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

      10. +-commutative N/A

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

      11. add-flip N/A

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

      12. metadata-eval N/A

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

      13. lower--.f64 55.8

          \[\leadsto \mathsf{fma}\left(-0.5 \cdot y - z, y, x - -1\right) \]

   6. Applied rewrites 55.8%

      \[\leadsto \mathsf{fma}\left(-0.5 \cdot y - z, \color{blue}{y}, x - -1\right) \]

   7. Taylor expanded in y around 0

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

      1. lower-*.f64 63.8

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

   9. Applied rewrites 63.8%

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

Alternative 10: 69.7% accurate, 4.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -2 \cdot 10^{+77}:\\ \;\;\;\;x - -1\\ \mathbf{elif}\;y \leq 66:\\ \;\;\;\;\left(x + 1\right) - z \cdot y\\ \mathbf{else}:\\ \;\;\;\;x - -1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= y -2e+77)
   (- x -1.0)
   (if (<= y 66.0) (- (+ x 1.0) (* z y)) (- x -1.0))))

C

double code(double x, double y, double z) {
	double tmp;
	if (y <= -2e+77) {
		tmp = x - -1.0;
	} else if (y <= 66.0) {
		tmp = (x + 1.0) - (z * y);
	} else {
		tmp = x - -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, 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 <= (-2d+77)) then
        tmp = x - (-1.0d0)
    else if (y <= 66.0d0) then
        tmp = (x + 1.0d0) - (z * y)
    else
        tmp = x - (-1.0d0)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (y <= -2e+77) {
		tmp = x - -1.0;
	} else if (y <= 66.0) {
		tmp = (x + 1.0) - (z * y);
	} else {
		tmp = x - -1.0;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if y <= -2e+77:
		tmp = x - -1.0
	elif y <= 66.0:
		tmp = (x + 1.0) - (z * y)
	else:
		tmp = x - -1.0
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (y <= -2e+77)
		tmp = Float64(x - -1.0);
	elseif (y <= 66.0)
		tmp = Float64(Float64(x + 1.0) - Float64(z * y));
	else
		tmp = Float64(x - -1.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (y <= -2e+77)
		tmp = x - -1.0;
	elseif (y <= 66.0)
		tmp = (x + 1.0) - (z * y);
	else
		tmp = x - -1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[y, -2e+77], N[(x - -1.0), $MachinePrecision], If[LessEqual[y, 66.0], N[(N[(x + 1.0), $MachinePrecision] - N[(z * y), $MachinePrecision]), $MachinePrecision], N[(x - -1.0), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if y < -1.99999999999999997e77 or 66 < y

   1. Initial program 99.9%

      \[\left(x + \cos y\right) - z \cdot \sin y \]

   2. Taylor expanded in y around 0

      \[\leadsto \color{blue}{1 + x} \]
   3. Step-by-step derivation

      1. lower-+.f64 61.1

         \[\leadsto 1 + \color{blue}{x} \]

   4. Applied rewrites 61.1%

      \[\leadsto \color{blue}{1 + x} \]
   5. Step-by-step derivation

      1. lift-+.f64 N/A

         \[\leadsto 1 + \color{blue}{x} \]

      2. +-commutative N/A

         \[\leadsto x + \color{blue}{1} \]

      3. add-flip N/A

         \[\leadsto x - \color{blue}{\left(\mathsf{neg}\left(1\right)\right)} \]

      4. metadata-eval N/A

         \[\leadsto x - -1 \]

      5. lower--.f64 61.1

         \[\leadsto x - \color{blue}{-1} \]

   6. Applied rewrites 61.1%

      \[\leadsto \color{blue}{x - -1} \]

   if -1.99999999999999997e77 < y < 66

   1. Initial program 99.9%

      \[\left(x + \cos y\right) - z \cdot \sin y \]

   2. Taylor expanded in y around 0

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

   4. Applied rewrites 88.4%

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

   5. Taylor expanded in y around 0

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

   7. Applied rewrites 63.8%

      \[\leadsto \left(x + 1\right) - z \cdot \color{blue}{y} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 11: 62.8% accurate, 5.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := -1 \cdot \left(z \cdot y\right)\\ \mathbf{if}\;z \leq -5.6 \cdot 10^{+219}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;z \leq 1.35 \cdot 10^{+233}:\\ \;\;\;\;x - -1\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* -1.0 (* z y))))
   (if (<= z -5.6e+219) t_0 (if (<= z 1.35e+233) (- x -1.0) t_0))))

C

double code(double x, double y, double z) {
	double t_0 = -1.0 * (z * y);
	double tmp;
	if (z <= -5.6e+219) {
		tmp = t_0;
	} else if (z <= 1.35e+233) {
		tmp = x - -1.0;
	} else {
		tmp = t_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, z)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: t_0
    real(8) :: tmp
    t_0 = (-1.0d0) * (z * y)
    if (z <= (-5.6d+219)) then
        tmp = t_0
    else if (z <= 1.35d+233) then
        tmp = x - (-1.0d0)
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = -1.0 * (z * y);
	double tmp;
	if (z <= -5.6e+219) {
		tmp = t_0;
	} else if (z <= 1.35e+233) {
		tmp = x - -1.0;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = -1.0 * (z * y)
	tmp = 0
	if z <= -5.6e+219:
		tmp = t_0
	elif z <= 1.35e+233:
		tmp = x - -1.0
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(-1.0 * Float64(z * y))
	tmp = 0.0
	if (z <= -5.6e+219)
		tmp = t_0;
	elseif (z <= 1.35e+233)
		tmp = Float64(x - -1.0);
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = -1.0 * (z * y);
	tmp = 0.0;
	if (z <= -5.6e+219)
		tmp = t_0;
	elseif (z <= 1.35e+233)
		tmp = x - -1.0;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(-1.0 * N[(z * y), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -5.6e+219], t$95$0, If[LessEqual[z, 1.35e+233], N[(x - -1.0), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if z < -5.60000000000000031e219 or 1.35000000000000004e233 < z

   1. Initial program 99.9%

      \[\left(x + \cos y\right) - z \cdot \sin y \]

   2. Taylor expanded in z around inf

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

      1. lower-*.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. lower-sin.f64 29.1

         \[\leadsto -1 \cdot \left(z \cdot \sin y\right) \]

   4. Applied rewrites 29.1%

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

   5. Taylor expanded in y around 0

      \[\leadsto -1 \cdot \left(z \cdot y\right) \]
   6. Step-by-step derivation

   7. Applied rewrites 11.4%

      \[\leadsto -1 \cdot \left(z \cdot y\right) \]

   if -5.60000000000000031e219 < z < 1.35000000000000004e233

   1. Initial program 99.9%

      \[\left(x + \cos y\right) - z \cdot \sin y \]

   2. Taylor expanded in y around 0

      \[\leadsto \color{blue}{1 + x} \]
   3. Step-by-step derivation

      1. lower-+.f64 61.1

         \[\leadsto 1 + \color{blue}{x} \]

   4. Applied rewrites 61.1%

      \[\leadsto \color{blue}{1 + x} \]
   5. Step-by-step derivation

      1. lift-+.f64 N/A

         \[\leadsto 1 + \color{blue}{x} \]

      2. +-commutative N/A

         \[\leadsto x + \color{blue}{1} \]

      3. add-flip N/A

         \[\leadsto x - \color{blue}{\left(\mathsf{neg}\left(1\right)\right)} \]

      4. metadata-eval N/A

         \[\leadsto x - -1 \]

      5. lower--.f64 61.1

         \[\leadsto x - \color{blue}{-1} \]

   6. Applied rewrites 61.1%

      \[\leadsto \color{blue}{x - -1} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 12: 61.1% accurate, 20.2× speedup

Math

\[\begin{array}{l} \\ x - -1 \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (- x -1.0))

C

double code(double x, double y, double z) {
	return x - -1.0;
}

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

Java

public static double code(double x, double y, double z) {
	return x - -1.0;
}

Python

def code(x, y, z):
	return x - -1.0

Julia

function code(x, y, z)
	return Float64(x - -1.0)
end

MATLAB

function tmp = code(x, y, z)
	tmp = x - -1.0;
end

Wolfram

code[x_, y_, z_] := N[(x - -1.0), $MachinePrecision]

Derivation

1. Initial program 99.9%

   \[\left(x + \cos y\right) - z \cdot \sin y \]

2. Taylor expanded in y around 0

   \[\leadsto \color{blue}{1 + x} \]
3. Step-by-step derivation

   1. lower-+.f64 61.1

      \[\leadsto 1 + \color{blue}{x} \]

4. Applied rewrites 61.1%

   \[\leadsto \color{blue}{1 + x} \]
5. Step-by-step derivation

   1. lift-+.f64 N/A

      \[\leadsto 1 + \color{blue}{x} \]

   2. +-commutative N/A

      \[\leadsto x + \color{blue}{1} \]

   3. add-flip N/A

      \[\leadsto x - \color{blue}{\left(\mathsf{neg}\left(1\right)\right)} \]

   4. metadata-eval N/A

      \[\leadsto x - -1 \]

   5. lower--.f64 61.1

      \[\leadsto x - \color{blue}{-1} \]

6. Applied rewrites 61.1%

   \[\leadsto \color{blue}{x - -1} \]
7. Add Preprocessing

Alternative 13: 21.8% accurate, 72.9× speedup

Math

\[\begin{array}{l} \\ 1 \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 1.0)

C

double code(double x, double y, double z) {
	return 1.0;
}

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

Java

public static double code(double x, double y, double z) {
	return 1.0;
}

Python

def code(x, y, z):
	return 1.0

Julia

function code(x, y, z)
	return 1.0
end

MATLAB

function tmp = code(x, y, z)
	tmp = 1.0;
end

Wolfram

code[x_, y_, z_] := 1.0

Derivation

1. Initial program 99.9%

   \[\left(x + \cos y\right) - z \cdot \sin y \]

2. Taylor expanded in y around 0

   \[\leadsto \color{blue}{1 + x} \]
3. Step-by-step derivation

   1. lower-+.f64 61.1

      \[\leadsto 1 + \color{blue}{x} \]

4. Applied rewrites 61.1%

   \[\leadsto \color{blue}{1 + x} \]

5. Taylor expanded in x around 0

   \[\leadsto 1 \]
6. Step-by-step derivation

7. Applied rewrites 21.8%

   \[\leadsto 1 \]
8. Add Preprocessing

Reproduce

herbie shell --seed 2025154 
(FPCore (x y z)
  :name "Graphics.Rasterific.Svg.PathConverter:segmentToBezier from rasterific-svg-0.2.3.1, B"
  :precision binary64
  (- (+ x (cos y)) (* z (sin y))))