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

Percentage Accurate: 99.9% → 99.9%

Time: 4.4s

Alternatives: 11

Speedup: 1.0×

Specification

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 99.9% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 99.9% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 99.9%

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

   1. lift-+.f64 N/A

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

   2. +-commutative N/A

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

   3. lift-*.f64 N/A

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

   4. *-commutative N/A

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

   5. lower-fma.f64 99.9

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

   6. lift-+.f64 N/A

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

   7. +-commutative N/A

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

   8. lower-+.f64 99.9

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

3. Applied rewrites 99.9%

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

Alternative 2: 88.9% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := z \cdot \cos y\\ \mathbf{if}\;z \leq -6.4 \cdot 10^{+188}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;z \leq 2.8 \cdot 10^{+32}:\\ \;\;\;\;\left(x + \sin y\right) + z\\ \mathbf{elif}\;z \leq 2.2 \cdot 10^{+108}:\\ \;\;\;\;t\_0\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(\cos y, z, x\right) + y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* z (cos y))))
   (if (<= z -6.4e+188)
     t_0
     (if (<= z 2.8e+32)
       (+ (+ x (sin y)) z)
       (if (<= z 2.2e+108) t_0 (+ (fma (cos y) z x) y))))))

C

double code(double x, double y, double z) {
	double t_0 = z * cos(y);
	double tmp;
	if (z <= -6.4e+188) {
		tmp = t_0;
	} else if (z <= 2.8e+32) {
		tmp = (x + sin(y)) + z;
	} else if (z <= 2.2e+108) {
		tmp = t_0;
	} else {
		tmp = fma(cos(y), z, x) + y;
	}
	return tmp;
}

Julia

function code(x, y, z)
	t_0 = Float64(z * cos(y))
	tmp = 0.0
	if (z <= -6.4e+188)
		tmp = t_0;
	elseif (z <= 2.8e+32)
		tmp = Float64(Float64(x + sin(y)) + z);
	elseif (z <= 2.2e+108)
		tmp = t_0;
	else
		tmp = Float64(fma(cos(y), z, x) + y);
	end
	return tmp
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(z * N[Cos[y], $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -6.4e+188], t$95$0, If[LessEqual[z, 2.8e+32], N[(N[(x + N[Sin[y], $MachinePrecision]), $MachinePrecision] + z), $MachinePrecision], If[LessEqual[z, 2.2e+108], t$95$0, N[(N[(N[Cos[y], $MachinePrecision] * z + x), $MachinePrecision] + y), $MachinePrecision]]]]]

Derivation

1. Split input into 3 regimes

2. if z < -6.39999999999999941e188 or 2.8e32 < z < 2.2000000000000001e108

   1. Initial program 99.9%

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

      1. lift-+.f64 N/A

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

      2. sum-to-mult N/A

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

      3. lower-*.f64 N/A

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

      4. +-commutative N/A

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

      5. lift-*.f64 N/A

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

      6. *-commutative N/A

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

      7. associate-/l* N/A

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

      8. lower-fma.f64 N/A

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

      9. lower-/.f64 96.0

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

      10. lift-+.f64 N/A

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

      11. +-commutative N/A

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

      12. lower-+.f64 96.0

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

      13. lift-+.f64 N/A

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

      14. +-commutative N/A

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

      15. lower-+.f64 96.0

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

   3. Applied rewrites 96.0%

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

      1. lift-/.f64 N/A

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

      2. div-flip N/A

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

      3. lower-/.f64 N/A

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

      4. lift-+.f64 N/A

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

      5. +-commutative N/A

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

      6. lift-sin.f64 N/A

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

      7. lower-/.f64 N/A

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

      8. lift-sin.f64 N/A

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

      9. +-commutative N/A

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

      10. lift-+.f64 95.9

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

   5. Applied rewrites 95.9%

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

   6. Taylor expanded in z around inf

      \[\leadsto \color{blue}{z \cdot \cos y} \]
   7. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto z \cdot \color{blue}{\cos y} \]

      2. lower-cos.f64 42.6

         \[\leadsto z \cdot \cos y \]

   8. Applied rewrites 42.6%

      \[\leadsto \color{blue}{z \cdot \cos y} \]

   if -6.39999999999999941e188 < z < 2.8e32

   1. Initial program 99.9%

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

   2. Taylor expanded in y around 0

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

   4. Applied rewrites 82.7%

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

   if 2.2000000000000001e108 < z

   1. Initial program 99.9%

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

   2. Taylor expanded in y around 0

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

   4. Applied rewrites 71.2%

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

      1. lift-+.f64 N/A

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

      2. +-commutative N/A

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

      3. lift-+.f64 N/A

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

      4. associate-+r+ N/A

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

      5. lower-+.f64 N/A

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

      6. lift-*.f64 N/A

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

      7. *-commutative N/A

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

      8. lower-fma.f64 71.2

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

   6. Applied rewrites 71.2%

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

Alternative 3: 88.7% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -4.4 \cdot 10^{-209}:\\ \;\;\;\;\left(x + \sin y\right) + z\\ \mathbf{elif}\;x \leq 220000000:\\ \;\;\;\;\mathsf{fma}\left(\cos y, z, \sin y\right)\\ \mathbf{else}:\\ \;\;\;\;\left(x - z\right) \cdot \frac{x + z}{x - z}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= x -4.4e-209)
   (+ (+ x (sin y)) z)
   (if (<= x 220000000.0)
     (fma (cos y) z (sin y))
     (* (- x z) (/ (+ x z) (- x z))))))

C

double code(double x, double y, double z) {
	double tmp;
	if (x <= -4.4e-209) {
		tmp = (x + sin(y)) + z;
	} else if (x <= 220000000.0) {
		tmp = fma(cos(y), z, sin(y));
	} else {
		tmp = (x - z) * ((x + z) / (x - z));
	}
	return tmp;
}

Julia

function code(x, y, z)
	tmp = 0.0
	if (x <= -4.4e-209)
		tmp = Float64(Float64(x + sin(y)) + z);
	elseif (x <= 220000000.0)
		tmp = fma(cos(y), z, sin(y));
	else
		tmp = Float64(Float64(x - z) * Float64(Float64(x + z) / Float64(x - z)));
	end
	return tmp
end

Wolfram

code[x_, y_, z_] := If[LessEqual[x, -4.4e-209], N[(N[(x + N[Sin[y], $MachinePrecision]), $MachinePrecision] + z), $MachinePrecision], If[LessEqual[x, 220000000.0], N[(N[Cos[y], $MachinePrecision] * z + N[Sin[y], $MachinePrecision]), $MachinePrecision], N[(N[(x - z), $MachinePrecision] * N[(N[(x + z), $MachinePrecision] / N[(x - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if x < -4.40000000000000019e-209

   1. Initial program 99.9%

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

   2. Taylor expanded in y around 0

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

   4. Applied rewrites 82.7%

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

   if -4.40000000000000019e-209 < x < 2.2e8

   1. Initial program 99.9%

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

      1. lift-+.f64 N/A

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

      2. +-commutative N/A

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

      3. lift-*.f64 N/A

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

      4. *-commutative N/A

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

      5. lower-fma.f64 99.9

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

      6. lift-+.f64 N/A

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

      7. +-commutative N/A

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

      8. lower-+.f64 99.9

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

   3. Applied rewrites 99.9%

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

   4. Taylor expanded in x around 0

      \[\leadsto \mathsf{fma}\left(\cos y, z, \color{blue}{\sin y}\right) \]
   5. Step-by-step derivation

      1. lower-sin.f64 58.8

         \[\leadsto \mathsf{fma}\left(\cos y, z, \sin y\right) \]

   6. Applied rewrites 58.8%

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

   if 2.2e8 < x

   1. Initial program 99.9%

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

   2. Taylor expanded in y around 0

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

      1. lower-+.f64 66.3

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

   4. Applied rewrites 66.3%

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

      1. lift-+.f64 N/A

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

      2. flip-+ N/A

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

      3. mult-flip N/A

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

      4. lower-*.f64 N/A

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

      5. difference-of-squares N/A

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

      6. lift-+.f64 N/A

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

      7. lower-*.f64 N/A

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

      8. lower--.f64 N/A

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

      9. lower-/.f64 N/A

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

      10. lower--.f64 34.6

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

   6. Applied rewrites 34.6%

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

      1. lift-*.f64 N/A

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

      2. lift-/.f64 N/A

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

      3. mult-flip-rev N/A

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

      4. lift-*.f64 N/A

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

      5. lift-+.f64 N/A

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

      6. lift--.f64 N/A

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

      7. difference-of-squares-rev N/A

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

      8. lift--.f64 N/A

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

      9. difference-of-squares-rev N/A

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

      10. lift-+.f64 N/A

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

      11. lift--.f64 N/A

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

      12. *-commutative N/A

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

      13. lift--.f64 N/A

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

      14. associate-/l* N/A

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

      15. lower-*.f64 N/A

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

      16. lower-/.f64 66.3

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

   8. Applied rewrites 66.3%

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

Alternative 4: 87.0% accurate, 1.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := z \cdot \cos y\\ \mathbf{if}\;z \leq -6.4 \cdot 10^{+188}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;z \leq 2.8 \cdot 10^{+32}:\\ \;\;\;\;\left(x + \sin y\right) + z\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* z (cos y))))
   (if (<= z -6.4e+188) t_0 (if (<= z 2.8e+32) (+ (+ x (sin y)) z) t_0))))

C

double code(double x, double y, double z) {
	double t_0 = z * cos(y);
	double tmp;
	if (z <= -6.4e+188) {
		tmp = t_0;
	} else if (z <= 2.8e+32) {
		tmp = (x + sin(y)) + 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 = z * cos(y)
    if (z <= (-6.4d+188)) then
        tmp = t_0
    else if (z <= 2.8d+32) then
        tmp = (x + sin(y)) + 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 = z * Math.cos(y);
	double tmp;
	if (z <= -6.4e+188) {
		tmp = t_0;
	} else if (z <= 2.8e+32) {
		tmp = (x + Math.sin(y)) + z;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = z * math.cos(y)
	tmp = 0
	if z <= -6.4e+188:
		tmp = t_0
	elif z <= 2.8e+32:
		tmp = (x + math.sin(y)) + z
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(z * cos(y))
	tmp = 0.0
	if (z <= -6.4e+188)
		tmp = t_0;
	elseif (z <= 2.8e+32)
		tmp = Float64(Float64(x + sin(y)) + z);
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = z * cos(y);
	tmp = 0.0;
	if (z <= -6.4e+188)
		tmp = t_0;
	elseif (z <= 2.8e+32)
		tmp = (x + sin(y)) + z;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(z * N[Cos[y], $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -6.4e+188], t$95$0, If[LessEqual[z, 2.8e+32], N[(N[(x + N[Sin[y], $MachinePrecision]), $MachinePrecision] + z), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if z < -6.39999999999999941e188 or 2.8e32 < z

   1. Initial program 99.9%

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

      1. lift-+.f64 N/A

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

      2. sum-to-mult N/A

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

      3. lower-*.f64 N/A

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

      4. +-commutative N/A

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

      5. lift-*.f64 N/A

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

      6. *-commutative N/A

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

      7. associate-/l* N/A

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

      8. lower-fma.f64 N/A

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

      9. lower-/.f64 96.0

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

      10. lift-+.f64 N/A

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

      11. +-commutative N/A

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

      12. lower-+.f64 96.0

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

      13. lift-+.f64 N/A

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

      14. +-commutative N/A

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

      15. lower-+.f64 96.0

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

   3. Applied rewrites 96.0%

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

      1. lift-/.f64 N/A

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

      2. div-flip N/A

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

      3. lower-/.f64 N/A

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

      4. lift-+.f64 N/A

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

      5. +-commutative N/A

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

      6. lift-sin.f64 N/A

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

      7. lower-/.f64 N/A

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

      8. lift-sin.f64 N/A

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

      9. +-commutative N/A

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

      10. lift-+.f64 95.9

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

   5. Applied rewrites 95.9%

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

   6. Taylor expanded in z around inf

      \[\leadsto \color{blue}{z \cdot \cos y} \]
   7. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto z \cdot \color{blue}{\cos y} \]

      2. lower-cos.f64 42.6

         \[\leadsto z \cdot \cos y \]

   8. Applied rewrites 42.6%

      \[\leadsto \color{blue}{z \cdot \cos y} \]

   if -6.39999999999999941e188 < z < 2.8e32

   1. Initial program 99.9%

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

   2. Taylor expanded in y around 0

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

   4. Applied rewrites 82.7%

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

Alternative 5: 81.3% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := 1 \cdot \sin y\\ t_1 := \left(x + \sin y\right) + z \cdot \cos y\\ \mathbf{if}\;t\_1 \leq -5:\\ \;\;\;\;x + z\\ \mathbf{elif}\;t\_1 \leq -0.2:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;t\_1 \leq 0.001:\\ \;\;\;\;x + \left(z + y \cdot \left(1 + y \cdot \mathsf{fma}\left(-0.5, z, -0.16666666666666666 \cdot y\right)\right)\right)\\ \mathbf{elif}\;t\_1 \leq 1:\\ \;\;\;\;t\_0\\ \mathbf{else}:\\ \;\;\;\;\left(x - z\right) \cdot \frac{x + z}{x - z}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* 1.0 (sin y))) (t_1 (+ (+ x (sin y)) (* z (cos y)))))
   (if (<= t_1 -5.0)
     (+ x z)
     (if (<= t_1 -0.2)
       t_0
       (if (<= t_1 0.001)
         (+
          x
          (+ z (* y (+ 1.0 (* y (fma -0.5 z (* -0.16666666666666666 y)))))))
         (if (<= t_1 1.0) t_0 (* (- x z) (/ (+ x z) (- x z)))))))))

C

double code(double x, double y, double z) {
	double t_0 = 1.0 * sin(y);
	double t_1 = (x + sin(y)) + (z * cos(y));
	double tmp;
	if (t_1 <= -5.0) {
		tmp = x + z;
	} else if (t_1 <= -0.2) {
		tmp = t_0;
	} else if (t_1 <= 0.001) {
		tmp = x + (z + (y * (1.0 + (y * fma(-0.5, z, (-0.16666666666666666 * y))))));
	} else if (t_1 <= 1.0) {
		tmp = t_0;
	} else {
		tmp = (x - z) * ((x + z) / (x - z));
	}
	return tmp;
}

Julia

function code(x, y, z)
	t_0 = Float64(1.0 * sin(y))
	t_1 = Float64(Float64(x + sin(y)) + Float64(z * cos(y)))
	tmp = 0.0
	if (t_1 <= -5.0)
		tmp = Float64(x + z);
	elseif (t_1 <= -0.2)
		tmp = t_0;
	elseif (t_1 <= 0.001)
		tmp = Float64(x + Float64(z + Float64(y * Float64(1.0 + Float64(y * fma(-0.5, z, Float64(-0.16666666666666666 * y)))))));
	elseif (t_1 <= 1.0)
		tmp = t_0;
	else
		tmp = Float64(Float64(x - z) * Float64(Float64(x + z) / Float64(x - z)));
	end
	return tmp
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(1.0 * N[Sin[y], $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(N[(x + N[Sin[y], $MachinePrecision]), $MachinePrecision] + N[(z * N[Cos[y], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, -5.0], N[(x + z), $MachinePrecision], If[LessEqual[t$95$1, -0.2], t$95$0, If[LessEqual[t$95$1, 0.001], N[(x + N[(z + N[(y * N[(1.0 + N[(y * N[(-0.5 * z + N[(-0.16666666666666666 * y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$1, 1.0], t$95$0, N[(N[(x - z), $MachinePrecision] * N[(N[(x + z), $MachinePrecision] / N[(x - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]]

Derivation

1. Split input into 4 regimes

2. if (+.f64 (+.f64 x (sin.f64 y)) (*.f64 z (cos.f64 y))) < -5

   1. Initial program 99.9%

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

   2. Taylor expanded in y around 0

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

      1. lower-+.f64 66.3

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

   4. Applied rewrites 66.3%

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

   if -5 < (+.f64 (+.f64 x (sin.f64 y)) (*.f64 z (cos.f64 y))) < -0.20000000000000001 or 1e-3 < (+.f64 (+.f64 x (sin.f64 y)) (*.f64 z (cos.f64 y))) < 1

   1. Initial program 99.9%

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

      1. lift-+.f64 N/A

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

      2. sum-to-mult N/A

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

      3. lower-*.f64 N/A

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

      4. +-commutative N/A

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

      5. lift-*.f64 N/A

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

      6. *-commutative N/A

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

      7. associate-/l* N/A

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

      8. lower-fma.f64 N/A

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

      9. lower-/.f64 96.0

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

      10. lift-+.f64 N/A

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

      11. +-commutative N/A

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

      12. lower-+.f64 96.0

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

      13. lift-+.f64 N/A

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

      14. +-commutative N/A

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

      15. lower-+.f64 96.0

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

   3. Applied rewrites 96.0%

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

   4. Taylor expanded in x around inf

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

   6. Applied rewrites 58.7%

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

   7. Taylor expanded in x around 0

      \[\leadsto 1 \cdot \color{blue}{\sin y} \]
   8. Step-by-step derivation

      1. lower-sin.f64 18.5

         \[\leadsto 1 \cdot \sin y \]

   9. Applied rewrites 18.5%

      \[\leadsto 1 \cdot \color{blue}{\sin y} \]

   if -0.20000000000000001 < (+.f64 (+.f64 x (sin.f64 y)) (*.f64 z (cos.f64 y))) < 1e-3

   1. Initial program 99.9%

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

   2. Taylor expanded in y around 0

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

      1. lower-+.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-+.f64 N/A

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

      5. lower-*.f64 N/A

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

      6. lower-fma.f64 N/A

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

      7. lower-*.f64 54.7

         \[\leadsto x + \left(z + y \cdot \left(1 + y \cdot \mathsf{fma}\left(-0.5, z, -0.16666666666666666 \cdot y\right)\right)\right) \]

   4. Applied rewrites 54.7%

      \[\leadsto \color{blue}{x + \left(z + y \cdot \left(1 + y \cdot \mathsf{fma}\left(-0.5, z, -0.16666666666666666 \cdot y\right)\right)\right)} \]

   if 1 < (+.f64 (+.f64 x (sin.f64 y)) (*.f64 z (cos.f64 y)))

   1. Initial program 99.9%

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

   2. Taylor expanded in y around 0

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

      1. lower-+.f64 66.3

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

   4. Applied rewrites 66.3%

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

      1. lift-+.f64 N/A

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

      2. flip-+ N/A

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

      3. mult-flip N/A

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

      4. lower-*.f64 N/A

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

      5. difference-of-squares N/A

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

      6. lift-+.f64 N/A

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

      7. lower-*.f64 N/A

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

      8. lower--.f64 N/A

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

      9. lower-/.f64 N/A

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

      10. lower--.f64 34.6

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

   6. Applied rewrites 34.6%

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

      1. lift-*.f64 N/A

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

      2. lift-/.f64 N/A

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

      3. mult-flip-rev N/A

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

      4. lift-*.f64 N/A

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

      5. lift-+.f64 N/A

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

      6. lift--.f64 N/A

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

      7. difference-of-squares-rev N/A

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

      8. lift--.f64 N/A

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

      9. difference-of-squares-rev N/A

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

      10. lift-+.f64 N/A

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

      11. lift--.f64 N/A

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

      12. *-commutative N/A

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

      13. lift--.f64 N/A

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

      14. associate-/l* N/A

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

      15. lower-*.f64 N/A

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

      16. lower-/.f64 66.3

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

   8. Applied rewrites 66.3%

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

Alternative 6: 70.6% accurate, 2.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left(x - z\right) \cdot \frac{x + z}{x - z}\\ \mathbf{if}\;y \leq -7.8 \cdot 10^{+65}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;y \leq 6 \cdot 10^{+55}:\\ \;\;\;\;\left(z + \mathsf{fma}\left(-0.5 \cdot z, y, 1\right) \cdot y\right) + x\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* (- x z) (/ (+ x z) (- x z)))))
   (if (<= y -7.8e+65)
     t_0
     (if (<= y 6e+55) (+ (+ z (* (fma (* -0.5 z) y 1.0) y)) x) t_0))))

C

double code(double x, double y, double z) {
	double t_0 = (x - z) * ((x + z) / (x - z));
	double tmp;
	if (y <= -7.8e+65) {
		tmp = t_0;
	} else if (y <= 6e+55) {
		tmp = (z + (fma((-0.5 * z), y, 1.0) * y)) + x;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Julia

function code(x, y, z)
	t_0 = Float64(Float64(x - z) * Float64(Float64(x + z) / Float64(x - z)))
	tmp = 0.0
	if (y <= -7.8e+65)
		tmp = t_0;
	elseif (y <= 6e+55)
		tmp = Float64(Float64(z + Float64(fma(Float64(-0.5 * z), y, 1.0) * y)) + x);
	else
		tmp = t_0;
	end
	return tmp
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(N[(x - z), $MachinePrecision] * N[(N[(x + z), $MachinePrecision] / N[(x - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -7.8e+65], t$95$0, If[LessEqual[y, 6e+55], N[(N[(z + N[(N[(N[(-0.5 * z), $MachinePrecision] * y + 1.0), $MachinePrecision] * y), $MachinePrecision]), $MachinePrecision] + x), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if y < -7.7999999999999996e65 or 6.00000000000000033e55 < y

   1. Initial program 99.9%

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

   2. Taylor expanded in y around 0

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

      1. lower-+.f64 66.3

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

   4. Applied rewrites 66.3%

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

      1. lift-+.f64 N/A

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

      2. flip-+ N/A

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

      3. mult-flip N/A

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

      4. lower-*.f64 N/A

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

      5. difference-of-squares N/A

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

      6. lift-+.f64 N/A

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

      7. lower-*.f64 N/A

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

      8. lower--.f64 N/A

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

      9. lower-/.f64 N/A

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

      10. lower--.f64 34.6

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

   6. Applied rewrites 34.6%

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

      1. lift-*.f64 N/A

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

      2. lift-/.f64 N/A

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

      3. mult-flip-rev N/A

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

      4. lift-*.f64 N/A

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

      5. lift-+.f64 N/A

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

      6. lift--.f64 N/A

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

      7. difference-of-squares-rev N/A

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

      8. lift--.f64 N/A

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

      9. difference-of-squares-rev N/A

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

      10. lift-+.f64 N/A

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

      11. lift--.f64 N/A

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

      12. *-commutative N/A

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

      13. lift--.f64 N/A

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

      14. associate-/l* N/A

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

      15. lower-*.f64 N/A

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

      16. lower-/.f64 66.3

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

   8. Applied rewrites 66.3%

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

   if -7.7999999999999996e65 < y < 6.00000000000000033e55

   1. Initial program 99.9%

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

   2. Taylor expanded in y around 0

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

      1. lower-+.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-+.f64 N/A

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

      5. lower-*.f64 N/A

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

      6. lower-fma.f64 N/A

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

      7. lower-*.f64 54.7

         \[\leadsto x + \left(z + y \cdot \left(1 + y \cdot \mathsf{fma}\left(-0.5, z, -0.16666666666666666 \cdot y\right)\right)\right) \]

   4. Applied rewrites 54.7%

      \[\leadsto \color{blue}{x + \left(z + y \cdot \left(1 + y \cdot \mathsf{fma}\left(-0.5, z, -0.16666666666666666 \cdot y\right)\right)\right)} \]
   5. Step-by-step derivation

      1. lift-+.f64 N/A

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

      2. +-commutative N/A

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

      3. lift-+.f64 N/A

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

      4. sum-to-mult N/A

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

      5. lower-fma.f64 N/A

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

      6. +-commutative N/A

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

      7. lift-*.f64 N/A

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

      8. *-commutative N/A

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

      9. associate-/l* N/A

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

      10. lower-fma.f64 N/A

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

      11. lift-+.f64 N/A

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

      12. +-commutative N/A

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

      13. lift-*.f64 N/A

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

      14. *-commutative N/A

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

      15. lower-fma.f64 N/A

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

      16. lower-/.f64 53.9

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(-0.5, z, -0.16666666666666666 \cdot y\right), y, 1\right), \frac{y}{z}, 1\right), z, x\right) \]

   6. Applied rewrites 53.9%

      \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(-0.5, z, -0.16666666666666666 \cdot y\right), y, 1\right), \frac{y}{z}, 1\right), \color{blue}{z}, x\right) \]

   7. Taylor expanded in y around 0

      \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{-1}{2} \cdot z, y, 1\right), \frac{y}{z}, 1\right), z, x\right) \]
   8. Step-by-step derivation

      1. lower-*.f64 56.0

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

   9. Applied rewrites 56.0%

      \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(-0.5 \cdot z, y, 1\right), \frac{y}{z}, 1\right), z, x\right) \]
   10. Step-by-step derivation

       1. lift-fma.f64 N/A

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

       2. lower-+.f64 N/A

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

       3. lift-fma.f64 N/A

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

       4. +-commutative N/A

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

       5. lift-/.f64 N/A

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

       6. associate-*r/ N/A

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

       7. sum-to-mult-rev N/A

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

       8. lower-+.f64 N/A

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

       9. lower-*.f64 57.7

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

   11. Applied rewrites 57.7%

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

Alternative 7: 70.6% accurate, 3.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left(x - z\right) \cdot \frac{x + z}{x - z}\\ \mathbf{if}\;y \leq -4.9 \cdot 10^{+73}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;y \leq 9.5 \cdot 10^{+56}:\\ \;\;\;\;x + \left(y + z\right)\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* (- x z) (/ (+ x z) (- x z)))))
   (if (<= y -4.9e+73) t_0 (if (<= y 9.5e+56) (+ x (+ y z)) t_0))))

C

double code(double x, double y, double z) {
	double t_0 = (x - z) * ((x + z) / (x - z));
	double tmp;
	if (y <= -4.9e+73) {
		tmp = t_0;
	} else if (y <= 9.5e+56) {
		tmp = x + (y + 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 - z) * ((x + z) / (x - z))
    if (y <= (-4.9d+73)) then
        tmp = t_0
    else if (y <= 9.5d+56) then
        tmp = x + (y + 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 - z) * ((x + z) / (x - z));
	double tmp;
	if (y <= -4.9e+73) {
		tmp = t_0;
	} else if (y <= 9.5e+56) {
		tmp = x + (y + z);
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = (x - z) * ((x + z) / (x - z))
	tmp = 0
	if y <= -4.9e+73:
		tmp = t_0
	elif y <= 9.5e+56:
		tmp = x + (y + z)
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(Float64(x - z) * Float64(Float64(x + z) / Float64(x - z)))
	tmp = 0.0
	if (y <= -4.9e+73)
		tmp = t_0;
	elseif (y <= 9.5e+56)
		tmp = Float64(x + Float64(y + z));
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = (x - z) * ((x + z) / (x - z));
	tmp = 0.0;
	if (y <= -4.9e+73)
		tmp = t_0;
	elseif (y <= 9.5e+56)
		tmp = x + (y + z);
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(N[(x - z), $MachinePrecision] * N[(N[(x + z), $MachinePrecision] / N[(x - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -4.9e+73], t$95$0, If[LessEqual[y, 9.5e+56], N[(x + N[(y + z), $MachinePrecision]), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if y < -4.8999999999999999e73 or 9.4999999999999997e56 < y

   1. Initial program 99.9%

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

   2. Taylor expanded in y around 0

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

      1. lower-+.f64 66.3

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

   4. Applied rewrites 66.3%

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

      1. lift-+.f64 N/A

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

      2. flip-+ N/A

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

      3. mult-flip N/A

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

      4. lower-*.f64 N/A

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

      5. difference-of-squares N/A

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

      6. lift-+.f64 N/A

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

      7. lower-*.f64 N/A

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

      8. lower--.f64 N/A

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

      9. lower-/.f64 N/A

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

      10. lower--.f64 34.6

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

   6. Applied rewrites 34.6%

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

      1. lift-*.f64 N/A

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

      2. lift-/.f64 N/A

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

      3. mult-flip-rev N/A

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

      4. lift-*.f64 N/A

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

      5. lift-+.f64 N/A

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

      6. lift--.f64 N/A

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

      7. difference-of-squares-rev N/A

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

      8. lift--.f64 N/A

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

      9. difference-of-squares-rev N/A

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

      10. lift-+.f64 N/A

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

      11. lift--.f64 N/A

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

      12. *-commutative N/A

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

      13. lift--.f64 N/A

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

      14. associate-/l* N/A

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

      15. lower-*.f64 N/A

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

      16. lower-/.f64 66.3

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

   8. Applied rewrites 66.3%

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

   if -4.8999999999999999e73 < y < 9.4999999999999997e56

   1. Initial program 99.9%

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

   2. Taylor expanded in y around 0

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

      1. lower-+.f64 N/A

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

      2. lower-+.f64 62.0

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

   4. Applied rewrites 62.0%

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

Alternative 8: 70.0% accurate, 5.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -4.9 \cdot 10^{+73}:\\ \;\;\;\;x + z\\ \mathbf{elif}\;y \leq 1.9 \cdot 10^{+40}:\\ \;\;\;\;x + \left(y + z\right)\\ \mathbf{else}:\\ \;\;\;\;x + z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= y -4.9e+73) (+ x z) (if (<= y 1.9e+40) (+ x (+ y z)) (+ x z))))

C

double code(double x, double y, double z) {
	double tmp;
	if (y <= -4.9e+73) {
		tmp = x + z;
	} else if (y <= 1.9e+40) {
		tmp = x + (y + z);
	} else {
		tmp = x + 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 (y <= (-4.9d+73)) then
        tmp = x + z
    else if (y <= 1.9d+40) then
        tmp = x + (y + z)
    else
        tmp = x + z
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (y <= -4.9e+73) {
		tmp = x + z;
	} else if (y <= 1.9e+40) {
		tmp = x + (y + z);
	} else {
		tmp = x + z;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if y <= -4.9e+73:
		tmp = x + z
	elif y <= 1.9e+40:
		tmp = x + (y + z)
	else:
		tmp = x + z
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (y <= -4.9e+73)
		tmp = Float64(x + z);
	elseif (y <= 1.9e+40)
		tmp = Float64(x + Float64(y + z));
	else
		tmp = Float64(x + z);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (y <= -4.9e+73)
		tmp = x + z;
	elseif (y <= 1.9e+40)
		tmp = x + (y + z);
	else
		tmp = x + z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[y, -4.9e+73], N[(x + z), $MachinePrecision], If[LessEqual[y, 1.9e+40], N[(x + N[(y + z), $MachinePrecision]), $MachinePrecision], N[(x + z), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if y < -4.8999999999999999e73 or 1.90000000000000002e40 < y

   1. Initial program 99.9%

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

   2. Taylor expanded in y around 0

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

      1. lower-+.f64 66.3

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

   4. Applied rewrites 66.3%

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

   if -4.8999999999999999e73 < y < 1.90000000000000002e40

   1. Initial program 99.9%

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

   2. Taylor expanded in y around 0

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

      1. lower-+.f64 N/A

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

      2. lower-+.f64 62.0

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

   4. Applied rewrites 62.0%

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

Alternative 9: 68.2% accurate, 6.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1.55 \cdot 10^{-236}:\\ \;\;\;\;x + z\\ \mathbf{elif}\;x \leq 8.8 \cdot 10^{-178}:\\ \;\;\;\;y + z\\ \mathbf{else}:\\ \;\;\;\;x + z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= x -1.55e-236) (+ x z) (if (<= x 8.8e-178) (+ y z) (+ x z))))

C

double code(double x, double y, double z) {
	double tmp;
	if (x <= -1.55e-236) {
		tmp = x + z;
	} else if (x <= 8.8e-178) {
		tmp = y + z;
	} else {
		tmp = x + 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 (x <= (-1.55d-236)) then
        tmp = x + z
    else if (x <= 8.8d-178) then
        tmp = y + z
    else
        tmp = x + z
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (x <= -1.55e-236) {
		tmp = x + z;
	} else if (x <= 8.8e-178) {
		tmp = y + z;
	} else {
		tmp = x + z;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if x <= -1.55e-236:
		tmp = x + z
	elif x <= 8.8e-178:
		tmp = y + z
	else:
		tmp = x + z
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (x <= -1.55e-236)
		tmp = Float64(x + z);
	elseif (x <= 8.8e-178)
		tmp = Float64(y + z);
	else
		tmp = Float64(x + z);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (x <= -1.55e-236)
		tmp = x + z;
	elseif (x <= 8.8e-178)
		tmp = y + z;
	else
		tmp = x + z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[x, -1.55e-236], N[(x + z), $MachinePrecision], If[LessEqual[x, 8.8e-178], N[(y + z), $MachinePrecision], N[(x + z), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if x < -1.5499999999999999e-236 or 8.8000000000000005e-178 < x

   1. Initial program 99.9%

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

   2. Taylor expanded in y around 0

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

      1. lower-+.f64 66.3

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

   4. Applied rewrites 66.3%

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

   if -1.5499999999999999e-236 < x < 8.8000000000000005e-178

   1. Initial program 99.9%

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

   2. Taylor expanded in y around 0

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

      1. lower-+.f64 N/A

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

      2. lower-+.f64 62.0

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

   4. Applied rewrites 62.0%

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

   5. Taylor expanded in x around 0

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

      1. lower-+.f64 30.1

         \[\leadsto y + z \]

   7. Applied rewrites 30.1%

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

Alternative 10: 30.1% accurate, 19.6× speedup

Math

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

FPCore

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

C

double code(double x, double y, double z) {
	return y + 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 = y + z
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 99.9%

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

2. Taylor expanded in y around 0

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

   1. lower-+.f64 N/A

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

   2. lower-+.f64 62.0

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

4. Applied rewrites 62.0%

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

5. Taylor expanded in x around 0

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

   1. lower-+.f64 30.1

      \[\leadsto y + z \]

7. Applied rewrites 30.1%

   \[\leadsto y + \color{blue}{z} \]
8. Add Preprocessing

Alternative 11: 26.3% accurate, 73.1× 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%

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

2. Taylor expanded in y around 0

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

   1. lower-+.f64 66.3

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

4. Applied rewrites 66.3%

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

5. Taylor expanded in x around 0

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

7. Applied rewrites 26.3%

   \[\leadsto z \]
8. Add Preprocessing

Reproduce

herbie shell --seed 2025149 
(FPCore (x y z)
  :name "Graphics.Rasterific.Svg.PathConverter:segmentToBezier from rasterific-svg-0.2.3.1, C"
  :precision binary64
  (+ (+ x (sin y)) (* z (cos y))))