Diagrams.Backend.Rasterific:$crender from diagrams-rasterific-1.3.1.3

Percentage Accurate: 97.8% → 99.9%

Time: 5.0s

Alternatives: 7

Speedup: 0.8×

• 20.9% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

\[\begin{array}{l} \\ x \cdot y + \left(1 - x\right) \cdot z \end{array} \]

FPCore

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

C

double code(double x, double y, double z) {
	return (x * y) + ((1.0 - x) * 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 = (x * y) + ((1.0d0 - x) * z)
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 97.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ x \cdot y + \left(1 - x\right) \cdot z \end{array} \]

FPCore

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

C

double code(double x, double y, double z) {
	return (x * y) + ((1.0 - x) * 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 = (x * y) + ((1.0d0 - x) * z)
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 99.9% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left(1 - x\right) \cdot z\\ \mathbf{if}\;x \cdot y + t\_0 \leq 2 \cdot 10^{+306}:\\ \;\;\;\;\mathsf{fma}\left(y, x, t\_0\right)\\ \mathbf{else}:\\ \;\;\;\;\left(y - z\right) \cdot x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* (- 1.0 x) z)))
   (if (<= (+ (* x y) t_0) 2e+306) (fma y x t_0) (* (- y z) x))))

C

double code(double x, double y, double z) {
	double t_0 = (1.0 - x) * z;
	double tmp;
	if (((x * y) + t_0) <= 2e+306) {
		tmp = fma(y, x, t_0);
	} else {
		tmp = (y - z) * x;
	}
	return tmp;
}

Julia

function code(x, y, z)
	t_0 = Float64(Float64(1.0 - x) * z)
	tmp = 0.0
	if (Float64(Float64(x * y) + t_0) <= 2e+306)
		tmp = fma(y, x, t_0);
	else
		tmp = Float64(Float64(y - z) * x);
	end
	return tmp
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(N[(1.0 - x), $MachinePrecision] * z), $MachinePrecision]}, If[LessEqual[N[(N[(x * y), $MachinePrecision] + t$95$0), $MachinePrecision], 2e+306], N[(y * x + t$95$0), $MachinePrecision], N[(N[(y - z), $MachinePrecision] * x), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if (+.f64 (*.f64 x y) (*.f64 (-.f64 #s(literal 1 binary64) x) z)) < 2.00000000000000003e306

   1. Initial program 100.0%

      \[x \cdot y + \left(1 - x\right) \cdot z \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift-+.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. *-commutative N/A

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

      4. lift--.f64 N/A

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

      5. lift-*.f64 N/A

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

      6. lower-fma.f64 N/A

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

      7. lift-*.f64 N/A

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

      8. lift--.f64 100.0

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

   4. Applied rewrites 100.0%

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

   if 2.00000000000000003e306 < (+.f64 (*.f64 x y) (*.f64 (-.f64 #s(literal 1 binary64) x) z))

   1. Initial program 71.4%

      \[x \cdot y + \left(1 - x\right) \cdot z \]
   2. Add Preprocessing

   3. Taylor expanded in x around -inf

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

   5. Applied rewrites 100.0%

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

      1. *-commutative 100.0

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

      2. lift--.f64 N/A

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

      3. lift-*.f64 N/A

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

      4. lift-neg.f64 N/A

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

      5. mul-1-neg N/A

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

      6. distribute-lft-out-- N/A

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

      7. associate-*r* N/A

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

      8. mul-1-neg N/A

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

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

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

      10. mul-1-neg N/A

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

      11. distribute-rgt-neg-in N/A

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

      12. mul-1-neg N/A

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

      13. distribute-lft-in N/A

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

      14. +-commutative N/A

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

      15. *-commutative N/A

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

      16. lower-*.f64 N/A

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

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

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

      18. metadata-eval N/A

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

      19. *-lft-identity N/A

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

      20. lower--.f64 100.0

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

   7. Applied rewrites 100.0%

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

Alternative 2: 98.6% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1 \lor \neg \left(x \leq 1.45 \cdot 10^{-18}\right):\\ \;\;\;\;\left(y - z\right) \cdot x\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(y, x, z\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= x -1.0) (not (<= x 1.45e-18))) (* (- y z) x) (fma y x z)))

C

double code(double x, double y, double z) {
	double tmp;
	if ((x <= -1.0) || !(x <= 1.45e-18)) {
		tmp = (y - z) * x;
	} else {
		tmp = fma(y, x, z);
	}
	return tmp;
}

Julia

function code(x, y, z)
	tmp = 0.0
	if ((x <= -1.0) || !(x <= 1.45e-18))
		tmp = Float64(Float64(y - z) * x);
	else
		tmp = fma(y, x, z);
	end
	return tmp
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[x, -1.0], N[Not[LessEqual[x, 1.45e-18]], $MachinePrecision]], N[(N[(y - z), $MachinePrecision] * x), $MachinePrecision], N[(y * x + z), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < -1 or 1.45e-18 < x

   1. Initial program 93.5%

      \[x \cdot y + \left(1 - x\right) \cdot z \]
   2. Add Preprocessing

   3. Taylor expanded in x around -inf

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

   5. Applied rewrites 99.0%

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

      1. *-commutative 99.0

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

      2. lift--.f64 N/A

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

      3. lift-*.f64 N/A

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

      4. lift-neg.f64 N/A

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

      5. mul-1-neg N/A

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

      6. distribute-lft-out-- N/A

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

      7. associate-*r* N/A

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

      8. mul-1-neg N/A

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

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

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

      10. mul-1-neg N/A

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

      11. distribute-rgt-neg-in N/A

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

      12. mul-1-neg N/A

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

      13. distribute-lft-in N/A

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

      14. +-commutative N/A

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

      15. *-commutative N/A

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

      16. lower-*.f64 N/A

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

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

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

      18. metadata-eval N/A

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

      19. *-lft-identity N/A

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

      20. lower--.f64 99.0

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

   7. Applied rewrites 99.0%

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

   if -1 < x < 1.45e-18

   1. Initial program 100.0%

      \[x \cdot y + \left(1 - x\right) \cdot z \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

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

   5. Applied rewrites 99.9%

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

      1. lift-+.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. *-commutative N/A

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

      4. lower-fma.f64 99.9

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

   7. Applied rewrites 99.9%

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

4. Final simplification 99.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -1 \lor \neg \left(x \leq 1.45 \cdot 10^{-18}\right):\\ \;\;\;\;\left(y - z\right) \cdot x\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(y, x, z\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 86.9% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -2.3 \cdot 10^{+83} \lor \neg \left(z \leq 3.4 \cdot 10^{+15}\right):\\ \;\;\;\;\left(1 - x\right) \cdot z\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(y, x, z\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= z -2.3e+83) (not (<= z 3.4e+15))) (* (- 1.0 x) z) (fma y x z)))

C

double code(double x, double y, double z) {
	double tmp;
	if ((z <= -2.3e+83) || !(z <= 3.4e+15)) {
		tmp = (1.0 - x) * z;
	} else {
		tmp = fma(y, x, z);
	}
	return tmp;
}

Julia

function code(x, y, z)
	tmp = 0.0
	if ((z <= -2.3e+83) || !(z <= 3.4e+15))
		tmp = Float64(Float64(1.0 - x) * z);
	else
		tmp = fma(y, x, z);
	end
	return tmp
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[z, -2.3e+83], N[Not[LessEqual[z, 3.4e+15]], $MachinePrecision]], N[(N[(1.0 - x), $MachinePrecision] * z), $MachinePrecision], N[(y * x + z), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if z < -2.29999999999999995e83 or 3.4e15 < z

   1. Initial program 93.9%

      \[x \cdot y + \left(1 - x\right) \cdot z \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0

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

      1. *-commutative N/A

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

      2. lift-*.f64 N/A

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

      3. lift--.f64 90.3

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

   5. Applied rewrites 90.3%

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

   if -2.29999999999999995e83 < z < 3.4e15

   1. Initial program 99.3%

      \[x \cdot y + \left(1 - x\right) \cdot z \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

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

   5. Applied rewrites 93.1%

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

      1. lift-+.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. *-commutative N/A

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

      4. lower-fma.f64 93.1

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

   7. Applied rewrites 93.1%

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

4. Final simplification 91.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -2.3 \cdot 10^{+83} \lor \neg \left(z \leq 3.4 \cdot 10^{+15}\right):\\ \;\;\;\;\left(1 - x\right) \cdot z\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(y, x, z\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 76.2% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -2.05 \cdot 10^{+234} \lor \neg \left(x \leq 1.35 \cdot 10^{+67}\right):\\ \;\;\;\;\left(-x\right) \cdot z\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(y, x, z\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= x -2.05e+234) (not (<= x 1.35e+67))) (* (- x) z) (fma y x z)))

C

double code(double x, double y, double z) {
	double tmp;
	if ((x <= -2.05e+234) || !(x <= 1.35e+67)) {
		tmp = -x * z;
	} else {
		tmp = fma(y, x, z);
	}
	return tmp;
}

Julia

function code(x, y, z)
	tmp = 0.0
	if ((x <= -2.05e+234) || !(x <= 1.35e+67))
		tmp = Float64(Float64(-x) * z);
	else
		tmp = fma(y, x, z);
	end
	return tmp
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[x, -2.05e+234], N[Not[LessEqual[x, 1.35e+67]], $MachinePrecision]], N[((-x) * z), $MachinePrecision], N[(y * x + z), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < -2.04999999999999987e234 or 1.35e67 < x

   1. Initial program 92.5%

      \[x \cdot y + \left(1 - x\right) \cdot z \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0

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

      1. *-commutative N/A

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

      2. lift-*.f64 N/A

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

      3. lift--.f64 66.5

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

   5. Applied rewrites 66.5%

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

   6. Taylor expanded in x around inf

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

      1. mul-1-neg N/A

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

      2. lower-neg.f64 66.5

         \[\leadsto \left(-x\right) \cdot z \]

   8. Applied rewrites 66.5%

      \[\leadsto \left(-x\right) \cdot z \]

   if -2.04999999999999987e234 < x < 1.35e67

   1. Initial program 98.0%

      \[x \cdot y + \left(1 - x\right) \cdot z \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

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

   5. Applied rewrites 89.3%

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

      1. lift-+.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. *-commutative N/A

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

      4. lower-fma.f64 89.3

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

   7. Applied rewrites 89.3%

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

4. Final simplification 84.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -2.05 \cdot 10^{+234} \lor \neg \left(x \leq 1.35 \cdot 10^{+67}\right):\\ \;\;\;\;\left(-x\right) \cdot z\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(y, x, z\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 62.0% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -4200 \lor \neg \left(x \leq 1.75 \cdot 10^{-45}\right):\\ \;\;\;\;y \cdot x\\ \mathbf{else}:\\ \;\;\;\;z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= x -4200.0) (not (<= x 1.75e-45))) (* y x) z))

C

double code(double x, double y, double z) {
	double tmp;
	if ((x <= -4200.0) || !(x <= 1.75e-45)) {
		tmp = y * x;
	} else {
		tmp = z;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if ((x <= (-4200.0d0)) .or. (.not. (x <= 1.75d-45))) then
        tmp = y * x
    else
        tmp = z
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if ((x <= -4200.0) || !(x <= 1.75e-45)) {
		tmp = y * x;
	} else {
		tmp = z;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (x <= -4200.0) or not (x <= 1.75e-45):
		tmp = y * x
	else:
		tmp = z
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((x <= -4200.0) || !(x <= 1.75e-45))
		tmp = Float64(y * x);
	else
		tmp = z;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((x <= -4200.0) || ~((x <= 1.75e-45)))
		tmp = y * x;
	else
		tmp = z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[x, -4200.0], N[Not[LessEqual[x, 1.75e-45]], $MachinePrecision]], N[(y * x), $MachinePrecision], z]

Derivation

1. Split input into 2 regimes

2. if x < -4200 or 1.75e-45 < x

   1. Initial program 93.8%

      \[x \cdot y + \left(1 - x\right) \cdot z \]
   2. Add Preprocessing

   3. Taylor expanded in y around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 58.1

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

   5. Applied rewrites 58.1%

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

   if -4200 < x < 1.75e-45

   1. Initial program 100.0%

      \[x \cdot y + \left(1 - x\right) \cdot z \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

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

   5. Applied rewrites 71.8%

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

4. Final simplification 64.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -4200 \lor \neg \left(x \leq 1.75 \cdot 10^{-45}\right):\\ \;\;\;\;y \cdot x\\ \mathbf{else}:\\ \;\;\;\;z\\ \end{array} \]
5. Add Preprocessing

Alternative 6: 76.5% accurate, 2.4× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 96.9%

   \[x \cdot y + \left(1 - x\right) \cdot z \]
2. Add Preprocessing

3. Taylor expanded in x around 0

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

5. Applied rewrites 79.3%

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

   1. lift-+.f64 N/A

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

   2. lift-*.f64 N/A

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

   3. *-commutative N/A

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

   4. lower-fma.f64 79.3

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

7. Applied rewrites 79.3%

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

Alternative 7: 36.8% accurate, 17.0× speedup

Math

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

FPCore

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

C

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

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

code[x_, y_, z_] := z

Derivation

1. Initial program 96.9%

   \[x \cdot y + \left(1 - x\right) \cdot z \]
2. Add Preprocessing

3. Taylor expanded in x around 0

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

5. Applied rewrites 37.6%

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

6. Final simplification 37.6%

   \[\leadsto z \]
7. Add Preprocessing

Reproduce

herbie shell --seed 2025084 
(FPCore (x y z)
  :name "Diagrams.Backend.Rasterific:$crender from diagrams-rasterific-1.3.1.3"
  :precision binary64
  (+ (* x y) (* (- 1.0 x) z)))