Diagrams.Color.HSV:lerp from diagrams-contrib-1.3.0.5

Percentage Accurate: 97.8% → 100.0%

Time: 7.6s

Alternatives: 7

Speedup: 1.7×

• 21.1% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 97.8% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 100.0% accurate, 1.7× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 97.2%

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

3. Taylor expanded in x around 0

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

   1. +-commutative N/A

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

   2. distribute-lft-in N/A

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

   3. mul-1-neg N/A

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

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

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

   5. mul-1-neg N/A

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

   6. associate-+r+ N/A

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

   7. mul-1-neg N/A

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

   8. unsub-neg N/A

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

   9. associate-+l- N/A

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

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

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

   11. unsub-neg N/A

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

   12. mul-1-neg N/A

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

   13. sub-neg N/A

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

   14. mul-1-neg N/A

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

   15. +-commutative N/A

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

   16. mul-1-neg N/A

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

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

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

   18. lower-fma.f64 N/A

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

5. Simplified 100.0%

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

Alternative 2: 60.7% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1.65 \cdot 10^{+159}:\\ \;\;\;\;x \cdot \left(-y\right)\\ \mathbf{elif}\;x \leq -1.56 \cdot 10^{-108}:\\ \;\;\;\;x \cdot z\\ \mathbf{elif}\;x \leq 0.0021:\\ \;\;\;\;\mathsf{fma}\left(y, x, y\right)\\ \mathbf{else}:\\ \;\;\;\;x \cdot z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= x -1.65e+159)
   (* x (- y))
   (if (<= x -1.56e-108) (* x z) (if (<= x 0.0021) (fma y x y) (* x z)))))

C

double code(double x, double y, double z) {
	double tmp;
	if (x <= -1.65e+159) {
		tmp = x * -y;
	} else if (x <= -1.56e-108) {
		tmp = x * z;
	} else if (x <= 0.0021) {
		tmp = fma(y, x, y);
	} else {
		tmp = x * z;
	}
	return tmp;
}

Julia

function code(x, y, z)
	tmp = 0.0
	if (x <= -1.65e+159)
		tmp = Float64(x * Float64(-y));
	elseif (x <= -1.56e-108)
		tmp = Float64(x * z);
	elseif (x <= 0.0021)
		tmp = fma(y, x, y);
	else
		tmp = Float64(x * z);
	end
	return tmp
end

Wolfram

code[x_, y_, z_] := If[LessEqual[x, -1.65e+159], N[(x * (-y)), $MachinePrecision], If[LessEqual[x, -1.56e-108], N[(x * z), $MachinePrecision], If[LessEqual[x, 0.0021], N[(y * x + y), $MachinePrecision], N[(x * z), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if x < -1.6499999999999999e159

   1. Initial program 90.8%

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

   3. Taylor expanded in x around inf

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

      1. +-commutative N/A

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

      2. remove-double-neg N/A

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

      3. mul-1-neg N/A

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

      4. mul-1-neg N/A

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

      5. distribute-neg-in N/A

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

      6. lower-*.f64 N/A

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

      7. +-commutative N/A

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

      8. distribute-neg-in N/A

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

      9. mul-1-neg N/A

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

      10. remove-double-neg N/A

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

      11. unsub-neg N/A

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

      12. lower--.f64 99.9

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

   5. Simplified 99.9%

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

   6. Taylor expanded in z around 0

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

      1. associate-*r* N/A

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

      2. *-commutative N/A

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

      3. lower-*.f64 N/A

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

      4. mul-1-neg N/A

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

      5. lower-neg.f64 58.7

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

   8. Simplified 58.7%

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

   if -1.6499999999999999e159 < x < -1.56000000000000009e-108 or 0.00209999999999999987 < x

   1. Initial program 95.9%

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

   3. Taylor expanded in y around 0

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

      1. lower-*.f64 69.7

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

   5. Simplified 69.7%

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

   if -1.56000000000000009e-108 < x < 0.00209999999999999987

   1. Initial program 100.0%

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

   3. Taylor expanded in y around inf

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

      1. distribute-rgt-out-- N/A

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

      2. *-lft-identity N/A

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

      3. lower--.f64 N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 70.7

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

   5. Simplified 70.7%

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

      1. *-commutative N/A

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

      2. cancel-sign-sub-inv N/A

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

      3. lift-neg.f64 N/A

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

      4. distribute-rgt1-in N/A

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

      5. distribute-lft1-in N/A

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

      6. lift-neg.f64 N/A

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

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

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

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

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

      9. *-commutative N/A

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

      10. lower-fma.f64 N/A

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

      11. lower-neg.f64 70.7

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

   7. Applied egg-rr 70.7%

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

      1. neg-sub0 N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{0 - y}, x, y\right) \]

      2. flip3-- N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{\frac{{0}^{3} - {y}^{3}}{0 \cdot 0 + \left(y \cdot y + 0 \cdot y\right)}}, x, y\right) \]

      3. metadata-eval N/A

         \[\leadsto \mathsf{fma}\left(\frac{{0}^{3} - {y}^{3}}{\color{blue}{0} + \left(y \cdot y + 0 \cdot y\right)}, x, y\right) \]

      4. +-lft-identity N/A

         \[\leadsto \mathsf{fma}\left(\frac{{0}^{3} - {y}^{3}}{\color{blue}{y \cdot y + 0 \cdot y}}, x, y\right) \]

      5. distribute-rgt-out N/A

         \[\leadsto \mathsf{fma}\left(\frac{{0}^{3} - {y}^{3}}{\color{blue}{y \cdot \left(y + 0\right)}}, x, y\right) \]

      6. +-commutative N/A

         \[\leadsto \mathsf{fma}\left(\frac{{0}^{3} - {y}^{3}}{y \cdot \color{blue}{\left(0 + y\right)}}, x, y\right) \]

      7. +-lft-identity N/A

         \[\leadsto \mathsf{fma}\left(\frac{{0}^{3} - {y}^{3}}{y \cdot \color{blue}{y}}, x, y\right) \]

      8. metadata-eval N/A

         \[\leadsto \mathsf{fma}\left(\frac{\color{blue}{0} - {y}^{3}}{y \cdot y}, x, y\right) \]

      9. sub0-neg N/A

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

      10. cube-neg N/A

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

      11. lift-neg.f64 N/A

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

      12. sqr-pow N/A

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

      13. pow-prod-down N/A

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

      14. lift-neg.f64 N/A

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

      15. lift-neg.f64 N/A

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

      16. sqr-neg N/A

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

      17. pow-prod-down N/A

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

      18. sqr-pow N/A

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

      19. pow2 N/A

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

      20. pow-div N/A

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

      21. metadata-eval N/A

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

      22. unpow1 69.9

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

   9. Applied egg-rr 69.9%

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

4. Final simplification 68.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -1.65 \cdot 10^{+159}:\\ \;\;\;\;x \cdot \left(-y\right)\\ \mathbf{elif}\;x \leq -1.56 \cdot 10^{-108}:\\ \;\;\;\;x \cdot z\\ \mathbf{elif}\;x \leq 0.0021:\\ \;\;\;\;\mathsf{fma}\left(y, x, y\right)\\ \mathbf{else}:\\ \;\;\;\;x \cdot z\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 84.2% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := x \cdot \left(z - y\right)\\ \mathbf{if}\;x \leq -1.56 \cdot 10^{-108}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;x \leq 3350000:\\ \;\;\;\;\mathsf{fma}\left(-y, x, y\right)\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* x (- z y))))
   (if (<= x -1.56e-108) t_0 (if (<= x 3350000.0) (fma (- y) x y) t_0))))

C

double code(double x, double y, double z) {
	double t_0 = x * (z - y);
	double tmp;
	if (x <= -1.56e-108) {
		tmp = t_0;
	} else if (x <= 3350000.0) {
		tmp = fma(-y, x, y);
	} else {
		tmp = t_0;
	}
	return tmp;
}

Julia

function code(x, y, z)
	t_0 = Float64(x * Float64(z - y))
	tmp = 0.0
	if (x <= -1.56e-108)
		tmp = t_0;
	elseif (x <= 3350000.0)
		tmp = fma(Float64(-y), x, y);
	else
		tmp = t_0;
	end
	return tmp
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(x * N[(z - y), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -1.56e-108], t$95$0, If[LessEqual[x, 3350000.0], N[((-y) * x + y), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if x < -1.56000000000000009e-108 or 3.35e6 < x

   1. Initial program 94.5%

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

   3. Taylor expanded in x around inf

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

      1. +-commutative N/A

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

      2. remove-double-neg N/A

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

      3. mul-1-neg N/A

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

      4. mul-1-neg N/A

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

      5. distribute-neg-in N/A

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

      6. lower-*.f64 N/A

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

      7. +-commutative N/A

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

      8. distribute-neg-in N/A

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

      9. mul-1-neg N/A

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

      10. remove-double-neg N/A

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

      11. unsub-neg N/A

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

      12. lower--.f64 95.7

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

   5. Simplified 95.7%

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

   if -1.56000000000000009e-108 < x < 3.35e6

   1. Initial program 100.0%

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

   3. Taylor expanded in y around inf

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

      1. distribute-rgt-out-- N/A

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

      2. *-lft-identity N/A

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

      3. lower--.f64 N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 71.3

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

   5. Simplified 71.3%

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

      1. *-commutative N/A

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

      2. cancel-sign-sub-inv N/A

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

      3. lift-neg.f64 N/A

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

      4. distribute-rgt1-in N/A

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

      5. distribute-lft1-in N/A

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

      6. lift-neg.f64 N/A

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

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

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

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

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

      9. *-commutative N/A

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

      10. lower-fma.f64 N/A

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

      11. lower-neg.f64 71.4

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

   7. Applied egg-rr 71.4%

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

Alternative 4: 84.2% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := x \cdot \left(z - y\right)\\ \mathbf{if}\;x \leq -1.56 \cdot 10^{-108}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;x \leq 3350000:\\ \;\;\;\;y - x \cdot y\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* x (- z y))))
   (if (<= x -1.56e-108) t_0 (if (<= x 3350000.0) (- y (* x y)) t_0))))

C

double code(double x, double y, double z) {
	double t_0 = x * (z - y);
	double tmp;
	if (x <= -1.56e-108) {
		tmp = t_0;
	} else if (x <= 3350000.0) {
		tmp = y - (x * y);
	} else {
		tmp = t_0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    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 - y)
    if (x <= (-1.56d-108)) then
        tmp = t_0
    else if (x <= 3350000.0d0) then
        tmp = y - (x * y)
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = x * (z - y);
	double tmp;
	if (x <= -1.56e-108) {
		tmp = t_0;
	} else if (x <= 3350000.0) {
		tmp = y - (x * y);
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = x * (z - y)
	tmp = 0
	if x <= -1.56e-108:
		tmp = t_0
	elif x <= 3350000.0:
		tmp = y - (x * y)
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(x * Float64(z - y))
	tmp = 0.0
	if (x <= -1.56e-108)
		tmp = t_0;
	elseif (x <= 3350000.0)
		tmp = Float64(y - Float64(x * y));
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = x * (z - y);
	tmp = 0.0;
	if (x <= -1.56e-108)
		tmp = t_0;
	elseif (x <= 3350000.0)
		tmp = y - (x * y);
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(x * N[(z - y), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -1.56e-108], t$95$0, If[LessEqual[x, 3350000.0], N[(y - N[(x * y), $MachinePrecision]), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if x < -1.56000000000000009e-108 or 3.35e6 < x

   1. Initial program 94.5%

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

   3. Taylor expanded in x around inf

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

      1. +-commutative N/A

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

      2. remove-double-neg N/A

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

      3. mul-1-neg N/A

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

      4. mul-1-neg N/A

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

      5. distribute-neg-in N/A

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

      6. lower-*.f64 N/A

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

      7. +-commutative N/A

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

      8. distribute-neg-in N/A

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

      9. mul-1-neg N/A

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

      10. remove-double-neg N/A

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

      11. unsub-neg N/A

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

      12. lower--.f64 95.7

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

   5. Simplified 95.7%

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

   if -1.56000000000000009e-108 < x < 3.35e6

   1. Initial program 100.0%

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

   3. Taylor expanded in y around inf

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

      1. distribute-rgt-out-- N/A

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

      2. *-lft-identity N/A

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

      3. lower--.f64 N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 71.3

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

   5. Simplified 71.3%

      \[\leadsto \color{blue}{y - y \cdot x} \]
3. Recombined 2 regimes into one program.

4. Final simplification 83.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -1.56 \cdot 10^{-108}:\\ \;\;\;\;x \cdot \left(z - y\right)\\ \mathbf{elif}\;x \leq 3350000:\\ \;\;\;\;y - x \cdot y\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(z - y\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 84.1% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := x \cdot \left(z - y\right)\\ \mathbf{if}\;x \leq -1.56 \cdot 10^{-108}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;x \leq 0.0021:\\ \;\;\;\;\mathsf{fma}\left(y, x, y\right)\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* x (- z y))))
   (if (<= x -1.56e-108) t_0 (if (<= x 0.0021) (fma y x y) t_0))))

C

double code(double x, double y, double z) {
	double t_0 = x * (z - y);
	double tmp;
	if (x <= -1.56e-108) {
		tmp = t_0;
	} else if (x <= 0.0021) {
		tmp = fma(y, x, y);
	} else {
		tmp = t_0;
	}
	return tmp;
}

Julia

function code(x, y, z)
	t_0 = Float64(x * Float64(z - y))
	tmp = 0.0
	if (x <= -1.56e-108)
		tmp = t_0;
	elseif (x <= 0.0021)
		tmp = fma(y, x, y);
	else
		tmp = t_0;
	end
	return tmp
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(x * N[(z - y), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -1.56e-108], t$95$0, If[LessEqual[x, 0.0021], N[(y * x + y), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if x < -1.56000000000000009e-108 or 0.00209999999999999987 < x

   1. Initial program 94.6%

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

   3. Taylor expanded in x around inf

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

      1. +-commutative N/A

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

      2. remove-double-neg N/A

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

      3. mul-1-neg N/A

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

      4. mul-1-neg N/A

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

      5. distribute-neg-in N/A

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

      6. lower-*.f64 N/A

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

      7. +-commutative N/A

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

      8. distribute-neg-in N/A

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

      9. mul-1-neg N/A

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

      10. remove-double-neg N/A

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

      11. unsub-neg N/A

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

      12. lower--.f64 94.6

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

   5. Simplified 94.6%

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

   if -1.56000000000000009e-108 < x < 0.00209999999999999987

   1. Initial program 100.0%

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

   3. Taylor expanded in y around inf

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

      1. distribute-rgt-out-- N/A

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

      2. *-lft-identity N/A

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

      3. lower--.f64 N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 70.7

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

   5. Simplified 70.7%

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

      1. *-commutative N/A

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

      2. cancel-sign-sub-inv N/A

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

      3. lift-neg.f64 N/A

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

      4. distribute-rgt1-in N/A

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

      5. distribute-lft1-in N/A

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

      6. lift-neg.f64 N/A

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

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

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

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

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

      9. *-commutative N/A

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

      10. lower-fma.f64 N/A

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

      11. lower-neg.f64 70.7

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

   7. Applied egg-rr 70.7%

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

      1. neg-sub0 N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{0 - y}, x, y\right) \]

      2. flip3-- N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{\frac{{0}^{3} - {y}^{3}}{0 \cdot 0 + \left(y \cdot y + 0 \cdot y\right)}}, x, y\right) \]

      3. metadata-eval N/A

         \[\leadsto \mathsf{fma}\left(\frac{{0}^{3} - {y}^{3}}{\color{blue}{0} + \left(y \cdot y + 0 \cdot y\right)}, x, y\right) \]

      4. +-lft-identity N/A

         \[\leadsto \mathsf{fma}\left(\frac{{0}^{3} - {y}^{3}}{\color{blue}{y \cdot y + 0 \cdot y}}, x, y\right) \]

      5. distribute-rgt-out N/A

         \[\leadsto \mathsf{fma}\left(\frac{{0}^{3} - {y}^{3}}{\color{blue}{y \cdot \left(y + 0\right)}}, x, y\right) \]

      6. +-commutative N/A

         \[\leadsto \mathsf{fma}\left(\frac{{0}^{3} - {y}^{3}}{y \cdot \color{blue}{\left(0 + y\right)}}, x, y\right) \]

      7. +-lft-identity N/A

         \[\leadsto \mathsf{fma}\left(\frac{{0}^{3} - {y}^{3}}{y \cdot \color{blue}{y}}, x, y\right) \]

      8. metadata-eval N/A

         \[\leadsto \mathsf{fma}\left(\frac{\color{blue}{0} - {y}^{3}}{y \cdot y}, x, y\right) \]

      9. sub0-neg N/A

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

      10. cube-neg N/A

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

      11. lift-neg.f64 N/A

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

      12. sqr-pow N/A

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

      13. pow-prod-down N/A

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

      14. lift-neg.f64 N/A

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

      15. lift-neg.f64 N/A

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

      16. sqr-neg N/A

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

      17. pow-prod-down N/A

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

      18. sqr-pow N/A

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

      19. pow2 N/A

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

      20. pow-div N/A

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

      21. metadata-eval N/A

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

      22. unpow1 69.9

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

   9. Applied egg-rr 69.9%

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

Alternative 6: 60.4% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1.56 \cdot 10^{-108}:\\ \;\;\;\;x \cdot z\\ \mathbf{elif}\;x \leq 0.0021:\\ \;\;\;\;\mathsf{fma}\left(y, x, y\right)\\ \mathbf{else}:\\ \;\;\;\;x \cdot z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= x -1.56e-108) (* x z) (if (<= x 0.0021) (fma y x y) (* x z))))

C

double code(double x, double y, double z) {
	double tmp;
	if (x <= -1.56e-108) {
		tmp = x * z;
	} else if (x <= 0.0021) {
		tmp = fma(y, x, y);
	} else {
		tmp = x * z;
	}
	return tmp;
}

Julia

function code(x, y, z)
	tmp = 0.0
	if (x <= -1.56e-108)
		tmp = Float64(x * z);
	elseif (x <= 0.0021)
		tmp = fma(y, x, y);
	else
		tmp = Float64(x * z);
	end
	return tmp
end

Wolfram

code[x_, y_, z_] := If[LessEqual[x, -1.56e-108], N[(x * z), $MachinePrecision], If[LessEqual[x, 0.0021], N[(y * x + y), $MachinePrecision], N[(x * z), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if x < -1.56000000000000009e-108 or 0.00209999999999999987 < x

   1. Initial program 94.6%

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

   3. Taylor expanded in y around 0

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

      1. lower-*.f64 63.8

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

   5. Simplified 63.8%

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

   if -1.56000000000000009e-108 < x < 0.00209999999999999987

   1. Initial program 100.0%

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

   3. Taylor expanded in y around inf

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

      1. distribute-rgt-out-- N/A

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

      2. *-lft-identity N/A

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

      3. lower--.f64 N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 70.7

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

   5. Simplified 70.7%

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

      1. *-commutative N/A

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

      2. cancel-sign-sub-inv N/A

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

      3. lift-neg.f64 N/A

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

      4. distribute-rgt1-in N/A

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

      5. distribute-lft1-in N/A

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

      6. lift-neg.f64 N/A

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

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

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

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

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

      9. *-commutative N/A

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

      10. lower-fma.f64 N/A

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

      11. lower-neg.f64 70.7

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

   7. Applied egg-rr 70.7%

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

      1. neg-sub0 N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{0 - y}, x, y\right) \]

      2. flip3-- N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{\frac{{0}^{3} - {y}^{3}}{0 \cdot 0 + \left(y \cdot y + 0 \cdot y\right)}}, x, y\right) \]

      3. metadata-eval N/A

         \[\leadsto \mathsf{fma}\left(\frac{{0}^{3} - {y}^{3}}{\color{blue}{0} + \left(y \cdot y + 0 \cdot y\right)}, x, y\right) \]

      4. +-lft-identity N/A

         \[\leadsto \mathsf{fma}\left(\frac{{0}^{3} - {y}^{3}}{\color{blue}{y \cdot y + 0 \cdot y}}, x, y\right) \]

      5. distribute-rgt-out N/A

         \[\leadsto \mathsf{fma}\left(\frac{{0}^{3} - {y}^{3}}{\color{blue}{y \cdot \left(y + 0\right)}}, x, y\right) \]

      6. +-commutative N/A

         \[\leadsto \mathsf{fma}\left(\frac{{0}^{3} - {y}^{3}}{y \cdot \color{blue}{\left(0 + y\right)}}, x, y\right) \]

      7. +-lft-identity N/A

         \[\leadsto \mathsf{fma}\left(\frac{{0}^{3} - {y}^{3}}{y \cdot \color{blue}{y}}, x, y\right) \]

      8. metadata-eval N/A

         \[\leadsto \mathsf{fma}\left(\frac{\color{blue}{0} - {y}^{3}}{y \cdot y}, x, y\right) \]

      9. sub0-neg N/A

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

      10. cube-neg N/A

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

      11. lift-neg.f64 N/A

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

      12. sqr-pow N/A

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

      13. pow-prod-down N/A

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

      14. lift-neg.f64 N/A

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

      15. lift-neg.f64 N/A

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

      16. sqr-neg N/A

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

      17. pow-prod-down N/A

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

      18. sqr-pow N/A

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

      19. pow2 N/A

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

      20. pow-div N/A

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

      21. metadata-eval N/A

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

      22. unpow1 69.9

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

   9. Applied egg-rr 69.9%

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

Alternative 7: 41.7% accurate, 2.8× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 97.2%

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

3. Taylor expanded in y around 0

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

   1. lower-*.f64 48.2

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

5. Simplified 48.2%

   \[\leadsto \color{blue}{x \cdot z} \]
6. Add Preprocessing

Developer Target 1: 100.0% accurate, 1.4× speedup

Math

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

FPCore

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

C

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

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = y - (x * (y - z))
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Reproduce

herbie shell --seed 2024207 
(FPCore (x y z)
  :name "Diagrams.Color.HSV:lerp  from diagrams-contrib-1.3.0.5"
  :precision binary64

  :alt
  (! :herbie-platform default (- y (* x (- y z))))

  (+ (* (- 1.0 x) y) (* x z)))