cos2 (problem 3.4.1)

Percentage Accurate: 50.3% → 99.6%

Time: 10.5s

Alternatives: 10

Speedup: 17.1×

Specification

Math

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

FPCore

(FPCore (x) :precision binary64 (/ (- 1.0 (cos x)) (* x x)))

C

double code(double x) {
	return (1.0 - cos(x)) / (x * x);
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = (1.0d0 - cos(x)) / (x * x)
end function

Java

public static double code(double x) {
	return (1.0 - Math.cos(x)) / (x * x);
}

Python

def code(x):
	return (1.0 - math.cos(x)) / (x * x)

Julia

function code(x)
	return Float64(Float64(1.0 - cos(x)) / Float64(x * x))
end

MATLAB

function tmp = code(x)
	tmp = (1.0 - cos(x)) / (x * x);
end

Wolfram

code[x_] := N[(N[(1.0 - N[Cos[x], $MachinePrecision]), $MachinePrecision] / N[(x * x), $MachinePrecision]), $MachinePrecision]

Initial Program: 50.3% accurate, 1.0× speedup

Math

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

FPCore

(FPCore (x) :precision binary64 (/ (- 1.0 (cos x)) (* x x)))

C

double code(double x) {
	return (1.0 - cos(x)) / (x * x);
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = (1.0d0 - cos(x)) / (x * x)
end function

Java

public static double code(double x) {
	return (1.0 - Math.cos(x)) / (x * x);
}

Python

def code(x):
	return (1.0 - math.cos(x)) / (x * x)

Julia

function code(x)
	return Float64(Float64(1.0 - cos(x)) / Float64(x * x))
end

MATLAB

function tmp = code(x)
	tmp = (1.0 - cos(x)) / (x * x);
end

Wolfram

code[x_] := N[(N[(1.0 - N[Cos[x], $MachinePrecision]), $MachinePrecision] / N[(x * x), $MachinePrecision]), $MachinePrecision]

Alternative 1: 99.6% accurate, 0.8× speedup

Math

\[\begin{array}{l} x_m = \left|x\right| \\ \begin{array}{l} \mathbf{if}\;x\_m \leq 0.032:\\ \;\;\;\;\mathsf{fma}\left(x\_m \cdot x\_m, \mathsf{fma}\left(x\_m \cdot x\_m, 0.001388888888888889, -0.041666666666666664\right), 0.5\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{-1}{\frac{x\_m}{\cos x\_m + -1}}}{x\_m}\\ \end{array} \end{array} \]

FPCore

x_m = (fabs.f64 x)
(FPCore (x_m)
 :precision binary64
 (if (<= x_m 0.032)
   (fma
    (* x_m x_m)
    (fma (* x_m x_m) 0.001388888888888889 -0.041666666666666664)
    0.5)
   (/ (/ -1.0 (/ x_m (+ (cos x_m) -1.0))) x_m)))

C

x_m = fabs(x);
double code(double x_m) {
	double tmp;
	if (x_m <= 0.032) {
		tmp = fma((x_m * x_m), fma((x_m * x_m), 0.001388888888888889, -0.041666666666666664), 0.5);
	} else {
		tmp = (-1.0 / (x_m / (cos(x_m) + -1.0))) / x_m;
	}
	return tmp;
}

Julia

x_m = abs(x)
function code(x_m)
	tmp = 0.0
	if (x_m <= 0.032)
		tmp = fma(Float64(x_m * x_m), fma(Float64(x_m * x_m), 0.001388888888888889, -0.041666666666666664), 0.5);
	else
		tmp = Float64(Float64(-1.0 / Float64(x_m / Float64(cos(x_m) + -1.0))) / x_m);
	end
	return tmp
end

Wolfram

x_m = N[Abs[x], $MachinePrecision]
code[x$95$m_] := If[LessEqual[x$95$m, 0.032], N[(N[(x$95$m * x$95$m), $MachinePrecision] * N[(N[(x$95$m * x$95$m), $MachinePrecision] * 0.001388888888888889 + -0.041666666666666664), $MachinePrecision] + 0.5), $MachinePrecision], N[(N[(-1.0 / N[(x$95$m / N[(N[Cos[x$95$m], $MachinePrecision] + -1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / x$95$m), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < 0.032000000000000001

   1. Initial program 39.6%

      \[\frac{1 - \cos x}{x \cdot x} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\frac{1}{2} + {x}^{2} \cdot \left(\frac{1}{720} \cdot {x}^{2} - \frac{1}{24}\right)} \]
   4. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \color{blue}{{x}^{2} \cdot \left(\frac{1}{720} \cdot {x}^{2} - \frac{1}{24}\right) + \frac{1}{2}} \]

      2. lower-fma.f64 N/A

         \[\leadsto \color{blue}{\mathsf{fma}\left({x}^{2}, \frac{1}{720} \cdot {x}^{2} - \frac{1}{24}, \frac{1}{2}\right)} \]

      3. unpow2 N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{x \cdot x}, \frac{1}{720} \cdot {x}^{2} - \frac{1}{24}, \frac{1}{2}\right) \]

      4. lower-*.f64 N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{x \cdot x}, \frac{1}{720} \cdot {x}^{2} - \frac{1}{24}, \frac{1}{2}\right) \]

      5. sub-neg N/A

         \[\leadsto \mathsf{fma}\left(x \cdot x, \color{blue}{\frac{1}{720} \cdot {x}^{2} + \left(\mathsf{neg}\left(\frac{1}{24}\right)\right)}, \frac{1}{2}\right) \]

      6. metadata-eval N/A

         \[\leadsto \mathsf{fma}\left(x \cdot x, \frac{1}{720} \cdot {x}^{2} + \color{blue}{\frac{-1}{24}}, \frac{1}{2}\right) \]

      7. *-commutative N/A

         \[\leadsto \mathsf{fma}\left(x \cdot x, \color{blue}{{x}^{2} \cdot \frac{1}{720}} + \frac{-1}{24}, \frac{1}{2}\right) \]

      8. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(x \cdot x, \color{blue}{\mathsf{fma}\left({x}^{2}, \frac{1}{720}, \frac{-1}{24}\right)}, \frac{1}{2}\right) \]

      9. unpow2 N/A

         \[\leadsto \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(\color{blue}{x \cdot x}, \frac{1}{720}, \frac{-1}{24}\right), \frac{1}{2}\right) \]

      10. lower-*.f64 63.4

          \[\leadsto \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(\color{blue}{x \cdot x}, 0.001388888888888889, -0.041666666666666664\right), 0.5\right) \]

   5. Simplified 63.4%

      \[\leadsto \color{blue}{\mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, 0.001388888888888889, -0.041666666666666664\right), 0.5\right)} \]

   if 0.032000000000000001 < x

   1. Initial program 96.7%

      \[\frac{1 - \cos x}{x \cdot x} \]
   2. Add Preprocessing

   4. Applied egg-rr 99.3%

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

      1. lift-cos.f64 N/A

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

      2. lift-+.f64 N/A

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

      3. clear-num N/A

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

      4. lower-/.f64 N/A

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

      5. lower-/.f64 99.3

         \[\leadsto \frac{\frac{1}{\color{blue}{\frac{x}{\cos x + -1}}}}{-x} \]

   6. Applied egg-rr 99.3%

      \[\leadsto \frac{\color{blue}{\frac{1}{\frac{x}{\cos x + -1}}}}{-x} \]
3. Recombined 2 regimes into one program.

4. Final simplification 73.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq 0.032:\\ \;\;\;\;\mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, 0.001388888888888889, -0.041666666666666664\right), 0.5\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{-1}{\frac{x}{\cos x + -1}}}{x}\\ \end{array} \]
5. Add Preprocessing

Alternative 2: 99.3% accurate, 0.5× speedup

Math

\[\begin{array}{l} x_m = \left|x\right| \\ \frac{\tan \left(x\_m \cdot 0.5\right)}{x\_m \cdot \frac{x\_m}{\sin x\_m}} \end{array} \]

FPCore

x_m = (fabs.f64 x)
(FPCore (x_m)
 :precision binary64
 (/ (tan (* x_m 0.5)) (* x_m (/ x_m (sin x_m)))))

C

x_m = fabs(x);
double code(double x_m) {
	return tan((x_m * 0.5)) / (x_m * (x_m / sin(x_m)));
}

Fortran

x_m = abs(x)
real(8) function code(x_m)
    real(8), intent (in) :: x_m
    code = tan((x_m * 0.5d0)) / (x_m * (x_m / sin(x_m)))
end function

Java

x_m = Math.abs(x);
public static double code(double x_m) {
	return Math.tan((x_m * 0.5)) / (x_m * (x_m / Math.sin(x_m)));
}

Python

x_m = math.fabs(x)
def code(x_m):
	return math.tan((x_m * 0.5)) / (x_m * (x_m / math.sin(x_m)))

Julia

x_m = abs(x)
function code(x_m)
	return Float64(tan(Float64(x_m * 0.5)) / Float64(x_m * Float64(x_m / sin(x_m))))
end

MATLAB

x_m = abs(x);
function tmp = code(x_m)
	tmp = tan((x_m * 0.5)) / (x_m * (x_m / sin(x_m)));
end

Wolfram

x_m = N[Abs[x], $MachinePrecision]
code[x$95$m_] := N[(N[Tan[N[(x$95$m * 0.5), $MachinePrecision]], $MachinePrecision] / N[(x$95$m * N[(x$95$m / N[Sin[x$95$m], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 55.0%

   \[\frac{1 - \cos x}{x \cdot x} \]
2. Add Preprocessing
3. Step-by-step derivation

   1. lift-cos.f64 N/A

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

   2. flip-- N/A

      \[\leadsto \frac{\color{blue}{\frac{1 \cdot 1 - \cos x \cdot \cos x}{1 + \cos x}}}{x \cdot x} \]

   3. lift-*.f64 N/A

      \[\leadsto \frac{\frac{1 \cdot 1 - \cos x \cdot \cos x}{1 + \cos x}}{\color{blue}{x \cdot x}} \]

   4. associate-/l/ N/A

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

   5. metadata-eval N/A

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

   6. lift-cos.f64 N/A

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

   7. lift-cos.f64 N/A

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

   8. 1-sub-cos N/A

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

   9. times-frac N/A

      \[\leadsto \color{blue}{\frac{\sin x}{x \cdot x} \cdot \frac{\sin x}{1 + \cos x}} \]

   10. lower-*.f64 N/A

       \[\leadsto \color{blue}{\frac{\sin x}{x \cdot x} \cdot \frac{\sin x}{1 + \cos x}} \]

   11. lower-/.f64 N/A

       \[\leadsto \color{blue}{\frac{\sin x}{x \cdot x}} \cdot \frac{\sin x}{1 + \cos x} \]

   12. lower-sin.f64 N/A

       \[\leadsto \frac{\color{blue}{\sin x}}{x \cdot x} \cdot \frac{\sin x}{1 + \cos x} \]

   13. lift-cos.f64 N/A

       \[\leadsto \frac{\sin x}{x \cdot x} \cdot \frac{\sin x}{1 + \color{blue}{\cos x}} \]

   14. hang-0p-tan N/A

       \[\leadsto \frac{\sin x}{x \cdot x} \cdot \color{blue}{\tan \left(\frac{x}{2}\right)} \]

   15. lower-tan.f64 N/A

       \[\leadsto \frac{\sin x}{x \cdot x} \cdot \color{blue}{\tan \left(\frac{x}{2}\right)} \]

   16. lower-/.f64 80.0

       \[\leadsto \frac{\sin x}{x \cdot x} \cdot \tan \color{blue}{\left(\frac{x}{2}\right)} \]

4. Applied egg-rr 80.0%

   \[\leadsto \color{blue}{\frac{\sin x}{x \cdot x} \cdot \tan \left(\frac{x}{2}\right)} \]
5. Step-by-step derivation

   1. lift-sin.f64 N/A

      \[\leadsto \frac{\color{blue}{\sin x}}{x \cdot x} \cdot \tan \left(\frac{x}{2}\right) \]

   2. lift-*.f64 N/A

      \[\leadsto \frac{\sin x}{\color{blue}{x \cdot x}} \cdot \tan \left(\frac{x}{2}\right) \]

   3. lift-/.f64 N/A

      \[\leadsto \frac{\sin x}{x \cdot x} \cdot \tan \color{blue}{\left(\frac{x}{2}\right)} \]

   4. lift-tan.f64 N/A

      \[\leadsto \frac{\sin x}{x \cdot x} \cdot \color{blue}{\tan \left(\frac{x}{2}\right)} \]

   5. associate-*l/ N/A

      \[\leadsto \color{blue}{\frac{\sin x \cdot \tan \left(\frac{x}{2}\right)}{x \cdot x}} \]

   6. lift-*.f64 N/A

      \[\leadsto \frac{\sin x \cdot \tan \left(\frac{x}{2}\right)}{\color{blue}{x \cdot x}} \]

   7. times-frac N/A

      \[\leadsto \color{blue}{\frac{\sin x}{x} \cdot \frac{\tan \left(\frac{x}{2}\right)}{x}} \]

   8. clear-num N/A

      \[\leadsto \color{blue}{\frac{1}{\frac{x}{\sin x}}} \cdot \frac{\tan \left(\frac{x}{2}\right)}{x} \]

   9. frac-times N/A

      \[\leadsto \color{blue}{\frac{1 \cdot \tan \left(\frac{x}{2}\right)}{\frac{x}{\sin x} \cdot x}} \]

   10. *-lft-identity N/A

       \[\leadsto \frac{\color{blue}{\tan \left(\frac{x}{2}\right)}}{\frac{x}{\sin x} \cdot x} \]

   11. lower-/.f64 N/A

       \[\leadsto \color{blue}{\frac{\tan \left(\frac{x}{2}\right)}{\frac{x}{\sin x} \cdot x}} \]

   12. lift-/.f64 N/A

       \[\leadsto \frac{\tan \color{blue}{\left(\frac{x}{2}\right)}}{\frac{x}{\sin x} \cdot x} \]

   13. div-inv N/A

       \[\leadsto \frac{\tan \color{blue}{\left(x \cdot \frac{1}{2}\right)}}{\frac{x}{\sin x} \cdot x} \]

   14. lower-*.f64 N/A

       \[\leadsto \frac{\tan \color{blue}{\left(x \cdot \frac{1}{2}\right)}}{\frac{x}{\sin x} \cdot x} \]

   15. metadata-eval N/A

       \[\leadsto \frac{\tan \left(x \cdot \color{blue}{\frac{1}{2}}\right)}{\frac{x}{\sin x} \cdot x} \]

   16. lower-*.f64 N/A

       \[\leadsto \frac{\tan \left(x \cdot \frac{1}{2}\right)}{\color{blue}{\frac{x}{\sin x} \cdot x}} \]

   17. lower-/.f64 99.1

       \[\leadsto \frac{\tan \left(x \cdot 0.5\right)}{\color{blue}{\frac{x}{\sin x}} \cdot x} \]

6. Applied egg-rr 99.1%

   \[\leadsto \color{blue}{\frac{\tan \left(x \cdot 0.5\right)}{\frac{x}{\sin x} \cdot x}} \]

7. Final simplification 99.1%

   \[\leadsto \frac{\tan \left(x \cdot 0.5\right)}{x \cdot \frac{x}{\sin x}} \]
8. Add Preprocessing

Alternative 3: 99.6% accurate, 0.9× speedup

Math

\[\begin{array}{l} x_m = \left|x\right| \\ \begin{array}{l} \mathbf{if}\;x\_m \leq 0.032:\\ \;\;\;\;\mathsf{fma}\left(x\_m \cdot x\_m, \mathsf{fma}\left(x\_m \cdot x\_m, 0.001388888888888889, -0.041666666666666664\right), 0.5\right)\\ \mathbf{else}:\\ \;\;\;\;\left(\cos x\_m + -1\right) \cdot \frac{\frac{-1}{x\_m}}{x\_m}\\ \end{array} \end{array} \]

FPCore

x_m = (fabs.f64 x)
(FPCore (x_m)
 :precision binary64
 (if (<= x_m 0.032)
   (fma
    (* x_m x_m)
    (fma (* x_m x_m) 0.001388888888888889 -0.041666666666666664)
    0.5)
   (* (+ (cos x_m) -1.0) (/ (/ -1.0 x_m) x_m))))

C

x_m = fabs(x);
double code(double x_m) {
	double tmp;
	if (x_m <= 0.032) {
		tmp = fma((x_m * x_m), fma((x_m * x_m), 0.001388888888888889, -0.041666666666666664), 0.5);
	} else {
		tmp = (cos(x_m) + -1.0) * ((-1.0 / x_m) / x_m);
	}
	return tmp;
}

Julia

x_m = abs(x)
function code(x_m)
	tmp = 0.0
	if (x_m <= 0.032)
		tmp = fma(Float64(x_m * x_m), fma(Float64(x_m * x_m), 0.001388888888888889, -0.041666666666666664), 0.5);
	else
		tmp = Float64(Float64(cos(x_m) + -1.0) * Float64(Float64(-1.0 / x_m) / x_m));
	end
	return tmp
end

Wolfram

x_m = N[Abs[x], $MachinePrecision]
code[x$95$m_] := If[LessEqual[x$95$m, 0.032], N[(N[(x$95$m * x$95$m), $MachinePrecision] * N[(N[(x$95$m * x$95$m), $MachinePrecision] * 0.001388888888888889 + -0.041666666666666664), $MachinePrecision] + 0.5), $MachinePrecision], N[(N[(N[Cos[x$95$m], $MachinePrecision] + -1.0), $MachinePrecision] * N[(N[(-1.0 / x$95$m), $MachinePrecision] / x$95$m), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < 0.032000000000000001

   1. Initial program 39.6%

      \[\frac{1 - \cos x}{x \cdot x} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\frac{1}{2} + {x}^{2} \cdot \left(\frac{1}{720} \cdot {x}^{2} - \frac{1}{24}\right)} \]
   4. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \color{blue}{{x}^{2} \cdot \left(\frac{1}{720} \cdot {x}^{2} - \frac{1}{24}\right) + \frac{1}{2}} \]

      2. lower-fma.f64 N/A

         \[\leadsto \color{blue}{\mathsf{fma}\left({x}^{2}, \frac{1}{720} \cdot {x}^{2} - \frac{1}{24}, \frac{1}{2}\right)} \]

      3. unpow2 N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{x \cdot x}, \frac{1}{720} \cdot {x}^{2} - \frac{1}{24}, \frac{1}{2}\right) \]

      4. lower-*.f64 N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{x \cdot x}, \frac{1}{720} \cdot {x}^{2} - \frac{1}{24}, \frac{1}{2}\right) \]

      5. sub-neg N/A

         \[\leadsto \mathsf{fma}\left(x \cdot x, \color{blue}{\frac{1}{720} \cdot {x}^{2} + \left(\mathsf{neg}\left(\frac{1}{24}\right)\right)}, \frac{1}{2}\right) \]

      6. metadata-eval N/A

         \[\leadsto \mathsf{fma}\left(x \cdot x, \frac{1}{720} \cdot {x}^{2} + \color{blue}{\frac{-1}{24}}, \frac{1}{2}\right) \]

      7. *-commutative N/A

         \[\leadsto \mathsf{fma}\left(x \cdot x, \color{blue}{{x}^{2} \cdot \frac{1}{720}} + \frac{-1}{24}, \frac{1}{2}\right) \]

      8. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(x \cdot x, \color{blue}{\mathsf{fma}\left({x}^{2}, \frac{1}{720}, \frac{-1}{24}\right)}, \frac{1}{2}\right) \]

      9. unpow2 N/A

         \[\leadsto \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(\color{blue}{x \cdot x}, \frac{1}{720}, \frac{-1}{24}\right), \frac{1}{2}\right) \]

      10. lower-*.f64 63.4

          \[\leadsto \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(\color{blue}{x \cdot x}, 0.001388888888888889, -0.041666666666666664\right), 0.5\right) \]

   5. Simplified 63.4%

      \[\leadsto \color{blue}{\mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, 0.001388888888888889, -0.041666666666666664\right), 0.5\right)} \]

   if 0.032000000000000001 < x

   1. Initial program 96.7%

      \[\frac{1 - \cos x}{x \cdot x} \]
   2. Add Preprocessing

   4. Applied egg-rr 99.3%

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

      1. lift-cos.f64 N/A

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

      2. lift-+.f64 N/A

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

      3. lift-/.f64 N/A

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

      4. lift-neg.f64 N/A

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

      5. clear-num N/A

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

      6. lower-/.f64 N/A

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

      7. clear-num N/A

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

      8. lift-/.f64 N/A

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

      9. associate-/l/ N/A

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

      10. lift-neg.f64 N/A

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

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

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

      12. lift-*.f64 N/A

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

      13. clear-num N/A

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

      14. frac-2neg N/A

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

      15. remove-double-neg N/A

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

      16. lower-/.f64 N/A

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

      17. lift-+.f64 N/A

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

      18. +-commutative N/A

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

      19. distribute-neg-in N/A

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

      20. metadata-eval N/A

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

      21. unsub-neg N/A

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

   6. Applied egg-rr 96.6%

      \[\leadsto \color{blue}{\frac{1}{\frac{x \cdot x}{1 - \cos x}}} \]
   7. Step-by-step derivation

      1. lift-cos.f64 N/A

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

      2. lift--.f64 N/A

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

      3. associate-/l* N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 N/A

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

      6. lower-/.f64 96.6

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

   8. Applied egg-rr 96.6%

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

   10. Applied egg-rr 99.3%

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

Alternative 4: 99.6% accurate, 0.9× speedup

Math

\[\begin{array}{l} x_m = \left|x\right| \\ \begin{array}{l} \mathbf{if}\;x\_m \leq 0.032:\\ \;\;\;\;\mathsf{fma}\left(x\_m \cdot x\_m, \mathsf{fma}\left(x\_m \cdot x\_m, 0.001388888888888889, -0.041666666666666664\right), 0.5\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{1 - \cos x\_m}{x\_m}}{x\_m}\\ \end{array} \end{array} \]

FPCore

x_m = (fabs.f64 x)
(FPCore (x_m)
 :precision binary64
 (if (<= x_m 0.032)
   (fma
    (* x_m x_m)
    (fma (* x_m x_m) 0.001388888888888889 -0.041666666666666664)
    0.5)
   (/ (/ (- 1.0 (cos x_m)) x_m) x_m)))

C

x_m = fabs(x);
double code(double x_m) {
	double tmp;
	if (x_m <= 0.032) {
		tmp = fma((x_m * x_m), fma((x_m * x_m), 0.001388888888888889, -0.041666666666666664), 0.5);
	} else {
		tmp = ((1.0 - cos(x_m)) / x_m) / x_m;
	}
	return tmp;
}

Julia

x_m = abs(x)
function code(x_m)
	tmp = 0.0
	if (x_m <= 0.032)
		tmp = fma(Float64(x_m * x_m), fma(Float64(x_m * x_m), 0.001388888888888889, -0.041666666666666664), 0.5);
	else
		tmp = Float64(Float64(Float64(1.0 - cos(x_m)) / x_m) / x_m);
	end
	return tmp
end

Wolfram

x_m = N[Abs[x], $MachinePrecision]
code[x$95$m_] := If[LessEqual[x$95$m, 0.032], N[(N[(x$95$m * x$95$m), $MachinePrecision] * N[(N[(x$95$m * x$95$m), $MachinePrecision] * 0.001388888888888889 + -0.041666666666666664), $MachinePrecision] + 0.5), $MachinePrecision], N[(N[(N[(1.0 - N[Cos[x$95$m], $MachinePrecision]), $MachinePrecision] / x$95$m), $MachinePrecision] / x$95$m), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < 0.032000000000000001

   1. Initial program 39.6%

      \[\frac{1 - \cos x}{x \cdot x} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\frac{1}{2} + {x}^{2} \cdot \left(\frac{1}{720} \cdot {x}^{2} - \frac{1}{24}\right)} \]
   4. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \color{blue}{{x}^{2} \cdot \left(\frac{1}{720} \cdot {x}^{2} - \frac{1}{24}\right) + \frac{1}{2}} \]

      2. lower-fma.f64 N/A

         \[\leadsto \color{blue}{\mathsf{fma}\left({x}^{2}, \frac{1}{720} \cdot {x}^{2} - \frac{1}{24}, \frac{1}{2}\right)} \]

      3. unpow2 N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{x \cdot x}, \frac{1}{720} \cdot {x}^{2} - \frac{1}{24}, \frac{1}{2}\right) \]

      4. lower-*.f64 N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{x \cdot x}, \frac{1}{720} \cdot {x}^{2} - \frac{1}{24}, \frac{1}{2}\right) \]

      5. sub-neg N/A

         \[\leadsto \mathsf{fma}\left(x \cdot x, \color{blue}{\frac{1}{720} \cdot {x}^{2} + \left(\mathsf{neg}\left(\frac{1}{24}\right)\right)}, \frac{1}{2}\right) \]

      6. metadata-eval N/A

         \[\leadsto \mathsf{fma}\left(x \cdot x, \frac{1}{720} \cdot {x}^{2} + \color{blue}{\frac{-1}{24}}, \frac{1}{2}\right) \]

      7. *-commutative N/A

         \[\leadsto \mathsf{fma}\left(x \cdot x, \color{blue}{{x}^{2} \cdot \frac{1}{720}} + \frac{-1}{24}, \frac{1}{2}\right) \]

      8. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(x \cdot x, \color{blue}{\mathsf{fma}\left({x}^{2}, \frac{1}{720}, \frac{-1}{24}\right)}, \frac{1}{2}\right) \]

      9. unpow2 N/A

         \[\leadsto \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(\color{blue}{x \cdot x}, \frac{1}{720}, \frac{-1}{24}\right), \frac{1}{2}\right) \]

      10. lower-*.f64 63.4

          \[\leadsto \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(\color{blue}{x \cdot x}, 0.001388888888888889, -0.041666666666666664\right), 0.5\right) \]

   5. Simplified 63.4%

      \[\leadsto \color{blue}{\mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, 0.001388888888888889, -0.041666666666666664\right), 0.5\right)} \]

   if 0.032000000000000001 < x

   1. Initial program 96.7%

      \[\frac{1 - \cos x}{x \cdot x} \]
   2. Add Preprocessing

   4. Applied egg-rr 99.3%

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

      1. lift-cos.f64 N/A

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

      2. lift-+.f64 N/A

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

      3. lift-/.f64 N/A

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

      4. distribute-frac-neg2 N/A

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

      5. distribute-frac-neg N/A

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

      6. lower-/.f64 N/A

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

      7. lift-/.f64 N/A

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

      8. distribute-neg-frac N/A

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

      9. lower-/.f64 N/A

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

      10. lift-+.f64 N/A

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

      11. +-commutative N/A

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

      12. distribute-neg-in N/A

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

      13. metadata-eval N/A

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

      14. unsub-neg N/A

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

      15. lower--.f64 99.3

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

   6. Applied egg-rr 99.3%

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

Alternative 5: 99.1% accurate, 1.0× speedup

Math

\[\begin{array}{l} x_m = \left|x\right| \\ \begin{array}{l} \mathbf{if}\;x\_m \leq 0.032:\\ \;\;\;\;\mathsf{fma}\left(x\_m \cdot x\_m, \mathsf{fma}\left(x\_m \cdot x\_m, 0.001388888888888889, -0.041666666666666664\right), 0.5\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{1 - \cos x\_m}{x\_m \cdot x\_m}\\ \end{array} \end{array} \]

FPCore

x_m = (fabs.f64 x)
(FPCore (x_m)
 :precision binary64
 (if (<= x_m 0.032)
   (fma
    (* x_m x_m)
    (fma (* x_m x_m) 0.001388888888888889 -0.041666666666666664)
    0.5)
   (/ (- 1.0 (cos x_m)) (* x_m x_m))))

C

x_m = fabs(x);
double code(double x_m) {
	double tmp;
	if (x_m <= 0.032) {
		tmp = fma((x_m * x_m), fma((x_m * x_m), 0.001388888888888889, -0.041666666666666664), 0.5);
	} else {
		tmp = (1.0 - cos(x_m)) / (x_m * x_m);
	}
	return tmp;
}

Julia

x_m = abs(x)
function code(x_m)
	tmp = 0.0
	if (x_m <= 0.032)
		tmp = fma(Float64(x_m * x_m), fma(Float64(x_m * x_m), 0.001388888888888889, -0.041666666666666664), 0.5);
	else
		tmp = Float64(Float64(1.0 - cos(x_m)) / Float64(x_m * x_m));
	end
	return tmp
end

Wolfram

x_m = N[Abs[x], $MachinePrecision]
code[x$95$m_] := If[LessEqual[x$95$m, 0.032], N[(N[(x$95$m * x$95$m), $MachinePrecision] * N[(N[(x$95$m * x$95$m), $MachinePrecision] * 0.001388888888888889 + -0.041666666666666664), $MachinePrecision] + 0.5), $MachinePrecision], N[(N[(1.0 - N[Cos[x$95$m], $MachinePrecision]), $MachinePrecision] / N[(x$95$m * x$95$m), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < 0.032000000000000001

   1. Initial program 39.6%

      \[\frac{1 - \cos x}{x \cdot x} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\frac{1}{2} + {x}^{2} \cdot \left(\frac{1}{720} \cdot {x}^{2} - \frac{1}{24}\right)} \]
   4. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \color{blue}{{x}^{2} \cdot \left(\frac{1}{720} \cdot {x}^{2} - \frac{1}{24}\right) + \frac{1}{2}} \]

      2. lower-fma.f64 N/A

         \[\leadsto \color{blue}{\mathsf{fma}\left({x}^{2}, \frac{1}{720} \cdot {x}^{2} - \frac{1}{24}, \frac{1}{2}\right)} \]

      3. unpow2 N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{x \cdot x}, \frac{1}{720} \cdot {x}^{2} - \frac{1}{24}, \frac{1}{2}\right) \]

      4. lower-*.f64 N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{x \cdot x}, \frac{1}{720} \cdot {x}^{2} - \frac{1}{24}, \frac{1}{2}\right) \]

      5. sub-neg N/A

         \[\leadsto \mathsf{fma}\left(x \cdot x, \color{blue}{\frac{1}{720} \cdot {x}^{2} + \left(\mathsf{neg}\left(\frac{1}{24}\right)\right)}, \frac{1}{2}\right) \]

      6. metadata-eval N/A

         \[\leadsto \mathsf{fma}\left(x \cdot x, \frac{1}{720} \cdot {x}^{2} + \color{blue}{\frac{-1}{24}}, \frac{1}{2}\right) \]

      7. *-commutative N/A

         \[\leadsto \mathsf{fma}\left(x \cdot x, \color{blue}{{x}^{2} \cdot \frac{1}{720}} + \frac{-1}{24}, \frac{1}{2}\right) \]

      8. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(x \cdot x, \color{blue}{\mathsf{fma}\left({x}^{2}, \frac{1}{720}, \frac{-1}{24}\right)}, \frac{1}{2}\right) \]

      9. unpow2 N/A

         \[\leadsto \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(\color{blue}{x \cdot x}, \frac{1}{720}, \frac{-1}{24}\right), \frac{1}{2}\right) \]

      10. lower-*.f64 63.4

          \[\leadsto \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(\color{blue}{x \cdot x}, 0.001388888888888889, -0.041666666666666664\right), 0.5\right) \]

   5. Simplified 63.4%

      \[\leadsto \color{blue}{\mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, 0.001388888888888889, -0.041666666666666664\right), 0.5\right)} \]

   if 0.032000000000000001 < x

   1. Initial program 96.7%

      \[\frac{1 - \cos x}{x \cdot x} \]
   2. Add Preprocessing
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 6: 75.9% accurate, 4.6× speedup

Math

\[\begin{array}{l} x_m = \left|x\right| \\ \begin{array}{l} \mathbf{if}\;x\_m \leq 3.5:\\ \;\;\;\;\mathsf{fma}\left(x\_m, x\_m \cdot -0.041666666666666664, 0.5\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{1 + -1}{x\_m \cdot x\_m}\\ \end{array} \end{array} \]

FPCore

x_m = (fabs.f64 x)
(FPCore (x_m)
 :precision binary64
 (if (<= x_m 3.5)
   (fma x_m (* x_m -0.041666666666666664) 0.5)
   (/ (+ 1.0 -1.0) (* x_m x_m))))

C

x_m = fabs(x);
double code(double x_m) {
	double tmp;
	if (x_m <= 3.5) {
		tmp = fma(x_m, (x_m * -0.041666666666666664), 0.5);
	} else {
		tmp = (1.0 + -1.0) / (x_m * x_m);
	}
	return tmp;
}

Julia

x_m = abs(x)
function code(x_m)
	tmp = 0.0
	if (x_m <= 3.5)
		tmp = fma(x_m, Float64(x_m * -0.041666666666666664), 0.5);
	else
		tmp = Float64(Float64(1.0 + -1.0) / Float64(x_m * x_m));
	end
	return tmp
end

Wolfram

x_m = N[Abs[x], $MachinePrecision]
code[x$95$m_] := If[LessEqual[x$95$m, 3.5], N[(x$95$m * N[(x$95$m * -0.041666666666666664), $MachinePrecision] + 0.5), $MachinePrecision], N[(N[(1.0 + -1.0), $MachinePrecision] / N[(x$95$m * x$95$m), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < 3.5

   1. Initial program 39.6%

      \[\frac{1 - \cos x}{x \cdot x} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\frac{1}{2} + \frac{-1}{24} \cdot {x}^{2}} \]
   4. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \color{blue}{\frac{-1}{24} \cdot {x}^{2} + \frac{1}{2}} \]

      2. unpow2 N/A

         \[\leadsto \frac{-1}{24} \cdot \color{blue}{\left(x \cdot x\right)} + \frac{1}{2} \]

      3. associate-*r* N/A

         \[\leadsto \color{blue}{\left(\frac{-1}{24} \cdot x\right) \cdot x} + \frac{1}{2} \]

      4. *-commutative N/A

         \[\leadsto \color{blue}{x \cdot \left(\frac{-1}{24} \cdot x\right)} + \frac{1}{2} \]

      5. lower-fma.f64 N/A

         \[\leadsto \color{blue}{\mathsf{fma}\left(x, \frac{-1}{24} \cdot x, \frac{1}{2}\right)} \]

      6. *-commutative N/A

         \[\leadsto \mathsf{fma}\left(x, \color{blue}{x \cdot \frac{-1}{24}}, \frac{1}{2}\right) \]

      7. lower-*.f64 63.1

         \[\leadsto \mathsf{fma}\left(x, \color{blue}{x \cdot -0.041666666666666664}, 0.5\right) \]

   5. Simplified 63.1%

      \[\leadsto \color{blue}{\mathsf{fma}\left(x, x \cdot -0.041666666666666664, 0.5\right)} \]

   if 3.5 < x

   1. Initial program 96.7%

      \[\frac{1 - \cos x}{x \cdot x} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \frac{1 - \color{blue}{1}}{x \cdot x} \]
   4. Step-by-step derivation

   5. Simplified 52.3%

      \[\leadsto \frac{1 - \color{blue}{1}}{x \cdot x} \]
3. Recombined 2 regimes into one program.

4. Final simplification 60.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq 3.5:\\ \;\;\;\;\mathsf{fma}\left(x, x \cdot -0.041666666666666664, 0.5\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{1 + -1}{x \cdot x}\\ \end{array} \]
5. Add Preprocessing

Alternative 7: 78.6% accurate, 5.2× speedup

Math

\[\begin{array}{l} x_m = \left|x\right| \\ \frac{1}{\mathsf{fma}\left(x\_m, x\_m \cdot 0.16666666666666666, 2\right)} \end{array} \]

FPCore

x_m = (fabs.f64 x)
(FPCore (x_m)
 :precision binary64
 (/ 1.0 (fma x_m (* x_m 0.16666666666666666) 2.0)))

C

x_m = fabs(x);
double code(double x_m) {
	return 1.0 / fma(x_m, (x_m * 0.16666666666666666), 2.0);
}

Julia

x_m = abs(x)
function code(x_m)
	return Float64(1.0 / fma(x_m, Float64(x_m * 0.16666666666666666), 2.0))
end

Wolfram

x_m = N[Abs[x], $MachinePrecision]
code[x$95$m_] := N[(1.0 / N[(x$95$m * N[(x$95$m * 0.16666666666666666), $MachinePrecision] + 2.0), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 55.0%

   \[\frac{1 - \cos x}{x \cdot x} \]
2. Add Preprocessing
3. Step-by-step derivation

   1. lift-cos.f64 N/A

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

   2. lift-*.f64 N/A

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

   3. div-sub N/A

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

   4. frac-sub N/A

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

   5. clear-num N/A

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

   6. lower-/.f64 N/A

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

   7. lower-/.f64 N/A

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

   8. lift-*.f64 N/A

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

   9. associate-*l* N/A

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

   10. lift-*.f64 N/A

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

   11. cube-unmult N/A

       \[\leadsto \frac{1}{\frac{x \cdot \color{blue}{{x}^{3}}}{1 \cdot \left(x \cdot x\right) - \left(x \cdot x\right) \cdot \cos x}} \]

   12. lower-*.f64 N/A

       \[\leadsto \frac{1}{\frac{\color{blue}{x \cdot {x}^{3}}}{1 \cdot \left(x \cdot x\right) - \left(x \cdot x\right) \cdot \cos x}} \]

   13. cube-unmult N/A

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

   14. lift-*.f64 N/A

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

   15. lower-*.f64 N/A

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

   16. *-lft-identity N/A

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

   17. lower--.f64 N/A

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

   18. lift-*.f64 N/A

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

   19. associate-*l* N/A

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

   20. lower-*.f64 N/A

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

   21. lower-*.f64 17.3

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

4. Applied egg-rr 17.3%

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

5. Taylor expanded in x around 0

   \[\leadsto \frac{1}{\color{blue}{2 + \frac{1}{6} \cdot {x}^{2}}} \]
6. Step-by-step derivation

   1. +-commutative N/A

      \[\leadsto \frac{1}{\color{blue}{\frac{1}{6} \cdot {x}^{2} + 2}} \]

   2. unpow2 N/A

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

   3. associate-*r* N/A

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

   4. *-commutative N/A

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

   5. lower-fma.f64 N/A

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

   6. *-commutative N/A

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

   7. lower-*.f64 76.3

      \[\leadsto \frac{1}{\mathsf{fma}\left(x, \color{blue}{x \cdot 0.16666666666666666}, 2\right)} \]

7. Simplified 76.3%

   \[\leadsto \frac{1}{\color{blue}{\mathsf{fma}\left(x, x \cdot 0.16666666666666666, 2\right)}} \]
8. Add Preprocessing

Alternative 8: 75.9% accurate, 6.7× speedup

Math

\[\begin{array}{l} x_m = \left|x\right| \\ \begin{array}{l} \mathbf{if}\;x\_m \leq 3.5:\\ \;\;\;\;\mathsf{fma}\left(x\_m, x\_m \cdot -0.041666666666666664, 0.5\right)\\ \mathbf{else}:\\ \;\;\;\;0\\ \end{array} \end{array} \]

FPCore

x_m = (fabs.f64 x)
(FPCore (x_m)
 :precision binary64
 (if (<= x_m 3.5) (fma x_m (* x_m -0.041666666666666664) 0.5) 0.0))

C

x_m = fabs(x);
double code(double x_m) {
	double tmp;
	if (x_m <= 3.5) {
		tmp = fma(x_m, (x_m * -0.041666666666666664), 0.5);
	} else {
		tmp = 0.0;
	}
	return tmp;
}

Julia

x_m = abs(x)
function code(x_m)
	tmp = 0.0
	if (x_m <= 3.5)
		tmp = fma(x_m, Float64(x_m * -0.041666666666666664), 0.5);
	else
		tmp = 0.0;
	end
	return tmp
end

Wolfram

x_m = N[Abs[x], $MachinePrecision]
code[x$95$m_] := If[LessEqual[x$95$m, 3.5], N[(x$95$m * N[(x$95$m * -0.041666666666666664), $MachinePrecision] + 0.5), $MachinePrecision], 0.0]

Derivation

1. Split input into 2 regimes

2. if x < 3.5

   1. Initial program 39.6%

      \[\frac{1 - \cos x}{x \cdot x} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\frac{1}{2} + \frac{-1}{24} \cdot {x}^{2}} \]
   4. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \color{blue}{\frac{-1}{24} \cdot {x}^{2} + \frac{1}{2}} \]

      2. unpow2 N/A

         \[\leadsto \frac{-1}{24} \cdot \color{blue}{\left(x \cdot x\right)} + \frac{1}{2} \]

      3. associate-*r* N/A

         \[\leadsto \color{blue}{\left(\frac{-1}{24} \cdot x\right) \cdot x} + \frac{1}{2} \]

      4. *-commutative N/A

         \[\leadsto \color{blue}{x \cdot \left(\frac{-1}{24} \cdot x\right)} + \frac{1}{2} \]

      5. lower-fma.f64 N/A

         \[\leadsto \color{blue}{\mathsf{fma}\left(x, \frac{-1}{24} \cdot x, \frac{1}{2}\right)} \]

      6. *-commutative N/A

         \[\leadsto \mathsf{fma}\left(x, \color{blue}{x \cdot \frac{-1}{24}}, \frac{1}{2}\right) \]

      7. lower-*.f64 63.1

         \[\leadsto \mathsf{fma}\left(x, \color{blue}{x \cdot -0.041666666666666664}, 0.5\right) \]

   5. Simplified 63.1%

      \[\leadsto \color{blue}{\mathsf{fma}\left(x, x \cdot -0.041666666666666664, 0.5\right)} \]

   if 3.5 < x

   1. Initial program 96.7%

      \[\frac{1 - \cos x}{x \cdot x} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \frac{1 - \color{blue}{1}}{x \cdot x} \]
   4. Step-by-step derivation

   5. Simplified 52.3%

      \[\leadsto \frac{1 - \color{blue}{1}}{x \cdot x} \]
   6. Step-by-step derivation

      1. metadata-eval N/A

         \[\leadsto \frac{\color{blue}{0}}{x \cdot x} \]

      2. lift-*.f64 N/A

         \[\leadsto \frac{0}{\color{blue}{x \cdot x}} \]

      3. div0 52.3

         \[\leadsto \color{blue}{0} \]

   7. Applied egg-rr 52.3%

      \[\leadsto \color{blue}{0} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 9: 76.0% accurate, 17.1× speedup

Math

\[\begin{array}{l} x_m = \left|x\right| \\ \begin{array}{l} \mathbf{if}\;x\_m \leq 1.6 \cdot 10^{+77}:\\ \;\;\;\;0.5\\ \mathbf{else}:\\ \;\;\;\;0\\ \end{array} \end{array} \]

FPCore

x_m = (fabs.f64 x)
(FPCore (x_m) :precision binary64 (if (<= x_m 1.6e+77) 0.5 0.0))

C

x_m = fabs(x);
double code(double x_m) {
	double tmp;
	if (x_m <= 1.6e+77) {
		tmp = 0.5;
	} else {
		tmp = 0.0;
	}
	return tmp;
}

Fortran

x_m = abs(x)
real(8) function code(x_m)
    real(8), intent (in) :: x_m
    real(8) :: tmp
    if (x_m <= 1.6d+77) then
        tmp = 0.5d0
    else
        tmp = 0.0d0
    end if
    code = tmp
end function

Java

x_m = Math.abs(x);
public static double code(double x_m) {
	double tmp;
	if (x_m <= 1.6e+77) {
		tmp = 0.5;
	} else {
		tmp = 0.0;
	}
	return tmp;
}

Python

x_m = math.fabs(x)
def code(x_m):
	tmp = 0
	if x_m <= 1.6e+77:
		tmp = 0.5
	else:
		tmp = 0.0
	return tmp

Julia

x_m = abs(x)
function code(x_m)
	tmp = 0.0
	if (x_m <= 1.6e+77)
		tmp = 0.5;
	else
		tmp = 0.0;
	end
	return tmp
end

MATLAB

x_m = abs(x);
function tmp_2 = code(x_m)
	tmp = 0.0;
	if (x_m <= 1.6e+77)
		tmp = 0.5;
	else
		tmp = 0.0;
	end
	tmp_2 = tmp;
end

Wolfram

x_m = N[Abs[x], $MachinePrecision]
code[x$95$m_] := If[LessEqual[x$95$m, 1.6e+77], 0.5, 0.0]

Derivation

1. Split input into 2 regimes

2. if x < 1.6000000000000001e77

   1. Initial program 44.5%

      \[\frac{1 - \cos x}{x \cdot x} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\frac{1}{2}} \]
   4. Step-by-step derivation

   5. Simplified 58.6%

      \[\leadsto \color{blue}{0.5} \]

   if 1.6000000000000001e77 < x

   1. Initial program 96.0%

      \[\frac{1 - \cos x}{x \cdot x} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \frac{1 - \color{blue}{1}}{x \cdot x} \]
   4. Step-by-step derivation

   5. Simplified 68.1%

      \[\leadsto \frac{1 - \color{blue}{1}}{x \cdot x} \]
   6. Step-by-step derivation

      1. metadata-eval N/A

         \[\leadsto \frac{\color{blue}{0}}{x \cdot x} \]

      2. lift-*.f64 N/A

         \[\leadsto \frac{0}{\color{blue}{x \cdot x}} \]

      3. div0 68.1

         \[\leadsto \color{blue}{0} \]

   7. Applied egg-rr 68.1%

      \[\leadsto \color{blue}{0} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 10: 27.2% accurate, 120.0× speedup

Math

\[\begin{array}{l} x_m = \left|x\right| \\ 0 \end{array} \]

FPCore

x_m = (fabs.f64 x)
(FPCore (x_m) :precision binary64 0.0)

C

x_m = fabs(x);
double code(double x_m) {
	return 0.0;
}

Fortran

x_m = abs(x)
real(8) function code(x_m)
    real(8), intent (in) :: x_m
    code = 0.0d0
end function

Java

x_m = Math.abs(x);
public static double code(double x_m) {
	return 0.0;
}

Python

x_m = math.fabs(x)
def code(x_m):
	return 0.0

Julia

x_m = abs(x)
function code(x_m)
	return 0.0
end

MATLAB

x_m = abs(x);
function tmp = code(x_m)
	tmp = 0.0;
end

Wolfram

x_m = N[Abs[x], $MachinePrecision]
code[x$95$m_] := 0.0

Derivation

1. Initial program 55.0%

   \[\frac{1 - \cos x}{x \cdot x} \]
2. Add Preprocessing

3. Taylor expanded in x around 0

   \[\leadsto \frac{1 - \color{blue}{1}}{x \cdot x} \]
4. Step-by-step derivation

5. Simplified 28.5%

   \[\leadsto \frac{1 - \color{blue}{1}}{x \cdot x} \]
6. Step-by-step derivation

   1. metadata-eval N/A

      \[\leadsto \frac{\color{blue}{0}}{x \cdot x} \]

   2. lift-*.f64 N/A

      \[\leadsto \frac{0}{\color{blue}{x \cdot x}} \]

   3. div0 29.1

      \[\leadsto \color{blue}{0} \]

7. Applied egg-rr 29.1%

   \[\leadsto \color{blue}{0} \]
8. Add Preprocessing

Reproduce

herbie shell --seed 2024219 
(FPCore (x)
  :name "cos2 (problem 3.4.1)"
  :precision binary64
  (/ (- 1.0 (cos x)) (* x x)))