Falkner and Boettcher, Equation (22+)

Percentage Accurate: 98.5% → 100.0%

Time: 5.1s

Alternatives: 5

Speedup: 1.1×

Specification

Math

\[\begin{array}{l} \\ \frac{4}{\left(\left(3 \cdot \pi\right) \cdot \left(1 - v \cdot v\right)\right) \cdot \sqrt{2 - 6 \cdot \left(v \cdot v\right)}} \end{array} \]

FPCore

(FPCore (v)
 :precision binary64
 (/ 4.0 (* (* (* 3.0 PI) (- 1.0 (* v v))) (sqrt (- 2.0 (* 6.0 (* v v)))))))

C

double code(double v) {
	return 4.0 / (((3.0 * ((double) M_PI)) * (1.0 - (v * v))) * sqrt((2.0 - (6.0 * (v * v)))));
}

Java

public static double code(double v) {
	return 4.0 / (((3.0 * Math.PI) * (1.0 - (v * v))) * Math.sqrt((2.0 - (6.0 * (v * v)))));
}

Python

def code(v):
	return 4.0 / (((3.0 * math.pi) * (1.0 - (v * v))) * math.sqrt((2.0 - (6.0 * (v * v)))))

Julia

function code(v)
	return Float64(4.0 / Float64(Float64(Float64(3.0 * pi) * Float64(1.0 - Float64(v * v))) * sqrt(Float64(2.0 - Float64(6.0 * Float64(v * v))))))
end

MATLAB

function tmp = code(v)
	tmp = 4.0 / (((3.0 * pi) * (1.0 - (v * v))) * sqrt((2.0 - (6.0 * (v * v)))));
end

Wolfram

code[v_] := N[(4.0 / N[(N[(N[(3.0 * Pi), $MachinePrecision] * N[(1.0 - N[(v * v), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * N[Sqrt[N[(2.0 - N[(6.0 * N[(v * v), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Initial Program: 98.5% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{4}{\left(\left(3 \cdot \pi\right) \cdot \left(1 - v \cdot v\right)\right) \cdot \sqrt{2 - 6 \cdot \left(v \cdot v\right)}} \end{array} \]

FPCore

(FPCore (v)
 :precision binary64
 (/ 4.0 (* (* (* 3.0 PI) (- 1.0 (* v v))) (sqrt (- 2.0 (* 6.0 (* v v)))))))

C

double code(double v) {
	return 4.0 / (((3.0 * ((double) M_PI)) * (1.0 - (v * v))) * sqrt((2.0 - (6.0 * (v * v)))));
}

Java

public static double code(double v) {
	return 4.0 / (((3.0 * Math.PI) * (1.0 - (v * v))) * Math.sqrt((2.0 - (6.0 * (v * v)))));
}

Python

def code(v):
	return 4.0 / (((3.0 * math.pi) * (1.0 - (v * v))) * math.sqrt((2.0 - (6.0 * (v * v)))))

Julia

function code(v)
	return Float64(4.0 / Float64(Float64(Float64(3.0 * pi) * Float64(1.0 - Float64(v * v))) * sqrt(Float64(2.0 - Float64(6.0 * Float64(v * v))))))
end

MATLAB

function tmp = code(v)
	tmp = 4.0 / (((3.0 * pi) * (1.0 - (v * v))) * sqrt((2.0 - (6.0 * (v * v)))));
end

Wolfram

code[v_] := N[(4.0 / N[(N[(N[(3.0 * Pi), $MachinePrecision] * N[(1.0 - N[(v * v), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * N[Sqrt[N[(2.0 - N[(6.0 * N[(v * v), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Alternative 1: 100.0% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \frac{\frac{4}{\left(\pi \cdot 3\right) \cdot \left(1 - {v}^{2}\right)}}{\sqrt{2 + \left(v \cdot v\right) \cdot -6}} \end{array} \]

FPCore

(FPCore (v)
 :precision binary64
 (/
  (/ 4.0 (* (* PI 3.0) (- 1.0 (pow v 2.0))))
  (sqrt (+ 2.0 (* (* v v) -6.0)))))

C

double code(double v) {
	return (4.0 / ((((double) M_PI) * 3.0) * (1.0 - pow(v, 2.0)))) / sqrt((2.0 + ((v * v) * -6.0)));
}

Java

public static double code(double v) {
	return (4.0 / ((Math.PI * 3.0) * (1.0 - Math.pow(v, 2.0)))) / Math.sqrt((2.0 + ((v * v) * -6.0)));
}

Python

def code(v):
	return (4.0 / ((math.pi * 3.0) * (1.0 - math.pow(v, 2.0)))) / math.sqrt((2.0 + ((v * v) * -6.0)))

Julia

function code(v)
	return Float64(Float64(4.0 / Float64(Float64(pi * 3.0) * Float64(1.0 - (v ^ 2.0)))) / sqrt(Float64(2.0 + Float64(Float64(v * v) * -6.0))))
end

MATLAB

function tmp = code(v)
	tmp = (4.0 / ((pi * 3.0) * (1.0 - (v ^ 2.0)))) / sqrt((2.0 + ((v * v) * -6.0)));
end

Wolfram

code[v_] := N[(N[(4.0 / N[(N[(Pi * 3.0), $MachinePrecision] * N[(1.0 - N[Power[v, 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[Sqrt[N[(2.0 + N[(N[(v * v), $MachinePrecision] * -6.0), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 98.5%

   \[\frac{4}{\left(\left(3 \cdot \pi\right) \cdot \left(1 - v \cdot v\right)\right) \cdot \sqrt{2 - 6 \cdot \left(v \cdot v\right)}} \]
2. Step-by-step derivation

   1. associate-/r* 100.0%

      \[\leadsto \color{blue}{\frac{\frac{4}{\left(3 \cdot \pi\right) \cdot \left(1 - v \cdot v\right)}}{\sqrt{2 - 6 \cdot \left(v \cdot v\right)}}} \]

   2. sqr-neg 100.0%

      \[\leadsto \frac{\frac{4}{\left(3 \cdot \pi\right) \cdot \left(1 - \color{blue}{\left(-v\right) \cdot \left(-v\right)}\right)}}{\sqrt{2 - 6 \cdot \left(v \cdot v\right)}} \]

   3. associate-*l* 100.0%

      \[\leadsto \frac{\frac{4}{\color{blue}{3 \cdot \left(\pi \cdot \left(1 - \left(-v\right) \cdot \left(-v\right)\right)\right)}}}{\sqrt{2 - 6 \cdot \left(v \cdot v\right)}} \]

   4. associate-/r* 100.0%

      \[\leadsto \frac{\color{blue}{\frac{\frac{4}{3}}{\pi \cdot \left(1 - \left(-v\right) \cdot \left(-v\right)\right)}}}{\sqrt{2 - 6 \cdot \left(v \cdot v\right)}} \]

   5. metadata-eval 100.0%

      \[\leadsto \frac{\frac{\color{blue}{1.3333333333333333}}{\pi \cdot \left(1 - \left(-v\right) \cdot \left(-v\right)\right)}}{\sqrt{2 - 6 \cdot \left(v \cdot v\right)}} \]

   6. sqr-neg 100.0%

      \[\leadsto \frac{\frac{1.3333333333333333}{\pi \cdot \left(1 - \color{blue}{v \cdot v}\right)}}{\sqrt{2 - 6 \cdot \left(v \cdot v\right)}} \]

   7. sub-neg 100.0%

      \[\leadsto \frac{\frac{1.3333333333333333}{\pi \cdot \left(1 - v \cdot v\right)}}{\sqrt{\color{blue}{2 + \left(-6 \cdot \left(v \cdot v\right)\right)}}} \]

   8. sqr-neg 100.0%

      \[\leadsto \frac{\frac{1.3333333333333333}{\pi \cdot \left(1 - v \cdot v\right)}}{\sqrt{2 + \left(-6 \cdot \color{blue}{\left(\left(-v\right) \cdot \left(-v\right)\right)}\right)}} \]

   9. *-commutative 100.0%

      \[\leadsto \frac{\frac{1.3333333333333333}{\pi \cdot \left(1 - v \cdot v\right)}}{\sqrt{2 + \left(-\color{blue}{\left(\left(-v\right) \cdot \left(-v\right)\right) \cdot 6}\right)}} \]

   10. distribute-rgt-neg-in 100.0%

       \[\leadsto \frac{\frac{1.3333333333333333}{\pi \cdot \left(1 - v \cdot v\right)}}{\sqrt{2 + \color{blue}{\left(\left(-v\right) \cdot \left(-v\right)\right) \cdot \left(-6\right)}}} \]

   11. sqr-neg 100.0%

       \[\leadsto \frac{\frac{1.3333333333333333}{\pi \cdot \left(1 - v \cdot v\right)}}{\sqrt{2 + \color{blue}{\left(v \cdot v\right)} \cdot \left(-6\right)}} \]

   12. metadata-eval 100.0%

       \[\leadsto \frac{\frac{1.3333333333333333}{\pi \cdot \left(1 - v \cdot v\right)}}{\sqrt{2 + \left(v \cdot v\right) \cdot \color{blue}{-6}}} \]

3. Simplified 100.0%

   \[\leadsto \color{blue}{\frac{\frac{1.3333333333333333}{\pi \cdot \left(1 - v \cdot v\right)}}{\sqrt{2 + \left(v \cdot v\right) \cdot -6}}} \]
4. Add Preprocessing
5. Step-by-step derivation

   1. metadata-eval 100.0%

      \[\leadsto \frac{\frac{\color{blue}{\frac{4}{3}}}{\pi \cdot \left(1 - v \cdot v\right)}}{\sqrt{2 + \left(v \cdot v\right) \cdot -6}} \]

   2. associate-/r* 100.0%

      \[\leadsto \frac{\color{blue}{\frac{4}{3 \cdot \left(\pi \cdot \left(1 - v \cdot v\right)\right)}}}{\sqrt{2 + \left(v \cdot v\right) \cdot -6}} \]

   3. associate-*l* 100.0%

      \[\leadsto \frac{\frac{4}{\color{blue}{\left(3 \cdot \pi\right) \cdot \left(1 - v \cdot v\right)}}}{\sqrt{2 + \left(v \cdot v\right) \cdot -6}} \]

   4. div-inv 100.0%

      \[\leadsto \frac{\color{blue}{4 \cdot \frac{1}{\left(3 \cdot \pi\right) \cdot \left(1 - v \cdot v\right)}}}{\sqrt{2 + \left(v \cdot v\right) \cdot -6}} \]

   5. associate-*l* 100.0%

      \[\leadsto \frac{4 \cdot \frac{1}{\color{blue}{3 \cdot \left(\pi \cdot \left(1 - v \cdot v\right)\right)}}}{\sqrt{2 + \left(v \cdot v\right) \cdot -6}} \]

   6. pow2 100.0%

      \[\leadsto \frac{4 \cdot \frac{1}{3 \cdot \left(\pi \cdot \left(1 - \color{blue}{{v}^{2}}\right)\right)}}{\sqrt{2 + \left(v \cdot v\right) \cdot -6}} \]

6. Applied egg-rr 100.0%

   \[\leadsto \frac{\color{blue}{4 \cdot \frac{1}{3 \cdot \left(\pi \cdot \left(1 - {v}^{2}\right)\right)}}}{\sqrt{2 + \left(v \cdot v\right) \cdot -6}} \]
7. Step-by-step derivation

   1. associate-*r/ 100.0%

      \[\leadsto \frac{\color{blue}{\frac{4 \cdot 1}{3 \cdot \left(\pi \cdot \left(1 - {v}^{2}\right)\right)}}}{\sqrt{2 + \left(v \cdot v\right) \cdot -6}} \]

   2. metadata-eval 100.0%

      \[\leadsto \frac{\frac{\color{blue}{4}}{3 \cdot \left(\pi \cdot \left(1 - {v}^{2}\right)\right)}}{\sqrt{2 + \left(v \cdot v\right) \cdot -6}} \]

   3. associate-*r* 100.0%

      \[\leadsto \frac{\frac{4}{\color{blue}{\left(3 \cdot \pi\right) \cdot \left(1 - {v}^{2}\right)}}}{\sqrt{2 + \left(v \cdot v\right) \cdot -6}} \]

   4. *-commutative 100.0%

      \[\leadsto \frac{\frac{4}{\color{blue}{\left(\pi \cdot 3\right)} \cdot \left(1 - {v}^{2}\right)}}{\sqrt{2 + \left(v \cdot v\right) \cdot -6}} \]

8. Simplified 100.0%

   \[\leadsto \frac{\color{blue}{\frac{4}{\left(\pi \cdot 3\right) \cdot \left(1 - {v}^{2}\right)}}}{\sqrt{2 + \left(v \cdot v\right) \cdot -6}} \]

9. Final simplification 100.0%

   \[\leadsto \frac{\frac{4}{\left(\pi \cdot 3\right) \cdot \left(1 - {v}^{2}\right)}}{\sqrt{2 + \left(v \cdot v\right) \cdot -6}} \]
10. Add Preprocessing

Alternative 2: 100.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{\frac{1.3333333333333333}{\pi \cdot \left(1 - v \cdot v\right)}}{\sqrt{2 + \left(v \cdot v\right) \cdot -6}} \end{array} \]

FPCore

(FPCore (v)
 :precision binary64
 (/
  (/ 1.3333333333333333 (* PI (- 1.0 (* v v))))
  (sqrt (+ 2.0 (* (* v v) -6.0)))))

C

double code(double v) {
	return (1.3333333333333333 / (((double) M_PI) * (1.0 - (v * v)))) / sqrt((2.0 + ((v * v) * -6.0)));
}

Java

public static double code(double v) {
	return (1.3333333333333333 / (Math.PI * (1.0 - (v * v)))) / Math.sqrt((2.0 + ((v * v) * -6.0)));
}

Python

def code(v):
	return (1.3333333333333333 / (math.pi * (1.0 - (v * v)))) / math.sqrt((2.0 + ((v * v) * -6.0)))

Julia

function code(v)
	return Float64(Float64(1.3333333333333333 / Float64(pi * Float64(1.0 - Float64(v * v)))) / sqrt(Float64(2.0 + Float64(Float64(v * v) * -6.0))))
end

MATLAB

function tmp = code(v)
	tmp = (1.3333333333333333 / (pi * (1.0 - (v * v)))) / sqrt((2.0 + ((v * v) * -6.0)));
end

Wolfram

code[v_] := N[(N[(1.3333333333333333 / N[(Pi * N[(1.0 - N[(v * v), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[Sqrt[N[(2.0 + N[(N[(v * v), $MachinePrecision] * -6.0), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 98.5%

   \[\frac{4}{\left(\left(3 \cdot \pi\right) \cdot \left(1 - v \cdot v\right)\right) \cdot \sqrt{2 - 6 \cdot \left(v \cdot v\right)}} \]
2. Step-by-step derivation

   1. associate-/r* 100.0%

      \[\leadsto \color{blue}{\frac{\frac{4}{\left(3 \cdot \pi\right) \cdot \left(1 - v \cdot v\right)}}{\sqrt{2 - 6 \cdot \left(v \cdot v\right)}}} \]

   2. sqr-neg 100.0%

      \[\leadsto \frac{\frac{4}{\left(3 \cdot \pi\right) \cdot \left(1 - \color{blue}{\left(-v\right) \cdot \left(-v\right)}\right)}}{\sqrt{2 - 6 \cdot \left(v \cdot v\right)}} \]

   3. associate-*l* 100.0%

      \[\leadsto \frac{\frac{4}{\color{blue}{3 \cdot \left(\pi \cdot \left(1 - \left(-v\right) \cdot \left(-v\right)\right)\right)}}}{\sqrt{2 - 6 \cdot \left(v \cdot v\right)}} \]

   4. associate-/r* 100.0%

      \[\leadsto \frac{\color{blue}{\frac{\frac{4}{3}}{\pi \cdot \left(1 - \left(-v\right) \cdot \left(-v\right)\right)}}}{\sqrt{2 - 6 \cdot \left(v \cdot v\right)}} \]

   5. metadata-eval 100.0%

      \[\leadsto \frac{\frac{\color{blue}{1.3333333333333333}}{\pi \cdot \left(1 - \left(-v\right) \cdot \left(-v\right)\right)}}{\sqrt{2 - 6 \cdot \left(v \cdot v\right)}} \]

   6. sqr-neg 100.0%

      \[\leadsto \frac{\frac{1.3333333333333333}{\pi \cdot \left(1 - \color{blue}{v \cdot v}\right)}}{\sqrt{2 - 6 \cdot \left(v \cdot v\right)}} \]

   7. sub-neg 100.0%

      \[\leadsto \frac{\frac{1.3333333333333333}{\pi \cdot \left(1 - v \cdot v\right)}}{\sqrt{\color{blue}{2 + \left(-6 \cdot \left(v \cdot v\right)\right)}}} \]

   8. sqr-neg 100.0%

      \[\leadsto \frac{\frac{1.3333333333333333}{\pi \cdot \left(1 - v \cdot v\right)}}{\sqrt{2 + \left(-6 \cdot \color{blue}{\left(\left(-v\right) \cdot \left(-v\right)\right)}\right)}} \]

   9. *-commutative 100.0%

      \[\leadsto \frac{\frac{1.3333333333333333}{\pi \cdot \left(1 - v \cdot v\right)}}{\sqrt{2 + \left(-\color{blue}{\left(\left(-v\right) \cdot \left(-v\right)\right) \cdot 6}\right)}} \]

   10. distribute-rgt-neg-in 100.0%

       \[\leadsto \frac{\frac{1.3333333333333333}{\pi \cdot \left(1 - v \cdot v\right)}}{\sqrt{2 + \color{blue}{\left(\left(-v\right) \cdot \left(-v\right)\right) \cdot \left(-6\right)}}} \]

   11. sqr-neg 100.0%

       \[\leadsto \frac{\frac{1.3333333333333333}{\pi \cdot \left(1 - v \cdot v\right)}}{\sqrt{2 + \color{blue}{\left(v \cdot v\right)} \cdot \left(-6\right)}} \]

   12. metadata-eval 100.0%

       \[\leadsto \frac{\frac{1.3333333333333333}{\pi \cdot \left(1 - v \cdot v\right)}}{\sqrt{2 + \left(v \cdot v\right) \cdot \color{blue}{-6}}} \]

3. Simplified 100.0%

   \[\leadsto \color{blue}{\frac{\frac{1.3333333333333333}{\pi \cdot \left(1 - v \cdot v\right)}}{\sqrt{2 + \left(v \cdot v\right) \cdot -6}}} \]
4. Add Preprocessing

5. Final simplification 100.0%

   \[\leadsto \frac{\frac{1.3333333333333333}{\pi \cdot \left(1 - v \cdot v\right)}}{\sqrt{2 + \left(v \cdot v\right) \cdot -6}} \]
6. Add Preprocessing

Alternative 3: 99.0% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \frac{\frac{1.3333333333333333}{\pi}}{\sqrt{2 + \left(v \cdot v\right) \cdot -6}} \end{array} \]

FPCore

(FPCore (v)
 :precision binary64
 (/ (/ 1.3333333333333333 PI) (sqrt (+ 2.0 (* (* v v) -6.0)))))

C

double code(double v) {
	return (1.3333333333333333 / ((double) M_PI)) / sqrt((2.0 + ((v * v) * -6.0)));
}

Java

public static double code(double v) {
	return (1.3333333333333333 / Math.PI) / Math.sqrt((2.0 + ((v * v) * -6.0)));
}

Python

def code(v):
	return (1.3333333333333333 / math.pi) / math.sqrt((2.0 + ((v * v) * -6.0)))

Julia

function code(v)
	return Float64(Float64(1.3333333333333333 / pi) / sqrt(Float64(2.0 + Float64(Float64(v * v) * -6.0))))
end

MATLAB

function tmp = code(v)
	tmp = (1.3333333333333333 / pi) / sqrt((2.0 + ((v * v) * -6.0)));
end

Wolfram

code[v_] := N[(N[(1.3333333333333333 / Pi), $MachinePrecision] / N[Sqrt[N[(2.0 + N[(N[(v * v), $MachinePrecision] * -6.0), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 98.5%

   \[\frac{4}{\left(\left(3 \cdot \pi\right) \cdot \left(1 - v \cdot v\right)\right) \cdot \sqrt{2 - 6 \cdot \left(v \cdot v\right)}} \]
2. Step-by-step derivation

   1. associate-/r* 100.0%

      \[\leadsto \color{blue}{\frac{\frac{4}{\left(3 \cdot \pi\right) \cdot \left(1 - v \cdot v\right)}}{\sqrt{2 - 6 \cdot \left(v \cdot v\right)}}} \]

   2. sqr-neg 100.0%

      \[\leadsto \frac{\frac{4}{\left(3 \cdot \pi\right) \cdot \left(1 - \color{blue}{\left(-v\right) \cdot \left(-v\right)}\right)}}{\sqrt{2 - 6 \cdot \left(v \cdot v\right)}} \]

   3. associate-*l* 100.0%

      \[\leadsto \frac{\frac{4}{\color{blue}{3 \cdot \left(\pi \cdot \left(1 - \left(-v\right) \cdot \left(-v\right)\right)\right)}}}{\sqrt{2 - 6 \cdot \left(v \cdot v\right)}} \]

   4. associate-/r* 100.0%

      \[\leadsto \frac{\color{blue}{\frac{\frac{4}{3}}{\pi \cdot \left(1 - \left(-v\right) \cdot \left(-v\right)\right)}}}{\sqrt{2 - 6 \cdot \left(v \cdot v\right)}} \]

   5. metadata-eval 100.0%

      \[\leadsto \frac{\frac{\color{blue}{1.3333333333333333}}{\pi \cdot \left(1 - \left(-v\right) \cdot \left(-v\right)\right)}}{\sqrt{2 - 6 \cdot \left(v \cdot v\right)}} \]

   6. sqr-neg 100.0%

      \[\leadsto \frac{\frac{1.3333333333333333}{\pi \cdot \left(1 - \color{blue}{v \cdot v}\right)}}{\sqrt{2 - 6 \cdot \left(v \cdot v\right)}} \]

   7. sub-neg 100.0%

      \[\leadsto \frac{\frac{1.3333333333333333}{\pi \cdot \left(1 - v \cdot v\right)}}{\sqrt{\color{blue}{2 + \left(-6 \cdot \left(v \cdot v\right)\right)}}} \]

   8. sqr-neg 100.0%

      \[\leadsto \frac{\frac{1.3333333333333333}{\pi \cdot \left(1 - v \cdot v\right)}}{\sqrt{2 + \left(-6 \cdot \color{blue}{\left(\left(-v\right) \cdot \left(-v\right)\right)}\right)}} \]

   9. *-commutative 100.0%

      \[\leadsto \frac{\frac{1.3333333333333333}{\pi \cdot \left(1 - v \cdot v\right)}}{\sqrt{2 + \left(-\color{blue}{\left(\left(-v\right) \cdot \left(-v\right)\right) \cdot 6}\right)}} \]

   10. distribute-rgt-neg-in 100.0%

       \[\leadsto \frac{\frac{1.3333333333333333}{\pi \cdot \left(1 - v \cdot v\right)}}{\sqrt{2 + \color{blue}{\left(\left(-v\right) \cdot \left(-v\right)\right) \cdot \left(-6\right)}}} \]

   11. sqr-neg 100.0%

       \[\leadsto \frac{\frac{1.3333333333333333}{\pi \cdot \left(1 - v \cdot v\right)}}{\sqrt{2 + \color{blue}{\left(v \cdot v\right)} \cdot \left(-6\right)}} \]

   12. metadata-eval 100.0%

       \[\leadsto \frac{\frac{1.3333333333333333}{\pi \cdot \left(1 - v \cdot v\right)}}{\sqrt{2 + \left(v \cdot v\right) \cdot \color{blue}{-6}}} \]

3. Simplified 100.0%

   \[\leadsto \color{blue}{\frac{\frac{1.3333333333333333}{\pi \cdot \left(1 - v \cdot v\right)}}{\sqrt{2 + \left(v \cdot v\right) \cdot -6}}} \]
4. Add Preprocessing

5. Taylor expanded in v around 0 99.1%

   \[\leadsto \frac{\color{blue}{\frac{1.3333333333333333}{\pi}}}{\sqrt{2 + \left(v \cdot v\right) \cdot -6}} \]

6. Final simplification 99.1%

   \[\leadsto \frac{\frac{1.3333333333333333}{\pi}}{\sqrt{2 + \left(v \cdot v\right) \cdot -6}} \]
7. Add Preprocessing

Alternative 4: 98.9% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \frac{1.3333333333333333}{\pi} \cdot \sqrt{0.5} \end{array} \]

FPCore

(FPCore (v) :precision binary64 (* (/ 1.3333333333333333 PI) (sqrt 0.5)))

C

double code(double v) {
	return (1.3333333333333333 / ((double) M_PI)) * sqrt(0.5);
}

Java

public static double code(double v) {
	return (1.3333333333333333 / Math.PI) * Math.sqrt(0.5);
}

Python

def code(v):
	return (1.3333333333333333 / math.pi) * math.sqrt(0.5)

Julia

function code(v)
	return Float64(Float64(1.3333333333333333 / pi) * sqrt(0.5))
end

MATLAB

function tmp = code(v)
	tmp = (1.3333333333333333 / pi) * sqrt(0.5);
end

Wolfram

code[v_] := N[(N[(1.3333333333333333 / Pi), $MachinePrecision] * N[Sqrt[0.5], $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 98.5%

   \[\frac{4}{\left(\left(3 \cdot \pi\right) \cdot \left(1 - v \cdot v\right)\right) \cdot \sqrt{2 - 6 \cdot \left(v \cdot v\right)}} \]
2. Add Preprocessing

3. Taylor expanded in v around 0 97.5%

   \[\leadsto \color{blue}{1.3333333333333333 \cdot \frac{\sqrt{0.5}}{\pi}} \]
4. Step-by-step derivation

   1. associate-*r/ 97.5%

      \[\leadsto \color{blue}{\frac{1.3333333333333333 \cdot \sqrt{0.5}}{\pi}} \]

5. Simplified 97.5%

   \[\leadsto \color{blue}{\frac{1.3333333333333333 \cdot \sqrt{0.5}}{\pi}} \]
6. Step-by-step derivation

   1. div-inv 97.5%

      \[\leadsto \color{blue}{\left(1.3333333333333333 \cdot \sqrt{0.5}\right) \cdot \frac{1}{\pi}} \]

   2. *-commutative 97.5%

      \[\leadsto \color{blue}{\left(\sqrt{0.5} \cdot 1.3333333333333333\right)} \cdot \frac{1}{\pi} \]

   3. associate-*l* 97.5%

      \[\leadsto \color{blue}{\sqrt{0.5} \cdot \left(1.3333333333333333 \cdot \frac{1}{\pi}\right)} \]

   4. div-inv 99.0%

      \[\leadsto \sqrt{0.5} \cdot \color{blue}{\frac{1.3333333333333333}{\pi}} \]

7. Applied egg-rr 99.0%

   \[\leadsto \color{blue}{\sqrt{0.5} \cdot \frac{1.3333333333333333}{\pi}} \]

8. Final simplification 99.0%

   \[\leadsto \frac{1.3333333333333333}{\pi} \cdot \sqrt{0.5} \]
9. Add Preprocessing

Alternative 5: 97.4% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \frac{\sqrt{0.8888888888888888}}{\pi} \end{array} \]

FPCore

(FPCore (v) :precision binary64 (/ (sqrt 0.8888888888888888) PI))

C

double code(double v) {
	return sqrt(0.8888888888888888) / ((double) M_PI);
}

Java

public static double code(double v) {
	return Math.sqrt(0.8888888888888888) / Math.PI;
}

Python

def code(v):
	return math.sqrt(0.8888888888888888) / math.pi

Julia

function code(v)
	return Float64(sqrt(0.8888888888888888) / pi)
end

MATLAB

function tmp = code(v)
	tmp = sqrt(0.8888888888888888) / pi;
end

Wolfram

code[v_] := N[(N[Sqrt[0.8888888888888888], $MachinePrecision] / Pi), $MachinePrecision]

Derivation

1. Initial program 98.5%

   \[\frac{4}{\left(\left(3 \cdot \pi\right) \cdot \left(1 - v \cdot v\right)\right) \cdot \sqrt{2 - 6 \cdot \left(v \cdot v\right)}} \]
2. Add Preprocessing

3. Taylor expanded in v around 0 97.5%

   \[\leadsto \color{blue}{1.3333333333333333 \cdot \frac{\sqrt{0.5}}{\pi}} \]
4. Step-by-step derivation

   1. associate-*r/ 97.5%

      \[\leadsto \color{blue}{\frac{1.3333333333333333 \cdot \sqrt{0.5}}{\pi}} \]

5. Simplified 97.5%

   \[\leadsto \color{blue}{\frac{1.3333333333333333 \cdot \sqrt{0.5}}{\pi}} \]
6. Step-by-step derivation

   1. *-un-lft-identity 97.5%

      \[\leadsto \color{blue}{1 \cdot \frac{1.3333333333333333 \cdot \sqrt{0.5}}{\pi}} \]

   2. add-sqr-sqrt 97.5%

      \[\leadsto 1 \cdot \frac{\color{blue}{\sqrt{1.3333333333333333 \cdot \sqrt{0.5}} \cdot \sqrt{1.3333333333333333 \cdot \sqrt{0.5}}}}{\pi} \]

   3. sqrt-unprod 97.5%

      \[\leadsto 1 \cdot \frac{\color{blue}{\sqrt{\left(1.3333333333333333 \cdot \sqrt{0.5}\right) \cdot \left(1.3333333333333333 \cdot \sqrt{0.5}\right)}}}{\pi} \]

   4. swap-sqr 97.5%

      \[\leadsto 1 \cdot \frac{\sqrt{\color{blue}{\left(1.3333333333333333 \cdot 1.3333333333333333\right) \cdot \left(\sqrt{0.5} \cdot \sqrt{0.5}\right)}}}{\pi} \]

   5. metadata-eval 97.5%

      \[\leadsto 1 \cdot \frac{\sqrt{\color{blue}{1.7777777777777777} \cdot \left(\sqrt{0.5} \cdot \sqrt{0.5}\right)}}{\pi} \]

   6. rem-square-sqrt 97.5%

      \[\leadsto 1 \cdot \frac{\sqrt{1.7777777777777777 \cdot \color{blue}{0.5}}}{\pi} \]

   7. metadata-eval 97.5%

      \[\leadsto 1 \cdot \frac{\sqrt{\color{blue}{0.8888888888888888}}}{\pi} \]

7. Applied egg-rr 97.5%

   \[\leadsto \color{blue}{1 \cdot \frac{\sqrt{0.8888888888888888}}{\pi}} \]
8. Step-by-step derivation

   1. *-lft-identity 97.5%

      \[\leadsto \color{blue}{\frac{\sqrt{0.8888888888888888}}{\pi}} \]

9. Simplified 97.5%

   \[\leadsto \color{blue}{\frac{\sqrt{0.8888888888888888}}{\pi}} \]

10. Final simplification 97.5%

    \[\leadsto \frac{\sqrt{0.8888888888888888}}{\pi} \]
11. Add Preprocessing

Reproduce

herbie shell --seed 2024058 
(FPCore (v)
  :name "Falkner and Boettcher, Equation (22+)"
  :precision binary64
  (/ 4.0 (* (* (* 3.0 PI) (- 1.0 (* v v))) (sqrt (- 2.0 (* 6.0 (* v v)))))))