Falkner and Boettcher, Equation (22+)

Percentage Accurate: 98.5% → 100.0%

Time: 4.4s

Alternatives: 6

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, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{\frac{1}{\left(0.75 - \frac{v \cdot v}{1.3333333333333333}\right) \cdot \pi}}{\sqrt{2 + \left(v \cdot v\right) \cdot -6}} \end{array} \]

FPCore

(FPCore (v)
 :precision binary64
 (/
  (/ 1.0 (* (- 0.75 (/ (* v v) 1.3333333333333333)) PI))
  (sqrt (+ 2.0 (* (* v v) -6.0)))))

C

double code(double v) {
	return (1.0 / ((0.75 - ((v * v) / 1.3333333333333333)) * ((double) M_PI))) / sqrt((2.0 + ((v * v) * -6.0)));
}

Java

public static double code(double v) {
	return (1.0 / ((0.75 - ((v * v) / 1.3333333333333333)) * Math.PI)) / Math.sqrt((2.0 + ((v * v) * -6.0)));
}

Python

def code(v):
	return (1.0 / ((0.75 - ((v * v) / 1.3333333333333333)) * math.pi)) / math.sqrt((2.0 + ((v * v) * -6.0)))

Julia

function code(v)
	return Float64(Float64(1.0 / Float64(Float64(0.75 - Float64(Float64(v * v) / 1.3333333333333333)) * pi)) / sqrt(Float64(2.0 + Float64(Float64(v * v) * -6.0))))
end

MATLAB

function tmp = code(v)
	tmp = (1.0 / ((0.75 - ((v * v) / 1.3333333333333333)) * pi)) / sqrt((2.0 + ((v * v) * -6.0)));
end

Wolfram

code[v_] := N[(N[(1.0 / N[(N[(0.75 - N[(N[(v * v), $MachinePrecision] / 1.3333333333333333), $MachinePrecision]), $MachinePrecision] * Pi), $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. associate-*l* 100.0%

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

   3. associate-/r* 100.0%

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

   4. metadata-eval 100.0%

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

   5. sub-neg 100.0%

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

   6. *-commutative 100.0%

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

   7. distribute-rgt-neg-in 100.0%

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

   8. 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. Step-by-step derivation

   1. associate-/r* 100.0%

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

   2. clear-num 98.5%

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

   3. inv-pow 98.5%

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

5. Applied egg-rr 98.5%

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

   1. unpow-1 98.5%

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

   2. associate-/r/ 100.0%

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

   3. unpow2 100.0%

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

   4. div-sub 100.0%

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

   5. metadata-eval 100.0%

      \[\leadsto \frac{\frac{1}{\left(\color{blue}{0.75} - \frac{{v}^{2}}{1.3333333333333333}\right) \cdot \pi}}{\sqrt{2 + \left(v \cdot v\right) \cdot -6}} \]

   6. unpow2 100.0%

      \[\leadsto \frac{\frac{1}{\left(0.75 - \frac{\color{blue}{v \cdot v}}{1.3333333333333333}\right) \cdot \pi}}{\sqrt{2 + \left(v \cdot v\right) \cdot -6}} \]

7. Simplified 100.0%

   \[\leadsto \frac{\color{blue}{\frac{1}{\left(0.75 - \frac{v \cdot v}{1.3333333333333333}\right) \cdot \pi}}}{\sqrt{2 + \left(v \cdot v\right) \cdot -6}} \]

8. Final simplification 100.0%

   \[\leadsto \frac{\frac{1}{\left(0.75 - \frac{v \cdot v}{1.3333333333333333}\right) \cdot \pi}}{\sqrt{2 + \left(v \cdot v\right) \cdot -6}} \]

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. associate-*l* 100.0%

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

   3. associate-/r* 100.0%

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

   4. metadata-eval 100.0%

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

   5. sub-neg 100.0%

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

   6. *-commutative 100.0%

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

   7. distribute-rgt-neg-in 100.0%

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

   8. 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. Final simplification 100.0%

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

Alternative 3: 98.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{\frac{1}{0.75 \cdot \pi}}{\sqrt{2 + \left(v \cdot v\right) \cdot -6}} \end{array} \]

FPCore

(FPCore (v)
 :precision binary64
 (/ (/ 1.0 (* 0.75 PI)) (sqrt (+ 2.0 (* (* v v) -6.0)))))

C

double code(double v) {
	return (1.0 / (0.75 * ((double) M_PI))) / sqrt((2.0 + ((v * v) * -6.0)));
}

Java

public static double code(double v) {
	return (1.0 / (0.75 * Math.PI)) / Math.sqrt((2.0 + ((v * v) * -6.0)));
}

Python

def code(v):
	return (1.0 / (0.75 * math.pi)) / math.sqrt((2.0 + ((v * v) * -6.0)))

Julia

function code(v)
	return Float64(Float64(1.0 / Float64(0.75 * pi)) / sqrt(Float64(2.0 + Float64(Float64(v * v) * -6.0))))
end

MATLAB

function tmp = code(v)
	tmp = (1.0 / (0.75 * pi)) / sqrt((2.0 + ((v * v) * -6.0)));
end

Wolfram

code[v_] := N[(N[(1.0 / N[(0.75 * Pi), $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. associate-*l* 100.0%

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

   3. associate-/r* 100.0%

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

   4. metadata-eval 100.0%

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

   5. sub-neg 100.0%

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

   6. *-commutative 100.0%

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

   7. distribute-rgt-neg-in 100.0%

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

   8. 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. Taylor expanded in v around 0 98.7%

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

   1. clear-num 98.7%

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

   2. inv-pow 98.7%

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

   3. div-inv 98.7%

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

   4. metadata-eval 98.7%

      \[\leadsto \frac{{\left(\pi \cdot \color{blue}{0.75}\right)}^{-1}}{\sqrt{2 + \left(v \cdot v\right) \cdot -6}} \]

6. Applied egg-rr 98.7%

   \[\leadsto \frac{\color{blue}{{\left(\pi \cdot 0.75\right)}^{-1}}}{\sqrt{2 + \left(v \cdot v\right) \cdot -6}} \]
7. Step-by-step derivation

   1. unpow-1 98.7%

      \[\leadsto \frac{\color{blue}{\frac{1}{\pi \cdot 0.75}}}{\sqrt{2 + \left(v \cdot v\right) \cdot -6}} \]

8. Simplified 98.7%

   \[\leadsto \frac{\color{blue}{\frac{1}{\pi \cdot 0.75}}}{\sqrt{2 + \left(v \cdot v\right) \cdot -6}} \]

9. Final simplification 98.7%

   \[\leadsto \frac{\frac{1}{0.75 \cdot \pi}}{\sqrt{2 + \left(v \cdot v\right) \cdot -6}} \]

Alternative 4: 98.9% accurate, 1.0× 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. associate-*l* 100.0%

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

   3. associate-/r* 100.0%

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

   4. metadata-eval 100.0%

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

   5. sub-neg 100.0%

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

   6. *-commutative 100.0%

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

   7. distribute-rgt-neg-in 100.0%

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

   8. 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. Taylor expanded in v around 0 98.7%

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

5. Final simplification 98.7%

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

Alternative 5: 97.3% accurate, 1.1× speedup

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

code[v_] := N[(1.3333333333333333 * N[(N[Sqrt[0.5], $MachinePrecision] / Pi), $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-*l* 98.5%

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

   2. cancel-sign-sub-inv 98.5%

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

   3. metadata-eval 98.5%

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

3. Simplified 98.5%

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

4. Taylor expanded in v around 0 97.2%

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

5. Final simplification 97.2%

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

Alternative 6: 98.8% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \frac{\frac{1.3333333333333333}{\pi}}{\sqrt{2}} \end{array} \]

FPCore

(FPCore (v) :precision binary64 (/ (/ 1.3333333333333333 PI) (sqrt 2.0)))

C

double code(double v) {
	return (1.3333333333333333 / ((double) M_PI)) / sqrt(2.0);
}

Java

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

Python

def code(v):
	return (1.3333333333333333 / math.pi) / math.sqrt(2.0)

Julia

function code(v)
	return Float64(Float64(1.3333333333333333 / pi) / sqrt(2.0))
end

MATLAB

function tmp = code(v)
	tmp = (1.3333333333333333 / pi) / sqrt(2.0);
end

Wolfram

code[v_] := N[(N[(1.3333333333333333 / Pi), $MachinePrecision] / N[Sqrt[2.0], $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. associate-*l* 100.0%

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

   3. associate-/r* 100.0%

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

   4. metadata-eval 100.0%

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

   5. sub-neg 100.0%

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

   6. *-commutative 100.0%

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

   7. distribute-rgt-neg-in 100.0%

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

   8. 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. Taylor expanded in v around 0 98.7%

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

5. Taylor expanded in v around 0 98.7%

   \[\leadsto \frac{\frac{1.3333333333333333}{\pi}}{\color{blue}{\sqrt{2}}} \]

6. Final simplification 98.7%

   \[\leadsto \frac{\frac{1.3333333333333333}{\pi}}{\sqrt{2}} \]

Reproduce

herbie shell --seed 2023195 
(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)))))))