Falkner and Boettcher, Equation (22+)

Percentage Accurate: 98.5% → 100.0%

Time: 9.2s

Alternatives: 9

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, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := v \cdot \left(v \cdot -6\right)\\ \frac{\frac{\frac{1.3333333333333333}{\pi}}{1 - v \cdot v}}{\sqrt{\frac{t_0 \cdot t_0 - 4}{t_0 - 2}}} \end{array} \end{array} \]

FPCore

(FPCore (v)
 :precision binary64
 (let* ((t_0 (* v (* v -6.0))))
   (/
    (/ (/ 1.3333333333333333 PI) (- 1.0 (* v v)))
    (sqrt (/ (- (* t_0 t_0) 4.0) (- t_0 2.0))))))

C

double code(double v) {
	double t_0 = v * (v * -6.0);
	return ((1.3333333333333333 / ((double) M_PI)) / (1.0 - (v * v))) / sqrt((((t_0 * t_0) - 4.0) / (t_0 - 2.0)));
}

Java

public static double code(double v) {
	double t_0 = v * (v * -6.0);
	return ((1.3333333333333333 / Math.PI) / (1.0 - (v * v))) / Math.sqrt((((t_0 * t_0) - 4.0) / (t_0 - 2.0)));
}

Python

def code(v):
	t_0 = v * (v * -6.0)
	return ((1.3333333333333333 / math.pi) / (1.0 - (v * v))) / math.sqrt((((t_0 * t_0) - 4.0) / (t_0 - 2.0)))

Julia

function code(v)
	t_0 = Float64(v * Float64(v * -6.0))
	return Float64(Float64(Float64(1.3333333333333333 / pi) / Float64(1.0 - Float64(v * v))) / sqrt(Float64(Float64(Float64(t_0 * t_0) - 4.0) / Float64(t_0 - 2.0))))
end

MATLAB

function tmp = code(v)
	t_0 = v * (v * -6.0);
	tmp = ((1.3333333333333333 / pi) / (1.0 - (v * v))) / sqrt((((t_0 * t_0) - 4.0) / (t_0 - 2.0)));
end

Wolfram

code[v_] := Block[{t$95$0 = N[(v * N[(v * -6.0), $MachinePrecision]), $MachinePrecision]}, N[(N[(N[(1.3333333333333333 / Pi), $MachinePrecision] / N[(1.0 - N[(v * v), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[Sqrt[N[(N[(N[(t$95$0 * t$95$0), $MachinePrecision] - 4.0), $MachinePrecision] / N[(t$95$0 - 2.0), $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. *-commutative 100.0%

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

   3. sqr-neg 100.0%

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

   4. associate-/l/ 100.0%

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

   5. associate-/r* 100.0%

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

   6. metadata-eval 100.0%

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

   7. sqr-neg 100.0%

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

   8. sub-neg 100.0%

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

   9. sqr-neg 100.0%

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

   10. +-commutative 100.0%

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

3. Simplified 100.0%

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

   1. fma-udef 100.0%

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

   2. flip-+ 100.0%

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

   3. metadata-eval 100.0%

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

5. Applied egg-rr 100.0%

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

6. Final simplification 100.0%

   \[\leadsto \frac{\frac{\frac{1.3333333333333333}{\pi}}{1 - v \cdot v}}{\sqrt{\frac{\left(v \cdot \left(v \cdot -6\right)\right) \cdot \left(v \cdot \left(v \cdot -6\right)\right) - 4}{v \cdot \left(v \cdot -6\right) - 2}}} \]

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. sqr-neg 100.0%

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

   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%

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

3. Simplified 100.0%

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

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: 100.0% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

code[v_] := N[(N[(N[(1.3333333333333333 / Pi), $MachinePrecision] / N[(1.0 - N[(v * v), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[Sqrt[N[(N[(v * N[(v * -6.0), $MachinePrecision]), $MachinePrecision] + 2.0), $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. *-commutative 100.0%

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

   3. sqr-neg 100.0%

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

   4. associate-/l/ 100.0%

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

   5. associate-/r* 100.0%

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

   6. metadata-eval 100.0%

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

   7. sqr-neg 100.0%

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

   8. sub-neg 100.0%

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

   9. sqr-neg 100.0%

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

   10. +-commutative 100.0%

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

3. Simplified 100.0%

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

   1. fma-udef 100.0%

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

5. Applied egg-rr 100.0%

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

6. Final simplification 100.0%

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

Alternative 4: 99.0% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

code[v_] := N[(4.0 * N[(1.0 / N[(Pi * N[(3.0 * N[Sqrt[N[(2.0 + N[(N[(v * v), $MachinePrecision] * -6.0), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $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. Taylor expanded in v around 0 97.0%

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

   1. *-commutative 97.0%

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

4. Simplified 97.0%

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

   1. div-inv 97.0%

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

   2. associate-*l* 98.5%

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

   3. cancel-sign-sub-inv 98.5%

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

   4. metadata-eval 98.5%

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

6. Applied egg-rr 98.5%

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

7. Final simplification 98.5%

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

Alternative 5: 99.0% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

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

   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%

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

3. Simplified 100.0%

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

4. Taylor expanded in v around 0 98.5%

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

   1. clear-num 98.5%

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

   2. inv-pow 98.5%

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

   3. div-inv 98.5%

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

   4. metadata-eval 98.5%

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

6. Applied egg-rr 98.5%

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

   1. unpow-1 98.5%

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

8. Simplified 98.5%

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

9. Final simplification 98.5%

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

Alternative 6: 99.0% 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. sqr-neg 100.0%

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

   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%

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

3. Simplified 100.0%

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

4. Taylor expanded in v around 0 98.5%

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

5. Final simplification 98.5%

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

Alternative 7: 99.0% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

code[v_] := N[(N[(1.3333333333333333 / N[Sqrt[N[(N[(v * N[(v * -6.0), $MachinePrecision]), $MachinePrecision] + 2.0), $MachinePrecision]], $MachinePrecision]), $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. 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. sqr-neg 100.0%

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

   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%

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

3. Simplified 100.0%

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

4. Taylor expanded in v around 0 98.5%

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

   1. expm1-log1p-u 98.5%

      \[\leadsto \color{blue}{\mathsf{expm1}\left(\mathsf{log1p}\left(\frac{\frac{1.3333333333333333}{\pi}}{\sqrt{2 - 6 \cdot \left(v \cdot v\right)}}\right)\right)} \]

   2. expm1-udef 97.0%

      \[\leadsto \color{blue}{e^{\mathsf{log1p}\left(\frac{\frac{1.3333333333333333}{\pi}}{\sqrt{2 - 6 \cdot \left(v \cdot v\right)}}\right)} - 1} \]

   3. associate-/l/ 97.0%

      \[\leadsto e^{\mathsf{log1p}\left(\color{blue}{\frac{1.3333333333333333}{\sqrt{2 - 6 \cdot \left(v \cdot v\right)} \cdot \pi}}\right)} - 1 \]

   4. cancel-sign-sub-inv 97.0%

      \[\leadsto e^{\mathsf{log1p}\left(\frac{1.3333333333333333}{\sqrt{\color{blue}{2 + \left(-6\right) \cdot \left(v \cdot v\right)}} \cdot \pi}\right)} - 1 \]

   5. metadata-eval 97.0%

      \[\leadsto e^{\mathsf{log1p}\left(\frac{1.3333333333333333}{\sqrt{2 + \color{blue}{-6} \cdot \left(v \cdot v\right)} \cdot \pi}\right)} - 1 \]

6. Applied egg-rr 97.0%

   \[\leadsto \color{blue}{e^{\mathsf{log1p}\left(\frac{1.3333333333333333}{\sqrt{2 + -6 \cdot \left(v \cdot v\right)} \cdot \pi}\right)} - 1} \]
7. Step-by-step derivation

   1. expm1-def 98.5%

      \[\leadsto \color{blue}{\mathsf{expm1}\left(\mathsf{log1p}\left(\frac{1.3333333333333333}{\sqrt{2 + -6 \cdot \left(v \cdot v\right)} \cdot \pi}\right)\right)} \]

   2. expm1-log1p 98.5%

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

   3. associate-/r* 98.5%

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

   4. *-commutative 98.5%

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

   5. associate-*l* 98.5%

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

8. Simplified 98.5%

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

9. Final simplification 98.5%

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

Alternative 8: 97.4% 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. Taylor expanded in v around 0 97.0%

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

3. Final simplification 97.0%

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

Alternative 9: 98.9% 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. sqr-neg 100.0%

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

   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%

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

3. Simplified 100.0%

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

4. Taylor expanded in v around 0 98.5%

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

5. Taylor expanded in v around 0 98.5%

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

6. Final simplification 98.5%

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

Reproduce

herbie shell --seed 2023287 
(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)))))))