Average Error: 1.0 → 0.0
Time: 2.9s
Precision: binary64
\[\frac{4}{\left(\left(3 \cdot \pi\right) \cdot \left(1 - v \cdot v\right)\right) \cdot \sqrt{2 - 6 \cdot \left(v \cdot v\right)}} \]
↓
\[\sqrt{\frac{\frac{1.7777777777777777}{{\left(\pi \cdot \mathsf{fma}\left(v, v, -1\right)\right)}^{2}}}{\mathsf{fma}\left(v, v \cdot -6, 2\right)}} \]
(FPCore (v)
 :precision binary64
 (/ 4.0 (* (* (* 3.0 PI) (- 1.0 (* v v))) (sqrt (- 2.0 (* 6.0 (* v v)))))))
↓
(FPCore (v)
 :precision binary64
 (sqrt
  (/
   (/ 1.7777777777777777 (pow (* PI (fma v v -1.0)) 2.0))
   (fma v (* v -6.0) 2.0))))
double code(double v) {
	return 4.0 / (((3.0 * ((double) M_PI)) * (1.0 - (v * v))) * sqrt((2.0 - (6.0 * (v * v)))));
}
↓
double code(double v) {
	return sqrt(((1.7777777777777777 / pow((((double) M_PI) * fma(v, v, -1.0)), 2.0)) / fma(v, (v * -6.0), 2.0)));
}

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
↓
function code(v)
	return sqrt(Float64(Float64(1.7777777777777777 / (Float64(pi * fma(v, v, -1.0)) ^ 2.0)) / fma(v, Float64(v * -6.0), 2.0)))
end

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]
↓
code[v_] := N[Sqrt[N[(N[(1.7777777777777777 / N[Power[N[(Pi * N[(v * v + -1.0), $MachinePrecision]), $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision] / N[(v * N[(v * -6.0), $MachinePrecision] + 2.0), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]

\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)}}
↓
\sqrt{\frac{\frac{1.7777777777777777}{{\left(\pi \cdot \mathsf{fma}\left(v, v, -1\right)\right)}^{2}}}{\mathsf{fma}\left(v, v \cdot -6, 2\right)}}

Error

Bits error versus v

Derivation

  1. Initial program 1.0

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

  2. Simplified0.0

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

  3. Applied egg-rr0.0

    \[\leadsto \color{blue}{\sqrt{\frac{\frac{1.7777777777777777}{{\left(\pi \cdot \mathsf{fma}\left(v, v, -1\right)\right)}^{2}}}{\mathsf{fma}\left(v, v \cdot -6, 2\right)}}} \]

  4. Final simplification0.0

    \[\leadsto \sqrt{\frac{\frac{1.7777777777777777}{{\left(\pi \cdot \mathsf{fma}\left(v, v, -1\right)\right)}^{2}}}{\mathsf{fma}\left(v, v \cdot -6, 2\right)}} \]

Reproduce

herbie shell --seed 2022146 
(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)))))))