Average Error: 1.0 → 0.0
Time: 2.8s
Precision: binary64
\[\frac{4}{\left(\left(3 \cdot \pi\right) \cdot \left(1 - v \cdot v\right)\right) \cdot \sqrt{2 - 6 \cdot \left(v \cdot v\right)}} \]
↓
\[\frac{1.3333333333333333}{\pi \cdot \left(1 - v \cdot v\right)} \cdot {\left(\mathsf{fma}\left(v \cdot v, -6, 2\right)\right)}^{-0.5} \]
(FPCore (v)
 :precision binary64
 (/ 4.0 (* (* (* 3.0 PI) (- 1.0 (* v v))) (sqrt (- 2.0 (* 6.0 (* v v)))))))
↓
(FPCore (v)
 :precision binary64
 (*
  (/ 1.3333333333333333 (* PI (- 1.0 (* v v))))
  (pow (fma (* v v) -6.0 2.0) -0.5)))
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 (1.3333333333333333 / (((double) M_PI) * (1.0 - (v * v)))) * pow(fma((v * v), -6.0, 2.0), -0.5);
}

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 Float64(Float64(1.3333333333333333 / Float64(pi * Float64(1.0 - Float64(v * v)))) * (fma(Float64(v * v), -6.0, 2.0) ^ -0.5))
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[(N[(1.3333333333333333 / N[(Pi * N[(1.0 - N[(v * v), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * N[Power[N[(N[(v * v), $MachinePrecision] * -6.0 + 2.0), $MachinePrecision], -0.5], $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)}}
↓
\frac{1.3333333333333333}{\pi \cdot \left(1 - v \cdot v\right)} \cdot {\left(\mathsf{fma}\left(v \cdot v, -6, 2\right)\right)}^{-0.5}

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

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

  3. Applied egg-rr0.0

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

  4. Applied egg-rr0.0

    \[\leadsto \frac{1.3333333333333333}{\pi \cdot \left(1 - v \cdot v\right)} \cdot \color{blue}{{\left(\mathsf{fma}\left(v \cdot v, -6, 2\right)\right)}^{-0.5}} \]

  5. Final simplification0.0

    \[\leadsto \frac{1.3333333333333333}{\pi \cdot \left(1 - v \cdot v\right)} \cdot {\left(\mathsf{fma}\left(v \cdot v, -6, 2\right)\right)}^{-0.5} \]

Reproduce

herbie shell --seed 2022155 
(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)))))))