Average Error: 1.0 → 0.0
Time: 5.5s
Precision: binary64
\[\frac{4}{\left(\left(3 \cdot \pi\right) \cdot \left(1 - v \cdot v\right)\right) \cdot \sqrt{2 - 6 \cdot \left(v \cdot v\right)}} \]
↓
\[\frac{1}{\pi \cdot \frac{\sqrt{\mathsf{fma}\left(v, v \cdot -6, 2\right)}}{\frac{-1.3333333333333333}{\mathsf{fma}\left(v, v, -1\right)}}} \]
\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}{\pi \cdot \frac{\sqrt{\mathsf{fma}\left(v, v \cdot -6, 2\right)}}{\frac{-1.3333333333333333}{\mathsf{fma}\left(v, v, -1\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
 (/
  1.0
  (*
   PI
   (/ (sqrt (fma v (* v -6.0) 2.0)) (/ -1.3333333333333333 (fma v v -1.0))))))
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 1.0 / (((double) M_PI) * (sqrt(fma(v, (v * -6.0), 2.0)) / (-1.3333333333333333 / fma(v, v, -1.0))));
}

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 clear-num_binary640.0

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

  4. Applied *-un-lft-identity_binary640.0

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

  5. Applied times-frac_binary641.0

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

  6. Applied *-un-lft-identity_binary641.0

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

  7. Applied sqrt-prod_binary641.0

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

  8. Applied times-frac_binary640.0

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

  9. Simplified0.0

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

  10. Final simplification0.0

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

Reproduce

herbie shell --seed 2021280 
(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)))))))