Average Error: 15.0 → 0.4
Time: 2.6s
Precision: binary64
\[\tan^{-1} \left(N + 1\right) - \tan^{-1} N\]
↓
\[\tan^{-1}_* \frac{1}{1 + \left(N \cdot N + 1 \cdot N\right)}\]
\tan^{-1} \left(N + 1\right) - \tan^{-1} N
↓
\tan^{-1}_* \frac{1}{1 + \left(N \cdot N + 1 \cdot N\right)}
double code(double N) {
	return ((double) (((double) atan(((double) (N + 1.0)))) - ((double) atan(N))));
}
↓
double code(double N) {
	return ((double) atan2(1.0, ((double) (1.0 + ((double) (((double) (N * N)) + ((double) (1.0 * N))))))));
}

Error

Bits error versus N

Try it out

Your Program's Arguments

Results

Enter valid numbers for all inputs

Target

Original15.0
Target0.4
Herbie0.4
\[\tan^{-1} \left(\frac{1}{1 + N \cdot \left(N + 1\right)}\right)\]

Derivation

  1. Initial program 15.0

    \[\tan^{-1} \left(N + 1\right) - \tan^{-1} N\]
  2. Using strategy rm

  3. Applied diff-atan13.9

    \[\leadsto \color{blue}{\tan^{-1}_* \frac{\left(N + 1\right) - N}{1 + \left(N + 1\right) \cdot N}}\]

  4. Simplified0.4

    \[\leadsto \tan^{-1}_* \frac{\color{blue}{1}}{1 + \left(N + 1\right) \cdot N}\]

  5. Simplified0.4

    \[\leadsto \tan^{-1}_* \frac{1}{\color{blue}{1 + N \cdot \left(N + 1\right)}}\]
  6. Using strategy rm

  7. Applied distribute-lft-in0.4

    \[\leadsto \tan^{-1}_* \frac{1}{1 + \color{blue}{\left(N \cdot N + N \cdot 1\right)}}\]

  8. Simplified0.4

    \[\leadsto \tan^{-1}_* \frac{1}{1 + \left(N \cdot N + \color{blue}{1 \cdot N}\right)}\]

  9. Final simplification0.4

    \[\leadsto \tan^{-1}_* \frac{1}{1 + \left(N \cdot N + 1 \cdot N\right)}\]

Reproduce

herbie shell --seed 2020184 
(FPCore (N)
  :name "2atan (example 3.5)"
  :precision binary64

  :herbie-target
  (atan (/ 1.0 (+ 1.0 (* N (+ N 1.0)))))

  (- (atan (+ N 1.0)) (atan N)))