Average Error: 0.1 → 0.1
Time: 6.4s
Precision: binary64
\[\frac{1 + \frac{2 \cdot t}{1 + t} \cdot \frac{2 \cdot t}{1 + t}}{2 + \frac{2 \cdot t}{1 + t} \cdot \frac{2 \cdot t}{1 + t}}\]
↓
\[\frac{1 + \frac{2 \cdot t}{1 + t} \cdot \frac{2 \cdot t}{1 + t}}{2 \cdot 2 - {\left(\frac{2 \cdot t}{1 + t}\right)}^{3} \cdot \frac{2 \cdot t}{1 + t}} \cdot \left(2 - \frac{2 \cdot t}{1 + t} \cdot \frac{2 \cdot t}{1 + t}\right)\]
\frac{1 + \frac{2 \cdot t}{1 + t} \cdot \frac{2 \cdot t}{1 + t}}{2 + \frac{2 \cdot t}{1 + t} \cdot \frac{2 \cdot t}{1 + t}}
↓
\frac{1 + \frac{2 \cdot t}{1 + t} \cdot \frac{2 \cdot t}{1 + t}}{2 \cdot 2 - {\left(\frac{2 \cdot t}{1 + t}\right)}^{3} \cdot \frac{2 \cdot t}{1 + t}} \cdot \left(2 - \frac{2 \cdot t}{1 + t} \cdot \frac{2 \cdot t}{1 + t}\right)
double code(double t) {
	return ((double) (((double) (1.0 + ((double) (((double) (((double) (2.0 * t)) / ((double) (1.0 + t)))) * ((double) (((double) (2.0 * t)) / ((double) (1.0 + t)))))))) / ((double) (2.0 + ((double) (((double) (((double) (2.0 * t)) / ((double) (1.0 + t)))) * ((double) (((double) (2.0 * t)) / ((double) (1.0 + t))))))))));
}
↓
double code(double t) {
	return ((double) (((double) (((double) (1.0 + ((double) (((double) (((double) (2.0 * t)) / ((double) (1.0 + t)))) * ((double) (((double) (2.0 * t)) / ((double) (1.0 + t)))))))) / ((double) (((double) (2.0 * 2.0)) - ((double) (((double) pow(((double) (((double) (2.0 * t)) / ((double) (1.0 + t)))), 3.0)) * ((double) (((double) (2.0 * t)) / ((double) (1.0 + t)))))))))) * ((double) (2.0 - ((double) (((double) (((double) (2.0 * t)) / ((double) (1.0 + t)))) * ((double) (((double) (2.0 * t)) / ((double) (1.0 + t))))))))));
}

Error

Bits error versus t

Try it out

Your Program's Arguments

Results

Enter valid numbers for all inputs

Derivation

  1. Initial program 0.1

    \[\frac{1 + \frac{2 \cdot t}{1 + t} \cdot \frac{2 \cdot t}{1 + t}}{2 + \frac{2 \cdot t}{1 + t} \cdot \frac{2 \cdot t}{1 + t}}\]
  2. Using strategy rm

  3. Applied flip-+0.1

    \[\leadsto \frac{1 + \frac{2 \cdot t}{1 + t} \cdot \frac{2 \cdot t}{1 + t}}{\color{blue}{\frac{2 \cdot 2 - \left(\frac{2 \cdot t}{1 + t} \cdot \frac{2 \cdot t}{1 + t}\right) \cdot \left(\frac{2 \cdot t}{1 + t} \cdot \frac{2 \cdot t}{1 + t}\right)}{2 - \frac{2 \cdot t}{1 + t} \cdot \frac{2 \cdot t}{1 + t}}}}\]

  4. Applied associate-/r/0.1

    \[\leadsto \color{blue}{\frac{1 + \frac{2 \cdot t}{1 + t} \cdot \frac{2 \cdot t}{1 + t}}{2 \cdot 2 - \left(\frac{2 \cdot t}{1 + t} \cdot \frac{2 \cdot t}{1 + t}\right) \cdot \left(\frac{2 \cdot t}{1 + t} \cdot \frac{2 \cdot t}{1 + t}\right)} \cdot \left(2 - \frac{2 \cdot t}{1 + t} \cdot \frac{2 \cdot t}{1 + t}\right)}\]

  5. Simplified0.1

    \[\leadsto \color{blue}{\frac{1 + \frac{2 \cdot t}{1 + t} \cdot \frac{2 \cdot t}{1 + t}}{2 \cdot 2 - {\left(\frac{2 \cdot t}{1 + t}\right)}^{3} \cdot \frac{2 \cdot t}{1 + t}}} \cdot \left(2 - \frac{2 \cdot t}{1 + t} \cdot \frac{2 \cdot t}{1 + t}\right)\]

  6. Final simplification0.1

    \[\leadsto \frac{1 + \frac{2 \cdot t}{1 + t} \cdot \frac{2 \cdot t}{1 + t}}{2 \cdot 2 - {\left(\frac{2 \cdot t}{1 + t}\right)}^{3} \cdot \frac{2 \cdot t}{1 + t}} \cdot \left(2 - \frac{2 \cdot t}{1 + t} \cdot \frac{2 \cdot t}{1 + t}\right)\]

Reproduce

herbie shell --seed 2020163 
(FPCore (t)
  :name "Kahan p13 Example 1"
  :precision binary64
  (/ (+ 1.0 (* (/ (* 2.0 t) (+ 1.0 t)) (/ (* 2.0 t) (+ 1.0 t)))) (+ 2.0 (* (/ (* 2.0 t) (+ 1.0 t)) (/ (* 2.0 t) (+ 1.0 t))))))