Average Error: 4.3 → 0.7
Time: 3.5m
Precision: 64
Internal Precision: 2880
\[\frac{{\left(\frac{1}{1 + e^{-s}}\right)}^{c_p} \cdot {\left(1 - \frac{1}{1 + e^{-s}}\right)}^{c_n}}{{\left(\frac{1}{1 + e^{-t}}\right)}^{c_p} \cdot {\left(1 - \frac{1}{1 + e^{-t}}\right)}^{c_n}}\]
\[e^{(c_n \cdot \left(\log_* (1 + \frac{-1}{1 + e^{-s}}) - \log_* (1 + \frac{-1}{1 + e^{-t}})\right) + \left(c_p \cdot \left((\left((\frac{1}{8} \cdot t + \frac{-1}{2})_*\right) \cdot t + \left(\log 2\right))_* - \log_* (1 + e^{-s})\right)\right))_*}\]

Error

Bits error versus c_p

Bits error versus c_n

Bits error versus t

Bits error versus s

Target

Original4.3
Target2.3
Herbie0.7
\[{\left(\frac{1 + e^{-t}}{1 + e^{-s}}\right)}^{c_p} \cdot {\left(\frac{1 + e^{t}}{1 + e^{s}}\right)}^{c_n}\]

Derivation

  1. Initial program 4.3

    \[\frac{{\left(\frac{1}{1 + e^{-s}}\right)}^{c_p} \cdot {\left(1 - \frac{1}{1 + e^{-s}}\right)}^{c_n}}{{\left(\frac{1}{1 + e^{-t}}\right)}^{c_p} \cdot {\left(1 - \frac{1}{1 + e^{-t}}\right)}^{c_n}}\]
  2. Initial simplification4.3

    \[\leadsto \frac{{\left(1 - \frac{1}{e^{-s} + 1}\right)}^{c_n}}{{\left(1 - \frac{1}{e^{-t} + 1}\right)}^{c_n}} \cdot \frac{{\left(\frac{1}{e^{-s} + 1}\right)}^{c_p}}{{\left(\frac{1}{e^{-t} + 1}\right)}^{c_p}}\]
  3. Using strategy rm
  4. Applied add-exp-log4.3

    \[\leadsto \frac{{\left(1 - \frac{1}{e^{-s} + 1}\right)}^{c_n}}{{\left(1 - \frac{1}{e^{-t} + 1}\right)}^{c_n}} \cdot \frac{{\left(\frac{1}{e^{-s} + 1}\right)}^{c_p}}{{\color{blue}{\left(e^{\log \left(\frac{1}{e^{-t} + 1}\right)}\right)}}^{c_p}}\]
  5. Applied pow-exp4.3

    \[\leadsto \frac{{\left(1 - \frac{1}{e^{-s} + 1}\right)}^{c_n}}{{\left(1 - \frac{1}{e^{-t} + 1}\right)}^{c_n}} \cdot \frac{{\left(\frac{1}{e^{-s} + 1}\right)}^{c_p}}{\color{blue}{e^{\log \left(\frac{1}{e^{-t} + 1}\right) \cdot c_p}}}\]
  6. Applied add-exp-log4.3

    \[\leadsto \frac{{\left(1 - \frac{1}{e^{-s} + 1}\right)}^{c_n}}{{\left(1 - \frac{1}{e^{-t} + 1}\right)}^{c_n}} \cdot \frac{\color{blue}{e^{\log \left({\left(\frac{1}{e^{-s} + 1}\right)}^{c_p}\right)}}}{e^{\log \left(\frac{1}{e^{-t} + 1}\right) \cdot c_p}}\]
  7. Applied div-exp4.0

    \[\leadsto \frac{{\left(1 - \frac{1}{e^{-s} + 1}\right)}^{c_n}}{{\left(1 - \frac{1}{e^{-t} + 1}\right)}^{c_n}} \cdot \color{blue}{e^{\log \left({\left(\frac{1}{e^{-s} + 1}\right)}^{c_p}\right) - \log \left(\frac{1}{e^{-t} + 1}\right) \cdot c_p}}\]
  8. Applied add-exp-log4.0

    \[\leadsto \frac{{\left(1 - \frac{1}{e^{-s} + 1}\right)}^{c_n}}{\color{blue}{e^{\log \left({\left(1 - \frac{1}{e^{-t} + 1}\right)}^{c_n}\right)}}} \cdot e^{\log \left({\left(\frac{1}{e^{-s} + 1}\right)}^{c_p}\right) - \log \left(\frac{1}{e^{-t} + 1}\right) \cdot c_p}\]
  9. Applied pow-to-exp4.0

    \[\leadsto \frac{\color{blue}{e^{\log \left(1 - \frac{1}{e^{-s} + 1}\right) \cdot c_n}}}{e^{\log \left({\left(1 - \frac{1}{e^{-t} + 1}\right)}^{c_n}\right)}} \cdot e^{\log \left({\left(\frac{1}{e^{-s} + 1}\right)}^{c_p}\right) - \log \left(\frac{1}{e^{-t} + 1}\right) \cdot c_p}\]
  10. Applied div-exp4.0

    \[\leadsto \color{blue}{e^{\log \left(1 - \frac{1}{e^{-s} + 1}\right) \cdot c_n - \log \left({\left(1 - \frac{1}{e^{-t} + 1}\right)}^{c_n}\right)}} \cdot e^{\log \left({\left(\frac{1}{e^{-s} + 1}\right)}^{c_p}\right) - \log \left(\frac{1}{e^{-t} + 1}\right) \cdot c_p}\]
  11. Applied prod-exp3.7

    \[\leadsto \color{blue}{e^{\left(\log \left(1 - \frac{1}{e^{-s} + 1}\right) \cdot c_n - \log \left({\left(1 - \frac{1}{e^{-t} + 1}\right)}^{c_n}\right)\right) + \left(\log \left({\left(\frac{1}{e^{-s} + 1}\right)}^{c_p}\right) - \log \left(\frac{1}{e^{-t} + 1}\right) \cdot c_p\right)}}\]
  12. Simplified1.9

    \[\leadsto e^{\color{blue}{(c_n \cdot \left(\log_* (1 + \frac{-1}{e^{-s} + 1}) - \log_* (1 + \frac{-1}{e^{-t} + 1})\right) + \left(\left(\log_* (1 + e^{-t}) - \log_* (1 + e^{-s})\right) \cdot c_p\right))_*}}\]
  13. Taylor expanded around 0 0.7

    \[\leadsto e^{(c_n \cdot \left(\log_* (1 + \frac{-1}{e^{-s} + 1}) - \log_* (1 + \frac{-1}{e^{-t} + 1})\right) + \left(\left(\color{blue}{\left(\left(\log 2 + \frac{1}{8} \cdot {t}^{2}\right) - \frac{1}{2} \cdot t\right)} - \log_* (1 + e^{-s})\right) \cdot c_p\right))_*}\]
  14. Simplified0.7

    \[\leadsto e^{(c_n \cdot \left(\log_* (1 + \frac{-1}{e^{-s} + 1}) - \log_* (1 + \frac{-1}{e^{-t} + 1})\right) + \left(\left(\color{blue}{(\left((\frac{1}{8} \cdot t + \frac{-1}{2})_*\right) \cdot t + \left(\log 2\right))_*} - \log_* (1 + e^{-s})\right) \cdot c_p\right))_*}\]
  15. Final simplification0.7

    \[\leadsto e^{(c_n \cdot \left(\log_* (1 + \frac{-1}{1 + e^{-s}}) - \log_* (1 + \frac{-1}{1 + e^{-t}})\right) + \left(c_p \cdot \left((\left((\frac{1}{8} \cdot t + \frac{-1}{2})_*\right) \cdot t + \left(\log 2\right))_* - \log_* (1 + e^{-s})\right)\right))_*}\]

Runtime

Time bar (total: 3.5m)Debug logProfile

herbie shell --seed 2018255 +o rules:numerics
(FPCore (c_p c_n t s)
  :name "Harley's example"
  :pre (and (< 0 c_p) (< 0 c_n))

  :herbie-target
  (* (pow (/ (+ 1 (exp (- t))) (+ 1 (exp (- s)))) c_p) (pow (/ (+ 1 (exp t)) (+ 1 (exp s))) c_n))

  (/ (* (pow (/ 1 (+ 1 (exp (- s)))) c_p) (pow (- 1 (/ 1 (+ 1 (exp (- s))))) c_n)) (* (pow (/ 1 (+ 1 (exp (- t)))) c_p) (pow (- 1 (/ 1 (+ 1 (exp (- t))))) c_n))))