Average Error: 2.0 → 1.5
Time: 57.0s
Precision: 64
Internal Precision: 128
\[\frac{x \cdot e^{\left(y \cdot \log z + \left(t - 1.0\right) \cdot \log a\right) - b}}{y}\]
\[\begin{array}{l} \mathbf{if}\;y \le -38408719121246.9 \lor \neg \left(y \le 2.0554642296841583 \cdot 10^{-11}\right):\\ \;\;\;\;\frac{x}{\frac{y}{e^{\left(\log a \cdot \left(t - 1.0\right) + \log z \cdot y\right) - b}}}\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{\frac{y}{x \cdot \left(e^{(t \cdot \left(\log a\right) + \left(-b\right))_*} \cdot \left({z}^{y} \cdot {a}^{\left(-1.0\right)}\right)\right)}}\\ \end{array}\]

Error

Bits error versus x

Bits error versus y

Bits error versus z

Bits error versus t

Bits error versus a

Bits error versus b

Derivation

  1. Split input into 2 regimes
  2. if y < -38408719121246.9 or 2.0554642296841583e-11 < y

    1. Initial program 0.1

      \[\frac{x \cdot e^{\left(y \cdot \log z + \left(t - 1.0\right) \cdot \log a\right) - b}}{y}\]
    2. Using strategy rm
    3. Applied associate-/l*0.1

      \[\leadsto \color{blue}{\frac{x}{\frac{y}{e^{\left(y \cdot \log z + \left(t - 1.0\right) \cdot \log a\right) - b}}}}\]

    if -38408719121246.9 < y < 2.0554642296841583e-11

    1. Initial program 3.7

      \[\frac{x \cdot e^{\left(y \cdot \log z + \left(t - 1.0\right) \cdot \log a\right) - b}}{y}\]
    2. Taylor expanded around inf 3.7

      \[\leadsto \frac{x \cdot \color{blue}{e^{1.0 \cdot \log \left(\frac{1}{a}\right) - \left(\log \left(\frac{1}{z}\right) \cdot y + \left(b + t \cdot \log \left(\frac{1}{a}\right)\right)\right)}}}{y}\]
    3. Simplified2.8

      \[\leadsto \frac{x \cdot \color{blue}{\left(\left({a}^{\left(-1.0\right)} \cdot {z}^{y}\right) \cdot e^{(t \cdot \left(\log a\right) + \left(-b\right))_*}\right)}}{y}\]
    4. Using strategy rm
    5. Applied clear-num2.8

      \[\leadsto \color{blue}{\frac{1}{\frac{y}{x \cdot \left(\left({a}^{\left(-1.0\right)} \cdot {z}^{y}\right) \cdot e^{(t \cdot \left(\log a\right) + \left(-b\right))_*}\right)}}}\]
  3. Recombined 2 regimes into one program.
  4. Final simplification1.5

    \[\leadsto \begin{array}{l} \mathbf{if}\;y \le -38408719121246.9 \lor \neg \left(y \le 2.0554642296841583 \cdot 10^{-11}\right):\\ \;\;\;\;\frac{x}{\frac{y}{e^{\left(\log a \cdot \left(t - 1.0\right) + \log z \cdot y\right) - b}}}\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{\frac{y}{x \cdot \left(e^{(t \cdot \left(\log a\right) + \left(-b\right))_*} \cdot \left({z}^{y} \cdot {a}^{\left(-1.0\right)}\right)\right)}}\\ \end{array}\]

Runtime

Time bar (total: 57.0s)Debug logProfile

BaselineHerbieOracleSpan%
Regimes2.11.50.02.027.9%
herbie shell --seed 2018355 +o rules:numerics
(FPCore (x y z t a b)
  :name "Numeric.SpecFunctions:incompleteBetaWorker from math-functions-0.1.5.2"
  (/ (* x (exp (- (+ (* y (log z)) (* (- t 1.0) (log a))) b))) y))