Average Error: 49.1 → 0
Time: 1.9s
Precision: 64
\[1.9 \le t \le 2.1\]
\[1.7 \cdot 10^{+308} \cdot t - 1.7 \cdot 10^{+308}\]
↓
\[\mathsf{fma}\left(\left( 1.7 \cdot 10^{+308} \right), t, \left(-1.7 \cdot 10^{+308}\right)\right)\]
1.7 \cdot 10^{+308} \cdot t - 1.7 \cdot 10^{+308}
↓
\mathsf{fma}\left(\left( 1.7 \cdot 10^{+308} \right), t, \left(-1.7 \cdot 10^{+308}\right)\right)
double f(double t) {
        double r3919290 = 1.7e+308;
        double r3919291 = t;
        double r3919292 = r3919290 * r3919291;
        double r3919293 = r3919292 - r3919290;
        return r3919293;
}
↓
double f(double t) {
        double r3919294 = 1.7e+308;
        double r3919295 = t;
        double r3919296 = -r3919294;
        double r3919297 = fma(r3919294, r3919295, r3919296);
        return r3919297;
}

Error

Bits error versus t

Target

Original49.1
Target0
Herbie0
\[\mathsf{fma}\left(\left( 1.7 \cdot 10^{+308} \right), t, \left(-1.7 \cdot 10^{+308}\right)\right)\]

Derivation

  1. Initial program 49.1

    \[1.7 \cdot 10^{+308} \cdot t - 1.7 \cdot 10^{+308}\]
  2. Using strategy rm

  3. Applied fma-neg0

    \[\leadsto \color{blue}{\mathsf{fma}\left(\left( 1.7 \cdot 10^{+308} \right), t, \left(-1.7 \cdot 10^{+308}\right)\right)}\]

  4. Final simplification0

    \[\leadsto \mathsf{fma}\left(\left( 1.7 \cdot 10^{+308} \right), t, \left(-1.7 \cdot 10^{+308}\right)\right)\]

Reproduce

herbie shell --seed 2019120 +o rules:numerics
(FPCore (t)
  :name "fma_test2"
  :pre (<= 1.9 t 2.1)

  :herbie-target
  (fma 1.7e+308 t (- 1.7e+308))

  (- (* 1.7e+308 t) 1.7e+308))