\[\frac{x}{y - z \cdot t}\]

↓

\[\frac{-x}{\mathsf{fma}\left(t, z, -y\right)}\]

\frac{x}{y - z \cdot t}

↓

\frac{-x}{\mathsf{fma}\left(t, z, -y\right)}

double f(double x, double y, double z, double t) {
        double r723482 = x;
        double r723483 = y;
        double r723484 = z;
        double r723485 = t;
        double r723486 = r723484 * r723485;
        double r723487 = r723483 - r723486;
        double r723488 = r723482 / r723487;
        return r723488;
}

↓

double f(double x, double y, double z, double t) {
        double r723489 = x;
        double r723490 = -r723489;
        double r723491 = t;
        double r723492 = z;
        double r723493 = y;
        double r723494 = -r723493;
        double r723495 = fma(r723491, r723492, r723494);
        double r723496 = r723490 / r723495;
        return r723496;
}

Error

The X axis uses an exponential scale — Bits error versus `x`

Target

Original	2.7
Target	1.9
Herbie	2.7

\[\begin{array}{l} \mathbf{if}\;x \lt -1.618195973607049 \cdot 10^{50}:\\ \;\;\;\;\frac{1}{\frac{y}{x} - \frac{z}{x} \cdot t}\\ \mathbf{elif}\;x \lt 2.13783064348764444 \cdot 10^{131}:\\ \;\;\;\;\frac{x}{y - z \cdot t}\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{\frac{y}{x} - \frac{z}{x} \cdot t}\\ \end{array}\]

Derivation

Initial program 2.7
\[\frac{x}{y - z \cdot t}\]
Using strategy rm
Applied frac-2neg2.7
\[\leadsto \color{blue}{\frac{-x}{-\left(y - z \cdot t\right)}}\]
Simplified2.7
\[\leadsto \frac{-x}{\color{blue}{\mathsf{fma}\left(t, z, -y\right)}}\]
Final simplification2.7
\[\leadsto \frac{-x}{\mathsf{fma}\left(t, z, -y\right)}\]

Reproduce

herbie shell --seed 2020062 +o rules:numerics
(FPCore (x y z t)
  :name "Diagrams.Solve.Tridiagonal:solveTriDiagonal from diagrams-solve-0.1, B"
  :precision binary64

  :herbie-target
  (if (< x -1.618195973607049e+50) (/ 1 (- (/ y x) (* (/ z x) t))) (if (< x 2.1378306434876444e+131) (/ x (- y (* z t))) (/ 1 (- (/ y x) (* (/ z x) t)))))

  (/ x (- y (* z t))))