Average Error: 0.0 → 0.0
Time: 13.4s
Precision: 64
\[\frac{\left(x + y\right) - z}{t \cdot 2.0}\]
↓
\[\frac{\left(y + x\right) + \left(-z\right)}{2.0 \cdot t}\]
\frac{\left(x + y\right) - z}{t \cdot 2.0}
↓
\frac{\left(y + x\right) + \left(-z\right)}{2.0 \cdot t}
double f(double x, double y, double z, double t) {
        double r3438931 = x;
        double r3438932 = y;
        double r3438933 = r3438931 + r3438932;
        double r3438934 = z;
        double r3438935 = r3438933 - r3438934;
        double r3438936 = t;
        double r3438937 = 2.0;
        double r3438938 = r3438936 * r3438937;
        double r3438939 = r3438935 / r3438938;
        return r3438939;
}
↓
double f(double x, double y, double z, double t) {
        double r3438940 = y;
        double r3438941 = x;
        double r3438942 = r3438940 + r3438941;
        double r3438943 = z;
        double r3438944 = -r3438943;
        double r3438945 = r3438942 + r3438944;
        double r3438946 = 2.0;
        double r3438947 = t;
        double r3438948 = r3438946 * r3438947;
        double r3438949 = r3438945 / r3438948;
        return r3438949;
}

Error

Bits error versus x

Bits error versus y

Bits error versus z

Bits error versus t

Try it out

Your Program's Arguments

Results

Enter valid numbers for all inputs

Derivation

  1. Initial program 0.0

    \[\frac{\left(x + y\right) - z}{t \cdot 2.0}\]
  2. Using strategy rm

  3. Applied sub-neg0.0

    \[\leadsto \frac{\color{blue}{\left(x + y\right) + \left(-z\right)}}{t \cdot 2.0}\]

  4. Final simplification0.0

    \[\leadsto \frac{\left(y + x\right) + \left(-z\right)}{2.0 \cdot t}\]

Reproduce

herbie shell --seed 2019168 +o rules:numerics
(FPCore (x y z t)
  :name "Optimisation.CirclePacking:place from circle-packing-0.1.0.4, B"
  (/ (- (+ x y) z) (* t 2.0)))