Average Error: 9.7 → 0.1
Time: 3.0s
Precision: binary64
\[\frac{x \cdot \left(\left(y - z\right) + 1\right)}{z} \]
\[\begin{array}{l} t_0 := \frac{x \cdot \left(\left(y - z\right) + 1\right)}{z}\\ t_1 := \mathsf{fma}\left(\frac{x}{z}, y, \frac{x}{z} - x\right)\\ \mathbf{if}\;t_0 \leq -5 \cdot 10^{+144}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t_0 \leq 2 \cdot 10^{+207}:\\ \;\;\;\;\frac{x \cdot \left(y + 1\right)}{z} - x\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \]
(FPCore (x y z) :precision binary64 (/ (* x (+ (- y z) 1.0)) z))
(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (/ (* x (+ (- y z) 1.0)) z)) (t_1 (fma (/ x z) y (- (/ x z) x))))
   (if (<= t_0 -5e+144)
     t_1
     (if (<= t_0 2e+207) (- (/ (* x (+ y 1.0)) z) x) t_1))))
double code(double x, double y, double z) {
	return (x * ((y - z) + 1.0)) / z;
}
double code(double x, double y, double z) {
	double t_0 = (x * ((y - z) + 1.0)) / z;
	double t_1 = fma((x / z), y, ((x / z) - x));
	double tmp;
	if (t_0 <= -5e+144) {
		tmp = t_1;
	} else if (t_0 <= 2e+207) {
		tmp = ((x * (y + 1.0)) / z) - x;
	} else {
		tmp = t_1;
	}
	return tmp;
}
function code(x, y, z)
	return Float64(Float64(x * Float64(Float64(y - z) + 1.0)) / z)
end
function code(x, y, z)
	t_0 = Float64(Float64(x * Float64(Float64(y - z) + 1.0)) / z)
	t_1 = fma(Float64(x / z), y, Float64(Float64(x / z) - x))
	tmp = 0.0
	if (t_0 <= -5e+144)
		tmp = t_1;
	elseif (t_0 <= 2e+207)
		tmp = Float64(Float64(Float64(x * Float64(y + 1.0)) / z) - x);
	else
		tmp = t_1;
	end
	return tmp
end
code[x_, y_, z_] := N[(N[(x * N[(N[(y - z), $MachinePrecision] + 1.0), $MachinePrecision]), $MachinePrecision] / z), $MachinePrecision]
code[x_, y_, z_] := Block[{t$95$0 = N[(N[(x * N[(N[(y - z), $MachinePrecision] + 1.0), $MachinePrecision]), $MachinePrecision] / z), $MachinePrecision]}, Block[{t$95$1 = N[(N[(x / z), $MachinePrecision] * y + N[(N[(x / z), $MachinePrecision] - x), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$0, -5e+144], t$95$1, If[LessEqual[t$95$0, 2e+207], N[(N[(N[(x * N[(y + 1.0), $MachinePrecision]), $MachinePrecision] / z), $MachinePrecision] - x), $MachinePrecision], t$95$1]]]]
\frac{x \cdot \left(\left(y - z\right) + 1\right)}{z}
\begin{array}{l}
t_0 := \frac{x \cdot \left(\left(y - z\right) + 1\right)}{z}\\
t_1 := \mathsf{fma}\left(\frac{x}{z}, y, \frac{x}{z} - x\right)\\
\mathbf{if}\;t_0 \leq -5 \cdot 10^{+144}:\\
\;\;\;\;t_1\\

\mathbf{elif}\;t_0 \leq 2 \cdot 10^{+207}:\\
\;\;\;\;\frac{x \cdot \left(y + 1\right)}{z} - x\\

\mathbf{else}:\\
\;\;\;\;t_1\\


\end{array}

Error

Bits error versus x

Bits error versus y

Bits error versus z

Target

Original9.7
Target0.5
Herbie0.1
\[\begin{array}{l} \mathbf{if}\;x < -2.71483106713436 \cdot 10^{-162}:\\ \;\;\;\;\left(1 + y\right) \cdot \frac{x}{z} - x\\ \mathbf{elif}\;x < 3.874108816439546 \cdot 10^{-197}:\\ \;\;\;\;\left(x \cdot \left(\left(y - z\right) + 1\right)\right) \cdot \frac{1}{z}\\ \mathbf{else}:\\ \;\;\;\;\left(1 + y\right) \cdot \frac{x}{z} - x\\ \end{array} \]

Derivation

  1. Split input into 2 regimes
  2. if (/.f64 (*.f64 x (+.f64 (-.f64 y z) 1)) z) < -4.9999999999999999e144 or 2.0000000000000001e207 < (/.f64 (*.f64 x (+.f64 (-.f64 y z) 1)) z)

    1. Initial program 30.6

      \[\frac{x \cdot \left(\left(y - z\right) + 1\right)}{z} \]
    2. Simplified10.2

      \[\leadsto \color{blue}{\frac{\mathsf{fma}\left(x, y, x\right)}{z} - x} \]
    3. Applied egg-rr10.7

      \[\leadsto \color{blue}{{\left(\sqrt[3]{\frac{\mathsf{fma}\left(x, y, x\right)}{z}}\right)}^{3}} - x \]
    4. Taylor expanded in x around 0 7.2

      \[\leadsto \color{blue}{\left(\left(\frac{y}{z} + \frac{1}{z}\right) - 1\right) \cdot x} \]
    5. Simplified0.1

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

    if -4.9999999999999999e144 < (/.f64 (*.f64 x (+.f64 (-.f64 y z) 1)) z) < 2.0000000000000001e207

    1. Initial program 0.1

      \[\frac{x \cdot \left(\left(y - z\right) + 1\right)}{z} \]
    2. Simplified0.1

      \[\leadsto \color{blue}{\frac{\mathsf{fma}\left(x, y, x\right)}{z} - x} \]
    3. Applied egg-rr0.7

      \[\leadsto \color{blue}{{\left(\sqrt[3]{\frac{\mathsf{fma}\left(x, y, x\right)}{z}}\right)}^{3}} - x \]
    4. Applied egg-rr0.8

      \[\leadsto \color{blue}{\frac{\frac{\mathsf{fma}\left(x, y, x\right)}{{\left(\sqrt[3]{z}\right)}^{2}}}{\sqrt[3]{z}}} - x \]
    5. Taylor expanded in x around 0 0.1

      \[\leadsto \color{blue}{\frac{x \cdot \left(1 + y\right)}{z}} - x \]
  3. Recombined 2 regimes into one program.
  4. Final simplification0.1

    \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{x \cdot \left(\left(y - z\right) + 1\right)}{z} \leq -5 \cdot 10^{+144}:\\ \;\;\;\;\mathsf{fma}\left(\frac{x}{z}, y, \frac{x}{z} - x\right)\\ \mathbf{elif}\;\frac{x \cdot \left(\left(y - z\right) + 1\right)}{z} \leq 2 \cdot 10^{+207}:\\ \;\;\;\;\frac{x \cdot \left(y + 1\right)}{z} - x\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(\frac{x}{z}, y, \frac{x}{z} - x\right)\\ \end{array} \]

Reproduce

herbie shell --seed 2022162 
(FPCore (x y z)
  :name "Diagrams.TwoD.Segment.Bernstein:evaluateBernstein from diagrams-lib-1.3.0.3"
  :precision binary64

  :herbie-target
  (if (< x -2.71483106713436e-162) (- (* (+ 1.0 y) (/ x z)) x) (if (< x 3.874108816439546e-197) (* (* x (+ (- y z) 1.0)) (/ 1.0 z)) (- (* (+ 1.0 y) (/ x z)) x)))

  (/ (* x (+ (- y z) 1.0)) z))