Average Error: 9.1 → 0.2
Time: 2.6s
Precision: binary64
\[\frac{x \cdot \left(\frac{x}{y} + 1\right)}{x + 1} \]
\[\begin{array}{l} t_0 := \frac{\mathsf{fma}\left(1 + \frac{x}{y}, y, -1\right)}{y}\\ \mathbf{if}\;x \leq -5.7098531931846154 \cdot 10^{+63}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;x \leq 4801249538.945384:\\ \;\;\;\;\frac{\mathsf{fma}\left(\frac{x}{y}, x, x\right)}{x + 1}\\ \mathbf{else}:\\ \;\;\;\;t_0\\ \end{array} \]
(FPCore (x y) :precision binary64 (/ (* x (+ (/ x y) 1.0)) (+ x 1.0)))
(FPCore (x y)
 :precision binary64
 (let* ((t_0 (/ (fma (+ 1.0 (/ x y)) y -1.0) y)))
   (if (<= x -5.7098531931846154e+63)
     t_0
     (if (<= x 4801249538.945384) (/ (fma (/ x y) x x) (+ x 1.0)) t_0))))
double code(double x, double y) {
	return (x * ((x / y) + 1.0)) / (x + 1.0);
}
double code(double x, double y) {
	double t_0 = fma((1.0 + (x / y)), y, -1.0) / y;
	double tmp;
	if (x <= -5.7098531931846154e+63) {
		tmp = t_0;
	} else if (x <= 4801249538.945384) {
		tmp = fma((x / y), x, x) / (x + 1.0);
	} else {
		tmp = t_0;
	}
	return tmp;
}
function code(x, y)
	return Float64(Float64(x * Float64(Float64(x / y) + 1.0)) / Float64(x + 1.0))
end
function code(x, y)
	t_0 = Float64(fma(Float64(1.0 + Float64(x / y)), y, -1.0) / y)
	tmp = 0.0
	if (x <= -5.7098531931846154e+63)
		tmp = t_0;
	elseif (x <= 4801249538.945384)
		tmp = Float64(fma(Float64(x / y), x, x) / Float64(x + 1.0));
	else
		tmp = t_0;
	end
	return tmp
end
code[x_, y_] := N[(N[(x * N[(N[(x / y), $MachinePrecision] + 1.0), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision]
code[x_, y_] := Block[{t$95$0 = N[(N[(N[(1.0 + N[(x / y), $MachinePrecision]), $MachinePrecision] * y + -1.0), $MachinePrecision] / y), $MachinePrecision]}, If[LessEqual[x, -5.7098531931846154e+63], t$95$0, If[LessEqual[x, 4801249538.945384], N[(N[(N[(x / y), $MachinePrecision] * x + x), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision], t$95$0]]]
\frac{x \cdot \left(\frac{x}{y} + 1\right)}{x + 1}
\begin{array}{l}
t_0 := \frac{\mathsf{fma}\left(1 + \frac{x}{y}, y, -1\right)}{y}\\
\mathbf{if}\;x \leq -5.7098531931846154 \cdot 10^{+63}:\\
\;\;\;\;t_0\\

\mathbf{elif}\;x \leq 4801249538.945384:\\
\;\;\;\;\frac{\mathsf{fma}\left(\frac{x}{y}, x, x\right)}{x + 1}\\

\mathbf{else}:\\
\;\;\;\;t_0\\


\end{array}

Error

Bits error versus x

Bits error versus y

Target

Original9.1
Target0.1
Herbie0.2
\[\frac{x}{1} \cdot \frac{\frac{x}{y} + 1}{x + 1} \]

Derivation

  1. Split input into 2 regimes
  2. if x < -5.7098531931846154e63 or 4801249538.94538403 < x

    1. Initial program 24.5

      \[\frac{x \cdot \left(\frac{x}{y} + 1\right)}{x + 1} \]
    2. Taylor expanded in x around inf 0.1

      \[\leadsto \color{blue}{\left(1 + \frac{x}{y}\right) - \frac{1}{y}} \]
    3. Applied egg-rr0.1

      \[\leadsto \color{blue}{\frac{\mathsf{fma}\left(1 + \frac{x}{y}, y, -1\right)}{y}} \]

    if -5.7098531931846154e63 < x < 4801249538.94538403

    1. Initial program 0.2

      \[\frac{x \cdot \left(\frac{x}{y} + 1\right)}{x + 1} \]
    2. Applied egg-rr0.2

      \[\leadsto \frac{\color{blue}{x + x \cdot \frac{x}{y}}}{x + 1} \]
    3. Applied egg-rr0.2

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

    \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -5.7098531931846154 \cdot 10^{+63}:\\ \;\;\;\;\frac{\mathsf{fma}\left(1 + \frac{x}{y}, y, -1\right)}{y}\\ \mathbf{elif}\;x \leq 4801249538.945384:\\ \;\;\;\;\frac{\mathsf{fma}\left(\frac{x}{y}, x, x\right)}{x + 1}\\ \mathbf{else}:\\ \;\;\;\;\frac{\mathsf{fma}\left(1 + \frac{x}{y}, y, -1\right)}{y}\\ \end{array} \]

Reproduce

herbie shell --seed 2022131 
(FPCore (x y)
  :name "Codec.Picture.Types:toneMapping from JuicyPixels-3.2.6.1"
  :precision binary64

  :herbie-target
  (* (/ x 1.0) (/ (+ (/ x y) 1.0) (+ x 1.0)))

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