Average Error: 0.0 → 0.0
Time: 2.3s
Precision: binary64
\[\left(x \cdot \left(y - 1\right) - y \cdot 0.5\right) + 0.918938533204673 \]
↓
\[\mathsf{fma}\left(x, y + -1, y \cdot -0.5\right) + 0.918938533204673 \]
(FPCore (x y)
 :precision binary64
 (+ (- (* x (- y 1.0)) (* y 0.5)) 0.918938533204673))
↓
(FPCore (x y)
 :precision binary64
 (+ (fma x (+ y -1.0) (* y -0.5)) 0.918938533204673))
double code(double x, double y) {
	return ((x * (y - 1.0)) - (y * 0.5)) + 0.918938533204673;
}
↓
double code(double x, double y) {
	return fma(x, (y + -1.0), (y * -0.5)) + 0.918938533204673;
}

function code(x, y)
	return Float64(Float64(Float64(x * Float64(y - 1.0)) - Float64(y * 0.5)) + 0.918938533204673)
end
↓
function code(x, y)
	return Float64(fma(x, Float64(y + -1.0), Float64(y * -0.5)) + 0.918938533204673)
end

code[x_, y_] := N[(N[(N[(x * N[(y - 1.0), $MachinePrecision]), $MachinePrecision] - N[(y * 0.5), $MachinePrecision]), $MachinePrecision] + 0.918938533204673), $MachinePrecision]
↓
code[x_, y_] := N[(N[(x * N[(y + -1.0), $MachinePrecision] + N[(y * -0.5), $MachinePrecision]), $MachinePrecision] + 0.918938533204673), $MachinePrecision]

\left(x \cdot \left(y - 1\right) - y \cdot 0.5\right) + 0.918938533204673
↓
\mathsf{fma}\left(x, y + -1, y \cdot -0.5\right) + 0.918938533204673

Error

Derivation

  1. Initial program 0.0

    \[\left(x \cdot \left(y - 1\right) - y \cdot 0.5\right) + 0.918938533204673 \]

  2. Applied egg-rr0.0

    \[\leadsto \color{blue}{\mathsf{fma}\left(x, y + -1, y \cdot -0.5\right)} + 0.918938533204673 \]

  3. Final simplification0.0

    \[\leadsto \mathsf{fma}\left(x, y + -1, y \cdot -0.5\right) + 0.918938533204673 \]

Reproduce

herbie shell --seed 2022210 
(FPCore (x y)
  :name "Numeric.SpecFunctions:logGamma from math-functions-0.1.5.2, A"
  :precision binary64
  (+ (- (* x (- y 1.0)) (* y 0.5)) 0.918938533204673))