Average Error: 0.2 → 0.0
Time: 2.3s
Precision: binary64
\[\left({\left(a \cdot a + b \cdot b\right)}^{2} + 4 \cdot \left(b \cdot b\right)\right) - 1 \]
↓
\[\mathsf{fma}\left(1, \mathsf{fma}\left(b, b \cdot 4, -1\right), {a}^{4} + \mathsf{fma}\left(2, {\left(b \cdot a\right)}^{2}, {b}^{4}\right)\right) \]
(FPCore (a b)
 :precision binary64
 (- (+ (pow (+ (* a a) (* b b)) 2.0) (* 4.0 (* b b))) 1.0))
↓
(FPCore (a b)
 :precision binary64
 (fma
  1.0
  (fma b (* b 4.0) -1.0)
  (+ (pow a 4.0) (fma 2.0 (pow (* b a) 2.0) (pow b 4.0)))))
double code(double a, double b) {
	return (pow(((a * a) + (b * b)), 2.0) + (4.0 * (b * b))) - 1.0;
}
↓
double code(double a, double b) {
	return fma(1.0, fma(b, (b * 4.0), -1.0), (pow(a, 4.0) + fma(2.0, pow((b * a), 2.0), pow(b, 4.0))));
}

function code(a, b)
	return Float64(Float64((Float64(Float64(a * a) + Float64(b * b)) ^ 2.0) + Float64(4.0 * Float64(b * b))) - 1.0)
end
↓
function code(a, b)
	return fma(1.0, fma(b, Float64(b * 4.0), -1.0), Float64((a ^ 4.0) + fma(2.0, (Float64(b * a) ^ 2.0), (b ^ 4.0))))
end

code[a_, b_] := N[(N[(N[Power[N[(N[(a * a), $MachinePrecision] + N[(b * b), $MachinePrecision]), $MachinePrecision], 2.0], $MachinePrecision] + N[(4.0 * N[(b * b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - 1.0), $MachinePrecision]
↓
code[a_, b_] := N[(1.0 * N[(b * N[(b * 4.0), $MachinePrecision] + -1.0), $MachinePrecision] + N[(N[Power[a, 4.0], $MachinePrecision] + N[(2.0 * N[Power[N[(b * a), $MachinePrecision], 2.0], $MachinePrecision] + N[Power[b, 4.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

\left({\left(a \cdot a + b \cdot b\right)}^{2} + 4 \cdot \left(b \cdot b\right)\right) - 1
↓
\mathsf{fma}\left(1, \mathsf{fma}\left(b, b \cdot 4, -1\right), {a}^{4} + \mathsf{fma}\left(2, {\left(b \cdot a\right)}^{2}, {b}^{4}\right)\right)

Error

Bits error versus a

Bits error versus b

Derivation

  1. Initial program 0.2

    \[\left({\left(a \cdot a + b \cdot b\right)}^{2} + 4 \cdot \left(b \cdot b\right)\right) - 1 \]

  2. Simplified0.0

    \[\leadsto \color{blue}{{\left(\mathsf{hypot}\left(a, b\right)\right)}^{4} + \mathsf{fma}\left(b, b \cdot 4, -1\right)} \]

  3. Taylor expanded in a around 0 0.0

    \[\leadsto \color{blue}{\left({a}^{4} + \left(2 \cdot \left({a}^{2} \cdot {b}^{2}\right) + {b}^{4}\right)\right)} + \mathsf{fma}\left(b, b \cdot 4, -1\right) \]

  4. Applied egg-rr0.0

    \[\leadsto \color{blue}{\mathsf{fma}\left(1, \mathsf{fma}\left(b, b \cdot 4, -1\right), {a}^{4} + \mathsf{fma}\left(2, {\left(a \cdot b\right)}^{2}, {b}^{4}\right)\right)} \]

  5. Final simplification0.0

    \[\leadsto \mathsf{fma}\left(1, \mathsf{fma}\left(b, b \cdot 4, -1\right), {a}^{4} + \mathsf{fma}\left(2, {\left(b \cdot a\right)}^{2}, {b}^{4}\right)\right) \]

Reproduce

herbie shell --seed 2022153 
(FPCore (a b)
  :name "Bouland and Aaronson, Equation (26)"
  :precision binary64
  (- (+ (pow (+ (* a a) (* b b)) 2.0) (* 4.0 (* b b))) 1.0))