Average Error: 0.2 → 0.0
Time: 3.5s
Precision: binary64
\[\left({\left(a \cdot a + b \cdot b\right)}^{2} + 4 \cdot \left(b \cdot b\right)\right) - 1 \]
↓
\[\left({a}^{4} + \left(2 \cdot \left({a}^{2} \cdot {b}^{2}\right) + \mathsf{fma}\left(4, b \cdot b, {b}^{4}\right)\right)\right) - 1 \]
(FPCore (a b)
 :precision binary64
 (- (+ (pow (+ (* a a) (* b b)) 2.0) (* 4.0 (* b b))) 1.0))
↓
(FPCore (a b)
 :precision binary64
 (-
  (+
   (pow a 4.0)
   (+ (* 2.0 (* (pow a 2.0) (pow b 2.0))) (fma 4.0 (* b b) (pow b 4.0))))
  1.0))
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 (pow(a, 4.0) + ((2.0 * (pow(a, 2.0) * pow(b, 2.0))) + fma(4.0, (b * b), pow(b, 4.0)))) - 1.0;
}

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 Float64(Float64((a ^ 4.0) + Float64(Float64(2.0 * Float64((a ^ 2.0) * (b ^ 2.0))) + fma(4.0, Float64(b * b), (b ^ 4.0)))) - 1.0)
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[(N[(N[Power[a, 4.0], $MachinePrecision] + N[(N[(2.0 * N[(N[Power[a, 2.0], $MachinePrecision] * N[Power[b, 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(4.0 * N[(b * b), $MachinePrecision] + N[Power[b, 4.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - 1.0), $MachinePrecision]

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

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

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

  4. Applied add-cbrt-cube_binary648.4

    \[\leadsto \left({a}^{4} + \left(2 \cdot \left({a}^{2} \cdot {b}^{2}\right) + \color{blue}{\sqrt[3]{\left(\left(4 \cdot {b}^{2} + {b}^{4}\right) \cdot \left(4 \cdot {b}^{2} + {b}^{4}\right)\right) \cdot \left(4 \cdot {b}^{2} + {b}^{4}\right)}}\right)\right) - 1 \]

  5. Simplified8.4

    \[\leadsto \left({a}^{4} + \left(2 \cdot \left({a}^{2} \cdot {b}^{2}\right) + \sqrt[3]{\color{blue}{{b}^{6} \cdot {\left(\mathsf{fma}\left(b, b, 4\right)\right)}^{3}}}\right)\right) - 1 \]

  6. Taylor expanded in b around 0 0.0

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

  7. Simplified0.0

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

  8. Final simplification0.0

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

Reproduce

herbie shell --seed 2022131 
(FPCore (a b)
  :name "Bouland and Aaronson, Equation (26)"
  :precision binary64
  (- (+ (pow (+ (* a a) (* b b)) 2.0) (* 4.0 (* b b))) 1.0))