Average Error: 43.8 → 2.8
Time: 4.0s
Precision: binary64
\[\left(\left(1.1102230246251565 \cdot 10^{-16} < a \land a < 9007199254740992\right) \land \left(1.1102230246251565 \cdot 10^{-16} < b \land b < 9007199254740992\right)\right) \land \left(1.1102230246251565 \cdot 10^{-16} < c \land c < 9007199254740992\right)\]
\[\frac{\left(-b\right) + \sqrt{b \cdot b - \left(3 \cdot a\right) \cdot c}}{3 \cdot a} \]
↓
\[\mathsf{fma}\left(-0.16666666666666666, 6.328125 \cdot \frac{{c}^{4} \cdot {a}^{3}}{{b}^{7}}, \mathsf{fma}\left(-0.5, \frac{c}{b}, \mathsf{fma}\left(-0.375, a \cdot \frac{c \cdot c}{{b}^{3}}, -0.5625 \cdot \left(a \cdot \frac{{c}^{3}}{\frac{{b}^{5}}{a}}\right)\right)\right)\right) \]
(FPCore (a b c)
 :precision binary64
 (/ (+ (- b) (sqrt (- (* b b) (* (* 3.0 a) c)))) (* 3.0 a)))
↓
(FPCore (a b c)
 :precision binary64
 (fma
  -0.16666666666666666
  (* 6.328125 (/ (* (pow c 4.0) (pow a 3.0)) (pow b 7.0)))
  (fma
   -0.5
   (/ c b)
   (fma
    -0.375
    (* a (/ (* c c) (pow b 3.0)))
    (* -0.5625 (* a (/ (pow c 3.0) (/ (pow b 5.0) a))))))))
double code(double a, double b, double c) {
	return (-b + sqrt(((b * b) - ((3.0 * a) * c)))) / (3.0 * a);
}
↓
double code(double a, double b, double c) {
	return fma(-0.16666666666666666, (6.328125 * ((pow(c, 4.0) * pow(a, 3.0)) / pow(b, 7.0))), fma(-0.5, (c / b), fma(-0.375, (a * ((c * c) / pow(b, 3.0))), (-0.5625 * (a * (pow(c, 3.0) / (pow(b, 5.0) / a)))))));
}

function code(a, b, c)
	return Float64(Float64(Float64(-b) + sqrt(Float64(Float64(b * b) - Float64(Float64(3.0 * a) * c)))) / Float64(3.0 * a))
end
↓
function code(a, b, c)
	return fma(-0.16666666666666666, Float64(6.328125 * Float64(Float64((c ^ 4.0) * (a ^ 3.0)) / (b ^ 7.0))), fma(-0.5, Float64(c / b), fma(-0.375, Float64(a * Float64(Float64(c * c) / (b ^ 3.0))), Float64(-0.5625 * Float64(a * Float64((c ^ 3.0) / Float64((b ^ 5.0) / a)))))))
end

code[a_, b_, c_] := N[(N[((-b) + N[Sqrt[N[(N[(b * b), $MachinePrecision] - N[(N[(3.0 * a), $MachinePrecision] * c), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] / N[(3.0 * a), $MachinePrecision]), $MachinePrecision]
↓
code[a_, b_, c_] := N[(-0.16666666666666666 * N[(6.328125 * N[(N[(N[Power[c, 4.0], $MachinePrecision] * N[Power[a, 3.0], $MachinePrecision]), $MachinePrecision] / N[Power[b, 7.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(-0.5 * N[(c / b), $MachinePrecision] + N[(-0.375 * N[(a * N[(N[(c * c), $MachinePrecision] / N[Power[b, 3.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(-0.5625 * N[(a * N[(N[Power[c, 3.0], $MachinePrecision] / N[(N[Power[b, 5.0], $MachinePrecision] / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

\frac{\left(-b\right) + \sqrt{b \cdot b - \left(3 \cdot a\right) \cdot c}}{3 \cdot a}
↓
\mathsf{fma}\left(-0.16666666666666666, 6.328125 \cdot \frac{{c}^{4} \cdot {a}^{3}}{{b}^{7}}, \mathsf{fma}\left(-0.5, \frac{c}{b}, \mathsf{fma}\left(-0.375, a \cdot \frac{c \cdot c}{{b}^{3}}, -0.5625 \cdot \left(a \cdot \frac{{c}^{3}}{\frac{{b}^{5}}{a}}\right)\right)\right)\right)

Error

Derivation

  1. Initial program 43.8

    \[\frac{\left(-b\right) + \sqrt{b \cdot b - \left(3 \cdot a\right) \cdot c}}{3 \cdot a} \]

  2. Simplified43.8

    \[\leadsto \color{blue}{\left(\sqrt{\mathsf{fma}\left(b, b, a \cdot \left(c \cdot -3\right)\right)} - b\right) \cdot \frac{0.3333333333333333}{a}} \]

  3. Taylor expanded in a around 0 2.9

    \[\leadsto \color{blue}{-0.16666666666666666 \cdot \frac{{a}^{3} \cdot \left(5.0625 \cdot \frac{{c}^{4}}{{b}^{6}} + {\left(-1.125 \cdot \frac{{c}^{2}}{{b}^{3}}\right)}^{2}\right)}{b} + \left(-0.5625 \cdot \frac{{c}^{3} \cdot {a}^{2}}{{b}^{5}} + \left(-0.5 \cdot \frac{c}{b} + -0.375 \cdot \frac{{c}^{2} \cdot a}{{b}^{3}}\right)\right)} \]

  4. Simplified2.8

    \[\leadsto \color{blue}{\mathsf{fma}\left(-0.16666666666666666, \frac{{a}^{3}}{b} \cdot \left(\frac{{c}^{4}}{{b}^{6}} \cdot 6.328125\right), \mathsf{fma}\left(-0.5, \frac{c}{b}, \mathsf{fma}\left(-0.375, \frac{c \cdot c}{{b}^{3}} \cdot a, -0.5625 \cdot \left(\frac{{c}^{3}}{\frac{{b}^{5}}{a}} \cdot a\right)\right)\right)\right)} \]

  5. Taylor expanded in a around 0 2.8

    \[\leadsto \mathsf{fma}\left(-0.16666666666666666, \color{blue}{6.328125 \cdot \frac{{c}^{4} \cdot {a}^{3}}{{b}^{7}}}, \mathsf{fma}\left(-0.5, \frac{c}{b}, \mathsf{fma}\left(-0.375, \frac{c \cdot c}{{b}^{3}} \cdot a, -0.5625 \cdot \left(\frac{{c}^{3}}{\frac{{b}^{5}}{a}} \cdot a\right)\right)\right)\right) \]

  6. Final simplification2.8

    \[\leadsto \mathsf{fma}\left(-0.16666666666666666, 6.328125 \cdot \frac{{c}^{4} \cdot {a}^{3}}{{b}^{7}}, \mathsf{fma}\left(-0.5, \frac{c}{b}, \mathsf{fma}\left(-0.375, a \cdot \frac{c \cdot c}{{b}^{3}}, -0.5625 \cdot \left(a \cdot \frac{{c}^{3}}{\frac{{b}^{5}}{a}}\right)\right)\right)\right) \]

Reproduce

herbie shell --seed 2022192 
(FPCore (a b c)
  :name "Cubic critical, medium range"
  :precision binary64
  :pre (and (and (and (< 1.1102230246251565e-16 a) (< a 9007199254740992.0)) (and (< 1.1102230246251565e-16 b) (< b 9007199254740992.0))) (and (< 1.1102230246251565e-16 c) (< c 9007199254740992.0)))
  (/ (+ (- b) (sqrt (- (* b b) (* (* 3.0 a) c)))) (* 3.0 a)))