Average Error: 52.8 → 1.4
Time: 6.5s
Precision: binary64
\[\left(\left(4.930380657631324 \cdot 10^{-32} < a \land a < 2.028240960365167 \cdot 10^{+31}\right) \land \left(4.930380657631324 \cdot 10^{-32} < b \land b < 2.028240960365167 \cdot 10^{+31}\right)\right) \land \left(4.930380657631324 \cdot 10^{-32} < c \land c < 2.028240960365167 \cdot 10^{+31}\right)\]
\[\frac{\left(-b\right) + \sqrt{b \cdot b - \left(4 \cdot a\right) \cdot c}}{2 \cdot a} \]
↓
\[\mathsf{fma}\left(\frac{{c}^{3} \cdot \left(a \cdot a\right)}{{b}^{5}}, -2, -5 \cdot \frac{{c}^{4} \cdot {a}^{3}}{{b}^{7}}\right) - \mathsf{fma}\left(\frac{c \cdot c}{{b}^{3}}, a, \frac{c}{b}\right) \]
(FPCore (a b c)
 :precision binary64
 (/ (+ (- b) (sqrt (- (* b b) (* (* 4.0 a) c)))) (* 2.0 a)))
↓
(FPCore (a b c)
 :precision binary64
 (-
  (fma
   (/ (* (pow c 3.0) (* a a)) (pow b 5.0))
   -2.0
   (* -5.0 (/ (* (pow c 4.0) (pow a 3.0)) (pow b 7.0))))
  (fma (/ (* c c) (pow b 3.0)) a (/ c b))))
double code(double a, double b, double c) {
	return (-b + sqrt(((b * b) - ((4.0 * a) * c)))) / (2.0 * a);
}
↓
double code(double a, double b, double c) {
	return fma(((pow(c, 3.0) * (a * a)) / pow(b, 5.0)), -2.0, (-5.0 * ((pow(c, 4.0) * pow(a, 3.0)) / pow(b, 7.0)))) - fma(((c * c) / pow(b, 3.0)), a, (c / b));
}

function code(a, b, c)
	return Float64(Float64(Float64(-b) + sqrt(Float64(Float64(b * b) - Float64(Float64(4.0 * a) * c)))) / Float64(2.0 * a))
end
↓
function code(a, b, c)
	return Float64(fma(Float64(Float64((c ^ 3.0) * Float64(a * a)) / (b ^ 5.0)), -2.0, Float64(-5.0 * Float64(Float64((c ^ 4.0) * (a ^ 3.0)) / (b ^ 7.0)))) - fma(Float64(Float64(c * c) / (b ^ 3.0)), a, Float64(c / b)))
end

code[a_, b_, c_] := N[(N[((-b) + N[Sqrt[N[(N[(b * b), $MachinePrecision] - N[(N[(4.0 * a), $MachinePrecision] * c), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] / N[(2.0 * a), $MachinePrecision]), $MachinePrecision]
↓
code[a_, b_, c_] := N[(N[(N[(N[(N[Power[c, 3.0], $MachinePrecision] * N[(a * a), $MachinePrecision]), $MachinePrecision] / N[Power[b, 5.0], $MachinePrecision]), $MachinePrecision] * -2.0 + N[(-5.0 * N[(N[(N[Power[c, 4.0], $MachinePrecision] * N[Power[a, 3.0], $MachinePrecision]), $MachinePrecision] / N[Power[b, 7.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(N[(N[(c * c), $MachinePrecision] / N[Power[b, 3.0], $MachinePrecision]), $MachinePrecision] * a + N[(c / b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

\frac{\left(-b\right) + \sqrt{b \cdot b - \left(4 \cdot a\right) \cdot c}}{2 \cdot a}
↓
\mathsf{fma}\left(\frac{{c}^{3} \cdot \left(a \cdot a\right)}{{b}^{5}}, -2, -5 \cdot \frac{{c}^{4} \cdot {a}^{3}}{{b}^{7}}\right) - \mathsf{fma}\left(\frac{c \cdot c}{{b}^{3}}, a, \frac{c}{b}\right)

Error

Bits error versus a

Bits error versus b

Bits error versus c

Derivation

  1. Initial program 52.8

    \[\frac{\left(-b\right) + \sqrt{b \cdot b - \left(4 \cdot a\right) \cdot c}}{2 \cdot a} \]

  2. Simplified52.8

    \[\leadsto \color{blue}{\left(\sqrt{\mathsf{fma}\left(a, c \cdot -4, b \cdot b\right)} - b\right) \cdot \frac{0.5}{a}} \]

  3. Taylor expanded in a around 0 1.8

    \[\leadsto \color{blue}{\left(-\left(2 \cdot \frac{c \cdot a}{b} + \left(2 \cdot \frac{{c}^{2} \cdot {a}^{2}}{{b}^{3}} + \left(10 \cdot \frac{{c}^{4} \cdot {a}^{4}}{{b}^{7}} + 4 \cdot \frac{{c}^{3} \cdot {a}^{3}}{{b}^{5}}\right)\right)\right)\right)} \cdot \frac{0.5}{a} \]

  4. Simplified1.7

    \[\leadsto \color{blue}{\left(-2 \cdot \mathsf{fma}\left(\frac{c}{b}, a, \frac{\left(c \cdot a\right) \cdot \left(c \cdot a\right)}{{b}^{3}}\right) - \mathsf{fma}\left(4, \frac{{c}^{3} \cdot {a}^{3}}{{b}^{5}}, 10 \cdot \frac{{c}^{4} \cdot {a}^{4}}{{b}^{7}}\right)\right)} \cdot \frac{0.5}{a} \]

  5. Applied egg-rr2.1

    \[\leadsto \color{blue}{\sqrt[3]{{\left(\left(-2 \cdot \mathsf{fma}\left(\frac{c}{b}, a, \frac{{\left(c \cdot a\right)}^{2}}{{b}^{3}}\right) - \mathsf{fma}\left(4, \frac{{\left(c \cdot a\right)}^{3}}{{b}^{5}}, 10 \cdot \frac{{\left(c \cdot a\right)}^{4}}{{b}^{7}}\right)\right) \cdot \frac{0.5}{a}\right)}^{3}}} \]

  6. Taylor expanded in a around -inf 1.4

    \[\leadsto \color{blue}{-\left(2 \cdot \frac{{c}^{3} \cdot {a}^{2}}{{b}^{5}} + \left(5 \cdot \frac{{c}^{4} \cdot {a}^{3}}{{b}^{7}} + \left(1 \cdot \frac{{c}^{2} \cdot a}{{b}^{3}} + 1 \cdot \frac{c}{b}\right)\right)\right)} \]

  7. Simplified1.4

    \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{{c}^{3} \cdot \left(a \cdot a\right)}{{b}^{5}}, -2, -5 \cdot \frac{{c}^{4} \cdot {a}^{3}}{{b}^{7}}\right) - \mathsf{fma}\left(\frac{c \cdot c}{{b}^{3}}, a, \frac{c}{b}\right)} \]

  8. Final simplification1.4

    \[\leadsto \mathsf{fma}\left(\frac{{c}^{3} \cdot \left(a \cdot a\right)}{{b}^{5}}, -2, -5 \cdot \frac{{c}^{4} \cdot {a}^{3}}{{b}^{7}}\right) - \mathsf{fma}\left(\frac{c \cdot c}{{b}^{3}}, a, \frac{c}{b}\right) \]

Reproduce

herbie shell --seed 2022134 
(FPCore (a b c)
  :name "Quadratic roots, wide range"
  :precision binary64
  :pre (and (and (and (< 4.930380657631324e-32 a) (< a 2.028240960365167e+31)) (and (< 4.930380657631324e-32 b) (< b 2.028240960365167e+31))) (and (< 4.930380657631324e-32 c) (< c 2.028240960365167e+31)))
  (/ (+ (- b) (sqrt (- (* b b) (* (* 4.0 a) c)))) (* 2.0 a)))