Quadratic roots, wide range

Percentage Accurate: 17.9% → 99.9%

Time: 13.2s

Alternatives: 6

Speedup: 3.6×

Specification

\[\left(\left(4.930380657631324 \cdot 10^{-32} < a \land a < 2.028240960365167 \cdot 10^{+31}\right) \land \left(4.930380657631324 \cdot 10^{-32} < b \land b < 2.028240960365167 \cdot 10^{+31}\right)\right) \land \left(4.930380657631324 \cdot 10^{-32} < c \land c < 2.028240960365167 \cdot 10^{+31}\right)\]

Math

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

FPCore

(FPCore (a b c)
 :precision binary64
 (/ (+ (- b) (sqrt (- (* b b) (* (* 4.0 a) c)))) (* 2.0 a)))

C

double code(double a, double b, double c) {
	return (-b + sqrt(((b * b) - ((4.0 * a) * c)))) / (2.0 * a);
}

Fortran

real(8) function code(a, b, c)
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    code = (-b + sqrt(((b * b) - ((4.0d0 * a) * c)))) / (2.0d0 * a)
end function

Java

public static double code(double a, double b, double c) {
	return (-b + Math.sqrt(((b * b) - ((4.0 * a) * c)))) / (2.0 * a);
}

Python

def code(a, b, c):
	return (-b + math.sqrt(((b * b) - ((4.0 * a) * c)))) / (2.0 * a)

Julia

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

MATLAB

function tmp = code(a, b, c)
	tmp = (-b + sqrt(((b * b) - ((4.0 * a) * c)))) / (2.0 * a);
end

Wolfram

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]

Initial Program: 17.9% accurate, 1.0× speedup

Math

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

FPCore

(FPCore (a b c)
 :precision binary64
 (/ (+ (- b) (sqrt (- (* b b) (* (* 4.0 a) c)))) (* 2.0 a)))

C

double code(double a, double b, double c) {
	return (-b + sqrt(((b * b) - ((4.0 * a) * c)))) / (2.0 * a);
}

Fortran

real(8) function code(a, b, c)
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    code = (-b + sqrt(((b * b) - ((4.0d0 * a) * c)))) / (2.0d0 * a)
end function

Java

public static double code(double a, double b, double c) {
	return (-b + Math.sqrt(((b * b) - ((4.0 * a) * c)))) / (2.0 * a);
}

Python

def code(a, b, c):
	return (-b + math.sqrt(((b * b) - ((4.0 * a) * c)))) / (2.0 * a)

Julia

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

MATLAB

function tmp = code(a, b, c)
	tmp = (-b + sqrt(((b * b) - ((4.0 * a) * c)))) / (2.0 * a);
end

Wolfram

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]

Alternative 1: 99.9% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \frac{-2 \cdot c}{b + \sqrt{\mathsf{fma}\left(c, a \cdot -4, b \cdot b\right)}} \end{array} \]

FPCore

(FPCore (a b c)
 :precision binary64
 (/ (* -2.0 c) (+ b (sqrt (fma c (* a -4.0) (* b b))))))

C

double code(double a, double b, double c) {
	return (-2.0 * c) / (b + sqrt(fma(c, (a * -4.0), (b * b))));
}

Julia

function code(a, b, c)
	return Float64(Float64(-2.0 * c) / Float64(b + sqrt(fma(c, Float64(a * -4.0), Float64(b * b)))))
end

Wolfram

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

Derivation

1. Initial program 16.6%

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

4. Applied egg-rr 16.7%

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

   1. flip-- N/A

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

   2. associate-*r/ N/A

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

   3. /-lowering-/.f64 N/A

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

6. Applied egg-rr 17.1%

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

7. Taylor expanded in a around 0

   \[\leadsto \frac{\color{blue}{-2 \cdot c}}{b + \sqrt{\mathsf{fma}\left(c, a \cdot -4, b \cdot b\right)}} \]
8. Step-by-step derivation

   1. *-lowering-*.f64 99.9

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

9. Simplified 99.9%

   \[\leadsto \frac{\color{blue}{-2 \cdot c}}{b + \sqrt{\mathsf{fma}\left(c, a \cdot -4, b \cdot b\right)}} \]
10. Add Preprocessing

Alternative 2: 99.5% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ c \cdot \frac{-2}{b + \sqrt{\mathsf{fma}\left(c, a \cdot -4, b \cdot b\right)}} \end{array} \]

FPCore

(FPCore (a b c)
 :precision binary64
 (* c (/ -2.0 (+ b (sqrt (fma c (* a -4.0) (* b b)))))))

C

double code(double a, double b, double c) {
	return c * (-2.0 / (b + sqrt(fma(c, (a * -4.0), (b * b)))));
}

Julia

function code(a, b, c)
	return Float64(c * Float64(-2.0 / Float64(b + sqrt(fma(c, Float64(a * -4.0), Float64(b * b))))))
end

Wolfram

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

Derivation

1. Initial program 16.6%

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

4. Applied egg-rr 16.7%

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

   1. flip-- N/A

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

   2. associate-*r/ N/A

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

   3. /-lowering-/.f64 N/A

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

6. Applied egg-rr 17.1%

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

7. Taylor expanded in a around 0

   \[\leadsto \frac{\color{blue}{-2 \cdot c}}{b + \sqrt{\mathsf{fma}\left(c, a \cdot -4, b \cdot b\right)}} \]
8. Step-by-step derivation

   1. *-lowering-*.f64 99.9

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

9. Simplified 99.9%

   \[\leadsto \frac{\color{blue}{-2 \cdot c}}{b + \sqrt{\mathsf{fma}\left(c, a \cdot -4, b \cdot b\right)}} \]
10. Step-by-step derivation

    1. *-commutative N/A

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

    2. associate-/l* N/A

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

    3. *-lowering-*.f64 N/A

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

    4. /-lowering-/.f64 N/A

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

    5. +-lowering-+.f64 N/A

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

    6. sqrt-lowering-sqrt.f64 N/A

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

    7. accelerator-lowering-fma.f64 N/A

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

    8. *-lowering-*.f64 N/A

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

    9. *-lowering-*.f64 99.5

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

11. Applied egg-rr 99.5%

    \[\leadsto \color{blue}{c \cdot \frac{-2}{b + \sqrt{\mathsf{fma}\left(c, a \cdot -4, b \cdot b\right)}}} \]
12. Add Preprocessing

Alternative 3: 95.4% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \frac{-2 \cdot c}{\mathsf{fma}\left(c, \frac{-2 \cdot a}{b}, b \cdot 2\right)} \end{array} \]

FPCore

(FPCore (a b c)
 :precision binary64
 (/ (* -2.0 c) (fma c (/ (* -2.0 a) b) (* b 2.0))))

C

double code(double a, double b, double c) {
	return (-2.0 * c) / fma(c, ((-2.0 * a) / b), (b * 2.0));
}

Julia

function code(a, b, c)
	return Float64(Float64(-2.0 * c) / fma(c, Float64(Float64(-2.0 * a) / b), Float64(b * 2.0)))
end

Wolfram

code[a_, b_, c_] := N[(N[(-2.0 * c), $MachinePrecision] / N[(c * N[(N[(-2.0 * a), $MachinePrecision] / b), $MachinePrecision] + N[(b * 2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 16.6%

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

4. Applied egg-rr 16.7%

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

   1. flip-- N/A

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

   2. associate-*r/ N/A

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

   3. /-lowering-/.f64 N/A

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

6. Applied egg-rr 17.1%

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

7. Taylor expanded in a around 0

   \[\leadsto \frac{\color{blue}{-2 \cdot c}}{b + \sqrt{\mathsf{fma}\left(c, a \cdot -4, b \cdot b\right)}} \]
8. Step-by-step derivation

   1. *-lowering-*.f64 99.9

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

9. Simplified 99.9%

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

10. Taylor expanded in c around 0

    \[\leadsto \frac{-2 \cdot c}{\color{blue}{-2 \cdot \frac{a \cdot c}{b} + 2 \cdot b}} \]
11. Step-by-step derivation

    1. *-commutative N/A

       \[\leadsto \frac{-2 \cdot c}{\color{blue}{\frac{a \cdot c}{b} \cdot -2} + 2 \cdot b} \]

    2. *-commutative N/A

       \[\leadsto \frac{-2 \cdot c}{\frac{\color{blue}{c \cdot a}}{b} \cdot -2 + 2 \cdot b} \]

    3. associate-/l* N/A

       \[\leadsto \frac{-2 \cdot c}{\color{blue}{\left(c \cdot \frac{a}{b}\right)} \cdot -2 + 2 \cdot b} \]

    4. associate-*r* N/A

       \[\leadsto \frac{-2 \cdot c}{\color{blue}{c \cdot \left(\frac{a}{b} \cdot -2\right)} + 2 \cdot b} \]

    5. *-commutative N/A

       \[\leadsto \frac{-2 \cdot c}{c \cdot \color{blue}{\left(-2 \cdot \frac{a}{b}\right)} + 2 \cdot b} \]

    6. accelerator-lowering-fma.f64 N/A

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

    7. associate-*r/ N/A

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

    8. /-lowering-/.f64 N/A

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

    9. *-lowering-*.f64 N/A

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

    10. *-commutative N/A

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

    11. *-lowering-*.f64 96.0

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

12. Simplified 96.0%

    \[\leadsto \frac{-2 \cdot c}{\color{blue}{\mathsf{fma}\left(c, \frac{-2 \cdot a}{b}, b \cdot 2\right)}} \]
13. Add Preprocessing

Alternative 4: 95.4% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \frac{-2 \cdot c}{b + \mathsf{fma}\left(c, \frac{-2 \cdot a}{b}, b\right)} \end{array} \]

FPCore

(FPCore (a b c)
 :precision binary64
 (/ (* -2.0 c) (+ b (fma c (/ (* -2.0 a) b) b))))

C

double code(double a, double b, double c) {
	return (-2.0 * c) / (b + fma(c, ((-2.0 * a) / b), b));
}

Julia

function code(a, b, c)
	return Float64(Float64(-2.0 * c) / Float64(b + fma(c, Float64(Float64(-2.0 * a) / b), b)))
end

Wolfram

code[a_, b_, c_] := N[(N[(-2.0 * c), $MachinePrecision] / N[(b + N[(c * N[(N[(-2.0 * a), $MachinePrecision] / b), $MachinePrecision] + b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 16.6%

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

4. Applied egg-rr 16.7%

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

   1. flip-- N/A

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

   2. associate-*r/ N/A

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

   3. /-lowering-/.f64 N/A

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

6. Applied egg-rr 17.1%

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

7. Taylor expanded in a around 0

   \[\leadsto \frac{\color{blue}{-2 \cdot c}}{b + \sqrt{\mathsf{fma}\left(c, a \cdot -4, b \cdot b\right)}} \]
8. Step-by-step derivation

   1. *-lowering-*.f64 99.9

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

9. Simplified 99.9%

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

10. Taylor expanded in c around 0

    \[\leadsto \frac{-2 \cdot c}{b + \color{blue}{\left(b + -2 \cdot \frac{a \cdot c}{b}\right)}} \]
11. Step-by-step derivation

    1. +-commutative N/A

       \[\leadsto \frac{-2 \cdot c}{b + \color{blue}{\left(-2 \cdot \frac{a \cdot c}{b} + b\right)}} \]

    2. *-commutative N/A

       \[\leadsto \frac{-2 \cdot c}{b + \left(\color{blue}{\frac{a \cdot c}{b} \cdot -2} + b\right)} \]

    3. *-commutative N/A

       \[\leadsto \frac{-2 \cdot c}{b + \left(\frac{\color{blue}{c \cdot a}}{b} \cdot -2 + b\right)} \]

    4. associate-/l* N/A

       \[\leadsto \frac{-2 \cdot c}{b + \left(\color{blue}{\left(c \cdot \frac{a}{b}\right)} \cdot -2 + b\right)} \]

    5. associate-*r* N/A

       \[\leadsto \frac{-2 \cdot c}{b + \left(\color{blue}{c \cdot \left(\frac{a}{b} \cdot -2\right)} + b\right)} \]

    6. *-commutative N/A

       \[\leadsto \frac{-2 \cdot c}{b + \left(c \cdot \color{blue}{\left(-2 \cdot \frac{a}{b}\right)} + b\right)} \]

    7. accelerator-lowering-fma.f64 N/A

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

    8. associate-*r/ N/A

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

    9. /-lowering-/.f64 N/A

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

    10. *-lowering-*.f64 96.0

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

12. Simplified 96.0%

    \[\leadsto \frac{-2 \cdot c}{b + \color{blue}{\mathsf{fma}\left(c, \frac{-2 \cdot a}{b}, b\right)}} \]
13. Add Preprocessing

Alternative 5: 95.3% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \frac{\mathsf{fma}\left(c \cdot c, \frac{a}{b \cdot b}, c\right)}{-b} \end{array} \]

FPCore

(FPCore (a b c) :precision binary64 (/ (fma (* c c) (/ a (* b b)) c) (- b)))

C

double code(double a, double b, double c) {
	return fma((c * c), (a / (b * b)), c) / -b;
}

Julia

function code(a, b, c)
	return Float64(fma(Float64(c * c), Float64(a / Float64(b * b)), c) / Float64(-b))
end

Wolfram

code[a_, b_, c_] := N[(N[(N[(c * c), $MachinePrecision] * N[(a / N[(b * b), $MachinePrecision]), $MachinePrecision] + c), $MachinePrecision] / (-b)), $MachinePrecision]

Derivation

1. Initial program 16.6%

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

3. Taylor expanded in b around inf

   \[\leadsto \color{blue}{\frac{-1 \cdot c + -1 \cdot \frac{a \cdot {c}^{2}}{{b}^{2}}}{b}} \]
4. Step-by-step derivation

   1. distribute-lft-out N/A

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

   2. associate-/l* N/A

      \[\leadsto \color{blue}{-1 \cdot \frac{c + \frac{a \cdot {c}^{2}}{{b}^{2}}}{b}} \]

   3. mul-1-neg N/A

      \[\leadsto \color{blue}{\mathsf{neg}\left(\frac{c + \frac{a \cdot {c}^{2}}{{b}^{2}}}{b}\right)} \]

   4. neg-lowering-neg.f64 N/A

      \[\leadsto \color{blue}{\mathsf{neg}\left(\frac{c + \frac{a \cdot {c}^{2}}{{b}^{2}}}{b}\right)} \]

   5. /-lowering-/.f64 N/A

      \[\leadsto \mathsf{neg}\left(\color{blue}{\frac{c + \frac{a \cdot {c}^{2}}{{b}^{2}}}{b}}\right) \]

   6. +-commutative N/A

      \[\leadsto \mathsf{neg}\left(\frac{\color{blue}{\frac{a \cdot {c}^{2}}{{b}^{2}} + c}}{b}\right) \]

   7. *-commutative N/A

      \[\leadsto \mathsf{neg}\left(\frac{\frac{\color{blue}{{c}^{2} \cdot a}}{{b}^{2}} + c}{b}\right) \]

   8. associate-/l* N/A

      \[\leadsto \mathsf{neg}\left(\frac{\color{blue}{{c}^{2} \cdot \frac{a}{{b}^{2}}} + c}{b}\right) \]

   9. accelerator-lowering-fma.f64 N/A

      \[\leadsto \mathsf{neg}\left(\frac{\color{blue}{\mathsf{fma}\left({c}^{2}, \frac{a}{{b}^{2}}, c\right)}}{b}\right) \]

   10. unpow2 N/A

       \[\leadsto \mathsf{neg}\left(\frac{\mathsf{fma}\left(\color{blue}{c \cdot c}, \frac{a}{{b}^{2}}, c\right)}{b}\right) \]

   11. *-lowering-*.f64 N/A

       \[\leadsto \mathsf{neg}\left(\frac{\mathsf{fma}\left(\color{blue}{c \cdot c}, \frac{a}{{b}^{2}}, c\right)}{b}\right) \]

   12. /-lowering-/.f64 N/A

       \[\leadsto \mathsf{neg}\left(\frac{\mathsf{fma}\left(c \cdot c, \color{blue}{\frac{a}{{b}^{2}}}, c\right)}{b}\right) \]

   13. unpow2 N/A

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

   14. *-lowering-*.f64 95.9

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

5. Simplified 95.9%

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

6. Final simplification 95.9%

   \[\leadsto \frac{\mathsf{fma}\left(c \cdot c, \frac{a}{b \cdot b}, c\right)}{-b} \]
7. Add Preprocessing

Alternative 6: 90.4% accurate, 3.6× speedup

Math

\[\begin{array}{l} \\ \frac{c}{-b} \end{array} \]

FPCore

(FPCore (a b c) :precision binary64 (/ c (- b)))

C

double code(double a, double b, double c) {
	return c / -b;
}

Fortran

real(8) function code(a, b, c)
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    code = c / -b
end function

Java

public static double code(double a, double b, double c) {
	return c / -b;
}

Python

def code(a, b, c):
	return c / -b

Julia

function code(a, b, c)
	return Float64(c / Float64(-b))
end

MATLAB

function tmp = code(a, b, c)
	tmp = c / -b;
end

Wolfram

code[a_, b_, c_] := N[(c / (-b)), $MachinePrecision]

Derivation

1. Initial program 16.6%

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

3. Taylor expanded in b around inf

   \[\leadsto \color{blue}{-1 \cdot \frac{c}{b}} \]
4. Step-by-step derivation

   1. mul-1-neg N/A

      \[\leadsto \color{blue}{\mathsf{neg}\left(\frac{c}{b}\right)} \]

   2. distribute-neg-frac2 N/A

      \[\leadsto \color{blue}{\frac{c}{\mathsf{neg}\left(b\right)}} \]

   3. /-lowering-/.f64 N/A

      \[\leadsto \color{blue}{\frac{c}{\mathsf{neg}\left(b\right)}} \]

   4. neg-lowering-neg.f64 91.1

      \[\leadsto \frac{c}{\color{blue}{-b}} \]

5. Simplified 91.1%

   \[\leadsto \color{blue}{\frac{c}{-b}} \]
6. Add Preprocessing

Reproduce

herbie shell --seed 2024198 
(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)))