Quadratic roots, wide range

Percentage Accurate: 18.4% → 97.5%

Time: 7.9s

Alternatives: 5

Speedup: 29.0×

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: 18.4% 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: 97.5% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \left(\mathsf{fma}\left(-2, \frac{{c}^{3}}{{b}^{5}} \cdot \left(a \cdot a\right), \frac{\frac{\left(-5 \cdot {a}^{3}\right) \cdot {c}^{4}}{{b}^{6}}}{b}\right) - \frac{c}{b}\right) - a \cdot \frac{c}{\frac{{b}^{3}}{c}} \end{array} \]

FPCore

(FPCore (a b c)
 :precision binary64
 (-
  (-
   (fma
    -2.0
    (* (/ (pow c 3.0) (pow b 5.0)) (* a a))
    (/ (/ (* (* -5.0 (pow a 3.0)) (pow c 4.0)) (pow b 6.0)) b))
   (/ c b))
  (* a (/ c (/ (pow b 3.0) c)))))

C

double code(double a, double b, double c) {
	return (fma(-2.0, ((pow(c, 3.0) / pow(b, 5.0)) * (a * a)), ((((-5.0 * pow(a, 3.0)) * pow(c, 4.0)) / pow(b, 6.0)) / b)) - (c / b)) - (a * (c / (pow(b, 3.0) / c)));
}

Julia

function code(a, b, c)
	return Float64(Float64(fma(-2.0, Float64(Float64((c ^ 3.0) / (b ^ 5.0)) * Float64(a * a)), Float64(Float64(Float64(Float64(-5.0 * (a ^ 3.0)) * (c ^ 4.0)) / (b ^ 6.0)) / b)) - Float64(c / b)) - Float64(a * Float64(c / Float64((b ^ 3.0) / c))))
end

Wolfram

code[a_, b_, c_] := N[(N[(N[(-2.0 * N[(N[(N[Power[c, 3.0], $MachinePrecision] / N[Power[b, 5.0], $MachinePrecision]), $MachinePrecision] * N[(a * a), $MachinePrecision]), $MachinePrecision] + N[(N[(N[(N[(-5.0 * N[Power[a, 3.0], $MachinePrecision]), $MachinePrecision] * N[Power[c, 4.0], $MachinePrecision]), $MachinePrecision] / N[Power[b, 6.0], $MachinePrecision]), $MachinePrecision] / b), $MachinePrecision]), $MachinePrecision] - N[(c / b), $MachinePrecision]), $MachinePrecision] - N[(a * N[(c / N[(N[Power[b, 3.0], $MachinePrecision] / c), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 18.4%

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

2. Taylor expanded in a around 0 97.4%

   \[\leadsto \color{blue}{-1 \cdot \frac{{c}^{2} \cdot a}{{b}^{3}} + \left(-1 \cdot \frac{c}{b} + \left(-0.25 \cdot \frac{{a}^{3} \cdot \left(16 \cdot \frac{{c}^{4}}{{b}^{6}} + {\left(-2 \cdot \frac{{c}^{2}}{{b}^{3}}\right)}^{2}\right)}{b} + -2 \cdot \frac{{c}^{3} \cdot {a}^{2}}{{b}^{5}}\right)\right)} \]

4. Simplified 97.4%

   \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-2, \frac{{c}^{3}}{{b}^{5}} \cdot \left(a \cdot a\right), \frac{\left(-0.25 \cdot {a}^{3}\right) \cdot \mathsf{fma}\left(16, \frac{{c}^{4}}{{b}^{6}}, 4 \cdot \frac{{c}^{4}}{{b}^{6}}\right)}{b}\right) - \frac{c}{b}\right) - \frac{c}{\frac{{b}^{3}}{c}} \cdot a} \]

5. Taylor expanded in c around 0 97.4%

   \[\leadsto \left(\mathsf{fma}\left(-2, \frac{{c}^{3}}{{b}^{5}} \cdot \left(a \cdot a\right), \frac{\color{blue}{-5 \cdot \frac{{c}^{4} \cdot {a}^{3}}{{b}^{6}}}}{b}\right) - \frac{c}{b}\right) - \frac{c}{\frac{{b}^{3}}{c}} \cdot a \]
6. Step-by-step derivation

   1. associate-*r/ 97.4%

      \[\leadsto \left(\mathsf{fma}\left(-2, \frac{{c}^{3}}{{b}^{5}} \cdot \left(a \cdot a\right), \frac{\color{blue}{\frac{-5 \cdot \left({c}^{4} \cdot {a}^{3}\right)}{{b}^{6}}}}{b}\right) - \frac{c}{b}\right) - \frac{c}{\frac{{b}^{3}}{c}} \cdot a \]

   2. *-commutative 97.4%

      \[\leadsto \left(\mathsf{fma}\left(-2, \frac{{c}^{3}}{{b}^{5}} \cdot \left(a \cdot a\right), \frac{\frac{-5 \cdot \color{blue}{\left({a}^{3} \cdot {c}^{4}\right)}}{{b}^{6}}}{b}\right) - \frac{c}{b}\right) - \frac{c}{\frac{{b}^{3}}{c}} \cdot a \]

   3. associate-*r* 97.4%

      \[\leadsto \left(\mathsf{fma}\left(-2, \frac{{c}^{3}}{{b}^{5}} \cdot \left(a \cdot a\right), \frac{\frac{\color{blue}{\left(-5 \cdot {a}^{3}\right) \cdot {c}^{4}}}{{b}^{6}}}{b}\right) - \frac{c}{b}\right) - \frac{c}{\frac{{b}^{3}}{c}} \cdot a \]

7. Simplified 97.4%

   \[\leadsto \left(\mathsf{fma}\left(-2, \frac{{c}^{3}}{{b}^{5}} \cdot \left(a \cdot a\right), \frac{\color{blue}{\frac{\left(-5 \cdot {a}^{3}\right) \cdot {c}^{4}}{{b}^{6}}}}{b}\right) - \frac{c}{b}\right) - \frac{c}{\frac{{b}^{3}}{c}} \cdot a \]

8. Final simplification 97.4%

   \[\leadsto \left(\mathsf{fma}\left(-2, \frac{{c}^{3}}{{b}^{5}} \cdot \left(a \cdot a\right), \frac{\frac{\left(-5 \cdot {a}^{3}\right) \cdot {c}^{4}}{{b}^{6}}}{b}\right) - \frac{c}{b}\right) - a \cdot \frac{c}{\frac{{b}^{3}}{c}} \]

Alternative 2: 96.6% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \left(\frac{-2 \cdot \left(a \cdot \left({c}^{3} \cdot a\right)\right)}{{b}^{5}} - \frac{c}{b}\right) - a \cdot \frac{c}{\frac{{b}^{3}}{c}} \end{array} \]

FPCore

(FPCore (a b c)
 :precision binary64
 (-
  (- (/ (* -2.0 (* a (* (pow c 3.0) a))) (pow b 5.0)) (/ c b))
  (* a (/ c (/ (pow b 3.0) c)))))

C

double code(double a, double b, double c) {
	return (((-2.0 * (a * (pow(c, 3.0) * a))) / pow(b, 5.0)) - (c / b)) - (a * (c / (pow(b, 3.0) / c)));
}

Fortran

real(8) function code(a, b, c)
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    code = ((((-2.0d0) * (a * ((c ** 3.0d0) * a))) / (b ** 5.0d0)) - (c / b)) - (a * (c / ((b ** 3.0d0) / c)))
end function

Java

public static double code(double a, double b, double c) {
	return (((-2.0 * (a * (Math.pow(c, 3.0) * a))) / Math.pow(b, 5.0)) - (c / b)) - (a * (c / (Math.pow(b, 3.0) / c)));
}

Python

def code(a, b, c):
	return (((-2.0 * (a * (math.pow(c, 3.0) * a))) / math.pow(b, 5.0)) - (c / b)) - (a * (c / (math.pow(b, 3.0) / c)))

Julia

function code(a, b, c)
	return Float64(Float64(Float64(Float64(-2.0 * Float64(a * Float64((c ^ 3.0) * a))) / (b ^ 5.0)) - Float64(c / b)) - Float64(a * Float64(c / Float64((b ^ 3.0) / c))))
end

MATLAB

function tmp = code(a, b, c)
	tmp = (((-2.0 * (a * ((c ^ 3.0) * a))) / (b ^ 5.0)) - (c / b)) - (a * (c / ((b ^ 3.0) / c)));
end

Wolfram

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

Derivation

1. Initial program 18.4%

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

2. Taylor expanded in b around inf 96.4%

   \[\leadsto \color{blue}{-1 \cdot \frac{{c}^{2} \cdot a}{{b}^{3}} + \left(-1 \cdot \frac{c}{b} + -2 \cdot \frac{{c}^{3} \cdot {a}^{2}}{{b}^{5}}\right)} \]
3. Step-by-step derivation

   1. +-commutative 96.4%

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

   2. mul-1-neg 96.4%

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

   3. unsub-neg 96.4%

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

   4. +-commutative 96.4%

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

   5. mul-1-neg 96.4%

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

   6. unsub-neg 96.4%

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

   7. associate-*r/ 96.4%

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

   8. *-commutative 96.4%

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

   9. unpow2 96.4%

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

   10. associate-*l* 96.4%

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

   11. associate-/l* 96.4%

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

   12. associate-/r/ 96.4%

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

4. Simplified 96.4%

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

5. Final simplification 96.4%

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

Alternative 3: 95.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \left(-\frac{c}{b}\right) - a \cdot \frac{c}{\frac{{b}^{3}}{c}} \end{array} \]

FPCore

(FPCore (a b c)
 :precision binary64
 (- (- (/ c b)) (* a (/ c (/ (pow b 3.0) c)))))

C

double code(double a, double b, double c) {
	return -(c / b) - (a * (c / (pow(b, 3.0) / c)));
}

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) - (a * (c / ((b ** 3.0d0) / c)))
end function

Java

public static double code(double a, double b, double c) {
	return -(c / b) - (a * (c / (Math.pow(b, 3.0) / c)));
}

Python

def code(a, b, c):
	return -(c / b) - (a * (c / (math.pow(b, 3.0) / c)))

Julia

function code(a, b, c)
	return Float64(Float64(-Float64(c / b)) - Float64(a * Float64(c / Float64((b ^ 3.0) / c))))
end

MATLAB

function tmp = code(a, b, c)
	tmp = -(c / b) - (a * (c / ((b ^ 3.0) / c)));
end

Wolfram

code[a_, b_, c_] := N[((-N[(c / b), $MachinePrecision]) - N[(a * N[(c / N[(N[Power[b, 3.0], $MachinePrecision] / c), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 18.4%

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

2. Taylor expanded in b around inf 94.6%

   \[\leadsto \color{blue}{-1 \cdot \frac{{c}^{2} \cdot a}{{b}^{3}} + -1 \cdot \frac{c}{b}} \]
3. Step-by-step derivation

   1. +-commutative 94.6%

      \[\leadsto \color{blue}{-1 \cdot \frac{c}{b} + -1 \cdot \frac{{c}^{2} \cdot a}{{b}^{3}}} \]

   2. mul-1-neg 94.6%

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

   3. unsub-neg 94.6%

      \[\leadsto \color{blue}{-1 \cdot \frac{c}{b} - \frac{{c}^{2} \cdot a}{{b}^{3}}} \]

   4. mul-1-neg 94.6%

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

   5. associate-/l* 94.6%

      \[\leadsto \left(-\frac{c}{b}\right) - \color{blue}{\frac{{c}^{2}}{\frac{{b}^{3}}{a}}} \]

   6. associate-/r/ 94.6%

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

   7. unpow2 94.6%

      \[\leadsto \left(-\frac{c}{b}\right) - \frac{\color{blue}{c \cdot c}}{{b}^{3}} \cdot a \]

   8. associate-/l* 94.6%

      \[\leadsto \left(-\frac{c}{b}\right) - \color{blue}{\frac{c}{\frac{{b}^{3}}{c}}} \cdot a \]

4. Simplified 94.6%

   \[\leadsto \color{blue}{\left(-\frac{c}{b}\right) - \frac{c}{\frac{{b}^{3}}{c}} \cdot a} \]

5. Final simplification 94.6%

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

Alternative 4: 94.7% accurate, 5.3× speedup

Math

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

FPCore

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

C

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

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 * (a * 4.0d0)) / ((-b - b) - (((-2.0d0) / b) * (c * a)))) / (a * 2.0d0)
end function

Java

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

Python

def code(a, b, c):
	return ((c * (a * 4.0)) / ((-b - b) - ((-2.0 / b) * (c * a)))) / (a * 2.0)

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 18.4%

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

2. Taylor expanded in b around inf 12.6%

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

   1. associate-*r/ 12.6%

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

4. Simplified 12.6%

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

   1. flip-+ 12.7%

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

   2. associate-/l* 12.7%

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

   3. associate-/l* 12.7%

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

   4. associate-/l* 12.7%

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

6. Applied egg-rr 12.7%

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

   1. sqr-neg 12.7%

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

   2. associate-/r/ 12.7%

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

   3. associate-/r/ 12.7%

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

   4. associate--r+ 12.7%

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

   5. associate-/r/ 12.7%

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

8. Simplified 12.7%

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

9. Taylor expanded in b around inf 94.4%

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

    1. *-commutative 94.4%

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

    2. *-commutative 94.4%

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

    3. *-commutative 94.4%

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

    4. associate-*l* 94.4%

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

11. Simplified 94.4%

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

12. Final simplification 94.4%

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

Alternative 5: 90.0% accurate, 29.0× 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(-Float64(c / 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 18.4%

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

2. Taylor expanded in b around inf 89.8%

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

   1. mul-1-neg 89.8%

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

4. Simplified 89.8%

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

5. Final simplification 89.8%

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

Reproduce

herbie shell --seed 2023273 
(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)))