Cubic critical, wide range

Percentage Accurate: 17.8% → 97.7%

Time: 10.3s

Alternatives: 5

Speedup: 23.2×

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

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

FPCore

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

C

double code(double a, double b, double c) {
	return (-b + sqrt(((b * b) - ((3.0 * a) * c)))) / (3.0 * 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) - ((3.0d0 * a) * c)))) / (3.0d0 * a)
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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]

Initial Program: 17.8% accurate, 1.0× speedup

Math

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

FPCore

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

C

double code(double a, double b, double c) {
	return (-b + sqrt(((b * b) - ((3.0 * a) * c)))) / (3.0 * 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) - ((3.0d0 * a) * c)))) / (3.0d0 * a)
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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]

Alternative 1: 97.7% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := {\left(a \cdot c\right)}^{4}\\ -0.5625 \cdot \frac{{a}^{2} \cdot {c}^{3}}{{b}^{5}} + \left(-0.5 \cdot \frac{c}{b} + \left(-0.375 \cdot \left({\left(\frac{c}{b}\right)}^{2} \cdot \frac{a}{b}\right) + -0.16666666666666666 \cdot \frac{5.0625 \cdot t_0 + t_0 \cdot 1.265625}{a \cdot {b}^{7}}\right)\right) \end{array} \end{array} \]

FPCore

(FPCore (a b c)
 :precision binary64
 (let* ((t_0 (pow (* a c) 4.0)))
   (+
    (* -0.5625 (/ (* (pow a 2.0) (pow c 3.0)) (pow b 5.0)))
    (+
     (* -0.5 (/ c b))
     (+
      (* -0.375 (* (pow (/ c b) 2.0) (/ a b)))
      (*
       -0.16666666666666666
       (/ (+ (* 5.0625 t_0) (* t_0 1.265625)) (* a (pow b 7.0)))))))))

C

double code(double a, double b, double c) {
	double t_0 = pow((a * c), 4.0);
	return (-0.5625 * ((pow(a, 2.0) * pow(c, 3.0)) / pow(b, 5.0))) + ((-0.5 * (c / b)) + ((-0.375 * (pow((c / b), 2.0) * (a / b))) + (-0.16666666666666666 * (((5.0625 * t_0) + (t_0 * 1.265625)) / (a * pow(b, 7.0))))));
}

Fortran

real(8) function code(a, b, c)
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8) :: t_0
    t_0 = (a * c) ** 4.0d0
    code = ((-0.5625d0) * (((a ** 2.0d0) * (c ** 3.0d0)) / (b ** 5.0d0))) + (((-0.5d0) * (c / b)) + (((-0.375d0) * (((c / b) ** 2.0d0) * (a / b))) + ((-0.16666666666666666d0) * (((5.0625d0 * t_0) + (t_0 * 1.265625d0)) / (a * (b ** 7.0d0))))))
end function

Java

public static double code(double a, double b, double c) {
	double t_0 = Math.pow((a * c), 4.0);
	return (-0.5625 * ((Math.pow(a, 2.0) * Math.pow(c, 3.0)) / Math.pow(b, 5.0))) + ((-0.5 * (c / b)) + ((-0.375 * (Math.pow((c / b), 2.0) * (a / b))) + (-0.16666666666666666 * (((5.0625 * t_0) + (t_0 * 1.265625)) / (a * Math.pow(b, 7.0))))));
}

Python

def code(a, b, c):
	t_0 = math.pow((a * c), 4.0)
	return (-0.5625 * ((math.pow(a, 2.0) * math.pow(c, 3.0)) / math.pow(b, 5.0))) + ((-0.5 * (c / b)) + ((-0.375 * (math.pow((c / b), 2.0) * (a / b))) + (-0.16666666666666666 * (((5.0625 * t_0) + (t_0 * 1.265625)) / (a * math.pow(b, 7.0))))))

Julia

function code(a, b, c)
	t_0 = Float64(a * c) ^ 4.0
	return Float64(Float64(-0.5625 * Float64(Float64((a ^ 2.0) * (c ^ 3.0)) / (b ^ 5.0))) + Float64(Float64(-0.5 * Float64(c / b)) + Float64(Float64(-0.375 * Float64((Float64(c / b) ^ 2.0) * Float64(a / b))) + Float64(-0.16666666666666666 * Float64(Float64(Float64(5.0625 * t_0) + Float64(t_0 * 1.265625)) / Float64(a * (b ^ 7.0)))))))
end

MATLAB

function tmp = code(a, b, c)
	t_0 = (a * c) ^ 4.0;
	tmp = (-0.5625 * (((a ^ 2.0) * (c ^ 3.0)) / (b ^ 5.0))) + ((-0.5 * (c / b)) + ((-0.375 * (((c / b) ^ 2.0) * (a / b))) + (-0.16666666666666666 * (((5.0625 * t_0) + (t_0 * 1.265625)) / (a * (b ^ 7.0))))));
end

Wolfram

code[a_, b_, c_] := Block[{t$95$0 = N[Power[N[(a * c), $MachinePrecision], 4.0], $MachinePrecision]}, N[(N[(-0.5625 * N[(N[(N[Power[a, 2.0], $MachinePrecision] * N[Power[c, 3.0], $MachinePrecision]), $MachinePrecision] / N[Power[b, 5.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(N[(-0.5 * N[(c / b), $MachinePrecision]), $MachinePrecision] + N[(N[(-0.375 * N[(N[Power[N[(c / b), $MachinePrecision], 2.0], $MachinePrecision] * N[(a / b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(-0.16666666666666666 * N[(N[(N[(5.0625 * t$95$0), $MachinePrecision] + N[(t$95$0 * 1.265625), $MachinePrecision]), $MachinePrecision] / N[(a * N[Power[b, 7.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Initial program 19.1%

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

2. Taylor expanded in b around inf 97.0%

   \[\leadsto \color{blue}{-0.5625 \cdot \frac{{a}^{2} \cdot {c}^{3}}{{b}^{5}} + \left(-0.5 \cdot \frac{c}{b} + \left(-0.375 \cdot \frac{a \cdot {c}^{2}}{{b}^{3}} + -0.16666666666666666 \cdot \frac{5.0625 \cdot \left({a}^{4} \cdot {c}^{4}\right) + {\left(-1.125 \cdot \left({a}^{2} \cdot {c}^{2}\right)\right)}^{2}}{a \cdot {b}^{7}}\right)\right)} \]
3. Step-by-step derivation

   1. unpow2 97.0%

      \[\leadsto -0.5625 \cdot \frac{{a}^{2} \cdot {c}^{3}}{{b}^{5}} + \left(-0.5 \cdot \frac{c}{b} + \left(-0.375 \cdot \frac{a \cdot {c}^{2}}{{b}^{3}} + -0.16666666666666666 \cdot \frac{5.0625 \cdot \left({a}^{4} \cdot {c}^{4}\right) + \color{blue}{\left(-1.125 \cdot \left({a}^{2} \cdot {c}^{2}\right)\right) \cdot \left(-1.125 \cdot \left({a}^{2} \cdot {c}^{2}\right)\right)}}{a \cdot {b}^{7}}\right)\right) \]

   2. *-commutative 97.0%

      \[\leadsto -0.5625 \cdot \frac{{a}^{2} \cdot {c}^{3}}{{b}^{5}} + \left(-0.5 \cdot \frac{c}{b} + \left(-0.375 \cdot \frac{a \cdot {c}^{2}}{{b}^{3}} + -0.16666666666666666 \cdot \frac{5.0625 \cdot \left({a}^{4} \cdot {c}^{4}\right) + \color{blue}{\left(\left({a}^{2} \cdot {c}^{2}\right) \cdot -1.125\right)} \cdot \left(-1.125 \cdot \left({a}^{2} \cdot {c}^{2}\right)\right)}{a \cdot {b}^{7}}\right)\right) \]

   3. *-commutative 97.0%

      \[\leadsto -0.5625 \cdot \frac{{a}^{2} \cdot {c}^{3}}{{b}^{5}} + \left(-0.5 \cdot \frac{c}{b} + \left(-0.375 \cdot \frac{a \cdot {c}^{2}}{{b}^{3}} + -0.16666666666666666 \cdot \frac{5.0625 \cdot \left({a}^{4} \cdot {c}^{4}\right) + \left(\left({a}^{2} \cdot {c}^{2}\right) \cdot -1.125\right) \cdot \color{blue}{\left(\left({a}^{2} \cdot {c}^{2}\right) \cdot -1.125\right)}}{a \cdot {b}^{7}}\right)\right) \]

   4. swap-sqr 97.0%

      \[\leadsto -0.5625 \cdot \frac{{a}^{2} \cdot {c}^{3}}{{b}^{5}} + \left(-0.5 \cdot \frac{c}{b} + \left(-0.375 \cdot \frac{a \cdot {c}^{2}}{{b}^{3}} + -0.16666666666666666 \cdot \frac{5.0625 \cdot \left({a}^{4} \cdot {c}^{4}\right) + \color{blue}{\left(\left({a}^{2} \cdot {c}^{2}\right) \cdot \left({a}^{2} \cdot {c}^{2}\right)\right) \cdot \left(-1.125 \cdot -1.125\right)}}{a \cdot {b}^{7}}\right)\right) \]

   5. pow-prod-down 97.0%

      \[\leadsto -0.5625 \cdot \frac{{a}^{2} \cdot {c}^{3}}{{b}^{5}} + \left(-0.5 \cdot \frac{c}{b} + \left(-0.375 \cdot \frac{a \cdot {c}^{2}}{{b}^{3}} + -0.16666666666666666 \cdot \frac{5.0625 \cdot \left({a}^{4} \cdot {c}^{4}\right) + \left(\color{blue}{{\left(a \cdot c\right)}^{2}} \cdot \left({a}^{2} \cdot {c}^{2}\right)\right) \cdot \left(-1.125 \cdot -1.125\right)}{a \cdot {b}^{7}}\right)\right) \]

   6. pow-prod-down 97.0%

      \[\leadsto -0.5625 \cdot \frac{{a}^{2} \cdot {c}^{3}}{{b}^{5}} + \left(-0.5 \cdot \frac{c}{b} + \left(-0.375 \cdot \frac{a \cdot {c}^{2}}{{b}^{3}} + -0.16666666666666666 \cdot \frac{5.0625 \cdot \left({a}^{4} \cdot {c}^{4}\right) + \left({\left(a \cdot c\right)}^{2} \cdot \color{blue}{{\left(a \cdot c\right)}^{2}}\right) \cdot \left(-1.125 \cdot -1.125\right)}{a \cdot {b}^{7}}\right)\right) \]

   7. pow-prod-up 97.0%

      \[\leadsto -0.5625 \cdot \frac{{a}^{2} \cdot {c}^{3}}{{b}^{5}} + \left(-0.5 \cdot \frac{c}{b} + \left(-0.375 \cdot \frac{a \cdot {c}^{2}}{{b}^{3}} + -0.16666666666666666 \cdot \frac{5.0625 \cdot \left({a}^{4} \cdot {c}^{4}\right) + \color{blue}{{\left(a \cdot c\right)}^{\left(2 + 2\right)}} \cdot \left(-1.125 \cdot -1.125\right)}{a \cdot {b}^{7}}\right)\right) \]

   8. metadata-eval 97.0%

      \[\leadsto -0.5625 \cdot \frac{{a}^{2} \cdot {c}^{3}}{{b}^{5}} + \left(-0.5 \cdot \frac{c}{b} + \left(-0.375 \cdot \frac{a \cdot {c}^{2}}{{b}^{3}} + -0.16666666666666666 \cdot \frac{5.0625 \cdot \left({a}^{4} \cdot {c}^{4}\right) + {\left(a \cdot c\right)}^{\color{blue}{4}} \cdot \left(-1.125 \cdot -1.125\right)}{a \cdot {b}^{7}}\right)\right) \]

   9. metadata-eval 97.0%

      \[\leadsto -0.5625 \cdot \frac{{a}^{2} \cdot {c}^{3}}{{b}^{5}} + \left(-0.5 \cdot \frac{c}{b} + \left(-0.375 \cdot \frac{a \cdot {c}^{2}}{{b}^{3}} + -0.16666666666666666 \cdot \frac{5.0625 \cdot \left({a}^{4} \cdot {c}^{4}\right) + {\left(a \cdot c\right)}^{4} \cdot \color{blue}{1.265625}}{a \cdot {b}^{7}}\right)\right) \]

4. Applied egg-rr 97.0%

   \[\leadsto -0.5625 \cdot \frac{{a}^{2} \cdot {c}^{3}}{{b}^{5}} + \left(-0.5 \cdot \frac{c}{b} + \left(-0.375 \cdot \frac{a \cdot {c}^{2}}{{b}^{3}} + -0.16666666666666666 \cdot \frac{5.0625 \cdot \left({a}^{4} \cdot {c}^{4}\right) + \color{blue}{{\left(a \cdot c\right)}^{4} \cdot 1.265625}}{a \cdot {b}^{7}}\right)\right) \]
5. Step-by-step derivation

   1. expm1-log1p-u 97.0%

      \[\leadsto -0.5625 \cdot \frac{{a}^{2} \cdot {c}^{3}}{{b}^{5}} + \left(-0.5 \cdot \frac{c}{b} + \left(-0.375 \cdot \frac{a \cdot {c}^{2}}{{b}^{3}} + -0.16666666666666666 \cdot \frac{5.0625 \cdot \color{blue}{\mathsf{expm1}\left(\mathsf{log1p}\left({a}^{4} \cdot {c}^{4}\right)\right)} + {\left(a \cdot c\right)}^{4} \cdot 1.265625}{a \cdot {b}^{7}}\right)\right) \]

   2. expm1-udef 96.7%

      \[\leadsto -0.5625 \cdot \frac{{a}^{2} \cdot {c}^{3}}{{b}^{5}} + \left(-0.5 \cdot \frac{c}{b} + \left(-0.375 \cdot \frac{a \cdot {c}^{2}}{{b}^{3}} + -0.16666666666666666 \cdot \frac{5.0625 \cdot \color{blue}{\left(e^{\mathsf{log1p}\left({a}^{4} \cdot {c}^{4}\right)} - 1\right)} + {\left(a \cdot c\right)}^{4} \cdot 1.265625}{a \cdot {b}^{7}}\right)\right) \]

   3. pow-prod-down 96.7%

      \[\leadsto -0.5625 \cdot \frac{{a}^{2} \cdot {c}^{3}}{{b}^{5}} + \left(-0.5 \cdot \frac{c}{b} + \left(-0.375 \cdot \frac{a \cdot {c}^{2}}{{b}^{3}} + -0.16666666666666666 \cdot \frac{5.0625 \cdot \left(e^{\mathsf{log1p}\left(\color{blue}{{\left(a \cdot c\right)}^{4}}\right)} - 1\right) + {\left(a \cdot c\right)}^{4} \cdot 1.265625}{a \cdot {b}^{7}}\right)\right) \]

6. Applied egg-rr 96.7%

   \[\leadsto -0.5625 \cdot \frac{{a}^{2} \cdot {c}^{3}}{{b}^{5}} + \left(-0.5 \cdot \frac{c}{b} + \left(-0.375 \cdot \frac{a \cdot {c}^{2}}{{b}^{3}} + -0.16666666666666666 \cdot \frac{5.0625 \cdot \color{blue}{\left(e^{\mathsf{log1p}\left({\left(a \cdot c\right)}^{4}\right)} - 1\right)} + {\left(a \cdot c\right)}^{4} \cdot 1.265625}{a \cdot {b}^{7}}\right)\right) \]
7. Step-by-step derivation

   1. expm1-def 97.0%

      \[\leadsto -0.5625 \cdot \frac{{a}^{2} \cdot {c}^{3}}{{b}^{5}} + \left(-0.5 \cdot \frac{c}{b} + \left(-0.375 \cdot \frac{a \cdot {c}^{2}}{{b}^{3}} + -0.16666666666666666 \cdot \frac{5.0625 \cdot \color{blue}{\mathsf{expm1}\left(\mathsf{log1p}\left({\left(a \cdot c\right)}^{4}\right)\right)} + {\left(a \cdot c\right)}^{4} \cdot 1.265625}{a \cdot {b}^{7}}\right)\right) \]

   2. expm1-log1p 97.0%

      \[\leadsto -0.5625 \cdot \frac{{a}^{2} \cdot {c}^{3}}{{b}^{5}} + \left(-0.5 \cdot \frac{c}{b} + \left(-0.375 \cdot \frac{a \cdot {c}^{2}}{{b}^{3}} + -0.16666666666666666 \cdot \frac{5.0625 \cdot \color{blue}{{\left(a \cdot c\right)}^{4}} + {\left(a \cdot c\right)}^{4} \cdot 1.265625}{a \cdot {b}^{7}}\right)\right) \]

8. Simplified 97.0%

   \[\leadsto -0.5625 \cdot \frac{{a}^{2} \cdot {c}^{3}}{{b}^{5}} + \left(-0.5 \cdot \frac{c}{b} + \left(-0.375 \cdot \frac{a \cdot {c}^{2}}{{b}^{3}} + -0.16666666666666666 \cdot \frac{5.0625 \cdot \color{blue}{{\left(a \cdot c\right)}^{4}} + {\left(a \cdot c\right)}^{4} \cdot 1.265625}{a \cdot {b}^{7}}\right)\right) \]
9. Step-by-step derivation

   1. *-commutative 97.0%

      \[\leadsto -0.5625 \cdot \frac{{a}^{2} \cdot {c}^{3}}{{b}^{5}} + \left(-0.5 \cdot \frac{c}{b} + \left(-0.375 \cdot \frac{\color{blue}{{c}^{2} \cdot a}}{{b}^{3}} + -0.16666666666666666 \cdot \frac{5.0625 \cdot {\left(a \cdot c\right)}^{4} + {\left(a \cdot c\right)}^{4} \cdot 1.265625}{a \cdot {b}^{7}}\right)\right) \]

   2. unpow3 97.0%

      \[\leadsto -0.5625 \cdot \frac{{a}^{2} \cdot {c}^{3}}{{b}^{5}} + \left(-0.5 \cdot \frac{c}{b} + \left(-0.375 \cdot \frac{{c}^{2} \cdot a}{\color{blue}{\left(b \cdot b\right) \cdot b}} + -0.16666666666666666 \cdot \frac{5.0625 \cdot {\left(a \cdot c\right)}^{4} + {\left(a \cdot c\right)}^{4} \cdot 1.265625}{a \cdot {b}^{7}}\right)\right) \]

   3. times-frac 97.0%

      \[\leadsto -0.5625 \cdot \frac{{a}^{2} \cdot {c}^{3}}{{b}^{5}} + \left(-0.5 \cdot \frac{c}{b} + \left(-0.375 \cdot \color{blue}{\left(\frac{{c}^{2}}{b \cdot b} \cdot \frac{a}{b}\right)} + -0.16666666666666666 \cdot \frac{5.0625 \cdot {\left(a \cdot c\right)}^{4} + {\left(a \cdot c\right)}^{4} \cdot 1.265625}{a \cdot {b}^{7}}\right)\right) \]

   4. unpow2 97.0%

      \[\leadsto -0.5625 \cdot \frac{{a}^{2} \cdot {c}^{3}}{{b}^{5}} + \left(-0.5 \cdot \frac{c}{b} + \left(-0.375 \cdot \left(\frac{\color{blue}{c \cdot c}}{b \cdot b} \cdot \frac{a}{b}\right) + -0.16666666666666666 \cdot \frac{5.0625 \cdot {\left(a \cdot c\right)}^{4} + {\left(a \cdot c\right)}^{4} \cdot 1.265625}{a \cdot {b}^{7}}\right)\right) \]

   5. frac-times 97.0%

      \[\leadsto -0.5625 \cdot \frac{{a}^{2} \cdot {c}^{3}}{{b}^{5}} + \left(-0.5 \cdot \frac{c}{b} + \left(-0.375 \cdot \left(\color{blue}{\left(\frac{c}{b} \cdot \frac{c}{b}\right)} \cdot \frac{a}{b}\right) + -0.16666666666666666 \cdot \frac{5.0625 \cdot {\left(a \cdot c\right)}^{4} + {\left(a \cdot c\right)}^{4} \cdot 1.265625}{a \cdot {b}^{7}}\right)\right) \]

   6. pow2 97.0%

      \[\leadsto -0.5625 \cdot \frac{{a}^{2} \cdot {c}^{3}}{{b}^{5}} + \left(-0.5 \cdot \frac{c}{b} + \left(-0.375 \cdot \left(\color{blue}{{\left(\frac{c}{b}\right)}^{2}} \cdot \frac{a}{b}\right) + -0.16666666666666666 \cdot \frac{5.0625 \cdot {\left(a \cdot c\right)}^{4} + {\left(a \cdot c\right)}^{4} \cdot 1.265625}{a \cdot {b}^{7}}\right)\right) \]

10. Applied egg-rr 97.0%

    \[\leadsto -0.5625 \cdot \frac{{a}^{2} \cdot {c}^{3}}{{b}^{5}} + \left(-0.5 \cdot \frac{c}{b} + \left(-0.375 \cdot \color{blue}{\left({\left(\frac{c}{b}\right)}^{2} \cdot \frac{a}{b}\right)} + -0.16666666666666666 \cdot \frac{5.0625 \cdot {\left(a \cdot c\right)}^{4} + {\left(a \cdot c\right)}^{4} \cdot 1.265625}{a \cdot {b}^{7}}\right)\right) \]

11. Final simplification 97.0%

    \[\leadsto -0.5625 \cdot \frac{{a}^{2} \cdot {c}^{3}}{{b}^{5}} + \left(-0.5 \cdot \frac{c}{b} + \left(-0.375 \cdot \left({\left(\frac{c}{b}\right)}^{2} \cdot \frac{a}{b}\right) + -0.16666666666666666 \cdot \frac{5.0625 \cdot {\left(a \cdot c\right)}^{4} + {\left(a \cdot c\right)}^{4} \cdot 1.265625}{a \cdot {b}^{7}}\right)\right) \]

Alternative 2: 96.9% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ -0.5625 \cdot \frac{{a}^{2} \cdot {c}^{3}}{{b}^{5}} + \left(-0.5 \cdot \frac{c}{b} + -0.375 \cdot \left({\left(\frac{c}{b}\right)}^{2} \cdot \frac{a}{b}\right)\right) \end{array} \]

FPCore

(FPCore (a b c)
 :precision binary64
 (+
  (* -0.5625 (/ (* (pow a 2.0) (pow c 3.0)) (pow b 5.0)))
  (+ (* -0.5 (/ c b)) (* -0.375 (* (pow (/ c b) 2.0) (/ a b))))))

C

double code(double a, double b, double c) {
	return (-0.5625 * ((pow(a, 2.0) * pow(c, 3.0)) / pow(b, 5.0))) + ((-0.5 * (c / b)) + (-0.375 * (pow((c / b), 2.0) * (a / 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 = ((-0.5625d0) * (((a ** 2.0d0) * (c ** 3.0d0)) / (b ** 5.0d0))) + (((-0.5d0) * (c / b)) + ((-0.375d0) * (((c / b) ** 2.0d0) * (a / b))))
end function

Java

public static double code(double a, double b, double c) {
	return (-0.5625 * ((Math.pow(a, 2.0) * Math.pow(c, 3.0)) / Math.pow(b, 5.0))) + ((-0.5 * (c / b)) + (-0.375 * (Math.pow((c / b), 2.0) * (a / b))));
}

Python

def code(a, b, c):
	return (-0.5625 * ((math.pow(a, 2.0) * math.pow(c, 3.0)) / math.pow(b, 5.0))) + ((-0.5 * (c / b)) + (-0.375 * (math.pow((c / b), 2.0) * (a / b))))

Julia

function code(a, b, c)
	return Float64(Float64(-0.5625 * Float64(Float64((a ^ 2.0) * (c ^ 3.0)) / (b ^ 5.0))) + Float64(Float64(-0.5 * Float64(c / b)) + Float64(-0.375 * Float64((Float64(c / b) ^ 2.0) * Float64(a / b)))))
end

MATLAB

function tmp = code(a, b, c)
	tmp = (-0.5625 * (((a ^ 2.0) * (c ^ 3.0)) / (b ^ 5.0))) + ((-0.5 * (c / b)) + (-0.375 * (((c / b) ^ 2.0) * (a / b))));
end

Wolfram

code[a_, b_, c_] := N[(N[(-0.5625 * N[(N[(N[Power[a, 2.0], $MachinePrecision] * N[Power[c, 3.0], $MachinePrecision]), $MachinePrecision] / N[Power[b, 5.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(N[(-0.5 * N[(c / b), $MachinePrecision]), $MachinePrecision] + N[(-0.375 * N[(N[Power[N[(c / b), $MachinePrecision], 2.0], $MachinePrecision] * N[(a / b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 19.1%

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

2. Taylor expanded in b around inf 96.1%

   \[\leadsto \color{blue}{-0.5625 \cdot \frac{{a}^{2} \cdot {c}^{3}}{{b}^{5}} + \left(-0.5 \cdot \frac{c}{b} + -0.375 \cdot \frac{a \cdot {c}^{2}}{{b}^{3}}\right)} \]
3. Step-by-step derivation

   1. *-commutative 97.0%

      \[\leadsto -0.5625 \cdot \frac{{a}^{2} \cdot {c}^{3}}{{b}^{5}} + \left(-0.5 \cdot \frac{c}{b} + \left(-0.375 \cdot \frac{\color{blue}{{c}^{2} \cdot a}}{{b}^{3}} + -0.16666666666666666 \cdot \frac{5.0625 \cdot {\left(a \cdot c\right)}^{4} + {\left(a \cdot c\right)}^{4} \cdot 1.265625}{a \cdot {b}^{7}}\right)\right) \]

   2. unpow3 97.0%

      \[\leadsto -0.5625 \cdot \frac{{a}^{2} \cdot {c}^{3}}{{b}^{5}} + \left(-0.5 \cdot \frac{c}{b} + \left(-0.375 \cdot \frac{{c}^{2} \cdot a}{\color{blue}{\left(b \cdot b\right) \cdot b}} + -0.16666666666666666 \cdot \frac{5.0625 \cdot {\left(a \cdot c\right)}^{4} + {\left(a \cdot c\right)}^{4} \cdot 1.265625}{a \cdot {b}^{7}}\right)\right) \]

   3. times-frac 97.0%

      \[\leadsto -0.5625 \cdot \frac{{a}^{2} \cdot {c}^{3}}{{b}^{5}} + \left(-0.5 \cdot \frac{c}{b} + \left(-0.375 \cdot \color{blue}{\left(\frac{{c}^{2}}{b \cdot b} \cdot \frac{a}{b}\right)} + -0.16666666666666666 \cdot \frac{5.0625 \cdot {\left(a \cdot c\right)}^{4} + {\left(a \cdot c\right)}^{4} \cdot 1.265625}{a \cdot {b}^{7}}\right)\right) \]

   4. unpow2 97.0%

      \[\leadsto -0.5625 \cdot \frac{{a}^{2} \cdot {c}^{3}}{{b}^{5}} + \left(-0.5 \cdot \frac{c}{b} + \left(-0.375 \cdot \left(\frac{\color{blue}{c \cdot c}}{b \cdot b} \cdot \frac{a}{b}\right) + -0.16666666666666666 \cdot \frac{5.0625 \cdot {\left(a \cdot c\right)}^{4} + {\left(a \cdot c\right)}^{4} \cdot 1.265625}{a \cdot {b}^{7}}\right)\right) \]

   5. frac-times 97.0%

      \[\leadsto -0.5625 \cdot \frac{{a}^{2} \cdot {c}^{3}}{{b}^{5}} + \left(-0.5 \cdot \frac{c}{b} + \left(-0.375 \cdot \left(\color{blue}{\left(\frac{c}{b} \cdot \frac{c}{b}\right)} \cdot \frac{a}{b}\right) + -0.16666666666666666 \cdot \frac{5.0625 \cdot {\left(a \cdot c\right)}^{4} + {\left(a \cdot c\right)}^{4} \cdot 1.265625}{a \cdot {b}^{7}}\right)\right) \]

   6. pow2 97.0%

      \[\leadsto -0.5625 \cdot \frac{{a}^{2} \cdot {c}^{3}}{{b}^{5}} + \left(-0.5 \cdot \frac{c}{b} + \left(-0.375 \cdot \left(\color{blue}{{\left(\frac{c}{b}\right)}^{2}} \cdot \frac{a}{b}\right) + -0.16666666666666666 \cdot \frac{5.0625 \cdot {\left(a \cdot c\right)}^{4} + {\left(a \cdot c\right)}^{4} \cdot 1.265625}{a \cdot {b}^{7}}\right)\right) \]

4. Applied egg-rr 96.1%

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

5. Final simplification 96.1%

   \[\leadsto -0.5625 \cdot \frac{{a}^{2} \cdot {c}^{3}}{{b}^{5}} + \left(-0.5 \cdot \frac{c}{b} + -0.375 \cdot \left({\left(\frac{c}{b}\right)}^{2} \cdot \frac{a}{b}\right)\right) \]

Alternative 3: 95.3% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ -0.5 \cdot \frac{c}{b} + -0.375 \cdot \left({\left(\frac{c}{b}\right)}^{2} \cdot \frac{a}{b}\right) \end{array} \]

FPCore

(FPCore (a b c)
 :precision binary64
 (+ (* -0.5 (/ c b)) (* -0.375 (* (pow (/ c b) 2.0) (/ a b)))))

C

double code(double a, double b, double c) {
	return (-0.5 * (c / b)) + (-0.375 * (pow((c / b), 2.0) * (a / 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 = ((-0.5d0) * (c / b)) + ((-0.375d0) * (((c / b) ** 2.0d0) * (a / b)))
end function

Java

public static double code(double a, double b, double c) {
	return (-0.5 * (c / b)) + (-0.375 * (Math.pow((c / b), 2.0) * (a / b)));
}

Python

def code(a, b, c):
	return (-0.5 * (c / b)) + (-0.375 * (math.pow((c / b), 2.0) * (a / b)))

Julia

function code(a, b, c)
	return Float64(Float64(-0.5 * Float64(c / b)) + Float64(-0.375 * Float64((Float64(c / b) ^ 2.0) * Float64(a / b))))
end

MATLAB

function tmp = code(a, b, c)
	tmp = (-0.5 * (c / b)) + (-0.375 * (((c / b) ^ 2.0) * (a / b)));
end

Wolfram

code[a_, b_, c_] := N[(N[(-0.5 * N[(c / b), $MachinePrecision]), $MachinePrecision] + N[(-0.375 * N[(N[Power[N[(c / b), $MachinePrecision], 2.0], $MachinePrecision] * N[(a / b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 19.1%

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

2. Taylor expanded in b around inf 94.7%

   \[\leadsto \color{blue}{-0.5 \cdot \frac{c}{b} + -0.375 \cdot \frac{a \cdot {c}^{2}}{{b}^{3}}} \]
3. Step-by-step derivation

   1. *-commutative 97.0%

      \[\leadsto -0.5625 \cdot \frac{{a}^{2} \cdot {c}^{3}}{{b}^{5}} + \left(-0.5 \cdot \frac{c}{b} + \left(-0.375 \cdot \frac{\color{blue}{{c}^{2} \cdot a}}{{b}^{3}} + -0.16666666666666666 \cdot \frac{5.0625 \cdot {\left(a \cdot c\right)}^{4} + {\left(a \cdot c\right)}^{4} \cdot 1.265625}{a \cdot {b}^{7}}\right)\right) \]

   2. unpow3 97.0%

      \[\leadsto -0.5625 \cdot \frac{{a}^{2} \cdot {c}^{3}}{{b}^{5}} + \left(-0.5 \cdot \frac{c}{b} + \left(-0.375 \cdot \frac{{c}^{2} \cdot a}{\color{blue}{\left(b \cdot b\right) \cdot b}} + -0.16666666666666666 \cdot \frac{5.0625 \cdot {\left(a \cdot c\right)}^{4} + {\left(a \cdot c\right)}^{4} \cdot 1.265625}{a \cdot {b}^{7}}\right)\right) \]

   3. times-frac 97.0%

      \[\leadsto -0.5625 \cdot \frac{{a}^{2} \cdot {c}^{3}}{{b}^{5}} + \left(-0.5 \cdot \frac{c}{b} + \left(-0.375 \cdot \color{blue}{\left(\frac{{c}^{2}}{b \cdot b} \cdot \frac{a}{b}\right)} + -0.16666666666666666 \cdot \frac{5.0625 \cdot {\left(a \cdot c\right)}^{4} + {\left(a \cdot c\right)}^{4} \cdot 1.265625}{a \cdot {b}^{7}}\right)\right) \]

   4. unpow2 97.0%

      \[\leadsto -0.5625 \cdot \frac{{a}^{2} \cdot {c}^{3}}{{b}^{5}} + \left(-0.5 \cdot \frac{c}{b} + \left(-0.375 \cdot \left(\frac{\color{blue}{c \cdot c}}{b \cdot b} \cdot \frac{a}{b}\right) + -0.16666666666666666 \cdot \frac{5.0625 \cdot {\left(a \cdot c\right)}^{4} + {\left(a \cdot c\right)}^{4} \cdot 1.265625}{a \cdot {b}^{7}}\right)\right) \]

   5. frac-times 97.0%

      \[\leadsto -0.5625 \cdot \frac{{a}^{2} \cdot {c}^{3}}{{b}^{5}} + \left(-0.5 \cdot \frac{c}{b} + \left(-0.375 \cdot \left(\color{blue}{\left(\frac{c}{b} \cdot \frac{c}{b}\right)} \cdot \frac{a}{b}\right) + -0.16666666666666666 \cdot \frac{5.0625 \cdot {\left(a \cdot c\right)}^{4} + {\left(a \cdot c\right)}^{4} \cdot 1.265625}{a \cdot {b}^{7}}\right)\right) \]

   6. pow2 97.0%

      \[\leadsto -0.5625 \cdot \frac{{a}^{2} \cdot {c}^{3}}{{b}^{5}} + \left(-0.5 \cdot \frac{c}{b} + \left(-0.375 \cdot \left(\color{blue}{{\left(\frac{c}{b}\right)}^{2}} \cdot \frac{a}{b}\right) + -0.16666666666666666 \cdot \frac{5.0625 \cdot {\left(a \cdot c\right)}^{4} + {\left(a \cdot c\right)}^{4} \cdot 1.265625}{a \cdot {b}^{7}}\right)\right) \]

4. Applied egg-rr 94.7%

   \[\leadsto -0.5 \cdot \frac{c}{b} + -0.375 \cdot \color{blue}{\left({\left(\frac{c}{b}\right)}^{2} \cdot \frac{a}{b}\right)} \]

5. Final simplification 94.7%

   \[\leadsto -0.5 \cdot \frac{c}{b} + -0.375 \cdot \left({\left(\frac{c}{b}\right)}^{2} \cdot \frac{a}{b}\right) \]

Alternative 4: 90.1% accurate, 23.2× speedup

Math

\[\begin{array}{l} \\ c \cdot \frac{-0.5}{b} \end{array} \]

FPCore

(FPCore (a b c) :precision binary64 (* c (/ -0.5 b)))

C

double code(double a, double b, double c) {
	return c * (-0.5 / 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 * ((-0.5d0) / b)
end function

Java

public static double code(double a, double b, double c) {
	return c * (-0.5 / b);
}

Python

def code(a, b, c):
	return c * (-0.5 / b)

Julia

function code(a, b, c)
	return Float64(c * Float64(-0.5 / b))
end

MATLAB

function tmp = code(a, b, c)
	tmp = c * (-0.5 / b);
end

Wolfram

code[a_, b_, c_] := N[(c * N[(-0.5 / b), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 19.1%

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

2. Taylor expanded in b around inf 89.0%

   \[\leadsto \frac{\color{blue}{-1.5 \cdot \frac{a \cdot c}{b}}}{3 \cdot a} \]
3. Step-by-step derivation

   1. associate-*r/ 89.0%

      \[\leadsto \frac{\color{blue}{\frac{-1.5 \cdot \left(a \cdot c\right)}{b}}}{3 \cdot a} \]

   2. associate-/l* 89.0%

      \[\leadsto \frac{\color{blue}{\frac{-1.5}{\frac{b}{a \cdot c}}}}{3 \cdot a} \]

4. Simplified 89.0%

   \[\leadsto \frac{\color{blue}{\frac{-1.5}{\frac{b}{a \cdot c}}}}{3 \cdot a} \]
5. Step-by-step derivation

   1. frac-2neg 89.0%

      \[\leadsto \color{blue}{\frac{-\frac{-1.5}{\frac{b}{a \cdot c}}}{-3 \cdot a}} \]

   2. div-inv 88.9%

      \[\leadsto \color{blue}{\left(-\frac{-1.5}{\frac{b}{a \cdot c}}\right) \cdot \frac{1}{-3 \cdot a}} \]

   3. div-inv 88.9%

      \[\leadsto \left(-\color{blue}{-1.5 \cdot \frac{1}{\frac{b}{a \cdot c}}}\right) \cdot \frac{1}{-3 \cdot a} \]

   4. distribute-lft-neg-in 88.9%

      \[\leadsto \color{blue}{\left(\left(--1.5\right) \cdot \frac{1}{\frac{b}{a \cdot c}}\right)} \cdot \frac{1}{-3 \cdot a} \]

   5. metadata-eval 88.9%

      \[\leadsto \left(\color{blue}{1.5} \cdot \frac{1}{\frac{b}{a \cdot c}}\right) \cdot \frac{1}{-3 \cdot a} \]

   6. clear-num 89.0%

      \[\leadsto \left(1.5 \cdot \color{blue}{\frac{a \cdot c}{b}}\right) \cdot \frac{1}{-3 \cdot a} \]

   7. *-commutative 89.0%

      \[\leadsto \left(1.5 \cdot \frac{a \cdot c}{b}\right) \cdot \frac{1}{-\color{blue}{a \cdot 3}} \]

   8. distribute-rgt-neg-in 89.0%

      \[\leadsto \left(1.5 \cdot \frac{a \cdot c}{b}\right) \cdot \frac{1}{\color{blue}{a \cdot \left(-3\right)}} \]

   9. metadata-eval 89.0%

      \[\leadsto \left(1.5 \cdot \frac{a \cdot c}{b}\right) \cdot \frac{1}{a \cdot \color{blue}{-3}} \]

6. Applied egg-rr 89.0%

   \[\leadsto \color{blue}{\left(1.5 \cdot \frac{a \cdot c}{b}\right) \cdot \frac{1}{a \cdot -3}} \]
7. Step-by-step derivation

   1. associate-*r/ 89.0%

      \[\leadsto \color{blue}{\frac{\left(1.5 \cdot \frac{a \cdot c}{b}\right) \cdot 1}{a \cdot -3}} \]

   2. *-rgt-identity 89.0%

      \[\leadsto \frac{\color{blue}{1.5 \cdot \frac{a \cdot c}{b}}}{a \cdot -3} \]

   3. metadata-eval 89.0%

      \[\leadsto \frac{\color{blue}{\left(--1.5\right)} \cdot \frac{a \cdot c}{b}}{a \cdot -3} \]

   4. distribute-lft-neg-in 89.0%

      \[\leadsto \frac{\color{blue}{--1.5 \cdot \frac{a \cdot c}{b}}}{a \cdot -3} \]

   5. associate-*r/ 89.0%

      \[\leadsto \frac{-\color{blue}{\frac{-1.5 \cdot \left(a \cdot c\right)}{b}}}{a \cdot -3} \]

   6. associate-*l/ 89.0%

      \[\leadsto \frac{-\color{blue}{\frac{-1.5}{b} \cdot \left(a \cdot c\right)}}{a \cdot -3} \]

   7. distribute-neg-frac 89.0%

      \[\leadsto \color{blue}{-\frac{\frac{-1.5}{b} \cdot \left(a \cdot c\right)}{a \cdot -3}} \]

   8. associate-/r* 89.0%

      \[\leadsto -\color{blue}{\frac{\frac{\frac{-1.5}{b} \cdot \left(a \cdot c\right)}{a}}{-3}} \]

   9. distribute-neg-frac 89.0%

      \[\leadsto \color{blue}{\frac{-\frac{\frac{-1.5}{b} \cdot \left(a \cdot c\right)}{a}}{-3}} \]

   10. associate-*r/ 89.1%

       \[\leadsto \frac{-\color{blue}{\frac{-1.5}{b} \cdot \frac{a \cdot c}{a}}}{-3} \]

   11. associate-/l* 89.1%

       \[\leadsto \frac{-\frac{-1.5}{b} \cdot \color{blue}{\frac{a}{\frac{a}{c}}}}{-3} \]

   12. associate-/r/ 89.1%

       \[\leadsto \frac{-\frac{-1.5}{b} \cdot \color{blue}{\left(\frac{a}{a} \cdot c\right)}}{-3} \]

   13. *-inverses 89.1%

       \[\leadsto \frac{-\frac{-1.5}{b} \cdot \left(\color{blue}{1} \cdot c\right)}{-3} \]

   14. associate-*l* 89.1%

       \[\leadsto \frac{-\color{blue}{\left(\frac{-1.5}{b} \cdot 1\right) \cdot c}}{-3} \]

   15. *-rgt-identity 89.1%

       \[\leadsto \frac{-\color{blue}{\frac{-1.5}{b}} \cdot c}{-3} \]

   16. *-commutative 89.1%

       \[\leadsto \frac{-\color{blue}{c \cdot \frac{-1.5}{b}}}{-3} \]

8. Simplified 89.1%

   \[\leadsto \color{blue}{\frac{-c \cdot \frac{-1.5}{b}}{-3}} \]

9. Taylor expanded in c around 0 89.6%

   \[\leadsto \color{blue}{-0.5 \cdot \frac{c}{b}} \]
10. Step-by-step derivation

    1. metadata-eval 89.6%

       \[\leadsto \color{blue}{\left(0.3333333333333333 \cdot -1.5\right)} \cdot \frac{c}{b} \]

    2. associate-*r* 89.2%

       \[\leadsto \color{blue}{0.3333333333333333 \cdot \left(-1.5 \cdot \frac{c}{b}\right)} \]

    3. *-commutative 89.2%

       \[\leadsto \color{blue}{\left(-1.5 \cdot \frac{c}{b}\right) \cdot 0.3333333333333333} \]

    4. associate-*r/ 89.0%

       \[\leadsto \color{blue}{\frac{-1.5 \cdot c}{b}} \cdot 0.3333333333333333 \]

    5. *-commutative 89.0%

       \[\leadsto \frac{\color{blue}{c \cdot -1.5}}{b} \cdot 0.3333333333333333 \]

    6. associate-*r/ 89.0%

       \[\leadsto \color{blue}{\left(c \cdot \frac{-1.5}{b}\right)} \cdot 0.3333333333333333 \]

    7. associate-*l* 89.0%

       \[\leadsto \color{blue}{c \cdot \left(\frac{-1.5}{b} \cdot 0.3333333333333333\right)} \]

    8. associate-*l/ 89.2%

       \[\leadsto c \cdot \color{blue}{\frac{-1.5 \cdot 0.3333333333333333}{b}} \]

    9. metadata-eval 89.2%

       \[\leadsto c \cdot \frac{\color{blue}{-0.5}}{b} \]

11. Simplified 89.2%

    \[\leadsto \color{blue}{c \cdot \frac{-0.5}{b}} \]

12. Final simplification 89.2%

    \[\leadsto c \cdot \frac{-0.5}{b} \]

Alternative 5: 90.4% accurate, 23.2× speedup

Math

\[\begin{array}{l} \\ \frac{c \cdot -0.5}{b} \end{array} \]

FPCore

(FPCore (a b c) :precision binary64 (/ (* c -0.5) b))

C

double code(double a, double b, double c) {
	return (c * -0.5) / 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 * (-0.5d0)) / b
end function

Java

public static double code(double a, double b, double c) {
	return (c * -0.5) / b;
}

Python

def code(a, b, c):
	return (c * -0.5) / b

Julia

function code(a, b, c)
	return Float64(Float64(c * -0.5) / b)
end

MATLAB

function tmp = code(a, b, c)
	tmp = (c * -0.5) / b;
end

Wolfram

code[a_, b_, c_] := N[(N[(c * -0.5), $MachinePrecision] / b), $MachinePrecision]

Derivation

1. Initial program 19.1%

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

2. Taylor expanded in b around inf 89.6%

   \[\leadsto \color{blue}{-0.5 \cdot \frac{c}{b}} \]
3. Step-by-step derivation

   1. *-commutative 89.6%

      \[\leadsto \color{blue}{\frac{c}{b} \cdot -0.5} \]

   2. associate-*l/ 89.6%

      \[\leadsto \color{blue}{\frac{c \cdot -0.5}{b}} \]

4. Simplified 89.6%

   \[\leadsto \color{blue}{\frac{c \cdot -0.5}{b}} \]

5. Final simplification 89.6%

   \[\leadsto \frac{c \cdot -0.5}{b} \]

Reproduce

herbie shell --seed 2023314 
(FPCore (a b c)
  :name "Cubic critical, 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) (* (* 3.0 a) c)))) (* 3.0 a)))