Q^3 (Cubic Equation Discriminant Part)

Percentage Accurate: 80.4% → 99.3%

Time: 5.3s

Alternatives: 12

Speedup: 1.8×

• 56.4% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

\[\left(\left(-1000000000 \leq a \land a \leq 1000000000\right) \land \left(-1000000000 \leq b \land b \leq 1000000000\right)\right) \land \left(-1000000000 \leq c \land c \leq 1000000000\right)\]

Math

\[{\left(\frac{3 \cdot \left(a \cdot c\right) - {b}^{2}}{9 \cdot {a}^{2}}\right)}^{3} \]

FPCore

(FPCore (a b c)
 :precision binary64
 (pow (/ (- (* 3.0 (* a c)) (pow b 2.0)) (* 9.0 (pow a 2.0))) 3.0))

C

double code(double a, double b, double c) {
	return pow((((3.0 * (a * c)) - pow(b, 2.0)) / (9.0 * pow(a, 2.0))), 3.0);
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(a, b, c)
use fmin_fmax_functions
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    code = (((3.0d0 * (a * c)) - (b ** 2.0d0)) / (9.0d0 * (a ** 2.0d0))) ** 3.0d0
end function

Java

public static double code(double a, double b, double c) {
	return Math.pow((((3.0 * (a * c)) - Math.pow(b, 2.0)) / (9.0 * Math.pow(a, 2.0))), 3.0);
}

Python

def code(a, b, c):
	return math.pow((((3.0 * (a * c)) - math.pow(b, 2.0)) / (9.0 * math.pow(a, 2.0))), 3.0)

Julia

function code(a, b, c)
	return Float64(Float64(Float64(3.0 * Float64(a * c)) - (b ^ 2.0)) / Float64(9.0 * (a ^ 2.0))) ^ 3.0
end

MATLAB

function tmp = code(a, b, c)
	tmp = (((3.0 * (a * c)) - (b ^ 2.0)) / (9.0 * (a ^ 2.0))) ^ 3.0;
end

Wolfram

code[a_, b_, c_] := N[Power[N[(N[(N[(3.0 * N[(a * c), $MachinePrecision]), $MachinePrecision] - N[Power[b, 2.0], $MachinePrecision]), $MachinePrecision] / N[(9.0 * N[Power[a, 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 3.0], $MachinePrecision]

Initial Program: 80.4% accurate, 1.0× speedup

Math

\[{\left(\frac{3 \cdot \left(a \cdot c\right) - {b}^{2}}{9 \cdot {a}^{2}}\right)}^{3} \]

FPCore

(FPCore (a b c)
 :precision binary64
 (pow (/ (- (* 3.0 (* a c)) (pow b 2.0)) (* 9.0 (pow a 2.0))) 3.0))

C

double code(double a, double b, double c) {
	return pow((((3.0 * (a * c)) - pow(b, 2.0)) / (9.0 * pow(a, 2.0))), 3.0);
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(a, b, c)
use fmin_fmax_functions
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    code = (((3.0d0 * (a * c)) - (b ** 2.0d0)) / (9.0d0 * (a ** 2.0d0))) ** 3.0d0
end function

Java

public static double code(double a, double b, double c) {
	return Math.pow((((3.0 * (a * c)) - Math.pow(b, 2.0)) / (9.0 * Math.pow(a, 2.0))), 3.0);
}

Python

def code(a, b, c):
	return math.pow((((3.0 * (a * c)) - math.pow(b, 2.0)) / (9.0 * math.pow(a, 2.0))), 3.0)

Julia

function code(a, b, c)
	return Float64(Float64(Float64(3.0 * Float64(a * c)) - (b ^ 2.0)) / Float64(9.0 * (a ^ 2.0))) ^ 3.0
end

MATLAB

function tmp = code(a, b, c)
	tmp = (((3.0 * (a * c)) - (b ^ 2.0)) / (9.0 * (a ^ 2.0))) ^ 3.0;
end

Wolfram

code[a_, b_, c_] := N[Power[N[(N[(N[(3.0 * N[(a * c), $MachinePrecision]), $MachinePrecision] - N[Power[b, 2.0], $MachinePrecision]), $MachinePrecision] / N[(9.0 * N[Power[a, 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 3.0], $MachinePrecision]

Alternative 1: 99.3% accurate, 1.2× speedup

Math

\[\begin{array}{l} t_0 := \mathsf{fma}\left(-3, c, \frac{b}{a} \cdot b\right)\\ \frac{t\_0 \cdot -0.1111111111111111}{a} \cdot \left({\left(\frac{t\_0}{a}\right)}^{2} \cdot 0.012345679012345678\right) \end{array} \]

FPCore

(FPCore (a b c)
 :precision binary64
 (let* ((t_0 (fma -3.0 c (* (/ b a) b))))
   (*
    (/ (* t_0 -0.1111111111111111) a)
    (* (pow (/ t_0 a) 2.0) 0.012345679012345678))))

C

double code(double a, double b, double c) {
	double t_0 = fma(-3.0, c, ((b / a) * b));
	return ((t_0 * -0.1111111111111111) / a) * (pow((t_0 / a), 2.0) * 0.012345679012345678);
}

Julia

function code(a, b, c)
	t_0 = fma(-3.0, c, Float64(Float64(b / a) * b))
	return Float64(Float64(Float64(t_0 * -0.1111111111111111) / a) * Float64((Float64(t_0 / a) ^ 2.0) * 0.012345679012345678))
end

Wolfram

code[a_, b_, c_] := Block[{t$95$0 = N[(-3.0 * c + N[(N[(b / a), $MachinePrecision] * b), $MachinePrecision]), $MachinePrecision]}, N[(N[(N[(t$95$0 * -0.1111111111111111), $MachinePrecision] / a), $MachinePrecision] * N[(N[Power[N[(t$95$0 / a), $MachinePrecision], 2.0], $MachinePrecision] * 0.012345679012345678), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Initial program 80.4%

   \[{\left(\frac{3 \cdot \left(a \cdot c\right) - {b}^{2}}{9 \cdot {a}^{2}}\right)}^{3} \]
2. Step-by-step derivation

   1. lift-/.f64 N/A

      \[\leadsto {\color{blue}{\left(\frac{3 \cdot \left(a \cdot c\right) - {b}^{2}}{9 \cdot {a}^{2}}\right)}}^{3} \]

   2. frac-2neg N/A

      \[\leadsto {\color{blue}{\left(\frac{\mathsf{neg}\left(\left(3 \cdot \left(a \cdot c\right) - {b}^{2}\right)\right)}{\mathsf{neg}\left(9 \cdot {a}^{2}\right)}\right)}}^{3} \]

   3. lift-*.f64 N/A

      \[\leadsto {\left(\frac{\mathsf{neg}\left(\left(3 \cdot \left(a \cdot c\right) - {b}^{2}\right)\right)}{\mathsf{neg}\left(\color{blue}{9 \cdot {a}^{2}}\right)}\right)}^{3} \]

   4. *-commutative N/A

      \[\leadsto {\left(\frac{\mathsf{neg}\left(\left(3 \cdot \left(a \cdot c\right) - {b}^{2}\right)\right)}{\mathsf{neg}\left(\color{blue}{{a}^{2} \cdot 9}\right)}\right)}^{3} \]

   5. distribute-rgt-neg-in N/A

      \[\leadsto {\left(\frac{\mathsf{neg}\left(\left(3 \cdot \left(a \cdot c\right) - {b}^{2}\right)\right)}{\color{blue}{{a}^{2} \cdot \left(\mathsf{neg}\left(9\right)\right)}}\right)}^{3} \]

   6. associate-/r* N/A

      \[\leadsto {\color{blue}{\left(\frac{\frac{\mathsf{neg}\left(\left(3 \cdot \left(a \cdot c\right) - {b}^{2}\right)\right)}{{a}^{2}}}{\mathsf{neg}\left(9\right)}\right)}}^{3} \]

   7. lower-/.f64 N/A

      \[\leadsto {\color{blue}{\left(\frac{\frac{\mathsf{neg}\left(\left(3 \cdot \left(a \cdot c\right) - {b}^{2}\right)\right)}{{a}^{2}}}{\mathsf{neg}\left(9\right)}\right)}}^{3} \]

3. Applied rewrites 80.4%

   \[\leadsto {\color{blue}{\left(\frac{\frac{\mathsf{fma}\left(-3, c \cdot a, b \cdot b\right)}{a \cdot a}}{-9}\right)}}^{3} \]
4. Step-by-step derivation

   1. lift-pow.f64 N/A

      \[\leadsto \color{blue}{{\left(\frac{\frac{\mathsf{fma}\left(-3, c \cdot a, b \cdot b\right)}{a \cdot a}}{-9}\right)}^{3}} \]

   2. cube-mult N/A

      \[\leadsto \color{blue}{\frac{\frac{\mathsf{fma}\left(-3, c \cdot a, b \cdot b\right)}{a \cdot a}}{-9} \cdot \left(\frac{\frac{\mathsf{fma}\left(-3, c \cdot a, b \cdot b\right)}{a \cdot a}}{-9} \cdot \frac{\frac{\mathsf{fma}\left(-3, c \cdot a, b \cdot b\right)}{a \cdot a}}{-9}\right)} \]

   3. lower-*.f64 N/A

      \[\leadsto \color{blue}{\frac{\frac{\mathsf{fma}\left(-3, c \cdot a, b \cdot b\right)}{a \cdot a}}{-9} \cdot \left(\frac{\frac{\mathsf{fma}\left(-3, c \cdot a, b \cdot b\right)}{a \cdot a}}{-9} \cdot \frac{\frac{\mathsf{fma}\left(-3, c \cdot a, b \cdot b\right)}{a \cdot a}}{-9}\right)} \]

5. Applied rewrites 99.3%

   \[\leadsto \color{blue}{\frac{\mathsf{fma}\left(-3, c, \frac{b}{a} \cdot b\right) \cdot -0.1111111111111111}{a} \cdot \left({\left(\frac{\mathsf{fma}\left(-3, c, \frac{b}{a} \cdot b\right)}{a}\right)}^{2} \cdot 0.012345679012345678\right)} \]
6. Add Preprocessing

Alternative 2: 99.2% accurate, 1.2× speedup

Math

\[\begin{array}{l} t_0 := \frac{\mathsf{fma}\left(-3, c, \frac{b}{a} \cdot b\right)}{a}\\ \left(\left({t\_0}^{2} \cdot 0.012345679012345678\right) \cdot t\_0\right) \cdot -0.1111111111111111 \end{array} \]

FPCore

(FPCore (a b c)
 :precision binary64
 (let* ((t_0 (/ (fma -3.0 c (* (/ b a) b)) a)))
   (* (* (* (pow t_0 2.0) 0.012345679012345678) t_0) -0.1111111111111111)))

C

double code(double a, double b, double c) {
	double t_0 = fma(-3.0, c, ((b / a) * b)) / a;
	return ((pow(t_0, 2.0) * 0.012345679012345678) * t_0) * -0.1111111111111111;
}

Julia

function code(a, b, c)
	t_0 = Float64(fma(-3.0, c, Float64(Float64(b / a) * b)) / a)
	return Float64(Float64(Float64((t_0 ^ 2.0) * 0.012345679012345678) * t_0) * -0.1111111111111111)
end

Wolfram

code[a_, b_, c_] := Block[{t$95$0 = N[(N[(-3.0 * c + N[(N[(b / a), $MachinePrecision] * b), $MachinePrecision]), $MachinePrecision] / a), $MachinePrecision]}, N[(N[(N[(N[Power[t$95$0, 2.0], $MachinePrecision] * 0.012345679012345678), $MachinePrecision] * t$95$0), $MachinePrecision] * -0.1111111111111111), $MachinePrecision]]

Derivation

1. Initial program 80.4%

   \[{\left(\frac{3 \cdot \left(a \cdot c\right) - {b}^{2}}{9 \cdot {a}^{2}}\right)}^{3} \]
2. Step-by-step derivation

   1. lift-/.f64 N/A

      \[\leadsto {\color{blue}{\left(\frac{3 \cdot \left(a \cdot c\right) - {b}^{2}}{9 \cdot {a}^{2}}\right)}}^{3} \]

   2. frac-2neg N/A

      \[\leadsto {\color{blue}{\left(\frac{\mathsf{neg}\left(\left(3 \cdot \left(a \cdot c\right) - {b}^{2}\right)\right)}{\mathsf{neg}\left(9 \cdot {a}^{2}\right)}\right)}}^{3} \]

   3. lift-*.f64 N/A

      \[\leadsto {\left(\frac{\mathsf{neg}\left(\left(3 \cdot \left(a \cdot c\right) - {b}^{2}\right)\right)}{\mathsf{neg}\left(\color{blue}{9 \cdot {a}^{2}}\right)}\right)}^{3} \]

   4. *-commutative N/A

      \[\leadsto {\left(\frac{\mathsf{neg}\left(\left(3 \cdot \left(a \cdot c\right) - {b}^{2}\right)\right)}{\mathsf{neg}\left(\color{blue}{{a}^{2} \cdot 9}\right)}\right)}^{3} \]

   5. distribute-rgt-neg-in N/A

      \[\leadsto {\left(\frac{\mathsf{neg}\left(\left(3 \cdot \left(a \cdot c\right) - {b}^{2}\right)\right)}{\color{blue}{{a}^{2} \cdot \left(\mathsf{neg}\left(9\right)\right)}}\right)}^{3} \]

   6. associate-/r* N/A

      \[\leadsto {\color{blue}{\left(\frac{\frac{\mathsf{neg}\left(\left(3 \cdot \left(a \cdot c\right) - {b}^{2}\right)\right)}{{a}^{2}}}{\mathsf{neg}\left(9\right)}\right)}}^{3} \]

   7. lower-/.f64 N/A

      \[\leadsto {\color{blue}{\left(\frac{\frac{\mathsf{neg}\left(\left(3 \cdot \left(a \cdot c\right) - {b}^{2}\right)\right)}{{a}^{2}}}{\mathsf{neg}\left(9\right)}\right)}}^{3} \]

3. Applied rewrites 80.4%

   \[\leadsto {\color{blue}{\left(\frac{\frac{\mathsf{fma}\left(-3, c \cdot a, b \cdot b\right)}{a \cdot a}}{-9}\right)}}^{3} \]
4. Step-by-step derivation

   1. lift-pow.f64 N/A

      \[\leadsto \color{blue}{{\left(\frac{\frac{\mathsf{fma}\left(-3, c \cdot a, b \cdot b\right)}{a \cdot a}}{-9}\right)}^{3}} \]

   2. unpow3 N/A

      \[\leadsto \color{blue}{\left(\frac{\frac{\mathsf{fma}\left(-3, c \cdot a, b \cdot b\right)}{a \cdot a}}{-9} \cdot \frac{\frac{\mathsf{fma}\left(-3, c \cdot a, b \cdot b\right)}{a \cdot a}}{-9}\right) \cdot \frac{\frac{\mathsf{fma}\left(-3, c \cdot a, b \cdot b\right)}{a \cdot a}}{-9}} \]

   3. lift-/.f64 N/A

      \[\leadsto \left(\frac{\frac{\mathsf{fma}\left(-3, c \cdot a, b \cdot b\right)}{a \cdot a}}{-9} \cdot \frac{\frac{\mathsf{fma}\left(-3, c \cdot a, b \cdot b\right)}{a \cdot a}}{-9}\right) \cdot \color{blue}{\frac{\frac{\mathsf{fma}\left(-3, c \cdot a, b \cdot b\right)}{a \cdot a}}{-9}} \]

   4. mult-flip N/A

      \[\leadsto \left(\frac{\frac{\mathsf{fma}\left(-3, c \cdot a, b \cdot b\right)}{a \cdot a}}{-9} \cdot \frac{\frac{\mathsf{fma}\left(-3, c \cdot a, b \cdot b\right)}{a \cdot a}}{-9}\right) \cdot \color{blue}{\left(\frac{\mathsf{fma}\left(-3, c \cdot a, b \cdot b\right)}{a \cdot a} \cdot \frac{1}{-9}\right)} \]

   5. associate-*r* N/A

      \[\leadsto \color{blue}{\left(\left(\frac{\frac{\mathsf{fma}\left(-3, c \cdot a, b \cdot b\right)}{a \cdot a}}{-9} \cdot \frac{\frac{\mathsf{fma}\left(-3, c \cdot a, b \cdot b\right)}{a \cdot a}}{-9}\right) \cdot \frac{\mathsf{fma}\left(-3, c \cdot a, b \cdot b\right)}{a \cdot a}\right) \cdot \frac{1}{-9}} \]

   6. lower-*.f64 N/A

      \[\leadsto \color{blue}{\left(\left(\frac{\frac{\mathsf{fma}\left(-3, c \cdot a, b \cdot b\right)}{a \cdot a}}{-9} \cdot \frac{\frac{\mathsf{fma}\left(-3, c \cdot a, b \cdot b\right)}{a \cdot a}}{-9}\right) \cdot \frac{\mathsf{fma}\left(-3, c \cdot a, b \cdot b\right)}{a \cdot a}\right) \cdot \frac{1}{-9}} \]

5. Applied rewrites 99.2%

   \[\leadsto \color{blue}{\left(\left({\left(\frac{\mathsf{fma}\left(-3, c, \frac{b}{a} \cdot b\right)}{a}\right)}^{2} \cdot 0.012345679012345678\right) \cdot \frac{\mathsf{fma}\left(-3, c, \frac{b}{a} \cdot b\right)}{a}\right) \cdot -0.1111111111111111} \]
6. Add Preprocessing

Alternative 3: 99.2% accurate, 1.8× speedup

Math

\[{\left(\frac{\mathsf{fma}\left(\frac{b}{a}, b, c \cdot -3\right)}{a} \cdot -0.1111111111111111\right)}^{3} \]

FPCore

(FPCore (a b c)
 :precision binary64
 (pow (* (/ (fma (/ b a) b (* c -3.0)) a) -0.1111111111111111) 3.0))

C

double code(double a, double b, double c) {
	return pow(((fma((b / a), b, (c * -3.0)) / a) * -0.1111111111111111), 3.0);
}

Julia

function code(a, b, c)
	return Float64(Float64(fma(Float64(b / a), b, Float64(c * -3.0)) / a) * -0.1111111111111111) ^ 3.0
end

Wolfram

code[a_, b_, c_] := N[Power[N[(N[(N[(N[(b / a), $MachinePrecision] * b + N[(c * -3.0), $MachinePrecision]), $MachinePrecision] / a), $MachinePrecision] * -0.1111111111111111), $MachinePrecision], 3.0], $MachinePrecision]

Derivation

1. Initial program 80.4%

   \[{\left(\frac{3 \cdot \left(a \cdot c\right) - {b}^{2}}{9 \cdot {a}^{2}}\right)}^{3} \]
2. Step-by-step derivation

   1. lift-/.f64 N/A

      \[\leadsto {\color{blue}{\left(\frac{3 \cdot \left(a \cdot c\right) - {b}^{2}}{9 \cdot {a}^{2}}\right)}}^{3} \]

   2. frac-2neg N/A

      \[\leadsto {\color{blue}{\left(\frac{\mathsf{neg}\left(\left(3 \cdot \left(a \cdot c\right) - {b}^{2}\right)\right)}{\mathsf{neg}\left(9 \cdot {a}^{2}\right)}\right)}}^{3} \]

   3. lift-*.f64 N/A

      \[\leadsto {\left(\frac{\mathsf{neg}\left(\left(3 \cdot \left(a \cdot c\right) - {b}^{2}\right)\right)}{\mathsf{neg}\left(\color{blue}{9 \cdot {a}^{2}}\right)}\right)}^{3} \]

   4. *-commutative N/A

      \[\leadsto {\left(\frac{\mathsf{neg}\left(\left(3 \cdot \left(a \cdot c\right) - {b}^{2}\right)\right)}{\mathsf{neg}\left(\color{blue}{{a}^{2} \cdot 9}\right)}\right)}^{3} \]

   5. distribute-rgt-neg-in N/A

      \[\leadsto {\left(\frac{\mathsf{neg}\left(\left(3 \cdot \left(a \cdot c\right) - {b}^{2}\right)\right)}{\color{blue}{{a}^{2} \cdot \left(\mathsf{neg}\left(9\right)\right)}}\right)}^{3} \]

   6. associate-/r* N/A

      \[\leadsto {\color{blue}{\left(\frac{\frac{\mathsf{neg}\left(\left(3 \cdot \left(a \cdot c\right) - {b}^{2}\right)\right)}{{a}^{2}}}{\mathsf{neg}\left(9\right)}\right)}}^{3} \]

   7. lower-/.f64 N/A

      \[\leadsto {\color{blue}{\left(\frac{\frac{\mathsf{neg}\left(\left(3 \cdot \left(a \cdot c\right) - {b}^{2}\right)\right)}{{a}^{2}}}{\mathsf{neg}\left(9\right)}\right)}}^{3} \]

3. Applied rewrites 80.4%

   \[\leadsto {\color{blue}{\left(\frac{\frac{\mathsf{fma}\left(-3, c \cdot a, b \cdot b\right)}{a \cdot a}}{-9}\right)}}^{3} \]
4. Step-by-step derivation

   1. lift-/.f64 N/A

      \[\leadsto {\color{blue}{\left(\frac{\frac{\mathsf{fma}\left(-3, c \cdot a, b \cdot b\right)}{a \cdot a}}{-9}\right)}}^{3} \]

   2. mult-flip N/A

      \[\leadsto {\color{blue}{\left(\frac{\mathsf{fma}\left(-3, c \cdot a, b \cdot b\right)}{a \cdot a} \cdot \frac{1}{-9}\right)}}^{3} \]

   3. lower-*.f64 N/A

      \[\leadsto {\color{blue}{\left(\frac{\mathsf{fma}\left(-3, c \cdot a, b \cdot b\right)}{a \cdot a} \cdot \frac{1}{-9}\right)}}^{3} \]

   4. lift-/.f64 N/A

      \[\leadsto {\left(\color{blue}{\frac{\mathsf{fma}\left(-3, c \cdot a, b \cdot b\right)}{a \cdot a}} \cdot \frac{1}{-9}\right)}^{3} \]

   5. lift-*.f64 N/A

      \[\leadsto {\left(\frac{\mathsf{fma}\left(-3, c \cdot a, b \cdot b\right)}{\color{blue}{a \cdot a}} \cdot \frac{1}{-9}\right)}^{3} \]

   6. associate-/r* N/A

      \[\leadsto {\left(\color{blue}{\frac{\frac{\mathsf{fma}\left(-3, c \cdot a, b \cdot b\right)}{a}}{a}} \cdot \frac{1}{-9}\right)}^{3} \]

   7. lower-/.f64 N/A

      \[\leadsto {\left(\color{blue}{\frac{\frac{\mathsf{fma}\left(-3, c \cdot a, b \cdot b\right)}{a}}{a}} \cdot \frac{1}{-9}\right)}^{3} \]

   8. lift-fma.f64 N/A

      \[\leadsto {\left(\frac{\frac{\color{blue}{-3 \cdot \left(c \cdot a\right) + b \cdot b}}{a}}{a} \cdot \frac{1}{-9}\right)}^{3} \]

   9. lift-*.f64 N/A

      \[\leadsto {\left(\frac{\frac{-3 \cdot \color{blue}{\left(c \cdot a\right)} + b \cdot b}{a}}{a} \cdot \frac{1}{-9}\right)}^{3} \]

   10. associate-*r* N/A

       \[\leadsto {\left(\frac{\frac{\color{blue}{\left(-3 \cdot c\right) \cdot a} + b \cdot b}{a}}{a} \cdot \frac{1}{-9}\right)}^{3} \]

   11. add-to-fraction-rev N/A

       \[\leadsto {\left(\frac{\color{blue}{-3 \cdot c + \frac{b \cdot b}{a}}}{a} \cdot \frac{1}{-9}\right)}^{3} \]

   12. lift-*.f64 N/A

       \[\leadsto {\left(\frac{-3 \cdot c + \frac{\color{blue}{b \cdot b}}{a}}{a} \cdot \frac{1}{-9}\right)}^{3} \]

   13. associate-*r/ N/A

       \[\leadsto {\left(\frac{-3 \cdot c + \color{blue}{b \cdot \frac{b}{a}}}{a} \cdot \frac{1}{-9}\right)}^{3} \]

   14. lift-/.f64 N/A

       \[\leadsto {\left(\frac{-3 \cdot c + b \cdot \color{blue}{\frac{b}{a}}}{a} \cdot \frac{1}{-9}\right)}^{3} \]

   15. lift-*.f64 N/A

       \[\leadsto {\left(\frac{-3 \cdot c + \color{blue}{b \cdot \frac{b}{a}}}{a} \cdot \frac{1}{-9}\right)}^{3} \]

   16. lower-fma.f64 N/A

       \[\leadsto {\left(\frac{\color{blue}{\mathsf{fma}\left(-3, c, b \cdot \frac{b}{a}\right)}}{a} \cdot \frac{1}{-9}\right)}^{3} \]

   17. lift-*.f64 N/A

       \[\leadsto {\left(\frac{\mathsf{fma}\left(-3, c, \color{blue}{b \cdot \frac{b}{a}}\right)}{a} \cdot \frac{1}{-9}\right)}^{3} \]

   18. *-commutative N/A

       \[\leadsto {\left(\frac{\mathsf{fma}\left(-3, c, \color{blue}{\frac{b}{a} \cdot b}\right)}{a} \cdot \frac{1}{-9}\right)}^{3} \]

   19. lower-*.f64 N/A

       \[\leadsto {\left(\frac{\mathsf{fma}\left(-3, c, \color{blue}{\frac{b}{a} \cdot b}\right)}{a} \cdot \frac{1}{-9}\right)}^{3} \]

   20. metadata-eval 99.2%

       \[\leadsto {\left(\frac{\mathsf{fma}\left(-3, c, \frac{b}{a} \cdot b\right)}{a} \cdot \color{blue}{-0.1111111111111111}\right)}^{3} \]

5. Applied rewrites 99.2%

   \[\leadsto {\color{blue}{\left(\frac{\mathsf{fma}\left(-3, c, \frac{b}{a} \cdot b\right)}{a} \cdot -0.1111111111111111\right)}}^{3} \]
6. Step-by-step derivation

   1. lift-fma.f64 N/A

      \[\leadsto {\left(\frac{\color{blue}{-3 \cdot c + \frac{b}{a} \cdot b}}{a} \cdot \frac{-1}{9}\right)}^{3} \]

   2. +-commutative N/A

      \[\leadsto {\left(\frac{\color{blue}{\frac{b}{a} \cdot b + -3 \cdot c}}{a} \cdot \frac{-1}{9}\right)}^{3} \]

   3. lift-*.f64 N/A

      \[\leadsto {\left(\frac{\color{blue}{\frac{b}{a} \cdot b} + -3 \cdot c}{a} \cdot \frac{-1}{9}\right)}^{3} \]

   4. lower-fma.f64 N/A

      \[\leadsto {\left(\frac{\color{blue}{\mathsf{fma}\left(\frac{b}{a}, b, -3 \cdot c\right)}}{a} \cdot \frac{-1}{9}\right)}^{3} \]

   5. *-commutative N/A

      \[\leadsto {\left(\frac{\mathsf{fma}\left(\frac{b}{a}, b, \color{blue}{c \cdot -3}\right)}{a} \cdot \frac{-1}{9}\right)}^{3} \]

   6. lower-*.f64 99.2%

      \[\leadsto {\left(\frac{\mathsf{fma}\left(\frac{b}{a}, b, \color{blue}{c \cdot -3}\right)}{a} \cdot -0.1111111111111111\right)}^{3} \]

7. Applied rewrites 99.2%

   \[\leadsto {\left(\frac{\color{blue}{\mathsf{fma}\left(\frac{b}{a}, b, c \cdot -3\right)}}{a} \cdot -0.1111111111111111\right)}^{3} \]
8. Add Preprocessing

Alternative 4: 99.2% accurate, 1.8× speedup

Math

\[{\left(\frac{\mathsf{fma}\left(-0.1111111111111111 \cdot \frac{b}{a}, b, 0.3333333333333333 \cdot c\right)}{a}\right)}^{3} \]

FPCore

(FPCore (a b c)
 :precision binary64
 (pow
  (/ (fma (* -0.1111111111111111 (/ b a)) b (* 0.3333333333333333 c)) a)
  3.0))

C

double code(double a, double b, double c) {
	return pow((fma((-0.1111111111111111 * (b / a)), b, (0.3333333333333333 * c)) / a), 3.0);
}

Julia

function code(a, b, c)
	return Float64(fma(Float64(-0.1111111111111111 * Float64(b / a)), b, Float64(0.3333333333333333 * c)) / a) ^ 3.0
end

Wolfram

code[a_, b_, c_] := N[Power[N[(N[(N[(-0.1111111111111111 * N[(b / a), $MachinePrecision]), $MachinePrecision] * b + N[(0.3333333333333333 * c), $MachinePrecision]), $MachinePrecision] / a), $MachinePrecision], 3.0], $MachinePrecision]

Derivation

1. Initial program 80.4%

   \[{\left(\frac{3 \cdot \left(a \cdot c\right) - {b}^{2}}{9 \cdot {a}^{2}}\right)}^{3} \]

2. Taylor expanded in a around inf

   \[\leadsto {\color{blue}{\left(\frac{\frac{-1}{9} \cdot \frac{{b}^{2}}{a} + \frac{1}{3} \cdot c}{a}\right)}}^{3} \]
3. Step-by-step derivation

   1. lower-/.f64 N/A

      \[\leadsto {\left(\frac{\frac{-1}{9} \cdot \frac{{b}^{2}}{a} + \frac{1}{3} \cdot c}{\color{blue}{a}}\right)}^{3} \]

   2. lower-fma.f64 N/A

      \[\leadsto {\left(\frac{\mathsf{fma}\left(\frac{-1}{9}, \frac{{b}^{2}}{a}, \frac{1}{3} \cdot c\right)}{a}\right)}^{3} \]

   3. lower-/.f64 N/A

      \[\leadsto {\left(\frac{\mathsf{fma}\left(\frac{-1}{9}, \frac{{b}^{2}}{a}, \frac{1}{3} \cdot c\right)}{a}\right)}^{3} \]

   4. lower-pow.f64 N/A

      \[\leadsto {\left(\frac{\mathsf{fma}\left(\frac{-1}{9}, \frac{{b}^{2}}{a}, \frac{1}{3} \cdot c\right)}{a}\right)}^{3} \]

   5. lower-*.f64 93.4%

      \[\leadsto {\left(\frac{\mathsf{fma}\left(-0.1111111111111111, \frac{{b}^{2}}{a}, 0.3333333333333333 \cdot c\right)}{a}\right)}^{3} \]

4. Applied rewrites 93.4%

   \[\leadsto {\color{blue}{\left(\frac{\mathsf{fma}\left(-0.1111111111111111, \frac{{b}^{2}}{a}, 0.3333333333333333 \cdot c\right)}{a}\right)}}^{3} \]
5. Step-by-step derivation

   1. lift-fma.f64 N/A

      \[\leadsto {\left(\frac{\frac{-1}{9} \cdot \frac{{b}^{2}}{a} + \frac{1}{3} \cdot c}{a}\right)}^{3} \]

   2. +-commutative N/A

      \[\leadsto {\left(\frac{\frac{1}{3} \cdot c + \frac{-1}{9} \cdot \frac{{b}^{2}}{a}}{a}\right)}^{3} \]

   3. fp-cancel-sign-sub-inv N/A

      \[\leadsto {\left(\frac{\frac{1}{3} \cdot c - \left(\mathsf{neg}\left(\frac{-1}{9}\right)\right) \cdot \frac{{b}^{2}}{a}}{a}\right)}^{3} \]

   4. lower--.f64 N/A

      \[\leadsto {\left(\frac{\frac{1}{3} \cdot c - \left(\mathsf{neg}\left(\frac{-1}{9}\right)\right) \cdot \frac{{b}^{2}}{a}}{a}\right)}^{3} \]

   5. metadata-eval N/A

      \[\leadsto {\left(\frac{\frac{1}{3} \cdot c - \frac{1}{9} \cdot \frac{{b}^{2}}{a}}{a}\right)}^{3} \]

   6. lower-*.f64 93.4%

      \[\leadsto {\left(\frac{0.3333333333333333 \cdot c - 0.1111111111111111 \cdot \frac{{b}^{2}}{a}}{a}\right)}^{3} \]

   7. lift-/.f64 N/A

      \[\leadsto {\left(\frac{\frac{1}{3} \cdot c - \frac{1}{9} \cdot \frac{{b}^{2}}{a}}{a}\right)}^{3} \]

   8. lift-pow.f64 N/A

      \[\leadsto {\left(\frac{\frac{1}{3} \cdot c - \frac{1}{9} \cdot \frac{{b}^{2}}{a}}{a}\right)}^{3} \]

   9. pow2 N/A

      \[\leadsto {\left(\frac{\frac{1}{3} \cdot c - \frac{1}{9} \cdot \frac{b \cdot b}{a}}{a}\right)}^{3} \]

   10. associate-/l* N/A

       \[\leadsto {\left(\frac{\frac{1}{3} \cdot c - \frac{1}{9} \cdot \left(b \cdot \frac{b}{a}\right)}{a}\right)}^{3} \]

   11. lower-*.f64 N/A

       \[\leadsto {\left(\frac{\frac{1}{3} \cdot c - \frac{1}{9} \cdot \left(b \cdot \frac{b}{a}\right)}{a}\right)}^{3} \]

   12. lower-/.f64 99.2%

       \[\leadsto {\left(\frac{0.3333333333333333 \cdot c - 0.1111111111111111 \cdot \left(b \cdot \frac{b}{a}\right)}{a}\right)}^{3} \]

6. Applied rewrites 99.2%

   \[\leadsto {\left(\frac{0.3333333333333333 \cdot c - 0.1111111111111111 \cdot \left(b \cdot \frac{b}{a}\right)}{a}\right)}^{3} \]
7. Step-by-step derivation

   1. lift--.f64 N/A

      \[\leadsto {\left(\frac{\frac{1}{3} \cdot c - \frac{1}{9} \cdot \left(b \cdot \frac{b}{a}\right)}{a}\right)}^{3} \]

   2. sub-flip N/A

      \[\leadsto {\left(\frac{\frac{1}{3} \cdot c + \left(\mathsf{neg}\left(\frac{1}{9} \cdot \left(b \cdot \frac{b}{a}\right)\right)\right)}{a}\right)}^{3} \]

   3. +-commutative N/A

      \[\leadsto {\left(\frac{\left(\mathsf{neg}\left(\frac{1}{9} \cdot \left(b \cdot \frac{b}{a}\right)\right)\right) + \frac{1}{3} \cdot c}{a}\right)}^{3} \]

   4. lift-*.f64 N/A

      \[\leadsto {\left(\frac{\left(\mathsf{neg}\left(\frac{1}{9} \cdot \left(b \cdot \frac{b}{a}\right)\right)\right) + \frac{1}{3} \cdot c}{a}\right)}^{3} \]

   5. distribute-lft-neg-out N/A

      \[\leadsto {\left(\frac{\left(\mathsf{neg}\left(\frac{1}{9}\right)\right) \cdot \left(b \cdot \frac{b}{a}\right) + \frac{1}{3} \cdot c}{a}\right)}^{3} \]

   6. metadata-eval N/A

      \[\leadsto {\left(\frac{\frac{-1}{9} \cdot \left(b \cdot \frac{b}{a}\right) + \frac{1}{3} \cdot c}{a}\right)}^{3} \]

   7. metadata-eval N/A

      \[\leadsto {\left(\frac{\frac{1}{-9} \cdot \left(b \cdot \frac{b}{a}\right) + \frac{1}{3} \cdot c}{a}\right)}^{3} \]

   8. lift-*.f64 N/A

      \[\leadsto {\left(\frac{\frac{1}{-9} \cdot \left(b \cdot \frac{b}{a}\right) + \frac{1}{3} \cdot c}{a}\right)}^{3} \]

   9. *-commutative N/A

      \[\leadsto {\left(\frac{\frac{1}{-9} \cdot \left(\frac{b}{a} \cdot b\right) + \frac{1}{3} \cdot c}{a}\right)}^{3} \]

   10. associate-*r* N/A

       \[\leadsto {\left(\frac{\left(\frac{1}{-9} \cdot \frac{b}{a}\right) \cdot b + \frac{1}{3} \cdot c}{a}\right)}^{3} \]

   11. lower-fma.f64 N/A

       \[\leadsto {\left(\frac{\mathsf{fma}\left(\frac{1}{-9} \cdot \frac{b}{a}, b, \frac{1}{3} \cdot c\right)}{a}\right)}^{3} \]

   12. lower-*.f64 N/A

       \[\leadsto {\left(\frac{\mathsf{fma}\left(\frac{1}{-9} \cdot \frac{b}{a}, b, \frac{1}{3} \cdot c\right)}{a}\right)}^{3} \]

   13. metadata-eval 99.2%

       \[\leadsto {\left(\frac{\mathsf{fma}\left(-0.1111111111111111 \cdot \frac{b}{a}, b, 0.3333333333333333 \cdot c\right)}{a}\right)}^{3} \]

8. Applied rewrites 99.2%

   \[\leadsto {\left(\frac{\mathsf{fma}\left(-0.1111111111111111 \cdot \frac{b}{a}, b, 0.3333333333333333 \cdot c\right)}{a}\right)}^{3} \]
9. Add Preprocessing

Alternative 5: 99.2% accurate, 1.8× speedup

Math

\[{\left(\frac{\mathsf{fma}\left(c, 0.3333333333333333, -0.1111111111111111 \cdot \left(\frac{b}{a} \cdot b\right)\right)}{a}\right)}^{3} \]

FPCore

(FPCore (a b c)
 :precision binary64
 (pow
  (/ (fma c 0.3333333333333333 (* -0.1111111111111111 (* (/ b a) b))) a)
  3.0))

C

double code(double a, double b, double c) {
	return pow((fma(c, 0.3333333333333333, (-0.1111111111111111 * ((b / a) * b))) / a), 3.0);
}

Julia

function code(a, b, c)
	return Float64(fma(c, 0.3333333333333333, Float64(-0.1111111111111111 * Float64(Float64(b / a) * b))) / a) ^ 3.0
end

Wolfram

code[a_, b_, c_] := N[Power[N[(N[(c * 0.3333333333333333 + N[(-0.1111111111111111 * N[(N[(b / a), $MachinePrecision] * b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / a), $MachinePrecision], 3.0], $MachinePrecision]

Derivation

1. Initial program 80.4%

   \[{\left(\frac{3 \cdot \left(a \cdot c\right) - {b}^{2}}{9 \cdot {a}^{2}}\right)}^{3} \]

2. Taylor expanded in a around inf

   \[\leadsto {\color{blue}{\left(\frac{\frac{-1}{9} \cdot \frac{{b}^{2}}{a} + \frac{1}{3} \cdot c}{a}\right)}}^{3} \]
3. Step-by-step derivation

   1. lower-/.f64 N/A

      \[\leadsto {\left(\frac{\frac{-1}{9} \cdot \frac{{b}^{2}}{a} + \frac{1}{3} \cdot c}{\color{blue}{a}}\right)}^{3} \]

   2. lower-fma.f64 N/A

      \[\leadsto {\left(\frac{\mathsf{fma}\left(\frac{-1}{9}, \frac{{b}^{2}}{a}, \frac{1}{3} \cdot c\right)}{a}\right)}^{3} \]

   3. lower-/.f64 N/A

      \[\leadsto {\left(\frac{\mathsf{fma}\left(\frac{-1}{9}, \frac{{b}^{2}}{a}, \frac{1}{3} \cdot c\right)}{a}\right)}^{3} \]

   4. lower-pow.f64 N/A

      \[\leadsto {\left(\frac{\mathsf{fma}\left(\frac{-1}{9}, \frac{{b}^{2}}{a}, \frac{1}{3} \cdot c\right)}{a}\right)}^{3} \]

   5. lower-*.f64 93.4%

      \[\leadsto {\left(\frac{\mathsf{fma}\left(-0.1111111111111111, \frac{{b}^{2}}{a}, 0.3333333333333333 \cdot c\right)}{a}\right)}^{3} \]

4. Applied rewrites 93.4%

   \[\leadsto {\color{blue}{\left(\frac{\mathsf{fma}\left(-0.1111111111111111, \frac{{b}^{2}}{a}, 0.3333333333333333 \cdot c\right)}{a}\right)}}^{3} \]
5. Step-by-step derivation

   1. lift-fma.f64 N/A

      \[\leadsto {\left(\frac{\frac{-1}{9} \cdot \frac{{b}^{2}}{a} + \frac{1}{3} \cdot c}{a}\right)}^{3} \]

   2. +-commutative N/A

      \[\leadsto {\left(\frac{\frac{1}{3} \cdot c + \frac{-1}{9} \cdot \frac{{b}^{2}}{a}}{a}\right)}^{3} \]

   3. fp-cancel-sign-sub-inv N/A

      \[\leadsto {\left(\frac{\frac{1}{3} \cdot c - \left(\mathsf{neg}\left(\frac{-1}{9}\right)\right) \cdot \frac{{b}^{2}}{a}}{a}\right)}^{3} \]

   4. lower--.f64 N/A

      \[\leadsto {\left(\frac{\frac{1}{3} \cdot c - \left(\mathsf{neg}\left(\frac{-1}{9}\right)\right) \cdot \frac{{b}^{2}}{a}}{a}\right)}^{3} \]

   5. metadata-eval N/A

      \[\leadsto {\left(\frac{\frac{1}{3} \cdot c - \frac{1}{9} \cdot \frac{{b}^{2}}{a}}{a}\right)}^{3} \]

   6. lower-*.f64 93.4%

      \[\leadsto {\left(\frac{0.3333333333333333 \cdot c - 0.1111111111111111 \cdot \frac{{b}^{2}}{a}}{a}\right)}^{3} \]

   7. lift-/.f64 N/A

      \[\leadsto {\left(\frac{\frac{1}{3} \cdot c - \frac{1}{9} \cdot \frac{{b}^{2}}{a}}{a}\right)}^{3} \]

   8. lift-pow.f64 N/A

      \[\leadsto {\left(\frac{\frac{1}{3} \cdot c - \frac{1}{9} \cdot \frac{{b}^{2}}{a}}{a}\right)}^{3} \]

   9. pow2 N/A

      \[\leadsto {\left(\frac{\frac{1}{3} \cdot c - \frac{1}{9} \cdot \frac{b \cdot b}{a}}{a}\right)}^{3} \]

   10. associate-/l* N/A

       \[\leadsto {\left(\frac{\frac{1}{3} \cdot c - \frac{1}{9} \cdot \left(b \cdot \frac{b}{a}\right)}{a}\right)}^{3} \]

   11. lower-*.f64 N/A

       \[\leadsto {\left(\frac{\frac{1}{3} \cdot c - \frac{1}{9} \cdot \left(b \cdot \frac{b}{a}\right)}{a}\right)}^{3} \]

   12. lower-/.f64 99.2%

       \[\leadsto {\left(\frac{0.3333333333333333 \cdot c - 0.1111111111111111 \cdot \left(b \cdot \frac{b}{a}\right)}{a}\right)}^{3} \]

6. Applied rewrites 99.2%

   \[\leadsto {\left(\frac{0.3333333333333333 \cdot c - 0.1111111111111111 \cdot \left(b \cdot \frac{b}{a}\right)}{a}\right)}^{3} \]
7. Step-by-step derivation

   1. lift--.f64 N/A

      \[\leadsto {\left(\frac{\frac{1}{3} \cdot c - \frac{1}{9} \cdot \left(b \cdot \frac{b}{a}\right)}{a}\right)}^{3} \]

   2. sub-flip N/A

      \[\leadsto {\left(\frac{\frac{1}{3} \cdot c + \left(\mathsf{neg}\left(\frac{1}{9} \cdot \left(b \cdot \frac{b}{a}\right)\right)\right)}{a}\right)}^{3} \]

   3. lift-*.f64 N/A

      \[\leadsto {\left(\frac{\frac{1}{3} \cdot c + \left(\mathsf{neg}\left(\frac{1}{9} \cdot \left(b \cdot \frac{b}{a}\right)\right)\right)}{a}\right)}^{3} \]

   4. *-commutative N/A

      \[\leadsto {\left(\frac{c \cdot \frac{1}{3} + \left(\mathsf{neg}\left(\frac{1}{9} \cdot \left(b \cdot \frac{b}{a}\right)\right)\right)}{a}\right)}^{3} \]

   5. lower-fma.f64 N/A

      \[\leadsto {\left(\frac{\mathsf{fma}\left(c, \frac{1}{3}, \mathsf{neg}\left(\frac{1}{9} \cdot \left(b \cdot \frac{b}{a}\right)\right)\right)}{a}\right)}^{3} \]

   6. lift-*.f64 N/A

      \[\leadsto {\left(\frac{\mathsf{fma}\left(c, \frac{1}{3}, \mathsf{neg}\left(\frac{1}{9} \cdot \left(b \cdot \frac{b}{a}\right)\right)\right)}{a}\right)}^{3} \]

   7. distribute-lft-neg-out N/A

      \[\leadsto {\left(\frac{\mathsf{fma}\left(c, \frac{1}{3}, \left(\mathsf{neg}\left(\frac{1}{9}\right)\right) \cdot \left(b \cdot \frac{b}{a}\right)\right)}{a}\right)}^{3} \]

   8. metadata-eval N/A

      \[\leadsto {\left(\frac{\mathsf{fma}\left(c, \frac{1}{3}, \frac{-1}{9} \cdot \left(b \cdot \frac{b}{a}\right)\right)}{a}\right)}^{3} \]

   9. metadata-eval N/A

      \[\leadsto {\left(\frac{\mathsf{fma}\left(c, \frac{1}{3}, \frac{1}{-9} \cdot \left(b \cdot \frac{b}{a}\right)\right)}{a}\right)}^{3} \]

   10. lower-*.f64 N/A

       \[\leadsto {\left(\frac{\mathsf{fma}\left(c, \frac{1}{3}, \frac{1}{-9} \cdot \left(b \cdot \frac{b}{a}\right)\right)}{a}\right)}^{3} \]

   11. metadata-eval 99.2%

       \[\leadsto {\left(\frac{\mathsf{fma}\left(c, 0.3333333333333333, -0.1111111111111111 \cdot \left(b \cdot \frac{b}{a}\right)\right)}{a}\right)}^{3} \]

   12. lift-*.f64 N/A

       \[\leadsto {\left(\frac{\mathsf{fma}\left(c, \frac{1}{3}, \frac{-1}{9} \cdot \left(b \cdot \frac{b}{a}\right)\right)}{a}\right)}^{3} \]

   13. *-commutative N/A

       \[\leadsto {\left(\frac{\mathsf{fma}\left(c, \frac{1}{3}, \frac{-1}{9} \cdot \left(\frac{b}{a} \cdot b\right)\right)}{a}\right)}^{3} \]

   14. lower-*.f64 99.2%

       \[\leadsto {\left(\frac{\mathsf{fma}\left(c, 0.3333333333333333, -0.1111111111111111 \cdot \left(\frac{b}{a} \cdot b\right)\right)}{a}\right)}^{3} \]

8. Applied rewrites 99.2%

   \[\leadsto {\left(\frac{\mathsf{fma}\left(c, 0.3333333333333333, -0.1111111111111111 \cdot \left(\frac{b}{a} \cdot b\right)\right)}{a}\right)}^{3} \]
9. Add Preprocessing

Alternative 6: 99.1% accurate, 1.8× speedup

Math

\[{\left(\frac{\mathsf{fma}\left(-3, c, \frac{b}{a} \cdot b\right)}{a}\right)}^{3} \cdot -0.0013717421124828531 \]

FPCore

(FPCore (a b c)
 :precision binary64
 (* (pow (/ (fma -3.0 c (* (/ b a) b)) a) 3.0) -0.0013717421124828531))

C

double code(double a, double b, double c) {
	return pow((fma(-3.0, c, ((b / a) * b)) / a), 3.0) * -0.0013717421124828531;
}

Julia

function code(a, b, c)
	return Float64((Float64(fma(-3.0, c, Float64(Float64(b / a) * b)) / a) ^ 3.0) * -0.0013717421124828531)
end

Wolfram

code[a_, b_, c_] := N[(N[Power[N[(N[(-3.0 * c + N[(N[(b / a), $MachinePrecision] * b), $MachinePrecision]), $MachinePrecision] / a), $MachinePrecision], 3.0], $MachinePrecision] * -0.0013717421124828531), $MachinePrecision]

Derivation

1. Initial program 80.4%

   \[{\left(\frac{3 \cdot \left(a \cdot c\right) - {b}^{2}}{9 \cdot {a}^{2}}\right)}^{3} \]
2. Step-by-step derivation

   1. lift-/.f64 N/A

      \[\leadsto {\color{blue}{\left(\frac{3 \cdot \left(a \cdot c\right) - {b}^{2}}{9 \cdot {a}^{2}}\right)}}^{3} \]

   2. frac-2neg N/A

      \[\leadsto {\color{blue}{\left(\frac{\mathsf{neg}\left(\left(3 \cdot \left(a \cdot c\right) - {b}^{2}\right)\right)}{\mathsf{neg}\left(9 \cdot {a}^{2}\right)}\right)}}^{3} \]

   3. lift-*.f64 N/A

      \[\leadsto {\left(\frac{\mathsf{neg}\left(\left(3 \cdot \left(a \cdot c\right) - {b}^{2}\right)\right)}{\mathsf{neg}\left(\color{blue}{9 \cdot {a}^{2}}\right)}\right)}^{3} \]

   4. *-commutative N/A

      \[\leadsto {\left(\frac{\mathsf{neg}\left(\left(3 \cdot \left(a \cdot c\right) - {b}^{2}\right)\right)}{\mathsf{neg}\left(\color{blue}{{a}^{2} \cdot 9}\right)}\right)}^{3} \]

   5. distribute-rgt-neg-in N/A

      \[\leadsto {\left(\frac{\mathsf{neg}\left(\left(3 \cdot \left(a \cdot c\right) - {b}^{2}\right)\right)}{\color{blue}{{a}^{2} \cdot \left(\mathsf{neg}\left(9\right)\right)}}\right)}^{3} \]

   6. associate-/r* N/A

      \[\leadsto {\color{blue}{\left(\frac{\frac{\mathsf{neg}\left(\left(3 \cdot \left(a \cdot c\right) - {b}^{2}\right)\right)}{{a}^{2}}}{\mathsf{neg}\left(9\right)}\right)}}^{3} \]

   7. lower-/.f64 N/A

      \[\leadsto {\color{blue}{\left(\frac{\frac{\mathsf{neg}\left(\left(3 \cdot \left(a \cdot c\right) - {b}^{2}\right)\right)}{{a}^{2}}}{\mathsf{neg}\left(9\right)}\right)}}^{3} \]

3. Applied rewrites 80.4%

   \[\leadsto {\color{blue}{\left(\frac{\frac{\mathsf{fma}\left(-3, c \cdot a, b \cdot b\right)}{a \cdot a}}{-9}\right)}}^{3} \]
4. Step-by-step derivation

   1. lift-pow.f64 N/A

      \[\leadsto \color{blue}{{\left(\frac{\frac{\mathsf{fma}\left(-3, c \cdot a, b \cdot b\right)}{a \cdot a}}{-9}\right)}^{3}} \]

   2. lift-/.f64 N/A

      \[\leadsto {\color{blue}{\left(\frac{\frac{\mathsf{fma}\left(-3, c \cdot a, b \cdot b\right)}{a \cdot a}}{-9}\right)}}^{3} \]

   3. mult-flip N/A

      \[\leadsto {\color{blue}{\left(\frac{\mathsf{fma}\left(-3, c \cdot a, b \cdot b\right)}{a \cdot a} \cdot \frac{1}{-9}\right)}}^{3} \]

   4. cube-prod N/A

      \[\leadsto \color{blue}{{\left(\frac{\mathsf{fma}\left(-3, c \cdot a, b \cdot b\right)}{a \cdot a}\right)}^{3} \cdot {\left(\frac{1}{-9}\right)}^{3}} \]

   5. lower-*.f64 N/A

      \[\leadsto \color{blue}{{\left(\frac{\mathsf{fma}\left(-3, c \cdot a, b \cdot b\right)}{a \cdot a}\right)}^{3} \cdot {\left(\frac{1}{-9}\right)}^{3}} \]

5. Applied rewrites 99.1%

   \[\leadsto \color{blue}{{\left(\frac{\mathsf{fma}\left(-3, c, \frac{b}{a} \cdot b\right)}{a}\right)}^{3} \cdot -0.0013717421124828531} \]
6. Add Preprocessing

Alternative 7: 64.3% accurate, 1.7× speedup

Math

\[\begin{array}{l} \mathbf{if}\;\left|b\right| \leq 1.05 \cdot 10^{-199}:\\ \;\;\;\;{\left(\frac{3}{9 \cdot a} \cdot \left(c \cdot 1\right)\right)}^{3}\\ \mathbf{else}:\\ \;\;\;\;{\left(\frac{a \cdot 3}{9} \cdot \frac{c}{a \cdot a}\right)}^{3}\\ \end{array} \]

FPCore

(FPCore (a b c)
 :precision binary64
 (if (<= (fabs b) 1.05e-199)
   (pow (* (/ 3.0 (* 9.0 a)) (* c 1.0)) 3.0)
   (pow (* (/ (* a 3.0) 9.0) (/ c (* a a))) 3.0)))

C

double code(double a, double b, double c) {
	double tmp;
	if (fabs(b) <= 1.05e-199) {
		tmp = pow(((3.0 / (9.0 * a)) * (c * 1.0)), 3.0);
	} else {
		tmp = pow((((a * 3.0) / 9.0) * (c / (a * a))), 3.0);
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(a, b, c)
use fmin_fmax_functions
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8) :: tmp
    if (abs(b) <= 1.05d-199) then
        tmp = ((3.0d0 / (9.0d0 * a)) * (c * 1.0d0)) ** 3.0d0
    else
        tmp = (((a * 3.0d0) / 9.0d0) * (c / (a * a))) ** 3.0d0
    end if
    code = tmp
end function

Java

public static double code(double a, double b, double c) {
	double tmp;
	if (Math.abs(b) <= 1.05e-199) {
		tmp = Math.pow(((3.0 / (9.0 * a)) * (c * 1.0)), 3.0);
	} else {
		tmp = Math.pow((((a * 3.0) / 9.0) * (c / (a * a))), 3.0);
	}
	return tmp;
}

Python

def code(a, b, c):
	tmp = 0
	if math.fabs(b) <= 1.05e-199:
		tmp = math.pow(((3.0 / (9.0 * a)) * (c * 1.0)), 3.0)
	else:
		tmp = math.pow((((a * 3.0) / 9.0) * (c / (a * a))), 3.0)
	return tmp

Julia

function code(a, b, c)
	tmp = 0.0
	if (abs(b) <= 1.05e-199)
		tmp = Float64(Float64(3.0 / Float64(9.0 * a)) * Float64(c * 1.0)) ^ 3.0;
	else
		tmp = Float64(Float64(Float64(a * 3.0) / 9.0) * Float64(c / Float64(a * a))) ^ 3.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(a, b, c)
	tmp = 0.0;
	if (abs(b) <= 1.05e-199)
		tmp = ((3.0 / (9.0 * a)) * (c * 1.0)) ^ 3.0;
	else
		tmp = (((a * 3.0) / 9.0) * (c / (a * a))) ^ 3.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[a_, b_, c_] := If[LessEqual[N[Abs[b], $MachinePrecision], 1.05e-199], N[Power[N[(N[(3.0 / N[(9.0 * a), $MachinePrecision]), $MachinePrecision] * N[(c * 1.0), $MachinePrecision]), $MachinePrecision], 3.0], $MachinePrecision], N[Power[N[(N[(N[(a * 3.0), $MachinePrecision] / 9.0), $MachinePrecision] * N[(c / N[(a * a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 3.0], $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if b < 1.05e-199

   1. Initial program 80.4%

      \[{\left(\frac{3 \cdot \left(a \cdot c\right) - {b}^{2}}{9 \cdot {a}^{2}}\right)}^{3} \]

   2. Taylor expanded in a around inf

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

      1. lower-*.f64 N/A

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

      2. lower-/.f64 58.0%

         \[\leadsto {\left(0.3333333333333333 \cdot \frac{c}{\color{blue}{a}}\right)}^{3} \]

   4. Applied rewrites 58.0%

      \[\leadsto {\color{blue}{\left(0.3333333333333333 \cdot \frac{c}{a}\right)}}^{3} \]
   5. Step-by-step derivation

      1. lift-*.f64 N/A

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

      2. lift-/.f64 N/A

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

      3. associate-*r/ N/A

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

      4. lift-*.f64 N/A

         \[\leadsto {\left(\frac{\frac{1}{3} \cdot c}{a}\right)}^{3} \]

      5. lower-/.f64 58.0%

         \[\leadsto {\left(\frac{0.3333333333333333 \cdot c}{\color{blue}{a}}\right)}^{3} \]

   6. Applied rewrites 58.0%

      \[\leadsto {\left(\frac{0.3333333333333333 \cdot c}{\color{blue}{a}}\right)}^{3} \]
   7. Step-by-step derivation

      1. lift-/.f64 N/A

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

      2. lift-*.f64 N/A

         \[\leadsto {\left(\frac{\frac{1}{3} \cdot c}{a}\right)}^{3} \]

      3. associate-/l* N/A

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

      4. mult-flip N/A

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

      5. *-inverses N/A

         \[\leadsto {\left(\frac{1}{3} \cdot \left(c \cdot \frac{\frac{a}{a}}{a}\right)\right)}^{3} \]

      6. associate-/r* N/A

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

      7. lift-*.f64 N/A

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

      8. mult-flip N/A

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

      9. associate-*l* N/A

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

      10. lift-*.f64 N/A

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

      11. metadata-eval N/A

          \[\leadsto {\left(\frac{3}{9} \cdot \left(\color{blue}{\left(c \cdot a\right)} \cdot \frac{1}{a \cdot a}\right)\right)}^{3} \]

      12. mult-flip N/A

          \[\leadsto {\left(\frac{3}{9} \cdot \frac{c \cdot a}{\color{blue}{a \cdot a}}\right)}^{3} \]

      13. times-frac N/A

          \[\leadsto {\left(\frac{3 \cdot \left(c \cdot a\right)}{\color{blue}{9 \cdot \left(a \cdot a\right)}}\right)}^{3} \]

      14. lift-*.f64 N/A

          \[\leadsto {\left(\frac{3 \cdot \left(c \cdot a\right)}{9 \cdot \left(a \cdot \color{blue}{a}\right)}\right)}^{3} \]

      15. associate-*r* N/A

          \[\leadsto {\left(\frac{3 \cdot \left(c \cdot a\right)}{\left(9 \cdot a\right) \cdot \color{blue}{a}}\right)}^{3} \]

      16. times-frac N/A

          \[\leadsto {\left(\frac{3}{9 \cdot a} \cdot \color{blue}{\frac{c \cdot a}{a}}\right)}^{3} \]

      17. lower-*.f64 N/A

          \[\leadsto {\left(\frac{3}{9 \cdot a} \cdot \color{blue}{\frac{c \cdot a}{a}}\right)}^{3} \]

      18. lower-/.f64 N/A

          \[\leadsto {\left(\frac{3}{9 \cdot a} \cdot \frac{\color{blue}{c \cdot a}}{a}\right)}^{3} \]

      19. lower-*.f64 N/A

          \[\leadsto {\left(\frac{3}{9 \cdot a} \cdot \frac{c \cdot \color{blue}{a}}{a}\right)}^{3} \]

      20. lift-*.f64 N/A

          \[\leadsto {\left(\frac{3}{9 \cdot a} \cdot \frac{c \cdot a}{a}\right)}^{3} \]

      21. associate-/l* N/A

          \[\leadsto {\left(\frac{3}{9 \cdot a} \cdot \left(c \cdot \color{blue}{\frac{a}{a}}\right)\right)}^{3} \]

      22. *-inverses N/A

          \[\leadsto {\left(\frac{3}{9 \cdot a} \cdot \left(c \cdot 1\right)\right)}^{3} \]

      23. lower-*.f64 58.0%

          \[\leadsto {\left(\frac{3}{9 \cdot a} \cdot \left(c \cdot \color{blue}{1}\right)\right)}^{3} \]

   8. Applied rewrites 58.0%

      \[\leadsto {\left(\frac{3}{9 \cdot a} \cdot \color{blue}{\left(c \cdot 1\right)}\right)}^{3} \]

   if 1.05e-199 < b

   1. Initial program 80.4%

      \[{\left(\frac{3 \cdot \left(a \cdot c\right) - {b}^{2}}{9 \cdot {a}^{2}}\right)}^{3} \]

   2. Taylor expanded in a around inf

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

      1. lower-*.f64 N/A

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

      2. lower-/.f64 58.0%

         \[\leadsto {\left(0.3333333333333333 \cdot \frac{c}{\color{blue}{a}}\right)}^{3} \]

   4. Applied rewrites 58.0%

      \[\leadsto {\color{blue}{\left(0.3333333333333333 \cdot \frac{c}{a}\right)}}^{3} \]
   5. Step-by-step derivation

      1. lift-*.f64 N/A

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

      2. lift-/.f64 N/A

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

      3. mult-flip N/A

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

      4. inv-pow N/A

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

      5. metadata-eval N/A

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

      6. metadata-eval N/A

         \[\leadsto {\left(\frac{1}{3} \cdot \left(c \cdot {a}^{\left(\left(\mathsf{neg}\left(2\right)\right) + 1\right)}\right)\right)}^{3} \]

      7. pow-plus N/A

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

      8. pow-flip N/A

         \[\leadsto {\left(\frac{1}{3} \cdot \left(c \cdot \left(\frac{1}{{a}^{2}} \cdot a\right)\right)\right)}^{3} \]

      9. pow2 N/A

         \[\leadsto {\left(\frac{1}{3} \cdot \left(c \cdot \left(\frac{1}{a \cdot a} \cdot a\right)\right)\right)}^{3} \]

      10. lift-*.f64 N/A

          \[\leadsto {\left(\frac{1}{3} \cdot \left(c \cdot \left(\frac{1}{a \cdot a} \cdot a\right)\right)\right)}^{3} \]

      11. associate-*l* N/A

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

      12. mult-flip N/A

          \[\leadsto {\left(\frac{1}{3} \cdot \left(\frac{c}{a \cdot a} \cdot a\right)\right)}^{3} \]

      13. lift-/.f64 N/A

          \[\leadsto {\left(\frac{1}{3} \cdot \left(\frac{c}{a \cdot a} \cdot a\right)\right)}^{3} \]

      14. *-commutative N/A

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

      15. lift-/.f64 N/A

          \[\leadsto {\left(\left(\frac{c}{a \cdot a} \cdot a\right) \cdot \frac{1}{3}\right)}^{3} \]

      16. associate-*l/ N/A

          \[\leadsto {\left(\frac{c \cdot a}{a \cdot a} \cdot \frac{1}{3}\right)}^{3} \]

      17. lift-*.f64 N/A

          \[\leadsto {\left(\frac{c \cdot a}{a \cdot a} \cdot \frac{1}{3}\right)}^{3} \]

      18. metadata-eval N/A

          \[\leadsto {\left(\frac{c \cdot a}{a \cdot a} \cdot \frac{3}{\color{blue}{9}}\right)}^{3} \]

      19. times-frac N/A

          \[\leadsto {\left(\frac{\left(c \cdot a\right) \cdot 3}{\color{blue}{\left(a \cdot a\right) \cdot 9}}\right)}^{3} \]

      20. *-commutative N/A

          \[\leadsto {\left(\frac{3 \cdot \left(c \cdot a\right)}{\color{blue}{\left(a \cdot a\right)} \cdot 9}\right)}^{3} \]

      21. lift-*.f64 N/A

          \[\leadsto {\left(\frac{3 \cdot \left(c \cdot a\right)}{\left(a \cdot \color{blue}{a}\right) \cdot 9}\right)}^{3} \]

      22. *-commutative N/A

          \[\leadsto {\left(\frac{3 \cdot \left(a \cdot c\right)}{\left(a \cdot \color{blue}{a}\right) \cdot 9}\right)}^{3} \]

      23. *-commutative N/A

          \[\leadsto {\left(\frac{3 \cdot \left(a \cdot c\right)}{9 \cdot \color{blue}{\left(a \cdot a\right)}}\right)}^{3} \]

      24. lift-*.f64 N/A

          \[\leadsto {\left(\frac{3 \cdot \left(a \cdot c\right)}{9 \cdot \left(a \cdot \color{blue}{a}\right)}\right)}^{3} \]

      25. pow2 N/A

          \[\leadsto {\left(\frac{3 \cdot \left(a \cdot c\right)}{9 \cdot {a}^{\color{blue}{2}}}\right)}^{3} \]

   6. Applied rewrites 58.4%

      \[\leadsto {\left(\frac{a \cdot 3}{9} \cdot \color{blue}{\frac{c}{a \cdot a}}\right)}^{3} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 8: 64.3% accurate, 1.9× speedup

Math

\[\begin{array}{l} \mathbf{if}\;\left|b\right| \leq 8.2 \cdot 10^{-200}:\\ \;\;\;\;{\left(\frac{3}{9 \cdot a} \cdot \left(c \cdot 1\right)\right)}^{3}\\ \mathbf{else}:\\ \;\;\;\;{\left(c \cdot \frac{0.3333333333333333 \cdot a}{a \cdot a}\right)}^{3}\\ \end{array} \]

FPCore

(FPCore (a b c)
 :precision binary64
 (if (<= (fabs b) 8.2e-200)
   (pow (* (/ 3.0 (* 9.0 a)) (* c 1.0)) 3.0)
   (pow (* c (/ (* 0.3333333333333333 a) (* a a))) 3.0)))

C

double code(double a, double b, double c) {
	double tmp;
	if (fabs(b) <= 8.2e-200) {
		tmp = pow(((3.0 / (9.0 * a)) * (c * 1.0)), 3.0);
	} else {
		tmp = pow((c * ((0.3333333333333333 * a) / (a * a))), 3.0);
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(a, b, c)
use fmin_fmax_functions
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8) :: tmp
    if (abs(b) <= 8.2d-200) then
        tmp = ((3.0d0 / (9.0d0 * a)) * (c * 1.0d0)) ** 3.0d0
    else
        tmp = (c * ((0.3333333333333333d0 * a) / (a * a))) ** 3.0d0
    end if
    code = tmp
end function

Java

public static double code(double a, double b, double c) {
	double tmp;
	if (Math.abs(b) <= 8.2e-200) {
		tmp = Math.pow(((3.0 / (9.0 * a)) * (c * 1.0)), 3.0);
	} else {
		tmp = Math.pow((c * ((0.3333333333333333 * a) / (a * a))), 3.0);
	}
	return tmp;
}

Python

def code(a, b, c):
	tmp = 0
	if math.fabs(b) <= 8.2e-200:
		tmp = math.pow(((3.0 / (9.0 * a)) * (c * 1.0)), 3.0)
	else:
		tmp = math.pow((c * ((0.3333333333333333 * a) / (a * a))), 3.0)
	return tmp

Julia

function code(a, b, c)
	tmp = 0.0
	if (abs(b) <= 8.2e-200)
		tmp = Float64(Float64(3.0 / Float64(9.0 * a)) * Float64(c * 1.0)) ^ 3.0;
	else
		tmp = Float64(c * Float64(Float64(0.3333333333333333 * a) / Float64(a * a))) ^ 3.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(a, b, c)
	tmp = 0.0;
	if (abs(b) <= 8.2e-200)
		tmp = ((3.0 / (9.0 * a)) * (c * 1.0)) ^ 3.0;
	else
		tmp = (c * ((0.3333333333333333 * a) / (a * a))) ^ 3.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[a_, b_, c_] := If[LessEqual[N[Abs[b], $MachinePrecision], 8.2e-200], N[Power[N[(N[(3.0 / N[(9.0 * a), $MachinePrecision]), $MachinePrecision] * N[(c * 1.0), $MachinePrecision]), $MachinePrecision], 3.0], $MachinePrecision], N[Power[N[(c * N[(N[(0.3333333333333333 * a), $MachinePrecision] / N[(a * a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 3.0], $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if b < 8.1999999999999997e-200

   1. Initial program 80.4%

      \[{\left(\frac{3 \cdot \left(a \cdot c\right) - {b}^{2}}{9 \cdot {a}^{2}}\right)}^{3} \]

   2. Taylor expanded in a around inf

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

      1. lower-*.f64 N/A

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

      2. lower-/.f64 58.0%

         \[\leadsto {\left(0.3333333333333333 \cdot \frac{c}{\color{blue}{a}}\right)}^{3} \]

   4. Applied rewrites 58.0%

      \[\leadsto {\color{blue}{\left(0.3333333333333333 \cdot \frac{c}{a}\right)}}^{3} \]
   5. Step-by-step derivation

      1. lift-*.f64 N/A

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

      2. lift-/.f64 N/A

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

      3. associate-*r/ N/A

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

      4. lift-*.f64 N/A

         \[\leadsto {\left(\frac{\frac{1}{3} \cdot c}{a}\right)}^{3} \]

      5. lower-/.f64 58.0%

         \[\leadsto {\left(\frac{0.3333333333333333 \cdot c}{\color{blue}{a}}\right)}^{3} \]

   6. Applied rewrites 58.0%

      \[\leadsto {\left(\frac{0.3333333333333333 \cdot c}{\color{blue}{a}}\right)}^{3} \]
   7. Step-by-step derivation

      1. lift-/.f64 N/A

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

      2. lift-*.f64 N/A

         \[\leadsto {\left(\frac{\frac{1}{3} \cdot c}{a}\right)}^{3} \]

      3. associate-/l* N/A

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

      4. mult-flip N/A

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

      5. *-inverses N/A

         \[\leadsto {\left(\frac{1}{3} \cdot \left(c \cdot \frac{\frac{a}{a}}{a}\right)\right)}^{3} \]

      6. associate-/r* N/A

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

      7. lift-*.f64 N/A

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

      8. mult-flip N/A

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

      9. associate-*l* N/A

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

      10. lift-*.f64 N/A

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

      11. metadata-eval N/A

          \[\leadsto {\left(\frac{3}{9} \cdot \left(\color{blue}{\left(c \cdot a\right)} \cdot \frac{1}{a \cdot a}\right)\right)}^{3} \]

      12. mult-flip N/A

          \[\leadsto {\left(\frac{3}{9} \cdot \frac{c \cdot a}{\color{blue}{a \cdot a}}\right)}^{3} \]

      13. times-frac N/A

          \[\leadsto {\left(\frac{3 \cdot \left(c \cdot a\right)}{\color{blue}{9 \cdot \left(a \cdot a\right)}}\right)}^{3} \]

      14. lift-*.f64 N/A

          \[\leadsto {\left(\frac{3 \cdot \left(c \cdot a\right)}{9 \cdot \left(a \cdot \color{blue}{a}\right)}\right)}^{3} \]

      15. associate-*r* N/A

          \[\leadsto {\left(\frac{3 \cdot \left(c \cdot a\right)}{\left(9 \cdot a\right) \cdot \color{blue}{a}}\right)}^{3} \]

      16. times-frac N/A

          \[\leadsto {\left(\frac{3}{9 \cdot a} \cdot \color{blue}{\frac{c \cdot a}{a}}\right)}^{3} \]

      17. lower-*.f64 N/A

          \[\leadsto {\left(\frac{3}{9 \cdot a} \cdot \color{blue}{\frac{c \cdot a}{a}}\right)}^{3} \]

      18. lower-/.f64 N/A

          \[\leadsto {\left(\frac{3}{9 \cdot a} \cdot \frac{\color{blue}{c \cdot a}}{a}\right)}^{3} \]

      19. lower-*.f64 N/A

          \[\leadsto {\left(\frac{3}{9 \cdot a} \cdot \frac{c \cdot \color{blue}{a}}{a}\right)}^{3} \]

      20. lift-*.f64 N/A

          \[\leadsto {\left(\frac{3}{9 \cdot a} \cdot \frac{c \cdot a}{a}\right)}^{3} \]

      21. associate-/l* N/A

          \[\leadsto {\left(\frac{3}{9 \cdot a} \cdot \left(c \cdot \color{blue}{\frac{a}{a}}\right)\right)}^{3} \]

      22. *-inverses N/A

          \[\leadsto {\left(\frac{3}{9 \cdot a} \cdot \left(c \cdot 1\right)\right)}^{3} \]

      23. lower-*.f64 58.0%

          \[\leadsto {\left(\frac{3}{9 \cdot a} \cdot \left(c \cdot \color{blue}{1}\right)\right)}^{3} \]

   8. Applied rewrites 58.0%

      \[\leadsto {\left(\frac{3}{9 \cdot a} \cdot \color{blue}{\left(c \cdot 1\right)}\right)}^{3} \]

   if 8.1999999999999997e-200 < b

   1. Initial program 80.4%

      \[{\left(\frac{3 \cdot \left(a \cdot c\right) - {b}^{2}}{9 \cdot {a}^{2}}\right)}^{3} \]

   2. Taylor expanded in a around inf

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

      1. lower-*.f64 N/A

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

      2. lower-/.f64 58.0%

         \[\leadsto {\left(0.3333333333333333 \cdot \frac{c}{\color{blue}{a}}\right)}^{3} \]

   4. Applied rewrites 58.0%

      \[\leadsto {\color{blue}{\left(0.3333333333333333 \cdot \frac{c}{a}\right)}}^{3} \]
   5. Step-by-step derivation

      1. lift-*.f64 N/A

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

      2. lift-/.f64 N/A

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

      3. mult-flip N/A

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

      4. inv-pow N/A

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

      5. metadata-eval N/A

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

      6. metadata-eval N/A

         \[\leadsto {\left(\frac{1}{3} \cdot \left(c \cdot {a}^{\left(\left(\mathsf{neg}\left(2\right)\right) + 1\right)}\right)\right)}^{3} \]

      7. pow-plus N/A

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

      8. pow-flip N/A

         \[\leadsto {\left(\frac{1}{3} \cdot \left(c \cdot \left(\frac{1}{{a}^{2}} \cdot a\right)\right)\right)}^{3} \]

      9. pow2 N/A

         \[\leadsto {\left(\frac{1}{3} \cdot \left(c \cdot \left(\frac{1}{a \cdot a} \cdot a\right)\right)\right)}^{3} \]

      10. lift-*.f64 N/A

          \[\leadsto {\left(\frac{1}{3} \cdot \left(c \cdot \left(\frac{1}{a \cdot a} \cdot a\right)\right)\right)}^{3} \]

      11. associate-*l* N/A

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

      12. mult-flip N/A

          \[\leadsto {\left(\frac{1}{3} \cdot \left(\frac{c}{a \cdot a} \cdot a\right)\right)}^{3} \]

      13. lift-/.f64 N/A

          \[\leadsto {\left(\frac{1}{3} \cdot \left(\frac{c}{a \cdot a} \cdot a\right)\right)}^{3} \]

      14. *-commutative N/A

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

      15. associate-*r* N/A

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

      16. lift-*.f64 N/A

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

      17. lift-/.f64 N/A

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

      18. associate-*l/ N/A

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

      19. associate-/l* N/A

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

      20. lower-*.f64 N/A

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

      21. lower-/.f64 58.4%

          \[\leadsto {\left(c \cdot \frac{a \cdot 0.3333333333333333}{\color{blue}{a \cdot a}}\right)}^{3} \]

      22. lift-*.f64 N/A

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

      23. *-commutative N/A

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

      24. lower-*.f64 58.4%

          \[\leadsto {\left(c \cdot \frac{0.3333333333333333 \cdot a}{\color{blue}{a} \cdot a}\right)}^{3} \]

   6. Applied rewrites 58.4%

      \[\leadsto {\left(c \cdot \color{blue}{\frac{0.3333333333333333 \cdot a}{a \cdot a}}\right)}^{3} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 9: 64.3% accurate, 1.9× speedup

Math

\[\begin{array}{l} \mathbf{if}\;\left|b\right| \leq 8.2 \cdot 10^{-200}:\\ \;\;\;\;{\left(c \cdot \frac{0.3333333333333333}{a}\right)}^{3}\\ \mathbf{else}:\\ \;\;\;\;{\left(c \cdot \frac{0.3333333333333333 \cdot a}{a \cdot a}\right)}^{3}\\ \end{array} \]

FPCore

(FPCore (a b c)
 :precision binary64
 (if (<= (fabs b) 8.2e-200)
   (pow (* c (/ 0.3333333333333333 a)) 3.0)
   (pow (* c (/ (* 0.3333333333333333 a) (* a a))) 3.0)))

C

double code(double a, double b, double c) {
	double tmp;
	if (fabs(b) <= 8.2e-200) {
		tmp = pow((c * (0.3333333333333333 / a)), 3.0);
	} else {
		tmp = pow((c * ((0.3333333333333333 * a) / (a * a))), 3.0);
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(a, b, c)
use fmin_fmax_functions
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8) :: tmp
    if (abs(b) <= 8.2d-200) then
        tmp = (c * (0.3333333333333333d0 / a)) ** 3.0d0
    else
        tmp = (c * ((0.3333333333333333d0 * a) / (a * a))) ** 3.0d0
    end if
    code = tmp
end function

Java

public static double code(double a, double b, double c) {
	double tmp;
	if (Math.abs(b) <= 8.2e-200) {
		tmp = Math.pow((c * (0.3333333333333333 / a)), 3.0);
	} else {
		tmp = Math.pow((c * ((0.3333333333333333 * a) / (a * a))), 3.0);
	}
	return tmp;
}

Python

def code(a, b, c):
	tmp = 0
	if math.fabs(b) <= 8.2e-200:
		tmp = math.pow((c * (0.3333333333333333 / a)), 3.0)
	else:
		tmp = math.pow((c * ((0.3333333333333333 * a) / (a * a))), 3.0)
	return tmp

Julia

function code(a, b, c)
	tmp = 0.0
	if (abs(b) <= 8.2e-200)
		tmp = Float64(c * Float64(0.3333333333333333 / a)) ^ 3.0;
	else
		tmp = Float64(c * Float64(Float64(0.3333333333333333 * a) / Float64(a * a))) ^ 3.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(a, b, c)
	tmp = 0.0;
	if (abs(b) <= 8.2e-200)
		tmp = (c * (0.3333333333333333 / a)) ^ 3.0;
	else
		tmp = (c * ((0.3333333333333333 * a) / (a * a))) ^ 3.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[a_, b_, c_] := If[LessEqual[N[Abs[b], $MachinePrecision], 8.2e-200], N[Power[N[(c * N[(0.3333333333333333 / a), $MachinePrecision]), $MachinePrecision], 3.0], $MachinePrecision], N[Power[N[(c * N[(N[(0.3333333333333333 * a), $MachinePrecision] / N[(a * a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 3.0], $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if b < 8.1999999999999997e-200

   1. Initial program 80.4%

      \[{\left(\frac{3 \cdot \left(a \cdot c\right) - {b}^{2}}{9 \cdot {a}^{2}}\right)}^{3} \]

   2. Taylor expanded in a around inf

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

      1. lower-*.f64 N/A

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

      2. lower-/.f64 58.0%

         \[\leadsto {\left(0.3333333333333333 \cdot \frac{c}{\color{blue}{a}}\right)}^{3} \]

   4. Applied rewrites 58.0%

      \[\leadsto {\color{blue}{\left(0.3333333333333333 \cdot \frac{c}{a}\right)}}^{3} \]
   5. Step-by-step derivation

      1. lift-*.f64 N/A

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

      2. *-commutative N/A

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

      3. lift-/.f64 N/A

         \[\leadsto {\left(\frac{c}{a} \cdot \frac{1}{3}\right)}^{3} \]

      4. associate-*l/ N/A

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

      5. associate-/l* N/A

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

      6. lower-*.f64 N/A

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

      7. lower-/.f64 58.0%

         \[\leadsto {\left(c \cdot \frac{0.3333333333333333}{\color{blue}{a}}\right)}^{3} \]

   6. Applied rewrites 58.0%

      \[\leadsto {\left(c \cdot \color{blue}{\frac{0.3333333333333333}{a}}\right)}^{3} \]

   if 8.1999999999999997e-200 < b

   1. Initial program 80.4%

      \[{\left(\frac{3 \cdot \left(a \cdot c\right) - {b}^{2}}{9 \cdot {a}^{2}}\right)}^{3} \]

   2. Taylor expanded in a around inf

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

      1. lower-*.f64 N/A

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

      2. lower-/.f64 58.0%

         \[\leadsto {\left(0.3333333333333333 \cdot \frac{c}{\color{blue}{a}}\right)}^{3} \]

   4. Applied rewrites 58.0%

      \[\leadsto {\color{blue}{\left(0.3333333333333333 \cdot \frac{c}{a}\right)}}^{3} \]
   5. Step-by-step derivation

      1. lift-*.f64 N/A

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

      2. lift-/.f64 N/A

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

      3. mult-flip N/A

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

      4. inv-pow N/A

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

      5. metadata-eval N/A

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

      6. metadata-eval N/A

         \[\leadsto {\left(\frac{1}{3} \cdot \left(c \cdot {a}^{\left(\left(\mathsf{neg}\left(2\right)\right) + 1\right)}\right)\right)}^{3} \]

      7. pow-plus N/A

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

      8. pow-flip N/A

         \[\leadsto {\left(\frac{1}{3} \cdot \left(c \cdot \left(\frac{1}{{a}^{2}} \cdot a\right)\right)\right)}^{3} \]

      9. pow2 N/A

         \[\leadsto {\left(\frac{1}{3} \cdot \left(c \cdot \left(\frac{1}{a \cdot a} \cdot a\right)\right)\right)}^{3} \]

      10. lift-*.f64 N/A

          \[\leadsto {\left(\frac{1}{3} \cdot \left(c \cdot \left(\frac{1}{a \cdot a} \cdot a\right)\right)\right)}^{3} \]

      11. associate-*l* N/A

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

      12. mult-flip N/A

          \[\leadsto {\left(\frac{1}{3} \cdot \left(\frac{c}{a \cdot a} \cdot a\right)\right)}^{3} \]

      13. lift-/.f64 N/A

          \[\leadsto {\left(\frac{1}{3} \cdot \left(\frac{c}{a \cdot a} \cdot a\right)\right)}^{3} \]

      14. *-commutative N/A

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

      15. associate-*r* N/A

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

      16. lift-*.f64 N/A

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

      17. lift-/.f64 N/A

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

      18. associate-*l/ N/A

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

      19. associate-/l* N/A

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

      20. lower-*.f64 N/A

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

      21. lower-/.f64 58.4%

          \[\leadsto {\left(c \cdot \frac{a \cdot 0.3333333333333333}{\color{blue}{a \cdot a}}\right)}^{3} \]

      22. lift-*.f64 N/A

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

      23. *-commutative N/A

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

      24. lower-*.f64 58.4%

          \[\leadsto {\left(c \cdot \frac{0.3333333333333333 \cdot a}{\color{blue}{a} \cdot a}\right)}^{3} \]

   6. Applied rewrites 58.4%

      \[\leadsto {\left(c \cdot \color{blue}{\frac{0.3333333333333333 \cdot a}{a \cdot a}}\right)}^{3} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 10: 58.0% accurate, 2.4× speedup

Math

\[\begin{array}{l} t_0 := \frac{0.3333333333333333}{a} \cdot c\\ \left(t\_0 \cdot t\_0\right) \cdot t\_0 \end{array} \]

FPCore

(FPCore (a b c)
 :precision binary64
 (let* ((t_0 (* (/ 0.3333333333333333 a) c))) (* (* t_0 t_0) t_0)))

C

double code(double a, double b, double c) {
	double t_0 = (0.3333333333333333 / a) * c;
	return (t_0 * t_0) * t_0;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(a, b, c)
use fmin_fmax_functions
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8) :: t_0
    t_0 = (0.3333333333333333d0 / a) * c
    code = (t_0 * t_0) * t_0
end function

Java

public static double code(double a, double b, double c) {
	double t_0 = (0.3333333333333333 / a) * c;
	return (t_0 * t_0) * t_0;
}

Python

def code(a, b, c):
	t_0 = (0.3333333333333333 / a) * c
	return (t_0 * t_0) * t_0

Julia

function code(a, b, c)
	t_0 = Float64(Float64(0.3333333333333333 / a) * c)
	return Float64(Float64(t_0 * t_0) * t_0)
end

MATLAB

function tmp = code(a, b, c)
	t_0 = (0.3333333333333333 / a) * c;
	tmp = (t_0 * t_0) * t_0;
end

Wolfram

code[a_, b_, c_] := Block[{t$95$0 = N[(N[(0.3333333333333333 / a), $MachinePrecision] * c), $MachinePrecision]}, N[(N[(t$95$0 * t$95$0), $MachinePrecision] * t$95$0), $MachinePrecision]]

Derivation

1. Initial program 80.4%

   \[{\left(\frac{3 \cdot \left(a \cdot c\right) - {b}^{2}}{9 \cdot {a}^{2}}\right)}^{3} \]

2. Taylor expanded in a around inf

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

   1. lower-*.f64 N/A

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

   2. lower-/.f64 58.0%

      \[\leadsto {\left(0.3333333333333333 \cdot \frac{c}{\color{blue}{a}}\right)}^{3} \]

4. Applied rewrites 58.0%

   \[\leadsto {\color{blue}{\left(0.3333333333333333 \cdot \frac{c}{a}\right)}}^{3} \]
5. Step-by-step derivation

   1. lift-*.f64 N/A

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

   2. *-commutative N/A

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

   3. lift-/.f64 N/A

      \[\leadsto {\left(\frac{c}{a} \cdot \frac{1}{3}\right)}^{3} \]

   4. associate-*l/ N/A

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

   5. associate-/l* N/A

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

   6. lower-*.f64 N/A

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

   7. lower-/.f64 58.0%

      \[\leadsto {\left(c \cdot \frac{0.3333333333333333}{\color{blue}{a}}\right)}^{3} \]

6. Applied rewrites 58.0%

   \[\leadsto {\left(c \cdot \color{blue}{\frac{0.3333333333333333}{a}}\right)}^{3} \]
7. Step-by-step derivation

   1. lift-pow.f64 N/A

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

   2. unpow3 N/A

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

   3. lower-*.f64 N/A

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

8. Applied rewrites 58.0%

   \[\leadsto \color{blue}{\left(\left(\frac{0.3333333333333333}{a} \cdot c\right) \cdot \left(\frac{0.3333333333333333}{a} \cdot c\right)\right) \cdot \left(\frac{0.3333333333333333}{a} \cdot c\right)} \]
9. Add Preprocessing

Alternative 11: 58.0% accurate, 2.7× speedup

Math

\[{\left(c \cdot \frac{0.3333333333333333}{a}\right)}^{3} \]

FPCore

(FPCore (a b c) :precision binary64 (pow (* c (/ 0.3333333333333333 a)) 3.0))

C

double code(double a, double b, double c) {
	return pow((c * (0.3333333333333333 / a)), 3.0);
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(a, b, c)
use fmin_fmax_functions
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    code = (c * (0.3333333333333333d0 / a)) ** 3.0d0
end function

Java

public static double code(double a, double b, double c) {
	return Math.pow((c * (0.3333333333333333 / a)), 3.0);
}

Python

def code(a, b, c):
	return math.pow((c * (0.3333333333333333 / a)), 3.0)

Julia

function code(a, b, c)
	return Float64(c * Float64(0.3333333333333333 / a)) ^ 3.0
end

MATLAB

function tmp = code(a, b, c)
	tmp = (c * (0.3333333333333333 / a)) ^ 3.0;
end

Wolfram

code[a_, b_, c_] := N[Power[N[(c * N[(0.3333333333333333 / a), $MachinePrecision]), $MachinePrecision], 3.0], $MachinePrecision]

Derivation

1. Initial program 80.4%

   \[{\left(\frac{3 \cdot \left(a \cdot c\right) - {b}^{2}}{9 \cdot {a}^{2}}\right)}^{3} \]

2. Taylor expanded in a around inf

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

   1. lower-*.f64 N/A

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

   2. lower-/.f64 58.0%

      \[\leadsto {\left(0.3333333333333333 \cdot \frac{c}{\color{blue}{a}}\right)}^{3} \]

4. Applied rewrites 58.0%

   \[\leadsto {\color{blue}{\left(0.3333333333333333 \cdot \frac{c}{a}\right)}}^{3} \]
5. Step-by-step derivation

   1. lift-*.f64 N/A

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

   2. *-commutative N/A

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

   3. lift-/.f64 N/A

      \[\leadsto {\left(\frac{c}{a} \cdot \frac{1}{3}\right)}^{3} \]

   4. associate-*l/ N/A

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

   5. associate-/l* N/A

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

   6. lower-*.f64 N/A

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

   7. lower-/.f64 58.0%

      \[\leadsto {\left(c \cdot \frac{0.3333333333333333}{\color{blue}{a}}\right)}^{3} \]

6. Applied rewrites 58.0%

   \[\leadsto {\left(c \cdot \color{blue}{\frac{0.3333333333333333}{a}}\right)}^{3} \]
7. Add Preprocessing

Alternative 12: 58.0% accurate, 2.7× speedup

Math

\[{\left(0.3333333333333333 \cdot \frac{c}{a}\right)}^{3} \]

FPCore

(FPCore (a b c) :precision binary64 (pow (* 0.3333333333333333 (/ c a)) 3.0))

C

double code(double a, double b, double c) {
	return pow((0.3333333333333333 * (c / a)), 3.0);
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(a, b, c)
use fmin_fmax_functions
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    code = (0.3333333333333333d0 * (c / a)) ** 3.0d0
end function

Java

public static double code(double a, double b, double c) {
	return Math.pow((0.3333333333333333 * (c / a)), 3.0);
}

Python

def code(a, b, c):
	return math.pow((0.3333333333333333 * (c / a)), 3.0)

Julia

function code(a, b, c)
	return Float64(0.3333333333333333 * Float64(c / a)) ^ 3.0
end

MATLAB

function tmp = code(a, b, c)
	tmp = (0.3333333333333333 * (c / a)) ^ 3.0;
end

Wolfram

code[a_, b_, c_] := N[Power[N[(0.3333333333333333 * N[(c / a), $MachinePrecision]), $MachinePrecision], 3.0], $MachinePrecision]

Derivation

1. Initial program 80.4%

   \[{\left(\frac{3 \cdot \left(a \cdot c\right) - {b}^{2}}{9 \cdot {a}^{2}}\right)}^{3} \]

2. Taylor expanded in a around inf

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

   1. lower-*.f64 N/A

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

   2. lower-/.f64 58.0%

      \[\leadsto {\left(0.3333333333333333 \cdot \frac{c}{\color{blue}{a}}\right)}^{3} \]

4. Applied rewrites 58.0%

   \[\leadsto {\color{blue}{\left(0.3333333333333333 \cdot \frac{c}{a}\right)}}^{3} \]
5. Add Preprocessing

Reproduce

herbie shell --seed 2025191 
(FPCore (a b c)
  :name "Q^3 (Cubic Equation Discriminant Part)"
  :precision binary64
  :pre (and (and (and (<= -1000000000.0 a) (<= a 1000000000.0)) (and (<= -1000000000.0 b) (<= b 1000000000.0))) (and (<= -1000000000.0 c) (<= c 1000000000.0)))
  (pow (/ (- (* 3.0 (* a c)) (pow b 2.0)) (* 9.0 (pow a 2.0))) 3.0))