Diagrams.Solve.Polynomial:cubForm from diagrams-solve-0.1, K

Percentage Accurate: 69.8% → 77.7%

Time: 17.7s

Alternatives: 9

Speedup: 1.2×

Specification

Math

\[\begin{array}{l} \\ \left(2 \cdot \sqrt{x}\right) \cdot \cos \left(y - \frac{z \cdot t}{3}\right) - \frac{a}{b \cdot 3} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (- (* (* 2.0 (sqrt x)) (cos (- y (/ (* z t) 3.0)))) (/ a (* b 3.0))))

C

double code(double x, double y, double z, double t, double a, double b) {
	return ((2.0 * sqrt(x)) * cos((y - ((z * t) / 3.0)))) - (a / (b * 3.0));
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    code = ((2.0d0 * sqrt(x)) * cos((y - ((z * t) / 3.0d0)))) - (a / (b * 3.0d0))
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	return ((2.0 * Math.sqrt(x)) * Math.cos((y - ((z * t) / 3.0)))) - (a / (b * 3.0));
}

Python

def code(x, y, z, t, a, b):
	return ((2.0 * math.sqrt(x)) * math.cos((y - ((z * t) / 3.0)))) - (a / (b * 3.0))

Julia

function code(x, y, z, t, a, b)
	return Float64(Float64(Float64(2.0 * sqrt(x)) * cos(Float64(y - Float64(Float64(z * t) / 3.0)))) - Float64(a / Float64(b * 3.0)))
end

MATLAB

function tmp = code(x, y, z, t, a, b)
	tmp = ((2.0 * sqrt(x)) * cos((y - ((z * t) / 3.0)))) - (a / (b * 3.0));
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := N[(N[(N[(2.0 * N[Sqrt[x], $MachinePrecision]), $MachinePrecision] * N[Cos[N[(y - N[(N[(z * t), $MachinePrecision] / 3.0), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] - N[(a / N[(b * 3.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Initial Program: 69.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \left(2 \cdot \sqrt{x}\right) \cdot \cos \left(y - \frac{z \cdot t}{3}\right) - \frac{a}{b \cdot 3} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (- (* (* 2.0 (sqrt x)) (cos (- y (/ (* z t) 3.0)))) (/ a (* b 3.0))))

C

double code(double x, double y, double z, double t, double a, double b) {
	return ((2.0 * sqrt(x)) * cos((y - ((z * t) / 3.0)))) - (a / (b * 3.0));
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    code = ((2.0d0 * sqrt(x)) * cos((y - ((z * t) / 3.0d0)))) - (a / (b * 3.0d0))
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	return ((2.0 * Math.sqrt(x)) * Math.cos((y - ((z * t) / 3.0)))) - (a / (b * 3.0));
}

Python

def code(x, y, z, t, a, b):
	return ((2.0 * math.sqrt(x)) * math.cos((y - ((z * t) / 3.0)))) - (a / (b * 3.0))

Julia

function code(x, y, z, t, a, b)
	return Float64(Float64(Float64(2.0 * sqrt(x)) * cos(Float64(y - Float64(Float64(z * t) / 3.0)))) - Float64(a / Float64(b * 3.0)))
end

MATLAB

function tmp = code(x, y, z, t, a, b)
	tmp = ((2.0 * sqrt(x)) * cos((y - ((z * t) / 3.0)))) - (a / (b * 3.0));
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := N[(N[(N[(2.0 * N[Sqrt[x], $MachinePrecision]), $MachinePrecision] * N[Cos[N[(y - N[(N[(z * t), $MachinePrecision] / 3.0), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] - N[(a / N[(b * 3.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Alternative 1: 77.7% accurate, 0.3× speedup

Math

\[\begin{array}{l} [x, y, z, t, a, b] = \mathsf{sort}([x, y, z, t, a, b])\\ \\ \begin{array}{l} t_1 := \frac{a}{b \cdot 3}\\ t_2 := \sqrt{x} \cdot 2\\ \mathbf{if}\;t\_2 \cdot \cos \left(y - \frac{t \cdot z}{3}\right) \leq 6 \cdot 10^{+145}:\\ \;\;\;\;t\_2 \cdot \mathsf{fma}\left(\cos \left(\left(-0.3333333333333333 \cdot t\right) \cdot z\right), \cos y, \sin y \cdot \sin \left(\left(0.3333333333333333 \cdot z\right) \cdot t\right)\right) - t\_1\\ \mathbf{else}:\\ \;\;\;\;\cos y \cdot \left(\left(\frac{x}{{x}^{0.25}} \cdot \frac{1}{{x}^{0.25}}\right) \cdot 2\right) - t\_1\\ \end{array} \end{array} \]

FPCore

NOTE: x, y, z, t, a, and b should be sorted in increasing order before calling this function.
(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (/ a (* b 3.0))) (t_2 (* (sqrt x) 2.0)))
   (if (<= (* t_2 (cos (- y (/ (* t z) 3.0)))) 6e+145)
     (-
      (*
       t_2
       (fma
        (cos (* (* -0.3333333333333333 t) z))
        (cos y)
        (* (sin y) (sin (* (* 0.3333333333333333 z) t)))))
      t_1)
     (- (* (cos y) (* (* (/ x (pow x 0.25)) (/ 1.0 (pow x 0.25))) 2.0)) t_1))))

C

assert(x < y && y < z && z < t && t < a && a < b);
double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = a / (b * 3.0);
	double t_2 = sqrt(x) * 2.0;
	double tmp;
	if ((t_2 * cos((y - ((t * z) / 3.0)))) <= 6e+145) {
		tmp = (t_2 * fma(cos(((-0.3333333333333333 * t) * z)), cos(y), (sin(y) * sin(((0.3333333333333333 * z) * t))))) - t_1;
	} else {
		tmp = (cos(y) * (((x / pow(x, 0.25)) * (1.0 / pow(x, 0.25))) * 2.0)) - t_1;
	}
	return tmp;
}

Julia

x, y, z, t, a, b = sort([x, y, z, t, a, b])
function code(x, y, z, t, a, b)
	t_1 = Float64(a / Float64(b * 3.0))
	t_2 = Float64(sqrt(x) * 2.0)
	tmp = 0.0
	if (Float64(t_2 * cos(Float64(y - Float64(Float64(t * z) / 3.0)))) <= 6e+145)
		tmp = Float64(Float64(t_2 * fma(cos(Float64(Float64(-0.3333333333333333 * t) * z)), cos(y), Float64(sin(y) * sin(Float64(Float64(0.3333333333333333 * z) * t))))) - t_1);
	else
		tmp = Float64(Float64(cos(y) * Float64(Float64(Float64(x / (x ^ 0.25)) * Float64(1.0 / (x ^ 0.25))) * 2.0)) - t_1);
	end
	return tmp
end

Wolfram

NOTE: x, y, z, t, a, and b should be sorted in increasing order before calling this function.
code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(a / N[(b * 3.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[Sqrt[x], $MachinePrecision] * 2.0), $MachinePrecision]}, If[LessEqual[N[(t$95$2 * N[Cos[N[(y - N[(N[(t * z), $MachinePrecision] / 3.0), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision], 6e+145], N[(N[(t$95$2 * N[(N[Cos[N[(N[(-0.3333333333333333 * t), $MachinePrecision] * z), $MachinePrecision]], $MachinePrecision] * N[Cos[y], $MachinePrecision] + N[(N[Sin[y], $MachinePrecision] * N[Sin[N[(N[(0.3333333333333333 * z), $MachinePrecision] * t), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - t$95$1), $MachinePrecision], N[(N[(N[Cos[y], $MachinePrecision] * N[(N[(N[(x / N[Power[x, 0.25], $MachinePrecision]), $MachinePrecision] * N[(1.0 / N[Power[x, 0.25], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * 2.0), $MachinePrecision]), $MachinePrecision] - t$95$1), $MachinePrecision]]]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (*.f64 #s(literal 2 binary64) (sqrt.f64 x)) (cos.f64 (-.f64 y (/.f64 (*.f64 z t) #s(literal 3 binary64))))) < 6.0000000000000005e145

   1. Initial program 83.0%

      \[\left(2 \cdot \sqrt{x}\right) \cdot \cos \left(y - \frac{z \cdot t}{3}\right) - \frac{a}{b \cdot 3} \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift--.f64 N/A

         \[\leadsto \left(2 \cdot \sqrt{x}\right) \cdot \cos \color{blue}{\left(y - \frac{z \cdot t}{3}\right)} - \frac{a}{b \cdot 3} \]

      2. flip-- N/A

         \[\leadsto \left(2 \cdot \sqrt{x}\right) \cdot \cos \color{blue}{\left(\frac{y \cdot y - \frac{z \cdot t}{3} \cdot \frac{z \cdot t}{3}}{y + \frac{z \cdot t}{3}}\right)} - \frac{a}{b \cdot 3} \]

      3. frac-2neg N/A

         \[\leadsto \left(2 \cdot \sqrt{x}\right) \cdot \cos \color{blue}{\left(\frac{\mathsf{neg}\left(\left(y \cdot y - \frac{z \cdot t}{3} \cdot \frac{z \cdot t}{3}\right)\right)}{\mathsf{neg}\left(\left(y + \frac{z \cdot t}{3}\right)\right)}\right)} - \frac{a}{b \cdot 3} \]

      4. div-inv N/A

         \[\leadsto \left(2 \cdot \sqrt{x}\right) \cdot \cos \color{blue}{\left(\left(\mathsf{neg}\left(\left(y \cdot y - \frac{z \cdot t}{3} \cdot \frac{z \cdot t}{3}\right)\right)\right) \cdot \frac{1}{\mathsf{neg}\left(\left(y + \frac{z \cdot t}{3}\right)\right)}\right)} - \frac{a}{b \cdot 3} \]

      5. lower-*.f64 N/A

         \[\leadsto \left(2 \cdot \sqrt{x}\right) \cdot \cos \color{blue}{\left(\left(\mathsf{neg}\left(\left(y \cdot y - \frac{z \cdot t}{3} \cdot \frac{z \cdot t}{3}\right)\right)\right) \cdot \frac{1}{\mathsf{neg}\left(\left(y + \frac{z \cdot t}{3}\right)\right)}\right)} - \frac{a}{b \cdot 3} \]

   4. Applied rewrites 54.6%

      \[\leadsto \left(2 \cdot \sqrt{x}\right) \cdot \cos \color{blue}{\left(\left(-\mathsf{fma}\left(y, y, {\left(t \cdot z\right)}^{2} \cdot -0.1111111111111111\right)\right) \cdot \frac{1}{-\mathsf{fma}\left(0.3333333333333333, t \cdot z, y\right)}\right)} - \frac{a}{b \cdot 3} \]
   5. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \left(2 \cdot \sqrt{x}\right) \cdot \cos \color{blue}{\left(\left(-\mathsf{fma}\left(y, y, {\left(t \cdot z\right)}^{2} \cdot \frac{-1}{9}\right)\right) \cdot \frac{1}{-\mathsf{fma}\left(\frac{1}{3}, t \cdot z, y\right)}\right)} - \frac{a}{b \cdot 3} \]

      2. lift-/.f64 N/A

         \[\leadsto \left(2 \cdot \sqrt{x}\right) \cdot \cos \left(\left(-\mathsf{fma}\left(y, y, {\left(t \cdot z\right)}^{2} \cdot \frac{-1}{9}\right)\right) \cdot \color{blue}{\frac{1}{-\mathsf{fma}\left(\frac{1}{3}, t \cdot z, y\right)}}\right) - \frac{a}{b \cdot 3} \]

      3. un-div-inv N/A

         \[\leadsto \left(2 \cdot \sqrt{x}\right) \cdot \cos \color{blue}{\left(\frac{-\mathsf{fma}\left(y, y, {\left(t \cdot z\right)}^{2} \cdot \frac{-1}{9}\right)}{-\mathsf{fma}\left(\frac{1}{3}, t \cdot z, y\right)}\right)} - \frac{a}{b \cdot 3} \]

      4. lift-neg.f64 N/A

         \[\leadsto \left(2 \cdot \sqrt{x}\right) \cdot \cos \left(\frac{-\mathsf{fma}\left(y, y, {\left(t \cdot z\right)}^{2} \cdot \frac{-1}{9}\right)}{\color{blue}{\mathsf{neg}\left(\mathsf{fma}\left(\frac{1}{3}, t \cdot z, y\right)\right)}}\right) - \frac{a}{b \cdot 3} \]

      5. lift-neg.f64 N/A

         \[\leadsto \left(2 \cdot \sqrt{x}\right) \cdot \cos \left(\frac{\color{blue}{\mathsf{neg}\left(\mathsf{fma}\left(y, y, {\left(t \cdot z\right)}^{2} \cdot \frac{-1}{9}\right)\right)}}{\mathsf{neg}\left(\mathsf{fma}\left(\frac{1}{3}, t \cdot z, y\right)\right)}\right) - \frac{a}{b \cdot 3} \]

      6. frac-2neg N/A

         \[\leadsto \left(2 \cdot \sqrt{x}\right) \cdot \cos \color{blue}{\left(\frac{\mathsf{fma}\left(y, y, {\left(t \cdot z\right)}^{2} \cdot \frac{-1}{9}\right)}{\mathsf{fma}\left(\frac{1}{3}, t \cdot z, y\right)}\right)} - \frac{a}{b \cdot 3} \]

   6. Applied rewrites 82.8%

      \[\leadsto \left(2 \cdot \sqrt{x}\right) \cdot \cos \color{blue}{\left(\mathsf{fma}\left(-0.3333333333333333 \cdot z, t, y\right)\right)} - \frac{a}{b \cdot 3} \]
   7. Step-by-step derivation

      1. lift-cos.f64 N/A

         \[\leadsto \left(2 \cdot \sqrt{x}\right) \cdot \color{blue}{\cos \left(\mathsf{fma}\left(\frac{-1}{3} \cdot z, t, y\right)\right)} - \frac{a}{b \cdot 3} \]

      2. lift-fma.f64 N/A

         \[\leadsto \left(2 \cdot \sqrt{x}\right) \cdot \cos \color{blue}{\left(\left(\frac{-1}{3} \cdot z\right) \cdot t + y\right)} - \frac{a}{b \cdot 3} \]

      3. +-commutative N/A

         \[\leadsto \left(2 \cdot \sqrt{x}\right) \cdot \cos \color{blue}{\left(y + \left(\frac{-1}{3} \cdot z\right) \cdot t\right)} - \frac{a}{b \cdot 3} \]

      4. lift-*.f64 N/A

         \[\leadsto \left(2 \cdot \sqrt{x}\right) \cdot \cos \left(y + \color{blue}{\left(\frac{-1}{3} \cdot z\right)} \cdot t\right) - \frac{a}{b \cdot 3} \]

      5. associate-*l* N/A

         \[\leadsto \left(2 \cdot \sqrt{x}\right) \cdot \cos \left(y + \color{blue}{\frac{-1}{3} \cdot \left(z \cdot t\right)}\right) - \frac{a}{b \cdot 3} \]

      6. metadata-eval N/A

         \[\leadsto \left(2 \cdot \sqrt{x}\right) \cdot \cos \left(y + \color{blue}{\left(\mathsf{neg}\left(\frac{1}{3}\right)\right)} \cdot \left(z \cdot t\right)\right) - \frac{a}{b \cdot 3} \]

      7. *-commutative N/A

         \[\leadsto \left(2 \cdot \sqrt{x}\right) \cdot \cos \left(y + \left(\mathsf{neg}\left(\frac{1}{3}\right)\right) \cdot \color{blue}{\left(t \cdot z\right)}\right) - \frac{a}{b \cdot 3} \]

      8. lift-*.f64 N/A

         \[\leadsto \left(2 \cdot \sqrt{x}\right) \cdot \cos \left(y + \left(\mathsf{neg}\left(\frac{1}{3}\right)\right) \cdot \color{blue}{\left(t \cdot z\right)}\right) - \frac{a}{b \cdot 3} \]

      9. cancel-sign-sub-inv N/A

         \[\leadsto \left(2 \cdot \sqrt{x}\right) \cdot \cos \color{blue}{\left(y - \frac{1}{3} \cdot \left(t \cdot z\right)\right)} - \frac{a}{b \cdot 3} \]

      10. *-commutative N/A

          \[\leadsto \left(2 \cdot \sqrt{x}\right) \cdot \cos \left(y - \color{blue}{\left(t \cdot z\right) \cdot \frac{1}{3}}\right) - \frac{a}{b \cdot 3} \]

      11. lift-*.f64 N/A

          \[\leadsto \left(2 \cdot \sqrt{x}\right) \cdot \cos \left(y - \color{blue}{\left(t \cdot z\right)} \cdot \frac{1}{3}\right) - \frac{a}{b \cdot 3} \]

      12. *-commutative N/A

          \[\leadsto \left(2 \cdot \sqrt{x}\right) \cdot \cos \left(y - \color{blue}{\left(z \cdot t\right)} \cdot \frac{1}{3}\right) - \frac{a}{b \cdot 3} \]

      13. metadata-eval N/A

          \[\leadsto \left(2 \cdot \sqrt{x}\right) \cdot \cos \left(y - \left(z \cdot t\right) \cdot \color{blue}{\frac{1}{3}}\right) - \frac{a}{b \cdot 3} \]

      14. div-inv N/A

          \[\leadsto \left(2 \cdot \sqrt{x}\right) \cdot \cos \left(y - \color{blue}{\frac{z \cdot t}{3}}\right) - \frac{a}{b \cdot 3} \]

      15. cos-diff N/A

          \[\leadsto \left(2 \cdot \sqrt{x}\right) \cdot \color{blue}{\left(\cos y \cdot \cos \left(\frac{z \cdot t}{3}\right) + \sin y \cdot \sin \left(\frac{z \cdot t}{3}\right)\right)} - \frac{a}{b \cdot 3} \]

      16. lift-cos.f64 N/A

          \[\leadsto \left(2 \cdot \sqrt{x}\right) \cdot \left(\color{blue}{\cos y} \cdot \cos \left(\frac{z \cdot t}{3}\right) + \sin y \cdot \sin \left(\frac{z \cdot t}{3}\right)\right) - \frac{a}{b \cdot 3} \]

      17. *-commutative N/A

          \[\leadsto \left(2 \cdot \sqrt{x}\right) \cdot \left(\color{blue}{\cos \left(\frac{z \cdot t}{3}\right) \cdot \cos y} + \sin y \cdot \sin \left(\frac{z \cdot t}{3}\right)\right) - \frac{a}{b \cdot 3} \]

      18. lower-fma.f64 N/A

          \[\leadsto \left(2 \cdot \sqrt{x}\right) \cdot \color{blue}{\mathsf{fma}\left(\cos \left(\frac{z \cdot t}{3}\right), \cos y, \sin y \cdot \sin \left(\frac{z \cdot t}{3}\right)\right)} - \frac{a}{b \cdot 3} \]

   8. Applied rewrites 83.1%

      \[\leadsto \left(2 \cdot \sqrt{x}\right) \cdot \color{blue}{\mathsf{fma}\left(\cos \left(\left(t \cdot z\right) \cdot -0.3333333333333333\right), \cos y, \sin \left(\left(0.3333333333333333 \cdot z\right) \cdot t\right) \cdot \sin y\right)} - \frac{a}{b \cdot 3} \]
   9. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \left(2 \cdot \sqrt{x}\right) \cdot \mathsf{fma}\left(\cos \color{blue}{\left(\left(t \cdot z\right) \cdot \frac{-1}{3}\right)}, \cos y, \sin \left(\left(\frac{1}{3} \cdot z\right) \cdot t\right) \cdot \sin y\right) - \frac{a}{b \cdot 3} \]

      2. lift-*.f64 N/A

         \[\leadsto \left(2 \cdot \sqrt{x}\right) \cdot \mathsf{fma}\left(\cos \left(\color{blue}{\left(t \cdot z\right)} \cdot \frac{-1}{3}\right), \cos y, \sin \left(\left(\frac{1}{3} \cdot z\right) \cdot t\right) \cdot \sin y\right) - \frac{a}{b \cdot 3} \]

      3. associate-*l* N/A

         \[\leadsto \left(2 \cdot \sqrt{x}\right) \cdot \mathsf{fma}\left(\cos \color{blue}{\left(t \cdot \left(z \cdot \frac{-1}{3}\right)\right)}, \cos y, \sin \left(\left(\frac{1}{3} \cdot z\right) \cdot t\right) \cdot \sin y\right) - \frac{a}{b \cdot 3} \]

      4. *-commutative N/A

         \[\leadsto \left(2 \cdot \sqrt{x}\right) \cdot \mathsf{fma}\left(\cos \left(t \cdot \color{blue}{\left(\frac{-1}{3} \cdot z\right)}\right), \cos y, \sin \left(\left(\frac{1}{3} \cdot z\right) \cdot t\right) \cdot \sin y\right) - \frac{a}{b \cdot 3} \]

      5. associate-*l* N/A

         \[\leadsto \left(2 \cdot \sqrt{x}\right) \cdot \mathsf{fma}\left(\cos \color{blue}{\left(\left(t \cdot \frac{-1}{3}\right) \cdot z\right)}, \cos y, \sin \left(\left(\frac{1}{3} \cdot z\right) \cdot t\right) \cdot \sin y\right) - \frac{a}{b \cdot 3} \]

      6. lift-*.f64 N/A

         \[\leadsto \left(2 \cdot \sqrt{x}\right) \cdot \mathsf{fma}\left(\cos \left(\color{blue}{\left(t \cdot \frac{-1}{3}\right)} \cdot z\right), \cos y, \sin \left(\left(\frac{1}{3} \cdot z\right) \cdot t\right) \cdot \sin y\right) - \frac{a}{b \cdot 3} \]

      7. lift-*.f64 83.1

         \[\leadsto \left(2 \cdot \sqrt{x}\right) \cdot \mathsf{fma}\left(\cos \color{blue}{\left(\left(t \cdot -0.3333333333333333\right) \cdot z\right)}, \cos y, \sin \left(\left(0.3333333333333333 \cdot z\right) \cdot t\right) \cdot \sin y\right) - \frac{a}{b \cdot 3} \]

      8. lift-*.f64 N/A

         \[\leadsto \left(2 \cdot \sqrt{x}\right) \cdot \mathsf{fma}\left(\cos \left(\color{blue}{\left(t \cdot \frac{-1}{3}\right)} \cdot z\right), \cos y, \sin \left(\left(\frac{1}{3} \cdot z\right) \cdot t\right) \cdot \sin y\right) - \frac{a}{b \cdot 3} \]

      9. *-commutative N/A

         \[\leadsto \left(2 \cdot \sqrt{x}\right) \cdot \mathsf{fma}\left(\cos \left(\color{blue}{\left(\frac{-1}{3} \cdot t\right)} \cdot z\right), \cos y, \sin \left(\left(\frac{1}{3} \cdot z\right) \cdot t\right) \cdot \sin y\right) - \frac{a}{b \cdot 3} \]

      10. lower-*.f64 83.1

          \[\leadsto \left(2 \cdot \sqrt{x}\right) \cdot \mathsf{fma}\left(\cos \left(\color{blue}{\left(-0.3333333333333333 \cdot t\right)} \cdot z\right), \cos y, \sin \left(\left(0.3333333333333333 \cdot z\right) \cdot t\right) \cdot \sin y\right) - \frac{a}{b \cdot 3} \]

   10. Applied rewrites 83.1%

       \[\leadsto \left(2 \cdot \sqrt{x}\right) \cdot \mathsf{fma}\left(\color{blue}{\cos \left(\left(-0.3333333333333333 \cdot t\right) \cdot z\right)}, \cos y, \sin \left(\left(0.3333333333333333 \cdot z\right) \cdot t\right) \cdot \sin y\right) - \frac{a}{b \cdot 3} \]

   if 6.0000000000000005e145 < (*.f64 (*.f64 #s(literal 2 binary64) (sqrt.f64 x)) (cos.f64 (-.f64 y (/.f64 (*.f64 z t) #s(literal 3 binary64)))))

   1. Initial program 9.5%

      \[\left(2 \cdot \sqrt{x}\right) \cdot \cos \left(y - \frac{z \cdot t}{3}\right) - \frac{a}{b \cdot 3} \]
   2. Add Preprocessing

   3. Taylor expanded in t around 0

      \[\leadsto \left(2 \cdot \sqrt{x}\right) \cdot \color{blue}{\cos y} - \frac{a}{b \cdot 3} \]
   4. Step-by-step derivation

      1. lower-cos.f64 59.2

         \[\leadsto \left(2 \cdot \sqrt{x}\right) \cdot \color{blue}{\cos y} - \frac{a}{b \cdot 3} \]

   5. Applied rewrites 59.2%

      \[\leadsto \left(2 \cdot \sqrt{x}\right) \cdot \color{blue}{\cos y} - \frac{a}{b \cdot 3} \]
   6. Step-by-step derivation

      1. lift-sqrt.f64 N/A

         \[\leadsto \left(2 \cdot \color{blue}{\sqrt{x}}\right) \cdot \cos y - \frac{a}{b \cdot 3} \]

      2. pow1/2 N/A

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

      3. metadata-eval N/A

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

      4. metadata-eval N/A

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

      5. pow-plus N/A

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

      6. metadata-eval N/A

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

      7. metadata-eval N/A

         \[\leadsto \left(2 \cdot \left({x}^{\color{blue}{\left(\mathsf{neg}\left(\frac{1}{2}\right)\right)}} \cdot x\right)\right) \cdot \cos y - \frac{a}{b \cdot 3} \]

      8. pow-flip N/A

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

      9. pow1/2 N/A

         \[\leadsto \left(2 \cdot \left(\frac{1}{\color{blue}{\sqrt{x}}} \cdot x\right)\right) \cdot \cos y - \frac{a}{b \cdot 3} \]

      10. lift-sqrt.f64 N/A

          \[\leadsto \left(2 \cdot \left(\frac{1}{\color{blue}{\sqrt{x}}} \cdot x\right)\right) \cdot \cos y - \frac{a}{b \cdot 3} \]

      11. associate-*l/ N/A

          \[\leadsto \left(2 \cdot \color{blue}{\frac{1 \cdot x}{\sqrt{x}}}\right) \cdot \cos y - \frac{a}{b \cdot 3} \]

      12. lift-sqrt.f64 N/A

          \[\leadsto \left(2 \cdot \frac{1 \cdot x}{\color{blue}{\sqrt{x}}}\right) \cdot \cos y - \frac{a}{b \cdot 3} \]

      13. pow1/2 N/A

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

      14. sqr-pow N/A

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

      15. times-frac N/A

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

      16. lower-*.f64 N/A

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

      17. lower-/.f64 N/A

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

      18. lower-pow.f64 N/A

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

      19. metadata-eval N/A

          \[\leadsto \left(2 \cdot \left(\frac{1}{{x}^{\color{blue}{\frac{1}{4}}}} \cdot \frac{x}{{x}^{\left(\frac{\frac{1}{2}}{2}\right)}}\right)\right) \cdot \cos y - \frac{a}{b \cdot 3} \]

      20. lower-/.f64 N/A

          \[\leadsto \left(2 \cdot \left(\frac{1}{{x}^{\frac{1}{4}}} \cdot \color{blue}{\frac{x}{{x}^{\left(\frac{\frac{1}{2}}{2}\right)}}}\right)\right) \cdot \cos y - \frac{a}{b \cdot 3} \]

      21. lower-pow.f64 N/A

          \[\leadsto \left(2 \cdot \left(\frac{1}{{x}^{\frac{1}{4}}} \cdot \frac{x}{\color{blue}{{x}^{\left(\frac{\frac{1}{2}}{2}\right)}}}\right)\right) \cdot \cos y - \frac{a}{b \cdot 3} \]

      22. metadata-eval 59.2

          \[\leadsto \left(2 \cdot \left(\frac{1}{{x}^{0.25}} \cdot \frac{x}{{x}^{\color{blue}{0.25}}}\right)\right) \cdot \cos y - \frac{a}{b \cdot 3} \]

   7. Applied rewrites 59.2%

      \[\leadsto \left(2 \cdot \color{blue}{\left(\frac{1}{{x}^{0.25}} \cdot \frac{x}{{x}^{0.25}}\right)}\right) \cdot \cos y - \frac{a}{b \cdot 3} \]
3. Recombined 2 regimes into one program.

4. Final simplification 79.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\left(\sqrt{x} \cdot 2\right) \cdot \cos \left(y - \frac{t \cdot z}{3}\right) \leq 6 \cdot 10^{+145}:\\ \;\;\;\;\left(\sqrt{x} \cdot 2\right) \cdot \mathsf{fma}\left(\cos \left(\left(-0.3333333333333333 \cdot t\right) \cdot z\right), \cos y, \sin y \cdot \sin \left(\left(0.3333333333333333 \cdot z\right) \cdot t\right)\right) - \frac{a}{b \cdot 3}\\ \mathbf{else}:\\ \;\;\;\;\cos y \cdot \left(\left(\frac{x}{{x}^{0.25}} \cdot \frac{1}{{x}^{0.25}}\right) \cdot 2\right) - \frac{a}{b \cdot 3}\\ \end{array} \]
5. Add Preprocessing

Alternative 2: 71.6% accurate, 0.9× speedup

Math

\[\begin{array}{l} [x, y, z, t, a, b] = \mathsf{sort}([x, y, z, t, a, b])\\ \\ \begin{array}{l} t_1 := \sqrt{x} \cdot 2\\ t_2 := \frac{a}{b \cdot 3}\\ t_3 := \mathsf{fma}\left(\frac{-1}{b}, \frac{a}{3}, 1 \cdot t\_1\right)\\ \mathbf{if}\;t\_2 \leq -2 \cdot 10^{-78}:\\ \;\;\;\;t\_3\\ \mathbf{elif}\;t\_2 \leq 2 \cdot 10^{-168}:\\ \;\;\;\;\cos \left(\mathsf{fma}\left(-0.3333333333333333 \cdot t, z, y\right)\right) \cdot t\_1\\ \mathbf{else}:\\ \;\;\;\;t\_3\\ \end{array} \end{array} \]

FPCore

NOTE: x, y, z, t, a, and b should be sorted in increasing order before calling this function.
(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (* (sqrt x) 2.0))
        (t_2 (/ a (* b 3.0)))
        (t_3 (fma (/ -1.0 b) (/ a 3.0) (* 1.0 t_1))))
   (if (<= t_2 -2e-78)
     t_3
     (if (<= t_2 2e-168)
       (* (cos (fma (* -0.3333333333333333 t) z y)) t_1)
       t_3))))

C

assert(x < y && y < z && z < t && t < a && a < b);
double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = sqrt(x) * 2.0;
	double t_2 = a / (b * 3.0);
	double t_3 = fma((-1.0 / b), (a / 3.0), (1.0 * t_1));
	double tmp;
	if (t_2 <= -2e-78) {
		tmp = t_3;
	} else if (t_2 <= 2e-168) {
		tmp = cos(fma((-0.3333333333333333 * t), z, y)) * t_1;
	} else {
		tmp = t_3;
	}
	return tmp;
}

Julia

x, y, z, t, a, b = sort([x, y, z, t, a, b])
function code(x, y, z, t, a, b)
	t_1 = Float64(sqrt(x) * 2.0)
	t_2 = Float64(a / Float64(b * 3.0))
	t_3 = fma(Float64(-1.0 / b), Float64(a / 3.0), Float64(1.0 * t_1))
	tmp = 0.0
	if (t_2 <= -2e-78)
		tmp = t_3;
	elseif (t_2 <= 2e-168)
		tmp = Float64(cos(fma(Float64(-0.3333333333333333 * t), z, y)) * t_1);
	else
		tmp = t_3;
	end
	return tmp
end

Wolfram

NOTE: x, y, z, t, a, and b should be sorted in increasing order before calling this function.
code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(N[Sqrt[x], $MachinePrecision] * 2.0), $MachinePrecision]}, Block[{t$95$2 = N[(a / N[(b * 3.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(N[(-1.0 / b), $MachinePrecision] * N[(a / 3.0), $MachinePrecision] + N[(1.0 * t$95$1), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$2, -2e-78], t$95$3, If[LessEqual[t$95$2, 2e-168], N[(N[Cos[N[(N[(-0.3333333333333333 * t), $MachinePrecision] * z + y), $MachinePrecision]], $MachinePrecision] * t$95$1), $MachinePrecision], t$95$3]]]]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 a (*.f64 b #s(literal 3 binary64))) < -2e-78 or 2.0000000000000001e-168 < (/.f64 a (*.f64 b #s(literal 3 binary64)))

   1. Initial program 77.6%

      \[\left(2 \cdot \sqrt{x}\right) \cdot \cos \left(y - \frac{z \cdot t}{3}\right) - \frac{a}{b \cdot 3} \]
   2. Add Preprocessing

   3. Taylor expanded in t around 0

      \[\leadsto \left(2 \cdot \sqrt{x}\right) \cdot \color{blue}{\cos y} - \frac{a}{b \cdot 3} \]
   4. Step-by-step derivation

      1. lower-cos.f64 86.4

         \[\leadsto \left(2 \cdot \sqrt{x}\right) \cdot \color{blue}{\cos y} - \frac{a}{b \cdot 3} \]

   5. Applied rewrites 86.4%

      \[\leadsto \left(2 \cdot \sqrt{x}\right) \cdot \color{blue}{\cos y} - \frac{a}{b \cdot 3} \]
   6. Step-by-step derivation

      1. lift--.f64 N/A

         \[\leadsto \color{blue}{\left(2 \cdot \sqrt{x}\right) \cdot \cos y - \frac{a}{b \cdot 3}} \]

      2. sub-neg N/A

         \[\leadsto \color{blue}{\left(2 \cdot \sqrt{x}\right) \cdot \cos y + \left(\mathsf{neg}\left(\frac{a}{b \cdot 3}\right)\right)} \]

      3. +-commutative N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(\frac{a}{b \cdot 3}\right)\right) + \left(2 \cdot \sqrt{x}\right) \cdot \cos y} \]

      4. lift-/.f64 N/A

         \[\leadsto \left(\mathsf{neg}\left(\color{blue}{\frac{a}{b \cdot 3}}\right)\right) + \left(2 \cdot \sqrt{x}\right) \cdot \cos y \]

      5. distribute-neg-frac N/A

         \[\leadsto \color{blue}{\frac{\mathsf{neg}\left(a\right)}{b \cdot 3}} + \left(2 \cdot \sqrt{x}\right) \cdot \cos y \]

      6. neg-mul-1 N/A

         \[\leadsto \frac{\color{blue}{-1 \cdot a}}{b \cdot 3} + \left(2 \cdot \sqrt{x}\right) \cdot \cos y \]

      7. lift-*.f64 N/A

         \[\leadsto \frac{-1 \cdot a}{\color{blue}{b \cdot 3}} + \left(2 \cdot \sqrt{x}\right) \cdot \cos y \]

      8. times-frac N/A

         \[\leadsto \color{blue}{\frac{-1}{b} \cdot \frac{a}{3}} + \left(2 \cdot \sqrt{x}\right) \cdot \cos y \]

      9. lower-fma.f64 N/A

         \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{-1}{b}, \frac{a}{3}, \left(2 \cdot \sqrt{x}\right) \cdot \cos y\right)} \]

      10. lower-/.f64 N/A

          \[\leadsto \mathsf{fma}\left(\color{blue}{\frac{-1}{b}}, \frac{a}{3}, \left(2 \cdot \sqrt{x}\right) \cdot \cos y\right) \]

      11. lower-/.f64 86.3

          \[\leadsto \mathsf{fma}\left(\frac{-1}{b}, \color{blue}{\frac{a}{3}}, \left(2 \cdot \sqrt{x}\right) \cdot \cos y\right) \]

      12. lift-*.f64 N/A

          \[\leadsto \mathsf{fma}\left(\frac{-1}{b}, \frac{a}{3}, \color{blue}{\left(2 \cdot \sqrt{x}\right) \cdot \cos y}\right) \]

      13. *-commutative N/A

          \[\leadsto \mathsf{fma}\left(\frac{-1}{b}, \frac{a}{3}, \color{blue}{\cos y \cdot \left(2 \cdot \sqrt{x}\right)}\right) \]

      14. lower-*.f64 86.3

          \[\leadsto \mathsf{fma}\left(\frac{-1}{b}, \frac{a}{3}, \color{blue}{\cos y \cdot \left(2 \cdot \sqrt{x}\right)}\right) \]

      15. lift-*.f64 N/A

          \[\leadsto \mathsf{fma}\left(\frac{-1}{b}, \frac{a}{3}, \cos y \cdot \color{blue}{\left(2 \cdot \sqrt{x}\right)}\right) \]

      16. *-commutative N/A

          \[\leadsto \mathsf{fma}\left(\frac{-1}{b}, \frac{a}{3}, \cos y \cdot \color{blue}{\left(\sqrt{x} \cdot 2\right)}\right) \]

      17. lower-*.f64 86.3

          \[\leadsto \mathsf{fma}\left(\frac{-1}{b}, \frac{a}{3}, \cos y \cdot \color{blue}{\left(\sqrt{x} \cdot 2\right)}\right) \]

   7. Applied rewrites 86.3%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{-1}{b}, \frac{a}{3}, \cos y \cdot \left(\sqrt{x} \cdot 2\right)\right)} \]

   8. Taylor expanded in y around 0

      \[\leadsto \mathsf{fma}\left(\frac{-1}{b}, \frac{a}{3}, 1 \cdot \left(\sqrt{x} \cdot 2\right)\right) \]
   9. Step-by-step derivation

   10. Applied rewrites 83.5%

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

   if -2e-78 < (/.f64 a (*.f64 b #s(literal 3 binary64))) < 2.0000000000000001e-168

   1. Initial program 60.9%

      \[\left(2 \cdot \sqrt{x}\right) \cdot \cos \left(y - \frac{z \cdot t}{3}\right) - \frac{a}{b \cdot 3} \]
   2. Add Preprocessing

   3. Taylor expanded in b around inf

      \[\leadsto \color{blue}{2 \cdot \left(\sqrt{x} \cdot \cos \left(y - \frac{1}{3} \cdot \left(t \cdot z\right)\right)\right)} \]

   5. Applied rewrites 58.8%

      \[\leadsto \color{blue}{\left(\sqrt{x} \cdot 2\right) \cdot \cos \left(\mathsf{fma}\left(t \cdot -0.3333333333333333, z, y\right)\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 76.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{a}{b \cdot 3} \leq -2 \cdot 10^{-78}:\\ \;\;\;\;\mathsf{fma}\left(\frac{-1}{b}, \frac{a}{3}, 1 \cdot \left(\sqrt{x} \cdot 2\right)\right)\\ \mathbf{elif}\;\frac{a}{b \cdot 3} \leq 2 \cdot 10^{-168}:\\ \;\;\;\;\cos \left(\mathsf{fma}\left(-0.3333333333333333 \cdot t, z, y\right)\right) \cdot \left(\sqrt{x} \cdot 2\right)\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(\frac{-1}{b}, \frac{a}{3}, 1 \cdot \left(\sqrt{x} \cdot 2\right)\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 76.8% accurate, 1.1× speedup

Math

\[\begin{array}{l} [x, y, z, t, a, b] = \mathsf{sort}([x, y, z, t, a, b])\\ \\ \left(\sqrt{x} \cdot 2\right) \cdot \cos y - \frac{a}{b \cdot 3} \end{array} \]

FPCore

NOTE: x, y, z, t, a, and b should be sorted in increasing order before calling this function.
(FPCore (x y z t a b)
 :precision binary64
 (- (* (* (sqrt x) 2.0) (cos y)) (/ a (* b 3.0))))

C

assert(x < y && y < z && z < t && t < a && a < b);
double code(double x, double y, double z, double t, double a, double b) {
	return ((sqrt(x) * 2.0) * cos(y)) - (a / (b * 3.0));
}

Fortran

NOTE: x, y, z, t, a, and b should be sorted in increasing order before calling this function.
real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    code = ((sqrt(x) * 2.0d0) * cos(y)) - (a / (b * 3.0d0))
end function

Java

assert x < y && y < z && z < t && t < a && a < b;
public static double code(double x, double y, double z, double t, double a, double b) {
	return ((Math.sqrt(x) * 2.0) * Math.cos(y)) - (a / (b * 3.0));
}

Python

[x, y, z, t, a, b] = sort([x, y, z, t, a, b])
def code(x, y, z, t, a, b):
	return ((math.sqrt(x) * 2.0) * math.cos(y)) - (a / (b * 3.0))

Julia

x, y, z, t, a, b = sort([x, y, z, t, a, b])
function code(x, y, z, t, a, b)
	return Float64(Float64(Float64(sqrt(x) * 2.0) * cos(y)) - Float64(a / Float64(b * 3.0)))
end

MATLAB

x, y, z, t, a, b = num2cell(sort([x, y, z, t, a, b])){:}
function tmp = code(x, y, z, t, a, b)
	tmp = ((sqrt(x) * 2.0) * cos(y)) - (a / (b * 3.0));
end

Wolfram

NOTE: x, y, z, t, a, and b should be sorted in increasing order before calling this function.
code[x_, y_, z_, t_, a_, b_] := N[(N[(N[(N[Sqrt[x], $MachinePrecision] * 2.0), $MachinePrecision] * N[Cos[y], $MachinePrecision]), $MachinePrecision] - N[(a / N[(b * 3.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 72.7%

   \[\left(2 \cdot \sqrt{x}\right) \cdot \cos \left(y - \frac{z \cdot t}{3}\right) - \frac{a}{b \cdot 3} \]
2. Add Preprocessing

3. Taylor expanded in t around 0

   \[\leadsto \left(2 \cdot \sqrt{x}\right) \cdot \color{blue}{\cos y} - \frac{a}{b \cdot 3} \]
4. Step-by-step derivation

   1. lower-cos.f64 78.3

      \[\leadsto \left(2 \cdot \sqrt{x}\right) \cdot \color{blue}{\cos y} - \frac{a}{b \cdot 3} \]

5. Applied rewrites 78.3%

   \[\leadsto \left(2 \cdot \sqrt{x}\right) \cdot \color{blue}{\cos y} - \frac{a}{b \cdot 3} \]

6. Final simplification 78.3%

   \[\leadsto \left(\sqrt{x} \cdot 2\right) \cdot \cos y - \frac{a}{b \cdot 3} \]
7. Add Preprocessing

Alternative 4: 76.7% accurate, 1.2× speedup

Math

\[\begin{array}{l} [x, y, z, t, a, b] = \mathsf{sort}([x, y, z, t, a, b])\\ \\ \mathsf{fma}\left(\frac{-0.3333333333333333}{b}, a, \left(\sqrt{x} \cdot 2\right) \cdot \cos y\right) \end{array} \]

FPCore

NOTE: x, y, z, t, a, and b should be sorted in increasing order before calling this function.
(FPCore (x y z t a b)
 :precision binary64
 (fma (/ -0.3333333333333333 b) a (* (* (sqrt x) 2.0) (cos y))))

C

assert(x < y && y < z && z < t && t < a && a < b);
double code(double x, double y, double z, double t, double a, double b) {
	return fma((-0.3333333333333333 / b), a, ((sqrt(x) * 2.0) * cos(y)));
}

Julia

x, y, z, t, a, b = sort([x, y, z, t, a, b])
function code(x, y, z, t, a, b)
	return fma(Float64(-0.3333333333333333 / b), a, Float64(Float64(sqrt(x) * 2.0) * cos(y)))
end

Wolfram

NOTE: x, y, z, t, a, and b should be sorted in increasing order before calling this function.
code[x_, y_, z_, t_, a_, b_] := N[(N[(-0.3333333333333333 / b), $MachinePrecision] * a + N[(N[(N[Sqrt[x], $MachinePrecision] * 2.0), $MachinePrecision] * N[Cos[y], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 72.7%

   \[\left(2 \cdot \sqrt{x}\right) \cdot \cos \left(y - \frac{z \cdot t}{3}\right) - \frac{a}{b \cdot 3} \]
2. Add Preprocessing

3. Taylor expanded in t around 0

   \[\leadsto \left(2 \cdot \sqrt{x}\right) \cdot \color{blue}{\cos y} - \frac{a}{b \cdot 3} \]
4. Step-by-step derivation

   1. lower-cos.f64 78.3

      \[\leadsto \left(2 \cdot \sqrt{x}\right) \cdot \color{blue}{\cos y} - \frac{a}{b \cdot 3} \]

5. Applied rewrites 78.3%

   \[\leadsto \left(2 \cdot \sqrt{x}\right) \cdot \color{blue}{\cos y} - \frac{a}{b \cdot 3} \]
6. Step-by-step derivation

   1. lift--.f64 N/A

      \[\leadsto \color{blue}{\left(2 \cdot \sqrt{x}\right) \cdot \cos y - \frac{a}{b \cdot 3}} \]

   2. sub-neg N/A

      \[\leadsto \color{blue}{\left(2 \cdot \sqrt{x}\right) \cdot \cos y + \left(\mathsf{neg}\left(\frac{a}{b \cdot 3}\right)\right)} \]

   3. +-commutative N/A

      \[\leadsto \color{blue}{\left(\mathsf{neg}\left(\frac{a}{b \cdot 3}\right)\right) + \left(2 \cdot \sqrt{x}\right) \cdot \cos y} \]

   4. lift-/.f64 N/A

      \[\leadsto \left(\mathsf{neg}\left(\color{blue}{\frac{a}{b \cdot 3}}\right)\right) + \left(2 \cdot \sqrt{x}\right) \cdot \cos y \]

   5. distribute-neg-frac N/A

      \[\leadsto \color{blue}{\frac{\mathsf{neg}\left(a\right)}{b \cdot 3}} + \left(2 \cdot \sqrt{x}\right) \cdot \cos y \]

   6. neg-mul-1 N/A

      \[\leadsto \frac{\color{blue}{-1 \cdot a}}{b \cdot 3} + \left(2 \cdot \sqrt{x}\right) \cdot \cos y \]

   7. lift-*.f64 N/A

      \[\leadsto \frac{-1 \cdot a}{\color{blue}{b \cdot 3}} + \left(2 \cdot \sqrt{x}\right) \cdot \cos y \]

   8. *-commutative N/A

      \[\leadsto \frac{-1 \cdot a}{\color{blue}{3 \cdot b}} + \left(2 \cdot \sqrt{x}\right) \cdot \cos y \]

   9. times-frac N/A

      \[\leadsto \color{blue}{\frac{-1}{3} \cdot \frac{a}{b}} + \left(2 \cdot \sqrt{x}\right) \cdot \cos y \]

   10. metadata-eval N/A

       \[\leadsto \color{blue}{\frac{-1}{3}} \cdot \frac{a}{b} + \left(2 \cdot \sqrt{x}\right) \cdot \cos y \]

   11. associate-/l* N/A

       \[\leadsto \color{blue}{\frac{\frac{-1}{3} \cdot a}{b}} + \left(2 \cdot \sqrt{x}\right) \cdot \cos y \]

   12. associate-*l/ N/A

       \[\leadsto \color{blue}{\frac{\frac{-1}{3}}{b} \cdot a} + \left(2 \cdot \sqrt{x}\right) \cdot \cos y \]

   13. lift-/.f64 N/A

       \[\leadsto \color{blue}{\frac{\frac{-1}{3}}{b}} \cdot a + \left(2 \cdot \sqrt{x}\right) \cdot \cos y \]

   14. lower-fma.f64 78.2

       \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{-0.3333333333333333}{b}, a, \left(2 \cdot \sqrt{x}\right) \cdot \cos y\right)} \]

   15. lift-*.f64 N/A

       \[\leadsto \mathsf{fma}\left(\frac{\frac{-1}{3}}{b}, a, \color{blue}{\left(2 \cdot \sqrt{x}\right) \cdot \cos y}\right) \]

   16. *-commutative N/A

       \[\leadsto \mathsf{fma}\left(\frac{\frac{-1}{3}}{b}, a, \color{blue}{\cos y \cdot \left(2 \cdot \sqrt{x}\right)}\right) \]

   17. lower-*.f64 78.2

       \[\leadsto \mathsf{fma}\left(\frac{-0.3333333333333333}{b}, a, \color{blue}{\cos y \cdot \left(2 \cdot \sqrt{x}\right)}\right) \]

   18. lift-*.f64 N/A

       \[\leadsto \mathsf{fma}\left(\frac{\frac{-1}{3}}{b}, a, \cos y \cdot \color{blue}{\left(2 \cdot \sqrt{x}\right)}\right) \]

7. Applied rewrites 78.2%

   \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{-0.3333333333333333}{b}, a, \cos y \cdot \left(\sqrt{x} \cdot 2\right)\right)} \]

8. Final simplification 78.2%

   \[\leadsto \mathsf{fma}\left(\frac{-0.3333333333333333}{b}, a, \left(\sqrt{x} \cdot 2\right) \cdot \cos y\right) \]
9. Add Preprocessing

Alternative 5: 76.7% accurate, 1.2× speedup

Math

\[\begin{array}{l} [x, y, z, t, a, b] = \mathsf{sort}([x, y, z, t, a, b])\\ \\ \mathsf{fma}\left(\cos y \cdot 2, \sqrt{x}, \frac{-0.3333333333333333}{b} \cdot a\right) \end{array} \]

FPCore

NOTE: x, y, z, t, a, and b should be sorted in increasing order before calling this function.
(FPCore (x y z t a b)
 :precision binary64
 (fma (* (cos y) 2.0) (sqrt x) (* (/ -0.3333333333333333 b) a)))

C

assert(x < y && y < z && z < t && t < a && a < b);
double code(double x, double y, double z, double t, double a, double b) {
	return fma((cos(y) * 2.0), sqrt(x), ((-0.3333333333333333 / b) * a));
}

Julia

x, y, z, t, a, b = sort([x, y, z, t, a, b])
function code(x, y, z, t, a, b)
	return fma(Float64(cos(y) * 2.0), sqrt(x), Float64(Float64(-0.3333333333333333 / b) * a))
end

Wolfram

NOTE: x, y, z, t, a, and b should be sorted in increasing order before calling this function.
code[x_, y_, z_, t_, a_, b_] := N[(N[(N[Cos[y], $MachinePrecision] * 2.0), $MachinePrecision] * N[Sqrt[x], $MachinePrecision] + N[(N[(-0.3333333333333333 / b), $MachinePrecision] * a), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 72.7%

   \[\left(2 \cdot \sqrt{x}\right) \cdot \cos \left(y - \frac{z \cdot t}{3}\right) - \frac{a}{b \cdot 3} \]
2. Add Preprocessing

3. Taylor expanded in t around 0

   \[\leadsto \color{blue}{2 \cdot \left(\sqrt{x} \cdot \cos y\right) - \frac{1}{3} \cdot \frac{a}{b}} \]
4. Step-by-step derivation

   1. sub-neg N/A

      \[\leadsto \color{blue}{2 \cdot \left(\sqrt{x} \cdot \cos y\right) + \left(\mathsf{neg}\left(\frac{1}{3} \cdot \frac{a}{b}\right)\right)} \]

   2. *-commutative N/A

      \[\leadsto 2 \cdot \color{blue}{\left(\cos y \cdot \sqrt{x}\right)} + \left(\mathsf{neg}\left(\frac{1}{3} \cdot \frac{a}{b}\right)\right) \]

   3. associate-*r* N/A

      \[\leadsto \color{blue}{\left(2 \cdot \cos y\right) \cdot \sqrt{x}} + \left(\mathsf{neg}\left(\frac{1}{3} \cdot \frac{a}{b}\right)\right) \]

   4. lower-fma.f64 N/A

      \[\leadsto \color{blue}{\mathsf{fma}\left(2 \cdot \cos y, \sqrt{x}, \mathsf{neg}\left(\frac{1}{3} \cdot \frac{a}{b}\right)\right)} \]

   5. lower-*.f64 N/A

      \[\leadsto \mathsf{fma}\left(\color{blue}{2 \cdot \cos y}, \sqrt{x}, \mathsf{neg}\left(\frac{1}{3} \cdot \frac{a}{b}\right)\right) \]

   6. lower-cos.f64 N/A

      \[\leadsto \mathsf{fma}\left(2 \cdot \color{blue}{\cos y}, \sqrt{x}, \mathsf{neg}\left(\frac{1}{3} \cdot \frac{a}{b}\right)\right) \]

   7. lower-sqrt.f64 N/A

      \[\leadsto \mathsf{fma}\left(2 \cdot \cos y, \color{blue}{\sqrt{x}}, \mathsf{neg}\left(\frac{1}{3} \cdot \frac{a}{b}\right)\right) \]

   8. associate-*r/ N/A

      \[\leadsto \mathsf{fma}\left(2 \cdot \cos y, \sqrt{x}, \mathsf{neg}\left(\color{blue}{\frac{\frac{1}{3} \cdot a}{b}}\right)\right) \]

   9. associate-*l/ N/A

      \[\leadsto \mathsf{fma}\left(2 \cdot \cos y, \sqrt{x}, \mathsf{neg}\left(\color{blue}{\frac{\frac{1}{3}}{b} \cdot a}\right)\right) \]

   10. metadata-eval N/A

       \[\leadsto \mathsf{fma}\left(2 \cdot \cos y, \sqrt{x}, \mathsf{neg}\left(\frac{\color{blue}{\frac{1}{3} \cdot 1}}{b} \cdot a\right)\right) \]

   11. associate-*r/ N/A

       \[\leadsto \mathsf{fma}\left(2 \cdot \cos y, \sqrt{x}, \mathsf{neg}\left(\color{blue}{\left(\frac{1}{3} \cdot \frac{1}{b}\right)} \cdot a\right)\right) \]

   12. distribute-lft-neg-in N/A

       \[\leadsto \mathsf{fma}\left(2 \cdot \cos y, \sqrt{x}, \color{blue}{\left(\mathsf{neg}\left(\frac{1}{3} \cdot \frac{1}{b}\right)\right) \cdot a}\right) \]

   13. lower-*.f64 N/A

       \[\leadsto \mathsf{fma}\left(2 \cdot \cos y, \sqrt{x}, \color{blue}{\left(\mathsf{neg}\left(\frac{1}{3} \cdot \frac{1}{b}\right)\right) \cdot a}\right) \]

   14. associate-*r/ N/A

       \[\leadsto \mathsf{fma}\left(2 \cdot \cos y, \sqrt{x}, \left(\mathsf{neg}\left(\color{blue}{\frac{\frac{1}{3} \cdot 1}{b}}\right)\right) \cdot a\right) \]

   15. metadata-eval N/A

       \[\leadsto \mathsf{fma}\left(2 \cdot \cos y, \sqrt{x}, \left(\mathsf{neg}\left(\frac{\color{blue}{\frac{1}{3}}}{b}\right)\right) \cdot a\right) \]

   16. distribute-neg-frac N/A

       \[\leadsto \mathsf{fma}\left(2 \cdot \cos y, \sqrt{x}, \color{blue}{\frac{\mathsf{neg}\left(\frac{1}{3}\right)}{b}} \cdot a\right) \]

   17. metadata-eval N/A

       \[\leadsto \mathsf{fma}\left(2 \cdot \cos y, \sqrt{x}, \frac{\color{blue}{\frac{-1}{3}}}{b} \cdot a\right) \]

   18. lower-/.f64 78.2

       \[\leadsto \mathsf{fma}\left(2 \cdot \cos y, \sqrt{x}, \color{blue}{\frac{-0.3333333333333333}{b}} \cdot a\right) \]

5. Applied rewrites 78.2%

   \[\leadsto \color{blue}{\mathsf{fma}\left(2 \cdot \cos y, \sqrt{x}, \frac{-0.3333333333333333}{b} \cdot a\right)} \]

6. Final simplification 78.2%

   \[\leadsto \mathsf{fma}\left(\cos y \cdot 2, \sqrt{x}, \frac{-0.3333333333333333}{b} \cdot a\right) \]
7. Add Preprocessing

Alternative 6: 65.6% accurate, 3.2× speedup

Math

\[\begin{array}{l} [x, y, z, t, a, b] = \mathsf{sort}([x, y, z, t, a, b])\\ \\ \mathsf{fma}\left(\frac{-1}{b}, \frac{a}{3}, 1 \cdot \left(\sqrt{x} \cdot 2\right)\right) \end{array} \]

FPCore

NOTE: x, y, z, t, a, and b should be sorted in increasing order before calling this function.
(FPCore (x y z t a b)
 :precision binary64
 (fma (/ -1.0 b) (/ a 3.0) (* 1.0 (* (sqrt x) 2.0))))

C

assert(x < y && y < z && z < t && t < a && a < b);
double code(double x, double y, double z, double t, double a, double b) {
	return fma((-1.0 / b), (a / 3.0), (1.0 * (sqrt(x) * 2.0)));
}

Julia

x, y, z, t, a, b = sort([x, y, z, t, a, b])
function code(x, y, z, t, a, b)
	return fma(Float64(-1.0 / b), Float64(a / 3.0), Float64(1.0 * Float64(sqrt(x) * 2.0)))
end

Wolfram

NOTE: x, y, z, t, a, and b should be sorted in increasing order before calling this function.
code[x_, y_, z_, t_, a_, b_] := N[(N[(-1.0 / b), $MachinePrecision] * N[(a / 3.0), $MachinePrecision] + N[(1.0 * N[(N[Sqrt[x], $MachinePrecision] * 2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 72.7%

   \[\left(2 \cdot \sqrt{x}\right) \cdot \cos \left(y - \frac{z \cdot t}{3}\right) - \frac{a}{b \cdot 3} \]
2. Add Preprocessing

3. Taylor expanded in t around 0

   \[\leadsto \left(2 \cdot \sqrt{x}\right) \cdot \color{blue}{\cos y} - \frac{a}{b \cdot 3} \]
4. Step-by-step derivation

   1. lower-cos.f64 78.3

      \[\leadsto \left(2 \cdot \sqrt{x}\right) \cdot \color{blue}{\cos y} - \frac{a}{b \cdot 3} \]

5. Applied rewrites 78.3%

   \[\leadsto \left(2 \cdot \sqrt{x}\right) \cdot \color{blue}{\cos y} - \frac{a}{b \cdot 3} \]
6. Step-by-step derivation

   1. lift--.f64 N/A

      \[\leadsto \color{blue}{\left(2 \cdot \sqrt{x}\right) \cdot \cos y - \frac{a}{b \cdot 3}} \]

   2. sub-neg N/A

      \[\leadsto \color{blue}{\left(2 \cdot \sqrt{x}\right) \cdot \cos y + \left(\mathsf{neg}\left(\frac{a}{b \cdot 3}\right)\right)} \]

   3. +-commutative N/A

      \[\leadsto \color{blue}{\left(\mathsf{neg}\left(\frac{a}{b \cdot 3}\right)\right) + \left(2 \cdot \sqrt{x}\right) \cdot \cos y} \]

   4. lift-/.f64 N/A

      \[\leadsto \left(\mathsf{neg}\left(\color{blue}{\frac{a}{b \cdot 3}}\right)\right) + \left(2 \cdot \sqrt{x}\right) \cdot \cos y \]

   5. distribute-neg-frac N/A

      \[\leadsto \color{blue}{\frac{\mathsf{neg}\left(a\right)}{b \cdot 3}} + \left(2 \cdot \sqrt{x}\right) \cdot \cos y \]

   6. neg-mul-1 N/A

      \[\leadsto \frac{\color{blue}{-1 \cdot a}}{b \cdot 3} + \left(2 \cdot \sqrt{x}\right) \cdot \cos y \]

   7. lift-*.f64 N/A

      \[\leadsto \frac{-1 \cdot a}{\color{blue}{b \cdot 3}} + \left(2 \cdot \sqrt{x}\right) \cdot \cos y \]

   8. times-frac N/A

      \[\leadsto \color{blue}{\frac{-1}{b} \cdot \frac{a}{3}} + \left(2 \cdot \sqrt{x}\right) \cdot \cos y \]

   9. lower-fma.f64 N/A

      \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{-1}{b}, \frac{a}{3}, \left(2 \cdot \sqrt{x}\right) \cdot \cos y\right)} \]

   10. lower-/.f64 N/A

       \[\leadsto \mathsf{fma}\left(\color{blue}{\frac{-1}{b}}, \frac{a}{3}, \left(2 \cdot \sqrt{x}\right) \cdot \cos y\right) \]

   11. lower-/.f64 78.3

       \[\leadsto \mathsf{fma}\left(\frac{-1}{b}, \color{blue}{\frac{a}{3}}, \left(2 \cdot \sqrt{x}\right) \cdot \cos y\right) \]

   12. lift-*.f64 N/A

       \[\leadsto \mathsf{fma}\left(\frac{-1}{b}, \frac{a}{3}, \color{blue}{\left(2 \cdot \sqrt{x}\right) \cdot \cos y}\right) \]

   13. *-commutative N/A

       \[\leadsto \mathsf{fma}\left(\frac{-1}{b}, \frac{a}{3}, \color{blue}{\cos y \cdot \left(2 \cdot \sqrt{x}\right)}\right) \]

   14. lower-*.f64 78.3

       \[\leadsto \mathsf{fma}\left(\frac{-1}{b}, \frac{a}{3}, \color{blue}{\cos y \cdot \left(2 \cdot \sqrt{x}\right)}\right) \]

   15. lift-*.f64 N/A

       \[\leadsto \mathsf{fma}\left(\frac{-1}{b}, \frac{a}{3}, \cos y \cdot \color{blue}{\left(2 \cdot \sqrt{x}\right)}\right) \]

   16. *-commutative N/A

       \[\leadsto \mathsf{fma}\left(\frac{-1}{b}, \frac{a}{3}, \cos y \cdot \color{blue}{\left(\sqrt{x} \cdot 2\right)}\right) \]

   17. lower-*.f64 78.3

       \[\leadsto \mathsf{fma}\left(\frac{-1}{b}, \frac{a}{3}, \cos y \cdot \color{blue}{\left(\sqrt{x} \cdot 2\right)}\right) \]

7. Applied rewrites 78.3%

   \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{-1}{b}, \frac{a}{3}, \cos y \cdot \left(\sqrt{x} \cdot 2\right)\right)} \]

8. Taylor expanded in y around 0

   \[\leadsto \mathsf{fma}\left(\frac{-1}{b}, \frac{a}{3}, 1 \cdot \left(\sqrt{x} \cdot 2\right)\right) \]
9. Step-by-step derivation

10. Applied rewrites 69.7%

    \[\leadsto \mathsf{fma}\left(\frac{-1}{b}, \frac{a}{3}, 1 \cdot \left(\sqrt{x} \cdot 2\right)\right) \]
11. Add Preprocessing

Alternative 7: 65.6% accurate, 4.8× speedup

Math

\[\begin{array}{l} [x, y, z, t, a, b] = \mathsf{sort}([x, y, z, t, a, b])\\ \\ \mathsf{fma}\left(\frac{-0.3333333333333333}{b}, a, \sqrt{x} \cdot 2\right) \end{array} \]

FPCore

NOTE: x, y, z, t, a, and b should be sorted in increasing order before calling this function.
(FPCore (x y z t a b)
 :precision binary64
 (fma (/ -0.3333333333333333 b) a (* (sqrt x) 2.0)))

C

assert(x < y && y < z && z < t && t < a && a < b);
double code(double x, double y, double z, double t, double a, double b) {
	return fma((-0.3333333333333333 / b), a, (sqrt(x) * 2.0));
}

Julia

x, y, z, t, a, b = sort([x, y, z, t, a, b])
function code(x, y, z, t, a, b)
	return fma(Float64(-0.3333333333333333 / b), a, Float64(sqrt(x) * 2.0))
end

Wolfram

NOTE: x, y, z, t, a, and b should be sorted in increasing order before calling this function.
code[x_, y_, z_, t_, a_, b_] := N[(N[(-0.3333333333333333 / b), $MachinePrecision] * a + N[(N[Sqrt[x], $MachinePrecision] * 2.0), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 72.7%

   \[\left(2 \cdot \sqrt{x}\right) \cdot \cos \left(y - \frac{z \cdot t}{3}\right) - \frac{a}{b \cdot 3} \]
2. Add Preprocessing

3. Taylor expanded in y around 0

   \[\leadsto \color{blue}{2 \cdot \left(\sqrt{x} \cdot \cos \left(\mathsf{neg}\left(\frac{1}{3} \cdot \left(t \cdot z\right)\right)\right)\right) - \frac{1}{3} \cdot \frac{a}{b}} \]
4. Step-by-step derivation

   1. sub-neg N/A

      \[\leadsto \color{blue}{2 \cdot \left(\sqrt{x} \cdot \cos \left(\mathsf{neg}\left(\frac{1}{3} \cdot \left(t \cdot z\right)\right)\right)\right) + \left(\mathsf{neg}\left(\frac{1}{3} \cdot \frac{a}{b}\right)\right)} \]

   2. associate-*r* N/A

      \[\leadsto \color{blue}{\left(2 \cdot \sqrt{x}\right) \cdot \cos \left(\mathsf{neg}\left(\frac{1}{3} \cdot \left(t \cdot z\right)\right)\right)} + \left(\mathsf{neg}\left(\frac{1}{3} \cdot \frac{a}{b}\right)\right) \]

   3. lower-fma.f64 N/A

      \[\leadsto \color{blue}{\mathsf{fma}\left(2 \cdot \sqrt{x}, \cos \left(\mathsf{neg}\left(\frac{1}{3} \cdot \left(t \cdot z\right)\right)\right), \mathsf{neg}\left(\frac{1}{3} \cdot \frac{a}{b}\right)\right)} \]

   4. *-commutative N/A

      \[\leadsto \mathsf{fma}\left(\color{blue}{\sqrt{x} \cdot 2}, \cos \left(\mathsf{neg}\left(\frac{1}{3} \cdot \left(t \cdot z\right)\right)\right), \mathsf{neg}\left(\frac{1}{3} \cdot \frac{a}{b}\right)\right) \]

   5. lower-*.f64 N/A

      \[\leadsto \mathsf{fma}\left(\color{blue}{\sqrt{x} \cdot 2}, \cos \left(\mathsf{neg}\left(\frac{1}{3} \cdot \left(t \cdot z\right)\right)\right), \mathsf{neg}\left(\frac{1}{3} \cdot \frac{a}{b}\right)\right) \]

   6. lower-sqrt.f64 N/A

      \[\leadsto \mathsf{fma}\left(\color{blue}{\sqrt{x}} \cdot 2, \cos \left(\mathsf{neg}\left(\frac{1}{3} \cdot \left(t \cdot z\right)\right)\right), \mathsf{neg}\left(\frac{1}{3} \cdot \frac{a}{b}\right)\right) \]

   7. lower-cos.f64 N/A

      \[\leadsto \mathsf{fma}\left(\sqrt{x} \cdot 2, \color{blue}{\cos \left(\mathsf{neg}\left(\frac{1}{3} \cdot \left(t \cdot z\right)\right)\right)}, \mathsf{neg}\left(\frac{1}{3} \cdot \frac{a}{b}\right)\right) \]

   8. distribute-lft-neg-in N/A

      \[\leadsto \mathsf{fma}\left(\sqrt{x} \cdot 2, \cos \color{blue}{\left(\left(\mathsf{neg}\left(\frac{1}{3}\right)\right) \cdot \left(t \cdot z\right)\right)}, \mathsf{neg}\left(\frac{1}{3} \cdot \frac{a}{b}\right)\right) \]

   9. metadata-eval N/A

      \[\leadsto \mathsf{fma}\left(\sqrt{x} \cdot 2, \cos \left(\color{blue}{\frac{-1}{3}} \cdot \left(t \cdot z\right)\right), \mathsf{neg}\left(\frac{1}{3} \cdot \frac{a}{b}\right)\right) \]

   10. associate-*r* N/A

       \[\leadsto \mathsf{fma}\left(\sqrt{x} \cdot 2, \cos \color{blue}{\left(\left(\frac{-1}{3} \cdot t\right) \cdot z\right)}, \mathsf{neg}\left(\frac{1}{3} \cdot \frac{a}{b}\right)\right) \]

   11. lower-*.f64 N/A

       \[\leadsto \mathsf{fma}\left(\sqrt{x} \cdot 2, \cos \color{blue}{\left(\left(\frac{-1}{3} \cdot t\right) \cdot z\right)}, \mathsf{neg}\left(\frac{1}{3} \cdot \frac{a}{b}\right)\right) \]

   12. *-commutative N/A

       \[\leadsto \mathsf{fma}\left(\sqrt{x} \cdot 2, \cos \left(\color{blue}{\left(t \cdot \frac{-1}{3}\right)} \cdot z\right), \mathsf{neg}\left(\frac{1}{3} \cdot \frac{a}{b}\right)\right) \]

   13. lower-*.f64 N/A

       \[\leadsto \mathsf{fma}\left(\sqrt{x} \cdot 2, \cos \left(\color{blue}{\left(t \cdot \frac{-1}{3}\right)} \cdot z\right), \mathsf{neg}\left(\frac{1}{3} \cdot \frac{a}{b}\right)\right) \]

   14. associate-*r/ N/A

       \[\leadsto \mathsf{fma}\left(\sqrt{x} \cdot 2, \cos \left(\left(t \cdot \frac{-1}{3}\right) \cdot z\right), \mathsf{neg}\left(\color{blue}{\frac{\frac{1}{3} \cdot a}{b}}\right)\right) \]

   15. associate-*l/ N/A

       \[\leadsto \mathsf{fma}\left(\sqrt{x} \cdot 2, \cos \left(\left(t \cdot \frac{-1}{3}\right) \cdot z\right), \mathsf{neg}\left(\color{blue}{\frac{\frac{1}{3}}{b} \cdot a}\right)\right) \]

   16. metadata-eval N/A

       \[\leadsto \mathsf{fma}\left(\sqrt{x} \cdot 2, \cos \left(\left(t \cdot \frac{-1}{3}\right) \cdot z\right), \mathsf{neg}\left(\frac{\color{blue}{\frac{1}{3} \cdot 1}}{b} \cdot a\right)\right) \]

   17. associate-*r/ N/A

       \[\leadsto \mathsf{fma}\left(\sqrt{x} \cdot 2, \cos \left(\left(t \cdot \frac{-1}{3}\right) \cdot z\right), \mathsf{neg}\left(\color{blue}{\left(\frac{1}{3} \cdot \frac{1}{b}\right)} \cdot a\right)\right) \]

   18. distribute-lft-neg-in N/A

       \[\leadsto \mathsf{fma}\left(\sqrt{x} \cdot 2, \cos \left(\left(t \cdot \frac{-1}{3}\right) \cdot z\right), \color{blue}{\left(\mathsf{neg}\left(\frac{1}{3} \cdot \frac{1}{b}\right)\right) \cdot a}\right) \]

   19. lower-*.f64 N/A

       \[\leadsto \mathsf{fma}\left(\sqrt{x} \cdot 2, \cos \left(\left(t \cdot \frac{-1}{3}\right) \cdot z\right), \color{blue}{\left(\mathsf{neg}\left(\frac{1}{3} \cdot \frac{1}{b}\right)\right) \cdot a}\right) \]

5. Applied rewrites 63.5%

   \[\leadsto \color{blue}{\mathsf{fma}\left(\sqrt{x} \cdot 2, \cos \left(\left(t \cdot -0.3333333333333333\right) \cdot z\right), \frac{-0.3333333333333333}{b} \cdot a\right)} \]

6. Taylor expanded in t around 0

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

8. Applied rewrites 69.6%

   \[\leadsto \mathsf{fma}\left(\frac{-0.3333333333333333}{b}, \color{blue}{a}, \sqrt{x} \cdot 2\right) \]
9. Add Preprocessing

Alternative 8: 50.6% accurate, 9.4× speedup

Math

\[\begin{array}{l} [x, y, z, t, a, b] = \mathsf{sort}([x, y, z, t, a, b])\\ \\ \frac{a}{-3 \cdot b} \end{array} \]

FPCore

NOTE: x, y, z, t, a, and b should be sorted in increasing order before calling this function.
(FPCore (x y z t a b) :precision binary64 (/ a (* -3.0 b)))

C

assert(x < y && y < z && z < t && t < a && a < b);
double code(double x, double y, double z, double t, double a, double b) {
	return a / (-3.0 * b);
}

Fortran

NOTE: x, y, z, t, a, and b should be sorted in increasing order before calling this function.
real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    code = a / ((-3.0d0) * b)
end function

Java

assert x < y && y < z && z < t && t < a && a < b;
public static double code(double x, double y, double z, double t, double a, double b) {
	return a / (-3.0 * b);
}

Python

[x, y, z, t, a, b] = sort([x, y, z, t, a, b])
def code(x, y, z, t, a, b):
	return a / (-3.0 * b)

Julia

x, y, z, t, a, b = sort([x, y, z, t, a, b])
function code(x, y, z, t, a, b)
	return Float64(a / Float64(-3.0 * b))
end

MATLAB

x, y, z, t, a, b = num2cell(sort([x, y, z, t, a, b])){:}
function tmp = code(x, y, z, t, a, b)
	tmp = a / (-3.0 * b);
end

Wolfram

NOTE: x, y, z, t, a, and b should be sorted in increasing order before calling this function.
code[x_, y_, z_, t_, a_, b_] := N[(a / N[(-3.0 * b), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 72.7%

   \[\left(2 \cdot \sqrt{x}\right) \cdot \cos \left(y - \frac{z \cdot t}{3}\right) - \frac{a}{b \cdot 3} \]
2. Add Preprocessing

3. Taylor expanded in b around 0

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

   1. associate-*r/ N/A

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

   2. associate-*l/ N/A

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

   3. metadata-eval N/A

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

   4. distribute-neg-frac N/A

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

   5. metadata-eval N/A

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

   6. associate-*r/ N/A

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

   7. lower-*.f64 N/A

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

   8. associate-*r/ N/A

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

   9. metadata-eval N/A

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

   10. distribute-neg-frac N/A

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

   11. metadata-eval N/A

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

   12. lower-/.f64 53.7

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

5. Applied rewrites 53.7%

   \[\leadsto \color{blue}{\frac{-0.3333333333333333}{b} \cdot a} \]
6. Step-by-step derivation

7. Applied rewrites 53.8%

   \[\leadsto \frac{a}{\color{blue}{-3 \cdot b}} \]
8. Add Preprocessing

Alternative 9: 50.5% accurate, 9.4× speedup

Math

\[\begin{array}{l} [x, y, z, t, a, b] = \mathsf{sort}([x, y, z, t, a, b])\\ \\ \frac{-0.3333333333333333}{b} \cdot a \end{array} \]

FPCore

NOTE: x, y, z, t, a, and b should be sorted in increasing order before calling this function.
(FPCore (x y z t a b) :precision binary64 (* (/ -0.3333333333333333 b) a))

C

assert(x < y && y < z && z < t && t < a && a < b);
double code(double x, double y, double z, double t, double a, double b) {
	return (-0.3333333333333333 / b) * a;
}

Fortran

NOTE: x, y, z, t, a, and b should be sorted in increasing order before calling this function.
real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    code = ((-0.3333333333333333d0) / b) * a
end function

Java

assert x < y && y < z && z < t && t < a && a < b;
public static double code(double x, double y, double z, double t, double a, double b) {
	return (-0.3333333333333333 / b) * a;
}

Python

[x, y, z, t, a, b] = sort([x, y, z, t, a, b])
def code(x, y, z, t, a, b):
	return (-0.3333333333333333 / b) * a

Julia

x, y, z, t, a, b = sort([x, y, z, t, a, b])
function code(x, y, z, t, a, b)
	return Float64(Float64(-0.3333333333333333 / b) * a)
end

MATLAB

x, y, z, t, a, b = num2cell(sort([x, y, z, t, a, b])){:}
function tmp = code(x, y, z, t, a, b)
	tmp = (-0.3333333333333333 / b) * a;
end

Wolfram

NOTE: x, y, z, t, a, and b should be sorted in increasing order before calling this function.
code[x_, y_, z_, t_, a_, b_] := N[(N[(-0.3333333333333333 / b), $MachinePrecision] * a), $MachinePrecision]

Derivation

1. Initial program 72.7%

   \[\left(2 \cdot \sqrt{x}\right) \cdot \cos \left(y - \frac{z \cdot t}{3}\right) - \frac{a}{b \cdot 3} \]
2. Add Preprocessing

3. Taylor expanded in b around 0

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

   1. associate-*r/ N/A

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

   2. associate-*l/ N/A

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

   3. metadata-eval N/A

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

   4. distribute-neg-frac N/A

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

   5. metadata-eval N/A

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

   6. associate-*r/ N/A

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

   7. lower-*.f64 N/A

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

   8. associate-*r/ N/A

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

   9. metadata-eval N/A

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

   10. distribute-neg-frac N/A

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

   11. metadata-eval N/A

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

   12. lower-/.f64 53.7

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

5. Applied rewrites 53.7%

   \[\leadsto \color{blue}{\frac{-0.3333333333333333}{b} \cdot a} \]
6. Add Preprocessing

Developer Target 1: 74.3% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{\frac{0.3333333333333333}{z}}{t}\\ t_2 := \frac{\frac{a}{3}}{b}\\ t_3 := 2 \cdot \sqrt{x}\\ \mathbf{if}\;z < -1.3793337487235141 \cdot 10^{+129}:\\ \;\;\;\;t\_3 \cdot \cos \left(\frac{1}{y} - t\_1\right) - t\_2\\ \mathbf{elif}\;z < 3.516290613555987 \cdot 10^{+106}:\\ \;\;\;\;\left(\sqrt{x} \cdot 2\right) \cdot \cos \left(y - \frac{t}{3} \cdot z\right) - t\_2\\ \mathbf{else}:\\ \;\;\;\;\cos \left(y - t\_1\right) \cdot t\_3 - \frac{\frac{a}{b}}{3}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (/ (/ 0.3333333333333333 z) t))
        (t_2 (/ (/ a 3.0) b))
        (t_3 (* 2.0 (sqrt x))))
   (if (< z -1.3793337487235141e+129)
     (- (* t_3 (cos (- (/ 1.0 y) t_1))) t_2)
     (if (< z 3.516290613555987e+106)
       (- (* (* (sqrt x) 2.0) (cos (- y (* (/ t 3.0) z)))) t_2)
       (- (* (cos (- y t_1)) t_3) (/ (/ a b) 3.0))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (0.3333333333333333 / z) / t;
	double t_2 = (a / 3.0) / b;
	double t_3 = 2.0 * sqrt(x);
	double tmp;
	if (z < -1.3793337487235141e+129) {
		tmp = (t_3 * cos(((1.0 / y) - t_1))) - t_2;
	} else if (z < 3.516290613555987e+106) {
		tmp = ((sqrt(x) * 2.0) * cos((y - ((t / 3.0) * z)))) - t_2;
	} else {
		tmp = (cos((y - t_1)) * t_3) - ((a / b) / 3.0);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: t_3
    real(8) :: tmp
    t_1 = (0.3333333333333333d0 / z) / t
    t_2 = (a / 3.0d0) / b
    t_3 = 2.0d0 * sqrt(x)
    if (z < (-1.3793337487235141d+129)) then
        tmp = (t_3 * cos(((1.0d0 / y) - t_1))) - t_2
    else if (z < 3.516290613555987d+106) then
        tmp = ((sqrt(x) * 2.0d0) * cos((y - ((t / 3.0d0) * z)))) - t_2
    else
        tmp = (cos((y - t_1)) * t_3) - ((a / b) / 3.0d0)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (0.3333333333333333 / z) / t;
	double t_2 = (a / 3.0) / b;
	double t_3 = 2.0 * Math.sqrt(x);
	double tmp;
	if (z < -1.3793337487235141e+129) {
		tmp = (t_3 * Math.cos(((1.0 / y) - t_1))) - t_2;
	} else if (z < 3.516290613555987e+106) {
		tmp = ((Math.sqrt(x) * 2.0) * Math.cos((y - ((t / 3.0) * z)))) - t_2;
	} else {
		tmp = (Math.cos((y - t_1)) * t_3) - ((a / b) / 3.0);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = (0.3333333333333333 / z) / t
	t_2 = (a / 3.0) / b
	t_3 = 2.0 * math.sqrt(x)
	tmp = 0
	if z < -1.3793337487235141e+129:
		tmp = (t_3 * math.cos(((1.0 / y) - t_1))) - t_2
	elif z < 3.516290613555987e+106:
		tmp = ((math.sqrt(x) * 2.0) * math.cos((y - ((t / 3.0) * z)))) - t_2
	else:
		tmp = (math.cos((y - t_1)) * t_3) - ((a / b) / 3.0)
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(0.3333333333333333 / z) / t)
	t_2 = Float64(Float64(a / 3.0) / b)
	t_3 = Float64(2.0 * sqrt(x))
	tmp = 0.0
	if (z < -1.3793337487235141e+129)
		tmp = Float64(Float64(t_3 * cos(Float64(Float64(1.0 / y) - t_1))) - t_2);
	elseif (z < 3.516290613555987e+106)
		tmp = Float64(Float64(Float64(sqrt(x) * 2.0) * cos(Float64(y - Float64(Float64(t / 3.0) * z)))) - t_2);
	else
		tmp = Float64(Float64(cos(Float64(y - t_1)) * t_3) - Float64(Float64(a / b) / 3.0));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = (0.3333333333333333 / z) / t;
	t_2 = (a / 3.0) / b;
	t_3 = 2.0 * sqrt(x);
	tmp = 0.0;
	if (z < -1.3793337487235141e+129)
		tmp = (t_3 * cos(((1.0 / y) - t_1))) - t_2;
	elseif (z < 3.516290613555987e+106)
		tmp = ((sqrt(x) * 2.0) * cos((y - ((t / 3.0) * z)))) - t_2;
	else
		tmp = (cos((y - t_1)) * t_3) - ((a / b) / 3.0);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(N[(0.3333333333333333 / z), $MachinePrecision] / t), $MachinePrecision]}, Block[{t$95$2 = N[(N[(a / 3.0), $MachinePrecision] / b), $MachinePrecision]}, Block[{t$95$3 = N[(2.0 * N[Sqrt[x], $MachinePrecision]), $MachinePrecision]}, If[Less[z, -1.3793337487235141e+129], N[(N[(t$95$3 * N[Cos[N[(N[(1.0 / y), $MachinePrecision] - t$95$1), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] - t$95$2), $MachinePrecision], If[Less[z, 3.516290613555987e+106], N[(N[(N[(N[Sqrt[x], $MachinePrecision] * 2.0), $MachinePrecision] * N[Cos[N[(y - N[(N[(t / 3.0), $MachinePrecision] * z), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] - t$95$2), $MachinePrecision], N[(N[(N[Cos[N[(y - t$95$1), $MachinePrecision]], $MachinePrecision] * t$95$3), $MachinePrecision] - N[(N[(a / b), $MachinePrecision] / 3.0), $MachinePrecision]), $MachinePrecision]]]]]]

Reproduce

herbie shell --seed 2024277 
(FPCore (x y z t a b)
  :name "Diagrams.Solve.Polynomial:cubForm  from diagrams-solve-0.1, K"
  :precision binary64

  :alt
  (! :herbie-platform default (if (< z -1379333748723514100000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000) (- (* (* 2 (sqrt x)) (cos (- (/ 1 y) (/ (/ 3333333333333333/10000000000000000 z) t)))) (/ (/ a 3) b)) (if (< z 35162906135559870000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000) (- (* (* (sqrt x) 2) (cos (- y (* (/ t 3) z)))) (/ (/ a 3) b)) (- (* (cos (- y (/ (/ 3333333333333333/10000000000000000 z) t))) (* 2 (sqrt x))) (/ (/ a b) 3)))))

  (- (* (* 2.0 (sqrt x)) (cos (- y (/ (* z t) 3.0)))) (/ a (* b 3.0))))