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

Percentage Accurate: 69.8% → 77.3%

Time: 22.5s

Alternatives: 17

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: 77.3% accurate, 0.2× speedup

Math

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

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 (* 3.0 b))) (t_2 (* 2.0 (sqrt x))))
   (if (<= (- (* t_2 (cos (- y (/ (* z t) 3.0)))) t_1) 1e+168)
     (-
      (*
       t_2
       (-
        (* (cos y) (cos (* z (* t 0.3333333333333333))))
        (* (sin (* t (* z -0.3333333333333333))) (sin y))))
      t_1)
     (fma 2.0 (sqrt x) (/ (* a -0.3333333333333333) 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) {
	double t_1 = a / (3.0 * b);
	double t_2 = 2.0 * sqrt(x);
	double tmp;
	if (((t_2 * cos((y - ((z * t) / 3.0)))) - t_1) <= 1e+168) {
		tmp = (t_2 * ((cos(y) * cos((z * (t * 0.3333333333333333)))) - (sin((t * (z * -0.3333333333333333))) * sin(y)))) - t_1;
	} else {
		tmp = fma(2.0, sqrt(x), ((a * -0.3333333333333333) / b));
	}
	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(3.0 * b))
	t_2 = Float64(2.0 * sqrt(x))
	tmp = 0.0
	if (Float64(Float64(t_2 * cos(Float64(y - Float64(Float64(z * t) / 3.0)))) - t_1) <= 1e+168)
		tmp = Float64(Float64(t_2 * Float64(Float64(cos(y) * cos(Float64(z * Float64(t * 0.3333333333333333)))) - Float64(sin(Float64(t * Float64(z * -0.3333333333333333))) * sin(y)))) - t_1);
	else
		tmp = fma(2.0, sqrt(x), Float64(Float64(a * -0.3333333333333333) / b));
	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[(3.0 * b), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(2.0 * N[Sqrt[x], $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[(N[(t$95$2 * N[Cos[N[(y - N[(N[(z * t), $MachinePrecision] / 3.0), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] - t$95$1), $MachinePrecision], 1e+168], N[(N[(t$95$2 * N[(N[(N[Cos[y], $MachinePrecision] * N[Cos[N[(z * N[(t * 0.3333333333333333), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] - N[(N[Sin[N[(t * N[(z * -0.3333333333333333), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] * N[Sin[y], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - t$95$1), $MachinePrecision], N[(2.0 * N[Sqrt[x], $MachinePrecision] + N[(N[(a * -0.3333333333333333), $MachinePrecision] / b), $MachinePrecision]), $MachinePrecision]]]]

Derivation

1. Split input into 2 regimes

2. if (-.f64 (*.f64 (*.f64 #s(literal 2 binary64) (sqrt.f64 x)) (cos.f64 (-.f64 y (/.f64 (*.f64 z t) #s(literal 3 binary64))))) (/.f64 a (*.f64 b #s(literal 3 binary64)))) < 9.9999999999999993e167

   1. Initial program 76.0%

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

      1. lift-*.f64 N/A

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

      2. lift-/.f64 N/A

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

      3. sub-neg N/A

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

      4. +-commutative N/A

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

      5. cos-sum N/A

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

      6. cos-neg N/A

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

      7. *-commutative N/A

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

      8. lower--.f64 N/A

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

   4. Applied egg-rr 77.1%

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

   if 9.9999999999999993e167 < (-.f64 (*.f64 (*.f64 #s(literal 2 binary64) (sqrt.f64 x)) (cos.f64 (-.f64 y (/.f64 (*.f64 z t) #s(literal 3 binary64))))) (/.f64 a (*.f64 b #s(literal 3 binary64))))

   1. Initial program 33.2%

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

   3. Taylor expanded in z 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 72.1

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

   5. Simplified 72.1%

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

   6. Taylor expanded in y around 0

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

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

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

      2. metadata-eval N/A

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

      3. lower-fma.f64 N/A

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

      4. lower-sqrt.f64 N/A

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

      5. associate-*r/ N/A

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

      6. lower-/.f64 N/A

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

      7. *-commutative N/A

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

      8. lower-*.f64 72.3

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

   8. Simplified 72.3%

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

4. Final simplification 76.2%

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

Alternative 2: 77.4% accurate, 0.3× speedup

Math

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

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 (* 3.0 b)))
        (t_2 (* 2.0 (sqrt x)))
        (t_3 (* z (* t 0.3333333333333333))))
   (if (<= (- (* t_2 (cos (- y (/ (* z t) 3.0)))) t_1) 1e+168)
     (- (* t_2 (fma (cos t_3) (cos y) (* (sin y) (sin t_3)))) t_1)
     (fma 2.0 (sqrt x) (/ (* a -0.3333333333333333) 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) {
	double t_1 = a / (3.0 * b);
	double t_2 = 2.0 * sqrt(x);
	double t_3 = z * (t * 0.3333333333333333);
	double tmp;
	if (((t_2 * cos((y - ((z * t) / 3.0)))) - t_1) <= 1e+168) {
		tmp = (t_2 * fma(cos(t_3), cos(y), (sin(y) * sin(t_3)))) - t_1;
	} else {
		tmp = fma(2.0, sqrt(x), ((a * -0.3333333333333333) / b));
	}
	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(3.0 * b))
	t_2 = Float64(2.0 * sqrt(x))
	t_3 = Float64(z * Float64(t * 0.3333333333333333))
	tmp = 0.0
	if (Float64(Float64(t_2 * cos(Float64(y - Float64(Float64(z * t) / 3.0)))) - t_1) <= 1e+168)
		tmp = Float64(Float64(t_2 * fma(cos(t_3), cos(y), Float64(sin(y) * sin(t_3)))) - t_1);
	else
		tmp = fma(2.0, sqrt(x), Float64(Float64(a * -0.3333333333333333) / b));
	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[(3.0 * b), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(2.0 * N[Sqrt[x], $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(z * N[(t * 0.3333333333333333), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[(N[(t$95$2 * N[Cos[N[(y - N[(N[(z * t), $MachinePrecision] / 3.0), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] - t$95$1), $MachinePrecision], 1e+168], N[(N[(t$95$2 * N[(N[Cos[t$95$3], $MachinePrecision] * N[Cos[y], $MachinePrecision] + N[(N[Sin[y], $MachinePrecision] * N[Sin[t$95$3], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - t$95$1), $MachinePrecision], N[(2.0 * N[Sqrt[x], $MachinePrecision] + N[(N[(a * -0.3333333333333333), $MachinePrecision] / b), $MachinePrecision]), $MachinePrecision]]]]]

Derivation

1. Split input into 2 regimes

2. if (-.f64 (*.f64 (*.f64 #s(literal 2 binary64) (sqrt.f64 x)) (cos.f64 (-.f64 y (/.f64 (*.f64 z t) #s(literal 3 binary64))))) (/.f64 a (*.f64 b #s(literal 3 binary64)))) < 9.9999999999999993e167

   1. Initial program 76.0%

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

      1. lift-*.f64 N/A

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

      2. lift-/.f64 N/A

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

      3. cos-diff N/A

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

      4. *-commutative N/A

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

      5. lower-fma.f64 N/A

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

      6. lower-cos.f64 N/A

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

      7. lift-/.f64 N/A

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

      8. lift-*.f64 N/A

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

      9. associate-/l* N/A

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

      10. lower-*.f64 N/A

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

      11. div-inv N/A

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

      12. lower-*.f64 N/A

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

      13. metadata-eval N/A

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

      14. lower-cos.f64 N/A

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

      15. lower-*.f64 N/A

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

      16. lower-sin.f64 N/A

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

      17. lower-sin.f64 77.0

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

      18. lift-/.f64 N/A

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

      19. lift-*.f64 N/A

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

      20. associate-/l* N/A

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

      21. lower-*.f64 N/A

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

      22. div-inv N/A

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

      23. lower-*.f64 N/A

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

      24. metadata-eval 77.0

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

   4. Applied egg-rr 77.0%

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

   if 9.9999999999999993e167 < (-.f64 (*.f64 (*.f64 #s(literal 2 binary64) (sqrt.f64 x)) (cos.f64 (-.f64 y (/.f64 (*.f64 z t) #s(literal 3 binary64))))) (/.f64 a (*.f64 b #s(literal 3 binary64))))

   1. Initial program 33.2%

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

   3. Taylor expanded in z 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 72.1

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

   5. Simplified 72.1%

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

   6. Taylor expanded in y around 0

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

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

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

      2. metadata-eval N/A

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

      3. lower-fma.f64 N/A

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

      4. lower-sqrt.f64 N/A

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

      5. associate-*r/ N/A

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

      6. lower-/.f64 N/A

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

      7. *-commutative N/A

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

      8. lower-*.f64 72.3

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

   8. Simplified 72.3%

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

4. Final simplification 76.2%

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

Alternative 3: 71.6% accurate, 0.9× speedup

Math

\[\begin{array}{l} [x, y, z, t, a, b] = \mathsf{sort}([x, y, z, t, a, b])\\ \\ \begin{array}{l} t_1 := \frac{a}{3 \cdot b}\\ t_2 := 2 \cdot \sqrt{x}\\ t_3 := t\_2 - t\_1\\ \mathbf{if}\;t\_1 \leq -2 \cdot 10^{-77}:\\ \;\;\;\;t\_3\\ \mathbf{elif}\;t\_1 \leq 2 \cdot 10^{-45}:\\ \;\;\;\;t\_2 \cdot \cos \left(\mathsf{fma}\left(-0.3333333333333333, z \cdot t, y\right)\right)\\ \mathbf{else}:\\ \;\;\;\;t\_3\\ \end{array} \end{array} \]

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 (/ a (* 3.0 b))) (t_2 (* 2.0 (sqrt x))) (t_3 (- t_2 t_1)))
   (if (<= t_1 -2e-77)
     t_3
     (if (<= t_1 2e-45)
       (* t_2 (cos (fma -0.3333333333333333 (* z t) y)))
       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 = a / (3.0 * b);
	double t_2 = 2.0 * sqrt(x);
	double t_3 = t_2 - t_1;
	double tmp;
	if (t_1 <= -2e-77) {
		tmp = t_3;
	} else if (t_1 <= 2e-45) {
		tmp = t_2 * cos(fma(-0.3333333333333333, (z * t), y));
	} 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(a / Float64(3.0 * b))
	t_2 = Float64(2.0 * sqrt(x))
	t_3 = Float64(t_2 - t_1)
	tmp = 0.0
	if (t_1 <= -2e-77)
		tmp = t_3;
	elseif (t_1 <= 2e-45)
		tmp = Float64(t_2 * cos(fma(-0.3333333333333333, Float64(z * t), y)));
	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[(a / N[(3.0 * b), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(2.0 * N[Sqrt[x], $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(t$95$2 - t$95$1), $MachinePrecision]}, If[LessEqual[t$95$1, -2e-77], t$95$3, If[LessEqual[t$95$1, 2e-45], N[(t$95$2 * N[Cos[N[(-0.3333333333333333 * N[(z * t), $MachinePrecision] + y), $MachinePrecision]], $MachinePrecision]), $MachinePrecision], t$95$3]]]]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 a (*.f64 b #s(literal 3 binary64))) < -1.9999999999999999e-77 or 1.99999999999999997e-45 < (/.f64 a (*.f64 b #s(literal 3 binary64)))

   1. Initial program 78.3%

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

   3. Taylor expanded in z 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 89.9

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

   5. Simplified 89.9%

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

   6. Taylor expanded in y around 0

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

      1. lower-*.f64 N/A

         \[\leadsto \color{blue}{2 \cdot \sqrt{x}} - \frac{a}{b \cdot 3} \]

      2. lower-sqrt.f64 88.1

         \[\leadsto 2 \cdot \color{blue}{\sqrt{x}} - \frac{a}{b \cdot 3} \]

   8. Simplified 88.1%

      \[\leadsto \color{blue}{2 \cdot \sqrt{x}} - \frac{a}{b \cdot 3} \]

   if -1.9999999999999999e-77 < (/.f64 a (*.f64 b #s(literal 3 binary64))) < 1.99999999999999997e-45

   1. Initial program 53.2%

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

      1. associate-/r* N/A

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

      2. lower-/.f64 N/A

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

      3. lower-/.f64 53.2

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

   4. Applied egg-rr 53.2%

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

   5. Taylor expanded in x around inf

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

      1. associate-*r* N/A

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

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

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

      3. metadata-eval N/A

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

      4. lower-*.f64 N/A

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

      5. lower-*.f64 N/A

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

      6. lower-sqrt.f64 N/A

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

      7. lower-cos.f64 N/A

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

      8. +-commutative N/A

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

      9. lower-fma.f64 N/A

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

      10. lower-*.f64 51.8

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

   7. Simplified 51.8%

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

4. Final simplification 73.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{a}{3 \cdot b} \leq -2 \cdot 10^{-77}:\\ \;\;\;\;2 \cdot \sqrt{x} - \frac{a}{3 \cdot b}\\ \mathbf{elif}\;\frac{a}{3 \cdot b} \leq 2 \cdot 10^{-45}:\\ \;\;\;\;\left(2 \cdot \sqrt{x}\right) \cdot \cos \left(\mathsf{fma}\left(-0.3333333333333333, z \cdot t, y\right)\right)\\ \mathbf{else}:\\ \;\;\;\;2 \cdot \sqrt{x} - \frac{a}{3 \cdot b}\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 71.6% accurate, 0.9× speedup

Math

\[\begin{array}{l} [x, y, z, t, a, b] = \mathsf{sort}([x, y, z, t, a, b])\\ \\ \begin{array}{l} t_1 := \frac{a}{3 \cdot b}\\ t_2 := 2 \cdot \sqrt{x}\\ t_3 := t\_2 - t\_1\\ \mathbf{if}\;t\_1 \leq -2 \cdot 10^{-77}:\\ \;\;\;\;t\_3\\ \mathbf{elif}\;t\_1 \leq 2 \cdot 10^{-45}:\\ \;\;\;\;t\_2 \cdot \cos \left(\mathsf{fma}\left(t, z \cdot -0.3333333333333333, y\right)\right)\\ \mathbf{else}:\\ \;\;\;\;t\_3\\ \end{array} \end{array} \]

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 (/ a (* 3.0 b))) (t_2 (* 2.0 (sqrt x))) (t_3 (- t_2 t_1)))
   (if (<= t_1 -2e-77)
     t_3
     (if (<= t_1 2e-45)
       (* t_2 (cos (fma t (* z -0.3333333333333333) y)))
       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 = a / (3.0 * b);
	double t_2 = 2.0 * sqrt(x);
	double t_3 = t_2 - t_1;
	double tmp;
	if (t_1 <= -2e-77) {
		tmp = t_3;
	} else if (t_1 <= 2e-45) {
		tmp = t_2 * cos(fma(t, (z * -0.3333333333333333), y));
	} 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(a / Float64(3.0 * b))
	t_2 = Float64(2.0 * sqrt(x))
	t_3 = Float64(t_2 - t_1)
	tmp = 0.0
	if (t_1 <= -2e-77)
		tmp = t_3;
	elseif (t_1 <= 2e-45)
		tmp = Float64(t_2 * cos(fma(t, Float64(z * -0.3333333333333333), y)));
	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[(a / N[(3.0 * b), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(2.0 * N[Sqrt[x], $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(t$95$2 - t$95$1), $MachinePrecision]}, If[LessEqual[t$95$1, -2e-77], t$95$3, If[LessEqual[t$95$1, 2e-45], N[(t$95$2 * N[Cos[N[(t * N[(z * -0.3333333333333333), $MachinePrecision] + y), $MachinePrecision]], $MachinePrecision]), $MachinePrecision], t$95$3]]]]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 a (*.f64 b #s(literal 3 binary64))) < -1.9999999999999999e-77 or 1.99999999999999997e-45 < (/.f64 a (*.f64 b #s(literal 3 binary64)))

   1. Initial program 78.3%

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

   3. Taylor expanded in z 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 89.9

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

   5. Simplified 89.9%

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

   6. Taylor expanded in y around 0

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

      1. lower-*.f64 N/A

         \[\leadsto \color{blue}{2 \cdot \sqrt{x}} - \frac{a}{b \cdot 3} \]

      2. lower-sqrt.f64 88.1

         \[\leadsto 2 \cdot \color{blue}{\sqrt{x}} - \frac{a}{b \cdot 3} \]

   8. Simplified 88.1%

      \[\leadsto \color{blue}{2 \cdot \sqrt{x}} - \frac{a}{b \cdot 3} \]

   if -1.9999999999999999e-77 < (/.f64 a (*.f64 b #s(literal 3 binary64))) < 1.99999999999999997e-45

   1. Initial program 53.2%

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

   3. Taylor expanded in x 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. Simplified 51.8%

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

4. Final simplification 73.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{a}{3 \cdot b} \leq -2 \cdot 10^{-77}:\\ \;\;\;\;2 \cdot \sqrt{x} - \frac{a}{3 \cdot b}\\ \mathbf{elif}\;\frac{a}{3 \cdot b} \leq 2 \cdot 10^{-45}:\\ \;\;\;\;\left(2 \cdot \sqrt{x}\right) \cdot \cos \left(\mathsf{fma}\left(t, z \cdot -0.3333333333333333, y\right)\right)\\ \mathbf{else}:\\ \;\;\;\;2 \cdot \sqrt{x} - \frac{a}{3 \cdot b}\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 71.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} [x, y, z, t, a, b] = \mathsf{sort}([x, y, z, t, a, b])\\ \\ \begin{array}{l} t_1 := \frac{a}{3 \cdot b}\\ t_2 := 2 \cdot \sqrt{x} - t\_1\\ \mathbf{if}\;t\_1 \leq -2 \cdot 10^{-68}:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;t\_1 \leq 2 \cdot 10^{-45}:\\ \;\;\;\;2 \cdot \left(\sqrt{x} \cdot \cos y\right)\\ \mathbf{else}:\\ \;\;\;\;t\_2\\ \end{array} \end{array} \]

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 (* 3.0 b))) (t_2 (- (* 2.0 (sqrt x)) t_1)))
   (if (<= t_1 -2e-68)
     t_2
     (if (<= t_1 2e-45) (* 2.0 (* (sqrt x) (cos y))) t_2))))

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 / (3.0 * b);
	double t_2 = (2.0 * sqrt(x)) - t_1;
	double tmp;
	if (t_1 <= -2e-68) {
		tmp = t_2;
	} else if (t_1 <= 2e-45) {
		tmp = 2.0 * (sqrt(x) * cos(y));
	} else {
		tmp = t_2;
	}
	return tmp;
}

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
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = a / (3.0d0 * b)
    t_2 = (2.0d0 * sqrt(x)) - t_1
    if (t_1 <= (-2d-68)) then
        tmp = t_2
    else if (t_1 <= 2d-45) then
        tmp = 2.0d0 * (sqrt(x) * cos(y))
    else
        tmp = t_2
    end if
    code = tmp
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) {
	double t_1 = a / (3.0 * b);
	double t_2 = (2.0 * Math.sqrt(x)) - t_1;
	double tmp;
	if (t_1 <= -2e-68) {
		tmp = t_2;
	} else if (t_1 <= 2e-45) {
		tmp = 2.0 * (Math.sqrt(x) * Math.cos(y));
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

[x, y, z, t, a, b] = sort([x, y, z, t, a, b])
def code(x, y, z, t, a, b):
	t_1 = a / (3.0 * b)
	t_2 = (2.0 * math.sqrt(x)) - t_1
	tmp = 0
	if t_1 <= -2e-68:
		tmp = t_2
	elif t_1 <= 2e-45:
		tmp = 2.0 * (math.sqrt(x) * math.cos(y))
	else:
		tmp = t_2
	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(3.0 * b))
	t_2 = Float64(Float64(2.0 * sqrt(x)) - t_1)
	tmp = 0.0
	if (t_1 <= -2e-68)
		tmp = t_2;
	elseif (t_1 <= 2e-45)
		tmp = Float64(2.0 * Float64(sqrt(x) * cos(y)));
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

x, y, z, t, a, b = num2cell(sort([x, y, z, t, a, b])){:}
function tmp_2 = code(x, y, z, t, a, b)
	t_1 = a / (3.0 * b);
	t_2 = (2.0 * sqrt(x)) - t_1;
	tmp = 0.0;
	if (t_1 <= -2e-68)
		tmp = t_2;
	elseif (t_1 <= 2e-45)
		tmp = 2.0 * (sqrt(x) * cos(y));
	else
		tmp = t_2;
	end
	tmp_2 = 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[(3.0 * b), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(2.0 * N[Sqrt[x], $MachinePrecision]), $MachinePrecision] - t$95$1), $MachinePrecision]}, If[LessEqual[t$95$1, -2e-68], t$95$2, If[LessEqual[t$95$1, 2e-45], N[(2.0 * N[(N[Sqrt[x], $MachinePrecision] * N[Cos[y], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$2]]]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 a (*.f64 b #s(literal 3 binary64))) < -2.00000000000000013e-68 or 1.99999999999999997e-45 < (/.f64 a (*.f64 b #s(literal 3 binary64)))

   1. Initial program 78.8%

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

   3. Taylor expanded in z 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 90.4

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

   5. Simplified 90.4%

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

   6. Taylor expanded in y around 0

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

      1. lower-*.f64 N/A

         \[\leadsto \color{blue}{2 \cdot \sqrt{x}} - \frac{a}{b \cdot 3} \]

      2. lower-sqrt.f64 88.6

         \[\leadsto 2 \cdot \color{blue}{\sqrt{x}} - \frac{a}{b \cdot 3} \]

   8. Simplified 88.6%

      \[\leadsto \color{blue}{2 \cdot \sqrt{x}} - \frac{a}{b \cdot 3} \]

   if -2.00000000000000013e-68 < (/.f64 a (*.f64 b #s(literal 3 binary64))) < 1.99999999999999997e-45

   1. Initial program 52.7%

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

   3. Taylor expanded in z 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 51.4

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

   5. Simplified 51.4%

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

   6. Taylor expanded in x around inf

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

      1. lower-*.f64 N/A

         \[\leadsto \color{blue}{2 \cdot \left(\sqrt{x} \cdot \cos y\right)} \]

      2. lower-*.f64 N/A

         \[\leadsto 2 \cdot \color{blue}{\left(\sqrt{x} \cdot \cos y\right)} \]

      3. lower-sqrt.f64 N/A

         \[\leadsto 2 \cdot \left(\color{blue}{\sqrt{x}} \cdot \cos y\right) \]

      4. lower-cos.f64 50.4

         \[\leadsto 2 \cdot \left(\sqrt{x} \cdot \color{blue}{\cos y}\right) \]

   8. Simplified 50.4%

      \[\leadsto \color{blue}{2 \cdot \left(\sqrt{x} \cdot \cos y\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 72.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{a}{3 \cdot b} \leq -2 \cdot 10^{-68}:\\ \;\;\;\;2 \cdot \sqrt{x} - \frac{a}{3 \cdot b}\\ \mathbf{elif}\;\frac{a}{3 \cdot b} \leq 2 \cdot 10^{-45}:\\ \;\;\;\;2 \cdot \left(\sqrt{x} \cdot \cos y\right)\\ \mathbf{else}:\\ \;\;\;\;2 \cdot \sqrt{x} - \frac{a}{3 \cdot b}\\ \end{array} \]
5. Add Preprocessing

Alternative 6: 76.4% accurate, 1.1× speedup

Math

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

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
 (- (* (* 2.0 (sqrt x)) (cos y)) (/ 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 ((2.0 * sqrt(x)) * cos(y)) - (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 = ((2.0d0 * sqrt(x)) * cos(y)) - (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 ((2.0 * Math.sqrt(x)) * Math.cos(y)) - (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 ((2.0 * math.sqrt(x)) * math.cos(y)) - (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(Float64(Float64(2.0 * sqrt(x)) * cos(y)) - 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 = ((2.0 * sqrt(x)) * cos(y)) - (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[(N[(N[(2.0 * N[Sqrt[x], $MachinePrecision]), $MachinePrecision] * N[Cos[y], $MachinePrecision]), $MachinePrecision] - N[(a / N[(3.0 * b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 68.0%

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

3. Taylor expanded in z 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 74.2

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

5. Simplified 74.2%

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

6. Final simplification 74.2%

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

Alternative 7: 76.4% accurate, 1.2× speedup

Math

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

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 (* 2.0 (sqrt x)) (cos y) (/ (* a -0.3333333333333333) 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 fma((2.0 * sqrt(x)), cos(y), ((a * -0.3333333333333333) / b));
}

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(2.0 * sqrt(x)), cos(y), Float64(Float64(a * -0.3333333333333333) / 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[(N[(2.0 * N[Sqrt[x], $MachinePrecision]), $MachinePrecision] * N[Cos[y], $MachinePrecision] + N[(N[(a * -0.3333333333333333), $MachinePrecision] / b), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 68.0%

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

3. Taylor expanded in z 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 74.2

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

5. Simplified 74.2%

   \[\leadsto \left(2 \cdot \sqrt{x}\right) \cdot \color{blue}{\cos y} - \frac{a}{b \cdot 3} \]
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. lift-*.f64 N/A

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

   3. lift-cos.f64 N/A

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

   4. lift-*.f64 N/A

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

   5. lift-*.f64 N/A

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

   6. lift-/.f64 N/A

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

   7. sub-neg N/A

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

   8. +-commutative N/A

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

   9. lift-/.f64 N/A

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

   10. lift-*.f64 N/A

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

   11. associate-/r* N/A

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

   12. lift-/.f64 N/A

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

   13. div-inv N/A

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

   14. metadata-eval N/A

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

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

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

   16. metadata-eval N/A

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

   17. lift-/.f64 N/A

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

   18. associate-*l/ N/A

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

   19. associate-/l* N/A

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

   20. lower-fma.f64 N/A

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

   21. lower-/.f64 74.2

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

   22. lift-*.f64 N/A

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

7. Applied egg-rr 74.2%

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

   1. lift-/.f64 N/A

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

   2. lift-sqrt.f64 N/A

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

   3. lift-cos.f64 N/A

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

   4. *-commutative N/A

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

   5. associate-*r* N/A

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

   6. lift-*.f64 N/A

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

   7. *-commutative N/A

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

   8. lift-*.f64 N/A

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

   9. +-commutative N/A

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

   10. lift-*.f64 N/A

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

   11. associate-*r* N/A

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

   12. lower-fma.f64 N/A

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

   13. *-commutative N/A

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

   14. lower-*.f64 74.2

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

   15. lift-*.f64 N/A

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

   16. *-commutative N/A

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

   17. lift-/.f64 N/A

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

   18. associate-*l/ N/A

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

   19. metadata-eval N/A

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

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

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

   21. lift-*.f64 N/A

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

   22. lower-/.f64 N/A

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

9. Applied egg-rr 74.2%

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

10. Final simplification 74.2%

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

Alternative 8: 76.3% accurate, 1.2× speedup

Math

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

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 (* 2.0 (sqrt x)) (cos y) (* a (/ -0.3333333333333333 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 fma((2.0 * sqrt(x)), cos(y), (a * (-0.3333333333333333 / b)));
}

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(2.0 * sqrt(x)), cos(y), Float64(a * Float64(-0.3333333333333333 / 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[(N[(2.0 * N[Sqrt[x], $MachinePrecision]), $MachinePrecision] * N[Cos[y], $MachinePrecision] + N[(a * N[(-0.3333333333333333 / b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 68.0%

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

3. Taylor expanded in z 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 74.2

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

5. Simplified 74.2%

   \[\leadsto \left(2 \cdot \sqrt{x}\right) \cdot \color{blue}{\cos y} - \frac{a}{b \cdot 3} \]
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. lift-*.f64 N/A

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

   3. lift-cos.f64 N/A

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

   4. lift-*.f64 N/A

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

   5. lift-*.f64 N/A

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

   6. lift-/.f64 N/A

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

   7. sub-neg N/A

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

   8. +-commutative N/A

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

   9. lift-/.f64 N/A

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

   10. lift-*.f64 N/A

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

   11. associate-/r* N/A

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

   12. lift-/.f64 N/A

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

   13. div-inv N/A

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

   14. metadata-eval N/A

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

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

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

   16. metadata-eval N/A

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

   17. lift-/.f64 N/A

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

   18. associate-*l/ N/A

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

   19. associate-/l* N/A

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

   20. lower-fma.f64 N/A

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

   21. lower-/.f64 74.2

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

   22. lift-*.f64 N/A

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

7. Applied egg-rr 74.2%

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

   1. lift-/.f64 N/A

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

   2. lift-sqrt.f64 N/A

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

   3. lift-cos.f64 N/A

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

   4. *-commutative N/A

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

   5. associate-*r* N/A

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

   6. lift-*.f64 N/A

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

   7. *-commutative N/A

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

   8. lift-*.f64 N/A

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

   9. +-commutative N/A

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

   10. lift-*.f64 N/A

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

   11. associate-*r* N/A

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

   12. lower-fma.f64 N/A

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

   13. *-commutative N/A

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

   14. lower-*.f64 74.2

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

   15. lift-*.f64 N/A

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

   16. *-commutative N/A

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

   17. lift-/.f64 N/A

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

   18. associate-*l/ N/A

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

   19. metadata-eval N/A

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

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

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

   21. lift-*.f64 N/A

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

   22. lower-/.f64 N/A

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

9. Applied egg-rr 74.2%

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

    1. associate-/l* N/A

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

    2. *-commutative N/A

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

    3. lower-*.f64 N/A

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

    4. lower-/.f64 74.2

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

11. Applied egg-rr 74.2%

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

12. Final simplification 74.2%

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

Alternative 9: 76.3% accurate, 1.2× speedup

Math

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

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 a (/ -0.3333333333333333 b) (* (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(a, (-0.3333333333333333 / b), (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(a, Float64(-0.3333333333333333 / b), Float64(sqrt(x) * Float64(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[(a * N[(-0.3333333333333333 / b), $MachinePrecision] + N[(N[Sqrt[x], $MachinePrecision] * N[(2.0 * N[Cos[y], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 68.0%

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

3. Taylor expanded in z 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 74.2

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

5. Simplified 74.2%

   \[\leadsto \left(2 \cdot \sqrt{x}\right) \cdot \color{blue}{\cos y} - \frac{a}{b \cdot 3} \]
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. lift-*.f64 N/A

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

   3. lift-cos.f64 N/A

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

   4. lift-*.f64 N/A

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

   5. lift-*.f64 N/A

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

   6. lift-/.f64 N/A

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

   7. sub-neg N/A

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

   8. +-commutative N/A

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

   9. lift-/.f64 N/A

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

   10. lift-*.f64 N/A

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

   11. associate-/r* N/A

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

   12. lift-/.f64 N/A

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

   13. div-inv N/A

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

   14. metadata-eval N/A

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

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

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

   16. metadata-eval N/A

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

   17. lift-/.f64 N/A

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

   18. associate-*l/ N/A

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

   19. associate-/l* N/A

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

   20. lower-fma.f64 N/A

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

   21. lower-/.f64 74.2

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

   22. lift-*.f64 N/A

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

7. Applied egg-rr 74.2%

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

Alternative 10: 76.3% accurate, 1.2× speedup

Math

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

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 2.0 (* (sqrt x) (cos y)) (* -0.3333333333333333 (/ a 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 fma(2.0, (sqrt(x) * cos(y)), (-0.3333333333333333 * (a / b)));
}

Julia

x, y, z, t, a, b = sort([x, y, z, t, a, b])
function code(x, y, z, t, a, b)
	return fma(2.0, Float64(sqrt(x) * cos(y)), Float64(-0.3333333333333333 * Float64(a / 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[(2.0 * N[(N[Sqrt[x], $MachinePrecision] * N[Cos[y], $MachinePrecision]), $MachinePrecision] + N[(-0.3333333333333333 * N[(a / b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 68.0%

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

3. Taylor expanded in z 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. cancel-sign-sub-inv N/A

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

   2. metadata-eval N/A

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

   3. lower-fma.f64 N/A

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

   4. lower-*.f64 N/A

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

   5. lower-sqrt.f64 N/A

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

   6. lower-cos.f64 N/A

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

   7. *-commutative N/A

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

   8. lower-*.f64 N/A

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

   9. lower-/.f64 74.1

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

5. Simplified 74.1%

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

6. Final simplification 74.1%

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

Alternative 11: 65.7% accurate, 4.5× speedup

Math

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

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 (- (* 2.0 (sqrt x)) (/ 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 (2.0 * sqrt(x)) - (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 = (2.0d0 * sqrt(x)) - (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 (2.0 * Math.sqrt(x)) - (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 (2.0 * math.sqrt(x)) - (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(Float64(2.0 * sqrt(x)) - 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 = (2.0 * sqrt(x)) - (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[(N[(2.0 * N[Sqrt[x], $MachinePrecision]), $MachinePrecision] - N[(a / N[(3.0 * b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 68.0%

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

3. Taylor expanded in z 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 74.2

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

5. Simplified 74.2%

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

6. Taylor expanded in y around 0

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

   1. lower-*.f64 N/A

      \[\leadsto \color{blue}{2 \cdot \sqrt{x}} - \frac{a}{b \cdot 3} \]

   2. lower-sqrt.f64 65.6

      \[\leadsto 2 \cdot \color{blue}{\sqrt{x}} - \frac{a}{b \cdot 3} \]

8. Simplified 65.6%

   \[\leadsto \color{blue}{2 \cdot \sqrt{x}} - \frac{a}{b \cdot 3} \]

9. Final simplification 65.6%

   \[\leadsto 2 \cdot \sqrt{x} - \frac{a}{3 \cdot b} \]
10. Add Preprocessing

Alternative 12: 65.7% accurate, 4.8× speedup

Math

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

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 2.0 (sqrt x) (/ (* a -0.3333333333333333) 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 fma(2.0, sqrt(x), ((a * -0.3333333333333333) / b));
}

Julia

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

Derivation

1. Initial program 68.0%

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

3. Taylor expanded in z 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 74.2

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

5. Simplified 74.2%

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

6. Taylor expanded in y around 0

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

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

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

   2. metadata-eval N/A

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

   3. lower-fma.f64 N/A

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

   4. lower-sqrt.f64 N/A

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

   5. associate-*r/ N/A

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

   6. lower-/.f64 N/A

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

   7. *-commutative N/A

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

   8. lower-*.f64 65.5

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

8. Simplified 65.5%

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

Alternative 13: 65.6% accurate, 4.8× speedup

Math

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

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 (/ a b) (* 2.0 (sqrt x))))

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, (a / b), (2.0 * sqrt(x)));
}

Julia

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

Derivation

1. Initial program 68.0%

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

3. Taylor expanded in z 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 74.2

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

5. Simplified 74.2%

   \[\leadsto \left(2 \cdot \sqrt{x}\right) \cdot \color{blue}{\cos y} - \frac{a}{b \cdot 3} \]
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. lift-*.f64 N/A

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

   3. lift-cos.f64 N/A

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

   4. lift-*.f64 N/A

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

   5. lift-*.f64 N/A

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

   6. lift-/.f64 N/A

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

   7. sub-neg N/A

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

   8. +-commutative N/A

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

   9. lift-/.f64 N/A

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

   10. lift-*.f64 N/A

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

   11. associate-/r* N/A

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

   12. lift-/.f64 N/A

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

   13. div-inv N/A

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

   14. metadata-eval N/A

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

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

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

   16. metadata-eval N/A

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

   17. lift-/.f64 N/A

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

   18. associate-*l/ N/A

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

   19. associate-/l* N/A

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

   20. lower-fma.f64 N/A

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

   21. lower-/.f64 74.2

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

   22. lift-*.f64 N/A

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

7. Applied egg-rr 74.2%

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

   1. lift-/.f64 N/A

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

   2. lift-sqrt.f64 N/A

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

   3. lift-cos.f64 N/A

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

   4. *-commutative N/A

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

   5. associate-*r* N/A

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

   6. lift-*.f64 N/A

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

   7. *-commutative N/A

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

   8. lift-*.f64 N/A

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

   9. +-commutative N/A

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

   10. lift-*.f64 N/A

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

   11. associate-*r* N/A

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

   12. lower-fma.f64 N/A

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

   13. *-commutative N/A

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

   14. lower-*.f64 74.2

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

   15. lift-*.f64 N/A

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

   16. *-commutative N/A

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

   17. lift-/.f64 N/A

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

   18. associate-*l/ N/A

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

   19. metadata-eval N/A

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

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

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

   21. lift-*.f64 N/A

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

   22. lower-/.f64 N/A

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

9. Applied egg-rr 74.2%

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

10. Taylor expanded in y around 0

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

    1. lower-fma.f64 N/A

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

    2. lower-/.f64 N/A

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

    3. lower-*.f64 N/A

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

    4. lower-sqrt.f64 65.5

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

12. Simplified 65.5%

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

Alternative 14: 50.8% accurate, 6.9× speedup

Math

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

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 (/ (/ 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 (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 = (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 (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 (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(a / 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 = (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[(a / b), $MachinePrecision] / -3.0), $MachinePrecision]

Derivation

1. Initial program 68.0%

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

3. Taylor expanded in a around inf

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

   1. *-commutative N/A

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

   2. lower-*.f64 N/A

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

   3. lower-/.f64 49.4

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

5. Simplified 49.4%

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

   1. lift-/.f64 N/A

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

   2. metadata-eval N/A

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

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

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

   4. metadata-eval N/A

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

   5. div-inv N/A

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

   6. distribute-neg-frac2 N/A

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

   7. lower-/.f64 N/A

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

   8. metadata-eval 49.5

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

7. Applied egg-rr 49.5%

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

Alternative 15: 50.9% accurate, 6.9× speedup

Math

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

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(Float64(a / -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[(N[(a / -3.0), $MachinePrecision] / b), $MachinePrecision]

Derivation

1. Initial program 68.0%

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

3. Taylor expanded in a around inf

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

   1. *-commutative N/A

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

   2. lower-*.f64 N/A

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

   3. lower-/.f64 49.4

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

5. Simplified 49.4%

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

   1. lift-/.f64 N/A

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

   2. metadata-eval N/A

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

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

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

   4. metadata-eval N/A

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

   5. div-inv N/A

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

   6. lift-/.f64 N/A

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

   7. associate-/r* N/A

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

   8. lift-*.f64 N/A

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

   9. distribute-neg-frac2 N/A

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

   10. lower-/.f64 N/A

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

   11. lift-*.f64 N/A

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

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

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

   13. lower-*.f64 N/A

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

   14. metadata-eval 49.4

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

7. Applied egg-rr 49.4%

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

   1. *-commutative N/A

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

   2. associate-/r* N/A

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

   3. lower-/.f64 N/A

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

   4. lower-/.f64 49.5

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

9. Applied egg-rr 49.5%

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

Alternative 16: 50.9% accurate, 9.4× speedup

Math

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

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 (/ 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 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 = 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 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 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(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 = 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[(a / N[(b * -3.0), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 68.0%

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

3. Taylor expanded in a around inf

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

   1. *-commutative N/A

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

   2. lower-*.f64 N/A

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

   3. lower-/.f64 49.4

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

5. Simplified 49.4%

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

   1. lift-/.f64 N/A

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

   2. metadata-eval N/A

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

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

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

   4. metadata-eval N/A

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

   5. div-inv N/A

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

   6. lift-/.f64 N/A

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

   7. associate-/r* N/A

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

   8. lift-*.f64 N/A

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

   9. distribute-neg-frac2 N/A

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

   10. lower-/.f64 N/A

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

   11. lift-*.f64 N/A

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

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

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

   13. lower-*.f64 N/A

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

   14. metadata-eval 49.4

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

7. Applied egg-rr 49.4%

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

Alternative 17: 50.8% accurate, 9.4× speedup

Math

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

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 (/ -0.3333333333333333 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 * (-0.3333333333333333 / 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 * ((-0.3333333333333333d0) / 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 * (-0.3333333333333333 / 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 * (-0.3333333333333333 / 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(-0.3333333333333333 / 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 * (-0.3333333333333333 / 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[(-0.3333333333333333 / b), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 68.0%

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

3. Taylor expanded in a around inf

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

   1. *-commutative N/A

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

   2. lower-*.f64 N/A

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

   3. lower-/.f64 49.4

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

5. Simplified 49.4%

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

   1. associate-*l/ N/A

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

   2. associate-/l* N/A

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

   3. lower-*.f64 N/A

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

   4. lower-/.f64 49.4

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

7. Applied egg-rr 49.4%

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

Developer Target 1: 74.1% 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 2024208 
(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))))