Rosa's FloatVsDoubleBenchmark

Percentage Accurate: 70.9% → 99.4%

Time: 1.0min

Alternatives: 24

Speedup: 1.3×

• 45.7% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left(3 \cdot x1\right) \cdot x1\\ t_1 := x1 \cdot x1 + 1\\ t_2 := \frac{\left(t_0 + 2 \cdot x2\right) - x1}{t_1}\\ x1 + \left(\left(\left(\left(\left(\left(\left(2 \cdot x1\right) \cdot t_2\right) \cdot \left(t_2 - 3\right) + \left(x1 \cdot x1\right) \cdot \left(4 \cdot t_2 - 6\right)\right) \cdot t_1 + t_0 \cdot t_2\right) + \left(x1 \cdot x1\right) \cdot x1\right) + x1\right) + 3 \cdot \frac{\left(t_0 - 2 \cdot x2\right) - x1}{t_1}\right) \end{array} \end{array} \]

FPCore

(FPCore (x1 x2)
 :precision binary64
 (let* ((t_0 (* (* 3.0 x1) x1))
        (t_1 (+ (* x1 x1) 1.0))
        (t_2 (/ (- (+ t_0 (* 2.0 x2)) x1) t_1)))
   (+
    x1
    (+
     (+
      (+
       (+
        (*
         (+
          (* (* (* 2.0 x1) t_2) (- t_2 3.0))
          (* (* x1 x1) (- (* 4.0 t_2) 6.0)))
         t_1)
        (* t_0 t_2))
       (* (* x1 x1) x1))
      x1)
     (* 3.0 (/ (- (- t_0 (* 2.0 x2)) x1) t_1))))))

C

double code(double x1, double x2) {
	double t_0 = (3.0 * x1) * x1;
	double t_1 = (x1 * x1) + 1.0;
	double t_2 = ((t_0 + (2.0 * x2)) - x1) / t_1;
	return x1 + (((((((((2.0 * x1) * t_2) * (t_2 - 3.0)) + ((x1 * x1) * ((4.0 * t_2) - 6.0))) * t_1) + (t_0 * t_2)) + ((x1 * x1) * x1)) + x1) + (3.0 * (((t_0 - (2.0 * x2)) - x1) / t_1)));
}

Fortran

real(8) function code(x1, x2)
    real(8), intent (in) :: x1
    real(8), intent (in) :: x2
    real(8) :: t_0
    real(8) :: t_1
    real(8) :: t_2
    t_0 = (3.0d0 * x1) * x1
    t_1 = (x1 * x1) + 1.0d0
    t_2 = ((t_0 + (2.0d0 * x2)) - x1) / t_1
    code = x1 + (((((((((2.0d0 * x1) * t_2) * (t_2 - 3.0d0)) + ((x1 * x1) * ((4.0d0 * t_2) - 6.0d0))) * t_1) + (t_0 * t_2)) + ((x1 * x1) * x1)) + x1) + (3.0d0 * (((t_0 - (2.0d0 * x2)) - x1) / t_1)))
end function

Java

public static double code(double x1, double x2) {
	double t_0 = (3.0 * x1) * x1;
	double t_1 = (x1 * x1) + 1.0;
	double t_2 = ((t_0 + (2.0 * x2)) - x1) / t_1;
	return x1 + (((((((((2.0 * x1) * t_2) * (t_2 - 3.0)) + ((x1 * x1) * ((4.0 * t_2) - 6.0))) * t_1) + (t_0 * t_2)) + ((x1 * x1) * x1)) + x1) + (3.0 * (((t_0 - (2.0 * x2)) - x1) / t_1)));
}

Python

def code(x1, x2):
	t_0 = (3.0 * x1) * x1
	t_1 = (x1 * x1) + 1.0
	t_2 = ((t_0 + (2.0 * x2)) - x1) / t_1
	return x1 + (((((((((2.0 * x1) * t_2) * (t_2 - 3.0)) + ((x1 * x1) * ((4.0 * t_2) - 6.0))) * t_1) + (t_0 * t_2)) + ((x1 * x1) * x1)) + x1) + (3.0 * (((t_0 - (2.0 * x2)) - x1) / t_1)))

Julia

function code(x1, x2)
	t_0 = Float64(Float64(3.0 * x1) * x1)
	t_1 = Float64(Float64(x1 * x1) + 1.0)
	t_2 = Float64(Float64(Float64(t_0 + Float64(2.0 * x2)) - x1) / t_1)
	return Float64(x1 + Float64(Float64(Float64(Float64(Float64(Float64(Float64(Float64(Float64(2.0 * x1) * t_2) * Float64(t_2 - 3.0)) + Float64(Float64(x1 * x1) * Float64(Float64(4.0 * t_2) - 6.0))) * t_1) + Float64(t_0 * t_2)) + Float64(Float64(x1 * x1) * x1)) + x1) + Float64(3.0 * Float64(Float64(Float64(t_0 - Float64(2.0 * x2)) - x1) / t_1))))
end

MATLAB

function tmp = code(x1, x2)
	t_0 = (3.0 * x1) * x1;
	t_1 = (x1 * x1) + 1.0;
	t_2 = ((t_0 + (2.0 * x2)) - x1) / t_1;
	tmp = x1 + (((((((((2.0 * x1) * t_2) * (t_2 - 3.0)) + ((x1 * x1) * ((4.0 * t_2) - 6.0))) * t_1) + (t_0 * t_2)) + ((x1 * x1) * x1)) + x1) + (3.0 * (((t_0 - (2.0 * x2)) - x1) / t_1)));
end

Wolfram

code[x1_, x2_] := Block[{t$95$0 = N[(N[(3.0 * x1), $MachinePrecision] * x1), $MachinePrecision]}, Block[{t$95$1 = N[(N[(x1 * x1), $MachinePrecision] + 1.0), $MachinePrecision]}, Block[{t$95$2 = N[(N[(N[(t$95$0 + N[(2.0 * x2), $MachinePrecision]), $MachinePrecision] - x1), $MachinePrecision] / t$95$1), $MachinePrecision]}, N[(x1 + N[(N[(N[(N[(N[(N[(N[(N[(N[(2.0 * x1), $MachinePrecision] * t$95$2), $MachinePrecision] * N[(t$95$2 - 3.0), $MachinePrecision]), $MachinePrecision] + N[(N[(x1 * x1), $MachinePrecision] * N[(N[(4.0 * t$95$2), $MachinePrecision] - 6.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * t$95$1), $MachinePrecision] + N[(t$95$0 * t$95$2), $MachinePrecision]), $MachinePrecision] + N[(N[(x1 * x1), $MachinePrecision] * x1), $MachinePrecision]), $MachinePrecision] + x1), $MachinePrecision] + N[(3.0 * N[(N[(N[(t$95$0 - N[(2.0 * x2), $MachinePrecision]), $MachinePrecision] - x1), $MachinePrecision] / t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]

Initial Program: 70.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left(3 \cdot x1\right) \cdot x1\\ t_1 := x1 \cdot x1 + 1\\ t_2 := \frac{\left(t_0 + 2 \cdot x2\right) - x1}{t_1}\\ x1 + \left(\left(\left(\left(\left(\left(\left(2 \cdot x1\right) \cdot t_2\right) \cdot \left(t_2 - 3\right) + \left(x1 \cdot x1\right) \cdot \left(4 \cdot t_2 - 6\right)\right) \cdot t_1 + t_0 \cdot t_2\right) + \left(x1 \cdot x1\right) \cdot x1\right) + x1\right) + 3 \cdot \frac{\left(t_0 - 2 \cdot x2\right) - x1}{t_1}\right) \end{array} \end{array} \]

FPCore

(FPCore (x1 x2)
 :precision binary64
 (let* ((t_0 (* (* 3.0 x1) x1))
        (t_1 (+ (* x1 x1) 1.0))
        (t_2 (/ (- (+ t_0 (* 2.0 x2)) x1) t_1)))
   (+
    x1
    (+
     (+
      (+
       (+
        (*
         (+
          (* (* (* 2.0 x1) t_2) (- t_2 3.0))
          (* (* x1 x1) (- (* 4.0 t_2) 6.0)))
         t_1)
        (* t_0 t_2))
       (* (* x1 x1) x1))
      x1)
     (* 3.0 (/ (- (- t_0 (* 2.0 x2)) x1) t_1))))))

C

double code(double x1, double x2) {
	double t_0 = (3.0 * x1) * x1;
	double t_1 = (x1 * x1) + 1.0;
	double t_2 = ((t_0 + (2.0 * x2)) - x1) / t_1;
	return x1 + (((((((((2.0 * x1) * t_2) * (t_2 - 3.0)) + ((x1 * x1) * ((4.0 * t_2) - 6.0))) * t_1) + (t_0 * t_2)) + ((x1 * x1) * x1)) + x1) + (3.0 * (((t_0 - (2.0 * x2)) - x1) / t_1)));
}

Fortran

real(8) function code(x1, x2)
    real(8), intent (in) :: x1
    real(8), intent (in) :: x2
    real(8) :: t_0
    real(8) :: t_1
    real(8) :: t_2
    t_0 = (3.0d0 * x1) * x1
    t_1 = (x1 * x1) + 1.0d0
    t_2 = ((t_0 + (2.0d0 * x2)) - x1) / t_1
    code = x1 + (((((((((2.0d0 * x1) * t_2) * (t_2 - 3.0d0)) + ((x1 * x1) * ((4.0d0 * t_2) - 6.0d0))) * t_1) + (t_0 * t_2)) + ((x1 * x1) * x1)) + x1) + (3.0d0 * (((t_0 - (2.0d0 * x2)) - x1) / t_1)))
end function

Java

public static double code(double x1, double x2) {
	double t_0 = (3.0 * x1) * x1;
	double t_1 = (x1 * x1) + 1.0;
	double t_2 = ((t_0 + (2.0 * x2)) - x1) / t_1;
	return x1 + (((((((((2.0 * x1) * t_2) * (t_2 - 3.0)) + ((x1 * x1) * ((4.0 * t_2) - 6.0))) * t_1) + (t_0 * t_2)) + ((x1 * x1) * x1)) + x1) + (3.0 * (((t_0 - (2.0 * x2)) - x1) / t_1)));
}

Python

def code(x1, x2):
	t_0 = (3.0 * x1) * x1
	t_1 = (x1 * x1) + 1.0
	t_2 = ((t_0 + (2.0 * x2)) - x1) / t_1
	return x1 + (((((((((2.0 * x1) * t_2) * (t_2 - 3.0)) + ((x1 * x1) * ((4.0 * t_2) - 6.0))) * t_1) + (t_0 * t_2)) + ((x1 * x1) * x1)) + x1) + (3.0 * (((t_0 - (2.0 * x2)) - x1) / t_1)))

Julia

function code(x1, x2)
	t_0 = Float64(Float64(3.0 * x1) * x1)
	t_1 = Float64(Float64(x1 * x1) + 1.0)
	t_2 = Float64(Float64(Float64(t_0 + Float64(2.0 * x2)) - x1) / t_1)
	return Float64(x1 + Float64(Float64(Float64(Float64(Float64(Float64(Float64(Float64(Float64(2.0 * x1) * t_2) * Float64(t_2 - 3.0)) + Float64(Float64(x1 * x1) * Float64(Float64(4.0 * t_2) - 6.0))) * t_1) + Float64(t_0 * t_2)) + Float64(Float64(x1 * x1) * x1)) + x1) + Float64(3.0 * Float64(Float64(Float64(t_0 - Float64(2.0 * x2)) - x1) / t_1))))
end

MATLAB

function tmp = code(x1, x2)
	t_0 = (3.0 * x1) * x1;
	t_1 = (x1 * x1) + 1.0;
	t_2 = ((t_0 + (2.0 * x2)) - x1) / t_1;
	tmp = x1 + (((((((((2.0 * x1) * t_2) * (t_2 - 3.0)) + ((x1 * x1) * ((4.0 * t_2) - 6.0))) * t_1) + (t_0 * t_2)) + ((x1 * x1) * x1)) + x1) + (3.0 * (((t_0 - (2.0 * x2)) - x1) / t_1)));
end

Wolfram

code[x1_, x2_] := Block[{t$95$0 = N[(N[(3.0 * x1), $MachinePrecision] * x1), $MachinePrecision]}, Block[{t$95$1 = N[(N[(x1 * x1), $MachinePrecision] + 1.0), $MachinePrecision]}, Block[{t$95$2 = N[(N[(N[(t$95$0 + N[(2.0 * x2), $MachinePrecision]), $MachinePrecision] - x1), $MachinePrecision] / t$95$1), $MachinePrecision]}, N[(x1 + N[(N[(N[(N[(N[(N[(N[(N[(N[(2.0 * x1), $MachinePrecision] * t$95$2), $MachinePrecision] * N[(t$95$2 - 3.0), $MachinePrecision]), $MachinePrecision] + N[(N[(x1 * x1), $MachinePrecision] * N[(N[(4.0 * t$95$2), $MachinePrecision] - 6.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * t$95$1), $MachinePrecision] + N[(t$95$0 * t$95$2), $MachinePrecision]), $MachinePrecision] + N[(N[(x1 * x1), $MachinePrecision] * x1), $MachinePrecision]), $MachinePrecision] + x1), $MachinePrecision] + N[(3.0 * N[(N[(N[(t$95$0 - N[(2.0 * x2), $MachinePrecision]), $MachinePrecision] - x1), $MachinePrecision] / t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]

Alternative 1: 99.4% accurate, 0.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{\mathsf{fma}\left(x1, x1 \cdot 3, 2 \cdot x2\right) - x1}{\mathsf{fma}\left(x1, x1, 1\right)}\\ t_1 := 3 \cdot \left(x1 \cdot x1\right)\\ \mathbf{if}\;x1 \leq -5 \cdot 10^{+102}:\\ \;\;\;\;x1 + \left(\left(x1 + \left(-3 \cdot {x1}^{3} + 6 \cdot {x1}^{4}\right)\right) + 9\right)\\ \mathbf{elif}\;x1 \leq 2 \cdot 10^{+153}:\\ \;\;\;\;x1 + \mathsf{fma}\left(3, \frac{t_1 - \mathsf{fma}\left(2, x2, x1\right)}{\mathsf{fma}\left(x1, x1, 1\right)}, x1 + \mathsf{fma}\left(\mathsf{fma}\left(x1, x1, 1\right), \mathsf{fma}\left(x1, x1 \cdot \mathsf{fma}\left(t_0, 4, -6\right), \left(x1 \cdot \left(2 \cdot t_0\right)\right) \cdot \left(-3 + t_0\right)\right), \mathsf{fma}\left(t_1, t_0, {x1}^{3}\right)\right)\right)\\ \mathbf{else}:\\ \;\;\;\;x1 + 9 \cdot {x1}^{2}\\ \end{array} \end{array} \]

FPCore

(FPCore (x1 x2)
 :precision binary64
 (let* ((t_0 (/ (- (fma x1 (* x1 3.0) (* 2.0 x2)) x1) (fma x1 x1 1.0)))
        (t_1 (* 3.0 (* x1 x1))))
   (if (<= x1 -5e+102)
     (+ x1 (+ (+ x1 (+ (* -3.0 (pow x1 3.0)) (* 6.0 (pow x1 4.0)))) 9.0))
     (if (<= x1 2e+153)
       (+
        x1
        (fma
         3.0
         (/ (- t_1 (fma 2.0 x2 x1)) (fma x1 x1 1.0))
         (+
          x1
          (fma
           (fma x1 x1 1.0)
           (fma
            x1
            (* x1 (fma t_0 4.0 -6.0))
            (* (* x1 (* 2.0 t_0)) (+ -3.0 t_0)))
           (fma t_1 t_0 (pow x1 3.0))))))
       (+ x1 (* 9.0 (pow x1 2.0)))))))

C

double code(double x1, double x2) {
	double t_0 = (fma(x1, (x1 * 3.0), (2.0 * x2)) - x1) / fma(x1, x1, 1.0);
	double t_1 = 3.0 * (x1 * x1);
	double tmp;
	if (x1 <= -5e+102) {
		tmp = x1 + ((x1 + ((-3.0 * pow(x1, 3.0)) + (6.0 * pow(x1, 4.0)))) + 9.0);
	} else if (x1 <= 2e+153) {
		tmp = x1 + fma(3.0, ((t_1 - fma(2.0, x2, x1)) / fma(x1, x1, 1.0)), (x1 + fma(fma(x1, x1, 1.0), fma(x1, (x1 * fma(t_0, 4.0, -6.0)), ((x1 * (2.0 * t_0)) * (-3.0 + t_0))), fma(t_1, t_0, pow(x1, 3.0)))));
	} else {
		tmp = x1 + (9.0 * pow(x1, 2.0));
	}
	return tmp;
}

Julia

function code(x1, x2)
	t_0 = Float64(Float64(fma(x1, Float64(x1 * 3.0), Float64(2.0 * x2)) - x1) / fma(x1, x1, 1.0))
	t_1 = Float64(3.0 * Float64(x1 * x1))
	tmp = 0.0
	if (x1 <= -5e+102)
		tmp = Float64(x1 + Float64(Float64(x1 + Float64(Float64(-3.0 * (x1 ^ 3.0)) + Float64(6.0 * (x1 ^ 4.0)))) + 9.0));
	elseif (x1 <= 2e+153)
		tmp = Float64(x1 + fma(3.0, Float64(Float64(t_1 - fma(2.0, x2, x1)) / fma(x1, x1, 1.0)), Float64(x1 + fma(fma(x1, x1, 1.0), fma(x1, Float64(x1 * fma(t_0, 4.0, -6.0)), Float64(Float64(x1 * Float64(2.0 * t_0)) * Float64(-3.0 + t_0))), fma(t_1, t_0, (x1 ^ 3.0))))));
	else
		tmp = Float64(x1 + Float64(9.0 * (x1 ^ 2.0)));
	end
	return tmp
end

Wolfram

code[x1_, x2_] := Block[{t$95$0 = N[(N[(N[(x1 * N[(x1 * 3.0), $MachinePrecision] + N[(2.0 * x2), $MachinePrecision]), $MachinePrecision] - x1), $MachinePrecision] / N[(x1 * x1 + 1.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(3.0 * N[(x1 * x1), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x1, -5e+102], N[(x1 + N[(N[(x1 + N[(N[(-3.0 * N[Power[x1, 3.0], $MachinePrecision]), $MachinePrecision] + N[(6.0 * N[Power[x1, 4.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + 9.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, 2e+153], N[(x1 + N[(3.0 * N[(N[(t$95$1 - N[(2.0 * x2 + x1), $MachinePrecision]), $MachinePrecision] / N[(x1 * x1 + 1.0), $MachinePrecision]), $MachinePrecision] + N[(x1 + N[(N[(x1 * x1 + 1.0), $MachinePrecision] * N[(x1 * N[(x1 * N[(t$95$0 * 4.0 + -6.0), $MachinePrecision]), $MachinePrecision] + N[(N[(x1 * N[(2.0 * t$95$0), $MachinePrecision]), $MachinePrecision] * N[(-3.0 + t$95$0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(t$95$1 * t$95$0 + N[Power[x1, 3.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x1 + N[(9.0 * N[Power[x1, 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 2: 99.0% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := 1 + {x1}^{2}\\ t_1 := x1 \cdot \left(x1 \cdot 3\right)\\ t_2 := x1 \cdot x1 + 1\\ t_3 := \frac{\left(2 \cdot x2 + t_1\right) - x1}{t_2}\\ \mathbf{if}\;x1 \leq -4 \cdot 10^{+106}:\\ \;\;\;\;x1 + \left(\left(x1 + \left(-3 \cdot {x1}^{3} + 6 \cdot {x1}^{4}\right)\right) + 9\right)\\ \mathbf{elif}\;x1 \leq 2 \cdot 10^{+153}:\\ \;\;\;\;x1 + \left(\left(x1 + \left(\left(t_2 \cdot \left(\left(\left(x1 \cdot 2\right) \cdot t_3\right) \cdot \left(t_3 - 3\right) + \left(x1 \cdot x1\right) \cdot \left(4 \cdot \left(\left(2 \cdot \frac{x2}{t_0} + 3 \cdot \frac{{x1}^{2}}{t_0}\right) - \frac{x1}{t_0}\right) - 6\right)\right) + t_1 \cdot t_3\right) + x1 \cdot \left(x1 \cdot x1\right)\right)\right) + 3 \cdot \frac{\left(t_1 - 2 \cdot x2\right) - x1}{t_2}\right)\\ \mathbf{else}:\\ \;\;\;\;x1 + 9 \cdot {x1}^{2}\\ \end{array} \end{array} \]

FPCore

(FPCore (x1 x2)
 :precision binary64
 (let* ((t_0 (+ 1.0 (pow x1 2.0)))
        (t_1 (* x1 (* x1 3.0)))
        (t_2 (+ (* x1 x1) 1.0))
        (t_3 (/ (- (+ (* 2.0 x2) t_1) x1) t_2)))
   (if (<= x1 -4e+106)
     (+ x1 (+ (+ x1 (+ (* -3.0 (pow x1 3.0)) (* 6.0 (pow x1 4.0)))) 9.0))
     (if (<= x1 2e+153)
       (+
        x1
        (+
         (+
          x1
          (+
           (+
            (*
             t_2
             (+
              (* (* (* x1 2.0) t_3) (- t_3 3.0))
              (*
               (* x1 x1)
               (-
                (*
                 4.0
                 (-
                  (+ (* 2.0 (/ x2 t_0)) (* 3.0 (/ (pow x1 2.0) t_0)))
                  (/ x1 t_0)))
                6.0))))
            (* t_1 t_3))
           (* x1 (* x1 x1))))
         (* 3.0 (/ (- (- t_1 (* 2.0 x2)) x1) t_2))))
       (+ x1 (* 9.0 (pow x1 2.0)))))))

C

double code(double x1, double x2) {
	double t_0 = 1.0 + pow(x1, 2.0);
	double t_1 = x1 * (x1 * 3.0);
	double t_2 = (x1 * x1) + 1.0;
	double t_3 = (((2.0 * x2) + t_1) - x1) / t_2;
	double tmp;
	if (x1 <= -4e+106) {
		tmp = x1 + ((x1 + ((-3.0 * pow(x1, 3.0)) + (6.0 * pow(x1, 4.0)))) + 9.0);
	} else if (x1 <= 2e+153) {
		tmp = x1 + ((x1 + (((t_2 * ((((x1 * 2.0) * t_3) * (t_3 - 3.0)) + ((x1 * x1) * ((4.0 * (((2.0 * (x2 / t_0)) + (3.0 * (pow(x1, 2.0) / t_0))) - (x1 / t_0))) - 6.0)))) + (t_1 * t_3)) + (x1 * (x1 * x1)))) + (3.0 * (((t_1 - (2.0 * x2)) - x1) / t_2)));
	} else {
		tmp = x1 + (9.0 * pow(x1, 2.0));
	}
	return tmp;
}

Fortran

real(8) function code(x1, x2)
    real(8), intent (in) :: x1
    real(8), intent (in) :: x2
    real(8) :: t_0
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: t_3
    real(8) :: tmp
    t_0 = 1.0d0 + (x1 ** 2.0d0)
    t_1 = x1 * (x1 * 3.0d0)
    t_2 = (x1 * x1) + 1.0d0
    t_3 = (((2.0d0 * x2) + t_1) - x1) / t_2
    if (x1 <= (-4d+106)) then
        tmp = x1 + ((x1 + (((-3.0d0) * (x1 ** 3.0d0)) + (6.0d0 * (x1 ** 4.0d0)))) + 9.0d0)
    else if (x1 <= 2d+153) then
        tmp = x1 + ((x1 + (((t_2 * ((((x1 * 2.0d0) * t_3) * (t_3 - 3.0d0)) + ((x1 * x1) * ((4.0d0 * (((2.0d0 * (x2 / t_0)) + (3.0d0 * ((x1 ** 2.0d0) / t_0))) - (x1 / t_0))) - 6.0d0)))) + (t_1 * t_3)) + (x1 * (x1 * x1)))) + (3.0d0 * (((t_1 - (2.0d0 * x2)) - x1) / t_2)))
    else
        tmp = x1 + (9.0d0 * (x1 ** 2.0d0))
    end if
    code = tmp
end function

Java

public static double code(double x1, double x2) {
	double t_0 = 1.0 + Math.pow(x1, 2.0);
	double t_1 = x1 * (x1 * 3.0);
	double t_2 = (x1 * x1) + 1.0;
	double t_3 = (((2.0 * x2) + t_1) - x1) / t_2;
	double tmp;
	if (x1 <= -4e+106) {
		tmp = x1 + ((x1 + ((-3.0 * Math.pow(x1, 3.0)) + (6.0 * Math.pow(x1, 4.0)))) + 9.0);
	} else if (x1 <= 2e+153) {
		tmp = x1 + ((x1 + (((t_2 * ((((x1 * 2.0) * t_3) * (t_3 - 3.0)) + ((x1 * x1) * ((4.0 * (((2.0 * (x2 / t_0)) + (3.0 * (Math.pow(x1, 2.0) / t_0))) - (x1 / t_0))) - 6.0)))) + (t_1 * t_3)) + (x1 * (x1 * x1)))) + (3.0 * (((t_1 - (2.0 * x2)) - x1) / t_2)));
	} else {
		tmp = x1 + (9.0 * Math.pow(x1, 2.0));
	}
	return tmp;
}

Python

def code(x1, x2):
	t_0 = 1.0 + math.pow(x1, 2.0)
	t_1 = x1 * (x1 * 3.0)
	t_2 = (x1 * x1) + 1.0
	t_3 = (((2.0 * x2) + t_1) - x1) / t_2
	tmp = 0
	if x1 <= -4e+106:
		tmp = x1 + ((x1 + ((-3.0 * math.pow(x1, 3.0)) + (6.0 * math.pow(x1, 4.0)))) + 9.0)
	elif x1 <= 2e+153:
		tmp = x1 + ((x1 + (((t_2 * ((((x1 * 2.0) * t_3) * (t_3 - 3.0)) + ((x1 * x1) * ((4.0 * (((2.0 * (x2 / t_0)) + (3.0 * (math.pow(x1, 2.0) / t_0))) - (x1 / t_0))) - 6.0)))) + (t_1 * t_3)) + (x1 * (x1 * x1)))) + (3.0 * (((t_1 - (2.0 * x2)) - x1) / t_2)))
	else:
		tmp = x1 + (9.0 * math.pow(x1, 2.0))
	return tmp

Julia

function code(x1, x2)
	t_0 = Float64(1.0 + (x1 ^ 2.0))
	t_1 = Float64(x1 * Float64(x1 * 3.0))
	t_2 = Float64(Float64(x1 * x1) + 1.0)
	t_3 = Float64(Float64(Float64(Float64(2.0 * x2) + t_1) - x1) / t_2)
	tmp = 0.0
	if (x1 <= -4e+106)
		tmp = Float64(x1 + Float64(Float64(x1 + Float64(Float64(-3.0 * (x1 ^ 3.0)) + Float64(6.0 * (x1 ^ 4.0)))) + 9.0));
	elseif (x1 <= 2e+153)
		tmp = Float64(x1 + Float64(Float64(x1 + Float64(Float64(Float64(t_2 * Float64(Float64(Float64(Float64(x1 * 2.0) * t_3) * Float64(t_3 - 3.0)) + Float64(Float64(x1 * x1) * Float64(Float64(4.0 * Float64(Float64(Float64(2.0 * Float64(x2 / t_0)) + Float64(3.0 * Float64((x1 ^ 2.0) / t_0))) - Float64(x1 / t_0))) - 6.0)))) + Float64(t_1 * t_3)) + Float64(x1 * Float64(x1 * x1)))) + Float64(3.0 * Float64(Float64(Float64(t_1 - Float64(2.0 * x2)) - x1) / t_2))));
	else
		tmp = Float64(x1 + Float64(9.0 * (x1 ^ 2.0)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x1, x2)
	t_0 = 1.0 + (x1 ^ 2.0);
	t_1 = x1 * (x1 * 3.0);
	t_2 = (x1 * x1) + 1.0;
	t_3 = (((2.0 * x2) + t_1) - x1) / t_2;
	tmp = 0.0;
	if (x1 <= -4e+106)
		tmp = x1 + ((x1 + ((-3.0 * (x1 ^ 3.0)) + (6.0 * (x1 ^ 4.0)))) + 9.0);
	elseif (x1 <= 2e+153)
		tmp = x1 + ((x1 + (((t_2 * ((((x1 * 2.0) * t_3) * (t_3 - 3.0)) + ((x1 * x1) * ((4.0 * (((2.0 * (x2 / t_0)) + (3.0 * ((x1 ^ 2.0) / t_0))) - (x1 / t_0))) - 6.0)))) + (t_1 * t_3)) + (x1 * (x1 * x1)))) + (3.0 * (((t_1 - (2.0 * x2)) - x1) / t_2)));
	else
		tmp = x1 + (9.0 * (x1 ^ 2.0));
	end
	tmp_2 = tmp;
end

Wolfram

code[x1_, x2_] := Block[{t$95$0 = N[(1.0 + N[Power[x1, 2.0], $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(x1 * N[(x1 * 3.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(x1 * x1), $MachinePrecision] + 1.0), $MachinePrecision]}, Block[{t$95$3 = N[(N[(N[(N[(2.0 * x2), $MachinePrecision] + t$95$1), $MachinePrecision] - x1), $MachinePrecision] / t$95$2), $MachinePrecision]}, If[LessEqual[x1, -4e+106], N[(x1 + N[(N[(x1 + N[(N[(-3.0 * N[Power[x1, 3.0], $MachinePrecision]), $MachinePrecision] + N[(6.0 * N[Power[x1, 4.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + 9.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, 2e+153], N[(x1 + N[(N[(x1 + N[(N[(N[(t$95$2 * N[(N[(N[(N[(x1 * 2.0), $MachinePrecision] * t$95$3), $MachinePrecision] * N[(t$95$3 - 3.0), $MachinePrecision]), $MachinePrecision] + N[(N[(x1 * x1), $MachinePrecision] * N[(N[(4.0 * N[(N[(N[(2.0 * N[(x2 / t$95$0), $MachinePrecision]), $MachinePrecision] + N[(3.0 * N[(N[Power[x1, 2.0], $MachinePrecision] / t$95$0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(x1 / t$95$0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - 6.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(t$95$1 * t$95$3), $MachinePrecision]), $MachinePrecision] + N[(x1 * N[(x1 * x1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(3.0 * N[(N[(N[(t$95$1 - N[(2.0 * x2), $MachinePrecision]), $MachinePrecision] - x1), $MachinePrecision] / t$95$2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x1 + N[(9.0 * N[Power[x1, 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 3: 95.9% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := x1 \cdot x1 + 1\\ t_1 := x1 \cdot \left(x1 \cdot 3\right)\\ t_2 := \frac{\left(2 \cdot x2 + t_1\right) - x1}{t_0}\\ t_3 := x1 + \left(3 \cdot \frac{\left(t_1 - 2 \cdot x2\right) - x1}{t_0} + \left(x1 + \left(x1 \cdot \left(x1 \cdot x1\right) + \left(t_1 \cdot t_2 + t_0 \cdot \left(\left(\left(x1 \cdot 2\right) \cdot t_2\right) \cdot \left(t_2 - 3\right) + \left(x1 \cdot x1\right) \cdot \left(4 \cdot t_2 - 6\right)\right)\right)\right)\right)\right)\\ \mathbf{if}\;t_3 \leq \infty:\\ \;\;\;\;t_3\\ \mathbf{else}:\\ \;\;\;\;x1 + 9 \cdot {x1}^{2}\\ \end{array} \end{array} \]

FPCore

(FPCore (x1 x2)
 :precision binary64
 (let* ((t_0 (+ (* x1 x1) 1.0))
        (t_1 (* x1 (* x1 3.0)))
        (t_2 (/ (- (+ (* 2.0 x2) t_1) x1) t_0))
        (t_3
         (+
          x1
          (+
           (* 3.0 (/ (- (- t_1 (* 2.0 x2)) x1) t_0))
           (+
            x1
            (+
             (* x1 (* x1 x1))
             (+
              (* t_1 t_2)
              (*
               t_0
               (+
                (* (* (* x1 2.0) t_2) (- t_2 3.0))
                (* (* x1 x1) (- (* 4.0 t_2) 6.0)))))))))))
   (if (<= t_3 INFINITY) t_3 (+ x1 (* 9.0 (pow x1 2.0))))))

C

double code(double x1, double x2) {
	double t_0 = (x1 * x1) + 1.0;
	double t_1 = x1 * (x1 * 3.0);
	double t_2 = (((2.0 * x2) + t_1) - x1) / t_0;
	double t_3 = x1 + ((3.0 * (((t_1 - (2.0 * x2)) - x1) / t_0)) + (x1 + ((x1 * (x1 * x1)) + ((t_1 * t_2) + (t_0 * ((((x1 * 2.0) * t_2) * (t_2 - 3.0)) + ((x1 * x1) * ((4.0 * t_2) - 6.0))))))));
	double tmp;
	if (t_3 <= ((double) INFINITY)) {
		tmp = t_3;
	} else {
		tmp = x1 + (9.0 * pow(x1, 2.0));
	}
	return tmp;
}

Java

public static double code(double x1, double x2) {
	double t_0 = (x1 * x1) + 1.0;
	double t_1 = x1 * (x1 * 3.0);
	double t_2 = (((2.0 * x2) + t_1) - x1) / t_0;
	double t_3 = x1 + ((3.0 * (((t_1 - (2.0 * x2)) - x1) / t_0)) + (x1 + ((x1 * (x1 * x1)) + ((t_1 * t_2) + (t_0 * ((((x1 * 2.0) * t_2) * (t_2 - 3.0)) + ((x1 * x1) * ((4.0 * t_2) - 6.0))))))));
	double tmp;
	if (t_3 <= Double.POSITIVE_INFINITY) {
		tmp = t_3;
	} else {
		tmp = x1 + (9.0 * Math.pow(x1, 2.0));
	}
	return tmp;
}

Python

def code(x1, x2):
	t_0 = (x1 * x1) + 1.0
	t_1 = x1 * (x1 * 3.0)
	t_2 = (((2.0 * x2) + t_1) - x1) / t_0
	t_3 = x1 + ((3.0 * (((t_1 - (2.0 * x2)) - x1) / t_0)) + (x1 + ((x1 * (x1 * x1)) + ((t_1 * t_2) + (t_0 * ((((x1 * 2.0) * t_2) * (t_2 - 3.0)) + ((x1 * x1) * ((4.0 * t_2) - 6.0))))))))
	tmp = 0
	if t_3 <= math.inf:
		tmp = t_3
	else:
		tmp = x1 + (9.0 * math.pow(x1, 2.0))
	return tmp

Julia

function code(x1, x2)
	t_0 = Float64(Float64(x1 * x1) + 1.0)
	t_1 = Float64(x1 * Float64(x1 * 3.0))
	t_2 = Float64(Float64(Float64(Float64(2.0 * x2) + t_1) - x1) / t_0)
	t_3 = Float64(x1 + Float64(Float64(3.0 * Float64(Float64(Float64(t_1 - Float64(2.0 * x2)) - x1) / t_0)) + Float64(x1 + Float64(Float64(x1 * Float64(x1 * x1)) + Float64(Float64(t_1 * t_2) + Float64(t_0 * Float64(Float64(Float64(Float64(x1 * 2.0) * t_2) * Float64(t_2 - 3.0)) + Float64(Float64(x1 * x1) * Float64(Float64(4.0 * t_2) - 6.0)))))))))
	tmp = 0.0
	if (t_3 <= Inf)
		tmp = t_3;
	else
		tmp = Float64(x1 + Float64(9.0 * (x1 ^ 2.0)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x1, x2)
	t_0 = (x1 * x1) + 1.0;
	t_1 = x1 * (x1 * 3.0);
	t_2 = (((2.0 * x2) + t_1) - x1) / t_0;
	t_3 = x1 + ((3.0 * (((t_1 - (2.0 * x2)) - x1) / t_0)) + (x1 + ((x1 * (x1 * x1)) + ((t_1 * t_2) + (t_0 * ((((x1 * 2.0) * t_2) * (t_2 - 3.0)) + ((x1 * x1) * ((4.0 * t_2) - 6.0))))))));
	tmp = 0.0;
	if (t_3 <= Inf)
		tmp = t_3;
	else
		tmp = x1 + (9.0 * (x1 ^ 2.0));
	end
	tmp_2 = tmp;
end

Wolfram

code[x1_, x2_] := Block[{t$95$0 = N[(N[(x1 * x1), $MachinePrecision] + 1.0), $MachinePrecision]}, Block[{t$95$1 = N[(x1 * N[(x1 * 3.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(N[(N[(2.0 * x2), $MachinePrecision] + t$95$1), $MachinePrecision] - x1), $MachinePrecision] / t$95$0), $MachinePrecision]}, Block[{t$95$3 = N[(x1 + N[(N[(3.0 * N[(N[(N[(t$95$1 - N[(2.0 * x2), $MachinePrecision]), $MachinePrecision] - x1), $MachinePrecision] / t$95$0), $MachinePrecision]), $MachinePrecision] + N[(x1 + N[(N[(x1 * N[(x1 * x1), $MachinePrecision]), $MachinePrecision] + N[(N[(t$95$1 * t$95$2), $MachinePrecision] + N[(t$95$0 * N[(N[(N[(N[(x1 * 2.0), $MachinePrecision] * t$95$2), $MachinePrecision] * N[(t$95$2 - 3.0), $MachinePrecision]), $MachinePrecision] + N[(N[(x1 * x1), $MachinePrecision] * N[(N[(4.0 * t$95$2), $MachinePrecision] - 6.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$3, Infinity], t$95$3, N[(x1 + N[(9.0 * N[Power[x1, 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 4: 99.2% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := x1 \cdot x1 + 1\\ t_1 := x1 \cdot \left(x1 \cdot 3\right)\\ t_2 := \frac{\left(2 \cdot x2 + t_1\right) - x1}{t_0}\\ \mathbf{if}\;x1 \leq -5.5 \cdot 10^{+102}:\\ \;\;\;\;x1 + \left(\left(x1 + \left(-3 \cdot {x1}^{3} + 6 \cdot {x1}^{4}\right)\right) + 9\right)\\ \mathbf{elif}\;x1 \leq 2 \cdot 10^{+153}:\\ \;\;\;\;x1 + \left(3 \cdot \frac{\left(t_1 - 2 \cdot x2\right) - x1}{t_0} + \left(x1 + \left(x1 \cdot \left(x1 \cdot x1\right) + \left(t_1 \cdot t_2 + t_0 \cdot \left(\left(\left(x1 \cdot 2\right) \cdot t_2\right) \cdot \left(t_2 - 3\right) + \left(x1 \cdot x1\right) \cdot \left(4 \cdot t_2 - 6\right)\right)\right)\right)\right)\right)\\ \mathbf{else}:\\ \;\;\;\;x1 + 9 \cdot {x1}^{2}\\ \end{array} \end{array} \]

FPCore

(FPCore (x1 x2)
 :precision binary64
 (let* ((t_0 (+ (* x1 x1) 1.0))
        (t_1 (* x1 (* x1 3.0)))
        (t_2 (/ (- (+ (* 2.0 x2) t_1) x1) t_0)))
   (if (<= x1 -5.5e+102)
     (+ x1 (+ (+ x1 (+ (* -3.0 (pow x1 3.0)) (* 6.0 (pow x1 4.0)))) 9.0))
     (if (<= x1 2e+153)
       (+
        x1
        (+
         (* 3.0 (/ (- (- t_1 (* 2.0 x2)) x1) t_0))
         (+
          x1
          (+
           (* x1 (* x1 x1))
           (+
            (* t_1 t_2)
            (*
             t_0
             (+
              (* (* (* x1 2.0) t_2) (- t_2 3.0))
              (* (* x1 x1) (- (* 4.0 t_2) 6.0)))))))))
       (+ x1 (* 9.0 (pow x1 2.0)))))))

C

double code(double x1, double x2) {
	double t_0 = (x1 * x1) + 1.0;
	double t_1 = x1 * (x1 * 3.0);
	double t_2 = (((2.0 * x2) + t_1) - x1) / t_0;
	double tmp;
	if (x1 <= -5.5e+102) {
		tmp = x1 + ((x1 + ((-3.0 * pow(x1, 3.0)) + (6.0 * pow(x1, 4.0)))) + 9.0);
	} else if (x1 <= 2e+153) {
		tmp = x1 + ((3.0 * (((t_1 - (2.0 * x2)) - x1) / t_0)) + (x1 + ((x1 * (x1 * x1)) + ((t_1 * t_2) + (t_0 * ((((x1 * 2.0) * t_2) * (t_2 - 3.0)) + ((x1 * x1) * ((4.0 * t_2) - 6.0))))))));
	} else {
		tmp = x1 + (9.0 * pow(x1, 2.0));
	}
	return tmp;
}

Fortran

real(8) function code(x1, x2)
    real(8), intent (in) :: x1
    real(8), intent (in) :: x2
    real(8) :: t_0
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_0 = (x1 * x1) + 1.0d0
    t_1 = x1 * (x1 * 3.0d0)
    t_2 = (((2.0d0 * x2) + t_1) - x1) / t_0
    if (x1 <= (-5.5d+102)) then
        tmp = x1 + ((x1 + (((-3.0d0) * (x1 ** 3.0d0)) + (6.0d0 * (x1 ** 4.0d0)))) + 9.0d0)
    else if (x1 <= 2d+153) then
        tmp = x1 + ((3.0d0 * (((t_1 - (2.0d0 * x2)) - x1) / t_0)) + (x1 + ((x1 * (x1 * x1)) + ((t_1 * t_2) + (t_0 * ((((x1 * 2.0d0) * t_2) * (t_2 - 3.0d0)) + ((x1 * x1) * ((4.0d0 * t_2) - 6.0d0))))))))
    else
        tmp = x1 + (9.0d0 * (x1 ** 2.0d0))
    end if
    code = tmp
end function

Java

public static double code(double x1, double x2) {
	double t_0 = (x1 * x1) + 1.0;
	double t_1 = x1 * (x1 * 3.0);
	double t_2 = (((2.0 * x2) + t_1) - x1) / t_0;
	double tmp;
	if (x1 <= -5.5e+102) {
		tmp = x1 + ((x1 + ((-3.0 * Math.pow(x1, 3.0)) + (6.0 * Math.pow(x1, 4.0)))) + 9.0);
	} else if (x1 <= 2e+153) {
		tmp = x1 + ((3.0 * (((t_1 - (2.0 * x2)) - x1) / t_0)) + (x1 + ((x1 * (x1 * x1)) + ((t_1 * t_2) + (t_0 * ((((x1 * 2.0) * t_2) * (t_2 - 3.0)) + ((x1 * x1) * ((4.0 * t_2) - 6.0))))))));
	} else {
		tmp = x1 + (9.0 * Math.pow(x1, 2.0));
	}
	return tmp;
}

Python

def code(x1, x2):
	t_0 = (x1 * x1) + 1.0
	t_1 = x1 * (x1 * 3.0)
	t_2 = (((2.0 * x2) + t_1) - x1) / t_0
	tmp = 0
	if x1 <= -5.5e+102:
		tmp = x1 + ((x1 + ((-3.0 * math.pow(x1, 3.0)) + (6.0 * math.pow(x1, 4.0)))) + 9.0)
	elif x1 <= 2e+153:
		tmp = x1 + ((3.0 * (((t_1 - (2.0 * x2)) - x1) / t_0)) + (x1 + ((x1 * (x1 * x1)) + ((t_1 * t_2) + (t_0 * ((((x1 * 2.0) * t_2) * (t_2 - 3.0)) + ((x1 * x1) * ((4.0 * t_2) - 6.0))))))))
	else:
		tmp = x1 + (9.0 * math.pow(x1, 2.0))
	return tmp

Julia

function code(x1, x2)
	t_0 = Float64(Float64(x1 * x1) + 1.0)
	t_1 = Float64(x1 * Float64(x1 * 3.0))
	t_2 = Float64(Float64(Float64(Float64(2.0 * x2) + t_1) - x1) / t_0)
	tmp = 0.0
	if (x1 <= -5.5e+102)
		tmp = Float64(x1 + Float64(Float64(x1 + Float64(Float64(-3.0 * (x1 ^ 3.0)) + Float64(6.0 * (x1 ^ 4.0)))) + 9.0));
	elseif (x1 <= 2e+153)
		tmp = Float64(x1 + Float64(Float64(3.0 * Float64(Float64(Float64(t_1 - Float64(2.0 * x2)) - x1) / t_0)) + Float64(x1 + Float64(Float64(x1 * Float64(x1 * x1)) + Float64(Float64(t_1 * t_2) + Float64(t_0 * Float64(Float64(Float64(Float64(x1 * 2.0) * t_2) * Float64(t_2 - 3.0)) + Float64(Float64(x1 * x1) * Float64(Float64(4.0 * t_2) - 6.0)))))))));
	else
		tmp = Float64(x1 + Float64(9.0 * (x1 ^ 2.0)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x1, x2)
	t_0 = (x1 * x1) + 1.0;
	t_1 = x1 * (x1 * 3.0);
	t_2 = (((2.0 * x2) + t_1) - x1) / t_0;
	tmp = 0.0;
	if (x1 <= -5.5e+102)
		tmp = x1 + ((x1 + ((-3.0 * (x1 ^ 3.0)) + (6.0 * (x1 ^ 4.0)))) + 9.0);
	elseif (x1 <= 2e+153)
		tmp = x1 + ((3.0 * (((t_1 - (2.0 * x2)) - x1) / t_0)) + (x1 + ((x1 * (x1 * x1)) + ((t_1 * t_2) + (t_0 * ((((x1 * 2.0) * t_2) * (t_2 - 3.0)) + ((x1 * x1) * ((4.0 * t_2) - 6.0))))))));
	else
		tmp = x1 + (9.0 * (x1 ^ 2.0));
	end
	tmp_2 = tmp;
end

Wolfram

code[x1_, x2_] := Block[{t$95$0 = N[(N[(x1 * x1), $MachinePrecision] + 1.0), $MachinePrecision]}, Block[{t$95$1 = N[(x1 * N[(x1 * 3.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(N[(N[(2.0 * x2), $MachinePrecision] + t$95$1), $MachinePrecision] - x1), $MachinePrecision] / t$95$0), $MachinePrecision]}, If[LessEqual[x1, -5.5e+102], N[(x1 + N[(N[(x1 + N[(N[(-3.0 * N[Power[x1, 3.0], $MachinePrecision]), $MachinePrecision] + N[(6.0 * N[Power[x1, 4.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + 9.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, 2e+153], N[(x1 + N[(N[(3.0 * N[(N[(N[(t$95$1 - N[(2.0 * x2), $MachinePrecision]), $MachinePrecision] - x1), $MachinePrecision] / t$95$0), $MachinePrecision]), $MachinePrecision] + N[(x1 + N[(N[(x1 * N[(x1 * x1), $MachinePrecision]), $MachinePrecision] + N[(N[(t$95$1 * t$95$2), $MachinePrecision] + N[(t$95$0 * N[(N[(N[(N[(x1 * 2.0), $MachinePrecision] * t$95$2), $MachinePrecision] * N[(t$95$2 - 3.0), $MachinePrecision]), $MachinePrecision] + N[(N[(x1 * x1), $MachinePrecision] * N[(N[(4.0 * t$95$2), $MachinePrecision] - 6.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x1 + N[(9.0 * N[Power[x1, 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 5: 99.1% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := x1 \cdot \left(x1 \cdot 3\right)\\ t_1 := x1 + 9 \cdot {x1}^{2}\\ t_2 := x1 \cdot x1 + 1\\ t_3 := 3 \cdot \frac{\left(t_0 - 2 \cdot x2\right) - x1}{t_2}\\ t_4 := \frac{\left(2 \cdot x2 + t_0\right) - x1}{t_2}\\ \mathbf{if}\;x1 \leq -5 \cdot 10^{+154}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x1 \leq -5.5 \cdot 10^{+102}:\\ \;\;\;\;x1 + \left(t_3 + \left(x1 + 6 \cdot {x1}^{4}\right)\right)\\ \mathbf{elif}\;x1 \leq 2 \cdot 10^{+153}:\\ \;\;\;\;x1 + \left(t_3 + \left(x1 + \left(x1 \cdot \left(x1 \cdot x1\right) + \left(t_0 \cdot t_4 + t_2 \cdot \left(\left(\left(x1 \cdot 2\right) \cdot t_4\right) \cdot \left(t_4 - 3\right) + \left(x1 \cdot x1\right) \cdot \left(4 \cdot t_4 - 6\right)\right)\right)\right)\right)\right)\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x1 x2)
 :precision binary64
 (let* ((t_0 (* x1 (* x1 3.0)))
        (t_1 (+ x1 (* 9.0 (pow x1 2.0))))
        (t_2 (+ (* x1 x1) 1.0))
        (t_3 (* 3.0 (/ (- (- t_0 (* 2.0 x2)) x1) t_2)))
        (t_4 (/ (- (+ (* 2.0 x2) t_0) x1) t_2)))
   (if (<= x1 -5e+154)
     t_1
     (if (<= x1 -5.5e+102)
       (+ x1 (+ t_3 (+ x1 (* 6.0 (pow x1 4.0)))))
       (if (<= x1 2e+153)
         (+
          x1
          (+
           t_3
           (+
            x1
            (+
             (* x1 (* x1 x1))
             (+
              (* t_0 t_4)
              (*
               t_2
               (+
                (* (* (* x1 2.0) t_4) (- t_4 3.0))
                (* (* x1 x1) (- (* 4.0 t_4) 6.0)))))))))
         t_1)))))

C

double code(double x1, double x2) {
	double t_0 = x1 * (x1 * 3.0);
	double t_1 = x1 + (9.0 * pow(x1, 2.0));
	double t_2 = (x1 * x1) + 1.0;
	double t_3 = 3.0 * (((t_0 - (2.0 * x2)) - x1) / t_2);
	double t_4 = (((2.0 * x2) + t_0) - x1) / t_2;
	double tmp;
	if (x1 <= -5e+154) {
		tmp = t_1;
	} else if (x1 <= -5.5e+102) {
		tmp = x1 + (t_3 + (x1 + (6.0 * pow(x1, 4.0))));
	} else if (x1 <= 2e+153) {
		tmp = x1 + (t_3 + (x1 + ((x1 * (x1 * x1)) + ((t_0 * t_4) + (t_2 * ((((x1 * 2.0) * t_4) * (t_4 - 3.0)) + ((x1 * x1) * ((4.0 * t_4) - 6.0))))))));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x1, x2)
    real(8), intent (in) :: x1
    real(8), intent (in) :: x2
    real(8) :: t_0
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: t_3
    real(8) :: t_4
    real(8) :: tmp
    t_0 = x1 * (x1 * 3.0d0)
    t_1 = x1 + (9.0d0 * (x1 ** 2.0d0))
    t_2 = (x1 * x1) + 1.0d0
    t_3 = 3.0d0 * (((t_0 - (2.0d0 * x2)) - x1) / t_2)
    t_4 = (((2.0d0 * x2) + t_0) - x1) / t_2
    if (x1 <= (-5d+154)) then
        tmp = t_1
    else if (x1 <= (-5.5d+102)) then
        tmp = x1 + (t_3 + (x1 + (6.0d0 * (x1 ** 4.0d0))))
    else if (x1 <= 2d+153) then
        tmp = x1 + (t_3 + (x1 + ((x1 * (x1 * x1)) + ((t_0 * t_4) + (t_2 * ((((x1 * 2.0d0) * t_4) * (t_4 - 3.0d0)) + ((x1 * x1) * ((4.0d0 * t_4) - 6.0d0))))))))
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x1, double x2) {
	double t_0 = x1 * (x1 * 3.0);
	double t_1 = x1 + (9.0 * Math.pow(x1, 2.0));
	double t_2 = (x1 * x1) + 1.0;
	double t_3 = 3.0 * (((t_0 - (2.0 * x2)) - x1) / t_2);
	double t_4 = (((2.0 * x2) + t_0) - x1) / t_2;
	double tmp;
	if (x1 <= -5e+154) {
		tmp = t_1;
	} else if (x1 <= -5.5e+102) {
		tmp = x1 + (t_3 + (x1 + (6.0 * Math.pow(x1, 4.0))));
	} else if (x1 <= 2e+153) {
		tmp = x1 + (t_3 + (x1 + ((x1 * (x1 * x1)) + ((t_0 * t_4) + (t_2 * ((((x1 * 2.0) * t_4) * (t_4 - 3.0)) + ((x1 * x1) * ((4.0 * t_4) - 6.0))))))));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x1, x2):
	t_0 = x1 * (x1 * 3.0)
	t_1 = x1 + (9.0 * math.pow(x1, 2.0))
	t_2 = (x1 * x1) + 1.0
	t_3 = 3.0 * (((t_0 - (2.0 * x2)) - x1) / t_2)
	t_4 = (((2.0 * x2) + t_0) - x1) / t_2
	tmp = 0
	if x1 <= -5e+154:
		tmp = t_1
	elif x1 <= -5.5e+102:
		tmp = x1 + (t_3 + (x1 + (6.0 * math.pow(x1, 4.0))))
	elif x1 <= 2e+153:
		tmp = x1 + (t_3 + (x1 + ((x1 * (x1 * x1)) + ((t_0 * t_4) + (t_2 * ((((x1 * 2.0) * t_4) * (t_4 - 3.0)) + ((x1 * x1) * ((4.0 * t_4) - 6.0))))))))
	else:
		tmp = t_1
	return tmp

Julia

function code(x1, x2)
	t_0 = Float64(x1 * Float64(x1 * 3.0))
	t_1 = Float64(x1 + Float64(9.0 * (x1 ^ 2.0)))
	t_2 = Float64(Float64(x1 * x1) + 1.0)
	t_3 = Float64(3.0 * Float64(Float64(Float64(t_0 - Float64(2.0 * x2)) - x1) / t_2))
	t_4 = Float64(Float64(Float64(Float64(2.0 * x2) + t_0) - x1) / t_2)
	tmp = 0.0
	if (x1 <= -5e+154)
		tmp = t_1;
	elseif (x1 <= -5.5e+102)
		tmp = Float64(x1 + Float64(t_3 + Float64(x1 + Float64(6.0 * (x1 ^ 4.0)))));
	elseif (x1 <= 2e+153)
		tmp = Float64(x1 + Float64(t_3 + Float64(x1 + Float64(Float64(x1 * Float64(x1 * x1)) + Float64(Float64(t_0 * t_4) + Float64(t_2 * Float64(Float64(Float64(Float64(x1 * 2.0) * t_4) * Float64(t_4 - 3.0)) + Float64(Float64(x1 * x1) * Float64(Float64(4.0 * t_4) - 6.0)))))))));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x1, x2)
	t_0 = x1 * (x1 * 3.0);
	t_1 = x1 + (9.0 * (x1 ^ 2.0));
	t_2 = (x1 * x1) + 1.0;
	t_3 = 3.0 * (((t_0 - (2.0 * x2)) - x1) / t_2);
	t_4 = (((2.0 * x2) + t_0) - x1) / t_2;
	tmp = 0.0;
	if (x1 <= -5e+154)
		tmp = t_1;
	elseif (x1 <= -5.5e+102)
		tmp = x1 + (t_3 + (x1 + (6.0 * (x1 ^ 4.0))));
	elseif (x1 <= 2e+153)
		tmp = x1 + (t_3 + (x1 + ((x1 * (x1 * x1)) + ((t_0 * t_4) + (t_2 * ((((x1 * 2.0) * t_4) * (t_4 - 3.0)) + ((x1 * x1) * ((4.0 * t_4) - 6.0))))))));
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x1_, x2_] := Block[{t$95$0 = N[(x1 * N[(x1 * 3.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(x1 + N[(9.0 * N[Power[x1, 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(x1 * x1), $MachinePrecision] + 1.0), $MachinePrecision]}, Block[{t$95$3 = N[(3.0 * N[(N[(N[(t$95$0 - N[(2.0 * x2), $MachinePrecision]), $MachinePrecision] - x1), $MachinePrecision] / t$95$2), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$4 = N[(N[(N[(N[(2.0 * x2), $MachinePrecision] + t$95$0), $MachinePrecision] - x1), $MachinePrecision] / t$95$2), $MachinePrecision]}, If[LessEqual[x1, -5e+154], t$95$1, If[LessEqual[x1, -5.5e+102], N[(x1 + N[(t$95$3 + N[(x1 + N[(6.0 * N[Power[x1, 4.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, 2e+153], N[(x1 + N[(t$95$3 + N[(x1 + N[(N[(x1 * N[(x1 * x1), $MachinePrecision]), $MachinePrecision] + N[(N[(t$95$0 * t$95$4), $MachinePrecision] + N[(t$95$2 * N[(N[(N[(N[(x1 * 2.0), $MachinePrecision] * t$95$4), $MachinePrecision] * N[(t$95$4 - 3.0), $MachinePrecision]), $MachinePrecision] + N[(N[(x1 * x1), $MachinePrecision] * N[(N[(4.0 * t$95$4), $MachinePrecision] - 6.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]]]]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 6: 93.5% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := x1 \cdot \left(x1 \cdot x1\right)\\ t_1 := x1 \cdot x1 + 1\\ t_2 := x1 + 9 \cdot {x1}^{2}\\ t_3 := x1 \cdot \left(x1 \cdot 3\right)\\ t_4 := \frac{\left(2 \cdot x2 + t_3\right) - x1}{t_1}\\ t_5 := t_1 \cdot \left(\left(\left(x1 \cdot 2\right) \cdot t_4\right) \cdot \left(t_4 - 3\right) + \left(x1 \cdot x1\right) \cdot \left(4 \cdot t_4 - 6\right)\right)\\ \mathbf{if}\;x1 \leq -8.5 \cdot 10^{+103}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;x1 \leq -0.85:\\ \;\;\;\;x1 + \left(9 + \left(x1 + \left(t_0 + \left(t_3 \cdot t_4 + t_5\right)\right)\right)\right)\\ \mathbf{elif}\;x1 \leq 4.4 \cdot 10^{+153}:\\ \;\;\;\;x1 + \left(3 \cdot \frac{\left(t_3 - 2 \cdot x2\right) - x1}{t_1} + \left(x1 + \left(t_0 + \left(t_5 + \left(2 \cdot x2\right) \cdot t_3\right)\right)\right)\right)\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x1 x2)
 :precision binary64
 (let* ((t_0 (* x1 (* x1 x1)))
        (t_1 (+ (* x1 x1) 1.0))
        (t_2 (+ x1 (* 9.0 (pow x1 2.0))))
        (t_3 (* x1 (* x1 3.0)))
        (t_4 (/ (- (+ (* 2.0 x2) t_3) x1) t_1))
        (t_5
         (*
          t_1
          (+
           (* (* (* x1 2.0) t_4) (- t_4 3.0))
           (* (* x1 x1) (- (* 4.0 t_4) 6.0))))))
   (if (<= x1 -8.5e+103)
     t_2
     (if (<= x1 -0.85)
       (+ x1 (+ 9.0 (+ x1 (+ t_0 (+ (* t_3 t_4) t_5)))))
       (if (<= x1 4.4e+153)
         (+
          x1
          (+
           (* 3.0 (/ (- (- t_3 (* 2.0 x2)) x1) t_1))
           (+ x1 (+ t_0 (+ t_5 (* (* 2.0 x2) t_3))))))
         t_2)))))

C

double code(double x1, double x2) {
	double t_0 = x1 * (x1 * x1);
	double t_1 = (x1 * x1) + 1.0;
	double t_2 = x1 + (9.0 * pow(x1, 2.0));
	double t_3 = x1 * (x1 * 3.0);
	double t_4 = (((2.0 * x2) + t_3) - x1) / t_1;
	double t_5 = t_1 * ((((x1 * 2.0) * t_4) * (t_4 - 3.0)) + ((x1 * x1) * ((4.0 * t_4) - 6.0)));
	double tmp;
	if (x1 <= -8.5e+103) {
		tmp = t_2;
	} else if (x1 <= -0.85) {
		tmp = x1 + (9.0 + (x1 + (t_0 + ((t_3 * t_4) + t_5))));
	} else if (x1 <= 4.4e+153) {
		tmp = x1 + ((3.0 * (((t_3 - (2.0 * x2)) - x1) / t_1)) + (x1 + (t_0 + (t_5 + ((2.0 * x2) * t_3)))));
	} else {
		tmp = t_2;
	}
	return tmp;
}

Fortran

real(8) function code(x1, x2)
    real(8), intent (in) :: x1
    real(8), intent (in) :: x2
    real(8) :: t_0
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: t_3
    real(8) :: t_4
    real(8) :: t_5
    real(8) :: tmp
    t_0 = x1 * (x1 * x1)
    t_1 = (x1 * x1) + 1.0d0
    t_2 = x1 + (9.0d0 * (x1 ** 2.0d0))
    t_3 = x1 * (x1 * 3.0d0)
    t_4 = (((2.0d0 * x2) + t_3) - x1) / t_1
    t_5 = t_1 * ((((x1 * 2.0d0) * t_4) * (t_4 - 3.0d0)) + ((x1 * x1) * ((4.0d0 * t_4) - 6.0d0)))
    if (x1 <= (-8.5d+103)) then
        tmp = t_2
    else if (x1 <= (-0.85d0)) then
        tmp = x1 + (9.0d0 + (x1 + (t_0 + ((t_3 * t_4) + t_5))))
    else if (x1 <= 4.4d+153) then
        tmp = x1 + ((3.0d0 * (((t_3 - (2.0d0 * x2)) - x1) / t_1)) + (x1 + (t_0 + (t_5 + ((2.0d0 * x2) * t_3)))))
    else
        tmp = t_2
    end if
    code = tmp
end function

Java

public static double code(double x1, double x2) {
	double t_0 = x1 * (x1 * x1);
	double t_1 = (x1 * x1) + 1.0;
	double t_2 = x1 + (9.0 * Math.pow(x1, 2.0));
	double t_3 = x1 * (x1 * 3.0);
	double t_4 = (((2.0 * x2) + t_3) - x1) / t_1;
	double t_5 = t_1 * ((((x1 * 2.0) * t_4) * (t_4 - 3.0)) + ((x1 * x1) * ((4.0 * t_4) - 6.0)));
	double tmp;
	if (x1 <= -8.5e+103) {
		tmp = t_2;
	} else if (x1 <= -0.85) {
		tmp = x1 + (9.0 + (x1 + (t_0 + ((t_3 * t_4) + t_5))));
	} else if (x1 <= 4.4e+153) {
		tmp = x1 + ((3.0 * (((t_3 - (2.0 * x2)) - x1) / t_1)) + (x1 + (t_0 + (t_5 + ((2.0 * x2) * t_3)))));
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x1, x2):
	t_0 = x1 * (x1 * x1)
	t_1 = (x1 * x1) + 1.0
	t_2 = x1 + (9.0 * math.pow(x1, 2.0))
	t_3 = x1 * (x1 * 3.0)
	t_4 = (((2.0 * x2) + t_3) - x1) / t_1
	t_5 = t_1 * ((((x1 * 2.0) * t_4) * (t_4 - 3.0)) + ((x1 * x1) * ((4.0 * t_4) - 6.0)))
	tmp = 0
	if x1 <= -8.5e+103:
		tmp = t_2
	elif x1 <= -0.85:
		tmp = x1 + (9.0 + (x1 + (t_0 + ((t_3 * t_4) + t_5))))
	elif x1 <= 4.4e+153:
		tmp = x1 + ((3.0 * (((t_3 - (2.0 * x2)) - x1) / t_1)) + (x1 + (t_0 + (t_5 + ((2.0 * x2) * t_3)))))
	else:
		tmp = t_2
	return tmp

Julia

function code(x1, x2)
	t_0 = Float64(x1 * Float64(x1 * x1))
	t_1 = Float64(Float64(x1 * x1) + 1.0)
	t_2 = Float64(x1 + Float64(9.0 * (x1 ^ 2.0)))
	t_3 = Float64(x1 * Float64(x1 * 3.0))
	t_4 = Float64(Float64(Float64(Float64(2.0 * x2) + t_3) - x1) / t_1)
	t_5 = Float64(t_1 * Float64(Float64(Float64(Float64(x1 * 2.0) * t_4) * Float64(t_4 - 3.0)) + Float64(Float64(x1 * x1) * Float64(Float64(4.0 * t_4) - 6.0))))
	tmp = 0.0
	if (x1 <= -8.5e+103)
		tmp = t_2;
	elseif (x1 <= -0.85)
		tmp = Float64(x1 + Float64(9.0 + Float64(x1 + Float64(t_0 + Float64(Float64(t_3 * t_4) + t_5)))));
	elseif (x1 <= 4.4e+153)
		tmp = Float64(x1 + Float64(Float64(3.0 * Float64(Float64(Float64(t_3 - Float64(2.0 * x2)) - x1) / t_1)) + Float64(x1 + Float64(t_0 + Float64(t_5 + Float64(Float64(2.0 * x2) * t_3))))));
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x1, x2)
	t_0 = x1 * (x1 * x1);
	t_1 = (x1 * x1) + 1.0;
	t_2 = x1 + (9.0 * (x1 ^ 2.0));
	t_3 = x1 * (x1 * 3.0);
	t_4 = (((2.0 * x2) + t_3) - x1) / t_1;
	t_5 = t_1 * ((((x1 * 2.0) * t_4) * (t_4 - 3.0)) + ((x1 * x1) * ((4.0 * t_4) - 6.0)));
	tmp = 0.0;
	if (x1 <= -8.5e+103)
		tmp = t_2;
	elseif (x1 <= -0.85)
		tmp = x1 + (9.0 + (x1 + (t_0 + ((t_3 * t_4) + t_5))));
	elseif (x1 <= 4.4e+153)
		tmp = x1 + ((3.0 * (((t_3 - (2.0 * x2)) - x1) / t_1)) + (x1 + (t_0 + (t_5 + ((2.0 * x2) * t_3)))));
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x1_, x2_] := Block[{t$95$0 = N[(x1 * N[(x1 * x1), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(N[(x1 * x1), $MachinePrecision] + 1.0), $MachinePrecision]}, Block[{t$95$2 = N[(x1 + N[(9.0 * N[Power[x1, 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(x1 * N[(x1 * 3.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$4 = N[(N[(N[(N[(2.0 * x2), $MachinePrecision] + t$95$3), $MachinePrecision] - x1), $MachinePrecision] / t$95$1), $MachinePrecision]}, Block[{t$95$5 = N[(t$95$1 * N[(N[(N[(N[(x1 * 2.0), $MachinePrecision] * t$95$4), $MachinePrecision] * N[(t$95$4 - 3.0), $MachinePrecision]), $MachinePrecision] + N[(N[(x1 * x1), $MachinePrecision] * N[(N[(4.0 * t$95$4), $MachinePrecision] - 6.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x1, -8.5e+103], t$95$2, If[LessEqual[x1, -0.85], N[(x1 + N[(9.0 + N[(x1 + N[(t$95$0 + N[(N[(t$95$3 * t$95$4), $MachinePrecision] + t$95$5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, 4.4e+153], N[(x1 + N[(N[(3.0 * N[(N[(N[(t$95$3 - N[(2.0 * x2), $MachinePrecision]), $MachinePrecision] - x1), $MachinePrecision] / t$95$1), $MachinePrecision]), $MachinePrecision] + N[(x1 + N[(t$95$0 + N[(t$95$5 + N[(N[(2.0 * x2), $MachinePrecision] * t$95$3), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$2]]]]]]]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 7: 95.2% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := x1 \cdot \left(x1 \cdot x1\right)\\ t_1 := 2 \cdot x2 - x1\\ t_2 := x1 + 9 \cdot {x1}^{2}\\ t_3 := x1 \cdot \left(x1 \cdot 3\right)\\ t_4 := x1 \cdot x1 + 1\\ t_5 := \frac{\left(2 \cdot x2 + t_3\right) - x1}{t_4}\\ t_6 := \left(x1 \cdot x1\right) \cdot \left(4 \cdot t_5 - 6\right)\\ t_7 := x1 + \left(9 + \left(x1 + \left(t_0 + \left(t_3 \cdot t_5 + t_4 \cdot \left(\left(\left(x1 \cdot 2\right) \cdot t_5\right) \cdot \left(t_5 - 3\right) + t_6\right)\right)\right)\right)\right)\\ \mathbf{if}\;x1 \leq -4.6 \cdot 10^{+105}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;x1 \leq -0.00165:\\ \;\;\;\;t_7\\ \mathbf{elif}\;x1 \leq 0.0025:\\ \;\;\;\;x1 + \left(3 \cdot \frac{\left(t_3 - 2 \cdot x2\right) - x1}{t_4} + \left(x1 + \left(t_0 + \left(t_4 \cdot \left(t_6 + \left(2 \cdot x2 - 3\right) \cdot \left(\left(x1 \cdot 2\right) \cdot t_1\right)\right) + t_3 \cdot t_1\right)\right)\right)\right)\\ \mathbf{elif}\;x1 \leq 2 \cdot 10^{+153}:\\ \;\;\;\;t_7\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x1 x2)
 :precision binary64
 (let* ((t_0 (* x1 (* x1 x1)))
        (t_1 (- (* 2.0 x2) x1))
        (t_2 (+ x1 (* 9.0 (pow x1 2.0))))
        (t_3 (* x1 (* x1 3.0)))
        (t_4 (+ (* x1 x1) 1.0))
        (t_5 (/ (- (+ (* 2.0 x2) t_3) x1) t_4))
        (t_6 (* (* x1 x1) (- (* 4.0 t_5) 6.0)))
        (t_7
         (+
          x1
          (+
           9.0
           (+
            x1
            (+
             t_0
             (+
              (* t_3 t_5)
              (* t_4 (+ (* (* (* x1 2.0) t_5) (- t_5 3.0)) t_6)))))))))
   (if (<= x1 -4.6e+105)
     t_2
     (if (<= x1 -0.00165)
       t_7
       (if (<= x1 0.0025)
         (+
          x1
          (+
           (* 3.0 (/ (- (- t_3 (* 2.0 x2)) x1) t_4))
           (+
            x1
            (+
             t_0
             (+
              (* t_4 (+ t_6 (* (- (* 2.0 x2) 3.0) (* (* x1 2.0) t_1))))
              (* t_3 t_1))))))
         (if (<= x1 2e+153) t_7 t_2))))))

C

double code(double x1, double x2) {
	double t_0 = x1 * (x1 * x1);
	double t_1 = (2.0 * x2) - x1;
	double t_2 = x1 + (9.0 * pow(x1, 2.0));
	double t_3 = x1 * (x1 * 3.0);
	double t_4 = (x1 * x1) + 1.0;
	double t_5 = (((2.0 * x2) + t_3) - x1) / t_4;
	double t_6 = (x1 * x1) * ((4.0 * t_5) - 6.0);
	double t_7 = x1 + (9.0 + (x1 + (t_0 + ((t_3 * t_5) + (t_4 * ((((x1 * 2.0) * t_5) * (t_5 - 3.0)) + t_6))))));
	double tmp;
	if (x1 <= -4.6e+105) {
		tmp = t_2;
	} else if (x1 <= -0.00165) {
		tmp = t_7;
	} else if (x1 <= 0.0025) {
		tmp = x1 + ((3.0 * (((t_3 - (2.0 * x2)) - x1) / t_4)) + (x1 + (t_0 + ((t_4 * (t_6 + (((2.0 * x2) - 3.0) * ((x1 * 2.0) * t_1)))) + (t_3 * t_1)))));
	} else if (x1 <= 2e+153) {
		tmp = t_7;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Fortran

real(8) function code(x1, x2)
    real(8), intent (in) :: x1
    real(8), intent (in) :: x2
    real(8) :: t_0
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: t_3
    real(8) :: t_4
    real(8) :: t_5
    real(8) :: t_6
    real(8) :: t_7
    real(8) :: tmp
    t_0 = x1 * (x1 * x1)
    t_1 = (2.0d0 * x2) - x1
    t_2 = x1 + (9.0d0 * (x1 ** 2.0d0))
    t_3 = x1 * (x1 * 3.0d0)
    t_4 = (x1 * x1) + 1.0d0
    t_5 = (((2.0d0 * x2) + t_3) - x1) / t_4
    t_6 = (x1 * x1) * ((4.0d0 * t_5) - 6.0d0)
    t_7 = x1 + (9.0d0 + (x1 + (t_0 + ((t_3 * t_5) + (t_4 * ((((x1 * 2.0d0) * t_5) * (t_5 - 3.0d0)) + t_6))))))
    if (x1 <= (-4.6d+105)) then
        tmp = t_2
    else if (x1 <= (-0.00165d0)) then
        tmp = t_7
    else if (x1 <= 0.0025d0) then
        tmp = x1 + ((3.0d0 * (((t_3 - (2.0d0 * x2)) - x1) / t_4)) + (x1 + (t_0 + ((t_4 * (t_6 + (((2.0d0 * x2) - 3.0d0) * ((x1 * 2.0d0) * t_1)))) + (t_3 * t_1)))))
    else if (x1 <= 2d+153) then
        tmp = t_7
    else
        tmp = t_2
    end if
    code = tmp
end function

Java

public static double code(double x1, double x2) {
	double t_0 = x1 * (x1 * x1);
	double t_1 = (2.0 * x2) - x1;
	double t_2 = x1 + (9.0 * Math.pow(x1, 2.0));
	double t_3 = x1 * (x1 * 3.0);
	double t_4 = (x1 * x1) + 1.0;
	double t_5 = (((2.0 * x2) + t_3) - x1) / t_4;
	double t_6 = (x1 * x1) * ((4.0 * t_5) - 6.0);
	double t_7 = x1 + (9.0 + (x1 + (t_0 + ((t_3 * t_5) + (t_4 * ((((x1 * 2.0) * t_5) * (t_5 - 3.0)) + t_6))))));
	double tmp;
	if (x1 <= -4.6e+105) {
		tmp = t_2;
	} else if (x1 <= -0.00165) {
		tmp = t_7;
	} else if (x1 <= 0.0025) {
		tmp = x1 + ((3.0 * (((t_3 - (2.0 * x2)) - x1) / t_4)) + (x1 + (t_0 + ((t_4 * (t_6 + (((2.0 * x2) - 3.0) * ((x1 * 2.0) * t_1)))) + (t_3 * t_1)))));
	} else if (x1 <= 2e+153) {
		tmp = t_7;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x1, x2):
	t_0 = x1 * (x1 * x1)
	t_1 = (2.0 * x2) - x1
	t_2 = x1 + (9.0 * math.pow(x1, 2.0))
	t_3 = x1 * (x1 * 3.0)
	t_4 = (x1 * x1) + 1.0
	t_5 = (((2.0 * x2) + t_3) - x1) / t_4
	t_6 = (x1 * x1) * ((4.0 * t_5) - 6.0)
	t_7 = x1 + (9.0 + (x1 + (t_0 + ((t_3 * t_5) + (t_4 * ((((x1 * 2.0) * t_5) * (t_5 - 3.0)) + t_6))))))
	tmp = 0
	if x1 <= -4.6e+105:
		tmp = t_2
	elif x1 <= -0.00165:
		tmp = t_7
	elif x1 <= 0.0025:
		tmp = x1 + ((3.0 * (((t_3 - (2.0 * x2)) - x1) / t_4)) + (x1 + (t_0 + ((t_4 * (t_6 + (((2.0 * x2) - 3.0) * ((x1 * 2.0) * t_1)))) + (t_3 * t_1)))))
	elif x1 <= 2e+153:
		tmp = t_7
	else:
		tmp = t_2
	return tmp

Julia

function code(x1, x2)
	t_0 = Float64(x1 * Float64(x1 * x1))
	t_1 = Float64(Float64(2.0 * x2) - x1)
	t_2 = Float64(x1 + Float64(9.0 * (x1 ^ 2.0)))
	t_3 = Float64(x1 * Float64(x1 * 3.0))
	t_4 = Float64(Float64(x1 * x1) + 1.0)
	t_5 = Float64(Float64(Float64(Float64(2.0 * x2) + t_3) - x1) / t_4)
	t_6 = Float64(Float64(x1 * x1) * Float64(Float64(4.0 * t_5) - 6.0))
	t_7 = Float64(x1 + Float64(9.0 + Float64(x1 + Float64(t_0 + Float64(Float64(t_3 * t_5) + Float64(t_4 * Float64(Float64(Float64(Float64(x1 * 2.0) * t_5) * Float64(t_5 - 3.0)) + t_6)))))))
	tmp = 0.0
	if (x1 <= -4.6e+105)
		tmp = t_2;
	elseif (x1 <= -0.00165)
		tmp = t_7;
	elseif (x1 <= 0.0025)
		tmp = Float64(x1 + Float64(Float64(3.0 * Float64(Float64(Float64(t_3 - Float64(2.0 * x2)) - x1) / t_4)) + Float64(x1 + Float64(t_0 + Float64(Float64(t_4 * Float64(t_6 + Float64(Float64(Float64(2.0 * x2) - 3.0) * Float64(Float64(x1 * 2.0) * t_1)))) + Float64(t_3 * t_1))))));
	elseif (x1 <= 2e+153)
		tmp = t_7;
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x1, x2)
	t_0 = x1 * (x1 * x1);
	t_1 = (2.0 * x2) - x1;
	t_2 = x1 + (9.0 * (x1 ^ 2.0));
	t_3 = x1 * (x1 * 3.0);
	t_4 = (x1 * x1) + 1.0;
	t_5 = (((2.0 * x2) + t_3) - x1) / t_4;
	t_6 = (x1 * x1) * ((4.0 * t_5) - 6.0);
	t_7 = x1 + (9.0 + (x1 + (t_0 + ((t_3 * t_5) + (t_4 * ((((x1 * 2.0) * t_5) * (t_5 - 3.0)) + t_6))))));
	tmp = 0.0;
	if (x1 <= -4.6e+105)
		tmp = t_2;
	elseif (x1 <= -0.00165)
		tmp = t_7;
	elseif (x1 <= 0.0025)
		tmp = x1 + ((3.0 * (((t_3 - (2.0 * x2)) - x1) / t_4)) + (x1 + (t_0 + ((t_4 * (t_6 + (((2.0 * x2) - 3.0) * ((x1 * 2.0) * t_1)))) + (t_3 * t_1)))));
	elseif (x1 <= 2e+153)
		tmp = t_7;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x1_, x2_] := Block[{t$95$0 = N[(x1 * N[(x1 * x1), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(N[(2.0 * x2), $MachinePrecision] - x1), $MachinePrecision]}, Block[{t$95$2 = N[(x1 + N[(9.0 * N[Power[x1, 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(x1 * N[(x1 * 3.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$4 = N[(N[(x1 * x1), $MachinePrecision] + 1.0), $MachinePrecision]}, Block[{t$95$5 = N[(N[(N[(N[(2.0 * x2), $MachinePrecision] + t$95$3), $MachinePrecision] - x1), $MachinePrecision] / t$95$4), $MachinePrecision]}, Block[{t$95$6 = N[(N[(x1 * x1), $MachinePrecision] * N[(N[(4.0 * t$95$5), $MachinePrecision] - 6.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$7 = N[(x1 + N[(9.0 + N[(x1 + N[(t$95$0 + N[(N[(t$95$3 * t$95$5), $MachinePrecision] + N[(t$95$4 * N[(N[(N[(N[(x1 * 2.0), $MachinePrecision] * t$95$5), $MachinePrecision] * N[(t$95$5 - 3.0), $MachinePrecision]), $MachinePrecision] + t$95$6), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x1, -4.6e+105], t$95$2, If[LessEqual[x1, -0.00165], t$95$7, If[LessEqual[x1, 0.0025], N[(x1 + N[(N[(3.0 * N[(N[(N[(t$95$3 - N[(2.0 * x2), $MachinePrecision]), $MachinePrecision] - x1), $MachinePrecision] / t$95$4), $MachinePrecision]), $MachinePrecision] + N[(x1 + N[(t$95$0 + N[(N[(t$95$4 * N[(t$95$6 + N[(N[(N[(2.0 * x2), $MachinePrecision] - 3.0), $MachinePrecision] * N[(N[(x1 * 2.0), $MachinePrecision] * t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(t$95$3 * t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, 2e+153], t$95$7, t$95$2]]]]]]]]]]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 8: 93.4% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := x1 \cdot \left(x1 \cdot 3\right)\\ t_1 := x1 \cdot x1 + 1\\ t_2 := \frac{\left(2 \cdot x2 + t_0\right) - x1}{t_1}\\ t_3 := t_0 \cdot t_2\\ t_4 := \left(x1 \cdot 2\right) \cdot t_2\\ t_5 := x1 \cdot \left(x1 \cdot x1\right)\\ t_6 := x1 + 9 \cdot {x1}^{2}\\ t_7 := \left(x1 \cdot x1\right) \cdot \left(4 \cdot t_2 - 6\right)\\ \mathbf{if}\;x1 \leq -8 \cdot 10^{+104}:\\ \;\;\;\;t_6\\ \mathbf{elif}\;x1 \leq 0.0072:\\ \;\;\;\;x1 + \left(3 \cdot \frac{\left(t_0 - 2 \cdot x2\right) - x1}{t_1} + \left(x1 + \left(t_5 + \left(t_3 + t_1 \cdot \left(t_7 + t_4 \cdot \left(2 \cdot x2 - 3\right)\right)\right)\right)\right)\right)\\ \mathbf{elif}\;x1 \leq 2 \cdot 10^{+153}:\\ \;\;\;\;x1 + \left(9 + \left(x1 + \left(t_5 + \left(t_3 + t_1 \cdot \left(t_4 \cdot \left(t_2 - 3\right) + t_7\right)\right)\right)\right)\right)\\ \mathbf{else}:\\ \;\;\;\;t_6\\ \end{array} \end{array} \]

FPCore

(FPCore (x1 x2)
 :precision binary64
 (let* ((t_0 (* x1 (* x1 3.0)))
        (t_1 (+ (* x1 x1) 1.0))
        (t_2 (/ (- (+ (* 2.0 x2) t_0) x1) t_1))
        (t_3 (* t_0 t_2))
        (t_4 (* (* x1 2.0) t_2))
        (t_5 (* x1 (* x1 x1)))
        (t_6 (+ x1 (* 9.0 (pow x1 2.0))))
        (t_7 (* (* x1 x1) (- (* 4.0 t_2) 6.0))))
   (if (<= x1 -8e+104)
     t_6
     (if (<= x1 0.0072)
       (+
        x1
        (+
         (* 3.0 (/ (- (- t_0 (* 2.0 x2)) x1) t_1))
         (+ x1 (+ t_5 (+ t_3 (* t_1 (+ t_7 (* t_4 (- (* 2.0 x2) 3.0)))))))))
       (if (<= x1 2e+153)
         (+
          x1
          (+ 9.0 (+ x1 (+ t_5 (+ t_3 (* t_1 (+ (* t_4 (- t_2 3.0)) t_7)))))))
         t_6)))))

C

double code(double x1, double x2) {
	double t_0 = x1 * (x1 * 3.0);
	double t_1 = (x1 * x1) + 1.0;
	double t_2 = (((2.0 * x2) + t_0) - x1) / t_1;
	double t_3 = t_0 * t_2;
	double t_4 = (x1 * 2.0) * t_2;
	double t_5 = x1 * (x1 * x1);
	double t_6 = x1 + (9.0 * pow(x1, 2.0));
	double t_7 = (x1 * x1) * ((4.0 * t_2) - 6.0);
	double tmp;
	if (x1 <= -8e+104) {
		tmp = t_6;
	} else if (x1 <= 0.0072) {
		tmp = x1 + ((3.0 * (((t_0 - (2.0 * x2)) - x1) / t_1)) + (x1 + (t_5 + (t_3 + (t_1 * (t_7 + (t_4 * ((2.0 * x2) - 3.0))))))));
	} else if (x1 <= 2e+153) {
		tmp = x1 + (9.0 + (x1 + (t_5 + (t_3 + (t_1 * ((t_4 * (t_2 - 3.0)) + t_7))))));
	} else {
		tmp = t_6;
	}
	return tmp;
}

Fortran

real(8) function code(x1, x2)
    real(8), intent (in) :: x1
    real(8), intent (in) :: x2
    real(8) :: t_0
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: t_3
    real(8) :: t_4
    real(8) :: t_5
    real(8) :: t_6
    real(8) :: t_7
    real(8) :: tmp
    t_0 = x1 * (x1 * 3.0d0)
    t_1 = (x1 * x1) + 1.0d0
    t_2 = (((2.0d0 * x2) + t_0) - x1) / t_1
    t_3 = t_0 * t_2
    t_4 = (x1 * 2.0d0) * t_2
    t_5 = x1 * (x1 * x1)
    t_6 = x1 + (9.0d0 * (x1 ** 2.0d0))
    t_7 = (x1 * x1) * ((4.0d0 * t_2) - 6.0d0)
    if (x1 <= (-8d+104)) then
        tmp = t_6
    else if (x1 <= 0.0072d0) then
        tmp = x1 + ((3.0d0 * (((t_0 - (2.0d0 * x2)) - x1) / t_1)) + (x1 + (t_5 + (t_3 + (t_1 * (t_7 + (t_4 * ((2.0d0 * x2) - 3.0d0))))))))
    else if (x1 <= 2d+153) then
        tmp = x1 + (9.0d0 + (x1 + (t_5 + (t_3 + (t_1 * ((t_4 * (t_2 - 3.0d0)) + t_7))))))
    else
        tmp = t_6
    end if
    code = tmp
end function

Java

public static double code(double x1, double x2) {
	double t_0 = x1 * (x1 * 3.0);
	double t_1 = (x1 * x1) + 1.0;
	double t_2 = (((2.0 * x2) + t_0) - x1) / t_1;
	double t_3 = t_0 * t_2;
	double t_4 = (x1 * 2.0) * t_2;
	double t_5 = x1 * (x1 * x1);
	double t_6 = x1 + (9.0 * Math.pow(x1, 2.0));
	double t_7 = (x1 * x1) * ((4.0 * t_2) - 6.0);
	double tmp;
	if (x1 <= -8e+104) {
		tmp = t_6;
	} else if (x1 <= 0.0072) {
		tmp = x1 + ((3.0 * (((t_0 - (2.0 * x2)) - x1) / t_1)) + (x1 + (t_5 + (t_3 + (t_1 * (t_7 + (t_4 * ((2.0 * x2) - 3.0))))))));
	} else if (x1 <= 2e+153) {
		tmp = x1 + (9.0 + (x1 + (t_5 + (t_3 + (t_1 * ((t_4 * (t_2 - 3.0)) + t_7))))));
	} else {
		tmp = t_6;
	}
	return tmp;
}

Python

def code(x1, x2):
	t_0 = x1 * (x1 * 3.0)
	t_1 = (x1 * x1) + 1.0
	t_2 = (((2.0 * x2) + t_0) - x1) / t_1
	t_3 = t_0 * t_2
	t_4 = (x1 * 2.0) * t_2
	t_5 = x1 * (x1 * x1)
	t_6 = x1 + (9.0 * math.pow(x1, 2.0))
	t_7 = (x1 * x1) * ((4.0 * t_2) - 6.0)
	tmp = 0
	if x1 <= -8e+104:
		tmp = t_6
	elif x1 <= 0.0072:
		tmp = x1 + ((3.0 * (((t_0 - (2.0 * x2)) - x1) / t_1)) + (x1 + (t_5 + (t_3 + (t_1 * (t_7 + (t_4 * ((2.0 * x2) - 3.0))))))))
	elif x1 <= 2e+153:
		tmp = x1 + (9.0 + (x1 + (t_5 + (t_3 + (t_1 * ((t_4 * (t_2 - 3.0)) + t_7))))))
	else:
		tmp = t_6
	return tmp

Julia

function code(x1, x2)
	t_0 = Float64(x1 * Float64(x1 * 3.0))
	t_1 = Float64(Float64(x1 * x1) + 1.0)
	t_2 = Float64(Float64(Float64(Float64(2.0 * x2) + t_0) - x1) / t_1)
	t_3 = Float64(t_0 * t_2)
	t_4 = Float64(Float64(x1 * 2.0) * t_2)
	t_5 = Float64(x1 * Float64(x1 * x1))
	t_6 = Float64(x1 + Float64(9.0 * (x1 ^ 2.0)))
	t_7 = Float64(Float64(x1 * x1) * Float64(Float64(4.0 * t_2) - 6.0))
	tmp = 0.0
	if (x1 <= -8e+104)
		tmp = t_6;
	elseif (x1 <= 0.0072)
		tmp = Float64(x1 + Float64(Float64(3.0 * Float64(Float64(Float64(t_0 - Float64(2.0 * x2)) - x1) / t_1)) + Float64(x1 + Float64(t_5 + Float64(t_3 + Float64(t_1 * Float64(t_7 + Float64(t_4 * Float64(Float64(2.0 * x2) - 3.0)))))))));
	elseif (x1 <= 2e+153)
		tmp = Float64(x1 + Float64(9.0 + Float64(x1 + Float64(t_5 + Float64(t_3 + Float64(t_1 * Float64(Float64(t_4 * Float64(t_2 - 3.0)) + t_7)))))));
	else
		tmp = t_6;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x1, x2)
	t_0 = x1 * (x1 * 3.0);
	t_1 = (x1 * x1) + 1.0;
	t_2 = (((2.0 * x2) + t_0) - x1) / t_1;
	t_3 = t_0 * t_2;
	t_4 = (x1 * 2.0) * t_2;
	t_5 = x1 * (x1 * x1);
	t_6 = x1 + (9.0 * (x1 ^ 2.0));
	t_7 = (x1 * x1) * ((4.0 * t_2) - 6.0);
	tmp = 0.0;
	if (x1 <= -8e+104)
		tmp = t_6;
	elseif (x1 <= 0.0072)
		tmp = x1 + ((3.0 * (((t_0 - (2.0 * x2)) - x1) / t_1)) + (x1 + (t_5 + (t_3 + (t_1 * (t_7 + (t_4 * ((2.0 * x2) - 3.0))))))));
	elseif (x1 <= 2e+153)
		tmp = x1 + (9.0 + (x1 + (t_5 + (t_3 + (t_1 * ((t_4 * (t_2 - 3.0)) + t_7))))));
	else
		tmp = t_6;
	end
	tmp_2 = tmp;
end

Wolfram

code[x1_, x2_] := Block[{t$95$0 = N[(x1 * N[(x1 * 3.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(N[(x1 * x1), $MachinePrecision] + 1.0), $MachinePrecision]}, Block[{t$95$2 = N[(N[(N[(N[(2.0 * x2), $MachinePrecision] + t$95$0), $MachinePrecision] - x1), $MachinePrecision] / t$95$1), $MachinePrecision]}, Block[{t$95$3 = N[(t$95$0 * t$95$2), $MachinePrecision]}, Block[{t$95$4 = N[(N[(x1 * 2.0), $MachinePrecision] * t$95$2), $MachinePrecision]}, Block[{t$95$5 = N[(x1 * N[(x1 * x1), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$6 = N[(x1 + N[(9.0 * N[Power[x1, 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$7 = N[(N[(x1 * x1), $MachinePrecision] * N[(N[(4.0 * t$95$2), $MachinePrecision] - 6.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x1, -8e+104], t$95$6, If[LessEqual[x1, 0.0072], N[(x1 + N[(N[(3.0 * N[(N[(N[(t$95$0 - N[(2.0 * x2), $MachinePrecision]), $MachinePrecision] - x1), $MachinePrecision] / t$95$1), $MachinePrecision]), $MachinePrecision] + N[(x1 + N[(t$95$5 + N[(t$95$3 + N[(t$95$1 * N[(t$95$7 + N[(t$95$4 * N[(N[(2.0 * x2), $MachinePrecision] - 3.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, 2e+153], N[(x1 + N[(9.0 + N[(x1 + N[(t$95$5 + N[(t$95$3 + N[(t$95$1 * N[(N[(t$95$4 * N[(t$95$2 - 3.0), $MachinePrecision]), $MachinePrecision] + t$95$7), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$6]]]]]]]]]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 9: 92.6% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := 2 \cdot x2 - x1\\ t_1 := x1 \cdot \left(x1 \cdot 3\right)\\ t_2 := x1 + 9 \cdot {x1}^{2}\\ t_3 := x1 \cdot x1 + 1\\ t_4 := 3 \cdot \frac{\left(t_1 - 2 \cdot x2\right) - x1}{t_3}\\ t_5 := \frac{\left(2 \cdot x2 + t_1\right) - x1}{t_3}\\ t_6 := t_5 - 3\\ t_7 := x1 \cdot \left(x1 \cdot x1\right)\\ \mathbf{if}\;x1 \leq -3.4 \cdot 10^{+104}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;x1 \leq 9.5:\\ \;\;\;\;x1 + \left(t_4 + \left(x1 + \left(t_7 + \left(t_1 \cdot t_0 + t_3 \cdot \left(\left(x1 \cdot x1\right) \cdot \left(4 \cdot t_5 - 6\right) + t_6 \cdot \left(\left(x1 \cdot 2\right) \cdot t_0\right)\right)\right)\right)\right)\right)\\ \mathbf{elif}\;x1 \leq 2 \cdot 10^{+153}:\\ \;\;\;\;x1 + \left(t_4 + \left(x1 + \left(t_7 + \left(t_1 \cdot t_5 + t_3 \cdot \left(\left(\left(x1 \cdot 2\right) \cdot t_5\right) \cdot t_6 + x1 \cdot \left(-4 + x1 \cdot 6\right)\right)\right)\right)\right)\right)\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x1 x2)
 :precision binary64
 (let* ((t_0 (- (* 2.0 x2) x1))
        (t_1 (* x1 (* x1 3.0)))
        (t_2 (+ x1 (* 9.0 (pow x1 2.0))))
        (t_3 (+ (* x1 x1) 1.0))
        (t_4 (* 3.0 (/ (- (- t_1 (* 2.0 x2)) x1) t_3)))
        (t_5 (/ (- (+ (* 2.0 x2) t_1) x1) t_3))
        (t_6 (- t_5 3.0))
        (t_7 (* x1 (* x1 x1))))
   (if (<= x1 -3.4e+104)
     t_2
     (if (<= x1 9.5)
       (+
        x1
        (+
         t_4
         (+
          x1
          (+
           t_7
           (+
            (* t_1 t_0)
            (*
             t_3
             (+
              (* (* x1 x1) (- (* 4.0 t_5) 6.0))
              (* t_6 (* (* x1 2.0) t_0)))))))))
       (if (<= x1 2e+153)
         (+
          x1
          (+
           t_4
           (+
            x1
            (+
             t_7
             (+
              (* t_1 t_5)
              (*
               t_3
               (+ (* (* (* x1 2.0) t_5) t_6) (* x1 (+ -4.0 (* x1 6.0))))))))))
         t_2)))))

C

double code(double x1, double x2) {
	double t_0 = (2.0 * x2) - x1;
	double t_1 = x1 * (x1 * 3.0);
	double t_2 = x1 + (9.0 * pow(x1, 2.0));
	double t_3 = (x1 * x1) + 1.0;
	double t_4 = 3.0 * (((t_1 - (2.0 * x2)) - x1) / t_3);
	double t_5 = (((2.0 * x2) + t_1) - x1) / t_3;
	double t_6 = t_5 - 3.0;
	double t_7 = x1 * (x1 * x1);
	double tmp;
	if (x1 <= -3.4e+104) {
		tmp = t_2;
	} else if (x1 <= 9.5) {
		tmp = x1 + (t_4 + (x1 + (t_7 + ((t_1 * t_0) + (t_3 * (((x1 * x1) * ((4.0 * t_5) - 6.0)) + (t_6 * ((x1 * 2.0) * t_0))))))));
	} else if (x1 <= 2e+153) {
		tmp = x1 + (t_4 + (x1 + (t_7 + ((t_1 * t_5) + (t_3 * ((((x1 * 2.0) * t_5) * t_6) + (x1 * (-4.0 + (x1 * 6.0)))))))));
	} else {
		tmp = t_2;
	}
	return tmp;
}

Fortran

real(8) function code(x1, x2)
    real(8), intent (in) :: x1
    real(8), intent (in) :: x2
    real(8) :: t_0
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: t_3
    real(8) :: t_4
    real(8) :: t_5
    real(8) :: t_6
    real(8) :: t_7
    real(8) :: tmp
    t_0 = (2.0d0 * x2) - x1
    t_1 = x1 * (x1 * 3.0d0)
    t_2 = x1 + (9.0d0 * (x1 ** 2.0d0))
    t_3 = (x1 * x1) + 1.0d0
    t_4 = 3.0d0 * (((t_1 - (2.0d0 * x2)) - x1) / t_3)
    t_5 = (((2.0d0 * x2) + t_1) - x1) / t_3
    t_6 = t_5 - 3.0d0
    t_7 = x1 * (x1 * x1)
    if (x1 <= (-3.4d+104)) then
        tmp = t_2
    else if (x1 <= 9.5d0) then
        tmp = x1 + (t_4 + (x1 + (t_7 + ((t_1 * t_0) + (t_3 * (((x1 * x1) * ((4.0d0 * t_5) - 6.0d0)) + (t_6 * ((x1 * 2.0d0) * t_0))))))))
    else if (x1 <= 2d+153) then
        tmp = x1 + (t_4 + (x1 + (t_7 + ((t_1 * t_5) + (t_3 * ((((x1 * 2.0d0) * t_5) * t_6) + (x1 * ((-4.0d0) + (x1 * 6.0d0)))))))))
    else
        tmp = t_2
    end if
    code = tmp
end function

Java

public static double code(double x1, double x2) {
	double t_0 = (2.0 * x2) - x1;
	double t_1 = x1 * (x1 * 3.0);
	double t_2 = x1 + (9.0 * Math.pow(x1, 2.0));
	double t_3 = (x1 * x1) + 1.0;
	double t_4 = 3.0 * (((t_1 - (2.0 * x2)) - x1) / t_3);
	double t_5 = (((2.0 * x2) + t_1) - x1) / t_3;
	double t_6 = t_5 - 3.0;
	double t_7 = x1 * (x1 * x1);
	double tmp;
	if (x1 <= -3.4e+104) {
		tmp = t_2;
	} else if (x1 <= 9.5) {
		tmp = x1 + (t_4 + (x1 + (t_7 + ((t_1 * t_0) + (t_3 * (((x1 * x1) * ((4.0 * t_5) - 6.0)) + (t_6 * ((x1 * 2.0) * t_0))))))));
	} else if (x1 <= 2e+153) {
		tmp = x1 + (t_4 + (x1 + (t_7 + ((t_1 * t_5) + (t_3 * ((((x1 * 2.0) * t_5) * t_6) + (x1 * (-4.0 + (x1 * 6.0)))))))));
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x1, x2):
	t_0 = (2.0 * x2) - x1
	t_1 = x1 * (x1 * 3.0)
	t_2 = x1 + (9.0 * math.pow(x1, 2.0))
	t_3 = (x1 * x1) + 1.0
	t_4 = 3.0 * (((t_1 - (2.0 * x2)) - x1) / t_3)
	t_5 = (((2.0 * x2) + t_1) - x1) / t_3
	t_6 = t_5 - 3.0
	t_7 = x1 * (x1 * x1)
	tmp = 0
	if x1 <= -3.4e+104:
		tmp = t_2
	elif x1 <= 9.5:
		tmp = x1 + (t_4 + (x1 + (t_7 + ((t_1 * t_0) + (t_3 * (((x1 * x1) * ((4.0 * t_5) - 6.0)) + (t_6 * ((x1 * 2.0) * t_0))))))))
	elif x1 <= 2e+153:
		tmp = x1 + (t_4 + (x1 + (t_7 + ((t_1 * t_5) + (t_3 * ((((x1 * 2.0) * t_5) * t_6) + (x1 * (-4.0 + (x1 * 6.0)))))))))
	else:
		tmp = t_2
	return tmp

Julia

function code(x1, x2)
	t_0 = Float64(Float64(2.0 * x2) - x1)
	t_1 = Float64(x1 * Float64(x1 * 3.0))
	t_2 = Float64(x1 + Float64(9.0 * (x1 ^ 2.0)))
	t_3 = Float64(Float64(x1 * x1) + 1.0)
	t_4 = Float64(3.0 * Float64(Float64(Float64(t_1 - Float64(2.0 * x2)) - x1) / t_3))
	t_5 = Float64(Float64(Float64(Float64(2.0 * x2) + t_1) - x1) / t_3)
	t_6 = Float64(t_5 - 3.0)
	t_7 = Float64(x1 * Float64(x1 * x1))
	tmp = 0.0
	if (x1 <= -3.4e+104)
		tmp = t_2;
	elseif (x1 <= 9.5)
		tmp = Float64(x1 + Float64(t_4 + Float64(x1 + Float64(t_7 + Float64(Float64(t_1 * t_0) + Float64(t_3 * Float64(Float64(Float64(x1 * x1) * Float64(Float64(4.0 * t_5) - 6.0)) + Float64(t_6 * Float64(Float64(x1 * 2.0) * t_0)))))))));
	elseif (x1 <= 2e+153)
		tmp = Float64(x1 + Float64(t_4 + Float64(x1 + Float64(t_7 + Float64(Float64(t_1 * t_5) + Float64(t_3 * Float64(Float64(Float64(Float64(x1 * 2.0) * t_5) * t_6) + Float64(x1 * Float64(-4.0 + Float64(x1 * 6.0))))))))));
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x1, x2)
	t_0 = (2.0 * x2) - x1;
	t_1 = x1 * (x1 * 3.0);
	t_2 = x1 + (9.0 * (x1 ^ 2.0));
	t_3 = (x1 * x1) + 1.0;
	t_4 = 3.0 * (((t_1 - (2.0 * x2)) - x1) / t_3);
	t_5 = (((2.0 * x2) + t_1) - x1) / t_3;
	t_6 = t_5 - 3.0;
	t_7 = x1 * (x1 * x1);
	tmp = 0.0;
	if (x1 <= -3.4e+104)
		tmp = t_2;
	elseif (x1 <= 9.5)
		tmp = x1 + (t_4 + (x1 + (t_7 + ((t_1 * t_0) + (t_3 * (((x1 * x1) * ((4.0 * t_5) - 6.0)) + (t_6 * ((x1 * 2.0) * t_0))))))));
	elseif (x1 <= 2e+153)
		tmp = x1 + (t_4 + (x1 + (t_7 + ((t_1 * t_5) + (t_3 * ((((x1 * 2.0) * t_5) * t_6) + (x1 * (-4.0 + (x1 * 6.0)))))))));
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x1_, x2_] := Block[{t$95$0 = N[(N[(2.0 * x2), $MachinePrecision] - x1), $MachinePrecision]}, Block[{t$95$1 = N[(x1 * N[(x1 * 3.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(x1 + N[(9.0 * N[Power[x1, 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(N[(x1 * x1), $MachinePrecision] + 1.0), $MachinePrecision]}, Block[{t$95$4 = N[(3.0 * N[(N[(N[(t$95$1 - N[(2.0 * x2), $MachinePrecision]), $MachinePrecision] - x1), $MachinePrecision] / t$95$3), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$5 = N[(N[(N[(N[(2.0 * x2), $MachinePrecision] + t$95$1), $MachinePrecision] - x1), $MachinePrecision] / t$95$3), $MachinePrecision]}, Block[{t$95$6 = N[(t$95$5 - 3.0), $MachinePrecision]}, Block[{t$95$7 = N[(x1 * N[(x1 * x1), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x1, -3.4e+104], t$95$2, If[LessEqual[x1, 9.5], N[(x1 + N[(t$95$4 + N[(x1 + N[(t$95$7 + N[(N[(t$95$1 * t$95$0), $MachinePrecision] + N[(t$95$3 * N[(N[(N[(x1 * x1), $MachinePrecision] * N[(N[(4.0 * t$95$5), $MachinePrecision] - 6.0), $MachinePrecision]), $MachinePrecision] + N[(t$95$6 * N[(N[(x1 * 2.0), $MachinePrecision] * t$95$0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, 2e+153], N[(x1 + N[(t$95$4 + N[(x1 + N[(t$95$7 + N[(N[(t$95$1 * t$95$5), $MachinePrecision] + N[(t$95$3 * N[(N[(N[(N[(x1 * 2.0), $MachinePrecision] * t$95$5), $MachinePrecision] * t$95$6), $MachinePrecision] + N[(x1 * N[(-4.0 + N[(x1 * 6.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$2]]]]]]]]]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 10: 91.7% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := 2 \cdot x2 - x1\\ t_1 := x1 \cdot \left(x1 \cdot 3\right)\\ t_2 := x1 + 9 \cdot {x1}^{2}\\ t_3 := x1 \cdot x1 + 1\\ t_4 := 3 \cdot \frac{\left(t_1 - 2 \cdot x2\right) - x1}{t_3}\\ t_5 := \frac{\left(2 \cdot x2 + t_1\right) - x1}{t_3}\\ t_6 := \left(x1 \cdot x1\right) \cdot \left(4 \cdot t_5 - 6\right)\\ t_7 := x1 \cdot \left(x1 \cdot x1\right)\\ \mathbf{if}\;x1 \leq -8 \cdot 10^{+103}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;x1 \leq 8.5 \cdot 10^{+34}:\\ \;\;\;\;x1 + \left(t_4 + \left(x1 + \left(t_7 + \left(t_1 \cdot t_0 + t_3 \cdot \left(t_6 + \left(t_5 - 3\right) \cdot \left(\left(x1 \cdot 2\right) \cdot t_0\right)\right)\right)\right)\right)\right)\\ \mathbf{elif}\;x1 \leq 2 \cdot 10^{+153}:\\ \;\;\;\;x1 + \left(t_4 + \left(x1 + \left(t_7 + \left(t_1 \cdot t_5 + t_3 \cdot \left(x1 \cdot 2 + t_6\right)\right)\right)\right)\right)\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x1 x2)
 :precision binary64
 (let* ((t_0 (- (* 2.0 x2) x1))
        (t_1 (* x1 (* x1 3.0)))
        (t_2 (+ x1 (* 9.0 (pow x1 2.0))))
        (t_3 (+ (* x1 x1) 1.0))
        (t_4 (* 3.0 (/ (- (- t_1 (* 2.0 x2)) x1) t_3)))
        (t_5 (/ (- (+ (* 2.0 x2) t_1) x1) t_3))
        (t_6 (* (* x1 x1) (- (* 4.0 t_5) 6.0)))
        (t_7 (* x1 (* x1 x1))))
   (if (<= x1 -8e+103)
     t_2
     (if (<= x1 8.5e+34)
       (+
        x1
        (+
         t_4
         (+
          x1
          (+
           t_7
           (+
            (* t_1 t_0)
            (* t_3 (+ t_6 (* (- t_5 3.0) (* (* x1 2.0) t_0)))))))))
       (if (<= x1 2e+153)
         (+
          x1
          (+ t_4 (+ x1 (+ t_7 (+ (* t_1 t_5) (* t_3 (+ (* x1 2.0) t_6)))))))
         t_2)))))

C

double code(double x1, double x2) {
	double t_0 = (2.0 * x2) - x1;
	double t_1 = x1 * (x1 * 3.0);
	double t_2 = x1 + (9.0 * pow(x1, 2.0));
	double t_3 = (x1 * x1) + 1.0;
	double t_4 = 3.0 * (((t_1 - (2.0 * x2)) - x1) / t_3);
	double t_5 = (((2.0 * x2) + t_1) - x1) / t_3;
	double t_6 = (x1 * x1) * ((4.0 * t_5) - 6.0);
	double t_7 = x1 * (x1 * x1);
	double tmp;
	if (x1 <= -8e+103) {
		tmp = t_2;
	} else if (x1 <= 8.5e+34) {
		tmp = x1 + (t_4 + (x1 + (t_7 + ((t_1 * t_0) + (t_3 * (t_6 + ((t_5 - 3.0) * ((x1 * 2.0) * t_0))))))));
	} else if (x1 <= 2e+153) {
		tmp = x1 + (t_4 + (x1 + (t_7 + ((t_1 * t_5) + (t_3 * ((x1 * 2.0) + t_6))))));
	} else {
		tmp = t_2;
	}
	return tmp;
}

Fortran

real(8) function code(x1, x2)
    real(8), intent (in) :: x1
    real(8), intent (in) :: x2
    real(8) :: t_0
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: t_3
    real(8) :: t_4
    real(8) :: t_5
    real(8) :: t_6
    real(8) :: t_7
    real(8) :: tmp
    t_0 = (2.0d0 * x2) - x1
    t_1 = x1 * (x1 * 3.0d0)
    t_2 = x1 + (9.0d0 * (x1 ** 2.0d0))
    t_3 = (x1 * x1) + 1.0d0
    t_4 = 3.0d0 * (((t_1 - (2.0d0 * x2)) - x1) / t_3)
    t_5 = (((2.0d0 * x2) + t_1) - x1) / t_3
    t_6 = (x1 * x1) * ((4.0d0 * t_5) - 6.0d0)
    t_7 = x1 * (x1 * x1)
    if (x1 <= (-8d+103)) then
        tmp = t_2
    else if (x1 <= 8.5d+34) then
        tmp = x1 + (t_4 + (x1 + (t_7 + ((t_1 * t_0) + (t_3 * (t_6 + ((t_5 - 3.0d0) * ((x1 * 2.0d0) * t_0))))))))
    else if (x1 <= 2d+153) then
        tmp = x1 + (t_4 + (x1 + (t_7 + ((t_1 * t_5) + (t_3 * ((x1 * 2.0d0) + t_6))))))
    else
        tmp = t_2
    end if
    code = tmp
end function

Java

public static double code(double x1, double x2) {
	double t_0 = (2.0 * x2) - x1;
	double t_1 = x1 * (x1 * 3.0);
	double t_2 = x1 + (9.0 * Math.pow(x1, 2.0));
	double t_3 = (x1 * x1) + 1.0;
	double t_4 = 3.0 * (((t_1 - (2.0 * x2)) - x1) / t_3);
	double t_5 = (((2.0 * x2) + t_1) - x1) / t_3;
	double t_6 = (x1 * x1) * ((4.0 * t_5) - 6.0);
	double t_7 = x1 * (x1 * x1);
	double tmp;
	if (x1 <= -8e+103) {
		tmp = t_2;
	} else if (x1 <= 8.5e+34) {
		tmp = x1 + (t_4 + (x1 + (t_7 + ((t_1 * t_0) + (t_3 * (t_6 + ((t_5 - 3.0) * ((x1 * 2.0) * t_0))))))));
	} else if (x1 <= 2e+153) {
		tmp = x1 + (t_4 + (x1 + (t_7 + ((t_1 * t_5) + (t_3 * ((x1 * 2.0) + t_6))))));
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x1, x2):
	t_0 = (2.0 * x2) - x1
	t_1 = x1 * (x1 * 3.0)
	t_2 = x1 + (9.0 * math.pow(x1, 2.0))
	t_3 = (x1 * x1) + 1.0
	t_4 = 3.0 * (((t_1 - (2.0 * x2)) - x1) / t_3)
	t_5 = (((2.0 * x2) + t_1) - x1) / t_3
	t_6 = (x1 * x1) * ((4.0 * t_5) - 6.0)
	t_7 = x1 * (x1 * x1)
	tmp = 0
	if x1 <= -8e+103:
		tmp = t_2
	elif x1 <= 8.5e+34:
		tmp = x1 + (t_4 + (x1 + (t_7 + ((t_1 * t_0) + (t_3 * (t_6 + ((t_5 - 3.0) * ((x1 * 2.0) * t_0))))))))
	elif x1 <= 2e+153:
		tmp = x1 + (t_4 + (x1 + (t_7 + ((t_1 * t_5) + (t_3 * ((x1 * 2.0) + t_6))))))
	else:
		tmp = t_2
	return tmp

Julia

function code(x1, x2)
	t_0 = Float64(Float64(2.0 * x2) - x1)
	t_1 = Float64(x1 * Float64(x1 * 3.0))
	t_2 = Float64(x1 + Float64(9.0 * (x1 ^ 2.0)))
	t_3 = Float64(Float64(x1 * x1) + 1.0)
	t_4 = Float64(3.0 * Float64(Float64(Float64(t_1 - Float64(2.0 * x2)) - x1) / t_3))
	t_5 = Float64(Float64(Float64(Float64(2.0 * x2) + t_1) - x1) / t_3)
	t_6 = Float64(Float64(x1 * x1) * Float64(Float64(4.0 * t_5) - 6.0))
	t_7 = Float64(x1 * Float64(x1 * x1))
	tmp = 0.0
	if (x1 <= -8e+103)
		tmp = t_2;
	elseif (x1 <= 8.5e+34)
		tmp = Float64(x1 + Float64(t_4 + Float64(x1 + Float64(t_7 + Float64(Float64(t_1 * t_0) + Float64(t_3 * Float64(t_6 + Float64(Float64(t_5 - 3.0) * Float64(Float64(x1 * 2.0) * t_0)))))))));
	elseif (x1 <= 2e+153)
		tmp = Float64(x1 + Float64(t_4 + Float64(x1 + Float64(t_7 + Float64(Float64(t_1 * t_5) + Float64(t_3 * Float64(Float64(x1 * 2.0) + t_6)))))));
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x1, x2)
	t_0 = (2.0 * x2) - x1;
	t_1 = x1 * (x1 * 3.0);
	t_2 = x1 + (9.0 * (x1 ^ 2.0));
	t_3 = (x1 * x1) + 1.0;
	t_4 = 3.0 * (((t_1 - (2.0 * x2)) - x1) / t_3);
	t_5 = (((2.0 * x2) + t_1) - x1) / t_3;
	t_6 = (x1 * x1) * ((4.0 * t_5) - 6.0);
	t_7 = x1 * (x1 * x1);
	tmp = 0.0;
	if (x1 <= -8e+103)
		tmp = t_2;
	elseif (x1 <= 8.5e+34)
		tmp = x1 + (t_4 + (x1 + (t_7 + ((t_1 * t_0) + (t_3 * (t_6 + ((t_5 - 3.0) * ((x1 * 2.0) * t_0))))))));
	elseif (x1 <= 2e+153)
		tmp = x1 + (t_4 + (x1 + (t_7 + ((t_1 * t_5) + (t_3 * ((x1 * 2.0) + t_6))))));
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x1_, x2_] := Block[{t$95$0 = N[(N[(2.0 * x2), $MachinePrecision] - x1), $MachinePrecision]}, Block[{t$95$1 = N[(x1 * N[(x1 * 3.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(x1 + N[(9.0 * N[Power[x1, 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(N[(x1 * x1), $MachinePrecision] + 1.0), $MachinePrecision]}, Block[{t$95$4 = N[(3.0 * N[(N[(N[(t$95$1 - N[(2.0 * x2), $MachinePrecision]), $MachinePrecision] - x1), $MachinePrecision] / t$95$3), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$5 = N[(N[(N[(N[(2.0 * x2), $MachinePrecision] + t$95$1), $MachinePrecision] - x1), $MachinePrecision] / t$95$3), $MachinePrecision]}, Block[{t$95$6 = N[(N[(x1 * x1), $MachinePrecision] * N[(N[(4.0 * t$95$5), $MachinePrecision] - 6.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$7 = N[(x1 * N[(x1 * x1), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x1, -8e+103], t$95$2, If[LessEqual[x1, 8.5e+34], N[(x1 + N[(t$95$4 + N[(x1 + N[(t$95$7 + N[(N[(t$95$1 * t$95$0), $MachinePrecision] + N[(t$95$3 * N[(t$95$6 + N[(N[(t$95$5 - 3.0), $MachinePrecision] * N[(N[(x1 * 2.0), $MachinePrecision] * t$95$0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, 2e+153], N[(x1 + N[(t$95$4 + N[(x1 + N[(t$95$7 + N[(N[(t$95$1 * t$95$5), $MachinePrecision] + N[(t$95$3 * N[(N[(x1 * 2.0), $MachinePrecision] + t$95$6), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$2]]]]]]]]]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 11: 75.9% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := x1 \cdot \left(x1 \cdot x1\right)\\ t_1 := 2 \cdot x2 - x1\\ t_2 := x1 \cdot \left(x1 \cdot 3\right)\\ t_3 := x1 \cdot x1 + 1\\ t_4 := 3 \cdot \frac{\left(t_2 - 2 \cdot x2\right) - x1}{t_3}\\ t_5 := \frac{\left(2 \cdot x2 + t_2\right) - x1}{t_3}\\ t_6 := \left(x1 \cdot x1\right) \cdot \left(4 \cdot t_5 - 6\right)\\ \mathbf{if}\;x1 \leq -5.5 \cdot 10^{+102}:\\ \;\;\;\;x1 + \left(9 + \left(x1 + -12 \cdot \left(x1 \cdot x2\right)\right)\right)\\ \mathbf{elif}\;x1 \leq 4.8 \cdot 10^{+34}:\\ \;\;\;\;x1 + \left(t_4 + \left(x1 + \left(t_0 + \left(t_2 \cdot t_1 + t_3 \cdot \left(t_6 + \left(t_5 - 3\right) \cdot \left(\left(x1 \cdot 2\right) \cdot t_1\right)\right)\right)\right)\right)\right)\\ \mathbf{elif}\;x1 \leq 1.35 \cdot 10^{+154}:\\ \;\;\;\;x1 + \left(t_4 + \left(x1 + \left(t_0 + \left(t_2 \cdot t_5 + t_3 \cdot \left(x1 \cdot 2 + t_6\right)\right)\right)\right)\right)\\ \mathbf{else}:\\ \;\;\;\;x1 + \left(9 + \left(x1 + 4 \cdot \left(x1 \cdot \left(x2 \cdot \left(2 \cdot x2 - 3\right)\right)\right)\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x1 x2)
 :precision binary64
 (let* ((t_0 (* x1 (* x1 x1)))
        (t_1 (- (* 2.0 x2) x1))
        (t_2 (* x1 (* x1 3.0)))
        (t_3 (+ (* x1 x1) 1.0))
        (t_4 (* 3.0 (/ (- (- t_2 (* 2.0 x2)) x1) t_3)))
        (t_5 (/ (- (+ (* 2.0 x2) t_2) x1) t_3))
        (t_6 (* (* x1 x1) (- (* 4.0 t_5) 6.0))))
   (if (<= x1 -5.5e+102)
     (+ x1 (+ 9.0 (+ x1 (* -12.0 (* x1 x2)))))
     (if (<= x1 4.8e+34)
       (+
        x1
        (+
         t_4
         (+
          x1
          (+
           t_0
           (+
            (* t_2 t_1)
            (* t_3 (+ t_6 (* (- t_5 3.0) (* (* x1 2.0) t_1)))))))))
       (if (<= x1 1.35e+154)
         (+
          x1
          (+ t_4 (+ x1 (+ t_0 (+ (* t_2 t_5) (* t_3 (+ (* x1 2.0) t_6)))))))
         (+ x1 (+ 9.0 (+ x1 (* 4.0 (* x1 (* x2 (- (* 2.0 x2) 3.0))))))))))))

C

double code(double x1, double x2) {
	double t_0 = x1 * (x1 * x1);
	double t_1 = (2.0 * x2) - x1;
	double t_2 = x1 * (x1 * 3.0);
	double t_3 = (x1 * x1) + 1.0;
	double t_4 = 3.0 * (((t_2 - (2.0 * x2)) - x1) / t_3);
	double t_5 = (((2.0 * x2) + t_2) - x1) / t_3;
	double t_6 = (x1 * x1) * ((4.0 * t_5) - 6.0);
	double tmp;
	if (x1 <= -5.5e+102) {
		tmp = x1 + (9.0 + (x1 + (-12.0 * (x1 * x2))));
	} else if (x1 <= 4.8e+34) {
		tmp = x1 + (t_4 + (x1 + (t_0 + ((t_2 * t_1) + (t_3 * (t_6 + ((t_5 - 3.0) * ((x1 * 2.0) * t_1))))))));
	} else if (x1 <= 1.35e+154) {
		tmp = x1 + (t_4 + (x1 + (t_0 + ((t_2 * t_5) + (t_3 * ((x1 * 2.0) + t_6))))));
	} else {
		tmp = x1 + (9.0 + (x1 + (4.0 * (x1 * (x2 * ((2.0 * x2) - 3.0))))));
	}
	return tmp;
}

Fortran

real(8) function code(x1, x2)
    real(8), intent (in) :: x1
    real(8), intent (in) :: x2
    real(8) :: t_0
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: t_3
    real(8) :: t_4
    real(8) :: t_5
    real(8) :: t_6
    real(8) :: tmp
    t_0 = x1 * (x1 * x1)
    t_1 = (2.0d0 * x2) - x1
    t_2 = x1 * (x1 * 3.0d0)
    t_3 = (x1 * x1) + 1.0d0
    t_4 = 3.0d0 * (((t_2 - (2.0d0 * x2)) - x1) / t_3)
    t_5 = (((2.0d0 * x2) + t_2) - x1) / t_3
    t_6 = (x1 * x1) * ((4.0d0 * t_5) - 6.0d0)
    if (x1 <= (-5.5d+102)) then
        tmp = x1 + (9.0d0 + (x1 + ((-12.0d0) * (x1 * x2))))
    else if (x1 <= 4.8d+34) then
        tmp = x1 + (t_4 + (x1 + (t_0 + ((t_2 * t_1) + (t_3 * (t_6 + ((t_5 - 3.0d0) * ((x1 * 2.0d0) * t_1))))))))
    else if (x1 <= 1.35d+154) then
        tmp = x1 + (t_4 + (x1 + (t_0 + ((t_2 * t_5) + (t_3 * ((x1 * 2.0d0) + t_6))))))
    else
        tmp = x1 + (9.0d0 + (x1 + (4.0d0 * (x1 * (x2 * ((2.0d0 * x2) - 3.0d0))))))
    end if
    code = tmp
end function

Java

public static double code(double x1, double x2) {
	double t_0 = x1 * (x1 * x1);
	double t_1 = (2.0 * x2) - x1;
	double t_2 = x1 * (x1 * 3.0);
	double t_3 = (x1 * x1) + 1.0;
	double t_4 = 3.0 * (((t_2 - (2.0 * x2)) - x1) / t_3);
	double t_5 = (((2.0 * x2) + t_2) - x1) / t_3;
	double t_6 = (x1 * x1) * ((4.0 * t_5) - 6.0);
	double tmp;
	if (x1 <= -5.5e+102) {
		tmp = x1 + (9.0 + (x1 + (-12.0 * (x1 * x2))));
	} else if (x1 <= 4.8e+34) {
		tmp = x1 + (t_4 + (x1 + (t_0 + ((t_2 * t_1) + (t_3 * (t_6 + ((t_5 - 3.0) * ((x1 * 2.0) * t_1))))))));
	} else if (x1 <= 1.35e+154) {
		tmp = x1 + (t_4 + (x1 + (t_0 + ((t_2 * t_5) + (t_3 * ((x1 * 2.0) + t_6))))));
	} else {
		tmp = x1 + (9.0 + (x1 + (4.0 * (x1 * (x2 * ((2.0 * x2) - 3.0))))));
	}
	return tmp;
}

Python

def code(x1, x2):
	t_0 = x1 * (x1 * x1)
	t_1 = (2.0 * x2) - x1
	t_2 = x1 * (x1 * 3.0)
	t_3 = (x1 * x1) + 1.0
	t_4 = 3.0 * (((t_2 - (2.0 * x2)) - x1) / t_3)
	t_5 = (((2.0 * x2) + t_2) - x1) / t_3
	t_6 = (x1 * x1) * ((4.0 * t_5) - 6.0)
	tmp = 0
	if x1 <= -5.5e+102:
		tmp = x1 + (9.0 + (x1 + (-12.0 * (x1 * x2))))
	elif x1 <= 4.8e+34:
		tmp = x1 + (t_4 + (x1 + (t_0 + ((t_2 * t_1) + (t_3 * (t_6 + ((t_5 - 3.0) * ((x1 * 2.0) * t_1))))))))
	elif x1 <= 1.35e+154:
		tmp = x1 + (t_4 + (x1 + (t_0 + ((t_2 * t_5) + (t_3 * ((x1 * 2.0) + t_6))))))
	else:
		tmp = x1 + (9.0 + (x1 + (4.0 * (x1 * (x2 * ((2.0 * x2) - 3.0))))))
	return tmp

Julia

function code(x1, x2)
	t_0 = Float64(x1 * Float64(x1 * x1))
	t_1 = Float64(Float64(2.0 * x2) - x1)
	t_2 = Float64(x1 * Float64(x1 * 3.0))
	t_3 = Float64(Float64(x1 * x1) + 1.0)
	t_4 = Float64(3.0 * Float64(Float64(Float64(t_2 - Float64(2.0 * x2)) - x1) / t_3))
	t_5 = Float64(Float64(Float64(Float64(2.0 * x2) + t_2) - x1) / t_3)
	t_6 = Float64(Float64(x1 * x1) * Float64(Float64(4.0 * t_5) - 6.0))
	tmp = 0.0
	if (x1 <= -5.5e+102)
		tmp = Float64(x1 + Float64(9.0 + Float64(x1 + Float64(-12.0 * Float64(x1 * x2)))));
	elseif (x1 <= 4.8e+34)
		tmp = Float64(x1 + Float64(t_4 + Float64(x1 + Float64(t_0 + Float64(Float64(t_2 * t_1) + Float64(t_3 * Float64(t_6 + Float64(Float64(t_5 - 3.0) * Float64(Float64(x1 * 2.0) * t_1)))))))));
	elseif (x1 <= 1.35e+154)
		tmp = Float64(x1 + Float64(t_4 + Float64(x1 + Float64(t_0 + Float64(Float64(t_2 * t_5) + Float64(t_3 * Float64(Float64(x1 * 2.0) + t_6)))))));
	else
		tmp = Float64(x1 + Float64(9.0 + Float64(x1 + Float64(4.0 * Float64(x1 * Float64(x2 * Float64(Float64(2.0 * x2) - 3.0)))))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x1, x2)
	t_0 = x1 * (x1 * x1);
	t_1 = (2.0 * x2) - x1;
	t_2 = x1 * (x1 * 3.0);
	t_3 = (x1 * x1) + 1.0;
	t_4 = 3.0 * (((t_2 - (2.0 * x2)) - x1) / t_3);
	t_5 = (((2.0 * x2) + t_2) - x1) / t_3;
	t_6 = (x1 * x1) * ((4.0 * t_5) - 6.0);
	tmp = 0.0;
	if (x1 <= -5.5e+102)
		tmp = x1 + (9.0 + (x1 + (-12.0 * (x1 * x2))));
	elseif (x1 <= 4.8e+34)
		tmp = x1 + (t_4 + (x1 + (t_0 + ((t_2 * t_1) + (t_3 * (t_6 + ((t_5 - 3.0) * ((x1 * 2.0) * t_1))))))));
	elseif (x1 <= 1.35e+154)
		tmp = x1 + (t_4 + (x1 + (t_0 + ((t_2 * t_5) + (t_3 * ((x1 * 2.0) + t_6))))));
	else
		tmp = x1 + (9.0 + (x1 + (4.0 * (x1 * (x2 * ((2.0 * x2) - 3.0))))));
	end
	tmp_2 = tmp;
end

Wolfram

code[x1_, x2_] := Block[{t$95$0 = N[(x1 * N[(x1 * x1), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(N[(2.0 * x2), $MachinePrecision] - x1), $MachinePrecision]}, Block[{t$95$2 = N[(x1 * N[(x1 * 3.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(N[(x1 * x1), $MachinePrecision] + 1.0), $MachinePrecision]}, Block[{t$95$4 = N[(3.0 * N[(N[(N[(t$95$2 - N[(2.0 * x2), $MachinePrecision]), $MachinePrecision] - x1), $MachinePrecision] / t$95$3), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$5 = N[(N[(N[(N[(2.0 * x2), $MachinePrecision] + t$95$2), $MachinePrecision] - x1), $MachinePrecision] / t$95$3), $MachinePrecision]}, Block[{t$95$6 = N[(N[(x1 * x1), $MachinePrecision] * N[(N[(4.0 * t$95$5), $MachinePrecision] - 6.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x1, -5.5e+102], N[(x1 + N[(9.0 + N[(x1 + N[(-12.0 * N[(x1 * x2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, 4.8e+34], N[(x1 + N[(t$95$4 + N[(x1 + N[(t$95$0 + N[(N[(t$95$2 * t$95$1), $MachinePrecision] + N[(t$95$3 * N[(t$95$6 + N[(N[(t$95$5 - 3.0), $MachinePrecision] * N[(N[(x1 * 2.0), $MachinePrecision] * t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, 1.35e+154], N[(x1 + N[(t$95$4 + N[(x1 + N[(t$95$0 + N[(N[(t$95$2 * t$95$5), $MachinePrecision] + N[(t$95$3 * N[(N[(x1 * 2.0), $MachinePrecision] + t$95$6), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x1 + N[(9.0 + N[(x1 + N[(4.0 * N[(x1 * N[(x2 * N[(N[(2.0 * x2), $MachinePrecision] - 3.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]]]]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 12: 71.3% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := x1 \cdot \left(x1 \cdot 3\right)\\ t_1 := x1 \cdot x1 + 1\\ t_2 := 3 \cdot \frac{\left(t_0 - 2 \cdot x2\right) - x1}{t_1}\\ t_3 := \frac{\left(2 \cdot x2 + t_0\right) - x1}{t_1}\\ t_4 := x1 + \left(t_2 + \left(x1 + \left(x1 \cdot \left(x1 \cdot x1\right) + \left(t_0 \cdot t_3 + t_1 \cdot \left(x1 \cdot 2 + \left(x1 \cdot x1\right) \cdot \left(4 \cdot t_3 - 6\right)\right)\right)\right)\right)\right)\\ t_5 := x1 + 4 \cdot \left(x1 \cdot \left(x2 \cdot \left(2 \cdot x2 - 3\right)\right)\right)\\ t_6 := x1 + \left(t_2 + t_5\right)\\ \mathbf{if}\;x1 \leq -5.5 \cdot 10^{+102}:\\ \;\;\;\;x1 + \left(9 + \left(x1 + -12 \cdot \left(x1 \cdot x2\right)\right)\right)\\ \mathbf{elif}\;x1 \leq -4:\\ \;\;\;\;t_4\\ \mathbf{elif}\;x1 \leq -2.4 \cdot 10^{-133}:\\ \;\;\;\;t_6\\ \mathbf{elif}\;x1 \leq 3.3 \cdot 10^{-167}:\\ \;\;\;\;x1 + \left(t_2 + \left(x1 + 4 \cdot \left(-3 \cdot \left(x1 \cdot x2\right)\right)\right)\right)\\ \mathbf{elif}\;x1 \leq 7.4:\\ \;\;\;\;t_6\\ \mathbf{elif}\;x1 \leq 1.35 \cdot 10^{+154}:\\ \;\;\;\;t_4\\ \mathbf{else}:\\ \;\;\;\;x1 + \left(9 + t_5\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x1 x2)
 :precision binary64
 (let* ((t_0 (* x1 (* x1 3.0)))
        (t_1 (+ (* x1 x1) 1.0))
        (t_2 (* 3.0 (/ (- (- t_0 (* 2.0 x2)) x1) t_1)))
        (t_3 (/ (- (+ (* 2.0 x2) t_0) x1) t_1))
        (t_4
         (+
          x1
          (+
           t_2
           (+
            x1
            (+
             (* x1 (* x1 x1))
             (+
              (* t_0 t_3)
              (* t_1 (+ (* x1 2.0) (* (* x1 x1) (- (* 4.0 t_3) 6.0))))))))))
        (t_5 (+ x1 (* 4.0 (* x1 (* x2 (- (* 2.0 x2) 3.0))))))
        (t_6 (+ x1 (+ t_2 t_5))))
   (if (<= x1 -5.5e+102)
     (+ x1 (+ 9.0 (+ x1 (* -12.0 (* x1 x2)))))
     (if (<= x1 -4.0)
       t_4
       (if (<= x1 -2.4e-133)
         t_6
         (if (<= x1 3.3e-167)
           (+ x1 (+ t_2 (+ x1 (* 4.0 (* -3.0 (* x1 x2))))))
           (if (<= x1 7.4)
             t_6
             (if (<= x1 1.35e+154) t_4 (+ x1 (+ 9.0 t_5))))))))))

C

double code(double x1, double x2) {
	double t_0 = x1 * (x1 * 3.0);
	double t_1 = (x1 * x1) + 1.0;
	double t_2 = 3.0 * (((t_0 - (2.0 * x2)) - x1) / t_1);
	double t_3 = (((2.0 * x2) + t_0) - x1) / t_1;
	double t_4 = x1 + (t_2 + (x1 + ((x1 * (x1 * x1)) + ((t_0 * t_3) + (t_1 * ((x1 * 2.0) + ((x1 * x1) * ((4.0 * t_3) - 6.0))))))));
	double t_5 = x1 + (4.0 * (x1 * (x2 * ((2.0 * x2) - 3.0))));
	double t_6 = x1 + (t_2 + t_5);
	double tmp;
	if (x1 <= -5.5e+102) {
		tmp = x1 + (9.0 + (x1 + (-12.0 * (x1 * x2))));
	} else if (x1 <= -4.0) {
		tmp = t_4;
	} else if (x1 <= -2.4e-133) {
		tmp = t_6;
	} else if (x1 <= 3.3e-167) {
		tmp = x1 + (t_2 + (x1 + (4.0 * (-3.0 * (x1 * x2)))));
	} else if (x1 <= 7.4) {
		tmp = t_6;
	} else if (x1 <= 1.35e+154) {
		tmp = t_4;
	} else {
		tmp = x1 + (9.0 + t_5);
	}
	return tmp;
}

Fortran

real(8) function code(x1, x2)
    real(8), intent (in) :: x1
    real(8), intent (in) :: x2
    real(8) :: t_0
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: t_3
    real(8) :: t_4
    real(8) :: t_5
    real(8) :: t_6
    real(8) :: tmp
    t_0 = x1 * (x1 * 3.0d0)
    t_1 = (x1 * x1) + 1.0d0
    t_2 = 3.0d0 * (((t_0 - (2.0d0 * x2)) - x1) / t_1)
    t_3 = (((2.0d0 * x2) + t_0) - x1) / t_1
    t_4 = x1 + (t_2 + (x1 + ((x1 * (x1 * x1)) + ((t_0 * t_3) + (t_1 * ((x1 * 2.0d0) + ((x1 * x1) * ((4.0d0 * t_3) - 6.0d0))))))))
    t_5 = x1 + (4.0d0 * (x1 * (x2 * ((2.0d0 * x2) - 3.0d0))))
    t_6 = x1 + (t_2 + t_5)
    if (x1 <= (-5.5d+102)) then
        tmp = x1 + (9.0d0 + (x1 + ((-12.0d0) * (x1 * x2))))
    else if (x1 <= (-4.0d0)) then
        tmp = t_4
    else if (x1 <= (-2.4d-133)) then
        tmp = t_6
    else if (x1 <= 3.3d-167) then
        tmp = x1 + (t_2 + (x1 + (4.0d0 * ((-3.0d0) * (x1 * x2)))))
    else if (x1 <= 7.4d0) then
        tmp = t_6
    else if (x1 <= 1.35d+154) then
        tmp = t_4
    else
        tmp = x1 + (9.0d0 + t_5)
    end if
    code = tmp
end function

Java

public static double code(double x1, double x2) {
	double t_0 = x1 * (x1 * 3.0);
	double t_1 = (x1 * x1) + 1.0;
	double t_2 = 3.0 * (((t_0 - (2.0 * x2)) - x1) / t_1);
	double t_3 = (((2.0 * x2) + t_0) - x1) / t_1;
	double t_4 = x1 + (t_2 + (x1 + ((x1 * (x1 * x1)) + ((t_0 * t_3) + (t_1 * ((x1 * 2.0) + ((x1 * x1) * ((4.0 * t_3) - 6.0))))))));
	double t_5 = x1 + (4.0 * (x1 * (x2 * ((2.0 * x2) - 3.0))));
	double t_6 = x1 + (t_2 + t_5);
	double tmp;
	if (x1 <= -5.5e+102) {
		tmp = x1 + (9.0 + (x1 + (-12.0 * (x1 * x2))));
	} else if (x1 <= -4.0) {
		tmp = t_4;
	} else if (x1 <= -2.4e-133) {
		tmp = t_6;
	} else if (x1 <= 3.3e-167) {
		tmp = x1 + (t_2 + (x1 + (4.0 * (-3.0 * (x1 * x2)))));
	} else if (x1 <= 7.4) {
		tmp = t_6;
	} else if (x1 <= 1.35e+154) {
		tmp = t_4;
	} else {
		tmp = x1 + (9.0 + t_5);
	}
	return tmp;
}

Python

def code(x1, x2):
	t_0 = x1 * (x1 * 3.0)
	t_1 = (x1 * x1) + 1.0
	t_2 = 3.0 * (((t_0 - (2.0 * x2)) - x1) / t_1)
	t_3 = (((2.0 * x2) + t_0) - x1) / t_1
	t_4 = x1 + (t_2 + (x1 + ((x1 * (x1 * x1)) + ((t_0 * t_3) + (t_1 * ((x1 * 2.0) + ((x1 * x1) * ((4.0 * t_3) - 6.0))))))))
	t_5 = x1 + (4.0 * (x1 * (x2 * ((2.0 * x2) - 3.0))))
	t_6 = x1 + (t_2 + t_5)
	tmp = 0
	if x1 <= -5.5e+102:
		tmp = x1 + (9.0 + (x1 + (-12.0 * (x1 * x2))))
	elif x1 <= -4.0:
		tmp = t_4
	elif x1 <= -2.4e-133:
		tmp = t_6
	elif x1 <= 3.3e-167:
		tmp = x1 + (t_2 + (x1 + (4.0 * (-3.0 * (x1 * x2)))))
	elif x1 <= 7.4:
		tmp = t_6
	elif x1 <= 1.35e+154:
		tmp = t_4
	else:
		tmp = x1 + (9.0 + t_5)
	return tmp

Julia

function code(x1, x2)
	t_0 = Float64(x1 * Float64(x1 * 3.0))
	t_1 = Float64(Float64(x1 * x1) + 1.0)
	t_2 = Float64(3.0 * Float64(Float64(Float64(t_0 - Float64(2.0 * x2)) - x1) / t_1))
	t_3 = Float64(Float64(Float64(Float64(2.0 * x2) + t_0) - x1) / t_1)
	t_4 = Float64(x1 + Float64(t_2 + Float64(x1 + Float64(Float64(x1 * Float64(x1 * x1)) + Float64(Float64(t_0 * t_3) + Float64(t_1 * Float64(Float64(x1 * 2.0) + Float64(Float64(x1 * x1) * Float64(Float64(4.0 * t_3) - 6.0)))))))))
	t_5 = Float64(x1 + Float64(4.0 * Float64(x1 * Float64(x2 * Float64(Float64(2.0 * x2) - 3.0)))))
	t_6 = Float64(x1 + Float64(t_2 + t_5))
	tmp = 0.0
	if (x1 <= -5.5e+102)
		tmp = Float64(x1 + Float64(9.0 + Float64(x1 + Float64(-12.0 * Float64(x1 * x2)))));
	elseif (x1 <= -4.0)
		tmp = t_4;
	elseif (x1 <= -2.4e-133)
		tmp = t_6;
	elseif (x1 <= 3.3e-167)
		tmp = Float64(x1 + Float64(t_2 + Float64(x1 + Float64(4.0 * Float64(-3.0 * Float64(x1 * x2))))));
	elseif (x1 <= 7.4)
		tmp = t_6;
	elseif (x1 <= 1.35e+154)
		tmp = t_4;
	else
		tmp = Float64(x1 + Float64(9.0 + t_5));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x1, x2)
	t_0 = x1 * (x1 * 3.0);
	t_1 = (x1 * x1) + 1.0;
	t_2 = 3.0 * (((t_0 - (2.0 * x2)) - x1) / t_1);
	t_3 = (((2.0 * x2) + t_0) - x1) / t_1;
	t_4 = x1 + (t_2 + (x1 + ((x1 * (x1 * x1)) + ((t_0 * t_3) + (t_1 * ((x1 * 2.0) + ((x1 * x1) * ((4.0 * t_3) - 6.0))))))));
	t_5 = x1 + (4.0 * (x1 * (x2 * ((2.0 * x2) - 3.0))));
	t_6 = x1 + (t_2 + t_5);
	tmp = 0.0;
	if (x1 <= -5.5e+102)
		tmp = x1 + (9.0 + (x1 + (-12.0 * (x1 * x2))));
	elseif (x1 <= -4.0)
		tmp = t_4;
	elseif (x1 <= -2.4e-133)
		tmp = t_6;
	elseif (x1 <= 3.3e-167)
		tmp = x1 + (t_2 + (x1 + (4.0 * (-3.0 * (x1 * x2)))));
	elseif (x1 <= 7.4)
		tmp = t_6;
	elseif (x1 <= 1.35e+154)
		tmp = t_4;
	else
		tmp = x1 + (9.0 + t_5);
	end
	tmp_2 = tmp;
end

Wolfram

code[x1_, x2_] := Block[{t$95$0 = N[(x1 * N[(x1 * 3.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(N[(x1 * x1), $MachinePrecision] + 1.0), $MachinePrecision]}, Block[{t$95$2 = N[(3.0 * N[(N[(N[(t$95$0 - N[(2.0 * x2), $MachinePrecision]), $MachinePrecision] - x1), $MachinePrecision] / t$95$1), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(N[(N[(N[(2.0 * x2), $MachinePrecision] + t$95$0), $MachinePrecision] - x1), $MachinePrecision] / t$95$1), $MachinePrecision]}, Block[{t$95$4 = N[(x1 + N[(t$95$2 + N[(x1 + N[(N[(x1 * N[(x1 * x1), $MachinePrecision]), $MachinePrecision] + N[(N[(t$95$0 * t$95$3), $MachinePrecision] + N[(t$95$1 * N[(N[(x1 * 2.0), $MachinePrecision] + N[(N[(x1 * x1), $MachinePrecision] * N[(N[(4.0 * t$95$3), $MachinePrecision] - 6.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$5 = N[(x1 + N[(4.0 * N[(x1 * N[(x2 * N[(N[(2.0 * x2), $MachinePrecision] - 3.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$6 = N[(x1 + N[(t$95$2 + t$95$5), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x1, -5.5e+102], N[(x1 + N[(9.0 + N[(x1 + N[(-12.0 * N[(x1 * x2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, -4.0], t$95$4, If[LessEqual[x1, -2.4e-133], t$95$6, If[LessEqual[x1, 3.3e-167], N[(x1 + N[(t$95$2 + N[(x1 + N[(4.0 * N[(-3.0 * N[(x1 * x2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, 7.4], t$95$6, If[LessEqual[x1, 1.35e+154], t$95$4, N[(x1 + N[(9.0 + t$95$5), $MachinePrecision]), $MachinePrecision]]]]]]]]]]]]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 13: 75.9% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := x1 \cdot x1 + 1\\ t_1 := x1 \cdot \left(x1 \cdot 3\right)\\ t_2 := 3 \cdot \frac{\left(t_1 - 2 \cdot x2\right) - x1}{t_0}\\ t_3 := x1 \cdot \left(x1 \cdot x1\right)\\ t_4 := \frac{\left(2 \cdot x2 + t_1\right) - x1}{t_0}\\ t_5 := t_1 \cdot t_4\\ t_6 := \left(x1 \cdot x1\right) \cdot \left(4 \cdot t_4 - 6\right)\\ t_7 := 2 \cdot x2 - x1\\ t_8 := \left(x1 \cdot 2\right) \cdot t_7\\ t_9 := 2 \cdot x2 - 3\\ \mathbf{if}\;x1 \leq -5.5 \cdot 10^{+102}:\\ \;\;\;\;x1 + \left(9 + \left(x1 + -12 \cdot \left(x1 \cdot x2\right)\right)\right)\\ \mathbf{elif}\;x1 \leq -0.96:\\ \;\;\;\;x1 + \left(9 + \left(x1 + \left(t_3 + \left(t_5 + t_0 \cdot \left(t_6 + \left(t_4 - 3\right) \cdot t_8\right)\right)\right)\right)\right)\\ \mathbf{elif}\;x1 \leq 580000000:\\ \;\;\;\;x1 + \left(t_2 + \left(x1 + \left(t_3 + \left(t_0 \cdot \left(t_6 + t_9 \cdot t_8\right) + t_1 \cdot t_7\right)\right)\right)\right)\\ \mathbf{elif}\;x1 \leq 1.35 \cdot 10^{+154}:\\ \;\;\;\;x1 + \left(t_2 + \left(x1 + \left(t_3 + \left(t_5 + t_0 \cdot \left(x1 \cdot 2 + t_6\right)\right)\right)\right)\right)\\ \mathbf{else}:\\ \;\;\;\;x1 + \left(9 + \left(x1 + 4 \cdot \left(x1 \cdot \left(x2 \cdot t_9\right)\right)\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x1 x2)
 :precision binary64
 (let* ((t_0 (+ (* x1 x1) 1.0))
        (t_1 (* x1 (* x1 3.0)))
        (t_2 (* 3.0 (/ (- (- t_1 (* 2.0 x2)) x1) t_0)))
        (t_3 (* x1 (* x1 x1)))
        (t_4 (/ (- (+ (* 2.0 x2) t_1) x1) t_0))
        (t_5 (* t_1 t_4))
        (t_6 (* (* x1 x1) (- (* 4.0 t_4) 6.0)))
        (t_7 (- (* 2.0 x2) x1))
        (t_8 (* (* x1 2.0) t_7))
        (t_9 (- (* 2.0 x2) 3.0)))
   (if (<= x1 -5.5e+102)
     (+ x1 (+ 9.0 (+ x1 (* -12.0 (* x1 x2)))))
     (if (<= x1 -0.96)
       (+
        x1
        (+ 9.0 (+ x1 (+ t_3 (+ t_5 (* t_0 (+ t_6 (* (- t_4 3.0) t_8))))))))
       (if (<= x1 580000000.0)
         (+
          x1
          (+ t_2 (+ x1 (+ t_3 (+ (* t_0 (+ t_6 (* t_9 t_8))) (* t_1 t_7))))))
         (if (<= x1 1.35e+154)
           (+ x1 (+ t_2 (+ x1 (+ t_3 (+ t_5 (* t_0 (+ (* x1 2.0) t_6)))))))
           (+ x1 (+ 9.0 (+ x1 (* 4.0 (* x1 (* x2 t_9))))))))))))

C

double code(double x1, double x2) {
	double t_0 = (x1 * x1) + 1.0;
	double t_1 = x1 * (x1 * 3.0);
	double t_2 = 3.0 * (((t_1 - (2.0 * x2)) - x1) / t_0);
	double t_3 = x1 * (x1 * x1);
	double t_4 = (((2.0 * x2) + t_1) - x1) / t_0;
	double t_5 = t_1 * t_4;
	double t_6 = (x1 * x1) * ((4.0 * t_4) - 6.0);
	double t_7 = (2.0 * x2) - x1;
	double t_8 = (x1 * 2.0) * t_7;
	double t_9 = (2.0 * x2) - 3.0;
	double tmp;
	if (x1 <= -5.5e+102) {
		tmp = x1 + (9.0 + (x1 + (-12.0 * (x1 * x2))));
	} else if (x1 <= -0.96) {
		tmp = x1 + (9.0 + (x1 + (t_3 + (t_5 + (t_0 * (t_6 + ((t_4 - 3.0) * t_8)))))));
	} else if (x1 <= 580000000.0) {
		tmp = x1 + (t_2 + (x1 + (t_3 + ((t_0 * (t_6 + (t_9 * t_8))) + (t_1 * t_7)))));
	} else if (x1 <= 1.35e+154) {
		tmp = x1 + (t_2 + (x1 + (t_3 + (t_5 + (t_0 * ((x1 * 2.0) + t_6))))));
	} else {
		tmp = x1 + (9.0 + (x1 + (4.0 * (x1 * (x2 * t_9)))));
	}
	return tmp;
}

Fortran

real(8) function code(x1, x2)
    real(8), intent (in) :: x1
    real(8), intent (in) :: x2
    real(8) :: t_0
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: t_3
    real(8) :: t_4
    real(8) :: t_5
    real(8) :: t_6
    real(8) :: t_7
    real(8) :: t_8
    real(8) :: t_9
    real(8) :: tmp
    t_0 = (x1 * x1) + 1.0d0
    t_1 = x1 * (x1 * 3.0d0)
    t_2 = 3.0d0 * (((t_1 - (2.0d0 * x2)) - x1) / t_0)
    t_3 = x1 * (x1 * x1)
    t_4 = (((2.0d0 * x2) + t_1) - x1) / t_0
    t_5 = t_1 * t_4
    t_6 = (x1 * x1) * ((4.0d0 * t_4) - 6.0d0)
    t_7 = (2.0d0 * x2) - x1
    t_8 = (x1 * 2.0d0) * t_7
    t_9 = (2.0d0 * x2) - 3.0d0
    if (x1 <= (-5.5d+102)) then
        tmp = x1 + (9.0d0 + (x1 + ((-12.0d0) * (x1 * x2))))
    else if (x1 <= (-0.96d0)) then
        tmp = x1 + (9.0d0 + (x1 + (t_3 + (t_5 + (t_0 * (t_6 + ((t_4 - 3.0d0) * t_8)))))))
    else if (x1 <= 580000000.0d0) then
        tmp = x1 + (t_2 + (x1 + (t_3 + ((t_0 * (t_6 + (t_9 * t_8))) + (t_1 * t_7)))))
    else if (x1 <= 1.35d+154) then
        tmp = x1 + (t_2 + (x1 + (t_3 + (t_5 + (t_0 * ((x1 * 2.0d0) + t_6))))))
    else
        tmp = x1 + (9.0d0 + (x1 + (4.0d0 * (x1 * (x2 * t_9)))))
    end if
    code = tmp
end function

Java

public static double code(double x1, double x2) {
	double t_0 = (x1 * x1) + 1.0;
	double t_1 = x1 * (x1 * 3.0);
	double t_2 = 3.0 * (((t_1 - (2.0 * x2)) - x1) / t_0);
	double t_3 = x1 * (x1 * x1);
	double t_4 = (((2.0 * x2) + t_1) - x1) / t_0;
	double t_5 = t_1 * t_4;
	double t_6 = (x1 * x1) * ((4.0 * t_4) - 6.0);
	double t_7 = (2.0 * x2) - x1;
	double t_8 = (x1 * 2.0) * t_7;
	double t_9 = (2.0 * x2) - 3.0;
	double tmp;
	if (x1 <= -5.5e+102) {
		tmp = x1 + (9.0 + (x1 + (-12.0 * (x1 * x2))));
	} else if (x1 <= -0.96) {
		tmp = x1 + (9.0 + (x1 + (t_3 + (t_5 + (t_0 * (t_6 + ((t_4 - 3.0) * t_8)))))));
	} else if (x1 <= 580000000.0) {
		tmp = x1 + (t_2 + (x1 + (t_3 + ((t_0 * (t_6 + (t_9 * t_8))) + (t_1 * t_7)))));
	} else if (x1 <= 1.35e+154) {
		tmp = x1 + (t_2 + (x1 + (t_3 + (t_5 + (t_0 * ((x1 * 2.0) + t_6))))));
	} else {
		tmp = x1 + (9.0 + (x1 + (4.0 * (x1 * (x2 * t_9)))));
	}
	return tmp;
}

Python

def code(x1, x2):
	t_0 = (x1 * x1) + 1.0
	t_1 = x1 * (x1 * 3.0)
	t_2 = 3.0 * (((t_1 - (2.0 * x2)) - x1) / t_0)
	t_3 = x1 * (x1 * x1)
	t_4 = (((2.0 * x2) + t_1) - x1) / t_0
	t_5 = t_1 * t_4
	t_6 = (x1 * x1) * ((4.0 * t_4) - 6.0)
	t_7 = (2.0 * x2) - x1
	t_8 = (x1 * 2.0) * t_7
	t_9 = (2.0 * x2) - 3.0
	tmp = 0
	if x1 <= -5.5e+102:
		tmp = x1 + (9.0 + (x1 + (-12.0 * (x1 * x2))))
	elif x1 <= -0.96:
		tmp = x1 + (9.0 + (x1 + (t_3 + (t_5 + (t_0 * (t_6 + ((t_4 - 3.0) * t_8)))))))
	elif x1 <= 580000000.0:
		tmp = x1 + (t_2 + (x1 + (t_3 + ((t_0 * (t_6 + (t_9 * t_8))) + (t_1 * t_7)))))
	elif x1 <= 1.35e+154:
		tmp = x1 + (t_2 + (x1 + (t_3 + (t_5 + (t_0 * ((x1 * 2.0) + t_6))))))
	else:
		tmp = x1 + (9.0 + (x1 + (4.0 * (x1 * (x2 * t_9)))))
	return tmp

Julia

function code(x1, x2)
	t_0 = Float64(Float64(x1 * x1) + 1.0)
	t_1 = Float64(x1 * Float64(x1 * 3.0))
	t_2 = Float64(3.0 * Float64(Float64(Float64(t_1 - Float64(2.0 * x2)) - x1) / t_0))
	t_3 = Float64(x1 * Float64(x1 * x1))
	t_4 = Float64(Float64(Float64(Float64(2.0 * x2) + t_1) - x1) / t_0)
	t_5 = Float64(t_1 * t_4)
	t_6 = Float64(Float64(x1 * x1) * Float64(Float64(4.0 * t_4) - 6.0))
	t_7 = Float64(Float64(2.0 * x2) - x1)
	t_8 = Float64(Float64(x1 * 2.0) * t_7)
	t_9 = Float64(Float64(2.0 * x2) - 3.0)
	tmp = 0.0
	if (x1 <= -5.5e+102)
		tmp = Float64(x1 + Float64(9.0 + Float64(x1 + Float64(-12.0 * Float64(x1 * x2)))));
	elseif (x1 <= -0.96)
		tmp = Float64(x1 + Float64(9.0 + Float64(x1 + Float64(t_3 + Float64(t_5 + Float64(t_0 * Float64(t_6 + Float64(Float64(t_4 - 3.0) * t_8))))))));
	elseif (x1 <= 580000000.0)
		tmp = Float64(x1 + Float64(t_2 + Float64(x1 + Float64(t_3 + Float64(Float64(t_0 * Float64(t_6 + Float64(t_9 * t_8))) + Float64(t_1 * t_7))))));
	elseif (x1 <= 1.35e+154)
		tmp = Float64(x1 + Float64(t_2 + Float64(x1 + Float64(t_3 + Float64(t_5 + Float64(t_0 * Float64(Float64(x1 * 2.0) + t_6)))))));
	else
		tmp = Float64(x1 + Float64(9.0 + Float64(x1 + Float64(4.0 * Float64(x1 * Float64(x2 * t_9))))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x1, x2)
	t_0 = (x1 * x1) + 1.0;
	t_1 = x1 * (x1 * 3.0);
	t_2 = 3.0 * (((t_1 - (2.0 * x2)) - x1) / t_0);
	t_3 = x1 * (x1 * x1);
	t_4 = (((2.0 * x2) + t_1) - x1) / t_0;
	t_5 = t_1 * t_4;
	t_6 = (x1 * x1) * ((4.0 * t_4) - 6.0);
	t_7 = (2.0 * x2) - x1;
	t_8 = (x1 * 2.0) * t_7;
	t_9 = (2.0 * x2) - 3.0;
	tmp = 0.0;
	if (x1 <= -5.5e+102)
		tmp = x1 + (9.0 + (x1 + (-12.0 * (x1 * x2))));
	elseif (x1 <= -0.96)
		tmp = x1 + (9.0 + (x1 + (t_3 + (t_5 + (t_0 * (t_6 + ((t_4 - 3.0) * t_8)))))));
	elseif (x1 <= 580000000.0)
		tmp = x1 + (t_2 + (x1 + (t_3 + ((t_0 * (t_6 + (t_9 * t_8))) + (t_1 * t_7)))));
	elseif (x1 <= 1.35e+154)
		tmp = x1 + (t_2 + (x1 + (t_3 + (t_5 + (t_0 * ((x1 * 2.0) + t_6))))));
	else
		tmp = x1 + (9.0 + (x1 + (4.0 * (x1 * (x2 * t_9)))));
	end
	tmp_2 = tmp;
end

Wolfram

code[x1_, x2_] := Block[{t$95$0 = N[(N[(x1 * x1), $MachinePrecision] + 1.0), $MachinePrecision]}, Block[{t$95$1 = N[(x1 * N[(x1 * 3.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(3.0 * N[(N[(N[(t$95$1 - N[(2.0 * x2), $MachinePrecision]), $MachinePrecision] - x1), $MachinePrecision] / t$95$0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(x1 * N[(x1 * x1), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$4 = N[(N[(N[(N[(2.0 * x2), $MachinePrecision] + t$95$1), $MachinePrecision] - x1), $MachinePrecision] / t$95$0), $MachinePrecision]}, Block[{t$95$5 = N[(t$95$1 * t$95$4), $MachinePrecision]}, Block[{t$95$6 = N[(N[(x1 * x1), $MachinePrecision] * N[(N[(4.0 * t$95$4), $MachinePrecision] - 6.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$7 = N[(N[(2.0 * x2), $MachinePrecision] - x1), $MachinePrecision]}, Block[{t$95$8 = N[(N[(x1 * 2.0), $MachinePrecision] * t$95$7), $MachinePrecision]}, Block[{t$95$9 = N[(N[(2.0 * x2), $MachinePrecision] - 3.0), $MachinePrecision]}, If[LessEqual[x1, -5.5e+102], N[(x1 + N[(9.0 + N[(x1 + N[(-12.0 * N[(x1 * x2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, -0.96], N[(x1 + N[(9.0 + N[(x1 + N[(t$95$3 + N[(t$95$5 + N[(t$95$0 * N[(t$95$6 + N[(N[(t$95$4 - 3.0), $MachinePrecision] * t$95$8), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, 580000000.0], N[(x1 + N[(t$95$2 + N[(x1 + N[(t$95$3 + N[(N[(t$95$0 * N[(t$95$6 + N[(t$95$9 * t$95$8), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(t$95$1 * t$95$7), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, 1.35e+154], N[(x1 + N[(t$95$2 + N[(x1 + N[(t$95$3 + N[(t$95$5 + N[(t$95$0 * N[(N[(x1 * 2.0), $MachinePrecision] + t$95$6), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x1 + N[(9.0 + N[(x1 + N[(4.0 * N[(x1 * N[(x2 * t$95$9), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]]]]]]]]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 14: 75.7% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := x1 \cdot \left(x1 \cdot 3\right)\\ t_1 := x1 \cdot \left(x1 \cdot x1\right)\\ t_2 := 2 \cdot x2 - 3\\ t_3 := x1 \cdot x1 + 1\\ t_4 := 3 \cdot \frac{\left(t_0 - 2 \cdot x2\right) - x1}{t_3}\\ t_5 := \frac{\left(2 \cdot x2 + t_0\right) - x1}{t_3}\\ t_6 := \left(x1 \cdot x1\right) \cdot \left(4 \cdot t_5 - 6\right)\\ t_7 := x1 + \left(t_4 + \left(x1 + \left(t_1 + \left(t_0 \cdot t_5 + t_3 \cdot \left(x1 \cdot 2 + t_6\right)\right)\right)\right)\right)\\ t_8 := 2 \cdot x2 - x1\\ \mathbf{if}\;x1 \leq -5.5 \cdot 10^{+102}:\\ \;\;\;\;x1 + \left(9 + \left(x1 + -12 \cdot \left(x1 \cdot x2\right)\right)\right)\\ \mathbf{elif}\;x1 \leq -660:\\ \;\;\;\;t_7\\ \mathbf{elif}\;x1 \leq 780000000:\\ \;\;\;\;x1 + \left(t_4 + \left(x1 + \left(t_1 + \left(t_3 \cdot \left(t_6 + t_2 \cdot \left(\left(x1 \cdot 2\right) \cdot t_8\right)\right) + t_0 \cdot t_8\right)\right)\right)\right)\\ \mathbf{elif}\;x1 \leq 1.35 \cdot 10^{+154}:\\ \;\;\;\;t_7\\ \mathbf{else}:\\ \;\;\;\;x1 + \left(9 + \left(x1 + 4 \cdot \left(x1 \cdot \left(x2 \cdot t_2\right)\right)\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x1 x2)
 :precision binary64
 (let* ((t_0 (* x1 (* x1 3.0)))
        (t_1 (* x1 (* x1 x1)))
        (t_2 (- (* 2.0 x2) 3.0))
        (t_3 (+ (* x1 x1) 1.0))
        (t_4 (* 3.0 (/ (- (- t_0 (* 2.0 x2)) x1) t_3)))
        (t_5 (/ (- (+ (* 2.0 x2) t_0) x1) t_3))
        (t_6 (* (* x1 x1) (- (* 4.0 t_5) 6.0)))
        (t_7
         (+
          x1
          (+ t_4 (+ x1 (+ t_1 (+ (* t_0 t_5) (* t_3 (+ (* x1 2.0) t_6))))))))
        (t_8 (- (* 2.0 x2) x1)))
   (if (<= x1 -5.5e+102)
     (+ x1 (+ 9.0 (+ x1 (* -12.0 (* x1 x2)))))
     (if (<= x1 -660.0)
       t_7
       (if (<= x1 780000000.0)
         (+
          x1
          (+
           t_4
           (+
            x1
            (+
             t_1
             (+ (* t_3 (+ t_6 (* t_2 (* (* x1 2.0) t_8)))) (* t_0 t_8))))))
         (if (<= x1 1.35e+154)
           t_7
           (+ x1 (+ 9.0 (+ x1 (* 4.0 (* x1 (* x2 t_2))))))))))))

C

double code(double x1, double x2) {
	double t_0 = x1 * (x1 * 3.0);
	double t_1 = x1 * (x1 * x1);
	double t_2 = (2.0 * x2) - 3.0;
	double t_3 = (x1 * x1) + 1.0;
	double t_4 = 3.0 * (((t_0 - (2.0 * x2)) - x1) / t_3);
	double t_5 = (((2.0 * x2) + t_0) - x1) / t_3;
	double t_6 = (x1 * x1) * ((4.0 * t_5) - 6.0);
	double t_7 = x1 + (t_4 + (x1 + (t_1 + ((t_0 * t_5) + (t_3 * ((x1 * 2.0) + t_6))))));
	double t_8 = (2.0 * x2) - x1;
	double tmp;
	if (x1 <= -5.5e+102) {
		tmp = x1 + (9.0 + (x1 + (-12.0 * (x1 * x2))));
	} else if (x1 <= -660.0) {
		tmp = t_7;
	} else if (x1 <= 780000000.0) {
		tmp = x1 + (t_4 + (x1 + (t_1 + ((t_3 * (t_6 + (t_2 * ((x1 * 2.0) * t_8)))) + (t_0 * t_8)))));
	} else if (x1 <= 1.35e+154) {
		tmp = t_7;
	} else {
		tmp = x1 + (9.0 + (x1 + (4.0 * (x1 * (x2 * t_2)))));
	}
	return tmp;
}

Fortran

real(8) function code(x1, x2)
    real(8), intent (in) :: x1
    real(8), intent (in) :: x2
    real(8) :: t_0
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: t_3
    real(8) :: t_4
    real(8) :: t_5
    real(8) :: t_6
    real(8) :: t_7
    real(8) :: t_8
    real(8) :: tmp
    t_0 = x1 * (x1 * 3.0d0)
    t_1 = x1 * (x1 * x1)
    t_2 = (2.0d0 * x2) - 3.0d0
    t_3 = (x1 * x1) + 1.0d0
    t_4 = 3.0d0 * (((t_0 - (2.0d0 * x2)) - x1) / t_3)
    t_5 = (((2.0d0 * x2) + t_0) - x1) / t_3
    t_6 = (x1 * x1) * ((4.0d0 * t_5) - 6.0d0)
    t_7 = x1 + (t_4 + (x1 + (t_1 + ((t_0 * t_5) + (t_3 * ((x1 * 2.0d0) + t_6))))))
    t_8 = (2.0d0 * x2) - x1
    if (x1 <= (-5.5d+102)) then
        tmp = x1 + (9.0d0 + (x1 + ((-12.0d0) * (x1 * x2))))
    else if (x1 <= (-660.0d0)) then
        tmp = t_7
    else if (x1 <= 780000000.0d0) then
        tmp = x1 + (t_4 + (x1 + (t_1 + ((t_3 * (t_6 + (t_2 * ((x1 * 2.0d0) * t_8)))) + (t_0 * t_8)))))
    else if (x1 <= 1.35d+154) then
        tmp = t_7
    else
        tmp = x1 + (9.0d0 + (x1 + (4.0d0 * (x1 * (x2 * t_2)))))
    end if
    code = tmp
end function

Java

public static double code(double x1, double x2) {
	double t_0 = x1 * (x1 * 3.0);
	double t_1 = x1 * (x1 * x1);
	double t_2 = (2.0 * x2) - 3.0;
	double t_3 = (x1 * x1) + 1.0;
	double t_4 = 3.0 * (((t_0 - (2.0 * x2)) - x1) / t_3);
	double t_5 = (((2.0 * x2) + t_0) - x1) / t_3;
	double t_6 = (x1 * x1) * ((4.0 * t_5) - 6.0);
	double t_7 = x1 + (t_4 + (x1 + (t_1 + ((t_0 * t_5) + (t_3 * ((x1 * 2.0) + t_6))))));
	double t_8 = (2.0 * x2) - x1;
	double tmp;
	if (x1 <= -5.5e+102) {
		tmp = x1 + (9.0 + (x1 + (-12.0 * (x1 * x2))));
	} else if (x1 <= -660.0) {
		tmp = t_7;
	} else if (x1 <= 780000000.0) {
		tmp = x1 + (t_4 + (x1 + (t_1 + ((t_3 * (t_6 + (t_2 * ((x1 * 2.0) * t_8)))) + (t_0 * t_8)))));
	} else if (x1 <= 1.35e+154) {
		tmp = t_7;
	} else {
		tmp = x1 + (9.0 + (x1 + (4.0 * (x1 * (x2 * t_2)))));
	}
	return tmp;
}

Python

def code(x1, x2):
	t_0 = x1 * (x1 * 3.0)
	t_1 = x1 * (x1 * x1)
	t_2 = (2.0 * x2) - 3.0
	t_3 = (x1 * x1) + 1.0
	t_4 = 3.0 * (((t_0 - (2.0 * x2)) - x1) / t_3)
	t_5 = (((2.0 * x2) + t_0) - x1) / t_3
	t_6 = (x1 * x1) * ((4.0 * t_5) - 6.0)
	t_7 = x1 + (t_4 + (x1 + (t_1 + ((t_0 * t_5) + (t_3 * ((x1 * 2.0) + t_6))))))
	t_8 = (2.0 * x2) - x1
	tmp = 0
	if x1 <= -5.5e+102:
		tmp = x1 + (9.0 + (x1 + (-12.0 * (x1 * x2))))
	elif x1 <= -660.0:
		tmp = t_7
	elif x1 <= 780000000.0:
		tmp = x1 + (t_4 + (x1 + (t_1 + ((t_3 * (t_6 + (t_2 * ((x1 * 2.0) * t_8)))) + (t_0 * t_8)))))
	elif x1 <= 1.35e+154:
		tmp = t_7
	else:
		tmp = x1 + (9.0 + (x1 + (4.0 * (x1 * (x2 * t_2)))))
	return tmp

Julia

function code(x1, x2)
	t_0 = Float64(x1 * Float64(x1 * 3.0))
	t_1 = Float64(x1 * Float64(x1 * x1))
	t_2 = Float64(Float64(2.0 * x2) - 3.0)
	t_3 = Float64(Float64(x1 * x1) + 1.0)
	t_4 = Float64(3.0 * Float64(Float64(Float64(t_0 - Float64(2.0 * x2)) - x1) / t_3))
	t_5 = Float64(Float64(Float64(Float64(2.0 * x2) + t_0) - x1) / t_3)
	t_6 = Float64(Float64(x1 * x1) * Float64(Float64(4.0 * t_5) - 6.0))
	t_7 = Float64(x1 + Float64(t_4 + Float64(x1 + Float64(t_1 + Float64(Float64(t_0 * t_5) + Float64(t_3 * Float64(Float64(x1 * 2.0) + t_6)))))))
	t_8 = Float64(Float64(2.0 * x2) - x1)
	tmp = 0.0
	if (x1 <= -5.5e+102)
		tmp = Float64(x1 + Float64(9.0 + Float64(x1 + Float64(-12.0 * Float64(x1 * x2)))));
	elseif (x1 <= -660.0)
		tmp = t_7;
	elseif (x1 <= 780000000.0)
		tmp = Float64(x1 + Float64(t_4 + Float64(x1 + Float64(t_1 + Float64(Float64(t_3 * Float64(t_6 + Float64(t_2 * Float64(Float64(x1 * 2.0) * t_8)))) + Float64(t_0 * t_8))))));
	elseif (x1 <= 1.35e+154)
		tmp = t_7;
	else
		tmp = Float64(x1 + Float64(9.0 + Float64(x1 + Float64(4.0 * Float64(x1 * Float64(x2 * t_2))))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x1, x2)
	t_0 = x1 * (x1 * 3.0);
	t_1 = x1 * (x1 * x1);
	t_2 = (2.0 * x2) - 3.0;
	t_3 = (x1 * x1) + 1.0;
	t_4 = 3.0 * (((t_0 - (2.0 * x2)) - x1) / t_3);
	t_5 = (((2.0 * x2) + t_0) - x1) / t_3;
	t_6 = (x1 * x1) * ((4.0 * t_5) - 6.0);
	t_7 = x1 + (t_4 + (x1 + (t_1 + ((t_0 * t_5) + (t_3 * ((x1 * 2.0) + t_6))))));
	t_8 = (2.0 * x2) - x1;
	tmp = 0.0;
	if (x1 <= -5.5e+102)
		tmp = x1 + (9.0 + (x1 + (-12.0 * (x1 * x2))));
	elseif (x1 <= -660.0)
		tmp = t_7;
	elseif (x1 <= 780000000.0)
		tmp = x1 + (t_4 + (x1 + (t_1 + ((t_3 * (t_6 + (t_2 * ((x1 * 2.0) * t_8)))) + (t_0 * t_8)))));
	elseif (x1 <= 1.35e+154)
		tmp = t_7;
	else
		tmp = x1 + (9.0 + (x1 + (4.0 * (x1 * (x2 * t_2)))));
	end
	tmp_2 = tmp;
end

Wolfram

code[x1_, x2_] := Block[{t$95$0 = N[(x1 * N[(x1 * 3.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(x1 * N[(x1 * x1), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(2.0 * x2), $MachinePrecision] - 3.0), $MachinePrecision]}, Block[{t$95$3 = N[(N[(x1 * x1), $MachinePrecision] + 1.0), $MachinePrecision]}, Block[{t$95$4 = N[(3.0 * N[(N[(N[(t$95$0 - N[(2.0 * x2), $MachinePrecision]), $MachinePrecision] - x1), $MachinePrecision] / t$95$3), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$5 = N[(N[(N[(N[(2.0 * x2), $MachinePrecision] + t$95$0), $MachinePrecision] - x1), $MachinePrecision] / t$95$3), $MachinePrecision]}, Block[{t$95$6 = N[(N[(x1 * x1), $MachinePrecision] * N[(N[(4.0 * t$95$5), $MachinePrecision] - 6.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$7 = N[(x1 + N[(t$95$4 + N[(x1 + N[(t$95$1 + N[(N[(t$95$0 * t$95$5), $MachinePrecision] + N[(t$95$3 * N[(N[(x1 * 2.0), $MachinePrecision] + t$95$6), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$8 = N[(N[(2.0 * x2), $MachinePrecision] - x1), $MachinePrecision]}, If[LessEqual[x1, -5.5e+102], N[(x1 + N[(9.0 + N[(x1 + N[(-12.0 * N[(x1 * x2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, -660.0], t$95$7, If[LessEqual[x1, 780000000.0], N[(x1 + N[(t$95$4 + N[(x1 + N[(t$95$1 + N[(N[(t$95$3 * N[(t$95$6 + N[(t$95$2 * N[(N[(x1 * 2.0), $MachinePrecision] * t$95$8), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(t$95$0 * t$95$8), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, 1.35e+154], t$95$7, N[(x1 + N[(9.0 + N[(x1 + N[(4.0 * N[(x1 * N[(x2 * t$95$2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]]]]]]]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 15: 70.5% accurate, 1.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := -2 \cdot \left(4 \cdot x2 - 3\right)\\ t_1 := x1 \cdot \left(x1 \cdot x1\right)\\ t_2 := x1 + 4 \cdot \left(x1 \cdot \left(x2 \cdot \left(2 \cdot x2 - 3\right)\right)\right)\\ t_3 := x1 \cdot x1 + 1\\ t_4 := x1 \cdot \left(x1 \cdot 3\right)\\ t_5 := 3 \cdot \frac{\left(t_4 - 2 \cdot x2\right) - x1}{t_3}\\ t_6 := t_4 \cdot \left(2 \cdot x2 - x1\right)\\ t_7 := x1 + \left(t_5 + t_2\right)\\ t_8 := \left(x1 \cdot x1\right) \cdot \left(4 \cdot \frac{\left(2 \cdot x2 + t_4\right) - x1}{t_3} - 6\right)\\ \mathbf{if}\;x1 \leq -5.5 \cdot 10^{+102}:\\ \;\;\;\;x1 + \left(9 + \left(x1 + -12 \cdot \left(x1 \cdot x2\right)\right)\right)\\ \mathbf{elif}\;x1 \leq -760:\\ \;\;\;\;x1 + \left(t_5 + \left(x1 + \left(t_1 + \left(t_6 + t_3 \cdot \left(x1 \cdot 2 + t_8\right)\right)\right)\right)\right)\\ \mathbf{elif}\;x1 \leq -2.4 \cdot 10^{-133}:\\ \;\;\;\;t_7\\ \mathbf{elif}\;x1 \leq 1.3 \cdot 10^{-166}:\\ \;\;\;\;x1 + \left(t_5 + \left(x1 + 4 \cdot \left(-3 \cdot \left(x1 \cdot x2\right)\right)\right)\right)\\ \mathbf{elif}\;x1 \leq 9:\\ \;\;\;\;t_7\\ \mathbf{elif}\;x1 \leq 5 \cdot 10^{+102}:\\ \;\;\;\;x1 + \left(9 + \left(x1 + \left(t_1 + \left(t_6 + t_3 \cdot \left(t_8 + \left(x1 \cdot 2 + t_0\right)\right)\right)\right)\right)\right)\\ \mathbf{elif}\;x1 \leq 1.35 \cdot 10^{+154}:\\ \;\;\;\;x1 + \left(t_5 + \left(x1 + \left(t_1 + t_0\right)\right)\right)\\ \mathbf{else}:\\ \;\;\;\;x1 + \left(9 + t_2\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x1 x2)
 :precision binary64
 (let* ((t_0 (* -2.0 (- (* 4.0 x2) 3.0)))
        (t_1 (* x1 (* x1 x1)))
        (t_2 (+ x1 (* 4.0 (* x1 (* x2 (- (* 2.0 x2) 3.0))))))
        (t_3 (+ (* x1 x1) 1.0))
        (t_4 (* x1 (* x1 3.0)))
        (t_5 (* 3.0 (/ (- (- t_4 (* 2.0 x2)) x1) t_3)))
        (t_6 (* t_4 (- (* 2.0 x2) x1)))
        (t_7 (+ x1 (+ t_5 t_2)))
        (t_8 (* (* x1 x1) (- (* 4.0 (/ (- (+ (* 2.0 x2) t_4) x1) t_3)) 6.0))))
   (if (<= x1 -5.5e+102)
     (+ x1 (+ 9.0 (+ x1 (* -12.0 (* x1 x2)))))
     (if (<= x1 -760.0)
       (+ x1 (+ t_5 (+ x1 (+ t_1 (+ t_6 (* t_3 (+ (* x1 2.0) t_8)))))))
       (if (<= x1 -2.4e-133)
         t_7
         (if (<= x1 1.3e-166)
           (+ x1 (+ t_5 (+ x1 (* 4.0 (* -3.0 (* x1 x2))))))
           (if (<= x1 9.0)
             t_7
             (if (<= x1 5e+102)
               (+
                x1
                (+
                 9.0
                 (+ x1 (+ t_1 (+ t_6 (* t_3 (+ t_8 (+ (* x1 2.0) t_0))))))))
               (if (<= x1 1.35e+154)
                 (+ x1 (+ t_5 (+ x1 (+ t_1 t_0))))
                 (+ x1 (+ 9.0 t_2)))))))))))

C

double code(double x1, double x2) {
	double t_0 = -2.0 * ((4.0 * x2) - 3.0);
	double t_1 = x1 * (x1 * x1);
	double t_2 = x1 + (4.0 * (x1 * (x2 * ((2.0 * x2) - 3.0))));
	double t_3 = (x1 * x1) + 1.0;
	double t_4 = x1 * (x1 * 3.0);
	double t_5 = 3.0 * (((t_4 - (2.0 * x2)) - x1) / t_3);
	double t_6 = t_4 * ((2.0 * x2) - x1);
	double t_7 = x1 + (t_5 + t_2);
	double t_8 = (x1 * x1) * ((4.0 * ((((2.0 * x2) + t_4) - x1) / t_3)) - 6.0);
	double tmp;
	if (x1 <= -5.5e+102) {
		tmp = x1 + (9.0 + (x1 + (-12.0 * (x1 * x2))));
	} else if (x1 <= -760.0) {
		tmp = x1 + (t_5 + (x1 + (t_1 + (t_6 + (t_3 * ((x1 * 2.0) + t_8))))));
	} else if (x1 <= -2.4e-133) {
		tmp = t_7;
	} else if (x1 <= 1.3e-166) {
		tmp = x1 + (t_5 + (x1 + (4.0 * (-3.0 * (x1 * x2)))));
	} else if (x1 <= 9.0) {
		tmp = t_7;
	} else if (x1 <= 5e+102) {
		tmp = x1 + (9.0 + (x1 + (t_1 + (t_6 + (t_3 * (t_8 + ((x1 * 2.0) + t_0)))))));
	} else if (x1 <= 1.35e+154) {
		tmp = x1 + (t_5 + (x1 + (t_1 + t_0)));
	} else {
		tmp = x1 + (9.0 + t_2);
	}
	return tmp;
}

Fortran

real(8) function code(x1, x2)
    real(8), intent (in) :: x1
    real(8), intent (in) :: x2
    real(8) :: t_0
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: t_3
    real(8) :: t_4
    real(8) :: t_5
    real(8) :: t_6
    real(8) :: t_7
    real(8) :: t_8
    real(8) :: tmp
    t_0 = (-2.0d0) * ((4.0d0 * x2) - 3.0d0)
    t_1 = x1 * (x1 * x1)
    t_2 = x1 + (4.0d0 * (x1 * (x2 * ((2.0d0 * x2) - 3.0d0))))
    t_3 = (x1 * x1) + 1.0d0
    t_4 = x1 * (x1 * 3.0d0)
    t_5 = 3.0d0 * (((t_4 - (2.0d0 * x2)) - x1) / t_3)
    t_6 = t_4 * ((2.0d0 * x2) - x1)
    t_7 = x1 + (t_5 + t_2)
    t_8 = (x1 * x1) * ((4.0d0 * ((((2.0d0 * x2) + t_4) - x1) / t_3)) - 6.0d0)
    if (x1 <= (-5.5d+102)) then
        tmp = x1 + (9.0d0 + (x1 + ((-12.0d0) * (x1 * x2))))
    else if (x1 <= (-760.0d0)) then
        tmp = x1 + (t_5 + (x1 + (t_1 + (t_6 + (t_3 * ((x1 * 2.0d0) + t_8))))))
    else if (x1 <= (-2.4d-133)) then
        tmp = t_7
    else if (x1 <= 1.3d-166) then
        tmp = x1 + (t_5 + (x1 + (4.0d0 * ((-3.0d0) * (x1 * x2)))))
    else if (x1 <= 9.0d0) then
        tmp = t_7
    else if (x1 <= 5d+102) then
        tmp = x1 + (9.0d0 + (x1 + (t_1 + (t_6 + (t_3 * (t_8 + ((x1 * 2.0d0) + t_0)))))))
    else if (x1 <= 1.35d+154) then
        tmp = x1 + (t_5 + (x1 + (t_1 + t_0)))
    else
        tmp = x1 + (9.0d0 + t_2)
    end if
    code = tmp
end function

Java

public static double code(double x1, double x2) {
	double t_0 = -2.0 * ((4.0 * x2) - 3.0);
	double t_1 = x1 * (x1 * x1);
	double t_2 = x1 + (4.0 * (x1 * (x2 * ((2.0 * x2) - 3.0))));
	double t_3 = (x1 * x1) + 1.0;
	double t_4 = x1 * (x1 * 3.0);
	double t_5 = 3.0 * (((t_4 - (2.0 * x2)) - x1) / t_3);
	double t_6 = t_4 * ((2.0 * x2) - x1);
	double t_7 = x1 + (t_5 + t_2);
	double t_8 = (x1 * x1) * ((4.0 * ((((2.0 * x2) + t_4) - x1) / t_3)) - 6.0);
	double tmp;
	if (x1 <= -5.5e+102) {
		tmp = x1 + (9.0 + (x1 + (-12.0 * (x1 * x2))));
	} else if (x1 <= -760.0) {
		tmp = x1 + (t_5 + (x1 + (t_1 + (t_6 + (t_3 * ((x1 * 2.0) + t_8))))));
	} else if (x1 <= -2.4e-133) {
		tmp = t_7;
	} else if (x1 <= 1.3e-166) {
		tmp = x1 + (t_5 + (x1 + (4.0 * (-3.0 * (x1 * x2)))));
	} else if (x1 <= 9.0) {
		tmp = t_7;
	} else if (x1 <= 5e+102) {
		tmp = x1 + (9.0 + (x1 + (t_1 + (t_6 + (t_3 * (t_8 + ((x1 * 2.0) + t_0)))))));
	} else if (x1 <= 1.35e+154) {
		tmp = x1 + (t_5 + (x1 + (t_1 + t_0)));
	} else {
		tmp = x1 + (9.0 + t_2);
	}
	return tmp;
}

Python

def code(x1, x2):
	t_0 = -2.0 * ((4.0 * x2) - 3.0)
	t_1 = x1 * (x1 * x1)
	t_2 = x1 + (4.0 * (x1 * (x2 * ((2.0 * x2) - 3.0))))
	t_3 = (x1 * x1) + 1.0
	t_4 = x1 * (x1 * 3.0)
	t_5 = 3.0 * (((t_4 - (2.0 * x2)) - x1) / t_3)
	t_6 = t_4 * ((2.0 * x2) - x1)
	t_7 = x1 + (t_5 + t_2)
	t_8 = (x1 * x1) * ((4.0 * ((((2.0 * x2) + t_4) - x1) / t_3)) - 6.0)
	tmp = 0
	if x1 <= -5.5e+102:
		tmp = x1 + (9.0 + (x1 + (-12.0 * (x1 * x2))))
	elif x1 <= -760.0:
		tmp = x1 + (t_5 + (x1 + (t_1 + (t_6 + (t_3 * ((x1 * 2.0) + t_8))))))
	elif x1 <= -2.4e-133:
		tmp = t_7
	elif x1 <= 1.3e-166:
		tmp = x1 + (t_5 + (x1 + (4.0 * (-3.0 * (x1 * x2)))))
	elif x1 <= 9.0:
		tmp = t_7
	elif x1 <= 5e+102:
		tmp = x1 + (9.0 + (x1 + (t_1 + (t_6 + (t_3 * (t_8 + ((x1 * 2.0) + t_0)))))))
	elif x1 <= 1.35e+154:
		tmp = x1 + (t_5 + (x1 + (t_1 + t_0)))
	else:
		tmp = x1 + (9.0 + t_2)
	return tmp

Julia

function code(x1, x2)
	t_0 = Float64(-2.0 * Float64(Float64(4.0 * x2) - 3.0))
	t_1 = Float64(x1 * Float64(x1 * x1))
	t_2 = Float64(x1 + Float64(4.0 * Float64(x1 * Float64(x2 * Float64(Float64(2.0 * x2) - 3.0)))))
	t_3 = Float64(Float64(x1 * x1) + 1.0)
	t_4 = Float64(x1 * Float64(x1 * 3.0))
	t_5 = Float64(3.0 * Float64(Float64(Float64(t_4 - Float64(2.0 * x2)) - x1) / t_3))
	t_6 = Float64(t_4 * Float64(Float64(2.0 * x2) - x1))
	t_7 = Float64(x1 + Float64(t_5 + t_2))
	t_8 = Float64(Float64(x1 * x1) * Float64(Float64(4.0 * Float64(Float64(Float64(Float64(2.0 * x2) + t_4) - x1) / t_3)) - 6.0))
	tmp = 0.0
	if (x1 <= -5.5e+102)
		tmp = Float64(x1 + Float64(9.0 + Float64(x1 + Float64(-12.0 * Float64(x1 * x2)))));
	elseif (x1 <= -760.0)
		tmp = Float64(x1 + Float64(t_5 + Float64(x1 + Float64(t_1 + Float64(t_6 + Float64(t_3 * Float64(Float64(x1 * 2.0) + t_8)))))));
	elseif (x1 <= -2.4e-133)
		tmp = t_7;
	elseif (x1 <= 1.3e-166)
		tmp = Float64(x1 + Float64(t_5 + Float64(x1 + Float64(4.0 * Float64(-3.0 * Float64(x1 * x2))))));
	elseif (x1 <= 9.0)
		tmp = t_7;
	elseif (x1 <= 5e+102)
		tmp = Float64(x1 + Float64(9.0 + Float64(x1 + Float64(t_1 + Float64(t_6 + Float64(t_3 * Float64(t_8 + Float64(Float64(x1 * 2.0) + t_0))))))));
	elseif (x1 <= 1.35e+154)
		tmp = Float64(x1 + Float64(t_5 + Float64(x1 + Float64(t_1 + t_0))));
	else
		tmp = Float64(x1 + Float64(9.0 + t_2));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x1, x2)
	t_0 = -2.0 * ((4.0 * x2) - 3.0);
	t_1 = x1 * (x1 * x1);
	t_2 = x1 + (4.0 * (x1 * (x2 * ((2.0 * x2) - 3.0))));
	t_3 = (x1 * x1) + 1.0;
	t_4 = x1 * (x1 * 3.0);
	t_5 = 3.0 * (((t_4 - (2.0 * x2)) - x1) / t_3);
	t_6 = t_4 * ((2.0 * x2) - x1);
	t_7 = x1 + (t_5 + t_2);
	t_8 = (x1 * x1) * ((4.0 * ((((2.0 * x2) + t_4) - x1) / t_3)) - 6.0);
	tmp = 0.0;
	if (x1 <= -5.5e+102)
		tmp = x1 + (9.0 + (x1 + (-12.0 * (x1 * x2))));
	elseif (x1 <= -760.0)
		tmp = x1 + (t_5 + (x1 + (t_1 + (t_6 + (t_3 * ((x1 * 2.0) + t_8))))));
	elseif (x1 <= -2.4e-133)
		tmp = t_7;
	elseif (x1 <= 1.3e-166)
		tmp = x1 + (t_5 + (x1 + (4.0 * (-3.0 * (x1 * x2)))));
	elseif (x1 <= 9.0)
		tmp = t_7;
	elseif (x1 <= 5e+102)
		tmp = x1 + (9.0 + (x1 + (t_1 + (t_6 + (t_3 * (t_8 + ((x1 * 2.0) + t_0)))))));
	elseif (x1 <= 1.35e+154)
		tmp = x1 + (t_5 + (x1 + (t_1 + t_0)));
	else
		tmp = x1 + (9.0 + t_2);
	end
	tmp_2 = tmp;
end

Wolfram

code[x1_, x2_] := Block[{t$95$0 = N[(-2.0 * N[(N[(4.0 * x2), $MachinePrecision] - 3.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(x1 * N[(x1 * x1), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(x1 + N[(4.0 * N[(x1 * N[(x2 * N[(N[(2.0 * x2), $MachinePrecision] - 3.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(N[(x1 * x1), $MachinePrecision] + 1.0), $MachinePrecision]}, Block[{t$95$4 = N[(x1 * N[(x1 * 3.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$5 = N[(3.0 * N[(N[(N[(t$95$4 - N[(2.0 * x2), $MachinePrecision]), $MachinePrecision] - x1), $MachinePrecision] / t$95$3), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$6 = N[(t$95$4 * N[(N[(2.0 * x2), $MachinePrecision] - x1), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$7 = N[(x1 + N[(t$95$5 + t$95$2), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$8 = N[(N[(x1 * x1), $MachinePrecision] * N[(N[(4.0 * N[(N[(N[(N[(2.0 * x2), $MachinePrecision] + t$95$4), $MachinePrecision] - x1), $MachinePrecision] / t$95$3), $MachinePrecision]), $MachinePrecision] - 6.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x1, -5.5e+102], N[(x1 + N[(9.0 + N[(x1 + N[(-12.0 * N[(x1 * x2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, -760.0], N[(x1 + N[(t$95$5 + N[(x1 + N[(t$95$1 + N[(t$95$6 + N[(t$95$3 * N[(N[(x1 * 2.0), $MachinePrecision] + t$95$8), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, -2.4e-133], t$95$7, If[LessEqual[x1, 1.3e-166], N[(x1 + N[(t$95$5 + N[(x1 + N[(4.0 * N[(-3.0 * N[(x1 * x2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, 9.0], t$95$7, If[LessEqual[x1, 5e+102], N[(x1 + N[(9.0 + N[(x1 + N[(t$95$1 + N[(t$95$6 + N[(t$95$3 * N[(t$95$8 + N[(N[(x1 * 2.0), $MachinePrecision] + t$95$0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, 1.35e+154], N[(x1 + N[(t$95$5 + N[(x1 + N[(t$95$1 + t$95$0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x1 + N[(9.0 + t$95$2), $MachinePrecision]), $MachinePrecision]]]]]]]]]]]]]]]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 16: 70.5% accurate, 1.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := x1 \cdot x1 + 1\\ t_1 := x1 \cdot \left(x1 \cdot x1\right)\\ t_2 := x1 \cdot \left(x1 \cdot 3\right)\\ t_3 := 3 \cdot \frac{\left(t_2 - 2 \cdot x2\right) - x1}{t_0}\\ t_4 := x1 + 4 \cdot \left(x1 \cdot \left(x2 \cdot \left(2 \cdot x2 - 3\right)\right)\right)\\ t_5 := x1 + \left(t_3 + t_4\right)\\ t_6 := -2 \cdot \left(4 \cdot x2 - 3\right)\\ t_7 := x1 + \left(9 + \left(x1 + \left(t_1 + \left(t_2 \cdot \left(2 \cdot x2 - x1\right) + t_0 \cdot \left(\left(x1 \cdot x1\right) \cdot \left(4 \cdot \frac{\left(2 \cdot x2 + t_2\right) - x1}{t_0} - 6\right) + \left(x1 \cdot 2 + t_6\right)\right)\right)\right)\right)\right)\\ \mathbf{if}\;x1 \leq -5.5 \cdot 10^{+102}:\\ \;\;\;\;x1 + \left(9 + \left(x1 + -12 \cdot \left(x1 \cdot x2\right)\right)\right)\\ \mathbf{elif}\;x1 \leq -38:\\ \;\;\;\;t_7\\ \mathbf{elif}\;x1 \leq -2.4 \cdot 10^{-133}:\\ \;\;\;\;t_5\\ \mathbf{elif}\;x1 \leq 3.3 \cdot 10^{-167}:\\ \;\;\;\;x1 + \left(t_3 + \left(x1 + 4 \cdot \left(-3 \cdot \left(x1 \cdot x2\right)\right)\right)\right)\\ \mathbf{elif}\;x1 \leq 9:\\ \;\;\;\;t_5\\ \mathbf{elif}\;x1 \leq 4.8 \cdot 10^{+102}:\\ \;\;\;\;t_7\\ \mathbf{elif}\;x1 \leq 1.35 \cdot 10^{+154}:\\ \;\;\;\;x1 + \left(t_3 + \left(x1 + \left(t_1 + t_6\right)\right)\right)\\ \mathbf{else}:\\ \;\;\;\;x1 + \left(9 + t_4\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x1 x2)
 :precision binary64
 (let* ((t_0 (+ (* x1 x1) 1.0))
        (t_1 (* x1 (* x1 x1)))
        (t_2 (* x1 (* x1 3.0)))
        (t_3 (* 3.0 (/ (- (- t_2 (* 2.0 x2)) x1) t_0)))
        (t_4 (+ x1 (* 4.0 (* x1 (* x2 (- (* 2.0 x2) 3.0))))))
        (t_5 (+ x1 (+ t_3 t_4)))
        (t_6 (* -2.0 (- (* 4.0 x2) 3.0)))
        (t_7
         (+
          x1
          (+
           9.0
           (+
            x1
            (+
             t_1
             (+
              (* t_2 (- (* 2.0 x2) x1))
              (*
               t_0
               (+
                (* (* x1 x1) (- (* 4.0 (/ (- (+ (* 2.0 x2) t_2) x1) t_0)) 6.0))
                (+ (* x1 2.0) t_6))))))))))
   (if (<= x1 -5.5e+102)
     (+ x1 (+ 9.0 (+ x1 (* -12.0 (* x1 x2)))))
     (if (<= x1 -38.0)
       t_7
       (if (<= x1 -2.4e-133)
         t_5
         (if (<= x1 3.3e-167)
           (+ x1 (+ t_3 (+ x1 (* 4.0 (* -3.0 (* x1 x2))))))
           (if (<= x1 9.0)
             t_5
             (if (<= x1 4.8e+102)
               t_7
               (if (<= x1 1.35e+154)
                 (+ x1 (+ t_3 (+ x1 (+ t_1 t_6))))
                 (+ x1 (+ 9.0 t_4)))))))))))

C

double code(double x1, double x2) {
	double t_0 = (x1 * x1) + 1.0;
	double t_1 = x1 * (x1 * x1);
	double t_2 = x1 * (x1 * 3.0);
	double t_3 = 3.0 * (((t_2 - (2.0 * x2)) - x1) / t_0);
	double t_4 = x1 + (4.0 * (x1 * (x2 * ((2.0 * x2) - 3.0))));
	double t_5 = x1 + (t_3 + t_4);
	double t_6 = -2.0 * ((4.0 * x2) - 3.0);
	double t_7 = x1 + (9.0 + (x1 + (t_1 + ((t_2 * ((2.0 * x2) - x1)) + (t_0 * (((x1 * x1) * ((4.0 * ((((2.0 * x2) + t_2) - x1) / t_0)) - 6.0)) + ((x1 * 2.0) + t_6)))))));
	double tmp;
	if (x1 <= -5.5e+102) {
		tmp = x1 + (9.0 + (x1 + (-12.0 * (x1 * x2))));
	} else if (x1 <= -38.0) {
		tmp = t_7;
	} else if (x1 <= -2.4e-133) {
		tmp = t_5;
	} else if (x1 <= 3.3e-167) {
		tmp = x1 + (t_3 + (x1 + (4.0 * (-3.0 * (x1 * x2)))));
	} else if (x1 <= 9.0) {
		tmp = t_5;
	} else if (x1 <= 4.8e+102) {
		tmp = t_7;
	} else if (x1 <= 1.35e+154) {
		tmp = x1 + (t_3 + (x1 + (t_1 + t_6)));
	} else {
		tmp = x1 + (9.0 + t_4);
	}
	return tmp;
}

Fortran

real(8) function code(x1, x2)
    real(8), intent (in) :: x1
    real(8), intent (in) :: x2
    real(8) :: t_0
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: t_3
    real(8) :: t_4
    real(8) :: t_5
    real(8) :: t_6
    real(8) :: t_7
    real(8) :: tmp
    t_0 = (x1 * x1) + 1.0d0
    t_1 = x1 * (x1 * x1)
    t_2 = x1 * (x1 * 3.0d0)
    t_3 = 3.0d0 * (((t_2 - (2.0d0 * x2)) - x1) / t_0)
    t_4 = x1 + (4.0d0 * (x1 * (x2 * ((2.0d0 * x2) - 3.0d0))))
    t_5 = x1 + (t_3 + t_4)
    t_6 = (-2.0d0) * ((4.0d0 * x2) - 3.0d0)
    t_7 = x1 + (9.0d0 + (x1 + (t_1 + ((t_2 * ((2.0d0 * x2) - x1)) + (t_0 * (((x1 * x1) * ((4.0d0 * ((((2.0d0 * x2) + t_2) - x1) / t_0)) - 6.0d0)) + ((x1 * 2.0d0) + t_6)))))))
    if (x1 <= (-5.5d+102)) then
        tmp = x1 + (9.0d0 + (x1 + ((-12.0d0) * (x1 * x2))))
    else if (x1 <= (-38.0d0)) then
        tmp = t_7
    else if (x1 <= (-2.4d-133)) then
        tmp = t_5
    else if (x1 <= 3.3d-167) then
        tmp = x1 + (t_3 + (x1 + (4.0d0 * ((-3.0d0) * (x1 * x2)))))
    else if (x1 <= 9.0d0) then
        tmp = t_5
    else if (x1 <= 4.8d+102) then
        tmp = t_7
    else if (x1 <= 1.35d+154) then
        tmp = x1 + (t_3 + (x1 + (t_1 + t_6)))
    else
        tmp = x1 + (9.0d0 + t_4)
    end if
    code = tmp
end function

Java

public static double code(double x1, double x2) {
	double t_0 = (x1 * x1) + 1.0;
	double t_1 = x1 * (x1 * x1);
	double t_2 = x1 * (x1 * 3.0);
	double t_3 = 3.0 * (((t_2 - (2.0 * x2)) - x1) / t_0);
	double t_4 = x1 + (4.0 * (x1 * (x2 * ((2.0 * x2) - 3.0))));
	double t_5 = x1 + (t_3 + t_4);
	double t_6 = -2.0 * ((4.0 * x2) - 3.0);
	double t_7 = x1 + (9.0 + (x1 + (t_1 + ((t_2 * ((2.0 * x2) - x1)) + (t_0 * (((x1 * x1) * ((4.0 * ((((2.0 * x2) + t_2) - x1) / t_0)) - 6.0)) + ((x1 * 2.0) + t_6)))))));
	double tmp;
	if (x1 <= -5.5e+102) {
		tmp = x1 + (9.0 + (x1 + (-12.0 * (x1 * x2))));
	} else if (x1 <= -38.0) {
		tmp = t_7;
	} else if (x1 <= -2.4e-133) {
		tmp = t_5;
	} else if (x1 <= 3.3e-167) {
		tmp = x1 + (t_3 + (x1 + (4.0 * (-3.0 * (x1 * x2)))));
	} else if (x1 <= 9.0) {
		tmp = t_5;
	} else if (x1 <= 4.8e+102) {
		tmp = t_7;
	} else if (x1 <= 1.35e+154) {
		tmp = x1 + (t_3 + (x1 + (t_1 + t_6)));
	} else {
		tmp = x1 + (9.0 + t_4);
	}
	return tmp;
}

Python

def code(x1, x2):
	t_0 = (x1 * x1) + 1.0
	t_1 = x1 * (x1 * x1)
	t_2 = x1 * (x1 * 3.0)
	t_3 = 3.0 * (((t_2 - (2.0 * x2)) - x1) / t_0)
	t_4 = x1 + (4.0 * (x1 * (x2 * ((2.0 * x2) - 3.0))))
	t_5 = x1 + (t_3 + t_4)
	t_6 = -2.0 * ((4.0 * x2) - 3.0)
	t_7 = x1 + (9.0 + (x1 + (t_1 + ((t_2 * ((2.0 * x2) - x1)) + (t_0 * (((x1 * x1) * ((4.0 * ((((2.0 * x2) + t_2) - x1) / t_0)) - 6.0)) + ((x1 * 2.0) + t_6)))))))
	tmp = 0
	if x1 <= -5.5e+102:
		tmp = x1 + (9.0 + (x1 + (-12.0 * (x1 * x2))))
	elif x1 <= -38.0:
		tmp = t_7
	elif x1 <= -2.4e-133:
		tmp = t_5
	elif x1 <= 3.3e-167:
		tmp = x1 + (t_3 + (x1 + (4.0 * (-3.0 * (x1 * x2)))))
	elif x1 <= 9.0:
		tmp = t_5
	elif x1 <= 4.8e+102:
		tmp = t_7
	elif x1 <= 1.35e+154:
		tmp = x1 + (t_3 + (x1 + (t_1 + t_6)))
	else:
		tmp = x1 + (9.0 + t_4)
	return tmp

Julia

function code(x1, x2)
	t_0 = Float64(Float64(x1 * x1) + 1.0)
	t_1 = Float64(x1 * Float64(x1 * x1))
	t_2 = Float64(x1 * Float64(x1 * 3.0))
	t_3 = Float64(3.0 * Float64(Float64(Float64(t_2 - Float64(2.0 * x2)) - x1) / t_0))
	t_4 = Float64(x1 + Float64(4.0 * Float64(x1 * Float64(x2 * Float64(Float64(2.0 * x2) - 3.0)))))
	t_5 = Float64(x1 + Float64(t_3 + t_4))
	t_6 = Float64(-2.0 * Float64(Float64(4.0 * x2) - 3.0))
	t_7 = Float64(x1 + Float64(9.0 + Float64(x1 + Float64(t_1 + Float64(Float64(t_2 * Float64(Float64(2.0 * x2) - x1)) + Float64(t_0 * Float64(Float64(Float64(x1 * x1) * Float64(Float64(4.0 * Float64(Float64(Float64(Float64(2.0 * x2) + t_2) - x1) / t_0)) - 6.0)) + Float64(Float64(x1 * 2.0) + t_6))))))))
	tmp = 0.0
	if (x1 <= -5.5e+102)
		tmp = Float64(x1 + Float64(9.0 + Float64(x1 + Float64(-12.0 * Float64(x1 * x2)))));
	elseif (x1 <= -38.0)
		tmp = t_7;
	elseif (x1 <= -2.4e-133)
		tmp = t_5;
	elseif (x1 <= 3.3e-167)
		tmp = Float64(x1 + Float64(t_3 + Float64(x1 + Float64(4.0 * Float64(-3.0 * Float64(x1 * x2))))));
	elseif (x1 <= 9.0)
		tmp = t_5;
	elseif (x1 <= 4.8e+102)
		tmp = t_7;
	elseif (x1 <= 1.35e+154)
		tmp = Float64(x1 + Float64(t_3 + Float64(x1 + Float64(t_1 + t_6))));
	else
		tmp = Float64(x1 + Float64(9.0 + t_4));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x1, x2)
	t_0 = (x1 * x1) + 1.0;
	t_1 = x1 * (x1 * x1);
	t_2 = x1 * (x1 * 3.0);
	t_3 = 3.0 * (((t_2 - (2.0 * x2)) - x1) / t_0);
	t_4 = x1 + (4.0 * (x1 * (x2 * ((2.0 * x2) - 3.0))));
	t_5 = x1 + (t_3 + t_4);
	t_6 = -2.0 * ((4.0 * x2) - 3.0);
	t_7 = x1 + (9.0 + (x1 + (t_1 + ((t_2 * ((2.0 * x2) - x1)) + (t_0 * (((x1 * x1) * ((4.0 * ((((2.0 * x2) + t_2) - x1) / t_0)) - 6.0)) + ((x1 * 2.0) + t_6)))))));
	tmp = 0.0;
	if (x1 <= -5.5e+102)
		tmp = x1 + (9.0 + (x1 + (-12.0 * (x1 * x2))));
	elseif (x1 <= -38.0)
		tmp = t_7;
	elseif (x1 <= -2.4e-133)
		tmp = t_5;
	elseif (x1 <= 3.3e-167)
		tmp = x1 + (t_3 + (x1 + (4.0 * (-3.0 * (x1 * x2)))));
	elseif (x1 <= 9.0)
		tmp = t_5;
	elseif (x1 <= 4.8e+102)
		tmp = t_7;
	elseif (x1 <= 1.35e+154)
		tmp = x1 + (t_3 + (x1 + (t_1 + t_6)));
	else
		tmp = x1 + (9.0 + t_4);
	end
	tmp_2 = tmp;
end

Wolfram

code[x1_, x2_] := Block[{t$95$0 = N[(N[(x1 * x1), $MachinePrecision] + 1.0), $MachinePrecision]}, Block[{t$95$1 = N[(x1 * N[(x1 * x1), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(x1 * N[(x1 * 3.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(3.0 * N[(N[(N[(t$95$2 - N[(2.0 * x2), $MachinePrecision]), $MachinePrecision] - x1), $MachinePrecision] / t$95$0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$4 = N[(x1 + N[(4.0 * N[(x1 * N[(x2 * N[(N[(2.0 * x2), $MachinePrecision] - 3.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$5 = N[(x1 + N[(t$95$3 + t$95$4), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$6 = N[(-2.0 * N[(N[(4.0 * x2), $MachinePrecision] - 3.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$7 = N[(x1 + N[(9.0 + N[(x1 + N[(t$95$1 + N[(N[(t$95$2 * N[(N[(2.0 * x2), $MachinePrecision] - x1), $MachinePrecision]), $MachinePrecision] + N[(t$95$0 * N[(N[(N[(x1 * x1), $MachinePrecision] * N[(N[(4.0 * N[(N[(N[(N[(2.0 * x2), $MachinePrecision] + t$95$2), $MachinePrecision] - x1), $MachinePrecision] / t$95$0), $MachinePrecision]), $MachinePrecision] - 6.0), $MachinePrecision]), $MachinePrecision] + N[(N[(x1 * 2.0), $MachinePrecision] + t$95$6), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x1, -5.5e+102], N[(x1 + N[(9.0 + N[(x1 + N[(-12.0 * N[(x1 * x2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, -38.0], t$95$7, If[LessEqual[x1, -2.4e-133], t$95$5, If[LessEqual[x1, 3.3e-167], N[(x1 + N[(t$95$3 + N[(x1 + N[(4.0 * N[(-3.0 * N[(x1 * x2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, 9.0], t$95$5, If[LessEqual[x1, 4.8e+102], t$95$7, If[LessEqual[x1, 1.35e+154], N[(x1 + N[(t$95$3 + N[(x1 + N[(t$95$1 + t$95$6), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x1 + N[(9.0 + t$95$4), $MachinePrecision]), $MachinePrecision]]]]]]]]]]]]]]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 17: 60.4% accurate, 2.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := 3 \cdot \frac{\left(x1 \cdot \left(x1 \cdot 3\right) - 2 \cdot x2\right) - x1}{x1 \cdot x1 + 1}\\ t_1 := x2 \cdot \left(2 \cdot x2 - 3\right)\\ t_2 := x1 + 4 \cdot \left(x1 \cdot t_1\right)\\ \mathbf{if}\;x1 \leq -8.2 \cdot 10^{-209}:\\ \;\;\;\;x1 + \left(x1 \cdot \left(4 \cdot t_1 - 2\right) + x2 \cdot -6\right)\\ \mathbf{elif}\;x1 \leq 3.5 \cdot 10^{-167}:\\ \;\;\;\;x1 + \left(t_0 + \left(x1 + 4 \cdot \left(-3 \cdot \left(x1 \cdot x2\right)\right)\right)\right)\\ \mathbf{elif}\;x1 \leq 3.5 \cdot 10^{+90}:\\ \;\;\;\;x1 + \left(t_0 + t_2\right)\\ \mathbf{elif}\;x1 \leq 1.35 \cdot 10^{+154}:\\ \;\;\;\;x1 + \left(t_0 + \left(x1 + \left(x1 \cdot \left(x1 \cdot x1\right) + -2 \cdot \left(4 \cdot x2 - 3\right)\right)\right)\right)\\ \mathbf{else}:\\ \;\;\;\;x1 + \left(9 + t_2\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x1 x2)
 :precision binary64
 (let* ((t_0
         (* 3.0 (/ (- (- (* x1 (* x1 3.0)) (* 2.0 x2)) x1) (+ (* x1 x1) 1.0))))
        (t_1 (* x2 (- (* 2.0 x2) 3.0)))
        (t_2 (+ x1 (* 4.0 (* x1 t_1)))))
   (if (<= x1 -8.2e-209)
     (+ x1 (+ (* x1 (- (* 4.0 t_1) 2.0)) (* x2 -6.0)))
     (if (<= x1 3.5e-167)
       (+ x1 (+ t_0 (+ x1 (* 4.0 (* -3.0 (* x1 x2))))))
       (if (<= x1 3.5e+90)
         (+ x1 (+ t_0 t_2))
         (if (<= x1 1.35e+154)
           (+
            x1
            (+ t_0 (+ x1 (+ (* x1 (* x1 x1)) (* -2.0 (- (* 4.0 x2) 3.0))))))
           (+ x1 (+ 9.0 t_2))))))))

C

double code(double x1, double x2) {
	double t_0 = 3.0 * ((((x1 * (x1 * 3.0)) - (2.0 * x2)) - x1) / ((x1 * x1) + 1.0));
	double t_1 = x2 * ((2.0 * x2) - 3.0);
	double t_2 = x1 + (4.0 * (x1 * t_1));
	double tmp;
	if (x1 <= -8.2e-209) {
		tmp = x1 + ((x1 * ((4.0 * t_1) - 2.0)) + (x2 * -6.0));
	} else if (x1 <= 3.5e-167) {
		tmp = x1 + (t_0 + (x1 + (4.0 * (-3.0 * (x1 * x2)))));
	} else if (x1 <= 3.5e+90) {
		tmp = x1 + (t_0 + t_2);
	} else if (x1 <= 1.35e+154) {
		tmp = x1 + (t_0 + (x1 + ((x1 * (x1 * x1)) + (-2.0 * ((4.0 * x2) - 3.0)))));
	} else {
		tmp = x1 + (9.0 + t_2);
	}
	return tmp;
}

Fortran

real(8) function code(x1, x2)
    real(8), intent (in) :: x1
    real(8), intent (in) :: x2
    real(8) :: t_0
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_0 = 3.0d0 * ((((x1 * (x1 * 3.0d0)) - (2.0d0 * x2)) - x1) / ((x1 * x1) + 1.0d0))
    t_1 = x2 * ((2.0d0 * x2) - 3.0d0)
    t_2 = x1 + (4.0d0 * (x1 * t_1))
    if (x1 <= (-8.2d-209)) then
        tmp = x1 + ((x1 * ((4.0d0 * t_1) - 2.0d0)) + (x2 * (-6.0d0)))
    else if (x1 <= 3.5d-167) then
        tmp = x1 + (t_0 + (x1 + (4.0d0 * ((-3.0d0) * (x1 * x2)))))
    else if (x1 <= 3.5d+90) then
        tmp = x1 + (t_0 + t_2)
    else if (x1 <= 1.35d+154) then
        tmp = x1 + (t_0 + (x1 + ((x1 * (x1 * x1)) + ((-2.0d0) * ((4.0d0 * x2) - 3.0d0)))))
    else
        tmp = x1 + (9.0d0 + t_2)
    end if
    code = tmp
end function

Java

public static double code(double x1, double x2) {
	double t_0 = 3.0 * ((((x1 * (x1 * 3.0)) - (2.0 * x2)) - x1) / ((x1 * x1) + 1.0));
	double t_1 = x2 * ((2.0 * x2) - 3.0);
	double t_2 = x1 + (4.0 * (x1 * t_1));
	double tmp;
	if (x1 <= -8.2e-209) {
		tmp = x1 + ((x1 * ((4.0 * t_1) - 2.0)) + (x2 * -6.0));
	} else if (x1 <= 3.5e-167) {
		tmp = x1 + (t_0 + (x1 + (4.0 * (-3.0 * (x1 * x2)))));
	} else if (x1 <= 3.5e+90) {
		tmp = x1 + (t_0 + t_2);
	} else if (x1 <= 1.35e+154) {
		tmp = x1 + (t_0 + (x1 + ((x1 * (x1 * x1)) + (-2.0 * ((4.0 * x2) - 3.0)))));
	} else {
		tmp = x1 + (9.0 + t_2);
	}
	return tmp;
}

Python

def code(x1, x2):
	t_0 = 3.0 * ((((x1 * (x1 * 3.0)) - (2.0 * x2)) - x1) / ((x1 * x1) + 1.0))
	t_1 = x2 * ((2.0 * x2) - 3.0)
	t_2 = x1 + (4.0 * (x1 * t_1))
	tmp = 0
	if x1 <= -8.2e-209:
		tmp = x1 + ((x1 * ((4.0 * t_1) - 2.0)) + (x2 * -6.0))
	elif x1 <= 3.5e-167:
		tmp = x1 + (t_0 + (x1 + (4.0 * (-3.0 * (x1 * x2)))))
	elif x1 <= 3.5e+90:
		tmp = x1 + (t_0 + t_2)
	elif x1 <= 1.35e+154:
		tmp = x1 + (t_0 + (x1 + ((x1 * (x1 * x1)) + (-2.0 * ((4.0 * x2) - 3.0)))))
	else:
		tmp = x1 + (9.0 + t_2)
	return tmp

Julia

function code(x1, x2)
	t_0 = Float64(3.0 * Float64(Float64(Float64(Float64(x1 * Float64(x1 * 3.0)) - Float64(2.0 * x2)) - x1) / Float64(Float64(x1 * x1) + 1.0)))
	t_1 = Float64(x2 * Float64(Float64(2.0 * x2) - 3.0))
	t_2 = Float64(x1 + Float64(4.0 * Float64(x1 * t_1)))
	tmp = 0.0
	if (x1 <= -8.2e-209)
		tmp = Float64(x1 + Float64(Float64(x1 * Float64(Float64(4.0 * t_1) - 2.0)) + Float64(x2 * -6.0)));
	elseif (x1 <= 3.5e-167)
		tmp = Float64(x1 + Float64(t_0 + Float64(x1 + Float64(4.0 * Float64(-3.0 * Float64(x1 * x2))))));
	elseif (x1 <= 3.5e+90)
		tmp = Float64(x1 + Float64(t_0 + t_2));
	elseif (x1 <= 1.35e+154)
		tmp = Float64(x1 + Float64(t_0 + Float64(x1 + Float64(Float64(x1 * Float64(x1 * x1)) + Float64(-2.0 * Float64(Float64(4.0 * x2) - 3.0))))));
	else
		tmp = Float64(x1 + Float64(9.0 + t_2));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x1, x2)
	t_0 = 3.0 * ((((x1 * (x1 * 3.0)) - (2.0 * x2)) - x1) / ((x1 * x1) + 1.0));
	t_1 = x2 * ((2.0 * x2) - 3.0);
	t_2 = x1 + (4.0 * (x1 * t_1));
	tmp = 0.0;
	if (x1 <= -8.2e-209)
		tmp = x1 + ((x1 * ((4.0 * t_1) - 2.0)) + (x2 * -6.0));
	elseif (x1 <= 3.5e-167)
		tmp = x1 + (t_0 + (x1 + (4.0 * (-3.0 * (x1 * x2)))));
	elseif (x1 <= 3.5e+90)
		tmp = x1 + (t_0 + t_2);
	elseif (x1 <= 1.35e+154)
		tmp = x1 + (t_0 + (x1 + ((x1 * (x1 * x1)) + (-2.0 * ((4.0 * x2) - 3.0)))));
	else
		tmp = x1 + (9.0 + t_2);
	end
	tmp_2 = tmp;
end

Wolfram

code[x1_, x2_] := Block[{t$95$0 = N[(3.0 * N[(N[(N[(N[(x1 * N[(x1 * 3.0), $MachinePrecision]), $MachinePrecision] - N[(2.0 * x2), $MachinePrecision]), $MachinePrecision] - x1), $MachinePrecision] / N[(N[(x1 * x1), $MachinePrecision] + 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(x2 * N[(N[(2.0 * x2), $MachinePrecision] - 3.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(x1 + N[(4.0 * N[(x1 * t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x1, -8.2e-209], N[(x1 + N[(N[(x1 * N[(N[(4.0 * t$95$1), $MachinePrecision] - 2.0), $MachinePrecision]), $MachinePrecision] + N[(x2 * -6.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, 3.5e-167], N[(x1 + N[(t$95$0 + N[(x1 + N[(4.0 * N[(-3.0 * N[(x1 * x2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, 3.5e+90], N[(x1 + N[(t$95$0 + t$95$2), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, 1.35e+154], N[(x1 + N[(t$95$0 + N[(x1 + N[(N[(x1 * N[(x1 * x1), $MachinePrecision]), $MachinePrecision] + N[(-2.0 * N[(N[(4.0 * x2), $MachinePrecision] - 3.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x1 + N[(9.0 + t$95$2), $MachinePrecision]), $MachinePrecision]]]]]]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 18: 58.3% accurate, 3.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := x1 + 4 \cdot \left(x1 \cdot \left(x2 \cdot \left(2 \cdot x2 - 3\right)\right)\right)\\ \mathbf{if}\;x1 \leq -2.1 \cdot 10^{+89}:\\ \;\;\;\;x1 + \left(9 + \left(x1 + -12 \cdot \left(x1 \cdot x2\right)\right)\right)\\ \mathbf{elif}\;x1 \leq 2 \cdot 10^{+141}:\\ \;\;\;\;x1 + \left(3 \cdot \frac{\left(x1 \cdot \left(x1 \cdot 3\right) - 2 \cdot x2\right) - x1}{x1 \cdot x1 + 1} + t_0\right)\\ \mathbf{else}:\\ \;\;\;\;x1 + \left(9 + t_0\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x1 x2)
 :precision binary64
 (let* ((t_0 (+ x1 (* 4.0 (* x1 (* x2 (- (* 2.0 x2) 3.0)))))))
   (if (<= x1 -2.1e+89)
     (+ x1 (+ 9.0 (+ x1 (* -12.0 (* x1 x2)))))
     (if (<= x1 2e+141)
       (+
        x1
        (+
         (* 3.0 (/ (- (- (* x1 (* x1 3.0)) (* 2.0 x2)) x1) (+ (* x1 x1) 1.0)))
         t_0))
       (+ x1 (+ 9.0 t_0))))))

C

double code(double x1, double x2) {
	double t_0 = x1 + (4.0 * (x1 * (x2 * ((2.0 * x2) - 3.0))));
	double tmp;
	if (x1 <= -2.1e+89) {
		tmp = x1 + (9.0 + (x1 + (-12.0 * (x1 * x2))));
	} else if (x1 <= 2e+141) {
		tmp = x1 + ((3.0 * ((((x1 * (x1 * 3.0)) - (2.0 * x2)) - x1) / ((x1 * x1) + 1.0))) + t_0);
	} else {
		tmp = x1 + (9.0 + t_0);
	}
	return tmp;
}

Fortran

real(8) function code(x1, x2)
    real(8), intent (in) :: x1
    real(8), intent (in) :: x2
    real(8) :: t_0
    real(8) :: tmp
    t_0 = x1 + (4.0d0 * (x1 * (x2 * ((2.0d0 * x2) - 3.0d0))))
    if (x1 <= (-2.1d+89)) then
        tmp = x1 + (9.0d0 + (x1 + ((-12.0d0) * (x1 * x2))))
    else if (x1 <= 2d+141) then
        tmp = x1 + ((3.0d0 * ((((x1 * (x1 * 3.0d0)) - (2.0d0 * x2)) - x1) / ((x1 * x1) + 1.0d0))) + t_0)
    else
        tmp = x1 + (9.0d0 + t_0)
    end if
    code = tmp
end function

Java

public static double code(double x1, double x2) {
	double t_0 = x1 + (4.0 * (x1 * (x2 * ((2.0 * x2) - 3.0))));
	double tmp;
	if (x1 <= -2.1e+89) {
		tmp = x1 + (9.0 + (x1 + (-12.0 * (x1 * x2))));
	} else if (x1 <= 2e+141) {
		tmp = x1 + ((3.0 * ((((x1 * (x1 * 3.0)) - (2.0 * x2)) - x1) / ((x1 * x1) + 1.0))) + t_0);
	} else {
		tmp = x1 + (9.0 + t_0);
	}
	return tmp;
}

Python

def code(x1, x2):
	t_0 = x1 + (4.0 * (x1 * (x2 * ((2.0 * x2) - 3.0))))
	tmp = 0
	if x1 <= -2.1e+89:
		tmp = x1 + (9.0 + (x1 + (-12.0 * (x1 * x2))))
	elif x1 <= 2e+141:
		tmp = x1 + ((3.0 * ((((x1 * (x1 * 3.0)) - (2.0 * x2)) - x1) / ((x1 * x1) + 1.0))) + t_0)
	else:
		tmp = x1 + (9.0 + t_0)
	return tmp

Julia

function code(x1, x2)
	t_0 = Float64(x1 + Float64(4.0 * Float64(x1 * Float64(x2 * Float64(Float64(2.0 * x2) - 3.0)))))
	tmp = 0.0
	if (x1 <= -2.1e+89)
		tmp = Float64(x1 + Float64(9.0 + Float64(x1 + Float64(-12.0 * Float64(x1 * x2)))));
	elseif (x1 <= 2e+141)
		tmp = Float64(x1 + Float64(Float64(3.0 * Float64(Float64(Float64(Float64(x1 * Float64(x1 * 3.0)) - Float64(2.0 * x2)) - x1) / Float64(Float64(x1 * x1) + 1.0))) + t_0));
	else
		tmp = Float64(x1 + Float64(9.0 + t_0));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x1, x2)
	t_0 = x1 + (4.0 * (x1 * (x2 * ((2.0 * x2) - 3.0))));
	tmp = 0.0;
	if (x1 <= -2.1e+89)
		tmp = x1 + (9.0 + (x1 + (-12.0 * (x1 * x2))));
	elseif (x1 <= 2e+141)
		tmp = x1 + ((3.0 * ((((x1 * (x1 * 3.0)) - (2.0 * x2)) - x1) / ((x1 * x1) + 1.0))) + t_0);
	else
		tmp = x1 + (9.0 + t_0);
	end
	tmp_2 = tmp;
end

Wolfram

code[x1_, x2_] := Block[{t$95$0 = N[(x1 + N[(4.0 * N[(x1 * N[(x2 * N[(N[(2.0 * x2), $MachinePrecision] - 3.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x1, -2.1e+89], N[(x1 + N[(9.0 + N[(x1 + N[(-12.0 * N[(x1 * x2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, 2e+141], N[(x1 + N[(N[(3.0 * N[(N[(N[(N[(x1 * N[(x1 * 3.0), $MachinePrecision]), $MachinePrecision] - N[(2.0 * x2), $MachinePrecision]), $MachinePrecision] - x1), $MachinePrecision] / N[(N[(x1 * x1), $MachinePrecision] + 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + t$95$0), $MachinePrecision]), $MachinePrecision], N[(x1 + N[(9.0 + t$95$0), $MachinePrecision]), $MachinePrecision]]]]

Derivation

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1. Add Preprocessing

Alternative 19: 57.1% accurate, 3.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x1 \leq -2.5 \cdot 10^{-207} \lor \neg \left(x1 \leq 9 \cdot 10^{-165}\right):\\ \;\;\;\;x1 + \left(x1 \cdot \left(4 \cdot \left(x2 \cdot \left(2 \cdot x2 - 3\right)\right) - 2\right) + x2 \cdot -6\right)\\ \mathbf{else}:\\ \;\;\;\;x1 + \left(3 \cdot \frac{\left(x1 \cdot \left(x1 \cdot 3\right) - 2 \cdot x2\right) - x1}{x1 \cdot x1 + 1} + \left(x1 + 4 \cdot \left(-3 \cdot \left(x1 \cdot x2\right)\right)\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x1 x2)
 :precision binary64
 (if (or (<= x1 -2.5e-207) (not (<= x1 9e-165)))
   (+ x1 (+ (* x1 (- (* 4.0 (* x2 (- (* 2.0 x2) 3.0))) 2.0)) (* x2 -6.0)))
   (+
    x1
    (+
     (* 3.0 (/ (- (- (* x1 (* x1 3.0)) (* 2.0 x2)) x1) (+ (* x1 x1) 1.0)))
     (+ x1 (* 4.0 (* -3.0 (* x1 x2))))))))

C

double code(double x1, double x2) {
	double tmp;
	if ((x1 <= -2.5e-207) || !(x1 <= 9e-165)) {
		tmp = x1 + ((x1 * ((4.0 * (x2 * ((2.0 * x2) - 3.0))) - 2.0)) + (x2 * -6.0));
	} else {
		tmp = x1 + ((3.0 * ((((x1 * (x1 * 3.0)) - (2.0 * x2)) - x1) / ((x1 * x1) + 1.0))) + (x1 + (4.0 * (-3.0 * (x1 * x2)))));
	}
	return tmp;
}

Fortran

real(8) function code(x1, x2)
    real(8), intent (in) :: x1
    real(8), intent (in) :: x2
    real(8) :: tmp
    if ((x1 <= (-2.5d-207)) .or. (.not. (x1 <= 9d-165))) then
        tmp = x1 + ((x1 * ((4.0d0 * (x2 * ((2.0d0 * x2) - 3.0d0))) - 2.0d0)) + (x2 * (-6.0d0)))
    else
        tmp = x1 + ((3.0d0 * ((((x1 * (x1 * 3.0d0)) - (2.0d0 * x2)) - x1) / ((x1 * x1) + 1.0d0))) + (x1 + (4.0d0 * ((-3.0d0) * (x1 * x2)))))
    end if
    code = tmp
end function

Java

public static double code(double x1, double x2) {
	double tmp;
	if ((x1 <= -2.5e-207) || !(x1 <= 9e-165)) {
		tmp = x1 + ((x1 * ((4.0 * (x2 * ((2.0 * x2) - 3.0))) - 2.0)) + (x2 * -6.0));
	} else {
		tmp = x1 + ((3.0 * ((((x1 * (x1 * 3.0)) - (2.0 * x2)) - x1) / ((x1 * x1) + 1.0))) + (x1 + (4.0 * (-3.0 * (x1 * x2)))));
	}
	return tmp;
}

Python

def code(x1, x2):
	tmp = 0
	if (x1 <= -2.5e-207) or not (x1 <= 9e-165):
		tmp = x1 + ((x1 * ((4.0 * (x2 * ((2.0 * x2) - 3.0))) - 2.0)) + (x2 * -6.0))
	else:
		tmp = x1 + ((3.0 * ((((x1 * (x1 * 3.0)) - (2.0 * x2)) - x1) / ((x1 * x1) + 1.0))) + (x1 + (4.0 * (-3.0 * (x1 * x2)))))
	return tmp

Julia

function code(x1, x2)
	tmp = 0.0
	if ((x1 <= -2.5e-207) || !(x1 <= 9e-165))
		tmp = Float64(x1 + Float64(Float64(x1 * Float64(Float64(4.0 * Float64(x2 * Float64(Float64(2.0 * x2) - 3.0))) - 2.0)) + Float64(x2 * -6.0)));
	else
		tmp = Float64(x1 + Float64(Float64(3.0 * Float64(Float64(Float64(Float64(x1 * Float64(x1 * 3.0)) - Float64(2.0 * x2)) - x1) / Float64(Float64(x1 * x1) + 1.0))) + Float64(x1 + Float64(4.0 * Float64(-3.0 * Float64(x1 * x2))))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x1, x2)
	tmp = 0.0;
	if ((x1 <= -2.5e-207) || ~((x1 <= 9e-165)))
		tmp = x1 + ((x1 * ((4.0 * (x2 * ((2.0 * x2) - 3.0))) - 2.0)) + (x2 * -6.0));
	else
		tmp = x1 + ((3.0 * ((((x1 * (x1 * 3.0)) - (2.0 * x2)) - x1) / ((x1 * x1) + 1.0))) + (x1 + (4.0 * (-3.0 * (x1 * x2)))));
	end
	tmp_2 = tmp;
end

Wolfram

code[x1_, x2_] := If[Or[LessEqual[x1, -2.5e-207], N[Not[LessEqual[x1, 9e-165]], $MachinePrecision]], N[(x1 + N[(N[(x1 * N[(N[(4.0 * N[(x2 * N[(N[(2.0 * x2), $MachinePrecision] - 3.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - 2.0), $MachinePrecision]), $MachinePrecision] + N[(x2 * -6.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x1 + N[(N[(3.0 * N[(N[(N[(N[(x1 * N[(x1 * 3.0), $MachinePrecision]), $MachinePrecision] - N[(2.0 * x2), $MachinePrecision]), $MachinePrecision] - x1), $MachinePrecision] / N[(N[(x1 * x1), $MachinePrecision] + 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(x1 + N[(4.0 * N[(-3.0 * N[(x1 * x2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 20: 55.1% accurate, 6.7× speedup

Math

\[\begin{array}{l} \\ x1 + \left(x1 \cdot \left(4 \cdot \left(x2 \cdot \left(2 \cdot x2 - 3\right)\right) - 2\right) + x2 \cdot -6\right) \end{array} \]

FPCore

(FPCore (x1 x2)
 :precision binary64
 (+ x1 (+ (* x1 (- (* 4.0 (* x2 (- (* 2.0 x2) 3.0))) 2.0)) (* x2 -6.0))))

C

double code(double x1, double x2) {
	return x1 + ((x1 * ((4.0 * (x2 * ((2.0 * x2) - 3.0))) - 2.0)) + (x2 * -6.0));
}

Fortran

real(8) function code(x1, x2)
    real(8), intent (in) :: x1
    real(8), intent (in) :: x2
    code = x1 + ((x1 * ((4.0d0 * (x2 * ((2.0d0 * x2) - 3.0d0))) - 2.0d0)) + (x2 * (-6.0d0)))
end function

Java

public static double code(double x1, double x2) {
	return x1 + ((x1 * ((4.0 * (x2 * ((2.0 * x2) - 3.0))) - 2.0)) + (x2 * -6.0));
}

Python

def code(x1, x2):
	return x1 + ((x1 * ((4.0 * (x2 * ((2.0 * x2) - 3.0))) - 2.0)) + (x2 * -6.0))

Julia

function code(x1, x2)
	return Float64(x1 + Float64(Float64(x1 * Float64(Float64(4.0 * Float64(x2 * Float64(Float64(2.0 * x2) - 3.0))) - 2.0)) + Float64(x2 * -6.0)))
end

MATLAB

function tmp = code(x1, x2)
	tmp = x1 + ((x1 * ((4.0 * (x2 * ((2.0 * x2) - 3.0))) - 2.0)) + (x2 * -6.0));
end

Wolfram

code[x1_, x2_] := N[(x1 + N[(N[(x1 * N[(N[(4.0 * N[(x2 * N[(N[(2.0 * x2), $MachinePrecision] - 3.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - 2.0), $MachinePrecision]), $MachinePrecision] + N[(x2 * -6.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 21: 33.9% accurate, 8.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x1 \leq -2.8 \cdot 10^{-55} \lor \neg \left(x1 \leq 1.35\right):\\ \;\;\;\;x1 + \left(9 + \left(x1 + -12 \cdot \left(x1 \cdot x2\right)\right)\right)\\ \mathbf{else}:\\ \;\;\;\;x2 \cdot -6\\ \end{array} \end{array} \]

FPCore

(FPCore (x1 x2)
 :precision binary64
 (if (or (<= x1 -2.8e-55) (not (<= x1 1.35)))
   (+ x1 (+ 9.0 (+ x1 (* -12.0 (* x1 x2)))))
   (* x2 -6.0)))

C

double code(double x1, double x2) {
	double tmp;
	if ((x1 <= -2.8e-55) || !(x1 <= 1.35)) {
		tmp = x1 + (9.0 + (x1 + (-12.0 * (x1 * x2))));
	} else {
		tmp = x2 * -6.0;
	}
	return tmp;
}

Fortran

real(8) function code(x1, x2)
    real(8), intent (in) :: x1
    real(8), intent (in) :: x2
    real(8) :: tmp
    if ((x1 <= (-2.8d-55)) .or. (.not. (x1 <= 1.35d0))) then
        tmp = x1 + (9.0d0 + (x1 + ((-12.0d0) * (x1 * x2))))
    else
        tmp = x2 * (-6.0d0)
    end if
    code = tmp
end function

Java

public static double code(double x1, double x2) {
	double tmp;
	if ((x1 <= -2.8e-55) || !(x1 <= 1.35)) {
		tmp = x1 + (9.0 + (x1 + (-12.0 * (x1 * x2))));
	} else {
		tmp = x2 * -6.0;
	}
	return tmp;
}

Python

def code(x1, x2):
	tmp = 0
	if (x1 <= -2.8e-55) or not (x1 <= 1.35):
		tmp = x1 + (9.0 + (x1 + (-12.0 * (x1 * x2))))
	else:
		tmp = x2 * -6.0
	return tmp

Julia

function code(x1, x2)
	tmp = 0.0
	if ((x1 <= -2.8e-55) || !(x1 <= 1.35))
		tmp = Float64(x1 + Float64(9.0 + Float64(x1 + Float64(-12.0 * Float64(x1 * x2)))));
	else
		tmp = Float64(x2 * -6.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x1, x2)
	tmp = 0.0;
	if ((x1 <= -2.8e-55) || ~((x1 <= 1.35)))
		tmp = x1 + (9.0 + (x1 + (-12.0 * (x1 * x2))));
	else
		tmp = x2 * -6.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x1_, x2_] := If[Or[LessEqual[x1, -2.8e-55], N[Not[LessEqual[x1, 1.35]], $MachinePrecision]], N[(x1 + N[(9.0 + N[(x1 + N[(-12.0 * N[(x1 * x2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x2 * -6.0), $MachinePrecision]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 22: 27.3% accurate, 25.4× speedup

Math

\[\begin{array}{l} \\ x1 + x2 \cdot -6 \end{array} \]

FPCore

(FPCore (x1 x2) :precision binary64 (+ x1 (* x2 -6.0)))

C

double code(double x1, double x2) {
	return x1 + (x2 * -6.0);
}

Fortran

real(8) function code(x1, x2)
    real(8), intent (in) :: x1
    real(8), intent (in) :: x2
    code = x1 + (x2 * (-6.0d0))
end function

Java

public static double code(double x1, double x2) {
	return x1 + (x2 * -6.0);
}

Python

def code(x1, x2):
	return x1 + (x2 * -6.0)

Julia

function code(x1, x2)
	return Float64(x1 + Float64(x2 * -6.0))
end

MATLAB

function tmp = code(x1, x2)
	tmp = x1 + (x2 * -6.0);
end

Wolfram

code[x1_, x2_] := N[(x1 + N[(x2 * -6.0), $MachinePrecision]), $MachinePrecision]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 23: 27.2% accurate, 42.3× speedup

Math

\[\begin{array}{l} \\ x2 \cdot -6 \end{array} \]

FPCore

(FPCore (x1 x2) :precision binary64 (* x2 -6.0))

C

double code(double x1, double x2) {
	return x2 * -6.0;
}

Fortran

real(8) function code(x1, x2)
    real(8), intent (in) :: x1
    real(8), intent (in) :: x2
    code = x2 * (-6.0d0)
end function

Java

public static double code(double x1, double x2) {
	return x2 * -6.0;
}

Python

def code(x1, x2):
	return x2 * -6.0

Julia

function code(x1, x2)
	return Float64(x2 * -6.0)
end

MATLAB

function tmp = code(x1, x2)
	tmp = x2 * -6.0;
end

Wolfram

code[x1_, x2_] := N[(x2 * -6.0), $MachinePrecision]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 24: 3.3% accurate, 127.0× speedup

Math

\[\begin{array}{l} \\ x1 \end{array} \]

FPCore

(FPCore (x1 x2) :precision binary64 x1)

C

double code(double x1, double x2) {
	return x1;
}

Fortran

real(8) function code(x1, x2)
    real(8), intent (in) :: x1
    real(8), intent (in) :: x2
    code = x1
end function

Java

public static double code(double x1, double x2) {
	return x1;
}

Python

def code(x1, x2):
	return x1

Julia

function code(x1, x2)
	return x1
end

MATLAB

function tmp = code(x1, x2)
	tmp = x1;
end

Wolfram

code[x1_, x2_] := x1

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Reproduce

herbie shell --seed 2023347 
(FPCore (x1 x2)
  :name "Rosa's FloatVsDoubleBenchmark"
  :precision binary64
  (+ x1 (+ (+ (+ (+ (* (+ (* (* (* 2.0 x1) (/ (- (+ (* (* 3.0 x1) x1) (* 2.0 x2)) x1) (+ (* x1 x1) 1.0))) (- (/ (- (+ (* (* 3.0 x1) x1) (* 2.0 x2)) x1) (+ (* x1 x1) 1.0)) 3.0)) (* (* x1 x1) (- (* 4.0 (/ (- (+ (* (* 3.0 x1) x1) (* 2.0 x2)) x1) (+ (* x1 x1) 1.0))) 6.0))) (+ (* x1 x1) 1.0)) (* (* (* 3.0 x1) x1) (/ (- (+ (* (* 3.0 x1) x1) (* 2.0 x2)) x1) (+ (* x1 x1) 1.0)))) (* (* x1 x1) x1)) x1) (* 3.0 (/ (- (- (* (* 3.0 x1) x1) (* 2.0 x2)) x1) (+ (* x1 x1) 1.0))))))