Rosa's FloatVsDoubleBenchmark

Percentage Accurate: 70.5% → 99.4%

Time: 54.6s

Alternatives: 21

Speedup: 1.4×

• 47.3% 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: 70.5% 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

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{\mathsf{fma}\left(x1, x1 \cdot 3, 2 \cdot x2\right) - x1}{\mathsf{fma}\left(x1, x1, 1\right)}\\ t_1 := x1 \cdot \left(x1 \cdot 3\right)\\ t_2 := x1 \cdot x1 + 1\\ t_3 := \frac{\left(t_1 + 2 \cdot x2\right) - x1}{t_2}\\ t_4 := 3 \cdot \left(x1 \cdot x1\right)\\ \mathbf{if}\;x1 + \left(\left(x1 + \left(\left(t_2 \cdot \left(\left(\left(x1 \cdot 2\right) \cdot t_3\right) \cdot \left(t_3 - 3\right) + \left(x1 \cdot x1\right) \cdot \left(t_3 \cdot 4 - 6\right)\right) + t_1 \cdot t_3\right) + x1 \cdot \left(x1 \cdot x1\right)\right)\right) + 3 \cdot \frac{\left(t_1 - 2 \cdot x2\right) - x1}{t_2}\right) \leq \infty:\\ \;\;\;\;x1 + \mathsf{fma}\left(3, \frac{t_4 - \mathsf{fma}\left(2, x2, x1\right)}{\mathsf{fma}\left(x1, x1, 1\right)}, x1 + \mathsf{fma}\left(\mathsf{fma}\left(x1, x1, 1\right), \mathsf{fma}\left(x1, x1 \cdot \mathsf{fma}\left(t_0, 4, -6\right), \left(x1 \cdot \left(2 \cdot t_0\right)\right) \cdot \left(t_0 + -3\right)\right), \mathsf{fma}\left(t_4, t_0, {x1}^{3}\right)\right)\right)\\ \mathbf{else}:\\ \;\;\;\;x1 + \left(\left(x1 + 6 \cdot {x1}^{4}\right) + 9\right)\\ \end{array} \end{array} \]

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 (* x1 (* x1 3.0)))
        (t_2 (+ (* x1 x1) 1.0))
        (t_3 (/ (- (+ t_1 (* 2.0 x2)) x1) t_2))
        (t_4 (* 3.0 (* x1 x1))))
   (if (<=
        (+
         x1
         (+
          (+
           x1
           (+
            (+
             (*
              t_2
              (+
               (* (* (* x1 2.0) t_3) (- t_3 3.0))
               (* (* x1 x1) (- (* t_3 4.0) 6.0))))
             (* t_1 t_3))
            (* x1 (* x1 x1))))
          (* 3.0 (/ (- (- t_1 (* 2.0 x2)) x1) t_2))))
        INFINITY)
     (+
      x1
      (fma
       3.0
       (/ (- t_4 (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)) (+ t_0 -3.0)))
         (fma t_4 t_0 (pow x1 3.0))))))
     (+ x1 (+ (+ x1 (* 6.0 (pow x1 4.0))) 9.0)))))

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 = x1 * (x1 * 3.0);
	double t_2 = (x1 * x1) + 1.0;
	double t_3 = ((t_1 + (2.0 * x2)) - x1) / t_2;
	double t_4 = 3.0 * (x1 * x1);
	double tmp;
	if ((x1 + ((x1 + (((t_2 * ((((x1 * 2.0) * t_3) * (t_3 - 3.0)) + ((x1 * x1) * ((t_3 * 4.0) - 6.0)))) + (t_1 * t_3)) + (x1 * (x1 * x1)))) + (3.0 * (((t_1 - (2.0 * x2)) - x1) / t_2)))) <= ((double) INFINITY)) {
		tmp = x1 + fma(3.0, ((t_4 - 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)) * (t_0 + -3.0))), fma(t_4, t_0, pow(x1, 3.0)))));
	} else {
		tmp = x1 + ((x1 + (6.0 * pow(x1, 4.0))) + 9.0);
	}
	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(x1 * Float64(x1 * 3.0))
	t_2 = Float64(Float64(x1 * x1) + 1.0)
	t_3 = Float64(Float64(Float64(t_1 + Float64(2.0 * x2)) - x1) / t_2)
	t_4 = Float64(3.0 * Float64(x1 * x1))
	tmp = 0.0
	if (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(t_3 * 4.0) - 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)))) <= Inf)
		tmp = Float64(x1 + fma(3.0, Float64(Float64(t_4 - 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(t_0 + -3.0))), fma(t_4, t_0, (x1 ^ 3.0))))));
	else
		tmp = Float64(x1 + Float64(Float64(x1 + Float64(6.0 * (x1 ^ 4.0))) + 9.0));
	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[(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[(t$95$1 + N[(2.0 * x2), $MachinePrecision]), $MachinePrecision] - x1), $MachinePrecision] / t$95$2), $MachinePrecision]}, Block[{t$95$4 = N[(3.0 * N[(x1 * x1), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[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[(t$95$3 * 4.0), $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], Infinity], N[(x1 + N[(3.0 * N[(N[(t$95$4 - 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[(t$95$0 + -3.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(t$95$4 * t$95$0 + N[Power[x1, 3.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x1 + N[(N[(x1 + N[(6.0 * N[Power[x1, 4.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + 9.0), $MachinePrecision]), $MachinePrecision]]]]]]]

Derivation

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

Alternative 2: 99.2% accurate, 0.2× speedup

Math

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

FPCore

(FPCore (x1 x2)
 :precision binary64
 (let* ((t_0 (* x1 (* x1 x1)))
        (t_1 (* x1 (* x1 3.0)))
        (t_2 (+ (* x1 x1) 1.0))
        (t_3 (/ (- (+ t_1 (* 2.0 x2)) x1) t_2))
        (t_4 (* (* x1 2.0) t_3))
        (t_5 (* t_1 t_3))
        (t_6 (* (* x1 x1) (- (* t_3 4.0) 6.0)))
        (t_7 (* 3.0 (/ (- (- t_1 (* 2.0 x2)) x1) t_2))))
   (if (<=
        (+
         x1
         (+ (+ x1 (+ (+ (* t_2 (+ (* t_4 (- t_3 3.0)) t_6)) t_5) t_0)) t_7))
        INFINITY)
     (+
      x1
      (+
       t_7
       (+
        x1
        (+
         t_0
         (+
          t_5
          (*
           t_2
           (+
            t_6
            (*
             t_4
             (-
              (*
               (fma 3.0 (pow x1 2.0) (- (* 2.0 x2) x1))
               (/ 1.0 (fma x1 x1 1.0)))
              3.0)))))))))
     (+ x1 (+ (+ x1 (* 6.0 (pow x1 4.0))) 9.0)))))

C

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

Julia

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

Wolfram

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

Derivation

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

Alternative 3: 99.2% accurate, 0.5× speedup

Math

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

FPCore

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

C

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

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

Python

def code(x1, x2):
	t_0 = x1 * (x1 * 3.0)
	t_1 = (x1 * x1) + 1.0
	t_2 = ((t_0 + (2.0 * x2)) - x1) / t_1
	t_3 = x1 + ((x1 + (((t_1 * ((((x1 * 2.0) * t_2) * (t_2 - 3.0)) + ((x1 * x1) * ((t_2 * 4.0) - 6.0)))) + (t_0 * t_2)) + (x1 * (x1 * x1)))) + (3.0 * (((t_0 - (2.0 * x2)) - x1) / t_1)))
	tmp = 0
	if t_3 <= math.inf:
		tmp = t_3
	else:
		tmp = x1 + ((x1 + (6.0 * math.pow(x1, 4.0))) + 9.0)
	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(t_0 + Float64(2.0 * x2)) - x1) / t_1)
	t_3 = Float64(x1 + Float64(Float64(x1 + Float64(Float64(Float64(t_1 * Float64(Float64(Float64(Float64(x1 * 2.0) * t_2) * Float64(t_2 - 3.0)) + Float64(Float64(x1 * x1) * Float64(Float64(t_2 * 4.0) - 6.0)))) + Float64(t_0 * t_2)) + Float64(x1 * Float64(x1 * x1)))) + Float64(3.0 * Float64(Float64(Float64(t_0 - Float64(2.0 * x2)) - x1) / t_1))))
	tmp = 0.0
	if (t_3 <= Inf)
		tmp = t_3;
	else
		tmp = Float64(x1 + Float64(Float64(x1 + Float64(6.0 * (x1 ^ 4.0))) + 9.0));
	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 = ((t_0 + (2.0 * x2)) - x1) / t_1;
	t_3 = x1 + ((x1 + (((t_1 * ((((x1 * 2.0) * t_2) * (t_2 - 3.0)) + ((x1 * x1) * ((t_2 * 4.0) - 6.0)))) + (t_0 * t_2)) + (x1 * (x1 * x1)))) + (3.0 * (((t_0 - (2.0 * x2)) - x1) / t_1)));
	tmp = 0.0;
	if (t_3 <= Inf)
		tmp = t_3;
	else
		tmp = x1 + ((x1 + (6.0 * (x1 ^ 4.0))) + 9.0);
	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[(t$95$0 + N[(2.0 * x2), $MachinePrecision]), $MachinePrecision] - x1), $MachinePrecision] / t$95$1), $MachinePrecision]}, Block[{t$95$3 = N[(x1 + N[(N[(x1 + N[(N[(N[(t$95$1 * 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[(t$95$2 * 4.0), $MachinePrecision] - 6.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(t$95$0 * t$95$2), $MachinePrecision]), $MachinePrecision] + N[(x1 * N[(x1 * x1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $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]}, If[LessEqual[t$95$3, Infinity], t$95$3, N[(x1 + N[(N[(x1 + N[(6.0 * N[Power[x1, 4.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + 9.0), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

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

Alternative 4: 96.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := x1 \cdot \left(x1 \cdot 3\right)\\ t_1 := x1 + \left(\left(x1 + 6 \cdot {x1}^{4}\right) + 9\right)\\ t_2 := x1 \cdot x1 + 1\\ t_3 := \frac{\left(t_0 + 2 \cdot x2\right) - x1}{t_2}\\ t_4 := \left(x1 \cdot x1\right) \cdot \left(t_3 \cdot 4 - 6\right)\\ t_5 := t_0 \cdot t_3\\ t_6 := \left(x1 \cdot 2\right) \cdot t_3\\ t_7 := x1 \cdot \left(x1 \cdot x1\right)\\ \mathbf{if}\;x1 \leq -5 \cdot 10^{+103}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x1 \leq -145:\\ \;\;\;\;x1 + \left(\left(x1 + \left(\left(t_2 \cdot \left(t_6 \cdot \left(t_3 - 3\right) + t_4\right) + t_5\right) + t_7\right)\right) + 9\right)\\ \mathbf{elif}\;x1 \leq 11200:\\ \;\;\;\;x1 + \left(3 \cdot \frac{\left(t_0 - 2 \cdot x2\right) - x1}{t_2} + \left(x1 + \left(t_7 + \left(t_5 + t_2 \cdot \left(t_4 + t_6 \cdot \left(\left(2 \cdot x2 - x1\right) - 3\right)\right)\right)\right)\right)\right)\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x1 x2)
 :precision binary64
 (let* ((t_0 (* x1 (* x1 3.0)))
        (t_1 (+ x1 (+ (+ x1 (* 6.0 (pow x1 4.0))) 9.0)))
        (t_2 (+ (* x1 x1) 1.0))
        (t_3 (/ (- (+ t_0 (* 2.0 x2)) x1) t_2))
        (t_4 (* (* x1 x1) (- (* t_3 4.0) 6.0)))
        (t_5 (* t_0 t_3))
        (t_6 (* (* x1 2.0) t_3))
        (t_7 (* x1 (* x1 x1))))
   (if (<= x1 -5e+103)
     t_1
     (if (<= x1 -145.0)
       (+
        x1
        (+ (+ x1 (+ (+ (* t_2 (+ (* t_6 (- t_3 3.0)) t_4)) t_5) t_7)) 9.0))
       (if (<= x1 11200.0)
         (+
          x1
          (+
           (* 3.0 (/ (- (- t_0 (* 2.0 x2)) x1) t_2))
           (+
            x1
            (+
             t_7
             (+ t_5 (* t_2 (+ t_4 (* t_6 (- (- (* 2.0 x2) x1) 3.0)))))))))
         t_1)))))

C

double code(double x1, double x2) {
	double t_0 = x1 * (x1 * 3.0);
	double t_1 = x1 + ((x1 + (6.0 * pow(x1, 4.0))) + 9.0);
	double t_2 = (x1 * x1) + 1.0;
	double t_3 = ((t_0 + (2.0 * x2)) - x1) / t_2;
	double t_4 = (x1 * x1) * ((t_3 * 4.0) - 6.0);
	double t_5 = t_0 * t_3;
	double t_6 = (x1 * 2.0) * t_3;
	double t_7 = x1 * (x1 * x1);
	double tmp;
	if (x1 <= -5e+103) {
		tmp = t_1;
	} else if (x1 <= -145.0) {
		tmp = x1 + ((x1 + (((t_2 * ((t_6 * (t_3 - 3.0)) + t_4)) + t_5) + t_7)) + 9.0);
	} else if (x1 <= 11200.0) {
		tmp = x1 + ((3.0 * (((t_0 - (2.0 * x2)) - x1) / t_2)) + (x1 + (t_7 + (t_5 + (t_2 * (t_4 + (t_6 * (((2.0 * x2) - x1) - 3.0))))))));
	} 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) :: t_5
    real(8) :: t_6
    real(8) :: t_7
    real(8) :: tmp
    t_0 = x1 * (x1 * 3.0d0)
    t_1 = x1 + ((x1 + (6.0d0 * (x1 ** 4.0d0))) + 9.0d0)
    t_2 = (x1 * x1) + 1.0d0
    t_3 = ((t_0 + (2.0d0 * x2)) - x1) / t_2
    t_4 = (x1 * x1) * ((t_3 * 4.0d0) - 6.0d0)
    t_5 = t_0 * t_3
    t_6 = (x1 * 2.0d0) * t_3
    t_7 = x1 * (x1 * x1)
    if (x1 <= (-5d+103)) then
        tmp = t_1
    else if (x1 <= (-145.0d0)) then
        tmp = x1 + ((x1 + (((t_2 * ((t_6 * (t_3 - 3.0d0)) + t_4)) + t_5) + t_7)) + 9.0d0)
    else if (x1 <= 11200.0d0) then
        tmp = x1 + ((3.0d0 * (((t_0 - (2.0d0 * x2)) - x1) / t_2)) + (x1 + (t_7 + (t_5 + (t_2 * (t_4 + (t_6 * (((2.0d0 * x2) - x1) - 3.0d0))))))))
    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 + ((x1 + (6.0 * Math.pow(x1, 4.0))) + 9.0);
	double t_2 = (x1 * x1) + 1.0;
	double t_3 = ((t_0 + (2.0 * x2)) - x1) / t_2;
	double t_4 = (x1 * x1) * ((t_3 * 4.0) - 6.0);
	double t_5 = t_0 * t_3;
	double t_6 = (x1 * 2.0) * t_3;
	double t_7 = x1 * (x1 * x1);
	double tmp;
	if (x1 <= -5e+103) {
		tmp = t_1;
	} else if (x1 <= -145.0) {
		tmp = x1 + ((x1 + (((t_2 * ((t_6 * (t_3 - 3.0)) + t_4)) + t_5) + t_7)) + 9.0);
	} else if (x1 <= 11200.0) {
		tmp = x1 + ((3.0 * (((t_0 - (2.0 * x2)) - x1) / t_2)) + (x1 + (t_7 + (t_5 + (t_2 * (t_4 + (t_6 * (((2.0 * x2) - x1) - 3.0))))))));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x1, x2):
	t_0 = x1 * (x1 * 3.0)
	t_1 = x1 + ((x1 + (6.0 * math.pow(x1, 4.0))) + 9.0)
	t_2 = (x1 * x1) + 1.0
	t_3 = ((t_0 + (2.0 * x2)) - x1) / t_2
	t_4 = (x1 * x1) * ((t_3 * 4.0) - 6.0)
	t_5 = t_0 * t_3
	t_6 = (x1 * 2.0) * t_3
	t_7 = x1 * (x1 * x1)
	tmp = 0
	if x1 <= -5e+103:
		tmp = t_1
	elif x1 <= -145.0:
		tmp = x1 + ((x1 + (((t_2 * ((t_6 * (t_3 - 3.0)) + t_4)) + t_5) + t_7)) + 9.0)
	elif x1 <= 11200.0:
		tmp = x1 + ((3.0 * (((t_0 - (2.0 * x2)) - x1) / t_2)) + (x1 + (t_7 + (t_5 + (t_2 * (t_4 + (t_6 * (((2.0 * x2) - x1) - 3.0))))))))
	else:
		tmp = t_1
	return tmp

Julia

function code(x1, x2)
	t_0 = Float64(x1 * Float64(x1 * 3.0))
	t_1 = Float64(x1 + Float64(Float64(x1 + Float64(6.0 * (x1 ^ 4.0))) + 9.0))
	t_2 = Float64(Float64(x1 * x1) + 1.0)
	t_3 = Float64(Float64(Float64(t_0 + Float64(2.0 * x2)) - x1) / t_2)
	t_4 = Float64(Float64(x1 * x1) * Float64(Float64(t_3 * 4.0) - 6.0))
	t_5 = Float64(t_0 * t_3)
	t_6 = Float64(Float64(x1 * 2.0) * t_3)
	t_7 = Float64(x1 * Float64(x1 * x1))
	tmp = 0.0
	if (x1 <= -5e+103)
		tmp = t_1;
	elseif (x1 <= -145.0)
		tmp = Float64(x1 + Float64(Float64(x1 + Float64(Float64(Float64(t_2 * Float64(Float64(t_6 * Float64(t_3 - 3.0)) + t_4)) + t_5) + t_7)) + 9.0));
	elseif (x1 <= 11200.0)
		tmp = Float64(x1 + Float64(Float64(3.0 * Float64(Float64(Float64(t_0 - Float64(2.0 * x2)) - x1) / t_2)) + Float64(x1 + Float64(t_7 + Float64(t_5 + Float64(t_2 * Float64(t_4 + Float64(t_6 * Float64(Float64(Float64(2.0 * x2) - x1) - 3.0)))))))));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x1, x2)
	t_0 = x1 * (x1 * 3.0);
	t_1 = x1 + ((x1 + (6.0 * (x1 ^ 4.0))) + 9.0);
	t_2 = (x1 * x1) + 1.0;
	t_3 = ((t_0 + (2.0 * x2)) - x1) / t_2;
	t_4 = (x1 * x1) * ((t_3 * 4.0) - 6.0);
	t_5 = t_0 * t_3;
	t_6 = (x1 * 2.0) * t_3;
	t_7 = x1 * (x1 * x1);
	tmp = 0.0;
	if (x1 <= -5e+103)
		tmp = t_1;
	elseif (x1 <= -145.0)
		tmp = x1 + ((x1 + (((t_2 * ((t_6 * (t_3 - 3.0)) + t_4)) + t_5) + t_7)) + 9.0);
	elseif (x1 <= 11200.0)
		tmp = x1 + ((3.0 * (((t_0 - (2.0 * x2)) - x1) / t_2)) + (x1 + (t_7 + (t_5 + (t_2 * (t_4 + (t_6 * (((2.0 * x2) - x1) - 3.0))))))));
	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[(N[(x1 + N[(6.0 * N[Power[x1, 4.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + 9.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(x1 * x1), $MachinePrecision] + 1.0), $MachinePrecision]}, Block[{t$95$3 = N[(N[(N[(t$95$0 + N[(2.0 * x2), $MachinePrecision]), $MachinePrecision] - x1), $MachinePrecision] / t$95$2), $MachinePrecision]}, Block[{t$95$4 = N[(N[(x1 * x1), $MachinePrecision] * N[(N[(t$95$3 * 4.0), $MachinePrecision] - 6.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$5 = N[(t$95$0 * t$95$3), $MachinePrecision]}, Block[{t$95$6 = N[(N[(x1 * 2.0), $MachinePrecision] * t$95$3), $MachinePrecision]}, Block[{t$95$7 = N[(x1 * N[(x1 * x1), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x1, -5e+103], t$95$1, If[LessEqual[x1, -145.0], N[(x1 + N[(N[(x1 + N[(N[(N[(t$95$2 * N[(N[(t$95$6 * N[(t$95$3 - 3.0), $MachinePrecision]), $MachinePrecision] + t$95$4), $MachinePrecision]), $MachinePrecision] + t$95$5), $MachinePrecision] + t$95$7), $MachinePrecision]), $MachinePrecision] + 9.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, 11200.0], N[(x1 + N[(N[(3.0 * N[(N[(N[(t$95$0 - N[(2.0 * x2), $MachinePrecision]), $MachinePrecision] - x1), $MachinePrecision] / t$95$2), $MachinePrecision]), $MachinePrecision] + N[(x1 + N[(t$95$7 + N[(t$95$5 + N[(t$95$2 * N[(t$95$4 + N[(t$95$6 * N[(N[(N[(2.0 * x2), $MachinePrecision] - x1), $MachinePrecision] - 3.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]]]]]]]]]

Derivation

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

Alternative 5: 96.7% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := x1 \cdot \left(x1 \cdot 3\right)\\ t_1 := x1 + \left(\left(x1 + 6 \cdot {x1}^{4}\right) + 9\right)\\ t_2 := x1 \cdot x1 + 1\\ t_3 := \frac{\left(t_0 + 2 \cdot x2\right) - x1}{t_2}\\ t_4 := \left(x1 \cdot x1\right) \cdot \left(t_3 \cdot 4 - 6\right)\\ t_5 := t_0 \cdot t_3\\ t_6 := \left(x1 \cdot 2\right) \cdot t_3\\ t_7 := x1 \cdot \left(x1 \cdot x1\right)\\ \mathbf{if}\;x1 \leq -5.5 \cdot 10^{+102}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x1 \leq -145:\\ \;\;\;\;x1 + \left(\left(x1 + \left(\left(t_2 \cdot \left(t_6 \cdot \left(t_3 - 3\right) + t_4\right) + t_5\right) + t_7\right)\right) + 9\right)\\ \mathbf{elif}\;x1 \leq 1.1 \cdot 10^{+45}:\\ \;\;\;\;x1 + \left(3 \cdot \frac{\left(t_0 - 2 \cdot x2\right) - x1}{t_2} + \left(x1 + \left(t_7 + \left(t_5 + t_2 \cdot \left(t_4 + t_6 \cdot \left(2 \cdot x2 - 3\right)\right)\right)\right)\right)\right)\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x1 x2)
 :precision binary64
 (let* ((t_0 (* x1 (* x1 3.0)))
        (t_1 (+ x1 (+ (+ x1 (* 6.0 (pow x1 4.0))) 9.0)))
        (t_2 (+ (* x1 x1) 1.0))
        (t_3 (/ (- (+ t_0 (* 2.0 x2)) x1) t_2))
        (t_4 (* (* x1 x1) (- (* t_3 4.0) 6.0)))
        (t_5 (* t_0 t_3))
        (t_6 (* (* x1 2.0) t_3))
        (t_7 (* x1 (* x1 x1))))
   (if (<= x1 -5.5e+102)
     t_1
     (if (<= x1 -145.0)
       (+
        x1
        (+ (+ x1 (+ (+ (* t_2 (+ (* t_6 (- t_3 3.0)) t_4)) t_5) t_7)) 9.0))
       (if (<= x1 1.1e+45)
         (+
          x1
          (+
           (* 3.0 (/ (- (- t_0 (* 2.0 x2)) x1) t_2))
           (+ x1 (+ t_7 (+ t_5 (* t_2 (+ t_4 (* t_6 (- (* 2.0 x2) 3.0)))))))))
         t_1)))))

C

double code(double x1, double x2) {
	double t_0 = x1 * (x1 * 3.0);
	double t_1 = x1 + ((x1 + (6.0 * pow(x1, 4.0))) + 9.0);
	double t_2 = (x1 * x1) + 1.0;
	double t_3 = ((t_0 + (2.0 * x2)) - x1) / t_2;
	double t_4 = (x1 * x1) * ((t_3 * 4.0) - 6.0);
	double t_5 = t_0 * t_3;
	double t_6 = (x1 * 2.0) * t_3;
	double t_7 = x1 * (x1 * x1);
	double tmp;
	if (x1 <= -5.5e+102) {
		tmp = t_1;
	} else if (x1 <= -145.0) {
		tmp = x1 + ((x1 + (((t_2 * ((t_6 * (t_3 - 3.0)) + t_4)) + t_5) + t_7)) + 9.0);
	} else if (x1 <= 1.1e+45) {
		tmp = x1 + ((3.0 * (((t_0 - (2.0 * x2)) - x1) / t_2)) + (x1 + (t_7 + (t_5 + (t_2 * (t_4 + (t_6 * ((2.0 * x2) - 3.0))))))));
	} 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) :: t_5
    real(8) :: t_6
    real(8) :: t_7
    real(8) :: tmp
    t_0 = x1 * (x1 * 3.0d0)
    t_1 = x1 + ((x1 + (6.0d0 * (x1 ** 4.0d0))) + 9.0d0)
    t_2 = (x1 * x1) + 1.0d0
    t_3 = ((t_0 + (2.0d0 * x2)) - x1) / t_2
    t_4 = (x1 * x1) * ((t_3 * 4.0d0) - 6.0d0)
    t_5 = t_0 * t_3
    t_6 = (x1 * 2.0d0) * t_3
    t_7 = x1 * (x1 * x1)
    if (x1 <= (-5.5d+102)) then
        tmp = t_1
    else if (x1 <= (-145.0d0)) then
        tmp = x1 + ((x1 + (((t_2 * ((t_6 * (t_3 - 3.0d0)) + t_4)) + t_5) + t_7)) + 9.0d0)
    else if (x1 <= 1.1d+45) then
        tmp = x1 + ((3.0d0 * (((t_0 - (2.0d0 * x2)) - x1) / t_2)) + (x1 + (t_7 + (t_5 + (t_2 * (t_4 + (t_6 * ((2.0d0 * x2) - 3.0d0))))))))
    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 + ((x1 + (6.0 * Math.pow(x1, 4.0))) + 9.0);
	double t_2 = (x1 * x1) + 1.0;
	double t_3 = ((t_0 + (2.0 * x2)) - x1) / t_2;
	double t_4 = (x1 * x1) * ((t_3 * 4.0) - 6.0);
	double t_5 = t_0 * t_3;
	double t_6 = (x1 * 2.0) * t_3;
	double t_7 = x1 * (x1 * x1);
	double tmp;
	if (x1 <= -5.5e+102) {
		tmp = t_1;
	} else if (x1 <= -145.0) {
		tmp = x1 + ((x1 + (((t_2 * ((t_6 * (t_3 - 3.0)) + t_4)) + t_5) + t_7)) + 9.0);
	} else if (x1 <= 1.1e+45) {
		tmp = x1 + ((3.0 * (((t_0 - (2.0 * x2)) - x1) / t_2)) + (x1 + (t_7 + (t_5 + (t_2 * (t_4 + (t_6 * ((2.0 * x2) - 3.0))))))));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x1, x2):
	t_0 = x1 * (x1 * 3.0)
	t_1 = x1 + ((x1 + (6.0 * math.pow(x1, 4.0))) + 9.0)
	t_2 = (x1 * x1) + 1.0
	t_3 = ((t_0 + (2.0 * x2)) - x1) / t_2
	t_4 = (x1 * x1) * ((t_3 * 4.0) - 6.0)
	t_5 = t_0 * t_3
	t_6 = (x1 * 2.0) * t_3
	t_7 = x1 * (x1 * x1)
	tmp = 0
	if x1 <= -5.5e+102:
		tmp = t_1
	elif x1 <= -145.0:
		tmp = x1 + ((x1 + (((t_2 * ((t_6 * (t_3 - 3.0)) + t_4)) + t_5) + t_7)) + 9.0)
	elif x1 <= 1.1e+45:
		tmp = x1 + ((3.0 * (((t_0 - (2.0 * x2)) - x1) / t_2)) + (x1 + (t_7 + (t_5 + (t_2 * (t_4 + (t_6 * ((2.0 * x2) - 3.0))))))))
	else:
		tmp = t_1
	return tmp

Julia

function code(x1, x2)
	t_0 = Float64(x1 * Float64(x1 * 3.0))
	t_1 = Float64(x1 + Float64(Float64(x1 + Float64(6.0 * (x1 ^ 4.0))) + 9.0))
	t_2 = Float64(Float64(x1 * x1) + 1.0)
	t_3 = Float64(Float64(Float64(t_0 + Float64(2.0 * x2)) - x1) / t_2)
	t_4 = Float64(Float64(x1 * x1) * Float64(Float64(t_3 * 4.0) - 6.0))
	t_5 = Float64(t_0 * t_3)
	t_6 = Float64(Float64(x1 * 2.0) * t_3)
	t_7 = Float64(x1 * Float64(x1 * x1))
	tmp = 0.0
	if (x1 <= -5.5e+102)
		tmp = t_1;
	elseif (x1 <= -145.0)
		tmp = Float64(x1 + Float64(Float64(x1 + Float64(Float64(Float64(t_2 * Float64(Float64(t_6 * Float64(t_3 - 3.0)) + t_4)) + t_5) + t_7)) + 9.0));
	elseif (x1 <= 1.1e+45)
		tmp = Float64(x1 + Float64(Float64(3.0 * Float64(Float64(Float64(t_0 - Float64(2.0 * x2)) - x1) / t_2)) + Float64(x1 + Float64(t_7 + Float64(t_5 + Float64(t_2 * Float64(t_4 + Float64(t_6 * Float64(Float64(2.0 * x2) - 3.0)))))))));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x1, x2)
	t_0 = x1 * (x1 * 3.0);
	t_1 = x1 + ((x1 + (6.0 * (x1 ^ 4.0))) + 9.0);
	t_2 = (x1 * x1) + 1.0;
	t_3 = ((t_0 + (2.0 * x2)) - x1) / t_2;
	t_4 = (x1 * x1) * ((t_3 * 4.0) - 6.0);
	t_5 = t_0 * t_3;
	t_6 = (x1 * 2.0) * t_3;
	t_7 = x1 * (x1 * x1);
	tmp = 0.0;
	if (x1 <= -5.5e+102)
		tmp = t_1;
	elseif (x1 <= -145.0)
		tmp = x1 + ((x1 + (((t_2 * ((t_6 * (t_3 - 3.0)) + t_4)) + t_5) + t_7)) + 9.0);
	elseif (x1 <= 1.1e+45)
		tmp = x1 + ((3.0 * (((t_0 - (2.0 * x2)) - x1) / t_2)) + (x1 + (t_7 + (t_5 + (t_2 * (t_4 + (t_6 * ((2.0 * x2) - 3.0))))))));
	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[(N[(x1 + N[(6.0 * N[Power[x1, 4.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + 9.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(x1 * x1), $MachinePrecision] + 1.0), $MachinePrecision]}, Block[{t$95$3 = N[(N[(N[(t$95$0 + N[(2.0 * x2), $MachinePrecision]), $MachinePrecision] - x1), $MachinePrecision] / t$95$2), $MachinePrecision]}, Block[{t$95$4 = N[(N[(x1 * x1), $MachinePrecision] * N[(N[(t$95$3 * 4.0), $MachinePrecision] - 6.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$5 = N[(t$95$0 * t$95$3), $MachinePrecision]}, Block[{t$95$6 = N[(N[(x1 * 2.0), $MachinePrecision] * t$95$3), $MachinePrecision]}, Block[{t$95$7 = N[(x1 * N[(x1 * x1), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x1, -5.5e+102], t$95$1, If[LessEqual[x1, -145.0], N[(x1 + N[(N[(x1 + N[(N[(N[(t$95$2 * N[(N[(t$95$6 * N[(t$95$3 - 3.0), $MachinePrecision]), $MachinePrecision] + t$95$4), $MachinePrecision]), $MachinePrecision] + t$95$5), $MachinePrecision] + t$95$7), $MachinePrecision]), $MachinePrecision] + 9.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, 1.1e+45], N[(x1 + N[(N[(3.0 * N[(N[(N[(t$95$0 - N[(2.0 * x2), $MachinePrecision]), $MachinePrecision] - x1), $MachinePrecision] / t$95$2), $MachinePrecision]), $MachinePrecision] + N[(x1 + N[(t$95$7 + N[(t$95$5 + N[(t$95$2 * N[(t$95$4 + N[(t$95$6 * N[(N[(2.0 * x2), $MachinePrecision] - 3.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]]]]]]]]]

Derivation

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

Alternative 6: 96.0% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := x1 \cdot \left(x1 \cdot 3\right)\\ t_1 := x1 + \left(\left(x1 + 6 \cdot {x1}^{4}\right) + 9\right)\\ t_2 := x1 \cdot x1 + 1\\ t_3 := x1 \cdot \left(x1 \cdot x1\right)\\ t_4 := \frac{\left(t_0 + 2 \cdot x2\right) - x1}{t_2}\\ t_5 := \left(x1 \cdot x1\right) \cdot \left(t_4 \cdot 4 - 6\right)\\ t_6 := t_0 \cdot t_4\\ \mathbf{if}\;x1 \leq -5.6 \cdot 10^{+102}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x1 \leq -145:\\ \;\;\;\;x1 + \left(\left(x1 + \left(\left(t_2 \cdot \left(\left(\left(x1 \cdot 2\right) \cdot t_4\right) \cdot \left(t_4 - 3\right) + t_5\right) + t_6\right) + t_3\right)\right) + 9\right)\\ \mathbf{elif}\;x1 \leq 980000:\\ \;\;\;\;x1 + \left(3 \cdot \frac{\left(t_0 - 2 \cdot x2\right) - x1}{t_2} + \left(x1 + \left(t_3 + \left(t_6 + t_2 \cdot \left(t_5 + \left(2 \cdot x2 - 3\right) \cdot \left(\left(x1 \cdot 2\right) \cdot \left(2 \cdot x2 - x1\right)\right)\right)\right)\right)\right)\right)\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x1 x2)
 :precision binary64
 (let* ((t_0 (* x1 (* x1 3.0)))
        (t_1 (+ x1 (+ (+ x1 (* 6.0 (pow x1 4.0))) 9.0)))
        (t_2 (+ (* x1 x1) 1.0))
        (t_3 (* x1 (* x1 x1)))
        (t_4 (/ (- (+ t_0 (* 2.0 x2)) x1) t_2))
        (t_5 (* (* x1 x1) (- (* t_4 4.0) 6.0)))
        (t_6 (* t_0 t_4)))
   (if (<= x1 -5.6e+102)
     t_1
     (if (<= x1 -145.0)
       (+
        x1
        (+
         (+
          x1
          (+ (+ (* t_2 (+ (* (* (* x1 2.0) t_4) (- t_4 3.0)) t_5)) t_6) t_3))
         9.0))
       (if (<= x1 980000.0)
         (+
          x1
          (+
           (* 3.0 (/ (- (- t_0 (* 2.0 x2)) x1) t_2))
           (+
            x1
            (+
             t_3
             (+
              t_6
              (*
               t_2
               (+
                t_5
                (* (- (* 2.0 x2) 3.0) (* (* x1 2.0) (- (* 2.0 x2) x1))))))))))
         t_1)))))

C

double code(double x1, double x2) {
	double t_0 = x1 * (x1 * 3.0);
	double t_1 = x1 + ((x1 + (6.0 * pow(x1, 4.0))) + 9.0);
	double t_2 = (x1 * x1) + 1.0;
	double t_3 = x1 * (x1 * x1);
	double t_4 = ((t_0 + (2.0 * x2)) - x1) / t_2;
	double t_5 = (x1 * x1) * ((t_4 * 4.0) - 6.0);
	double t_6 = t_0 * t_4;
	double tmp;
	if (x1 <= -5.6e+102) {
		tmp = t_1;
	} else if (x1 <= -145.0) {
		tmp = x1 + ((x1 + (((t_2 * ((((x1 * 2.0) * t_4) * (t_4 - 3.0)) + t_5)) + t_6) + t_3)) + 9.0);
	} else if (x1 <= 980000.0) {
		tmp = x1 + ((3.0 * (((t_0 - (2.0 * x2)) - x1) / t_2)) + (x1 + (t_3 + (t_6 + (t_2 * (t_5 + (((2.0 * x2) - 3.0) * ((x1 * 2.0) * ((2.0 * x2) - x1)))))))));
	} 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) :: t_5
    real(8) :: t_6
    real(8) :: tmp
    t_0 = x1 * (x1 * 3.0d0)
    t_1 = x1 + ((x1 + (6.0d0 * (x1 ** 4.0d0))) + 9.0d0)
    t_2 = (x1 * x1) + 1.0d0
    t_3 = x1 * (x1 * x1)
    t_4 = ((t_0 + (2.0d0 * x2)) - x1) / t_2
    t_5 = (x1 * x1) * ((t_4 * 4.0d0) - 6.0d0)
    t_6 = t_0 * t_4
    if (x1 <= (-5.6d+102)) then
        tmp = t_1
    else if (x1 <= (-145.0d0)) then
        tmp = x1 + ((x1 + (((t_2 * ((((x1 * 2.0d0) * t_4) * (t_4 - 3.0d0)) + t_5)) + t_6) + t_3)) + 9.0d0)
    else if (x1 <= 980000.0d0) then
        tmp = x1 + ((3.0d0 * (((t_0 - (2.0d0 * x2)) - x1) / t_2)) + (x1 + (t_3 + (t_6 + (t_2 * (t_5 + (((2.0d0 * x2) - 3.0d0) * ((x1 * 2.0d0) * ((2.0d0 * x2) - x1)))))))))
    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 + ((x1 + (6.0 * Math.pow(x1, 4.0))) + 9.0);
	double t_2 = (x1 * x1) + 1.0;
	double t_3 = x1 * (x1 * x1);
	double t_4 = ((t_0 + (2.0 * x2)) - x1) / t_2;
	double t_5 = (x1 * x1) * ((t_4 * 4.0) - 6.0);
	double t_6 = t_0 * t_4;
	double tmp;
	if (x1 <= -5.6e+102) {
		tmp = t_1;
	} else if (x1 <= -145.0) {
		tmp = x1 + ((x1 + (((t_2 * ((((x1 * 2.0) * t_4) * (t_4 - 3.0)) + t_5)) + t_6) + t_3)) + 9.0);
	} else if (x1 <= 980000.0) {
		tmp = x1 + ((3.0 * (((t_0 - (2.0 * x2)) - x1) / t_2)) + (x1 + (t_3 + (t_6 + (t_2 * (t_5 + (((2.0 * x2) - 3.0) * ((x1 * 2.0) * ((2.0 * x2) - x1)))))))));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x1, x2):
	t_0 = x1 * (x1 * 3.0)
	t_1 = x1 + ((x1 + (6.0 * math.pow(x1, 4.0))) + 9.0)
	t_2 = (x1 * x1) + 1.0
	t_3 = x1 * (x1 * x1)
	t_4 = ((t_0 + (2.0 * x2)) - x1) / t_2
	t_5 = (x1 * x1) * ((t_4 * 4.0) - 6.0)
	t_6 = t_0 * t_4
	tmp = 0
	if x1 <= -5.6e+102:
		tmp = t_1
	elif x1 <= -145.0:
		tmp = x1 + ((x1 + (((t_2 * ((((x1 * 2.0) * t_4) * (t_4 - 3.0)) + t_5)) + t_6) + t_3)) + 9.0)
	elif x1 <= 980000.0:
		tmp = x1 + ((3.0 * (((t_0 - (2.0 * x2)) - x1) / t_2)) + (x1 + (t_3 + (t_6 + (t_2 * (t_5 + (((2.0 * x2) - 3.0) * ((x1 * 2.0) * ((2.0 * x2) - x1)))))))))
	else:
		tmp = t_1
	return tmp

Julia

function code(x1, x2)
	t_0 = Float64(x1 * Float64(x1 * 3.0))
	t_1 = Float64(x1 + Float64(Float64(x1 + Float64(6.0 * (x1 ^ 4.0))) + 9.0))
	t_2 = Float64(Float64(x1 * x1) + 1.0)
	t_3 = Float64(x1 * Float64(x1 * x1))
	t_4 = Float64(Float64(Float64(t_0 + Float64(2.0 * x2)) - x1) / t_2)
	t_5 = Float64(Float64(x1 * x1) * Float64(Float64(t_4 * 4.0) - 6.0))
	t_6 = Float64(t_0 * t_4)
	tmp = 0.0
	if (x1 <= -5.6e+102)
		tmp = t_1;
	elseif (x1 <= -145.0)
		tmp = Float64(x1 + Float64(Float64(x1 + Float64(Float64(Float64(t_2 * Float64(Float64(Float64(Float64(x1 * 2.0) * t_4) * Float64(t_4 - 3.0)) + t_5)) + t_6) + t_3)) + 9.0));
	elseif (x1 <= 980000.0)
		tmp = Float64(x1 + Float64(Float64(3.0 * Float64(Float64(Float64(t_0 - Float64(2.0 * x2)) - x1) / t_2)) + Float64(x1 + Float64(t_3 + Float64(t_6 + Float64(t_2 * Float64(t_5 + Float64(Float64(Float64(2.0 * x2) - 3.0) * Float64(Float64(x1 * 2.0) * Float64(Float64(2.0 * x2) - x1))))))))));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x1, x2)
	t_0 = x1 * (x1 * 3.0);
	t_1 = x1 + ((x1 + (6.0 * (x1 ^ 4.0))) + 9.0);
	t_2 = (x1 * x1) + 1.0;
	t_3 = x1 * (x1 * x1);
	t_4 = ((t_0 + (2.0 * x2)) - x1) / t_2;
	t_5 = (x1 * x1) * ((t_4 * 4.0) - 6.0);
	t_6 = t_0 * t_4;
	tmp = 0.0;
	if (x1 <= -5.6e+102)
		tmp = t_1;
	elseif (x1 <= -145.0)
		tmp = x1 + ((x1 + (((t_2 * ((((x1 * 2.0) * t_4) * (t_4 - 3.0)) + t_5)) + t_6) + t_3)) + 9.0);
	elseif (x1 <= 980000.0)
		tmp = x1 + ((3.0 * (((t_0 - (2.0 * x2)) - x1) / t_2)) + (x1 + (t_3 + (t_6 + (t_2 * (t_5 + (((2.0 * x2) - 3.0) * ((x1 * 2.0) * ((2.0 * x2) - x1)))))))));
	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[(N[(x1 + N[(6.0 * N[Power[x1, 4.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + 9.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(x1 * x1), $MachinePrecision] + 1.0), $MachinePrecision]}, Block[{t$95$3 = N[(x1 * N[(x1 * x1), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$4 = N[(N[(N[(t$95$0 + N[(2.0 * x2), $MachinePrecision]), $MachinePrecision] - x1), $MachinePrecision] / t$95$2), $MachinePrecision]}, Block[{t$95$5 = N[(N[(x1 * x1), $MachinePrecision] * N[(N[(t$95$4 * 4.0), $MachinePrecision] - 6.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$6 = N[(t$95$0 * t$95$4), $MachinePrecision]}, If[LessEqual[x1, -5.6e+102], t$95$1, If[LessEqual[x1, -145.0], N[(x1 + N[(N[(x1 + N[(N[(N[(t$95$2 * N[(N[(N[(N[(x1 * 2.0), $MachinePrecision] * t$95$4), $MachinePrecision] * N[(t$95$4 - 3.0), $MachinePrecision]), $MachinePrecision] + t$95$5), $MachinePrecision]), $MachinePrecision] + t$95$6), $MachinePrecision] + t$95$3), $MachinePrecision]), $MachinePrecision] + 9.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, 980000.0], N[(x1 + N[(N[(3.0 * N[(N[(N[(t$95$0 - N[(2.0 * x2), $MachinePrecision]), $MachinePrecision] - x1), $MachinePrecision] / t$95$2), $MachinePrecision]), $MachinePrecision] + N[(x1 + N[(t$95$3 + N[(t$95$6 + N[(t$95$2 * N[(t$95$5 + N[(N[(N[(2.0 * x2), $MachinePrecision] - 3.0), $MachinePrecision] * N[(N[(x1 * 2.0), $MachinePrecision] * N[(N[(2.0 * x2), $MachinePrecision] - x1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]]]]]]]]

Derivation

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

Alternative 7: 75.9% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := x1 \cdot \left(x1 \cdot x1\right)\\ t_1 := x1 \cdot \left(x1 \cdot 3\right)\\ t_2 := x1 \cdot x1 + 1\\ t_3 := \frac{\left(t_1 + 2 \cdot x2\right) - x1}{t_2}\\ t_4 := t_1 \cdot t_3\\ t_5 := \left(x1 \cdot x1\right) \cdot \left(t_3 \cdot 4 - 6\right)\\ t_6 := x1 + \left(\left(x1 + \left(\left(t_2 \cdot \left(\left(\left(x1 \cdot 2\right) \cdot t_3\right) \cdot \left(t_3 - 3\right) + t_5\right) + t_4\right) + t_0\right)\right) + 9\right)\\ t_7 := 2 \cdot x2 - 3\\ \mathbf{if}\;x1 \leq -145:\\ \;\;\;\;t_6\\ \mathbf{elif}\;x1 \leq 2.95 \cdot 10^{-9}:\\ \;\;\;\;x1 + \left(3 \cdot \frac{\left(t_1 - 2 \cdot x2\right) - x1}{t_2} + \left(x1 + \left(t_0 + \left(t_4 + t_2 \cdot \left(t_5 + t_7 \cdot \left(\left(x1 \cdot 2\right) \cdot \left(2 \cdot x2 - x1\right)\right)\right)\right)\right)\right)\right)\\ \mathbf{elif}\;x1 \leq 1.32 \cdot 10^{+154}:\\ \;\;\;\;t_6\\ \mathbf{else}:\\ \;\;\;\;x1 + x1 \cdot \left(1 + 4 \cdot \left(x2 \cdot t_7\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x1 x2)
 :precision binary64
 (let* ((t_0 (* x1 (* x1 x1)))
        (t_1 (* x1 (* x1 3.0)))
        (t_2 (+ (* x1 x1) 1.0))
        (t_3 (/ (- (+ t_1 (* 2.0 x2)) x1) t_2))
        (t_4 (* t_1 t_3))
        (t_5 (* (* x1 x1) (- (* t_3 4.0) 6.0)))
        (t_6
         (+
          x1
          (+
           (+
            x1
            (+ (+ (* t_2 (+ (* (* (* x1 2.0) t_3) (- t_3 3.0)) t_5)) t_4) t_0))
           9.0)))
        (t_7 (- (* 2.0 x2) 3.0)))
   (if (<= x1 -145.0)
     t_6
     (if (<= x1 2.95e-9)
       (+
        x1
        (+
         (* 3.0 (/ (- (- t_1 (* 2.0 x2)) x1) t_2))
         (+
          x1
          (+
           t_0
           (+ t_4 (* t_2 (+ t_5 (* t_7 (* (* x1 2.0) (- (* 2.0 x2) x1))))))))))
       (if (<= x1 1.32e+154) t_6 (+ x1 (* x1 (+ 1.0 (* 4.0 (* x2 t_7))))))))))

C

double code(double x1, double x2) {
	double t_0 = x1 * (x1 * x1);
	double t_1 = x1 * (x1 * 3.0);
	double t_2 = (x1 * x1) + 1.0;
	double t_3 = ((t_1 + (2.0 * x2)) - x1) / t_2;
	double t_4 = t_1 * t_3;
	double t_5 = (x1 * x1) * ((t_3 * 4.0) - 6.0);
	double t_6 = x1 + ((x1 + (((t_2 * ((((x1 * 2.0) * t_3) * (t_3 - 3.0)) + t_5)) + t_4) + t_0)) + 9.0);
	double t_7 = (2.0 * x2) - 3.0;
	double tmp;
	if (x1 <= -145.0) {
		tmp = t_6;
	} else if (x1 <= 2.95e-9) {
		tmp = x1 + ((3.0 * (((t_1 - (2.0 * x2)) - x1) / t_2)) + (x1 + (t_0 + (t_4 + (t_2 * (t_5 + (t_7 * ((x1 * 2.0) * ((2.0 * x2) - x1)))))))));
	} else if (x1 <= 1.32e+154) {
		tmp = t_6;
	} else {
		tmp = x1 + (x1 * (1.0 + (4.0 * (x2 * t_7))));
	}
	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 = x1 * (x1 * 3.0d0)
    t_2 = (x1 * x1) + 1.0d0
    t_3 = ((t_1 + (2.0d0 * x2)) - x1) / t_2
    t_4 = t_1 * t_3
    t_5 = (x1 * x1) * ((t_3 * 4.0d0) - 6.0d0)
    t_6 = x1 + ((x1 + (((t_2 * ((((x1 * 2.0d0) * t_3) * (t_3 - 3.0d0)) + t_5)) + t_4) + t_0)) + 9.0d0)
    t_7 = (2.0d0 * x2) - 3.0d0
    if (x1 <= (-145.0d0)) then
        tmp = t_6
    else if (x1 <= 2.95d-9) then
        tmp = x1 + ((3.0d0 * (((t_1 - (2.0d0 * x2)) - x1) / t_2)) + (x1 + (t_0 + (t_4 + (t_2 * (t_5 + (t_7 * ((x1 * 2.0d0) * ((2.0d0 * x2) - x1)))))))))
    else if (x1 <= 1.32d+154) then
        tmp = t_6
    else
        tmp = x1 + (x1 * (1.0d0 + (4.0d0 * (x2 * t_7))))
    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 * 3.0);
	double t_2 = (x1 * x1) + 1.0;
	double t_3 = ((t_1 + (2.0 * x2)) - x1) / t_2;
	double t_4 = t_1 * t_3;
	double t_5 = (x1 * x1) * ((t_3 * 4.0) - 6.0);
	double t_6 = x1 + ((x1 + (((t_2 * ((((x1 * 2.0) * t_3) * (t_3 - 3.0)) + t_5)) + t_4) + t_0)) + 9.0);
	double t_7 = (2.0 * x2) - 3.0;
	double tmp;
	if (x1 <= -145.0) {
		tmp = t_6;
	} else if (x1 <= 2.95e-9) {
		tmp = x1 + ((3.0 * (((t_1 - (2.0 * x2)) - x1) / t_2)) + (x1 + (t_0 + (t_4 + (t_2 * (t_5 + (t_7 * ((x1 * 2.0) * ((2.0 * x2) - x1)))))))));
	} else if (x1 <= 1.32e+154) {
		tmp = t_6;
	} else {
		tmp = x1 + (x1 * (1.0 + (4.0 * (x2 * t_7))));
	}
	return tmp;
}

Python

def code(x1, x2):
	t_0 = x1 * (x1 * x1)
	t_1 = x1 * (x1 * 3.0)
	t_2 = (x1 * x1) + 1.0
	t_3 = ((t_1 + (2.0 * x2)) - x1) / t_2
	t_4 = t_1 * t_3
	t_5 = (x1 * x1) * ((t_3 * 4.0) - 6.0)
	t_6 = x1 + ((x1 + (((t_2 * ((((x1 * 2.0) * t_3) * (t_3 - 3.0)) + t_5)) + t_4) + t_0)) + 9.0)
	t_7 = (2.0 * x2) - 3.0
	tmp = 0
	if x1 <= -145.0:
		tmp = t_6
	elif x1 <= 2.95e-9:
		tmp = x1 + ((3.0 * (((t_1 - (2.0 * x2)) - x1) / t_2)) + (x1 + (t_0 + (t_4 + (t_2 * (t_5 + (t_7 * ((x1 * 2.0) * ((2.0 * x2) - x1)))))))))
	elif x1 <= 1.32e+154:
		tmp = t_6
	else:
		tmp = x1 + (x1 * (1.0 + (4.0 * (x2 * t_7))))
	return tmp

Julia

function code(x1, x2)
	t_0 = Float64(x1 * Float64(x1 * x1))
	t_1 = Float64(x1 * Float64(x1 * 3.0))
	t_2 = Float64(Float64(x1 * x1) + 1.0)
	t_3 = Float64(Float64(Float64(t_1 + Float64(2.0 * x2)) - x1) / t_2)
	t_4 = Float64(t_1 * t_3)
	t_5 = Float64(Float64(x1 * x1) * Float64(Float64(t_3 * 4.0) - 6.0))
	t_6 = Float64(x1 + Float64(Float64(x1 + Float64(Float64(Float64(t_2 * Float64(Float64(Float64(Float64(x1 * 2.0) * t_3) * Float64(t_3 - 3.0)) + t_5)) + t_4) + t_0)) + 9.0))
	t_7 = Float64(Float64(2.0 * x2) - 3.0)
	tmp = 0.0
	if (x1 <= -145.0)
		tmp = t_6;
	elseif (x1 <= 2.95e-9)
		tmp = Float64(x1 + Float64(Float64(3.0 * Float64(Float64(Float64(t_1 - Float64(2.0 * x2)) - x1) / t_2)) + Float64(x1 + Float64(t_0 + Float64(t_4 + Float64(t_2 * Float64(t_5 + Float64(t_7 * Float64(Float64(x1 * 2.0) * Float64(Float64(2.0 * x2) - x1))))))))));
	elseif (x1 <= 1.32e+154)
		tmp = t_6;
	else
		tmp = Float64(x1 + Float64(x1 * Float64(1.0 + Float64(4.0 * Float64(x2 * t_7)))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x1, x2)
	t_0 = x1 * (x1 * x1);
	t_1 = x1 * (x1 * 3.0);
	t_2 = (x1 * x1) + 1.0;
	t_3 = ((t_1 + (2.0 * x2)) - x1) / t_2;
	t_4 = t_1 * t_3;
	t_5 = (x1 * x1) * ((t_3 * 4.0) - 6.0);
	t_6 = x1 + ((x1 + (((t_2 * ((((x1 * 2.0) * t_3) * (t_3 - 3.0)) + t_5)) + t_4) + t_0)) + 9.0);
	t_7 = (2.0 * x2) - 3.0;
	tmp = 0.0;
	if (x1 <= -145.0)
		tmp = t_6;
	elseif (x1 <= 2.95e-9)
		tmp = x1 + ((3.0 * (((t_1 - (2.0 * x2)) - x1) / t_2)) + (x1 + (t_0 + (t_4 + (t_2 * (t_5 + (t_7 * ((x1 * 2.0) * ((2.0 * x2) - x1)))))))));
	elseif (x1 <= 1.32e+154)
		tmp = t_6;
	else
		tmp = x1 + (x1 * (1.0 + (4.0 * (x2 * t_7))));
	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[(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[(t$95$1 + N[(2.0 * x2), $MachinePrecision]), $MachinePrecision] - x1), $MachinePrecision] / t$95$2), $MachinePrecision]}, Block[{t$95$4 = N[(t$95$1 * t$95$3), $MachinePrecision]}, Block[{t$95$5 = N[(N[(x1 * x1), $MachinePrecision] * N[(N[(t$95$3 * 4.0), $MachinePrecision] - 6.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$6 = 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] + t$95$5), $MachinePrecision]), $MachinePrecision] + t$95$4), $MachinePrecision] + t$95$0), $MachinePrecision]), $MachinePrecision] + 9.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$7 = N[(N[(2.0 * x2), $MachinePrecision] - 3.0), $MachinePrecision]}, If[LessEqual[x1, -145.0], t$95$6, If[LessEqual[x1, 2.95e-9], N[(x1 + N[(N[(3.0 * N[(N[(N[(t$95$1 - N[(2.0 * x2), $MachinePrecision]), $MachinePrecision] - x1), $MachinePrecision] / t$95$2), $MachinePrecision]), $MachinePrecision] + N[(x1 + N[(t$95$0 + N[(t$95$4 + N[(t$95$2 * N[(t$95$5 + N[(t$95$7 * N[(N[(x1 * 2.0), $MachinePrecision] * N[(N[(2.0 * x2), $MachinePrecision] - x1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, 1.32e+154], t$95$6, N[(x1 + N[(x1 * N[(1.0 + N[(4.0 * N[(x2 * t$95$7), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]]]]]]]

Derivation

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

Alternative 8: 73.0% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left(x1 \cdot 2\right) \cdot \left(2 \cdot x2 - x1\right)\\ t_1 := 2 \cdot x2 - 3\\ t_2 := x1 \cdot x1 + 1\\ t_3 := x1 \cdot \left(x1 \cdot x1\right)\\ t_4 := x1 \cdot \left(x1 \cdot 3\right)\\ t_5 := \frac{\left(t_4 + 2 \cdot x2\right) - x1}{t_2}\\ t_6 := \left(x1 \cdot x1\right) \cdot \left(t_5 \cdot 4 - 6\right)\\ t_7 := t_4 \cdot t_5\\ \mathbf{if}\;x1 \leq -145:\\ \;\;\;\;x1 + \left(9 + \left(x1 + \left(t_3 + \left(t_7 + t_2 \cdot \left(t_6 + \left(t_5 - 3\right) \cdot t_0\right)\right)\right)\right)\right)\\ \mathbf{elif}\;x1 \leq 16200:\\ \;\;\;\;x1 + \left(3 \cdot \frac{\left(t_4 - 2 \cdot x2\right) - x1}{t_2} + \left(x1 + \left(t_3 + \left(t_7 + t_2 \cdot \left(t_6 + t_1 \cdot t_0\right)\right)\right)\right)\right)\\ \mathbf{elif}\;x1 \leq 1.32 \cdot 10^{+154}:\\ \;\;\;\;x1 + \left(9 + \left(x1 + \left(t_3 + \left(t_7 + t_2 \cdot \left(t_6 + 12 \cdot \frac{x2}{x1}\right)\right)\right)\right)\right)\\ \mathbf{else}:\\ \;\;\;\;x1 + x1 \cdot \left(1 + 4 \cdot \left(x2 \cdot t_1\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x1 x2)
 :precision binary64
 (let* ((t_0 (* (* x1 2.0) (- (* 2.0 x2) x1)))
        (t_1 (- (* 2.0 x2) 3.0))
        (t_2 (+ (* x1 x1) 1.0))
        (t_3 (* x1 (* x1 x1)))
        (t_4 (* x1 (* x1 3.0)))
        (t_5 (/ (- (+ t_4 (* 2.0 x2)) x1) t_2))
        (t_6 (* (* x1 x1) (- (* t_5 4.0) 6.0)))
        (t_7 (* t_4 t_5)))
   (if (<= x1 -145.0)
     (+ x1 (+ 9.0 (+ x1 (+ t_3 (+ t_7 (* t_2 (+ t_6 (* (- t_5 3.0) t_0))))))))
     (if (<= x1 16200.0)
       (+
        x1
        (+
         (* 3.0 (/ (- (- t_4 (* 2.0 x2)) x1) t_2))
         (+ x1 (+ t_3 (+ t_7 (* t_2 (+ t_6 (* t_1 t_0))))))))
       (if (<= x1 1.32e+154)
         (+
          x1
          (+ 9.0 (+ x1 (+ t_3 (+ t_7 (* t_2 (+ t_6 (* 12.0 (/ x2 x1)))))))))
         (+ x1 (* x1 (+ 1.0 (* 4.0 (* x2 t_1))))))))))

C

double code(double x1, double x2) {
	double t_0 = (x1 * 2.0) * ((2.0 * x2) - x1);
	double t_1 = (2.0 * x2) - 3.0;
	double t_2 = (x1 * x1) + 1.0;
	double t_3 = x1 * (x1 * x1);
	double t_4 = x1 * (x1 * 3.0);
	double t_5 = ((t_4 + (2.0 * x2)) - x1) / t_2;
	double t_6 = (x1 * x1) * ((t_5 * 4.0) - 6.0);
	double t_7 = t_4 * t_5;
	double tmp;
	if (x1 <= -145.0) {
		tmp = x1 + (9.0 + (x1 + (t_3 + (t_7 + (t_2 * (t_6 + ((t_5 - 3.0) * t_0)))))));
	} else if (x1 <= 16200.0) {
		tmp = x1 + ((3.0 * (((t_4 - (2.0 * x2)) - x1) / t_2)) + (x1 + (t_3 + (t_7 + (t_2 * (t_6 + (t_1 * t_0)))))));
	} else if (x1 <= 1.32e+154) {
		tmp = x1 + (9.0 + (x1 + (t_3 + (t_7 + (t_2 * (t_6 + (12.0 * (x2 / x1))))))));
	} else {
		tmp = x1 + (x1 * (1.0 + (4.0 * (x2 * 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) :: t_5
    real(8) :: t_6
    real(8) :: t_7
    real(8) :: tmp
    t_0 = (x1 * 2.0d0) * ((2.0d0 * x2) - x1)
    t_1 = (2.0d0 * x2) - 3.0d0
    t_2 = (x1 * x1) + 1.0d0
    t_3 = x1 * (x1 * x1)
    t_4 = x1 * (x1 * 3.0d0)
    t_5 = ((t_4 + (2.0d0 * x2)) - x1) / t_2
    t_6 = (x1 * x1) * ((t_5 * 4.0d0) - 6.0d0)
    t_7 = t_4 * t_5
    if (x1 <= (-145.0d0)) then
        tmp = x1 + (9.0d0 + (x1 + (t_3 + (t_7 + (t_2 * (t_6 + ((t_5 - 3.0d0) * t_0)))))))
    else if (x1 <= 16200.0d0) then
        tmp = x1 + ((3.0d0 * (((t_4 - (2.0d0 * x2)) - x1) / t_2)) + (x1 + (t_3 + (t_7 + (t_2 * (t_6 + (t_1 * t_0)))))))
    else if (x1 <= 1.32d+154) then
        tmp = x1 + (9.0d0 + (x1 + (t_3 + (t_7 + (t_2 * (t_6 + (12.0d0 * (x2 / x1))))))))
    else
        tmp = x1 + (x1 * (1.0d0 + (4.0d0 * (x2 * t_1))))
    end if
    code = tmp
end function

Java

public static double code(double x1, double x2) {
	double t_0 = (x1 * 2.0) * ((2.0 * x2) - x1);
	double t_1 = (2.0 * x2) - 3.0;
	double t_2 = (x1 * x1) + 1.0;
	double t_3 = x1 * (x1 * x1);
	double t_4 = x1 * (x1 * 3.0);
	double t_5 = ((t_4 + (2.0 * x2)) - x1) / t_2;
	double t_6 = (x1 * x1) * ((t_5 * 4.0) - 6.0);
	double t_7 = t_4 * t_5;
	double tmp;
	if (x1 <= -145.0) {
		tmp = x1 + (9.0 + (x1 + (t_3 + (t_7 + (t_2 * (t_6 + ((t_5 - 3.0) * t_0)))))));
	} else if (x1 <= 16200.0) {
		tmp = x1 + ((3.0 * (((t_4 - (2.0 * x2)) - x1) / t_2)) + (x1 + (t_3 + (t_7 + (t_2 * (t_6 + (t_1 * t_0)))))));
	} else if (x1 <= 1.32e+154) {
		tmp = x1 + (9.0 + (x1 + (t_3 + (t_7 + (t_2 * (t_6 + (12.0 * (x2 / x1))))))));
	} else {
		tmp = x1 + (x1 * (1.0 + (4.0 * (x2 * t_1))));
	}
	return tmp;
}

Python

def code(x1, x2):
	t_0 = (x1 * 2.0) * ((2.0 * x2) - x1)
	t_1 = (2.0 * x2) - 3.0
	t_2 = (x1 * x1) + 1.0
	t_3 = x1 * (x1 * x1)
	t_4 = x1 * (x1 * 3.0)
	t_5 = ((t_4 + (2.0 * x2)) - x1) / t_2
	t_6 = (x1 * x1) * ((t_5 * 4.0) - 6.0)
	t_7 = t_4 * t_5
	tmp = 0
	if x1 <= -145.0:
		tmp = x1 + (9.0 + (x1 + (t_3 + (t_7 + (t_2 * (t_6 + ((t_5 - 3.0) * t_0)))))))
	elif x1 <= 16200.0:
		tmp = x1 + ((3.0 * (((t_4 - (2.0 * x2)) - x1) / t_2)) + (x1 + (t_3 + (t_7 + (t_2 * (t_6 + (t_1 * t_0)))))))
	elif x1 <= 1.32e+154:
		tmp = x1 + (9.0 + (x1 + (t_3 + (t_7 + (t_2 * (t_6 + (12.0 * (x2 / x1))))))))
	else:
		tmp = x1 + (x1 * (1.0 + (4.0 * (x2 * t_1))))
	return tmp

Julia

function code(x1, x2)
	t_0 = Float64(Float64(x1 * 2.0) * Float64(Float64(2.0 * x2) - x1))
	t_1 = Float64(Float64(2.0 * x2) - 3.0)
	t_2 = Float64(Float64(x1 * x1) + 1.0)
	t_3 = Float64(x1 * Float64(x1 * x1))
	t_4 = Float64(x1 * Float64(x1 * 3.0))
	t_5 = Float64(Float64(Float64(t_4 + Float64(2.0 * x2)) - x1) / t_2)
	t_6 = Float64(Float64(x1 * x1) * Float64(Float64(t_5 * 4.0) - 6.0))
	t_7 = Float64(t_4 * t_5)
	tmp = 0.0
	if (x1 <= -145.0)
		tmp = Float64(x1 + Float64(9.0 + Float64(x1 + Float64(t_3 + Float64(t_7 + Float64(t_2 * Float64(t_6 + Float64(Float64(t_5 - 3.0) * t_0))))))));
	elseif (x1 <= 16200.0)
		tmp = Float64(x1 + Float64(Float64(3.0 * Float64(Float64(Float64(t_4 - Float64(2.0 * x2)) - x1) / t_2)) + Float64(x1 + Float64(t_3 + Float64(t_7 + Float64(t_2 * Float64(t_6 + Float64(t_1 * t_0))))))));
	elseif (x1 <= 1.32e+154)
		tmp = Float64(x1 + Float64(9.0 + Float64(x1 + Float64(t_3 + Float64(t_7 + Float64(t_2 * Float64(t_6 + Float64(12.0 * Float64(x2 / x1)))))))));
	else
		tmp = Float64(x1 + Float64(x1 * Float64(1.0 + Float64(4.0 * Float64(x2 * t_1)))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x1, x2)
	t_0 = (x1 * 2.0) * ((2.0 * x2) - x1);
	t_1 = (2.0 * x2) - 3.0;
	t_2 = (x1 * x1) + 1.0;
	t_3 = x1 * (x1 * x1);
	t_4 = x1 * (x1 * 3.0);
	t_5 = ((t_4 + (2.0 * x2)) - x1) / t_2;
	t_6 = (x1 * x1) * ((t_5 * 4.0) - 6.0);
	t_7 = t_4 * t_5;
	tmp = 0.0;
	if (x1 <= -145.0)
		tmp = x1 + (9.0 + (x1 + (t_3 + (t_7 + (t_2 * (t_6 + ((t_5 - 3.0) * t_0)))))));
	elseif (x1 <= 16200.0)
		tmp = x1 + ((3.0 * (((t_4 - (2.0 * x2)) - x1) / t_2)) + (x1 + (t_3 + (t_7 + (t_2 * (t_6 + (t_1 * t_0)))))));
	elseif (x1 <= 1.32e+154)
		tmp = x1 + (9.0 + (x1 + (t_3 + (t_7 + (t_2 * (t_6 + (12.0 * (x2 / x1))))))));
	else
		tmp = x1 + (x1 * (1.0 + (4.0 * (x2 * t_1))));
	end
	tmp_2 = tmp;
end

Wolfram

code[x1_, x2_] := Block[{t$95$0 = N[(N[(x1 * 2.0), $MachinePrecision] * N[(N[(2.0 * x2), $MachinePrecision] - x1), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(N[(2.0 * x2), $MachinePrecision] - 3.0), $MachinePrecision]}, Block[{t$95$2 = N[(N[(x1 * x1), $MachinePrecision] + 1.0), $MachinePrecision]}, Block[{t$95$3 = N[(x1 * N[(x1 * x1), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$4 = N[(x1 * N[(x1 * 3.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$5 = N[(N[(N[(t$95$4 + N[(2.0 * x2), $MachinePrecision]), $MachinePrecision] - x1), $MachinePrecision] / t$95$2), $MachinePrecision]}, Block[{t$95$6 = N[(N[(x1 * x1), $MachinePrecision] * N[(N[(t$95$5 * 4.0), $MachinePrecision] - 6.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$7 = N[(t$95$4 * t$95$5), $MachinePrecision]}, If[LessEqual[x1, -145.0], N[(x1 + N[(9.0 + N[(x1 + N[(t$95$3 + N[(t$95$7 + N[(t$95$2 * N[(t$95$6 + N[(N[(t$95$5 - 3.0), $MachinePrecision] * t$95$0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, 16200.0], N[(x1 + N[(N[(3.0 * N[(N[(N[(t$95$4 - N[(2.0 * x2), $MachinePrecision]), $MachinePrecision] - x1), $MachinePrecision] / t$95$2), $MachinePrecision]), $MachinePrecision] + N[(x1 + N[(t$95$3 + N[(t$95$7 + N[(t$95$2 * N[(t$95$6 + N[(t$95$1 * t$95$0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, 1.32e+154], N[(x1 + N[(9.0 + N[(x1 + N[(t$95$3 + N[(t$95$7 + N[(t$95$2 * N[(t$95$6 + N[(12.0 * N[(x2 / x1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x1 + N[(x1 * N[(1.0 + N[(4.0 * N[(x2 * t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]]]]]]]

Derivation

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

Alternative 9: 72.4% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := x1 \cdot x1 + 1\\ t_1 := 2 \cdot x2 - x1\\ t_2 := x1 \cdot \left(x1 \cdot 3\right)\\ t_3 := 3 \cdot \frac{\left(t_2 - 2 \cdot x2\right) - x1}{t_0}\\ t_4 := \frac{\left(t_2 + 2 \cdot x2\right) - x1}{t_0}\\ t_5 := t_2 \cdot t_4\\ t_6 := \left(x1 \cdot x1\right) \cdot \left(t_4 \cdot 4 - 6\right)\\ t_7 := x1 \cdot \left(x1 \cdot x1\right)\\ t_8 := 2 \cdot x2 - 3\\ t_9 := x2 \cdot t_8\\ \mathbf{if}\;x1 \leq -145:\\ \;\;\;\;x1 + \left(t_3 + \left(x1 + \left(t_7 + \left(t_5 + t_0 \cdot \left(t_6 + 4 \cdot \left(x1 \cdot t_9\right)\right)\right)\right)\right)\right)\\ \mathbf{elif}\;x1 \leq 106000:\\ \;\;\;\;x1 + \left(t_3 + \left(x1 + \left(t_7 + \left(t_0 \cdot \left(t_6 + t_8 \cdot \left(\left(x1 \cdot 2\right) \cdot t_1\right)\right) + t_2 \cdot t_1\right)\right)\right)\right)\\ \mathbf{elif}\;x1 \leq 1.32 \cdot 10^{+154}:\\ \;\;\;\;x1 + \left(9 + \left(x1 + \left(t_7 + \left(t_5 + t_0 \cdot \left(t_6 + 12 \cdot \frac{x2}{x1}\right)\right)\right)\right)\right)\\ \mathbf{else}:\\ \;\;\;\;x1 + x1 \cdot \left(1 + 4 \cdot t_9\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x1 x2)
 :precision binary64
 (let* ((t_0 (+ (* x1 x1) 1.0))
        (t_1 (- (* 2.0 x2) x1))
        (t_2 (* x1 (* x1 3.0)))
        (t_3 (* 3.0 (/ (- (- t_2 (* 2.0 x2)) x1) t_0)))
        (t_4 (/ (- (+ t_2 (* 2.0 x2)) x1) t_0))
        (t_5 (* t_2 t_4))
        (t_6 (* (* x1 x1) (- (* t_4 4.0) 6.0)))
        (t_7 (* x1 (* x1 x1)))
        (t_8 (- (* 2.0 x2) 3.0))
        (t_9 (* x2 t_8)))
   (if (<= x1 -145.0)
     (+ x1 (+ t_3 (+ x1 (+ t_7 (+ t_5 (* t_0 (+ t_6 (* 4.0 (* x1 t_9)))))))))
     (if (<= x1 106000.0)
       (+
        x1
        (+
         t_3
         (+
          x1
          (+ t_7 (+ (* t_0 (+ t_6 (* t_8 (* (* x1 2.0) t_1)))) (* t_2 t_1))))))
       (if (<= x1 1.32e+154)
         (+
          x1
          (+ 9.0 (+ x1 (+ t_7 (+ t_5 (* t_0 (+ t_6 (* 12.0 (/ x2 x1)))))))))
         (+ x1 (* x1 (+ 1.0 (* 4.0 t_9)))))))))

C

double code(double x1, double x2) {
	double t_0 = (x1 * x1) + 1.0;
	double t_1 = (2.0 * x2) - x1;
	double t_2 = x1 * (x1 * 3.0);
	double t_3 = 3.0 * (((t_2 - (2.0 * x2)) - x1) / t_0);
	double t_4 = ((t_2 + (2.0 * x2)) - x1) / t_0;
	double t_5 = t_2 * t_4;
	double t_6 = (x1 * x1) * ((t_4 * 4.0) - 6.0);
	double t_7 = x1 * (x1 * x1);
	double t_8 = (2.0 * x2) - 3.0;
	double t_9 = x2 * t_8;
	double tmp;
	if (x1 <= -145.0) {
		tmp = x1 + (t_3 + (x1 + (t_7 + (t_5 + (t_0 * (t_6 + (4.0 * (x1 * t_9))))))));
	} else if (x1 <= 106000.0) {
		tmp = x1 + (t_3 + (x1 + (t_7 + ((t_0 * (t_6 + (t_8 * ((x1 * 2.0) * t_1)))) + (t_2 * t_1)))));
	} else if (x1 <= 1.32e+154) {
		tmp = x1 + (9.0 + (x1 + (t_7 + (t_5 + (t_0 * (t_6 + (12.0 * (x2 / x1))))))));
	} else {
		tmp = x1 + (x1 * (1.0 + (4.0 * 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 = (2.0d0 * x2) - x1
    t_2 = x1 * (x1 * 3.0d0)
    t_3 = 3.0d0 * (((t_2 - (2.0d0 * x2)) - x1) / t_0)
    t_4 = ((t_2 + (2.0d0 * x2)) - x1) / t_0
    t_5 = t_2 * t_4
    t_6 = (x1 * x1) * ((t_4 * 4.0d0) - 6.0d0)
    t_7 = x1 * (x1 * x1)
    t_8 = (2.0d0 * x2) - 3.0d0
    t_9 = x2 * t_8
    if (x1 <= (-145.0d0)) then
        tmp = x1 + (t_3 + (x1 + (t_7 + (t_5 + (t_0 * (t_6 + (4.0d0 * (x1 * t_9))))))))
    else if (x1 <= 106000.0d0) then
        tmp = x1 + (t_3 + (x1 + (t_7 + ((t_0 * (t_6 + (t_8 * ((x1 * 2.0d0) * t_1)))) + (t_2 * t_1)))))
    else if (x1 <= 1.32d+154) then
        tmp = x1 + (9.0d0 + (x1 + (t_7 + (t_5 + (t_0 * (t_6 + (12.0d0 * (x2 / x1))))))))
    else
        tmp = x1 + (x1 * (1.0d0 + (4.0d0 * 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 = (2.0 * x2) - x1;
	double t_2 = x1 * (x1 * 3.0);
	double t_3 = 3.0 * (((t_2 - (2.0 * x2)) - x1) / t_0);
	double t_4 = ((t_2 + (2.0 * x2)) - x1) / t_0;
	double t_5 = t_2 * t_4;
	double t_6 = (x1 * x1) * ((t_4 * 4.0) - 6.0);
	double t_7 = x1 * (x1 * x1);
	double t_8 = (2.0 * x2) - 3.0;
	double t_9 = x2 * t_8;
	double tmp;
	if (x1 <= -145.0) {
		tmp = x1 + (t_3 + (x1 + (t_7 + (t_5 + (t_0 * (t_6 + (4.0 * (x1 * t_9))))))));
	} else if (x1 <= 106000.0) {
		tmp = x1 + (t_3 + (x1 + (t_7 + ((t_0 * (t_6 + (t_8 * ((x1 * 2.0) * t_1)))) + (t_2 * t_1)))));
	} else if (x1 <= 1.32e+154) {
		tmp = x1 + (9.0 + (x1 + (t_7 + (t_5 + (t_0 * (t_6 + (12.0 * (x2 / x1))))))));
	} else {
		tmp = x1 + (x1 * (1.0 + (4.0 * t_9)));
	}
	return tmp;
}

Python

def code(x1, x2):
	t_0 = (x1 * x1) + 1.0
	t_1 = (2.0 * x2) - x1
	t_2 = x1 * (x1 * 3.0)
	t_3 = 3.0 * (((t_2 - (2.0 * x2)) - x1) / t_0)
	t_4 = ((t_2 + (2.0 * x2)) - x1) / t_0
	t_5 = t_2 * t_4
	t_6 = (x1 * x1) * ((t_4 * 4.0) - 6.0)
	t_7 = x1 * (x1 * x1)
	t_8 = (2.0 * x2) - 3.0
	t_9 = x2 * t_8
	tmp = 0
	if x1 <= -145.0:
		tmp = x1 + (t_3 + (x1 + (t_7 + (t_5 + (t_0 * (t_6 + (4.0 * (x1 * t_9))))))))
	elif x1 <= 106000.0:
		tmp = x1 + (t_3 + (x1 + (t_7 + ((t_0 * (t_6 + (t_8 * ((x1 * 2.0) * t_1)))) + (t_2 * t_1)))))
	elif x1 <= 1.32e+154:
		tmp = x1 + (9.0 + (x1 + (t_7 + (t_5 + (t_0 * (t_6 + (12.0 * (x2 / x1))))))))
	else:
		tmp = x1 + (x1 * (1.0 + (4.0 * t_9)))
	return tmp

Julia

function code(x1, x2)
	t_0 = Float64(Float64(x1 * x1) + 1.0)
	t_1 = Float64(Float64(2.0 * x2) - 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(Float64(Float64(t_2 + Float64(2.0 * x2)) - x1) / t_0)
	t_5 = Float64(t_2 * t_4)
	t_6 = Float64(Float64(x1 * x1) * Float64(Float64(t_4 * 4.0) - 6.0))
	t_7 = Float64(x1 * Float64(x1 * x1))
	t_8 = Float64(Float64(2.0 * x2) - 3.0)
	t_9 = Float64(x2 * t_8)
	tmp = 0.0
	if (x1 <= -145.0)
		tmp = Float64(x1 + Float64(t_3 + Float64(x1 + Float64(t_7 + Float64(t_5 + Float64(t_0 * Float64(t_6 + Float64(4.0 * Float64(x1 * t_9)))))))));
	elseif (x1 <= 106000.0)
		tmp = Float64(x1 + Float64(t_3 + Float64(x1 + Float64(t_7 + Float64(Float64(t_0 * Float64(t_6 + Float64(t_8 * Float64(Float64(x1 * 2.0) * t_1)))) + Float64(t_2 * t_1))))));
	elseif (x1 <= 1.32e+154)
		tmp = Float64(x1 + Float64(9.0 + Float64(x1 + Float64(t_7 + Float64(t_5 + Float64(t_0 * Float64(t_6 + Float64(12.0 * Float64(x2 / x1)))))))));
	else
		tmp = Float64(x1 + Float64(x1 * Float64(1.0 + Float64(4.0 * t_9))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x1, x2)
	t_0 = (x1 * x1) + 1.0;
	t_1 = (2.0 * x2) - x1;
	t_2 = x1 * (x1 * 3.0);
	t_3 = 3.0 * (((t_2 - (2.0 * x2)) - x1) / t_0);
	t_4 = ((t_2 + (2.0 * x2)) - x1) / t_0;
	t_5 = t_2 * t_4;
	t_6 = (x1 * x1) * ((t_4 * 4.0) - 6.0);
	t_7 = x1 * (x1 * x1);
	t_8 = (2.0 * x2) - 3.0;
	t_9 = x2 * t_8;
	tmp = 0.0;
	if (x1 <= -145.0)
		tmp = x1 + (t_3 + (x1 + (t_7 + (t_5 + (t_0 * (t_6 + (4.0 * (x1 * t_9))))))));
	elseif (x1 <= 106000.0)
		tmp = x1 + (t_3 + (x1 + (t_7 + ((t_0 * (t_6 + (t_8 * ((x1 * 2.0) * t_1)))) + (t_2 * t_1)))));
	elseif (x1 <= 1.32e+154)
		tmp = x1 + (9.0 + (x1 + (t_7 + (t_5 + (t_0 * (t_6 + (12.0 * (x2 / x1))))))));
	else
		tmp = x1 + (x1 * (1.0 + (4.0 * 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[(N[(2.0 * x2), $MachinePrecision] - x1), $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[(N[(N[(t$95$2 + N[(2.0 * x2), $MachinePrecision]), $MachinePrecision] - x1), $MachinePrecision] / t$95$0), $MachinePrecision]}, Block[{t$95$5 = N[(t$95$2 * t$95$4), $MachinePrecision]}, Block[{t$95$6 = N[(N[(x1 * x1), $MachinePrecision] * N[(N[(t$95$4 * 4.0), $MachinePrecision] - 6.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$7 = N[(x1 * N[(x1 * x1), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$8 = N[(N[(2.0 * x2), $MachinePrecision] - 3.0), $MachinePrecision]}, Block[{t$95$9 = N[(x2 * t$95$8), $MachinePrecision]}, If[LessEqual[x1, -145.0], N[(x1 + N[(t$95$3 + N[(x1 + N[(t$95$7 + N[(t$95$5 + N[(t$95$0 * N[(t$95$6 + N[(4.0 * N[(x1 * t$95$9), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, 106000.0], N[(x1 + N[(t$95$3 + N[(x1 + N[(t$95$7 + N[(N[(t$95$0 * N[(t$95$6 + N[(t$95$8 * N[(N[(x1 * 2.0), $MachinePrecision] * t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(t$95$2 * t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, 1.32e+154], N[(x1 + N[(9.0 + N[(x1 + N[(t$95$7 + N[(t$95$5 + N[(t$95$0 * N[(t$95$6 + N[(12.0 * N[(x2 / x1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x1 + N[(x1 * N[(1.0 + N[(4.0 * t$95$9), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]]]]]]]]]

Derivation

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

Alternative 10: 73.1% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := x1 \cdot x1 + 1\\ t_1 := x1 \cdot \left(x1 \cdot x1\right)\\ t_2 := x1 \cdot \left(x1 \cdot 3\right)\\ t_3 := \frac{\left(t_2 + 2 \cdot x2\right) - x1}{t_0}\\ t_4 := \left(x1 \cdot x1\right) \cdot \left(t_3 \cdot 4 - 6\right)\\ t_5 := t_2 \cdot t_3\\ t_6 := 2 \cdot x2 - x1\\ t_7 := \left(x1 \cdot 2\right) \cdot t_6\\ t_8 := 2 \cdot x2 - 3\\ \mathbf{if}\;x1 \leq -145:\\ \;\;\;\;x1 + \left(9 + \left(x1 + \left(t_1 + \left(t_5 + t_0 \cdot \left(t_4 + \left(t_3 - 3\right) \cdot t_7\right)\right)\right)\right)\right)\\ \mathbf{elif}\;x1 \leq 39000:\\ \;\;\;\;x1 + \left(3 \cdot \frac{\left(t_2 - 2 \cdot x2\right) - x1}{t_0} + \left(x1 + \left(t_1 + \left(t_0 \cdot \left(t_4 + t_8 \cdot t_7\right) + t_2 \cdot t_6\right)\right)\right)\right)\\ \mathbf{elif}\;x1 \leq 1.32 \cdot 10^{+154}:\\ \;\;\;\;x1 + \left(9 + \left(x1 + \left(t_1 + \left(t_5 + t_0 \cdot \left(t_4 + 12 \cdot \frac{x2}{x1}\right)\right)\right)\right)\right)\\ \mathbf{else}:\\ \;\;\;\;x1 + x1 \cdot \left(1 + 4 \cdot \left(x2 \cdot t_8\right)\right)\\ \end{array} \end{array} \]

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 (/ (- (+ t_2 (* 2.0 x2)) x1) t_0))
        (t_4 (* (* x1 x1) (- (* t_3 4.0) 6.0)))
        (t_5 (* t_2 t_3))
        (t_6 (- (* 2.0 x2) x1))
        (t_7 (* (* x1 2.0) t_6))
        (t_8 (- (* 2.0 x2) 3.0)))
   (if (<= x1 -145.0)
     (+ x1 (+ 9.0 (+ x1 (+ t_1 (+ t_5 (* t_0 (+ t_4 (* (- t_3 3.0) t_7))))))))
     (if (<= x1 39000.0)
       (+
        x1
        (+
         (* 3.0 (/ (- (- t_2 (* 2.0 x2)) x1) t_0))
         (+ x1 (+ t_1 (+ (* t_0 (+ t_4 (* t_8 t_7))) (* t_2 t_6))))))
       (if (<= x1 1.32e+154)
         (+
          x1
          (+ 9.0 (+ x1 (+ t_1 (+ t_5 (* t_0 (+ t_4 (* 12.0 (/ x2 x1)))))))))
         (+ x1 (* x1 (+ 1.0 (* 4.0 (* x2 t_8))))))))))

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 = ((t_2 + (2.0 * x2)) - x1) / t_0;
	double t_4 = (x1 * x1) * ((t_3 * 4.0) - 6.0);
	double t_5 = t_2 * t_3;
	double t_6 = (2.0 * x2) - x1;
	double t_7 = (x1 * 2.0) * t_6;
	double t_8 = (2.0 * x2) - 3.0;
	double tmp;
	if (x1 <= -145.0) {
		tmp = x1 + (9.0 + (x1 + (t_1 + (t_5 + (t_0 * (t_4 + ((t_3 - 3.0) * t_7)))))));
	} else if (x1 <= 39000.0) {
		tmp = x1 + ((3.0 * (((t_2 - (2.0 * x2)) - x1) / t_0)) + (x1 + (t_1 + ((t_0 * (t_4 + (t_8 * t_7))) + (t_2 * t_6)))));
	} else if (x1 <= 1.32e+154) {
		tmp = x1 + (9.0 + (x1 + (t_1 + (t_5 + (t_0 * (t_4 + (12.0 * (x2 / x1))))))));
	} else {
		tmp = x1 + (x1 * (1.0 + (4.0 * (x2 * t_8))));
	}
	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) + 1.0d0
    t_1 = x1 * (x1 * x1)
    t_2 = x1 * (x1 * 3.0d0)
    t_3 = ((t_2 + (2.0d0 * x2)) - x1) / t_0
    t_4 = (x1 * x1) * ((t_3 * 4.0d0) - 6.0d0)
    t_5 = t_2 * t_3
    t_6 = (2.0d0 * x2) - x1
    t_7 = (x1 * 2.0d0) * t_6
    t_8 = (2.0d0 * x2) - 3.0d0
    if (x1 <= (-145.0d0)) then
        tmp = x1 + (9.0d0 + (x1 + (t_1 + (t_5 + (t_0 * (t_4 + ((t_3 - 3.0d0) * t_7)))))))
    else if (x1 <= 39000.0d0) then
        tmp = x1 + ((3.0d0 * (((t_2 - (2.0d0 * x2)) - x1) / t_0)) + (x1 + (t_1 + ((t_0 * (t_4 + (t_8 * t_7))) + (t_2 * t_6)))))
    else if (x1 <= 1.32d+154) then
        tmp = x1 + (9.0d0 + (x1 + (t_1 + (t_5 + (t_0 * (t_4 + (12.0d0 * (x2 / x1))))))))
    else
        tmp = x1 + (x1 * (1.0d0 + (4.0d0 * (x2 * t_8))))
    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 = ((t_2 + (2.0 * x2)) - x1) / t_0;
	double t_4 = (x1 * x1) * ((t_3 * 4.0) - 6.0);
	double t_5 = t_2 * t_3;
	double t_6 = (2.0 * x2) - x1;
	double t_7 = (x1 * 2.0) * t_6;
	double t_8 = (2.0 * x2) - 3.0;
	double tmp;
	if (x1 <= -145.0) {
		tmp = x1 + (9.0 + (x1 + (t_1 + (t_5 + (t_0 * (t_4 + ((t_3 - 3.0) * t_7)))))));
	} else if (x1 <= 39000.0) {
		tmp = x1 + ((3.0 * (((t_2 - (2.0 * x2)) - x1) / t_0)) + (x1 + (t_1 + ((t_0 * (t_4 + (t_8 * t_7))) + (t_2 * t_6)))));
	} else if (x1 <= 1.32e+154) {
		tmp = x1 + (9.0 + (x1 + (t_1 + (t_5 + (t_0 * (t_4 + (12.0 * (x2 / x1))))))));
	} else {
		tmp = x1 + (x1 * (1.0 + (4.0 * (x2 * t_8))));
	}
	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 = ((t_2 + (2.0 * x2)) - x1) / t_0
	t_4 = (x1 * x1) * ((t_3 * 4.0) - 6.0)
	t_5 = t_2 * t_3
	t_6 = (2.0 * x2) - x1
	t_7 = (x1 * 2.0) * t_6
	t_8 = (2.0 * x2) - 3.0
	tmp = 0
	if x1 <= -145.0:
		tmp = x1 + (9.0 + (x1 + (t_1 + (t_5 + (t_0 * (t_4 + ((t_3 - 3.0) * t_7)))))))
	elif x1 <= 39000.0:
		tmp = x1 + ((3.0 * (((t_2 - (2.0 * x2)) - x1) / t_0)) + (x1 + (t_1 + ((t_0 * (t_4 + (t_8 * t_7))) + (t_2 * t_6)))))
	elif x1 <= 1.32e+154:
		tmp = x1 + (9.0 + (x1 + (t_1 + (t_5 + (t_0 * (t_4 + (12.0 * (x2 / x1))))))))
	else:
		tmp = x1 + (x1 * (1.0 + (4.0 * (x2 * t_8))))
	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(Float64(Float64(t_2 + Float64(2.0 * x2)) - x1) / t_0)
	t_4 = Float64(Float64(x1 * x1) * Float64(Float64(t_3 * 4.0) - 6.0))
	t_5 = Float64(t_2 * t_3)
	t_6 = Float64(Float64(2.0 * x2) - x1)
	t_7 = Float64(Float64(x1 * 2.0) * t_6)
	t_8 = Float64(Float64(2.0 * x2) - 3.0)
	tmp = 0.0
	if (x1 <= -145.0)
		tmp = Float64(x1 + Float64(9.0 + Float64(x1 + Float64(t_1 + Float64(t_5 + Float64(t_0 * Float64(t_4 + Float64(Float64(t_3 - 3.0) * t_7))))))));
	elseif (x1 <= 39000.0)
		tmp = Float64(x1 + Float64(Float64(3.0 * Float64(Float64(Float64(t_2 - Float64(2.0 * x2)) - x1) / t_0)) + Float64(x1 + Float64(t_1 + Float64(Float64(t_0 * Float64(t_4 + Float64(t_8 * t_7))) + Float64(t_2 * t_6))))));
	elseif (x1 <= 1.32e+154)
		tmp = Float64(x1 + Float64(9.0 + Float64(x1 + Float64(t_1 + Float64(t_5 + Float64(t_0 * Float64(t_4 + Float64(12.0 * Float64(x2 / x1)))))))));
	else
		tmp = Float64(x1 + Float64(x1 * Float64(1.0 + Float64(4.0 * Float64(x2 * t_8)))));
	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 = ((t_2 + (2.0 * x2)) - x1) / t_0;
	t_4 = (x1 * x1) * ((t_3 * 4.0) - 6.0);
	t_5 = t_2 * t_3;
	t_6 = (2.0 * x2) - x1;
	t_7 = (x1 * 2.0) * t_6;
	t_8 = (2.0 * x2) - 3.0;
	tmp = 0.0;
	if (x1 <= -145.0)
		tmp = x1 + (9.0 + (x1 + (t_1 + (t_5 + (t_0 * (t_4 + ((t_3 - 3.0) * t_7)))))));
	elseif (x1 <= 39000.0)
		tmp = x1 + ((3.0 * (((t_2 - (2.0 * x2)) - x1) / t_0)) + (x1 + (t_1 + ((t_0 * (t_4 + (t_8 * t_7))) + (t_2 * t_6)))));
	elseif (x1 <= 1.32e+154)
		tmp = x1 + (9.0 + (x1 + (t_1 + (t_5 + (t_0 * (t_4 + (12.0 * (x2 / x1))))))));
	else
		tmp = x1 + (x1 * (1.0 + (4.0 * (x2 * t_8))));
	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[(N[(N[(t$95$2 + N[(2.0 * x2), $MachinePrecision]), $MachinePrecision] - x1), $MachinePrecision] / t$95$0), $MachinePrecision]}, Block[{t$95$4 = N[(N[(x1 * x1), $MachinePrecision] * N[(N[(t$95$3 * 4.0), $MachinePrecision] - 6.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$5 = N[(t$95$2 * t$95$3), $MachinePrecision]}, Block[{t$95$6 = N[(N[(2.0 * x2), $MachinePrecision] - x1), $MachinePrecision]}, Block[{t$95$7 = N[(N[(x1 * 2.0), $MachinePrecision] * t$95$6), $MachinePrecision]}, Block[{t$95$8 = N[(N[(2.0 * x2), $MachinePrecision] - 3.0), $MachinePrecision]}, If[LessEqual[x1, -145.0], N[(x1 + N[(9.0 + N[(x1 + N[(t$95$1 + N[(t$95$5 + N[(t$95$0 * N[(t$95$4 + N[(N[(t$95$3 - 3.0), $MachinePrecision] * t$95$7), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, 39000.0], N[(x1 + N[(N[(3.0 * N[(N[(N[(t$95$2 - N[(2.0 * x2), $MachinePrecision]), $MachinePrecision] - x1), $MachinePrecision] / t$95$0), $MachinePrecision]), $MachinePrecision] + N[(x1 + N[(t$95$1 + N[(N[(t$95$0 * N[(t$95$4 + N[(t$95$8 * t$95$7), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(t$95$2 * t$95$6), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, 1.32e+154], N[(x1 + N[(9.0 + N[(x1 + N[(t$95$1 + N[(t$95$5 + N[(t$95$0 * N[(t$95$4 + N[(12.0 * N[(x2 / x1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x1 + N[(x1 * N[(1.0 + N[(4.0 * N[(x2 * t$95$8), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]]]]]]]]

Derivation

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

Alternative 11: 73.0% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := 2 \cdot x2 - 3\\ t_1 := x1 \cdot x1 + 1\\ t_2 := x1 \cdot \left(x1 \cdot x1\right)\\ t_3 := x1 \cdot \left(x1 \cdot 3\right)\\ t_4 := \frac{\left(t_3 + 2 \cdot x2\right) - x1}{t_1}\\ t_5 := \left(x1 \cdot x1\right) \cdot \left(t_4 \cdot 4 - 6\right)\\ t_6 := x1 + \left(9 + \left(x1 + \left(t_2 + \left(t_3 \cdot t_4 + t_1 \cdot \left(t_5 + 12 \cdot \frac{x2}{x1}\right)\right)\right)\right)\right)\\ t_7 := 2 \cdot x2 - x1\\ \mathbf{if}\;x1 \leq -700:\\ \;\;\;\;t_6\\ \mathbf{elif}\;x1 \leq 290000:\\ \;\;\;\;x1 + \left(3 \cdot \frac{\left(t_3 - 2 \cdot x2\right) - x1}{t_1} + \left(x1 + \left(t_2 + \left(t_1 \cdot \left(t_5 + t_0 \cdot \left(\left(x1 \cdot 2\right) \cdot t_7\right)\right) + t_3 \cdot t_7\right)\right)\right)\right)\\ \mathbf{elif}\;x1 \leq 1.32 \cdot 10^{+154}:\\ \;\;\;\;t_6\\ \mathbf{else}:\\ \;\;\;\;x1 + x1 \cdot \left(1 + 4 \cdot \left(x2 \cdot t_0\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x1 x2)
 :precision binary64
 (let* ((t_0 (- (* 2.0 x2) 3.0))
        (t_1 (+ (* x1 x1) 1.0))
        (t_2 (* x1 (* x1 x1)))
        (t_3 (* x1 (* x1 3.0)))
        (t_4 (/ (- (+ t_3 (* 2.0 x2)) x1) t_1))
        (t_5 (* (* x1 x1) (- (* t_4 4.0) 6.0)))
        (t_6
         (+
          x1
          (+
           9.0
           (+ x1 (+ t_2 (+ (* t_3 t_4) (* t_1 (+ t_5 (* 12.0 (/ x2 x1))))))))))
        (t_7 (- (* 2.0 x2) x1)))
   (if (<= x1 -700.0)
     t_6
     (if (<= x1 290000.0)
       (+
        x1
        (+
         (* 3.0 (/ (- (- t_3 (* 2.0 x2)) x1) t_1))
         (+
          x1
          (+ t_2 (+ (* t_1 (+ t_5 (* t_0 (* (* x1 2.0) t_7)))) (* t_3 t_7))))))
       (if (<= x1 1.32e+154) t_6 (+ x1 (* x1 (+ 1.0 (* 4.0 (* x2 t_0))))))))))

C

double code(double x1, double x2) {
	double t_0 = (2.0 * x2) - 3.0;
	double t_1 = (x1 * x1) + 1.0;
	double t_2 = x1 * (x1 * x1);
	double t_3 = x1 * (x1 * 3.0);
	double t_4 = ((t_3 + (2.0 * x2)) - x1) / t_1;
	double t_5 = (x1 * x1) * ((t_4 * 4.0) - 6.0);
	double t_6 = x1 + (9.0 + (x1 + (t_2 + ((t_3 * t_4) + (t_1 * (t_5 + (12.0 * (x2 / x1))))))));
	double t_7 = (2.0 * x2) - x1;
	double tmp;
	if (x1 <= -700.0) {
		tmp = t_6;
	} else if (x1 <= 290000.0) {
		tmp = x1 + ((3.0 * (((t_3 - (2.0 * x2)) - x1) / t_1)) + (x1 + (t_2 + ((t_1 * (t_5 + (t_0 * ((x1 * 2.0) * t_7)))) + (t_3 * t_7)))));
	} else if (x1 <= 1.32e+154) {
		tmp = t_6;
	} else {
		tmp = x1 + (x1 * (1.0 + (4.0 * (x2 * 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) :: 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) - 3.0d0
    t_1 = (x1 * x1) + 1.0d0
    t_2 = x1 * (x1 * x1)
    t_3 = x1 * (x1 * 3.0d0)
    t_4 = ((t_3 + (2.0d0 * x2)) - x1) / t_1
    t_5 = (x1 * x1) * ((t_4 * 4.0d0) - 6.0d0)
    t_6 = x1 + (9.0d0 + (x1 + (t_2 + ((t_3 * t_4) + (t_1 * (t_5 + (12.0d0 * (x2 / x1))))))))
    t_7 = (2.0d0 * x2) - x1
    if (x1 <= (-700.0d0)) then
        tmp = t_6
    else if (x1 <= 290000.0d0) then
        tmp = x1 + ((3.0d0 * (((t_3 - (2.0d0 * x2)) - x1) / t_1)) + (x1 + (t_2 + ((t_1 * (t_5 + (t_0 * ((x1 * 2.0d0) * t_7)))) + (t_3 * t_7)))))
    else if (x1 <= 1.32d+154) then
        tmp = t_6
    else
        tmp = x1 + (x1 * (1.0d0 + (4.0d0 * (x2 * t_0))))
    end if
    code = tmp
end function

Java

public static double code(double x1, double x2) {
	double t_0 = (2.0 * x2) - 3.0;
	double t_1 = (x1 * x1) + 1.0;
	double t_2 = x1 * (x1 * x1);
	double t_3 = x1 * (x1 * 3.0);
	double t_4 = ((t_3 + (2.0 * x2)) - x1) / t_1;
	double t_5 = (x1 * x1) * ((t_4 * 4.0) - 6.0);
	double t_6 = x1 + (9.0 + (x1 + (t_2 + ((t_3 * t_4) + (t_1 * (t_5 + (12.0 * (x2 / x1))))))));
	double t_7 = (2.0 * x2) - x1;
	double tmp;
	if (x1 <= -700.0) {
		tmp = t_6;
	} else if (x1 <= 290000.0) {
		tmp = x1 + ((3.0 * (((t_3 - (2.0 * x2)) - x1) / t_1)) + (x1 + (t_2 + ((t_1 * (t_5 + (t_0 * ((x1 * 2.0) * t_7)))) + (t_3 * t_7)))));
	} else if (x1 <= 1.32e+154) {
		tmp = t_6;
	} else {
		tmp = x1 + (x1 * (1.0 + (4.0 * (x2 * t_0))));
	}
	return tmp;
}

Python

def code(x1, x2):
	t_0 = (2.0 * x2) - 3.0
	t_1 = (x1 * x1) + 1.0
	t_2 = x1 * (x1 * x1)
	t_3 = x1 * (x1 * 3.0)
	t_4 = ((t_3 + (2.0 * x2)) - x1) / t_1
	t_5 = (x1 * x1) * ((t_4 * 4.0) - 6.0)
	t_6 = x1 + (9.0 + (x1 + (t_2 + ((t_3 * t_4) + (t_1 * (t_5 + (12.0 * (x2 / x1))))))))
	t_7 = (2.0 * x2) - x1
	tmp = 0
	if x1 <= -700.0:
		tmp = t_6
	elif x1 <= 290000.0:
		tmp = x1 + ((3.0 * (((t_3 - (2.0 * x2)) - x1) / t_1)) + (x1 + (t_2 + ((t_1 * (t_5 + (t_0 * ((x1 * 2.0) * t_7)))) + (t_3 * t_7)))))
	elif x1 <= 1.32e+154:
		tmp = t_6
	else:
		tmp = x1 + (x1 * (1.0 + (4.0 * (x2 * t_0))))
	return tmp

Julia

function code(x1, x2)
	t_0 = Float64(Float64(2.0 * x2) - 3.0)
	t_1 = Float64(Float64(x1 * x1) + 1.0)
	t_2 = Float64(x1 * Float64(x1 * x1))
	t_3 = Float64(x1 * Float64(x1 * 3.0))
	t_4 = Float64(Float64(Float64(t_3 + Float64(2.0 * x2)) - x1) / t_1)
	t_5 = Float64(Float64(x1 * x1) * Float64(Float64(t_4 * 4.0) - 6.0))
	t_6 = Float64(x1 + Float64(9.0 + Float64(x1 + Float64(t_2 + Float64(Float64(t_3 * t_4) + Float64(t_1 * Float64(t_5 + Float64(12.0 * Float64(x2 / x1)))))))))
	t_7 = Float64(Float64(2.0 * x2) - x1)
	tmp = 0.0
	if (x1 <= -700.0)
		tmp = t_6;
	elseif (x1 <= 290000.0)
		tmp = Float64(x1 + Float64(Float64(3.0 * Float64(Float64(Float64(t_3 - Float64(2.0 * x2)) - x1) / t_1)) + Float64(x1 + Float64(t_2 + Float64(Float64(t_1 * Float64(t_5 + Float64(t_0 * Float64(Float64(x1 * 2.0) * t_7)))) + Float64(t_3 * t_7))))));
	elseif (x1 <= 1.32e+154)
		tmp = t_6;
	else
		tmp = Float64(x1 + Float64(x1 * Float64(1.0 + Float64(4.0 * Float64(x2 * t_0)))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x1, x2)
	t_0 = (2.0 * x2) - 3.0;
	t_1 = (x1 * x1) + 1.0;
	t_2 = x1 * (x1 * x1);
	t_3 = x1 * (x1 * 3.0);
	t_4 = ((t_3 + (2.0 * x2)) - x1) / t_1;
	t_5 = (x1 * x1) * ((t_4 * 4.0) - 6.0);
	t_6 = x1 + (9.0 + (x1 + (t_2 + ((t_3 * t_4) + (t_1 * (t_5 + (12.0 * (x2 / x1))))))));
	t_7 = (2.0 * x2) - x1;
	tmp = 0.0;
	if (x1 <= -700.0)
		tmp = t_6;
	elseif (x1 <= 290000.0)
		tmp = x1 + ((3.0 * (((t_3 - (2.0 * x2)) - x1) / t_1)) + (x1 + (t_2 + ((t_1 * (t_5 + (t_0 * ((x1 * 2.0) * t_7)))) + (t_3 * t_7)))));
	elseif (x1 <= 1.32e+154)
		tmp = t_6;
	else
		tmp = x1 + (x1 * (1.0 + (4.0 * (x2 * t_0))));
	end
	tmp_2 = tmp;
end

Wolfram

code[x1_, x2_] := Block[{t$95$0 = N[(N[(2.0 * x2), $MachinePrecision] - 3.0), $MachinePrecision]}, Block[{t$95$1 = N[(N[(x1 * x1), $MachinePrecision] + 1.0), $MachinePrecision]}, Block[{t$95$2 = N[(x1 * N[(x1 * x1), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(x1 * N[(x1 * 3.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$4 = N[(N[(N[(t$95$3 + N[(2.0 * x2), $MachinePrecision]), $MachinePrecision] - x1), $MachinePrecision] / t$95$1), $MachinePrecision]}, Block[{t$95$5 = N[(N[(x1 * x1), $MachinePrecision] * N[(N[(t$95$4 * 4.0), $MachinePrecision] - 6.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$6 = N[(x1 + N[(9.0 + N[(x1 + N[(t$95$2 + N[(N[(t$95$3 * t$95$4), $MachinePrecision] + N[(t$95$1 * N[(t$95$5 + N[(12.0 * N[(x2 / x1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$7 = N[(N[(2.0 * x2), $MachinePrecision] - x1), $MachinePrecision]}, If[LessEqual[x1, -700.0], t$95$6, If[LessEqual[x1, 290000.0], 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$2 + N[(N[(t$95$1 * N[(t$95$5 + N[(t$95$0 * N[(N[(x1 * 2.0), $MachinePrecision] * t$95$7), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(t$95$3 * t$95$7), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, 1.32e+154], t$95$6, N[(x1 + N[(x1 * N[(1.0 + N[(4.0 * N[(x2 * t$95$0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]]]]]]]

Derivation

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

Alternative 12: 73.0% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := 2 \cdot x2 - 3\\ t_1 := x1 \cdot x1 + 1\\ t_2 := x1 \cdot \left(x1 \cdot x1\right)\\ t_3 := x1 \cdot \left(x1 \cdot 3\right)\\ t_4 := \frac{\left(t_3 + 2 \cdot x2\right) - x1}{t_1}\\ t_5 := \left(x1 \cdot x1\right) \cdot \left(t_4 \cdot 4 - 6\right)\\ t_6 := x1 + \left(9 + \left(x1 + \left(t_2 + \left(t_3 \cdot t_4 + t_1 \cdot \left(t_5 + 12 \cdot \frac{x2}{x1}\right)\right)\right)\right)\right)\\ \mathbf{if}\;x1 \leq -700:\\ \;\;\;\;t_6\\ \mathbf{elif}\;x1 \leq 135000:\\ \;\;\;\;x1 + \left(3 \cdot \frac{\left(t_3 - 2 \cdot x2\right) - x1}{t_1} + \left(x1 + \left(t_2 + \left(t_1 \cdot \left(t_5 + t_0 \cdot \left(\left(x1 \cdot 2\right) \cdot \left(2 \cdot x2 - x1\right)\right)\right) + t_3 \cdot \left(2 \cdot x2\right)\right)\right)\right)\right)\\ \mathbf{elif}\;x1 \leq 1.32 \cdot 10^{+154}:\\ \;\;\;\;t_6\\ \mathbf{else}:\\ \;\;\;\;x1 + x1 \cdot \left(1 + 4 \cdot \left(x2 \cdot t_0\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x1 x2)
 :precision binary64
 (let* ((t_0 (- (* 2.0 x2) 3.0))
        (t_1 (+ (* x1 x1) 1.0))
        (t_2 (* x1 (* x1 x1)))
        (t_3 (* x1 (* x1 3.0)))
        (t_4 (/ (- (+ t_3 (* 2.0 x2)) x1) t_1))
        (t_5 (* (* x1 x1) (- (* t_4 4.0) 6.0)))
        (t_6
         (+
          x1
          (+
           9.0
           (+
            x1
            (+ t_2 (+ (* t_3 t_4) (* t_1 (+ t_5 (* 12.0 (/ x2 x1)))))))))))
   (if (<= x1 -700.0)
     t_6
     (if (<= x1 135000.0)
       (+
        x1
        (+
         (* 3.0 (/ (- (- t_3 (* 2.0 x2)) x1) t_1))
         (+
          x1
          (+
           t_2
           (+
            (* t_1 (+ t_5 (* t_0 (* (* x1 2.0) (- (* 2.0 x2) x1)))))
            (* t_3 (* 2.0 x2)))))))
       (if (<= x1 1.32e+154) t_6 (+ x1 (* x1 (+ 1.0 (* 4.0 (* x2 t_0))))))))))

C

double code(double x1, double x2) {
	double t_0 = (2.0 * x2) - 3.0;
	double t_1 = (x1 * x1) + 1.0;
	double t_2 = x1 * (x1 * x1);
	double t_3 = x1 * (x1 * 3.0);
	double t_4 = ((t_3 + (2.0 * x2)) - x1) / t_1;
	double t_5 = (x1 * x1) * ((t_4 * 4.0) - 6.0);
	double t_6 = x1 + (9.0 + (x1 + (t_2 + ((t_3 * t_4) + (t_1 * (t_5 + (12.0 * (x2 / x1))))))));
	double tmp;
	if (x1 <= -700.0) {
		tmp = t_6;
	} else if (x1 <= 135000.0) {
		tmp = x1 + ((3.0 * (((t_3 - (2.0 * x2)) - x1) / t_1)) + (x1 + (t_2 + ((t_1 * (t_5 + (t_0 * ((x1 * 2.0) * ((2.0 * x2) - x1))))) + (t_3 * (2.0 * x2))))));
	} else if (x1 <= 1.32e+154) {
		tmp = t_6;
	} else {
		tmp = x1 + (x1 * (1.0 + (4.0 * (x2 * 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) :: 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 = (2.0d0 * x2) - 3.0d0
    t_1 = (x1 * x1) + 1.0d0
    t_2 = x1 * (x1 * x1)
    t_3 = x1 * (x1 * 3.0d0)
    t_4 = ((t_3 + (2.0d0 * x2)) - x1) / t_1
    t_5 = (x1 * x1) * ((t_4 * 4.0d0) - 6.0d0)
    t_6 = x1 + (9.0d0 + (x1 + (t_2 + ((t_3 * t_4) + (t_1 * (t_5 + (12.0d0 * (x2 / x1))))))))
    if (x1 <= (-700.0d0)) then
        tmp = t_6
    else if (x1 <= 135000.0d0) then
        tmp = x1 + ((3.0d0 * (((t_3 - (2.0d0 * x2)) - x1) / t_1)) + (x1 + (t_2 + ((t_1 * (t_5 + (t_0 * ((x1 * 2.0d0) * ((2.0d0 * x2) - x1))))) + (t_3 * (2.0d0 * x2))))))
    else if (x1 <= 1.32d+154) then
        tmp = t_6
    else
        tmp = x1 + (x1 * (1.0d0 + (4.0d0 * (x2 * t_0))))
    end if
    code = tmp
end function

Java

public static double code(double x1, double x2) {
	double t_0 = (2.0 * x2) - 3.0;
	double t_1 = (x1 * x1) + 1.0;
	double t_2 = x1 * (x1 * x1);
	double t_3 = x1 * (x1 * 3.0);
	double t_4 = ((t_3 + (2.0 * x2)) - x1) / t_1;
	double t_5 = (x1 * x1) * ((t_4 * 4.0) - 6.0);
	double t_6 = x1 + (9.0 + (x1 + (t_2 + ((t_3 * t_4) + (t_1 * (t_5 + (12.0 * (x2 / x1))))))));
	double tmp;
	if (x1 <= -700.0) {
		tmp = t_6;
	} else if (x1 <= 135000.0) {
		tmp = x1 + ((3.0 * (((t_3 - (2.0 * x2)) - x1) / t_1)) + (x1 + (t_2 + ((t_1 * (t_5 + (t_0 * ((x1 * 2.0) * ((2.0 * x2) - x1))))) + (t_3 * (2.0 * x2))))));
	} else if (x1 <= 1.32e+154) {
		tmp = t_6;
	} else {
		tmp = x1 + (x1 * (1.0 + (4.0 * (x2 * t_0))));
	}
	return tmp;
}

Python

def code(x1, x2):
	t_0 = (2.0 * x2) - 3.0
	t_1 = (x1 * x1) + 1.0
	t_2 = x1 * (x1 * x1)
	t_3 = x1 * (x1 * 3.0)
	t_4 = ((t_3 + (2.0 * x2)) - x1) / t_1
	t_5 = (x1 * x1) * ((t_4 * 4.0) - 6.0)
	t_6 = x1 + (9.0 + (x1 + (t_2 + ((t_3 * t_4) + (t_1 * (t_5 + (12.0 * (x2 / x1))))))))
	tmp = 0
	if x1 <= -700.0:
		tmp = t_6
	elif x1 <= 135000.0:
		tmp = x1 + ((3.0 * (((t_3 - (2.0 * x2)) - x1) / t_1)) + (x1 + (t_2 + ((t_1 * (t_5 + (t_0 * ((x1 * 2.0) * ((2.0 * x2) - x1))))) + (t_3 * (2.0 * x2))))))
	elif x1 <= 1.32e+154:
		tmp = t_6
	else:
		tmp = x1 + (x1 * (1.0 + (4.0 * (x2 * t_0))))
	return tmp

Julia

function code(x1, x2)
	t_0 = Float64(Float64(2.0 * x2) - 3.0)
	t_1 = Float64(Float64(x1 * x1) + 1.0)
	t_2 = Float64(x1 * Float64(x1 * x1))
	t_3 = Float64(x1 * Float64(x1 * 3.0))
	t_4 = Float64(Float64(Float64(t_3 + Float64(2.0 * x2)) - x1) / t_1)
	t_5 = Float64(Float64(x1 * x1) * Float64(Float64(t_4 * 4.0) - 6.0))
	t_6 = Float64(x1 + Float64(9.0 + Float64(x1 + Float64(t_2 + Float64(Float64(t_3 * t_4) + Float64(t_1 * Float64(t_5 + Float64(12.0 * Float64(x2 / x1)))))))))
	tmp = 0.0
	if (x1 <= -700.0)
		tmp = t_6;
	elseif (x1 <= 135000.0)
		tmp = Float64(x1 + Float64(Float64(3.0 * Float64(Float64(Float64(t_3 - Float64(2.0 * x2)) - x1) / t_1)) + Float64(x1 + Float64(t_2 + Float64(Float64(t_1 * Float64(t_5 + Float64(t_0 * Float64(Float64(x1 * 2.0) * Float64(Float64(2.0 * x2) - x1))))) + Float64(t_3 * Float64(2.0 * x2)))))));
	elseif (x1 <= 1.32e+154)
		tmp = t_6;
	else
		tmp = Float64(x1 + Float64(x1 * Float64(1.0 + Float64(4.0 * Float64(x2 * t_0)))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x1, x2)
	t_0 = (2.0 * x2) - 3.0;
	t_1 = (x1 * x1) + 1.0;
	t_2 = x1 * (x1 * x1);
	t_3 = x1 * (x1 * 3.0);
	t_4 = ((t_3 + (2.0 * x2)) - x1) / t_1;
	t_5 = (x1 * x1) * ((t_4 * 4.0) - 6.0);
	t_6 = x1 + (9.0 + (x1 + (t_2 + ((t_3 * t_4) + (t_1 * (t_5 + (12.0 * (x2 / x1))))))));
	tmp = 0.0;
	if (x1 <= -700.0)
		tmp = t_6;
	elseif (x1 <= 135000.0)
		tmp = x1 + ((3.0 * (((t_3 - (2.0 * x2)) - x1) / t_1)) + (x1 + (t_2 + ((t_1 * (t_5 + (t_0 * ((x1 * 2.0) * ((2.0 * x2) - x1))))) + (t_3 * (2.0 * x2))))));
	elseif (x1 <= 1.32e+154)
		tmp = t_6;
	else
		tmp = x1 + (x1 * (1.0 + (4.0 * (x2 * t_0))));
	end
	tmp_2 = tmp;
end

Wolfram

code[x1_, x2_] := Block[{t$95$0 = N[(N[(2.0 * x2), $MachinePrecision] - 3.0), $MachinePrecision]}, Block[{t$95$1 = N[(N[(x1 * x1), $MachinePrecision] + 1.0), $MachinePrecision]}, Block[{t$95$2 = N[(x1 * N[(x1 * x1), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(x1 * N[(x1 * 3.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$4 = N[(N[(N[(t$95$3 + N[(2.0 * x2), $MachinePrecision]), $MachinePrecision] - x1), $MachinePrecision] / t$95$1), $MachinePrecision]}, Block[{t$95$5 = N[(N[(x1 * x1), $MachinePrecision] * N[(N[(t$95$4 * 4.0), $MachinePrecision] - 6.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$6 = N[(x1 + N[(9.0 + N[(x1 + N[(t$95$2 + N[(N[(t$95$3 * t$95$4), $MachinePrecision] + N[(t$95$1 * N[(t$95$5 + N[(12.0 * N[(x2 / x1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x1, -700.0], t$95$6, If[LessEqual[x1, 135000.0], 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$2 + N[(N[(t$95$1 * N[(t$95$5 + N[(t$95$0 * N[(N[(x1 * 2.0), $MachinePrecision] * N[(N[(2.0 * x2), $MachinePrecision] - x1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(t$95$3 * N[(2.0 * x2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, 1.32e+154], t$95$6, N[(x1 + N[(x1 * N[(1.0 + N[(4.0 * N[(x2 * t$95$0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]]]]]]

Derivation

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

Alternative 13: 68.6% accurate, 1.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := x2 \cdot \left(2 \cdot x2 - 3\right)\\ t_1 := 4 \cdot \left(x1 \cdot t_0\right)\\ t_2 := x1 \cdot x1 + 1\\ t_3 := x1 \cdot \left(x1 \cdot x1\right)\\ t_4 := x1 \cdot \left(x1 \cdot 3\right)\\ t_5 := \frac{\left(t_4 + 2 \cdot x2\right) - x1}{t_2}\\ t_6 := 3 \cdot \frac{\left(t_4 - 2 \cdot x2\right) - x1}{t_2}\\ t_7 := t_4 \cdot t_5\\ t_8 := x1 + \left(9 + \left(x1 + \left(t_3 + \left(t_7 + t_2 \cdot \left(\left(x1 \cdot x1\right) \cdot \left(t_5 \cdot 4 - 6\right) + 12 \cdot \frac{x2}{x1}\right)\right)\right)\right)\right)\\ \mathbf{if}\;x1 \leq -680:\\ \;\;\;\;t_8\\ \mathbf{elif}\;x1 \leq -8.5 \cdot 10^{-165}:\\ \;\;\;\;x1 + \left(t_6 + \left(x1 + \left(t_3 + \left(t_7 + t_2 \cdot t_1\right)\right)\right)\right)\\ \mathbf{elif}\;x1 \leq 1.08 \cdot 10^{-181}:\\ \;\;\;\;x1 + \left(t_6 + \left(x1 + 4 \cdot \left(x2 \cdot \left(x1 \cdot -3\right)\right)\right)\right)\\ \mathbf{elif}\;x1 \leq 21500:\\ \;\;\;\;x1 + \left(t_6 + \left(x1 + t_1\right)\right)\\ \mathbf{elif}\;x1 \leq 1.32 \cdot 10^{+154}:\\ \;\;\;\;t_8\\ \mathbf{else}:\\ \;\;\;\;x1 + x1 \cdot \left(1 + 4 \cdot t_0\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x1 x2)
 :precision binary64
 (let* ((t_0 (* x2 (- (* 2.0 x2) 3.0)))
        (t_1 (* 4.0 (* x1 t_0)))
        (t_2 (+ (* x1 x1) 1.0))
        (t_3 (* x1 (* x1 x1)))
        (t_4 (* x1 (* x1 3.0)))
        (t_5 (/ (- (+ t_4 (* 2.0 x2)) x1) t_2))
        (t_6 (* 3.0 (/ (- (- t_4 (* 2.0 x2)) x1) t_2)))
        (t_7 (* t_4 t_5))
        (t_8
         (+
          x1
          (+
           9.0
           (+
            x1
            (+
             t_3
             (+
              t_7
              (*
               t_2
               (+ (* (* x1 x1) (- (* t_5 4.0) 6.0)) (* 12.0 (/ x2 x1)))))))))))
   (if (<= x1 -680.0)
     t_8
     (if (<= x1 -8.5e-165)
       (+ x1 (+ t_6 (+ x1 (+ t_3 (+ t_7 (* t_2 t_1))))))
       (if (<= x1 1.08e-181)
         (+ x1 (+ t_6 (+ x1 (* 4.0 (* x2 (* x1 -3.0))))))
         (if (<= x1 21500.0)
           (+ x1 (+ t_6 (+ x1 t_1)))
           (if (<= x1 1.32e+154) t_8 (+ x1 (* x1 (+ 1.0 (* 4.0 t_0)))))))))))

C

double code(double x1, double x2) {
	double t_0 = x2 * ((2.0 * x2) - 3.0);
	double t_1 = 4.0 * (x1 * t_0);
	double t_2 = (x1 * x1) + 1.0;
	double t_3 = x1 * (x1 * x1);
	double t_4 = x1 * (x1 * 3.0);
	double t_5 = ((t_4 + (2.0 * x2)) - x1) / t_2;
	double t_6 = 3.0 * (((t_4 - (2.0 * x2)) - x1) / t_2);
	double t_7 = t_4 * t_5;
	double t_8 = x1 + (9.0 + (x1 + (t_3 + (t_7 + (t_2 * (((x1 * x1) * ((t_5 * 4.0) - 6.0)) + (12.0 * (x2 / x1))))))));
	double tmp;
	if (x1 <= -680.0) {
		tmp = t_8;
	} else if (x1 <= -8.5e-165) {
		tmp = x1 + (t_6 + (x1 + (t_3 + (t_7 + (t_2 * t_1)))));
	} else if (x1 <= 1.08e-181) {
		tmp = x1 + (t_6 + (x1 + (4.0 * (x2 * (x1 * -3.0)))));
	} else if (x1 <= 21500.0) {
		tmp = x1 + (t_6 + (x1 + t_1));
	} else if (x1 <= 1.32e+154) {
		tmp = t_8;
	} else {
		tmp = x1 + (x1 * (1.0 + (4.0 * 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) :: 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 = x2 * ((2.0d0 * x2) - 3.0d0)
    t_1 = 4.0d0 * (x1 * t_0)
    t_2 = (x1 * x1) + 1.0d0
    t_3 = x1 * (x1 * x1)
    t_4 = x1 * (x1 * 3.0d0)
    t_5 = ((t_4 + (2.0d0 * x2)) - x1) / t_2
    t_6 = 3.0d0 * (((t_4 - (2.0d0 * x2)) - x1) / t_2)
    t_7 = t_4 * t_5
    t_8 = x1 + (9.0d0 + (x1 + (t_3 + (t_7 + (t_2 * (((x1 * x1) * ((t_5 * 4.0d0) - 6.0d0)) + (12.0d0 * (x2 / x1))))))))
    if (x1 <= (-680.0d0)) then
        tmp = t_8
    else if (x1 <= (-8.5d-165)) then
        tmp = x1 + (t_6 + (x1 + (t_3 + (t_7 + (t_2 * t_1)))))
    else if (x1 <= 1.08d-181) then
        tmp = x1 + (t_6 + (x1 + (4.0d0 * (x2 * (x1 * (-3.0d0))))))
    else if (x1 <= 21500.0d0) then
        tmp = x1 + (t_6 + (x1 + t_1))
    else if (x1 <= 1.32d+154) then
        tmp = t_8
    else
        tmp = x1 + (x1 * (1.0d0 + (4.0d0 * t_0)))
    end if
    code = tmp
end function

Java

public static double code(double x1, double x2) {
	double t_0 = x2 * ((2.0 * x2) - 3.0);
	double t_1 = 4.0 * (x1 * t_0);
	double t_2 = (x1 * x1) + 1.0;
	double t_3 = x1 * (x1 * x1);
	double t_4 = x1 * (x1 * 3.0);
	double t_5 = ((t_4 + (2.0 * x2)) - x1) / t_2;
	double t_6 = 3.0 * (((t_4 - (2.0 * x2)) - x1) / t_2);
	double t_7 = t_4 * t_5;
	double t_8 = x1 + (9.0 + (x1 + (t_3 + (t_7 + (t_2 * (((x1 * x1) * ((t_5 * 4.0) - 6.0)) + (12.0 * (x2 / x1))))))));
	double tmp;
	if (x1 <= -680.0) {
		tmp = t_8;
	} else if (x1 <= -8.5e-165) {
		tmp = x1 + (t_6 + (x1 + (t_3 + (t_7 + (t_2 * t_1)))));
	} else if (x1 <= 1.08e-181) {
		tmp = x1 + (t_6 + (x1 + (4.0 * (x2 * (x1 * -3.0)))));
	} else if (x1 <= 21500.0) {
		tmp = x1 + (t_6 + (x1 + t_1));
	} else if (x1 <= 1.32e+154) {
		tmp = t_8;
	} else {
		tmp = x1 + (x1 * (1.0 + (4.0 * t_0)));
	}
	return tmp;
}

Python

def code(x1, x2):
	t_0 = x2 * ((2.0 * x2) - 3.0)
	t_1 = 4.0 * (x1 * t_0)
	t_2 = (x1 * x1) + 1.0
	t_3 = x1 * (x1 * x1)
	t_4 = x1 * (x1 * 3.0)
	t_5 = ((t_4 + (2.0 * x2)) - x1) / t_2
	t_6 = 3.0 * (((t_4 - (2.0 * x2)) - x1) / t_2)
	t_7 = t_4 * t_5
	t_8 = x1 + (9.0 + (x1 + (t_3 + (t_7 + (t_2 * (((x1 * x1) * ((t_5 * 4.0) - 6.0)) + (12.0 * (x2 / x1))))))))
	tmp = 0
	if x1 <= -680.0:
		tmp = t_8
	elif x1 <= -8.5e-165:
		tmp = x1 + (t_6 + (x1 + (t_3 + (t_7 + (t_2 * t_1)))))
	elif x1 <= 1.08e-181:
		tmp = x1 + (t_6 + (x1 + (4.0 * (x2 * (x1 * -3.0)))))
	elif x1 <= 21500.0:
		tmp = x1 + (t_6 + (x1 + t_1))
	elif x1 <= 1.32e+154:
		tmp = t_8
	else:
		tmp = x1 + (x1 * (1.0 + (4.0 * t_0)))
	return tmp

Julia

function code(x1, x2)
	t_0 = Float64(x2 * Float64(Float64(2.0 * x2) - 3.0))
	t_1 = Float64(4.0 * Float64(x1 * t_0))
	t_2 = Float64(Float64(x1 * x1) + 1.0)
	t_3 = Float64(x1 * Float64(x1 * x1))
	t_4 = Float64(x1 * Float64(x1 * 3.0))
	t_5 = Float64(Float64(Float64(t_4 + Float64(2.0 * x2)) - x1) / t_2)
	t_6 = Float64(3.0 * Float64(Float64(Float64(t_4 - Float64(2.0 * x2)) - x1) / t_2))
	t_7 = Float64(t_4 * t_5)
	t_8 = Float64(x1 + Float64(9.0 + Float64(x1 + Float64(t_3 + Float64(t_7 + Float64(t_2 * Float64(Float64(Float64(x1 * x1) * Float64(Float64(t_5 * 4.0) - 6.0)) + Float64(12.0 * Float64(x2 / x1)))))))))
	tmp = 0.0
	if (x1 <= -680.0)
		tmp = t_8;
	elseif (x1 <= -8.5e-165)
		tmp = Float64(x1 + Float64(t_6 + Float64(x1 + Float64(t_3 + Float64(t_7 + Float64(t_2 * t_1))))));
	elseif (x1 <= 1.08e-181)
		tmp = Float64(x1 + Float64(t_6 + Float64(x1 + Float64(4.0 * Float64(x2 * Float64(x1 * -3.0))))));
	elseif (x1 <= 21500.0)
		tmp = Float64(x1 + Float64(t_6 + Float64(x1 + t_1)));
	elseif (x1 <= 1.32e+154)
		tmp = t_8;
	else
		tmp = Float64(x1 + Float64(x1 * Float64(1.0 + Float64(4.0 * t_0))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x1, x2)
	t_0 = x2 * ((2.0 * x2) - 3.0);
	t_1 = 4.0 * (x1 * t_0);
	t_2 = (x1 * x1) + 1.0;
	t_3 = x1 * (x1 * x1);
	t_4 = x1 * (x1 * 3.0);
	t_5 = ((t_4 + (2.0 * x2)) - x1) / t_2;
	t_6 = 3.0 * (((t_4 - (2.0 * x2)) - x1) / t_2);
	t_7 = t_4 * t_5;
	t_8 = x1 + (9.0 + (x1 + (t_3 + (t_7 + (t_2 * (((x1 * x1) * ((t_5 * 4.0) - 6.0)) + (12.0 * (x2 / x1))))))));
	tmp = 0.0;
	if (x1 <= -680.0)
		tmp = t_8;
	elseif (x1 <= -8.5e-165)
		tmp = x1 + (t_6 + (x1 + (t_3 + (t_7 + (t_2 * t_1)))));
	elseif (x1 <= 1.08e-181)
		tmp = x1 + (t_6 + (x1 + (4.0 * (x2 * (x1 * -3.0)))));
	elseif (x1 <= 21500.0)
		tmp = x1 + (t_6 + (x1 + t_1));
	elseif (x1 <= 1.32e+154)
		tmp = t_8;
	else
		tmp = x1 + (x1 * (1.0 + (4.0 * t_0)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x1_, x2_] := Block[{t$95$0 = N[(x2 * N[(N[(2.0 * x2), $MachinePrecision] - 3.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(4.0 * N[(x1 * t$95$0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(x1 * x1), $MachinePrecision] + 1.0), $MachinePrecision]}, Block[{t$95$3 = N[(x1 * N[(x1 * x1), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$4 = N[(x1 * N[(x1 * 3.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$5 = N[(N[(N[(t$95$4 + N[(2.0 * x2), $MachinePrecision]), $MachinePrecision] - x1), $MachinePrecision] / t$95$2), $MachinePrecision]}, Block[{t$95$6 = N[(3.0 * N[(N[(N[(t$95$4 - N[(2.0 * x2), $MachinePrecision]), $MachinePrecision] - x1), $MachinePrecision] / t$95$2), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$7 = N[(t$95$4 * t$95$5), $MachinePrecision]}, Block[{t$95$8 = N[(x1 + N[(9.0 + N[(x1 + N[(t$95$3 + N[(t$95$7 + N[(t$95$2 * N[(N[(N[(x1 * x1), $MachinePrecision] * N[(N[(t$95$5 * 4.0), $MachinePrecision] - 6.0), $MachinePrecision]), $MachinePrecision] + N[(12.0 * N[(x2 / x1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x1, -680.0], t$95$8, If[LessEqual[x1, -8.5e-165], N[(x1 + N[(t$95$6 + N[(x1 + N[(t$95$3 + N[(t$95$7 + N[(t$95$2 * t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, 1.08e-181], N[(x1 + N[(t$95$6 + N[(x1 + N[(4.0 * N[(x2 * N[(x1 * -3.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, 21500.0], N[(x1 + N[(t$95$6 + N[(x1 + t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, 1.32e+154], t$95$8, N[(x1 + N[(x1 * N[(1.0 + N[(4.0 * t$95$0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]]]]]]]]]]

Derivation

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

Alternative 14: 66.6% accurate, 1.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := x1 \cdot x1 + 1\\ t_1 := x1 \cdot \left(x1 \cdot 3\right)\\ t_2 := 3 \cdot \frac{\left(t_1 - 2 \cdot x2\right) - x1}{t_0}\\ t_3 := x1 + \left(9 + \left(x1 + \left(x1 \cdot \left(x1 \cdot x1\right) + \left(t_1 \cdot \left(2 \cdot x2\right) + t_0 \cdot \left(\left(x1 \cdot x1\right) \cdot \left(\frac{\left(t_1 + 2 \cdot x2\right) - x1}{t_0} \cdot 4 - 6\right) + \left(x1 \cdot 2 + -2 \cdot \left(x2 \cdot 4 - 3\right)\right)\right)\right)\right)\right)\right)\\ t_4 := x2 \cdot \left(2 \cdot x2 - 3\right)\\ t_5 := 4 \cdot t_4\\ \mathbf{if}\;x1 \leq -1.56 \cdot 10^{+21}:\\ \;\;\;\;t_3\\ \mathbf{elif}\;x1 \leq -9 \cdot 10^{-165}:\\ \;\;\;\;x1 + \left(x1 \cdot \left(t_5 - 2\right) + x2 \cdot -6\right)\\ \mathbf{elif}\;x1 \leq 5.6 \cdot 10^{-182}:\\ \;\;\;\;x1 + \left(t_2 + \left(x1 + 4 \cdot \left(x2 \cdot \left(x1 \cdot -3\right)\right)\right)\right)\\ \mathbf{elif}\;x1 \leq 1100000:\\ \;\;\;\;x1 + \left(t_2 + \left(x1 + 4 \cdot \left(x1 \cdot t_4\right)\right)\right)\\ \mathbf{elif}\;x1 \leq 9.2 \cdot 10^{+153}:\\ \;\;\;\;t_3\\ \mathbf{else}:\\ \;\;\;\;x1 + x1 \cdot \left(1 + t_5\right)\\ \end{array} \end{array} \]

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
          (+
           9.0
           (+
            x1
            (+
             (* x1 (* x1 x1))
             (+
              (* t_1 (* 2.0 x2))
              (*
               t_0
               (+
                (* (* x1 x1) (- (* (/ (- (+ t_1 (* 2.0 x2)) x1) t_0) 4.0) 6.0))
                (+ (* x1 2.0) (* -2.0 (- (* x2 4.0) 3.0)))))))))))
        (t_4 (* x2 (- (* 2.0 x2) 3.0)))
        (t_5 (* 4.0 t_4)))
   (if (<= x1 -1.56e+21)
     t_3
     (if (<= x1 -9e-165)
       (+ x1 (+ (* x1 (- t_5 2.0)) (* x2 -6.0)))
       (if (<= x1 5.6e-182)
         (+ x1 (+ t_2 (+ x1 (* 4.0 (* x2 (* x1 -3.0))))))
         (if (<= x1 1100000.0)
           (+ x1 (+ t_2 (+ x1 (* 4.0 (* x1 t_4)))))
           (if (<= x1 9.2e+153) t_3 (+ x1 (* x1 (+ 1.0 t_5))))))))))

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 + (9.0 + (x1 + ((x1 * (x1 * x1)) + ((t_1 * (2.0 * x2)) + (t_0 * (((x1 * x1) * (((((t_1 + (2.0 * x2)) - x1) / t_0) * 4.0) - 6.0)) + ((x1 * 2.0) + (-2.0 * ((x2 * 4.0) - 3.0)))))))));
	double t_4 = x2 * ((2.0 * x2) - 3.0);
	double t_5 = 4.0 * t_4;
	double tmp;
	if (x1 <= -1.56e+21) {
		tmp = t_3;
	} else if (x1 <= -9e-165) {
		tmp = x1 + ((x1 * (t_5 - 2.0)) + (x2 * -6.0));
	} else if (x1 <= 5.6e-182) {
		tmp = x1 + (t_2 + (x1 + (4.0 * (x2 * (x1 * -3.0)))));
	} else if (x1 <= 1100000.0) {
		tmp = x1 + (t_2 + (x1 + (4.0 * (x1 * t_4))));
	} else if (x1 <= 9.2e+153) {
		tmp = t_3;
	} else {
		tmp = x1 + (x1 * (1.0 + 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) :: 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 + (9.0d0 + (x1 + ((x1 * (x1 * x1)) + ((t_1 * (2.0d0 * x2)) + (t_0 * (((x1 * x1) * (((((t_1 + (2.0d0 * x2)) - x1) / t_0) * 4.0d0) - 6.0d0)) + ((x1 * 2.0d0) + ((-2.0d0) * ((x2 * 4.0d0) - 3.0d0)))))))))
    t_4 = x2 * ((2.0d0 * x2) - 3.0d0)
    t_5 = 4.0d0 * t_4
    if (x1 <= (-1.56d+21)) then
        tmp = t_3
    else if (x1 <= (-9d-165)) then
        tmp = x1 + ((x1 * (t_5 - 2.0d0)) + (x2 * (-6.0d0)))
    else if (x1 <= 5.6d-182) then
        tmp = x1 + (t_2 + (x1 + (4.0d0 * (x2 * (x1 * (-3.0d0))))))
    else if (x1 <= 1100000.0d0) then
        tmp = x1 + (t_2 + (x1 + (4.0d0 * (x1 * t_4))))
    else if (x1 <= 9.2d+153) then
        tmp = t_3
    else
        tmp = x1 + (x1 * (1.0d0 + t_5))
    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 + (9.0 + (x1 + ((x1 * (x1 * x1)) + ((t_1 * (2.0 * x2)) + (t_0 * (((x1 * x1) * (((((t_1 + (2.0 * x2)) - x1) / t_0) * 4.0) - 6.0)) + ((x1 * 2.0) + (-2.0 * ((x2 * 4.0) - 3.0)))))))));
	double t_4 = x2 * ((2.0 * x2) - 3.0);
	double t_5 = 4.0 * t_4;
	double tmp;
	if (x1 <= -1.56e+21) {
		tmp = t_3;
	} else if (x1 <= -9e-165) {
		tmp = x1 + ((x1 * (t_5 - 2.0)) + (x2 * -6.0));
	} else if (x1 <= 5.6e-182) {
		tmp = x1 + (t_2 + (x1 + (4.0 * (x2 * (x1 * -3.0)))));
	} else if (x1 <= 1100000.0) {
		tmp = x1 + (t_2 + (x1 + (4.0 * (x1 * t_4))));
	} else if (x1 <= 9.2e+153) {
		tmp = t_3;
	} else {
		tmp = x1 + (x1 * (1.0 + t_5));
	}
	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 + (9.0 + (x1 + ((x1 * (x1 * x1)) + ((t_1 * (2.0 * x2)) + (t_0 * (((x1 * x1) * (((((t_1 + (2.0 * x2)) - x1) / t_0) * 4.0) - 6.0)) + ((x1 * 2.0) + (-2.0 * ((x2 * 4.0) - 3.0)))))))))
	t_4 = x2 * ((2.0 * x2) - 3.0)
	t_5 = 4.0 * t_4
	tmp = 0
	if x1 <= -1.56e+21:
		tmp = t_3
	elif x1 <= -9e-165:
		tmp = x1 + ((x1 * (t_5 - 2.0)) + (x2 * -6.0))
	elif x1 <= 5.6e-182:
		tmp = x1 + (t_2 + (x1 + (4.0 * (x2 * (x1 * -3.0)))))
	elif x1 <= 1100000.0:
		tmp = x1 + (t_2 + (x1 + (4.0 * (x1 * t_4))))
	elif x1 <= 9.2e+153:
		tmp = t_3
	else:
		tmp = x1 + (x1 * (1.0 + t_5))
	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(9.0 + Float64(x1 + Float64(Float64(x1 * Float64(x1 * x1)) + Float64(Float64(t_1 * Float64(2.0 * x2)) + Float64(t_0 * Float64(Float64(Float64(x1 * x1) * Float64(Float64(Float64(Float64(Float64(t_1 + Float64(2.0 * x2)) - x1) / t_0) * 4.0) - 6.0)) + Float64(Float64(x1 * 2.0) + Float64(-2.0 * Float64(Float64(x2 * 4.0) - 3.0))))))))))
	t_4 = Float64(x2 * Float64(Float64(2.0 * x2) - 3.0))
	t_5 = Float64(4.0 * t_4)
	tmp = 0.0
	if (x1 <= -1.56e+21)
		tmp = t_3;
	elseif (x1 <= -9e-165)
		tmp = Float64(x1 + Float64(Float64(x1 * Float64(t_5 - 2.0)) + Float64(x2 * -6.0)));
	elseif (x1 <= 5.6e-182)
		tmp = Float64(x1 + Float64(t_2 + Float64(x1 + Float64(4.0 * Float64(x2 * Float64(x1 * -3.0))))));
	elseif (x1 <= 1100000.0)
		tmp = Float64(x1 + Float64(t_2 + Float64(x1 + Float64(4.0 * Float64(x1 * t_4)))));
	elseif (x1 <= 9.2e+153)
		tmp = t_3;
	else
		tmp = Float64(x1 + Float64(x1 * Float64(1.0 + t_5)));
	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 + (9.0 + (x1 + ((x1 * (x1 * x1)) + ((t_1 * (2.0 * x2)) + (t_0 * (((x1 * x1) * (((((t_1 + (2.0 * x2)) - x1) / t_0) * 4.0) - 6.0)) + ((x1 * 2.0) + (-2.0 * ((x2 * 4.0) - 3.0)))))))));
	t_4 = x2 * ((2.0 * x2) - 3.0);
	t_5 = 4.0 * t_4;
	tmp = 0.0;
	if (x1 <= -1.56e+21)
		tmp = t_3;
	elseif (x1 <= -9e-165)
		tmp = x1 + ((x1 * (t_5 - 2.0)) + (x2 * -6.0));
	elseif (x1 <= 5.6e-182)
		tmp = x1 + (t_2 + (x1 + (4.0 * (x2 * (x1 * -3.0)))));
	elseif (x1 <= 1100000.0)
		tmp = x1 + (t_2 + (x1 + (4.0 * (x1 * t_4))));
	elseif (x1 <= 9.2e+153)
		tmp = t_3;
	else
		tmp = x1 + (x1 * (1.0 + t_5));
	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[(9.0 + N[(x1 + N[(N[(x1 * N[(x1 * x1), $MachinePrecision]), $MachinePrecision] + N[(N[(t$95$1 * N[(2.0 * x2), $MachinePrecision]), $MachinePrecision] + N[(t$95$0 * N[(N[(N[(x1 * x1), $MachinePrecision] * N[(N[(N[(N[(N[(t$95$1 + N[(2.0 * x2), $MachinePrecision]), $MachinePrecision] - x1), $MachinePrecision] / t$95$0), $MachinePrecision] * 4.0), $MachinePrecision] - 6.0), $MachinePrecision]), $MachinePrecision] + N[(N[(x1 * 2.0), $MachinePrecision] + N[(-2.0 * N[(N[(x2 * 4.0), $MachinePrecision] - 3.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$4 = N[(x2 * N[(N[(2.0 * x2), $MachinePrecision] - 3.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$5 = N[(4.0 * t$95$4), $MachinePrecision]}, If[LessEqual[x1, -1.56e+21], t$95$3, If[LessEqual[x1, -9e-165], N[(x1 + N[(N[(x1 * N[(t$95$5 - 2.0), $MachinePrecision]), $MachinePrecision] + N[(x2 * -6.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, 5.6e-182], N[(x1 + N[(t$95$2 + N[(x1 + N[(4.0 * N[(x2 * N[(x1 * -3.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, 1100000.0], N[(x1 + N[(t$95$2 + N[(x1 + N[(4.0 * N[(x1 * t$95$4), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, 9.2e+153], t$95$3, N[(x1 + N[(x1 * N[(1.0 + t$95$5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]]]]]]]

Derivation

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

Alternative 15: 56.4% accurate, 3.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x1 \leq -8.5 \cdot 10^{-165} \lor \neg \left(x1 \leq 2.4 \cdot 10^{-182}\right):\\ \;\;\;\;x1 + \left(x1 \cdot \left(4 \cdot \left(x2 \cdot \left(2 \cdot x2 - 3\right)\right) - 2\right) + x2 \cdot -6\right)\\ \mathbf{else}:\\ \;\;\;\;x1 + \left(3 \cdot \frac{\left(x1 \cdot \left(x1 \cdot 3\right) - 2 \cdot x2\right) - x1}{x1 \cdot x1 + 1} + \left(x1 + 4 \cdot \left(x2 \cdot \left(x1 \cdot -3\right)\right)\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x1 x2)
 :precision binary64
 (if (or (<= x1 -8.5e-165) (not (<= x1 2.4e-182)))
   (+ 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 (* x2 (* x1 -3.0))))))))

C

double code(double x1, double x2) {
	double tmp;
	if ((x1 <= -8.5e-165) || !(x1 <= 2.4e-182)) {
		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 * (x2 * (x1 * -3.0)))));
	}
	return tmp;
}

Fortran

real(8) function code(x1, x2)
    real(8), intent (in) :: x1
    real(8), intent (in) :: x2
    real(8) :: tmp
    if ((x1 <= (-8.5d-165)) .or. (.not. (x1 <= 2.4d-182))) 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 * (x2 * (x1 * (-3.0d0))))))
    end if
    code = tmp
end function

Java

public static double code(double x1, double x2) {
	double tmp;
	if ((x1 <= -8.5e-165) || !(x1 <= 2.4e-182)) {
		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 * (x2 * (x1 * -3.0)))));
	}
	return tmp;
}

Python

def code(x1, x2):
	tmp = 0
	if (x1 <= -8.5e-165) or not (x1 <= 2.4e-182):
		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 * (x2 * (x1 * -3.0)))))
	return tmp

Julia

function code(x1, x2)
	tmp = 0.0
	if ((x1 <= -8.5e-165) || !(x1 <= 2.4e-182))
		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(x2 * Float64(x1 * -3.0))))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x1, x2)
	tmp = 0.0;
	if ((x1 <= -8.5e-165) || ~((x1 <= 2.4e-182)))
		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 * (x2 * (x1 * -3.0)))));
	end
	tmp_2 = tmp;
end

Wolfram

code[x1_, x2_] := If[Or[LessEqual[x1, -8.5e-165], N[Not[LessEqual[x1, 2.4e-182]], $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[(x2 * N[(x1 * -3.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 16: 39.5% accurate, 6.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x1 \leq -4.4 \cdot 10^{-97} \lor \neg \left(x1 \leq 9.5 \cdot 10^{-67}\right):\\ \;\;\;\;x1 + x1 \cdot \left(1 + 4 \cdot \left(x2 \cdot \left(2 \cdot x2 - 3\right)\right)\right)\\ \mathbf{else}:\\ \;\;\;\;x2 \cdot -6\\ \end{array} \end{array} \]

FPCore

(FPCore (x1 x2)
 :precision binary64
 (if (or (<= x1 -4.4e-97) (not (<= x1 9.5e-67)))
   (+ x1 (* x1 (+ 1.0 (* 4.0 (* x2 (- (* 2.0 x2) 3.0))))))
   (* x2 -6.0)))

C

double code(double x1, double x2) {
	double tmp;
	if ((x1 <= -4.4e-97) || !(x1 <= 9.5e-67)) {
		tmp = x1 + (x1 * (1.0 + (4.0 * (x2 * ((2.0 * x2) - 3.0)))));
	} 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 <= (-4.4d-97)) .or. (.not. (x1 <= 9.5d-67))) then
        tmp = x1 + (x1 * (1.0d0 + (4.0d0 * (x2 * ((2.0d0 * x2) - 3.0d0)))))
    else
        tmp = x2 * (-6.0d0)
    end if
    code = tmp
end function

Java

public static double code(double x1, double x2) {
	double tmp;
	if ((x1 <= -4.4e-97) || !(x1 <= 9.5e-67)) {
		tmp = x1 + (x1 * (1.0 + (4.0 * (x2 * ((2.0 * x2) - 3.0)))));
	} else {
		tmp = x2 * -6.0;
	}
	return tmp;
}

Python

def code(x1, x2):
	tmp = 0
	if (x1 <= -4.4e-97) or not (x1 <= 9.5e-67):
		tmp = x1 + (x1 * (1.0 + (4.0 * (x2 * ((2.0 * x2) - 3.0)))))
	else:
		tmp = x2 * -6.0
	return tmp

Julia

function code(x1, x2)
	tmp = 0.0
	if ((x1 <= -4.4e-97) || !(x1 <= 9.5e-67))
		tmp = Float64(x1 + Float64(x1 * Float64(1.0 + Float64(4.0 * Float64(x2 * Float64(Float64(2.0 * x2) - 3.0))))));
	else
		tmp = Float64(x2 * -6.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x1, x2)
	tmp = 0.0;
	if ((x1 <= -4.4e-97) || ~((x1 <= 9.5e-67)))
		tmp = x1 + (x1 * (1.0 + (4.0 * (x2 * ((2.0 * x2) - 3.0)))));
	else
		tmp = x2 * -6.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x1_, x2_] := If[Or[LessEqual[x1, -4.4e-97], N[Not[LessEqual[x1, 9.5e-67]], $MachinePrecision]], N[(x1 + N[(x1 * N[(1.0 + N[(4.0 * N[(x2 * N[(N[(2.0 * x2), $MachinePrecision] - 3.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x2 * -6.0), $MachinePrecision]]

Derivation

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

Alternative 17: 39.8% accurate, 6.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := x1 \cdot \left(1 + 4 \cdot \left(x2 \cdot \left(2 \cdot x2 - 3\right)\right)\right)\\ \mathbf{if}\;x1 \leq -3.9 \cdot 10^{-97}:\\ \;\;\;\;x1 + \left(9 + t_0\right)\\ \mathbf{elif}\;x1 \leq 3.6 \cdot 10^{-69}:\\ \;\;\;\;x2 \cdot -6\\ \mathbf{else}:\\ \;\;\;\;x1 + t_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x1 x2)
 :precision binary64
 (let* ((t_0 (* x1 (+ 1.0 (* 4.0 (* x2 (- (* 2.0 x2) 3.0)))))))
   (if (<= x1 -3.9e-97)
     (+ x1 (+ 9.0 t_0))
     (if (<= x1 3.6e-69) (* x2 -6.0) (+ x1 t_0)))))

C

double code(double x1, double x2) {
	double t_0 = x1 * (1.0 + (4.0 * (x2 * ((2.0 * x2) - 3.0))));
	double tmp;
	if (x1 <= -3.9e-97) {
		tmp = x1 + (9.0 + t_0);
	} else if (x1 <= 3.6e-69) {
		tmp = x2 * -6.0;
	} else {
		tmp = x1 + 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 * (1.0d0 + (4.0d0 * (x2 * ((2.0d0 * x2) - 3.0d0))))
    if (x1 <= (-3.9d-97)) then
        tmp = x1 + (9.0d0 + t_0)
    else if (x1 <= 3.6d-69) then
        tmp = x2 * (-6.0d0)
    else
        tmp = x1 + t_0
    end if
    code = tmp
end function

Java

public static double code(double x1, double x2) {
	double t_0 = x1 * (1.0 + (4.0 * (x2 * ((2.0 * x2) - 3.0))));
	double tmp;
	if (x1 <= -3.9e-97) {
		tmp = x1 + (9.0 + t_0);
	} else if (x1 <= 3.6e-69) {
		tmp = x2 * -6.0;
	} else {
		tmp = x1 + t_0;
	}
	return tmp;
}

Python

def code(x1, x2):
	t_0 = x1 * (1.0 + (4.0 * (x2 * ((2.0 * x2) - 3.0))))
	tmp = 0
	if x1 <= -3.9e-97:
		tmp = x1 + (9.0 + t_0)
	elif x1 <= 3.6e-69:
		tmp = x2 * -6.0
	else:
		tmp = x1 + t_0
	return tmp

Julia

function code(x1, x2)
	t_0 = Float64(x1 * Float64(1.0 + Float64(4.0 * Float64(x2 * Float64(Float64(2.0 * x2) - 3.0)))))
	tmp = 0.0
	if (x1 <= -3.9e-97)
		tmp = Float64(x1 + Float64(9.0 + t_0));
	elseif (x1 <= 3.6e-69)
		tmp = Float64(x2 * -6.0);
	else
		tmp = Float64(x1 + t_0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x1, x2)
	t_0 = x1 * (1.0 + (4.0 * (x2 * ((2.0 * x2) - 3.0))));
	tmp = 0.0;
	if (x1 <= -3.9e-97)
		tmp = x1 + (9.0 + t_0);
	elseif (x1 <= 3.6e-69)
		tmp = x2 * -6.0;
	else
		tmp = x1 + t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x1_, x2_] := Block[{t$95$0 = N[(x1 * N[(1.0 + N[(4.0 * N[(x2 * N[(N[(2.0 * x2), $MachinePrecision] - 3.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x1, -3.9e-97], N[(x1 + N[(9.0 + t$95$0), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, 3.6e-69], N[(x2 * -6.0), $MachinePrecision], N[(x1 + t$95$0), $MachinePrecision]]]]

Derivation

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

Alternative 18: 54.5% 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 19: 25.7% 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 20: 25.5% 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 21: 3.2% 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 2023343 
(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))))))