Rosa's FloatVsDoubleBenchmark

Percentage Accurate: 70.5% → 99.4%

Time: 50.5s

Alternatives: 27

Speedup: 1.3×

• 46.0% 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

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

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 (+ (* 3.0 (* x2 -2.0)) (+ x1 (* 6.0 (pow x1 4.0))))))))

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 + ((3.0 * (x2 * -2.0)) + (x1 + (6.0 * pow(x1, 4.0))));
	}
	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(3.0 * Float64(x2 * -2.0)) + Float64(x1 + Float64(6.0 * (x1 ^ 4.0)))));
	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[(3.0 * N[(x2 * -2.0), $MachinePrecision]), $MachinePrecision] + N[(x1 + N[(6.0 * N[Power[x1, 4.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]]

Derivation

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

Alternative 2: 99.3% accurate, 0.2× speedup

Math

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

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

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

Derivation

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

Alternative 3: 99.3% accurate, 0.5× speedup

Math

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

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 (+ (* 3.0 (* x2 -2.0)) (+ x1 (* 6.0 (pow x1 4.0))))))))

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 + ((3.0 * (x2 * -2.0)) + (x1 + (6.0 * pow(x1, 4.0))));
	}
	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 + ((3.0 * (x2 * -2.0)) + (x1 + (6.0 * Math.pow(x1, 4.0))));
	}
	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 + ((3.0 * (x2 * -2.0)) + (x1 + (6.0 * math.pow(x1, 4.0))))
	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(3.0 * Float64(x2 * -2.0)) + Float64(x1 + Float64(6.0 * (x1 ^ 4.0)))));
	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 + ((3.0 * (x2 * -2.0)) + (x1 + (6.0 * (x1 ^ 4.0))));
	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[(3.0 * N[(x2 * -2.0), $MachinePrecision]), $MachinePrecision] + N[(x1 + N[(6.0 * N[Power[x1, 4.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

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

Alternative 4: 98.3% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := x1 \cdot \left(x1 \cdot 3\right)\\ t_1 := x1 \cdot x1 + 1\\ t_2 := x1 + 6 \cdot {x1}^{4}\\ t_3 := \frac{\left(t_0 + 2 \cdot x2\right) - x1}{t_1}\\ \mathbf{if}\;x1 \leq -5.6 \cdot 10^{+102}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;x1 \leq 5 \cdot 10^{+100}:\\ \;\;\;\;x1 + \left(3 \cdot \frac{\left(t_0 - 2 \cdot x2\right) - x1}{t_1} + \left(x1 + \left(x1 \cdot \left(x1 \cdot x1\right) + \left(t_1 \cdot \left(\left(\left(x1 \cdot 2\right) \cdot t_3\right) \cdot \left(t_3 - 3\right) + \left(x1 \cdot x1\right) \cdot \left(t_3 \cdot 4 - 6\right)\right) + 3 \cdot t_0\right)\right)\right)\right)\\ \mathbf{else}:\\ \;\;\;\;x1 + \left(3 \cdot \left(x2 \cdot -2\right) + t_2\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x1 x2)
 :precision binary64
 (let* ((t_0 (* x1 (* x1 3.0)))
        (t_1 (+ (* x1 x1) 1.0))
        (t_2 (+ x1 (* 6.0 (pow x1 4.0))))
        (t_3 (/ (- (+ t_0 (* 2.0 x2)) x1) t_1)))
   (if (<= x1 -5.6e+102)
     t_2
     (if (<= x1 5e+100)
       (+
        x1
        (+
         (* 3.0 (/ (- (- t_0 (* 2.0 x2)) x1) t_1))
         (+
          x1
          (+
           (* x1 (* x1 x1))
           (+
            (*
             t_1
             (+
              (* (* (* x1 2.0) t_3) (- t_3 3.0))
              (* (* x1 x1) (- (* t_3 4.0) 6.0))))
            (* 3.0 t_0))))))
       (+ x1 (+ (* 3.0 (* x2 -2.0)) t_2))))))

C

double code(double x1, double x2) {
	double t_0 = x1 * (x1 * 3.0);
	double t_1 = (x1 * x1) + 1.0;
	double t_2 = x1 + (6.0 * pow(x1, 4.0));
	double t_3 = ((t_0 + (2.0 * x2)) - x1) / t_1;
	double tmp;
	if (x1 <= -5.6e+102) {
		tmp = t_2;
	} else if (x1 <= 5e+100) {
		tmp = x1 + ((3.0 * (((t_0 - (2.0 * x2)) - x1) / t_1)) + (x1 + ((x1 * (x1 * x1)) + ((t_1 * ((((x1 * 2.0) * t_3) * (t_3 - 3.0)) + ((x1 * x1) * ((t_3 * 4.0) - 6.0)))) + (3.0 * t_0)))));
	} else {
		tmp = x1 + ((3.0 * (x2 * -2.0)) + t_2);
	}
	return tmp;
}

Fortran

real(8) function code(x1, x2)
    real(8), intent (in) :: x1
    real(8), intent (in) :: x2
    real(8) :: t_0
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: t_3
    real(8) :: tmp
    t_0 = x1 * (x1 * 3.0d0)
    t_1 = (x1 * x1) + 1.0d0
    t_2 = x1 + (6.0d0 * (x1 ** 4.0d0))
    t_3 = ((t_0 + (2.0d0 * x2)) - x1) / t_1
    if (x1 <= (-5.6d+102)) then
        tmp = t_2
    else if (x1 <= 5d+100) then
        tmp = x1 + ((3.0d0 * (((t_0 - (2.0d0 * x2)) - x1) / t_1)) + (x1 + ((x1 * (x1 * x1)) + ((t_1 * ((((x1 * 2.0d0) * t_3) * (t_3 - 3.0d0)) + ((x1 * x1) * ((t_3 * 4.0d0) - 6.0d0)))) + (3.0d0 * t_0)))))
    else
        tmp = x1 + ((3.0d0 * (x2 * (-2.0d0))) + t_2)
    end if
    code = tmp
end function

Java

public static double code(double x1, double x2) {
	double t_0 = x1 * (x1 * 3.0);
	double t_1 = (x1 * x1) + 1.0;
	double t_2 = x1 + (6.0 * Math.pow(x1, 4.0));
	double t_3 = ((t_0 + (2.0 * x2)) - x1) / t_1;
	double tmp;
	if (x1 <= -5.6e+102) {
		tmp = t_2;
	} else if (x1 <= 5e+100) {
		tmp = x1 + ((3.0 * (((t_0 - (2.0 * x2)) - x1) / t_1)) + (x1 + ((x1 * (x1 * x1)) + ((t_1 * ((((x1 * 2.0) * t_3) * (t_3 - 3.0)) + ((x1 * x1) * ((t_3 * 4.0) - 6.0)))) + (3.0 * t_0)))));
	} else {
		tmp = x1 + ((3.0 * (x2 * -2.0)) + t_2);
	}
	return tmp;
}

Python

def code(x1, x2):
	t_0 = x1 * (x1 * 3.0)
	t_1 = (x1 * x1) + 1.0
	t_2 = x1 + (6.0 * math.pow(x1, 4.0))
	t_3 = ((t_0 + (2.0 * x2)) - x1) / t_1
	tmp = 0
	if x1 <= -5.6e+102:
		tmp = t_2
	elif x1 <= 5e+100:
		tmp = x1 + ((3.0 * (((t_0 - (2.0 * x2)) - x1) / t_1)) + (x1 + ((x1 * (x1 * x1)) + ((t_1 * ((((x1 * 2.0) * t_3) * (t_3 - 3.0)) + ((x1 * x1) * ((t_3 * 4.0) - 6.0)))) + (3.0 * t_0)))))
	else:
		tmp = x1 + ((3.0 * (x2 * -2.0)) + t_2)
	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(x1 + Float64(6.0 * (x1 ^ 4.0)))
	t_3 = Float64(Float64(Float64(t_0 + Float64(2.0 * x2)) - x1) / t_1)
	tmp = 0.0
	if (x1 <= -5.6e+102)
		tmp = t_2;
	elseif (x1 <= 5e+100)
		tmp = Float64(x1 + Float64(Float64(3.0 * Float64(Float64(Float64(t_0 - Float64(2.0 * x2)) - x1) / t_1)) + Float64(x1 + Float64(Float64(x1 * Float64(x1 * x1)) + Float64(Float64(t_1 * 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(3.0 * t_0))))));
	else
		tmp = Float64(x1 + Float64(Float64(3.0 * Float64(x2 * -2.0)) + t_2));
	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 = x1 + (6.0 * (x1 ^ 4.0));
	t_3 = ((t_0 + (2.0 * x2)) - x1) / t_1;
	tmp = 0.0;
	if (x1 <= -5.6e+102)
		tmp = t_2;
	elseif (x1 <= 5e+100)
		tmp = x1 + ((3.0 * (((t_0 - (2.0 * x2)) - x1) / t_1)) + (x1 + ((x1 * (x1 * x1)) + ((t_1 * ((((x1 * 2.0) * t_3) * (t_3 - 3.0)) + ((x1 * x1) * ((t_3 * 4.0) - 6.0)))) + (3.0 * t_0)))));
	else
		tmp = x1 + ((3.0 * (x2 * -2.0)) + t_2);
	end
	tmp_2 = tmp;
end

Wolfram

code[x1_, x2_] := Block[{t$95$0 = N[(x1 * N[(x1 * 3.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(N[(x1 * x1), $MachinePrecision] + 1.0), $MachinePrecision]}, Block[{t$95$2 = N[(x1 + N[(6.0 * N[Power[x1, 4.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(N[(N[(t$95$0 + N[(2.0 * x2), $MachinePrecision]), $MachinePrecision] - x1), $MachinePrecision] / t$95$1), $MachinePrecision]}, If[LessEqual[x1, -5.6e+102], t$95$2, If[LessEqual[x1, 5e+100], N[(x1 + N[(N[(3.0 * N[(N[(N[(t$95$0 - N[(2.0 * x2), $MachinePrecision]), $MachinePrecision] - x1), $MachinePrecision] / t$95$1), $MachinePrecision]), $MachinePrecision] + N[(x1 + N[(N[(x1 * N[(x1 * x1), $MachinePrecision]), $MachinePrecision] + N[(N[(t$95$1 * 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[(3.0 * t$95$0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x1 + N[(N[(3.0 * N[(x2 * -2.0), $MachinePrecision]), $MachinePrecision] + t$95$2), $MachinePrecision]), $MachinePrecision]]]]]]]

Derivation

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

Alternative 5: 97.9% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := x1 \cdot \left(x1 \cdot 3\right)\\ t_1 := x1 \cdot x1 + 1\\ t_2 := \frac{\left(t_0 + 2 \cdot x2\right) - x1}{t_1}\\ \mathbf{if}\;x1 \leq -5 \cdot 10^{+102} \lor \neg \left(x1 \leq 6.7 \cdot 10^{+62}\right):\\ \;\;\;\;x1 + 6 \cdot {x1}^{4}\\ \mathbf{else}:\\ \;\;\;\;x1 + \left(3 \cdot \frac{\left(t_0 - 2 \cdot x2\right) - x1}{t_1} + \left(x1 + \left(x1 \cdot \left(x1 \cdot x1\right) + \left(t_1 \cdot \left(\left(\left(x1 \cdot 2\right) \cdot t_2\right) \cdot \left(t_2 - 3\right) + \left(x1 \cdot x1\right) \cdot \left(t_2 \cdot 4 - 6\right)\right) + 3 \cdot t_0\right)\right)\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x1 x2)
 :precision binary64
 (let* ((t_0 (* x1 (* x1 3.0)))
        (t_1 (+ (* x1 x1) 1.0))
        (t_2 (/ (- (+ t_0 (* 2.0 x2)) x1) t_1)))
   (if (or (<= x1 -5e+102) (not (<= x1 6.7e+62)))
     (+ x1 (* 6.0 (pow x1 4.0)))
     (+
      x1
      (+
       (* 3.0 (/ (- (- t_0 (* 2.0 x2)) x1) t_1))
       (+
        x1
        (+
         (* x1 (* x1 x1))
         (+
          (*
           t_1
           (+
            (* (* (* x1 2.0) t_2) (- t_2 3.0))
            (* (* x1 x1) (- (* t_2 4.0) 6.0))))
          (* 3.0 t_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 tmp;
	if ((x1 <= -5e+102) || !(x1 <= 6.7e+62)) {
		tmp = x1 + (6.0 * pow(x1, 4.0));
	} else {
		tmp = x1 + ((3.0 * (((t_0 - (2.0 * x2)) - x1) / t_1)) + (x1 + ((x1 * (x1 * x1)) + ((t_1 * ((((x1 * 2.0) * t_2) * (t_2 - 3.0)) + ((x1 * x1) * ((t_2 * 4.0) - 6.0)))) + (3.0 * 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) :: tmp
    t_0 = x1 * (x1 * 3.0d0)
    t_1 = (x1 * x1) + 1.0d0
    t_2 = ((t_0 + (2.0d0 * x2)) - x1) / t_1
    if ((x1 <= (-5d+102)) .or. (.not. (x1 <= 6.7d+62))) then
        tmp = x1 + (6.0d0 * (x1 ** 4.0d0))
    else
        tmp = x1 + ((3.0d0 * (((t_0 - (2.0d0 * x2)) - x1) / t_1)) + (x1 + ((x1 * (x1 * x1)) + ((t_1 * ((((x1 * 2.0d0) * t_2) * (t_2 - 3.0d0)) + ((x1 * x1) * ((t_2 * 4.0d0) - 6.0d0)))) + (3.0d0 * t_0)))))
    end if
    code = tmp
end function

Java

public static double code(double x1, double x2) {
	double t_0 = x1 * (x1 * 3.0);
	double t_1 = (x1 * x1) + 1.0;
	double t_2 = ((t_0 + (2.0 * x2)) - x1) / t_1;
	double tmp;
	if ((x1 <= -5e+102) || !(x1 <= 6.7e+62)) {
		tmp = x1 + (6.0 * Math.pow(x1, 4.0));
	} else {
		tmp = x1 + ((3.0 * (((t_0 - (2.0 * x2)) - x1) / t_1)) + (x1 + ((x1 * (x1 * x1)) + ((t_1 * ((((x1 * 2.0) * t_2) * (t_2 - 3.0)) + ((x1 * x1) * ((t_2 * 4.0) - 6.0)))) + (3.0 * t_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
	tmp = 0
	if (x1 <= -5e+102) or not (x1 <= 6.7e+62):
		tmp = x1 + (6.0 * math.pow(x1, 4.0))
	else:
		tmp = x1 + ((3.0 * (((t_0 - (2.0 * x2)) - x1) / t_1)) + (x1 + ((x1 * (x1 * x1)) + ((t_1 * ((((x1 * 2.0) * t_2) * (t_2 - 3.0)) + ((x1 * x1) * ((t_2 * 4.0) - 6.0)))) + (3.0 * t_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)
	tmp = 0.0
	if ((x1 <= -5e+102) || !(x1 <= 6.7e+62))
		tmp = Float64(x1 + Float64(6.0 * (x1 ^ 4.0)));
	else
		tmp = Float64(x1 + Float64(Float64(3.0 * Float64(Float64(Float64(t_0 - Float64(2.0 * x2)) - x1) / t_1)) + Float64(x1 + Float64(Float64(x1 * Float64(x1 * x1)) + 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(3.0 * t_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;
	tmp = 0.0;
	if ((x1 <= -5e+102) || ~((x1 <= 6.7e+62)))
		tmp = x1 + (6.0 * (x1 ^ 4.0));
	else
		tmp = x1 + ((3.0 * (((t_0 - (2.0 * x2)) - x1) / t_1)) + (x1 + ((x1 * (x1 * x1)) + ((t_1 * ((((x1 * 2.0) * t_2) * (t_2 - 3.0)) + ((x1 * x1) * ((t_2 * 4.0) - 6.0)))) + (3.0 * t_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]}, If[Or[LessEqual[x1, -5e+102], N[Not[LessEqual[x1, 6.7e+62]], $MachinePrecision]], N[(x1 + N[(6.0 * N[Power[x1, 4.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x1 + N[(N[(3.0 * N[(N[(N[(t$95$0 - N[(2.0 * x2), $MachinePrecision]), $MachinePrecision] - x1), $MachinePrecision] / t$95$1), $MachinePrecision]), $MachinePrecision] + N[(x1 + N[(N[(x1 * N[(x1 * x1), $MachinePrecision]), $MachinePrecision] + 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[(3.0 * t$95$0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]

Derivation

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

Alternative 6: 98.7% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := 2 \cdot x2 - x1\\ t_1 := x1 \cdot \left(x1 \cdot 3\right)\\ t_2 := x1 + 6 \cdot {x1}^{4}\\ t_3 := x1 \cdot \left(x1 \cdot x1\right)\\ t_4 := x1 \cdot x1 + 1\\ t_5 := \frac{\left(t_1 + 2 \cdot x2\right) - x1}{t_4}\\ t_6 := \left(\left(x1 \cdot 2\right) \cdot t_5\right) \cdot \left(t_5 - 3\right)\\ t_7 := x1 + \left(3 \cdot \left(x2 \cdot -2\right) + \left(x1 + \left(t_3 + \left(t_4 \cdot \left(t_6 + \left(x1 \cdot x1\right) \cdot \left(t_5 \cdot 4 - 6\right)\right) + t_1 \cdot \left(3 - \frac{1}{x1}\right)\right)\right)\right)\right)\\ \mathbf{if}\;x1 \leq -5 \cdot 10^{+102}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;x1 \leq -2.4 \cdot 10^{-5}:\\ \;\;\;\;t_7\\ \mathbf{elif}\;x1 \leq 2 \cdot 10^{-148}:\\ \;\;\;\;x1 + \left(3 \cdot \frac{\left(t_1 - 2 \cdot x2\right) - x1}{t_4} + \left(x1 + \left(t_3 + \left(t_4 \cdot \left(t_6 + \left(x1 \cdot x1\right) \cdot \left(4 \cdot t_0 - 6\right)\right) + t_1 \cdot t_0\right)\right)\right)\right)\\ \mathbf{elif}\;x1 \leq 6.7 \cdot 10^{+62}:\\ \;\;\;\;t_7\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x1 x2)
 :precision binary64
 (let* ((t_0 (- (* 2.0 x2) x1))
        (t_1 (* x1 (* x1 3.0)))
        (t_2 (+ x1 (* 6.0 (pow x1 4.0))))
        (t_3 (* x1 (* x1 x1)))
        (t_4 (+ (* x1 x1) 1.0))
        (t_5 (/ (- (+ t_1 (* 2.0 x2)) x1) t_4))
        (t_6 (* (* (* x1 2.0) t_5) (- t_5 3.0)))
        (t_7
         (+
          x1
          (+
           (* 3.0 (* x2 -2.0))
           (+
            x1
            (+
             t_3
             (+
              (* t_4 (+ t_6 (* (* x1 x1) (- (* t_5 4.0) 6.0))))
              (* t_1 (- 3.0 (/ 1.0 x1))))))))))
   (if (<= x1 -5e+102)
     t_2
     (if (<= x1 -2.4e-5)
       t_7
       (if (<= x1 2e-148)
         (+
          x1
          (+
           (* 3.0 (/ (- (- t_1 (* 2.0 x2)) x1) t_4))
           (+
            x1
            (+
             t_3
             (+
              (* t_4 (+ t_6 (* (* x1 x1) (- (* 4.0 t_0) 6.0))))
              (* t_1 t_0))))))
         (if (<= x1 6.7e+62) t_7 t_2))))))

C

double code(double x1, double x2) {
	double t_0 = (2.0 * x2) - x1;
	double t_1 = x1 * (x1 * 3.0);
	double t_2 = x1 + (6.0 * pow(x1, 4.0));
	double t_3 = x1 * (x1 * x1);
	double t_4 = (x1 * x1) + 1.0;
	double t_5 = ((t_1 + (2.0 * x2)) - x1) / t_4;
	double t_6 = ((x1 * 2.0) * t_5) * (t_5 - 3.0);
	double t_7 = x1 + ((3.0 * (x2 * -2.0)) + (x1 + (t_3 + ((t_4 * (t_6 + ((x1 * x1) * ((t_5 * 4.0) - 6.0)))) + (t_1 * (3.0 - (1.0 / x1)))))));
	double tmp;
	if (x1 <= -5e+102) {
		tmp = t_2;
	} else if (x1 <= -2.4e-5) {
		tmp = t_7;
	} else if (x1 <= 2e-148) {
		tmp = x1 + ((3.0 * (((t_1 - (2.0 * x2)) - x1) / t_4)) + (x1 + (t_3 + ((t_4 * (t_6 + ((x1 * x1) * ((4.0 * t_0) - 6.0)))) + (t_1 * t_0)))));
	} else if (x1 <= 6.7e+62) {
		tmp = t_7;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Fortran

real(8) function code(x1, x2)
    real(8), intent (in) :: x1
    real(8), intent (in) :: x2
    real(8) :: t_0
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: t_3
    real(8) :: t_4
    real(8) :: t_5
    real(8) :: t_6
    real(8) :: t_7
    real(8) :: tmp
    t_0 = (2.0d0 * x2) - x1
    t_1 = x1 * (x1 * 3.0d0)
    t_2 = x1 + (6.0d0 * (x1 ** 4.0d0))
    t_3 = x1 * (x1 * x1)
    t_4 = (x1 * x1) + 1.0d0
    t_5 = ((t_1 + (2.0d0 * x2)) - x1) / t_4
    t_6 = ((x1 * 2.0d0) * t_5) * (t_5 - 3.0d0)
    t_7 = x1 + ((3.0d0 * (x2 * (-2.0d0))) + (x1 + (t_3 + ((t_4 * (t_6 + ((x1 * x1) * ((t_5 * 4.0d0) - 6.0d0)))) + (t_1 * (3.0d0 - (1.0d0 / x1)))))))
    if (x1 <= (-5d+102)) then
        tmp = t_2
    else if (x1 <= (-2.4d-5)) then
        tmp = t_7
    else if (x1 <= 2d-148) then
        tmp = x1 + ((3.0d0 * (((t_1 - (2.0d0 * x2)) - x1) / t_4)) + (x1 + (t_3 + ((t_4 * (t_6 + ((x1 * x1) * ((4.0d0 * t_0) - 6.0d0)))) + (t_1 * t_0)))))
    else if (x1 <= 6.7d+62) then
        tmp = t_7
    else
        tmp = t_2
    end if
    code = tmp
end function

Java

public static double code(double x1, double x2) {
	double t_0 = (2.0 * x2) - x1;
	double t_1 = x1 * (x1 * 3.0);
	double t_2 = x1 + (6.0 * Math.pow(x1, 4.0));
	double t_3 = x1 * (x1 * x1);
	double t_4 = (x1 * x1) + 1.0;
	double t_5 = ((t_1 + (2.0 * x2)) - x1) / t_4;
	double t_6 = ((x1 * 2.0) * t_5) * (t_5 - 3.0);
	double t_7 = x1 + ((3.0 * (x2 * -2.0)) + (x1 + (t_3 + ((t_4 * (t_6 + ((x1 * x1) * ((t_5 * 4.0) - 6.0)))) + (t_1 * (3.0 - (1.0 / x1)))))));
	double tmp;
	if (x1 <= -5e+102) {
		tmp = t_2;
	} else if (x1 <= -2.4e-5) {
		tmp = t_7;
	} else if (x1 <= 2e-148) {
		tmp = x1 + ((3.0 * (((t_1 - (2.0 * x2)) - x1) / t_4)) + (x1 + (t_3 + ((t_4 * (t_6 + ((x1 * x1) * ((4.0 * t_0) - 6.0)))) + (t_1 * t_0)))));
	} else if (x1 <= 6.7e+62) {
		tmp = t_7;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x1, x2):
	t_0 = (2.0 * x2) - x1
	t_1 = x1 * (x1 * 3.0)
	t_2 = x1 + (6.0 * math.pow(x1, 4.0))
	t_3 = x1 * (x1 * x1)
	t_4 = (x1 * x1) + 1.0
	t_5 = ((t_1 + (2.0 * x2)) - x1) / t_4
	t_6 = ((x1 * 2.0) * t_5) * (t_5 - 3.0)
	t_7 = x1 + ((3.0 * (x2 * -2.0)) + (x1 + (t_3 + ((t_4 * (t_6 + ((x1 * x1) * ((t_5 * 4.0) - 6.0)))) + (t_1 * (3.0 - (1.0 / x1)))))))
	tmp = 0
	if x1 <= -5e+102:
		tmp = t_2
	elif x1 <= -2.4e-5:
		tmp = t_7
	elif x1 <= 2e-148:
		tmp = x1 + ((3.0 * (((t_1 - (2.0 * x2)) - x1) / t_4)) + (x1 + (t_3 + ((t_4 * (t_6 + ((x1 * x1) * ((4.0 * t_0) - 6.0)))) + (t_1 * t_0)))))
	elif x1 <= 6.7e+62:
		tmp = t_7
	else:
		tmp = t_2
	return tmp

Julia

function code(x1, x2)
	t_0 = Float64(Float64(2.0 * x2) - x1)
	t_1 = Float64(x1 * Float64(x1 * 3.0))
	t_2 = Float64(x1 + Float64(6.0 * (x1 ^ 4.0)))
	t_3 = Float64(x1 * Float64(x1 * x1))
	t_4 = Float64(Float64(x1 * x1) + 1.0)
	t_5 = Float64(Float64(Float64(t_1 + Float64(2.0 * x2)) - x1) / t_4)
	t_6 = Float64(Float64(Float64(x1 * 2.0) * t_5) * Float64(t_5 - 3.0))
	t_7 = Float64(x1 + Float64(Float64(3.0 * Float64(x2 * -2.0)) + Float64(x1 + Float64(t_3 + Float64(Float64(t_4 * Float64(t_6 + Float64(Float64(x1 * x1) * Float64(Float64(t_5 * 4.0) - 6.0)))) + Float64(t_1 * Float64(3.0 - Float64(1.0 / x1))))))))
	tmp = 0.0
	if (x1 <= -5e+102)
		tmp = t_2;
	elseif (x1 <= -2.4e-5)
		tmp = t_7;
	elseif (x1 <= 2e-148)
		tmp = Float64(x1 + Float64(Float64(3.0 * Float64(Float64(Float64(t_1 - Float64(2.0 * x2)) - x1) / t_4)) + Float64(x1 + Float64(t_3 + Float64(Float64(t_4 * Float64(t_6 + Float64(Float64(x1 * x1) * Float64(Float64(4.0 * t_0) - 6.0)))) + Float64(t_1 * t_0))))));
	elseif (x1 <= 6.7e+62)
		tmp = t_7;
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x1, x2)
	t_0 = (2.0 * x2) - x1;
	t_1 = x1 * (x1 * 3.0);
	t_2 = x1 + (6.0 * (x1 ^ 4.0));
	t_3 = x1 * (x1 * x1);
	t_4 = (x1 * x1) + 1.0;
	t_5 = ((t_1 + (2.0 * x2)) - x1) / t_4;
	t_6 = ((x1 * 2.0) * t_5) * (t_5 - 3.0);
	t_7 = x1 + ((3.0 * (x2 * -2.0)) + (x1 + (t_3 + ((t_4 * (t_6 + ((x1 * x1) * ((t_5 * 4.0) - 6.0)))) + (t_1 * (3.0 - (1.0 / x1)))))));
	tmp = 0.0;
	if (x1 <= -5e+102)
		tmp = t_2;
	elseif (x1 <= -2.4e-5)
		tmp = t_7;
	elseif (x1 <= 2e-148)
		tmp = x1 + ((3.0 * (((t_1 - (2.0 * x2)) - x1) / t_4)) + (x1 + (t_3 + ((t_4 * (t_6 + ((x1 * x1) * ((4.0 * t_0) - 6.0)))) + (t_1 * t_0)))));
	elseif (x1 <= 6.7e+62)
		tmp = t_7;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x1_, x2_] := Block[{t$95$0 = N[(N[(2.0 * x2), $MachinePrecision] - x1), $MachinePrecision]}, Block[{t$95$1 = N[(x1 * N[(x1 * 3.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(x1 + N[(6.0 * N[Power[x1, 4.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(x1 * N[(x1 * x1), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$4 = N[(N[(x1 * x1), $MachinePrecision] + 1.0), $MachinePrecision]}, Block[{t$95$5 = N[(N[(N[(t$95$1 + N[(2.0 * x2), $MachinePrecision]), $MachinePrecision] - x1), $MachinePrecision] / t$95$4), $MachinePrecision]}, Block[{t$95$6 = N[(N[(N[(x1 * 2.0), $MachinePrecision] * t$95$5), $MachinePrecision] * N[(t$95$5 - 3.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$7 = N[(x1 + N[(N[(3.0 * N[(x2 * -2.0), $MachinePrecision]), $MachinePrecision] + N[(x1 + N[(t$95$3 + N[(N[(t$95$4 * N[(t$95$6 + N[(N[(x1 * x1), $MachinePrecision] * N[(N[(t$95$5 * 4.0), $MachinePrecision] - 6.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(t$95$1 * N[(3.0 - N[(1.0 / x1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x1, -5e+102], t$95$2, If[LessEqual[x1, -2.4e-5], t$95$7, If[LessEqual[x1, 2e-148], N[(x1 + N[(N[(3.0 * N[(N[(N[(t$95$1 - N[(2.0 * x2), $MachinePrecision]), $MachinePrecision] - x1), $MachinePrecision] / t$95$4), $MachinePrecision]), $MachinePrecision] + N[(x1 + N[(t$95$3 + N[(N[(t$95$4 * N[(t$95$6 + N[(N[(x1 * x1), $MachinePrecision] * N[(N[(4.0 * t$95$0), $MachinePrecision] - 6.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(t$95$1 * t$95$0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, 6.7e+62], t$95$7, t$95$2]]]]]]]]]]]]

Derivation

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

Alternative 7: 96.3% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := x1 \cdot \left(x1 \cdot 3\right)\\ t_1 := x1 \cdot x1 + 1\\ t_2 := \frac{\left(t_0 + 2 \cdot x2\right) - x1}{t_1}\\ \mathbf{if}\;x1 \leq -1.55 \cdot 10^{+53} \lor \neg \left(x1 \leq 4 \cdot 10^{+126}\right):\\ \;\;\;\;x1 + 6 \cdot {x1}^{4}\\ \mathbf{else}:\\ \;\;\;\;x1 + \left(3 \cdot \frac{\left(t_0 - 2 \cdot x2\right) - x1}{t_1} + \left(x1 + \left(x1 \cdot \left(x1 \cdot x1\right) + \left(t_0 \cdot t_2 + t_1 \cdot \left(\left(\left(x1 \cdot 2\right) \cdot t_2\right) \cdot \left(t_2 - 3\right) + \left(x1 \cdot x1\right) \cdot 6\right)\right)\right)\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x1 x2)
 :precision binary64
 (let* ((t_0 (* x1 (* x1 3.0)))
        (t_1 (+ (* x1 x1) 1.0))
        (t_2 (/ (- (+ t_0 (* 2.0 x2)) x1) t_1)))
   (if (or (<= x1 -1.55e+53) (not (<= x1 4e+126)))
     (+ x1 (* 6.0 (pow x1 4.0)))
     (+
      x1
      (+
       (* 3.0 (/ (- (- t_0 (* 2.0 x2)) x1) t_1))
       (+
        x1
        (+
         (* x1 (* x1 x1))
         (+
          (* t_0 t_2)
          (*
           t_1
           (+ (* (* (* x1 2.0) t_2) (- t_2 3.0)) (* (* x1 x1) 6.0)))))))))))

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 tmp;
	if ((x1 <= -1.55e+53) || !(x1 <= 4e+126)) {
		tmp = x1 + (6.0 * pow(x1, 4.0));
	} else {
		tmp = x1 + ((3.0 * (((t_0 - (2.0 * x2)) - x1) / t_1)) + (x1 + ((x1 * (x1 * x1)) + ((t_0 * t_2) + (t_1 * ((((x1 * 2.0) * t_2) * (t_2 - 3.0)) + ((x1 * x1) * 6.0)))))));
	}
	return tmp;
}

Fortran

real(8) function code(x1, x2)
    real(8), intent (in) :: x1
    real(8), intent (in) :: x2
    real(8) :: t_0
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_0 = x1 * (x1 * 3.0d0)
    t_1 = (x1 * x1) + 1.0d0
    t_2 = ((t_0 + (2.0d0 * x2)) - x1) / t_1
    if ((x1 <= (-1.55d+53)) .or. (.not. (x1 <= 4d+126))) then
        tmp = x1 + (6.0d0 * (x1 ** 4.0d0))
    else
        tmp = x1 + ((3.0d0 * (((t_0 - (2.0d0 * x2)) - x1) / t_1)) + (x1 + ((x1 * (x1 * x1)) + ((t_0 * t_2) + (t_1 * ((((x1 * 2.0d0) * t_2) * (t_2 - 3.0d0)) + ((x1 * x1) * 6.0d0)))))))
    end if
    code = tmp
end function

Java

public static double code(double x1, double x2) {
	double t_0 = x1 * (x1 * 3.0);
	double t_1 = (x1 * x1) + 1.0;
	double t_2 = ((t_0 + (2.0 * x2)) - x1) / t_1;
	double tmp;
	if ((x1 <= -1.55e+53) || !(x1 <= 4e+126)) {
		tmp = x1 + (6.0 * Math.pow(x1, 4.0));
	} else {
		tmp = x1 + ((3.0 * (((t_0 - (2.0 * x2)) - x1) / t_1)) + (x1 + ((x1 * (x1 * x1)) + ((t_0 * t_2) + (t_1 * ((((x1 * 2.0) * t_2) * (t_2 - 3.0)) + ((x1 * x1) * 6.0)))))));
	}
	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
	tmp = 0
	if (x1 <= -1.55e+53) or not (x1 <= 4e+126):
		tmp = x1 + (6.0 * math.pow(x1, 4.0))
	else:
		tmp = x1 + ((3.0 * (((t_0 - (2.0 * x2)) - x1) / t_1)) + (x1 + ((x1 * (x1 * x1)) + ((t_0 * t_2) + (t_1 * ((((x1 * 2.0) * t_2) * (t_2 - 3.0)) + ((x1 * x1) * 6.0)))))))
	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)
	tmp = 0.0
	if ((x1 <= -1.55e+53) || !(x1 <= 4e+126))
		tmp = Float64(x1 + Float64(6.0 * (x1 ^ 4.0)));
	else
		tmp = Float64(x1 + Float64(Float64(3.0 * Float64(Float64(Float64(t_0 - Float64(2.0 * x2)) - x1) / t_1)) + Float64(x1 + Float64(Float64(x1 * Float64(x1 * x1)) + Float64(Float64(t_0 * t_2) + Float64(t_1 * Float64(Float64(Float64(Float64(x1 * 2.0) * t_2) * Float64(t_2 - 3.0)) + Float64(Float64(x1 * x1) * 6.0))))))));
	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;
	tmp = 0.0;
	if ((x1 <= -1.55e+53) || ~((x1 <= 4e+126)))
		tmp = x1 + (6.0 * (x1 ^ 4.0));
	else
		tmp = x1 + ((3.0 * (((t_0 - (2.0 * x2)) - x1) / t_1)) + (x1 + ((x1 * (x1 * x1)) + ((t_0 * t_2) + (t_1 * ((((x1 * 2.0) * t_2) * (t_2 - 3.0)) + ((x1 * x1) * 6.0)))))));
	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]}, If[Or[LessEqual[x1, -1.55e+53], N[Not[LessEqual[x1, 4e+126]], $MachinePrecision]], N[(x1 + N[(6.0 * N[Power[x1, 4.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x1 + N[(N[(3.0 * N[(N[(N[(t$95$0 - N[(2.0 * x2), $MachinePrecision]), $MachinePrecision] - x1), $MachinePrecision] / t$95$1), $MachinePrecision]), $MachinePrecision] + N[(x1 + N[(N[(x1 * N[(x1 * x1), $MachinePrecision]), $MachinePrecision] + N[(N[(t$95$0 * t$95$2), $MachinePrecision] + 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] * 6.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]

Derivation

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

Alternative 8: 79.5% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := x1 \cdot x1 + 1\\ t_1 := 2 \cdot x2 - 3\\ t_2 := 3 \cdot \left(x2 \cdot -2\right)\\ t_3 := x1 \cdot \left(x1 \cdot 3\right)\\ t_4 := \frac{\left(t_3 + 2 \cdot x2\right) - x1}{t_0}\\ t_5 := \left(\left(x1 \cdot 2\right) \cdot t_4\right) \cdot \left(t_4 - 3\right)\\ t_6 := x1 \cdot \left(x1 \cdot x1\right)\\ t_7 := x1 + \left(t_2 + \left(x1 + \left(t_6 + \left(t_0 \cdot \left(t_5 + \left(x1 \cdot x1\right) \cdot \left(t_4 \cdot 4 - 6\right)\right) + t_3 \cdot \left(3 - \frac{1}{x1}\right)\right)\right)\right)\right)\\ \mathbf{if}\;x1 \leq -4.1 \cdot 10^{+108}:\\ \;\;\;\;x1 + \left(t_2 + \left(x1 + x1 \cdot \left(6 \cdot t_1 - 4\right)\right)\right)\\ \mathbf{elif}\;x1 \leq -2.4 \cdot 10^{-5}:\\ \;\;\;\;t_7\\ \mathbf{elif}\;x1 \leq 2 \cdot 10^{-148}:\\ \;\;\;\;x1 + \left(3 \cdot \frac{\left(t_3 - 2 \cdot x2\right) - x1}{t_0} + \left(x1 + \left(t_6 + \left(t_3 \cdot \left(2 \cdot x2 - x1\right) + t_0 \cdot \left(t_5 + \left(x1 \cdot x1\right) \cdot \left(\left(2 \cdot x2\right) \cdot 4 - 6\right)\right)\right)\right)\right)\right)\\ \mathbf{elif}\;x1 \leq 1.35 \cdot 10^{+154}:\\ \;\;\;\;t_7\\ \mathbf{else}:\\ \;\;\;\;x1 + x1 \cdot \left(1 + 4 \cdot \left(x2 \cdot t_1\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x1 x2)
 :precision binary64
 (let* ((t_0 (+ (* x1 x1) 1.0))
        (t_1 (- (* 2.0 x2) 3.0))
        (t_2 (* 3.0 (* x2 -2.0)))
        (t_3 (* x1 (* x1 3.0)))
        (t_4 (/ (- (+ t_3 (* 2.0 x2)) x1) t_0))
        (t_5 (* (* (* x1 2.0) t_4) (- t_4 3.0)))
        (t_6 (* x1 (* x1 x1)))
        (t_7
         (+
          x1
          (+
           t_2
           (+
            x1
            (+
             t_6
             (+
              (* t_0 (+ t_5 (* (* x1 x1) (- (* t_4 4.0) 6.0))))
              (* t_3 (- 3.0 (/ 1.0 x1))))))))))
   (if (<= x1 -4.1e+108)
     (+ x1 (+ t_2 (+ x1 (* x1 (- (* 6.0 t_1) 4.0)))))
     (if (<= x1 -2.4e-5)
       t_7
       (if (<= x1 2e-148)
         (+
          x1
          (+
           (* 3.0 (/ (- (- t_3 (* 2.0 x2)) x1) t_0))
           (+
            x1
            (+
             t_6
             (+
              (* t_3 (- (* 2.0 x2) x1))
              (* t_0 (+ t_5 (* (* x1 x1) (- (* (* 2.0 x2) 4.0) 6.0)))))))))
         (if (<= x1 1.35e+154)
           t_7
           (+ x1 (* x1 (+ 1.0 (* 4.0 (* x2 t_1)))))))))))

C

double code(double x1, double x2) {
	double t_0 = (x1 * x1) + 1.0;
	double t_1 = (2.0 * x2) - 3.0;
	double t_2 = 3.0 * (x2 * -2.0);
	double t_3 = x1 * (x1 * 3.0);
	double t_4 = ((t_3 + (2.0 * x2)) - x1) / t_0;
	double t_5 = ((x1 * 2.0) * t_4) * (t_4 - 3.0);
	double t_6 = x1 * (x1 * x1);
	double t_7 = x1 + (t_2 + (x1 + (t_6 + ((t_0 * (t_5 + ((x1 * x1) * ((t_4 * 4.0) - 6.0)))) + (t_3 * (3.0 - (1.0 / x1)))))));
	double tmp;
	if (x1 <= -4.1e+108) {
		tmp = x1 + (t_2 + (x1 + (x1 * ((6.0 * t_1) - 4.0))));
	} else if (x1 <= -2.4e-5) {
		tmp = t_7;
	} else if (x1 <= 2e-148) {
		tmp = x1 + ((3.0 * (((t_3 - (2.0 * x2)) - x1) / t_0)) + (x1 + (t_6 + ((t_3 * ((2.0 * x2) - x1)) + (t_0 * (t_5 + ((x1 * x1) * (((2.0 * x2) * 4.0) - 6.0))))))));
	} else if (x1 <= 1.35e+154) {
		tmp = t_7;
	} 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 * x1) + 1.0d0
    t_1 = (2.0d0 * x2) - 3.0d0
    t_2 = 3.0d0 * (x2 * (-2.0d0))
    t_3 = x1 * (x1 * 3.0d0)
    t_4 = ((t_3 + (2.0d0 * x2)) - x1) / t_0
    t_5 = ((x1 * 2.0d0) * t_4) * (t_4 - 3.0d0)
    t_6 = x1 * (x1 * x1)
    t_7 = x1 + (t_2 + (x1 + (t_6 + ((t_0 * (t_5 + ((x1 * x1) * ((t_4 * 4.0d0) - 6.0d0)))) + (t_3 * (3.0d0 - (1.0d0 / x1)))))))
    if (x1 <= (-4.1d+108)) then
        tmp = x1 + (t_2 + (x1 + (x1 * ((6.0d0 * t_1) - 4.0d0))))
    else if (x1 <= (-2.4d-5)) then
        tmp = t_7
    else if (x1 <= 2d-148) then
        tmp = x1 + ((3.0d0 * (((t_3 - (2.0d0 * x2)) - x1) / t_0)) + (x1 + (t_6 + ((t_3 * ((2.0d0 * x2) - x1)) + (t_0 * (t_5 + ((x1 * x1) * (((2.0d0 * x2) * 4.0d0) - 6.0d0))))))))
    else if (x1 <= 1.35d+154) then
        tmp = t_7
    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 * x1) + 1.0;
	double t_1 = (2.0 * x2) - 3.0;
	double t_2 = 3.0 * (x2 * -2.0);
	double t_3 = x1 * (x1 * 3.0);
	double t_4 = ((t_3 + (2.0 * x2)) - x1) / t_0;
	double t_5 = ((x1 * 2.0) * t_4) * (t_4 - 3.0);
	double t_6 = x1 * (x1 * x1);
	double t_7 = x1 + (t_2 + (x1 + (t_6 + ((t_0 * (t_5 + ((x1 * x1) * ((t_4 * 4.0) - 6.0)))) + (t_3 * (3.0 - (1.0 / x1)))))));
	double tmp;
	if (x1 <= -4.1e+108) {
		tmp = x1 + (t_2 + (x1 + (x1 * ((6.0 * t_1) - 4.0))));
	} else if (x1 <= -2.4e-5) {
		tmp = t_7;
	} else if (x1 <= 2e-148) {
		tmp = x1 + ((3.0 * (((t_3 - (2.0 * x2)) - x1) / t_0)) + (x1 + (t_6 + ((t_3 * ((2.0 * x2) - x1)) + (t_0 * (t_5 + ((x1 * x1) * (((2.0 * x2) * 4.0) - 6.0))))))));
	} else if (x1 <= 1.35e+154) {
		tmp = t_7;
	} else {
		tmp = x1 + (x1 * (1.0 + (4.0 * (x2 * t_1))));
	}
	return tmp;
}

Python

def code(x1, x2):
	t_0 = (x1 * x1) + 1.0
	t_1 = (2.0 * x2) - 3.0
	t_2 = 3.0 * (x2 * -2.0)
	t_3 = x1 * (x1 * 3.0)
	t_4 = ((t_3 + (2.0 * x2)) - x1) / t_0
	t_5 = ((x1 * 2.0) * t_4) * (t_4 - 3.0)
	t_6 = x1 * (x1 * x1)
	t_7 = x1 + (t_2 + (x1 + (t_6 + ((t_0 * (t_5 + ((x1 * x1) * ((t_4 * 4.0) - 6.0)))) + (t_3 * (3.0 - (1.0 / x1)))))))
	tmp = 0
	if x1 <= -4.1e+108:
		tmp = x1 + (t_2 + (x1 + (x1 * ((6.0 * t_1) - 4.0))))
	elif x1 <= -2.4e-5:
		tmp = t_7
	elif x1 <= 2e-148:
		tmp = x1 + ((3.0 * (((t_3 - (2.0 * x2)) - x1) / t_0)) + (x1 + (t_6 + ((t_3 * ((2.0 * x2) - x1)) + (t_0 * (t_5 + ((x1 * x1) * (((2.0 * x2) * 4.0) - 6.0))))))))
	elif x1 <= 1.35e+154:
		tmp = t_7
	else:
		tmp = x1 + (x1 * (1.0 + (4.0 * (x2 * t_1))))
	return tmp

Julia

function code(x1, x2)
	t_0 = Float64(Float64(x1 * x1) + 1.0)
	t_1 = Float64(Float64(2.0 * x2) - 3.0)
	t_2 = Float64(3.0 * Float64(x2 * -2.0))
	t_3 = Float64(x1 * Float64(x1 * 3.0))
	t_4 = Float64(Float64(Float64(t_3 + Float64(2.0 * x2)) - x1) / t_0)
	t_5 = Float64(Float64(Float64(x1 * 2.0) * t_4) * Float64(t_4 - 3.0))
	t_6 = Float64(x1 * Float64(x1 * x1))
	t_7 = Float64(x1 + Float64(t_2 + Float64(x1 + Float64(t_6 + Float64(Float64(t_0 * Float64(t_5 + Float64(Float64(x1 * x1) * Float64(Float64(t_4 * 4.0) - 6.0)))) + Float64(t_3 * Float64(3.0 - Float64(1.0 / x1))))))))
	tmp = 0.0
	if (x1 <= -4.1e+108)
		tmp = Float64(x1 + Float64(t_2 + Float64(x1 + Float64(x1 * Float64(Float64(6.0 * t_1) - 4.0)))));
	elseif (x1 <= -2.4e-5)
		tmp = t_7;
	elseif (x1 <= 2e-148)
		tmp = Float64(x1 + Float64(Float64(3.0 * Float64(Float64(Float64(t_3 - Float64(2.0 * x2)) - x1) / t_0)) + Float64(x1 + Float64(t_6 + Float64(Float64(t_3 * Float64(Float64(2.0 * x2) - x1)) + Float64(t_0 * Float64(t_5 + Float64(Float64(x1 * x1) * Float64(Float64(Float64(2.0 * x2) * 4.0) - 6.0)))))))));
	elseif (x1 <= 1.35e+154)
		tmp = t_7;
	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 * x1) + 1.0;
	t_1 = (2.0 * x2) - 3.0;
	t_2 = 3.0 * (x2 * -2.0);
	t_3 = x1 * (x1 * 3.0);
	t_4 = ((t_3 + (2.0 * x2)) - x1) / t_0;
	t_5 = ((x1 * 2.0) * t_4) * (t_4 - 3.0);
	t_6 = x1 * (x1 * x1);
	t_7 = x1 + (t_2 + (x1 + (t_6 + ((t_0 * (t_5 + ((x1 * x1) * ((t_4 * 4.0) - 6.0)))) + (t_3 * (3.0 - (1.0 / x1)))))));
	tmp = 0.0;
	if (x1 <= -4.1e+108)
		tmp = x1 + (t_2 + (x1 + (x1 * ((6.0 * t_1) - 4.0))));
	elseif (x1 <= -2.4e-5)
		tmp = t_7;
	elseif (x1 <= 2e-148)
		tmp = x1 + ((3.0 * (((t_3 - (2.0 * x2)) - x1) / t_0)) + (x1 + (t_6 + ((t_3 * ((2.0 * x2) - x1)) + (t_0 * (t_5 + ((x1 * x1) * (((2.0 * x2) * 4.0) - 6.0))))))));
	elseif (x1 <= 1.35e+154)
		tmp = t_7;
	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 * x1), $MachinePrecision] + 1.0), $MachinePrecision]}, Block[{t$95$1 = N[(N[(2.0 * x2), $MachinePrecision] - 3.0), $MachinePrecision]}, Block[{t$95$2 = N[(3.0 * N[(x2 * -2.0), $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$0), $MachinePrecision]}, Block[{t$95$5 = N[(N[(N[(x1 * 2.0), $MachinePrecision] * t$95$4), $MachinePrecision] * N[(t$95$4 - 3.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$6 = N[(x1 * N[(x1 * x1), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$7 = N[(x1 + N[(t$95$2 + N[(x1 + N[(t$95$6 + N[(N[(t$95$0 * N[(t$95$5 + N[(N[(x1 * x1), $MachinePrecision] * N[(N[(t$95$4 * 4.0), $MachinePrecision] - 6.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(t$95$3 * N[(3.0 - N[(1.0 / x1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x1, -4.1e+108], N[(x1 + N[(t$95$2 + N[(x1 + N[(x1 * N[(N[(6.0 * t$95$1), $MachinePrecision] - 4.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, -2.4e-5], t$95$7, If[LessEqual[x1, 2e-148], N[(x1 + N[(N[(3.0 * N[(N[(N[(t$95$3 - N[(2.0 * x2), $MachinePrecision]), $MachinePrecision] - x1), $MachinePrecision] / t$95$0), $MachinePrecision]), $MachinePrecision] + N[(x1 + N[(t$95$6 + N[(N[(t$95$3 * N[(N[(2.0 * x2), $MachinePrecision] - x1), $MachinePrecision]), $MachinePrecision] + N[(t$95$0 * N[(t$95$5 + N[(N[(x1 * x1), $MachinePrecision] * N[(N[(N[(2.0 * x2), $MachinePrecision] * 4.0), $MachinePrecision] - 6.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, 1.35e+154], t$95$7, 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: 79.6% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := x1 \cdot x1 + 1\\ t_1 := 2 \cdot x2 - 3\\ t_2 := 3 \cdot \left(x2 \cdot -2\right)\\ t_3 := x1 \cdot \left(x1 \cdot 3\right)\\ t_4 := \frac{\left(t_3 + 2 \cdot x2\right) - x1}{t_0}\\ t_5 := x1 \cdot \left(x1 \cdot x1\right)\\ t_6 := \left(\left(x1 \cdot 2\right) \cdot t_4\right) \cdot \left(t_4 - 3\right)\\ t_7 := x1 + \left(t_2 + \left(x1 + \left(t_5 + \left(t_0 \cdot \left(t_6 + \left(x1 \cdot x1\right) \cdot \left(t_4 \cdot 4 - 6\right)\right) + t_3 \cdot \left(3 - \frac{1}{x1}\right)\right)\right)\right)\right)\\ t_8 := 2 \cdot x2 - x1\\ \mathbf{if}\;x1 \leq -3.4 \cdot 10^{+107}:\\ \;\;\;\;x1 + \left(t_2 + \left(x1 + x1 \cdot \left(6 \cdot t_1 - 4\right)\right)\right)\\ \mathbf{elif}\;x1 \leq -2.4 \cdot 10^{-5}:\\ \;\;\;\;t_7\\ \mathbf{elif}\;x1 \leq 2 \cdot 10^{-148}:\\ \;\;\;\;x1 + \left(3 \cdot \frac{\left(t_3 - 2 \cdot x2\right) - x1}{t_0} + \left(x1 + \left(t_5 + \left(t_0 \cdot \left(t_6 + \left(x1 \cdot x1\right) \cdot \left(4 \cdot t_8 - 6\right)\right) + t_3 \cdot t_8\right)\right)\right)\right)\\ \mathbf{elif}\;x1 \leq 1.35 \cdot 10^{+154}:\\ \;\;\;\;t_7\\ \mathbf{else}:\\ \;\;\;\;x1 + x1 \cdot \left(1 + 4 \cdot \left(x2 \cdot t_1\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x1 x2)
 :precision binary64
 (let* ((t_0 (+ (* x1 x1) 1.0))
        (t_1 (- (* 2.0 x2) 3.0))
        (t_2 (* 3.0 (* x2 -2.0)))
        (t_3 (* x1 (* x1 3.0)))
        (t_4 (/ (- (+ t_3 (* 2.0 x2)) x1) t_0))
        (t_5 (* x1 (* x1 x1)))
        (t_6 (* (* (* x1 2.0) t_4) (- t_4 3.0)))
        (t_7
         (+
          x1
          (+
           t_2
           (+
            x1
            (+
             t_5
             (+
              (* t_0 (+ t_6 (* (* x1 x1) (- (* t_4 4.0) 6.0))))
              (* t_3 (- 3.0 (/ 1.0 x1)))))))))
        (t_8 (- (* 2.0 x2) x1)))
   (if (<= x1 -3.4e+107)
     (+ x1 (+ t_2 (+ x1 (* x1 (- (* 6.0 t_1) 4.0)))))
     (if (<= x1 -2.4e-5)
       t_7
       (if (<= x1 2e-148)
         (+
          x1
          (+
           (* 3.0 (/ (- (- t_3 (* 2.0 x2)) x1) t_0))
           (+
            x1
            (+
             t_5
             (+
              (* t_0 (+ t_6 (* (* x1 x1) (- (* 4.0 t_8) 6.0))))
              (* t_3 t_8))))))
         (if (<= x1 1.35e+154)
           t_7
           (+ x1 (* x1 (+ 1.0 (* 4.0 (* x2 t_1)))))))))))

C

double code(double x1, double x2) {
	double t_0 = (x1 * x1) + 1.0;
	double t_1 = (2.0 * x2) - 3.0;
	double t_2 = 3.0 * (x2 * -2.0);
	double t_3 = x1 * (x1 * 3.0);
	double t_4 = ((t_3 + (2.0 * x2)) - x1) / t_0;
	double t_5 = x1 * (x1 * x1);
	double t_6 = ((x1 * 2.0) * t_4) * (t_4 - 3.0);
	double t_7 = x1 + (t_2 + (x1 + (t_5 + ((t_0 * (t_6 + ((x1 * x1) * ((t_4 * 4.0) - 6.0)))) + (t_3 * (3.0 - (1.0 / x1)))))));
	double t_8 = (2.0 * x2) - x1;
	double tmp;
	if (x1 <= -3.4e+107) {
		tmp = x1 + (t_2 + (x1 + (x1 * ((6.0 * t_1) - 4.0))));
	} else if (x1 <= -2.4e-5) {
		tmp = t_7;
	} else if (x1 <= 2e-148) {
		tmp = x1 + ((3.0 * (((t_3 - (2.0 * x2)) - x1) / t_0)) + (x1 + (t_5 + ((t_0 * (t_6 + ((x1 * x1) * ((4.0 * t_8) - 6.0)))) + (t_3 * t_8)))));
	} else if (x1 <= 1.35e+154) {
		tmp = t_7;
	} 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) :: t_8
    real(8) :: tmp
    t_0 = (x1 * x1) + 1.0d0
    t_1 = (2.0d0 * x2) - 3.0d0
    t_2 = 3.0d0 * (x2 * (-2.0d0))
    t_3 = x1 * (x1 * 3.0d0)
    t_4 = ((t_3 + (2.0d0 * x2)) - x1) / t_0
    t_5 = x1 * (x1 * x1)
    t_6 = ((x1 * 2.0d0) * t_4) * (t_4 - 3.0d0)
    t_7 = x1 + (t_2 + (x1 + (t_5 + ((t_0 * (t_6 + ((x1 * x1) * ((t_4 * 4.0d0) - 6.0d0)))) + (t_3 * (3.0d0 - (1.0d0 / x1)))))))
    t_8 = (2.0d0 * x2) - x1
    if (x1 <= (-3.4d+107)) then
        tmp = x1 + (t_2 + (x1 + (x1 * ((6.0d0 * t_1) - 4.0d0))))
    else if (x1 <= (-2.4d-5)) then
        tmp = t_7
    else if (x1 <= 2d-148) then
        tmp = x1 + ((3.0d0 * (((t_3 - (2.0d0 * x2)) - x1) / t_0)) + (x1 + (t_5 + ((t_0 * (t_6 + ((x1 * x1) * ((4.0d0 * t_8) - 6.0d0)))) + (t_3 * t_8)))))
    else if (x1 <= 1.35d+154) then
        tmp = t_7
    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 * x1) + 1.0;
	double t_1 = (2.0 * x2) - 3.0;
	double t_2 = 3.0 * (x2 * -2.0);
	double t_3 = x1 * (x1 * 3.0);
	double t_4 = ((t_3 + (2.0 * x2)) - x1) / t_0;
	double t_5 = x1 * (x1 * x1);
	double t_6 = ((x1 * 2.0) * t_4) * (t_4 - 3.0);
	double t_7 = x1 + (t_2 + (x1 + (t_5 + ((t_0 * (t_6 + ((x1 * x1) * ((t_4 * 4.0) - 6.0)))) + (t_3 * (3.0 - (1.0 / x1)))))));
	double t_8 = (2.0 * x2) - x1;
	double tmp;
	if (x1 <= -3.4e+107) {
		tmp = x1 + (t_2 + (x1 + (x1 * ((6.0 * t_1) - 4.0))));
	} else if (x1 <= -2.4e-5) {
		tmp = t_7;
	} else if (x1 <= 2e-148) {
		tmp = x1 + ((3.0 * (((t_3 - (2.0 * x2)) - x1) / t_0)) + (x1 + (t_5 + ((t_0 * (t_6 + ((x1 * x1) * ((4.0 * t_8) - 6.0)))) + (t_3 * t_8)))));
	} else if (x1 <= 1.35e+154) {
		tmp = t_7;
	} else {
		tmp = x1 + (x1 * (1.0 + (4.0 * (x2 * t_1))));
	}
	return tmp;
}

Python

def code(x1, x2):
	t_0 = (x1 * x1) + 1.0
	t_1 = (2.0 * x2) - 3.0
	t_2 = 3.0 * (x2 * -2.0)
	t_3 = x1 * (x1 * 3.0)
	t_4 = ((t_3 + (2.0 * x2)) - x1) / t_0
	t_5 = x1 * (x1 * x1)
	t_6 = ((x1 * 2.0) * t_4) * (t_4 - 3.0)
	t_7 = x1 + (t_2 + (x1 + (t_5 + ((t_0 * (t_6 + ((x1 * x1) * ((t_4 * 4.0) - 6.0)))) + (t_3 * (3.0 - (1.0 / x1)))))))
	t_8 = (2.0 * x2) - x1
	tmp = 0
	if x1 <= -3.4e+107:
		tmp = x1 + (t_2 + (x1 + (x1 * ((6.0 * t_1) - 4.0))))
	elif x1 <= -2.4e-5:
		tmp = t_7
	elif x1 <= 2e-148:
		tmp = x1 + ((3.0 * (((t_3 - (2.0 * x2)) - x1) / t_0)) + (x1 + (t_5 + ((t_0 * (t_6 + ((x1 * x1) * ((4.0 * t_8) - 6.0)))) + (t_3 * t_8)))))
	elif x1 <= 1.35e+154:
		tmp = t_7
	else:
		tmp = x1 + (x1 * (1.0 + (4.0 * (x2 * t_1))))
	return tmp

Julia

function code(x1, x2)
	t_0 = Float64(Float64(x1 * x1) + 1.0)
	t_1 = Float64(Float64(2.0 * x2) - 3.0)
	t_2 = Float64(3.0 * Float64(x2 * -2.0))
	t_3 = Float64(x1 * Float64(x1 * 3.0))
	t_4 = Float64(Float64(Float64(t_3 + Float64(2.0 * x2)) - x1) / t_0)
	t_5 = Float64(x1 * Float64(x1 * x1))
	t_6 = Float64(Float64(Float64(x1 * 2.0) * t_4) * Float64(t_4 - 3.0))
	t_7 = Float64(x1 + Float64(t_2 + Float64(x1 + Float64(t_5 + Float64(Float64(t_0 * Float64(t_6 + Float64(Float64(x1 * x1) * Float64(Float64(t_4 * 4.0) - 6.0)))) + Float64(t_3 * Float64(3.0 - Float64(1.0 / x1))))))))
	t_8 = Float64(Float64(2.0 * x2) - x1)
	tmp = 0.0
	if (x1 <= -3.4e+107)
		tmp = Float64(x1 + Float64(t_2 + Float64(x1 + Float64(x1 * Float64(Float64(6.0 * t_1) - 4.0)))));
	elseif (x1 <= -2.4e-5)
		tmp = t_7;
	elseif (x1 <= 2e-148)
		tmp = Float64(x1 + Float64(Float64(3.0 * Float64(Float64(Float64(t_3 - Float64(2.0 * x2)) - x1) / t_0)) + Float64(x1 + Float64(t_5 + Float64(Float64(t_0 * Float64(t_6 + Float64(Float64(x1 * x1) * Float64(Float64(4.0 * t_8) - 6.0)))) + Float64(t_3 * t_8))))));
	elseif (x1 <= 1.35e+154)
		tmp = t_7;
	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 * x1) + 1.0;
	t_1 = (2.0 * x2) - 3.0;
	t_2 = 3.0 * (x2 * -2.0);
	t_3 = x1 * (x1 * 3.0);
	t_4 = ((t_3 + (2.0 * x2)) - x1) / t_0;
	t_5 = x1 * (x1 * x1);
	t_6 = ((x1 * 2.0) * t_4) * (t_4 - 3.0);
	t_7 = x1 + (t_2 + (x1 + (t_5 + ((t_0 * (t_6 + ((x1 * x1) * ((t_4 * 4.0) - 6.0)))) + (t_3 * (3.0 - (1.0 / x1)))))));
	t_8 = (2.0 * x2) - x1;
	tmp = 0.0;
	if (x1 <= -3.4e+107)
		tmp = x1 + (t_2 + (x1 + (x1 * ((6.0 * t_1) - 4.0))));
	elseif (x1 <= -2.4e-5)
		tmp = t_7;
	elseif (x1 <= 2e-148)
		tmp = x1 + ((3.0 * (((t_3 - (2.0 * x2)) - x1) / t_0)) + (x1 + (t_5 + ((t_0 * (t_6 + ((x1 * x1) * ((4.0 * t_8) - 6.0)))) + (t_3 * t_8)))));
	elseif (x1 <= 1.35e+154)
		tmp = t_7;
	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 * x1), $MachinePrecision] + 1.0), $MachinePrecision]}, Block[{t$95$1 = N[(N[(2.0 * x2), $MachinePrecision] - 3.0), $MachinePrecision]}, Block[{t$95$2 = N[(3.0 * N[(x2 * -2.0), $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$0), $MachinePrecision]}, Block[{t$95$5 = N[(x1 * N[(x1 * x1), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$6 = N[(N[(N[(x1 * 2.0), $MachinePrecision] * t$95$4), $MachinePrecision] * N[(t$95$4 - 3.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$7 = N[(x1 + N[(t$95$2 + N[(x1 + N[(t$95$5 + N[(N[(t$95$0 * N[(t$95$6 + N[(N[(x1 * x1), $MachinePrecision] * N[(N[(t$95$4 * 4.0), $MachinePrecision] - 6.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(t$95$3 * N[(3.0 - N[(1.0 / x1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$8 = N[(N[(2.0 * x2), $MachinePrecision] - x1), $MachinePrecision]}, If[LessEqual[x1, -3.4e+107], N[(x1 + N[(t$95$2 + N[(x1 + N[(x1 * N[(N[(6.0 * t$95$1), $MachinePrecision] - 4.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, -2.4e-5], t$95$7, If[LessEqual[x1, 2e-148], N[(x1 + N[(N[(3.0 * N[(N[(N[(t$95$3 - N[(2.0 * x2), $MachinePrecision]), $MachinePrecision] - x1), $MachinePrecision] / t$95$0), $MachinePrecision]), $MachinePrecision] + N[(x1 + N[(t$95$5 + N[(N[(t$95$0 * N[(t$95$6 + N[(N[(x1 * x1), $MachinePrecision] * N[(N[(4.0 * t$95$8), $MachinePrecision] - 6.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(t$95$3 * t$95$8), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, 1.35e+154], t$95$7, 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 10: 79.1% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := x1 \cdot x1 + 1\\ t_1 := 2 \cdot x2 - 3\\ t_2 := 3 \cdot \left(x2 \cdot -2\right)\\ t_3 := x1 \cdot \left(x1 \cdot 3\right)\\ t_4 := \frac{\left(t_3 + 2 \cdot x2\right) - x1}{t_0}\\ t_5 := \left(\left(x1 \cdot 2\right) \cdot t_4\right) \cdot \left(t_4 - 3\right)\\ t_6 := x1 \cdot \left(x1 \cdot x1\right)\\ t_7 := x1 + \left(t_2 + \left(x1 + \left(t_6 + \left(t_0 \cdot \left(t_5 + \left(x1 \cdot x1\right) \cdot \left(t_4 \cdot 4 - 6\right)\right) + 3 \cdot t_3\right)\right)\right)\right)\\ \mathbf{if}\;x1 \leq -1.02 \cdot 10^{+106}:\\ \;\;\;\;x1 + \left(t_2 + \left(x1 + x1 \cdot \left(6 \cdot t_1 - 4\right)\right)\right)\\ \mathbf{elif}\;x1 \leq -0.5:\\ \;\;\;\;t_7\\ \mathbf{elif}\;x1 \leq 1.35:\\ \;\;\;\;x1 + \left(3 \cdot \frac{\left(t_3 - 2 \cdot x2\right) - x1}{t_0} + \left(x1 + \left(t_6 + \left(t_3 \cdot \left(2 \cdot x2 - x1\right) + t_0 \cdot \left(t_5 + \left(x1 \cdot x1\right) \cdot \left(\left(2 \cdot x2\right) \cdot 4 - 6\right)\right)\right)\right)\right)\right)\\ \mathbf{elif}\;x1 \leq 1.35 \cdot 10^{+154}:\\ \;\;\;\;t_7\\ \mathbf{else}:\\ \;\;\;\;x1 + x1 \cdot \left(1 + 4 \cdot \left(x2 \cdot t_1\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x1 x2)
 :precision binary64
 (let* ((t_0 (+ (* x1 x1) 1.0))
        (t_1 (- (* 2.0 x2) 3.0))
        (t_2 (* 3.0 (* x2 -2.0)))
        (t_3 (* x1 (* x1 3.0)))
        (t_4 (/ (- (+ t_3 (* 2.0 x2)) x1) t_0))
        (t_5 (* (* (* x1 2.0) t_4) (- t_4 3.0)))
        (t_6 (* x1 (* x1 x1)))
        (t_7
         (+
          x1
          (+
           t_2
           (+
            x1
            (+
             t_6
             (+
              (* t_0 (+ t_5 (* (* x1 x1) (- (* t_4 4.0) 6.0))))
              (* 3.0 t_3))))))))
   (if (<= x1 -1.02e+106)
     (+ x1 (+ t_2 (+ x1 (* x1 (- (* 6.0 t_1) 4.0)))))
     (if (<= x1 -0.5)
       t_7
       (if (<= x1 1.35)
         (+
          x1
          (+
           (* 3.0 (/ (- (- t_3 (* 2.0 x2)) x1) t_0))
           (+
            x1
            (+
             t_6
             (+
              (* t_3 (- (* 2.0 x2) x1))
              (* t_0 (+ t_5 (* (* x1 x1) (- (* (* 2.0 x2) 4.0) 6.0)))))))))
         (if (<= x1 1.35e+154)
           t_7
           (+ x1 (* x1 (+ 1.0 (* 4.0 (* x2 t_1)))))))))))

C

double code(double x1, double x2) {
	double t_0 = (x1 * x1) + 1.0;
	double t_1 = (2.0 * x2) - 3.0;
	double t_2 = 3.0 * (x2 * -2.0);
	double t_3 = x1 * (x1 * 3.0);
	double t_4 = ((t_3 + (2.0 * x2)) - x1) / t_0;
	double t_5 = ((x1 * 2.0) * t_4) * (t_4 - 3.0);
	double t_6 = x1 * (x1 * x1);
	double t_7 = x1 + (t_2 + (x1 + (t_6 + ((t_0 * (t_5 + ((x1 * x1) * ((t_4 * 4.0) - 6.0)))) + (3.0 * t_3)))));
	double tmp;
	if (x1 <= -1.02e+106) {
		tmp = x1 + (t_2 + (x1 + (x1 * ((6.0 * t_1) - 4.0))));
	} else if (x1 <= -0.5) {
		tmp = t_7;
	} else if (x1 <= 1.35) {
		tmp = x1 + ((3.0 * (((t_3 - (2.0 * x2)) - x1) / t_0)) + (x1 + (t_6 + ((t_3 * ((2.0 * x2) - x1)) + (t_0 * (t_5 + ((x1 * x1) * (((2.0 * x2) * 4.0) - 6.0))))))));
	} else if (x1 <= 1.35e+154) {
		tmp = t_7;
	} 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 * x1) + 1.0d0
    t_1 = (2.0d0 * x2) - 3.0d0
    t_2 = 3.0d0 * (x2 * (-2.0d0))
    t_3 = x1 * (x1 * 3.0d0)
    t_4 = ((t_3 + (2.0d0 * x2)) - x1) / t_0
    t_5 = ((x1 * 2.0d0) * t_4) * (t_4 - 3.0d0)
    t_6 = x1 * (x1 * x1)
    t_7 = x1 + (t_2 + (x1 + (t_6 + ((t_0 * (t_5 + ((x1 * x1) * ((t_4 * 4.0d0) - 6.0d0)))) + (3.0d0 * t_3)))))
    if (x1 <= (-1.02d+106)) then
        tmp = x1 + (t_2 + (x1 + (x1 * ((6.0d0 * t_1) - 4.0d0))))
    else if (x1 <= (-0.5d0)) then
        tmp = t_7
    else if (x1 <= 1.35d0) then
        tmp = x1 + ((3.0d0 * (((t_3 - (2.0d0 * x2)) - x1) / t_0)) + (x1 + (t_6 + ((t_3 * ((2.0d0 * x2) - x1)) + (t_0 * (t_5 + ((x1 * x1) * (((2.0d0 * x2) * 4.0d0) - 6.0d0))))))))
    else if (x1 <= 1.35d+154) then
        tmp = t_7
    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 * x1) + 1.0;
	double t_1 = (2.0 * x2) - 3.0;
	double t_2 = 3.0 * (x2 * -2.0);
	double t_3 = x1 * (x1 * 3.0);
	double t_4 = ((t_3 + (2.0 * x2)) - x1) / t_0;
	double t_5 = ((x1 * 2.0) * t_4) * (t_4 - 3.0);
	double t_6 = x1 * (x1 * x1);
	double t_7 = x1 + (t_2 + (x1 + (t_6 + ((t_0 * (t_5 + ((x1 * x1) * ((t_4 * 4.0) - 6.0)))) + (3.0 * t_3)))));
	double tmp;
	if (x1 <= -1.02e+106) {
		tmp = x1 + (t_2 + (x1 + (x1 * ((6.0 * t_1) - 4.0))));
	} else if (x1 <= -0.5) {
		tmp = t_7;
	} else if (x1 <= 1.35) {
		tmp = x1 + ((3.0 * (((t_3 - (2.0 * x2)) - x1) / t_0)) + (x1 + (t_6 + ((t_3 * ((2.0 * x2) - x1)) + (t_0 * (t_5 + ((x1 * x1) * (((2.0 * x2) * 4.0) - 6.0))))))));
	} else if (x1 <= 1.35e+154) {
		tmp = t_7;
	} else {
		tmp = x1 + (x1 * (1.0 + (4.0 * (x2 * t_1))));
	}
	return tmp;
}

Python

def code(x1, x2):
	t_0 = (x1 * x1) + 1.0
	t_1 = (2.0 * x2) - 3.0
	t_2 = 3.0 * (x2 * -2.0)
	t_3 = x1 * (x1 * 3.0)
	t_4 = ((t_3 + (2.0 * x2)) - x1) / t_0
	t_5 = ((x1 * 2.0) * t_4) * (t_4 - 3.0)
	t_6 = x1 * (x1 * x1)
	t_7 = x1 + (t_2 + (x1 + (t_6 + ((t_0 * (t_5 + ((x1 * x1) * ((t_4 * 4.0) - 6.0)))) + (3.0 * t_3)))))
	tmp = 0
	if x1 <= -1.02e+106:
		tmp = x1 + (t_2 + (x1 + (x1 * ((6.0 * t_1) - 4.0))))
	elif x1 <= -0.5:
		tmp = t_7
	elif x1 <= 1.35:
		tmp = x1 + ((3.0 * (((t_3 - (2.0 * x2)) - x1) / t_0)) + (x1 + (t_6 + ((t_3 * ((2.0 * x2) - x1)) + (t_0 * (t_5 + ((x1 * x1) * (((2.0 * x2) * 4.0) - 6.0))))))))
	elif x1 <= 1.35e+154:
		tmp = t_7
	else:
		tmp = x1 + (x1 * (1.0 + (4.0 * (x2 * t_1))))
	return tmp

Julia

function code(x1, x2)
	t_0 = Float64(Float64(x1 * x1) + 1.0)
	t_1 = Float64(Float64(2.0 * x2) - 3.0)
	t_2 = Float64(3.0 * Float64(x2 * -2.0))
	t_3 = Float64(x1 * Float64(x1 * 3.0))
	t_4 = Float64(Float64(Float64(t_3 + Float64(2.0 * x2)) - x1) / t_0)
	t_5 = Float64(Float64(Float64(x1 * 2.0) * t_4) * Float64(t_4 - 3.0))
	t_6 = Float64(x1 * Float64(x1 * x1))
	t_7 = Float64(x1 + Float64(t_2 + Float64(x1 + Float64(t_6 + Float64(Float64(t_0 * Float64(t_5 + Float64(Float64(x1 * x1) * Float64(Float64(t_4 * 4.0) - 6.0)))) + Float64(3.0 * t_3))))))
	tmp = 0.0
	if (x1 <= -1.02e+106)
		tmp = Float64(x1 + Float64(t_2 + Float64(x1 + Float64(x1 * Float64(Float64(6.0 * t_1) - 4.0)))));
	elseif (x1 <= -0.5)
		tmp = t_7;
	elseif (x1 <= 1.35)
		tmp = Float64(x1 + Float64(Float64(3.0 * Float64(Float64(Float64(t_3 - Float64(2.0 * x2)) - x1) / t_0)) + Float64(x1 + Float64(t_6 + Float64(Float64(t_3 * Float64(Float64(2.0 * x2) - x1)) + Float64(t_0 * Float64(t_5 + Float64(Float64(x1 * x1) * Float64(Float64(Float64(2.0 * x2) * 4.0) - 6.0)))))))));
	elseif (x1 <= 1.35e+154)
		tmp = t_7;
	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 * x1) + 1.0;
	t_1 = (2.0 * x2) - 3.0;
	t_2 = 3.0 * (x2 * -2.0);
	t_3 = x1 * (x1 * 3.0);
	t_4 = ((t_3 + (2.0 * x2)) - x1) / t_0;
	t_5 = ((x1 * 2.0) * t_4) * (t_4 - 3.0);
	t_6 = x1 * (x1 * x1);
	t_7 = x1 + (t_2 + (x1 + (t_6 + ((t_0 * (t_5 + ((x1 * x1) * ((t_4 * 4.0) - 6.0)))) + (3.0 * t_3)))));
	tmp = 0.0;
	if (x1 <= -1.02e+106)
		tmp = x1 + (t_2 + (x1 + (x1 * ((6.0 * t_1) - 4.0))));
	elseif (x1 <= -0.5)
		tmp = t_7;
	elseif (x1 <= 1.35)
		tmp = x1 + ((3.0 * (((t_3 - (2.0 * x2)) - x1) / t_0)) + (x1 + (t_6 + ((t_3 * ((2.0 * x2) - x1)) + (t_0 * (t_5 + ((x1 * x1) * (((2.0 * x2) * 4.0) - 6.0))))))));
	elseif (x1 <= 1.35e+154)
		tmp = t_7;
	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 * x1), $MachinePrecision] + 1.0), $MachinePrecision]}, Block[{t$95$1 = N[(N[(2.0 * x2), $MachinePrecision] - 3.0), $MachinePrecision]}, Block[{t$95$2 = N[(3.0 * N[(x2 * -2.0), $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$0), $MachinePrecision]}, Block[{t$95$5 = N[(N[(N[(x1 * 2.0), $MachinePrecision] * t$95$4), $MachinePrecision] * N[(t$95$4 - 3.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$6 = N[(x1 * N[(x1 * x1), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$7 = N[(x1 + N[(t$95$2 + N[(x1 + N[(t$95$6 + N[(N[(t$95$0 * N[(t$95$5 + N[(N[(x1 * x1), $MachinePrecision] * N[(N[(t$95$4 * 4.0), $MachinePrecision] - 6.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(3.0 * t$95$3), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x1, -1.02e+106], N[(x1 + N[(t$95$2 + N[(x1 + N[(x1 * N[(N[(6.0 * t$95$1), $MachinePrecision] - 4.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, -0.5], t$95$7, If[LessEqual[x1, 1.35], N[(x1 + N[(N[(3.0 * N[(N[(N[(t$95$3 - N[(2.0 * x2), $MachinePrecision]), $MachinePrecision] - x1), $MachinePrecision] / t$95$0), $MachinePrecision]), $MachinePrecision] + N[(x1 + N[(t$95$6 + N[(N[(t$95$3 * N[(N[(2.0 * x2), $MachinePrecision] - x1), $MachinePrecision]), $MachinePrecision] + N[(t$95$0 * N[(t$95$5 + N[(N[(x1 * x1), $MachinePrecision] * N[(N[(N[(2.0 * x2), $MachinePrecision] * 4.0), $MachinePrecision] - 6.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, 1.35e+154], t$95$7, 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 11: 72.6% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := x1 \cdot x1 + 1\\ t_1 := 2 \cdot x2 - 3\\ t_2 := x1 \cdot \left(x1 \cdot 3\right)\\ t_3 := \frac{\left(t_2 + 2 \cdot x2\right) - x1}{t_0}\\ t_4 := 3 \cdot \left(x2 \cdot -2\right)\\ t_5 := x1 + \left(t_4 + \left(x1 + \left(x1 \cdot \left(x1 \cdot x1\right) + \left(t_0 \cdot \left(\left(\left(x1 \cdot 2\right) \cdot t_3\right) \cdot \left(t_3 - 3\right) + \left(x1 \cdot x1\right) \cdot \left(t_3 \cdot 4 - 6\right)\right) + 3 \cdot t_2\right)\right)\right)\right)\\ t_6 := 4 \cdot \left(x2 \cdot t_1\right)\\ \mathbf{if}\;x1 \leq -4.8 \cdot 10^{+108}:\\ \;\;\;\;x1 + \left(t_4 + \left(x1 + x1 \cdot \left(6 \cdot t_1 - 4\right)\right)\right)\\ \mathbf{elif}\;x1 \leq -0.00033:\\ \;\;\;\;t_5\\ \mathbf{elif}\;x1 \leq 1.92 \cdot 10^{-13}:\\ \;\;\;\;x1 + \left(x1 \cdot \left(t_6 - 2\right) + x2 \cdot -6\right)\\ \mathbf{elif}\;x1 \leq 1.35 \cdot 10^{+154}:\\ \;\;\;\;t_5\\ \mathbf{else}:\\ \;\;\;\;x1 + x1 \cdot \left(1 + t_6\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x1 x2)
 :precision binary64
 (let* ((t_0 (+ (* x1 x1) 1.0))
        (t_1 (- (* 2.0 x2) 3.0))
        (t_2 (* x1 (* x1 3.0)))
        (t_3 (/ (- (+ t_2 (* 2.0 x2)) x1) t_0))
        (t_4 (* 3.0 (* x2 -2.0)))
        (t_5
         (+
          x1
          (+
           t_4
           (+
            x1
            (+
             (* x1 (* x1 x1))
             (+
              (*
               t_0
               (+
                (* (* (* x1 2.0) t_3) (- t_3 3.0))
                (* (* x1 x1) (- (* t_3 4.0) 6.0))))
              (* 3.0 t_2)))))))
        (t_6 (* 4.0 (* x2 t_1))))
   (if (<= x1 -4.8e+108)
     (+ x1 (+ t_4 (+ x1 (* x1 (- (* 6.0 t_1) 4.0)))))
     (if (<= x1 -0.00033)
       t_5
       (if (<= x1 1.92e-13)
         (+ x1 (+ (* x1 (- t_6 2.0)) (* x2 -6.0)))
         (if (<= x1 1.35e+154) t_5 (+ x1 (* x1 (+ 1.0 t_6)))))))))

C

double code(double x1, double x2) {
	double t_0 = (x1 * x1) + 1.0;
	double t_1 = (2.0 * x2) - 3.0;
	double t_2 = x1 * (x1 * 3.0);
	double t_3 = ((t_2 + (2.0 * x2)) - x1) / t_0;
	double t_4 = 3.0 * (x2 * -2.0);
	double t_5 = x1 + (t_4 + (x1 + ((x1 * (x1 * x1)) + ((t_0 * ((((x1 * 2.0) * t_3) * (t_3 - 3.0)) + ((x1 * x1) * ((t_3 * 4.0) - 6.0)))) + (3.0 * t_2)))));
	double t_6 = 4.0 * (x2 * t_1);
	double tmp;
	if (x1 <= -4.8e+108) {
		tmp = x1 + (t_4 + (x1 + (x1 * ((6.0 * t_1) - 4.0))));
	} else if (x1 <= -0.00033) {
		tmp = t_5;
	} else if (x1 <= 1.92e-13) {
		tmp = x1 + ((x1 * (t_6 - 2.0)) + (x2 * -6.0));
	} else if (x1 <= 1.35e+154) {
		tmp = t_5;
	} else {
		tmp = x1 + (x1 * (1.0 + t_6));
	}
	return tmp;
}

Fortran

real(8) function code(x1, x2)
    real(8), intent (in) :: x1
    real(8), intent (in) :: x2
    real(8) :: t_0
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: t_3
    real(8) :: t_4
    real(8) :: t_5
    real(8) :: t_6
    real(8) :: tmp
    t_0 = (x1 * x1) + 1.0d0
    t_1 = (2.0d0 * x2) - 3.0d0
    t_2 = x1 * (x1 * 3.0d0)
    t_3 = ((t_2 + (2.0d0 * x2)) - x1) / t_0
    t_4 = 3.0d0 * (x2 * (-2.0d0))
    t_5 = x1 + (t_4 + (x1 + ((x1 * (x1 * x1)) + ((t_0 * ((((x1 * 2.0d0) * t_3) * (t_3 - 3.0d0)) + ((x1 * x1) * ((t_3 * 4.0d0) - 6.0d0)))) + (3.0d0 * t_2)))))
    t_6 = 4.0d0 * (x2 * t_1)
    if (x1 <= (-4.8d+108)) then
        tmp = x1 + (t_4 + (x1 + (x1 * ((6.0d0 * t_1) - 4.0d0))))
    else if (x1 <= (-0.00033d0)) then
        tmp = t_5
    else if (x1 <= 1.92d-13) then
        tmp = x1 + ((x1 * (t_6 - 2.0d0)) + (x2 * (-6.0d0)))
    else if (x1 <= 1.35d+154) then
        tmp = t_5
    else
        tmp = x1 + (x1 * (1.0d0 + t_6))
    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) - 3.0;
	double t_2 = x1 * (x1 * 3.0);
	double t_3 = ((t_2 + (2.0 * x2)) - x1) / t_0;
	double t_4 = 3.0 * (x2 * -2.0);
	double t_5 = x1 + (t_4 + (x1 + ((x1 * (x1 * x1)) + ((t_0 * ((((x1 * 2.0) * t_3) * (t_3 - 3.0)) + ((x1 * x1) * ((t_3 * 4.0) - 6.0)))) + (3.0 * t_2)))));
	double t_6 = 4.0 * (x2 * t_1);
	double tmp;
	if (x1 <= -4.8e+108) {
		tmp = x1 + (t_4 + (x1 + (x1 * ((6.0 * t_1) - 4.0))));
	} else if (x1 <= -0.00033) {
		tmp = t_5;
	} else if (x1 <= 1.92e-13) {
		tmp = x1 + ((x1 * (t_6 - 2.0)) + (x2 * -6.0));
	} else if (x1 <= 1.35e+154) {
		tmp = t_5;
	} else {
		tmp = x1 + (x1 * (1.0 + t_6));
	}
	return tmp;
}

Python

def code(x1, x2):
	t_0 = (x1 * x1) + 1.0
	t_1 = (2.0 * x2) - 3.0
	t_2 = x1 * (x1 * 3.0)
	t_3 = ((t_2 + (2.0 * x2)) - x1) / t_0
	t_4 = 3.0 * (x2 * -2.0)
	t_5 = x1 + (t_4 + (x1 + ((x1 * (x1 * x1)) + ((t_0 * ((((x1 * 2.0) * t_3) * (t_3 - 3.0)) + ((x1 * x1) * ((t_3 * 4.0) - 6.0)))) + (3.0 * t_2)))))
	t_6 = 4.0 * (x2 * t_1)
	tmp = 0
	if x1 <= -4.8e+108:
		tmp = x1 + (t_4 + (x1 + (x1 * ((6.0 * t_1) - 4.0))))
	elif x1 <= -0.00033:
		tmp = t_5
	elif x1 <= 1.92e-13:
		tmp = x1 + ((x1 * (t_6 - 2.0)) + (x2 * -6.0))
	elif x1 <= 1.35e+154:
		tmp = t_5
	else:
		tmp = x1 + (x1 * (1.0 + t_6))
	return tmp

Julia

function code(x1, x2)
	t_0 = Float64(Float64(x1 * x1) + 1.0)
	t_1 = Float64(Float64(2.0 * x2) - 3.0)
	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(3.0 * Float64(x2 * -2.0))
	t_5 = Float64(x1 + Float64(t_4 + Float64(x1 + Float64(Float64(x1 * Float64(x1 * x1)) + Float64(Float64(t_0 * 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(3.0 * t_2))))))
	t_6 = Float64(4.0 * Float64(x2 * t_1))
	tmp = 0.0
	if (x1 <= -4.8e+108)
		tmp = Float64(x1 + Float64(t_4 + Float64(x1 + Float64(x1 * Float64(Float64(6.0 * t_1) - 4.0)))));
	elseif (x1 <= -0.00033)
		tmp = t_5;
	elseif (x1 <= 1.92e-13)
		tmp = Float64(x1 + Float64(Float64(x1 * Float64(t_6 - 2.0)) + Float64(x2 * -6.0)));
	elseif (x1 <= 1.35e+154)
		tmp = t_5;
	else
		tmp = Float64(x1 + Float64(x1 * Float64(1.0 + t_6)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x1, x2)
	t_0 = (x1 * x1) + 1.0;
	t_1 = (2.0 * x2) - 3.0;
	t_2 = x1 * (x1 * 3.0);
	t_3 = ((t_2 + (2.0 * x2)) - x1) / t_0;
	t_4 = 3.0 * (x2 * -2.0);
	t_5 = x1 + (t_4 + (x1 + ((x1 * (x1 * x1)) + ((t_0 * ((((x1 * 2.0) * t_3) * (t_3 - 3.0)) + ((x1 * x1) * ((t_3 * 4.0) - 6.0)))) + (3.0 * t_2)))));
	t_6 = 4.0 * (x2 * t_1);
	tmp = 0.0;
	if (x1 <= -4.8e+108)
		tmp = x1 + (t_4 + (x1 + (x1 * ((6.0 * t_1) - 4.0))));
	elseif (x1 <= -0.00033)
		tmp = t_5;
	elseif (x1 <= 1.92e-13)
		tmp = x1 + ((x1 * (t_6 - 2.0)) + (x2 * -6.0));
	elseif (x1 <= 1.35e+154)
		tmp = t_5;
	else
		tmp = x1 + (x1 * (1.0 + t_6));
	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] - 3.0), $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[(3.0 * N[(x2 * -2.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$5 = N[(x1 + N[(t$95$4 + N[(x1 + N[(N[(x1 * N[(x1 * x1), $MachinePrecision]), $MachinePrecision] + N[(N[(t$95$0 * 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[(3.0 * t$95$2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$6 = N[(4.0 * N[(x2 * t$95$1), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x1, -4.8e+108], N[(x1 + N[(t$95$4 + N[(x1 + N[(x1 * N[(N[(6.0 * t$95$1), $MachinePrecision] - 4.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, -0.00033], t$95$5, If[LessEqual[x1, 1.92e-13], N[(x1 + N[(N[(x1 * N[(t$95$6 - 2.0), $MachinePrecision]), $MachinePrecision] + N[(x2 * -6.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, 1.35e+154], t$95$5, N[(x1 + N[(x1 * N[(1.0 + t$95$6), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]]]]]]]

Derivation

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

Alternative 12: 71.2% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := x1 \cdot \left(x1 \cdot 3\right)\\ t_1 := 2 \cdot x2 - 3\\ t_2 := 3 \cdot \left(x2 \cdot -2\right)\\ t_3 := x1 \cdot x1 + 1\\ t_4 := \frac{\left(t_0 + 2 \cdot x2\right) - x1}{t_3}\\ t_5 := x1 + \left(t_2 + \left(x1 + \left(x1 \cdot \left(x1 \cdot x1\right) + \left(t_0 \cdot t_4 + t_3 \cdot \left(\left(\left(x1 \cdot 2\right) \cdot t_4\right) \cdot \left(t_4 - 3\right) + \left(x1 \cdot x1\right) \cdot 6\right)\right)\right)\right)\right)\\ t_6 := 4 \cdot \left(x2 \cdot t_1\right)\\ \mathbf{if}\;x1 \leq -1.85 \cdot 10^{+105}:\\ \;\;\;\;x1 + \left(t_2 + \left(x1 + x1 \cdot \left(6 \cdot t_1 - 4\right)\right)\right)\\ \mathbf{elif}\;x1 \leq -0.00112:\\ \;\;\;\;t_5\\ \mathbf{elif}\;x1 \leq 1.92 \cdot 10^{-13}:\\ \;\;\;\;x1 + \left(x1 \cdot \left(t_6 - 2\right) + x2 \cdot -6\right)\\ \mathbf{elif}\;x1 \leq 1.35 \cdot 10^{+154}:\\ \;\;\;\;t_5\\ \mathbf{else}:\\ \;\;\;\;x1 + x1 \cdot \left(1 + t_6\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x1 x2)
 :precision binary64
 (let* ((t_0 (* x1 (* x1 3.0)))
        (t_1 (- (* 2.0 x2) 3.0))
        (t_2 (* 3.0 (* x2 -2.0)))
        (t_3 (+ (* x1 x1) 1.0))
        (t_4 (/ (- (+ t_0 (* 2.0 x2)) x1) t_3))
        (t_5
         (+
          x1
          (+
           t_2
           (+
            x1
            (+
             (* x1 (* x1 x1))
             (+
              (* t_0 t_4)
              (*
               t_3
               (+ (* (* (* x1 2.0) t_4) (- t_4 3.0)) (* (* x1 x1) 6.0)))))))))
        (t_6 (* 4.0 (* x2 t_1))))
   (if (<= x1 -1.85e+105)
     (+ x1 (+ t_2 (+ x1 (* x1 (- (* 6.0 t_1) 4.0)))))
     (if (<= x1 -0.00112)
       t_5
       (if (<= x1 1.92e-13)
         (+ x1 (+ (* x1 (- t_6 2.0)) (* x2 -6.0)))
         (if (<= x1 1.35e+154) t_5 (+ x1 (* x1 (+ 1.0 t_6)))))))))

C

double code(double x1, double x2) {
	double t_0 = x1 * (x1 * 3.0);
	double t_1 = (2.0 * x2) - 3.0;
	double t_2 = 3.0 * (x2 * -2.0);
	double t_3 = (x1 * x1) + 1.0;
	double t_4 = ((t_0 + (2.0 * x2)) - x1) / t_3;
	double t_5 = x1 + (t_2 + (x1 + ((x1 * (x1 * x1)) + ((t_0 * t_4) + (t_3 * ((((x1 * 2.0) * t_4) * (t_4 - 3.0)) + ((x1 * x1) * 6.0)))))));
	double t_6 = 4.0 * (x2 * t_1);
	double tmp;
	if (x1 <= -1.85e+105) {
		tmp = x1 + (t_2 + (x1 + (x1 * ((6.0 * t_1) - 4.0))));
	} else if (x1 <= -0.00112) {
		tmp = t_5;
	} else if (x1 <= 1.92e-13) {
		tmp = x1 + ((x1 * (t_6 - 2.0)) + (x2 * -6.0));
	} else if (x1 <= 1.35e+154) {
		tmp = t_5;
	} else {
		tmp = x1 + (x1 * (1.0 + t_6));
	}
	return tmp;
}

Fortran

real(8) function code(x1, x2)
    real(8), intent (in) :: x1
    real(8), intent (in) :: x2
    real(8) :: t_0
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: t_3
    real(8) :: t_4
    real(8) :: t_5
    real(8) :: t_6
    real(8) :: tmp
    t_0 = x1 * (x1 * 3.0d0)
    t_1 = (2.0d0 * x2) - 3.0d0
    t_2 = 3.0d0 * (x2 * (-2.0d0))
    t_3 = (x1 * x1) + 1.0d0
    t_4 = ((t_0 + (2.0d0 * x2)) - x1) / t_3
    t_5 = x1 + (t_2 + (x1 + ((x1 * (x1 * x1)) + ((t_0 * t_4) + (t_3 * ((((x1 * 2.0d0) * t_4) * (t_4 - 3.0d0)) + ((x1 * x1) * 6.0d0)))))))
    t_6 = 4.0d0 * (x2 * t_1)
    if (x1 <= (-1.85d+105)) then
        tmp = x1 + (t_2 + (x1 + (x1 * ((6.0d0 * t_1) - 4.0d0))))
    else if (x1 <= (-0.00112d0)) then
        tmp = t_5
    else if (x1 <= 1.92d-13) then
        tmp = x1 + ((x1 * (t_6 - 2.0d0)) + (x2 * (-6.0d0)))
    else if (x1 <= 1.35d+154) then
        tmp = t_5
    else
        tmp = x1 + (x1 * (1.0d0 + t_6))
    end if
    code = tmp
end function

Java

public static double code(double x1, double x2) {
	double t_0 = x1 * (x1 * 3.0);
	double t_1 = (2.0 * x2) - 3.0;
	double t_2 = 3.0 * (x2 * -2.0);
	double t_3 = (x1 * x1) + 1.0;
	double t_4 = ((t_0 + (2.0 * x2)) - x1) / t_3;
	double t_5 = x1 + (t_2 + (x1 + ((x1 * (x1 * x1)) + ((t_0 * t_4) + (t_3 * ((((x1 * 2.0) * t_4) * (t_4 - 3.0)) + ((x1 * x1) * 6.0)))))));
	double t_6 = 4.0 * (x2 * t_1);
	double tmp;
	if (x1 <= -1.85e+105) {
		tmp = x1 + (t_2 + (x1 + (x1 * ((6.0 * t_1) - 4.0))));
	} else if (x1 <= -0.00112) {
		tmp = t_5;
	} else if (x1 <= 1.92e-13) {
		tmp = x1 + ((x1 * (t_6 - 2.0)) + (x2 * -6.0));
	} else if (x1 <= 1.35e+154) {
		tmp = t_5;
	} else {
		tmp = x1 + (x1 * (1.0 + t_6));
	}
	return tmp;
}

Python

def code(x1, x2):
	t_0 = x1 * (x1 * 3.0)
	t_1 = (2.0 * x2) - 3.0
	t_2 = 3.0 * (x2 * -2.0)
	t_3 = (x1 * x1) + 1.0
	t_4 = ((t_0 + (2.0 * x2)) - x1) / t_3
	t_5 = x1 + (t_2 + (x1 + ((x1 * (x1 * x1)) + ((t_0 * t_4) + (t_3 * ((((x1 * 2.0) * t_4) * (t_4 - 3.0)) + ((x1 * x1) * 6.0)))))))
	t_6 = 4.0 * (x2 * t_1)
	tmp = 0
	if x1 <= -1.85e+105:
		tmp = x1 + (t_2 + (x1 + (x1 * ((6.0 * t_1) - 4.0))))
	elif x1 <= -0.00112:
		tmp = t_5
	elif x1 <= 1.92e-13:
		tmp = x1 + ((x1 * (t_6 - 2.0)) + (x2 * -6.0))
	elif x1 <= 1.35e+154:
		tmp = t_5
	else:
		tmp = x1 + (x1 * (1.0 + t_6))
	return tmp

Julia

function code(x1, x2)
	t_0 = Float64(x1 * Float64(x1 * 3.0))
	t_1 = Float64(Float64(2.0 * x2) - 3.0)
	t_2 = Float64(3.0 * Float64(x2 * -2.0))
	t_3 = Float64(Float64(x1 * x1) + 1.0)
	t_4 = Float64(Float64(Float64(t_0 + Float64(2.0 * x2)) - x1) / t_3)
	t_5 = Float64(x1 + Float64(t_2 + Float64(x1 + Float64(Float64(x1 * Float64(x1 * x1)) + Float64(Float64(t_0 * t_4) + Float64(t_3 * Float64(Float64(Float64(Float64(x1 * 2.0) * t_4) * Float64(t_4 - 3.0)) + Float64(Float64(x1 * x1) * 6.0))))))))
	t_6 = Float64(4.0 * Float64(x2 * t_1))
	tmp = 0.0
	if (x1 <= -1.85e+105)
		tmp = Float64(x1 + Float64(t_2 + Float64(x1 + Float64(x1 * Float64(Float64(6.0 * t_1) - 4.0)))));
	elseif (x1 <= -0.00112)
		tmp = t_5;
	elseif (x1 <= 1.92e-13)
		tmp = Float64(x1 + Float64(Float64(x1 * Float64(t_6 - 2.0)) + Float64(x2 * -6.0)));
	elseif (x1 <= 1.35e+154)
		tmp = t_5;
	else
		tmp = Float64(x1 + Float64(x1 * Float64(1.0 + t_6)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x1, x2)
	t_0 = x1 * (x1 * 3.0);
	t_1 = (2.0 * x2) - 3.0;
	t_2 = 3.0 * (x2 * -2.0);
	t_3 = (x1 * x1) + 1.0;
	t_4 = ((t_0 + (2.0 * x2)) - x1) / t_3;
	t_5 = x1 + (t_2 + (x1 + ((x1 * (x1 * x1)) + ((t_0 * t_4) + (t_3 * ((((x1 * 2.0) * t_4) * (t_4 - 3.0)) + ((x1 * x1) * 6.0)))))));
	t_6 = 4.0 * (x2 * t_1);
	tmp = 0.0;
	if (x1 <= -1.85e+105)
		tmp = x1 + (t_2 + (x1 + (x1 * ((6.0 * t_1) - 4.0))));
	elseif (x1 <= -0.00112)
		tmp = t_5;
	elseif (x1 <= 1.92e-13)
		tmp = x1 + ((x1 * (t_6 - 2.0)) + (x2 * -6.0));
	elseif (x1 <= 1.35e+154)
		tmp = t_5;
	else
		tmp = x1 + (x1 * (1.0 + t_6));
	end
	tmp_2 = tmp;
end

Wolfram

code[x1_, x2_] := Block[{t$95$0 = N[(x1 * N[(x1 * 3.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(N[(2.0 * x2), $MachinePrecision] - 3.0), $MachinePrecision]}, Block[{t$95$2 = N[(3.0 * N[(x2 * -2.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(N[(x1 * x1), $MachinePrecision] + 1.0), $MachinePrecision]}, Block[{t$95$4 = N[(N[(N[(t$95$0 + N[(2.0 * x2), $MachinePrecision]), $MachinePrecision] - x1), $MachinePrecision] / t$95$3), $MachinePrecision]}, Block[{t$95$5 = N[(x1 + N[(t$95$2 + N[(x1 + N[(N[(x1 * N[(x1 * x1), $MachinePrecision]), $MachinePrecision] + N[(N[(t$95$0 * t$95$4), $MachinePrecision] + N[(t$95$3 * N[(N[(N[(N[(x1 * 2.0), $MachinePrecision] * t$95$4), $MachinePrecision] * N[(t$95$4 - 3.0), $MachinePrecision]), $MachinePrecision] + N[(N[(x1 * x1), $MachinePrecision] * 6.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$6 = N[(4.0 * N[(x2 * t$95$1), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x1, -1.85e+105], N[(x1 + N[(t$95$2 + N[(x1 + N[(x1 * N[(N[(6.0 * t$95$1), $MachinePrecision] - 4.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, -0.00112], t$95$5, If[LessEqual[x1, 1.92e-13], N[(x1 + N[(N[(x1 * N[(t$95$6 - 2.0), $MachinePrecision]), $MachinePrecision] + N[(x2 * -6.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, 1.35e+154], t$95$5, N[(x1 + N[(x1 * N[(1.0 + t$95$6), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]]]]]]]

Derivation

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

Alternative 13: 68.9% accurate, 1.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := x1 \cdot \left(x1 \cdot 3\right)\\ t_1 := 2 \cdot x2 - 3\\ t_2 := x1 \cdot x1 + 1\\ t_3 := \frac{\left(t_0 + 2 \cdot x2\right) - x1}{t_2}\\ t_4 := x1 \cdot \left(x1 \cdot x1\right)\\ t_5 := \left(x1 \cdot 2\right) \cdot 3\\ t_6 := 3 \cdot \left(x2 \cdot -2\right)\\ t_7 := 4 \cdot \left(x2 \cdot t_1\right)\\ \mathbf{if}\;x1 \leq -1.9 \cdot 10^{+108}:\\ \;\;\;\;x1 + \left(t_6 + \left(x1 + x1 \cdot \left(6 \cdot t_1 - 4\right)\right)\right)\\ \mathbf{elif}\;x1 \leq -170:\\ \;\;\;\;x1 + \left(t_6 + \left(x1 + \left(t_4 + \left(3 \cdot t_0 + t_2 \cdot \left(\left(x1 \cdot x1\right) \cdot \left(t_3 \cdot 4 - 6\right) + \left(t_3 - 3\right) \cdot t_5\right)\right)\right)\right)\right)\\ \mathbf{elif}\;x1 \leq 2300000:\\ \;\;\;\;x1 + \left(x1 \cdot \left(t_7 - 2\right) + x2 \cdot -6\right)\\ \mathbf{elif}\;x1 \leq 1.35 \cdot 10^{+154}:\\ \;\;\;\;x1 + \left(t_6 + \left(x1 + \left(t_4 + \left(t_0 \cdot t_3 + t_2 \cdot \left(t_5 \cdot \frac{-1}{x1} + \left(x1 \cdot x1\right) \cdot \left(4 \cdot \left(3 - \frac{1}{x1}\right) - 6\right)\right)\right)\right)\right)\right)\\ \mathbf{else}:\\ \;\;\;\;x1 + x1 \cdot \left(1 + t_7\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x1 x2)
 :precision binary64
 (let* ((t_0 (* x1 (* x1 3.0)))
        (t_1 (- (* 2.0 x2) 3.0))
        (t_2 (+ (* x1 x1) 1.0))
        (t_3 (/ (- (+ t_0 (* 2.0 x2)) x1) t_2))
        (t_4 (* x1 (* x1 x1)))
        (t_5 (* (* x1 2.0) 3.0))
        (t_6 (* 3.0 (* x2 -2.0)))
        (t_7 (* 4.0 (* x2 t_1))))
   (if (<= x1 -1.9e+108)
     (+ x1 (+ t_6 (+ x1 (* x1 (- (* 6.0 t_1) 4.0)))))
     (if (<= x1 -170.0)
       (+
        x1
        (+
         t_6
         (+
          x1
          (+
           t_4
           (+
            (* 3.0 t_0)
            (*
             t_2
             (+ (* (* x1 x1) (- (* t_3 4.0) 6.0)) (* (- t_3 3.0) t_5))))))))
       (if (<= x1 2300000.0)
         (+ x1 (+ (* x1 (- t_7 2.0)) (* x2 -6.0)))
         (if (<= x1 1.35e+154)
           (+
            x1
            (+
             t_6
             (+
              x1
              (+
               t_4
               (+
                (* t_0 t_3)
                (*
                 t_2
                 (+
                  (* t_5 (/ -1.0 x1))
                  (* (* x1 x1) (- (* 4.0 (- 3.0 (/ 1.0 x1))) 6.0)))))))))
           (+ x1 (* x1 (+ 1.0 t_7)))))))))

C

double code(double x1, double x2) {
	double t_0 = x1 * (x1 * 3.0);
	double t_1 = (2.0 * x2) - 3.0;
	double t_2 = (x1 * x1) + 1.0;
	double t_3 = ((t_0 + (2.0 * x2)) - x1) / t_2;
	double t_4 = x1 * (x1 * x1);
	double t_5 = (x1 * 2.0) * 3.0;
	double t_6 = 3.0 * (x2 * -2.0);
	double t_7 = 4.0 * (x2 * t_1);
	double tmp;
	if (x1 <= -1.9e+108) {
		tmp = x1 + (t_6 + (x1 + (x1 * ((6.0 * t_1) - 4.0))));
	} else if (x1 <= -170.0) {
		tmp = x1 + (t_6 + (x1 + (t_4 + ((3.0 * t_0) + (t_2 * (((x1 * x1) * ((t_3 * 4.0) - 6.0)) + ((t_3 - 3.0) * t_5)))))));
	} else if (x1 <= 2300000.0) {
		tmp = x1 + ((x1 * (t_7 - 2.0)) + (x2 * -6.0));
	} else if (x1 <= 1.35e+154) {
		tmp = x1 + (t_6 + (x1 + (t_4 + ((t_0 * t_3) + (t_2 * ((t_5 * (-1.0 / x1)) + ((x1 * x1) * ((4.0 * (3.0 - (1.0 / x1))) - 6.0))))))));
	} else {
		tmp = x1 + (x1 * (1.0 + 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 * 3.0d0)
    t_1 = (2.0d0 * x2) - 3.0d0
    t_2 = (x1 * x1) + 1.0d0
    t_3 = ((t_0 + (2.0d0 * x2)) - x1) / t_2
    t_4 = x1 * (x1 * x1)
    t_5 = (x1 * 2.0d0) * 3.0d0
    t_6 = 3.0d0 * (x2 * (-2.0d0))
    t_7 = 4.0d0 * (x2 * t_1)
    if (x1 <= (-1.9d+108)) then
        tmp = x1 + (t_6 + (x1 + (x1 * ((6.0d0 * t_1) - 4.0d0))))
    else if (x1 <= (-170.0d0)) then
        tmp = x1 + (t_6 + (x1 + (t_4 + ((3.0d0 * t_0) + (t_2 * (((x1 * x1) * ((t_3 * 4.0d0) - 6.0d0)) + ((t_3 - 3.0d0) * t_5)))))))
    else if (x1 <= 2300000.0d0) then
        tmp = x1 + ((x1 * (t_7 - 2.0d0)) + (x2 * (-6.0d0)))
    else if (x1 <= 1.35d+154) then
        tmp = x1 + (t_6 + (x1 + (t_4 + ((t_0 * t_3) + (t_2 * ((t_5 * ((-1.0d0) / x1)) + ((x1 * x1) * ((4.0d0 * (3.0d0 - (1.0d0 / x1))) - 6.0d0))))))))
    else
        tmp = x1 + (x1 * (1.0d0 + t_7))
    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 = (2.0 * x2) - 3.0;
	double t_2 = (x1 * x1) + 1.0;
	double t_3 = ((t_0 + (2.0 * x2)) - x1) / t_2;
	double t_4 = x1 * (x1 * x1);
	double t_5 = (x1 * 2.0) * 3.0;
	double t_6 = 3.0 * (x2 * -2.0);
	double t_7 = 4.0 * (x2 * t_1);
	double tmp;
	if (x1 <= -1.9e+108) {
		tmp = x1 + (t_6 + (x1 + (x1 * ((6.0 * t_1) - 4.0))));
	} else if (x1 <= -170.0) {
		tmp = x1 + (t_6 + (x1 + (t_4 + ((3.0 * t_0) + (t_2 * (((x1 * x1) * ((t_3 * 4.0) - 6.0)) + ((t_3 - 3.0) * t_5)))))));
	} else if (x1 <= 2300000.0) {
		tmp = x1 + ((x1 * (t_7 - 2.0)) + (x2 * -6.0));
	} else if (x1 <= 1.35e+154) {
		tmp = x1 + (t_6 + (x1 + (t_4 + ((t_0 * t_3) + (t_2 * ((t_5 * (-1.0 / x1)) + ((x1 * x1) * ((4.0 * (3.0 - (1.0 / x1))) - 6.0))))))));
	} else {
		tmp = x1 + (x1 * (1.0 + t_7));
	}
	return tmp;
}

Python

def code(x1, x2):
	t_0 = x1 * (x1 * 3.0)
	t_1 = (2.0 * x2) - 3.0
	t_2 = (x1 * x1) + 1.0
	t_3 = ((t_0 + (2.0 * x2)) - x1) / t_2
	t_4 = x1 * (x1 * x1)
	t_5 = (x1 * 2.0) * 3.0
	t_6 = 3.0 * (x2 * -2.0)
	t_7 = 4.0 * (x2 * t_1)
	tmp = 0
	if x1 <= -1.9e+108:
		tmp = x1 + (t_6 + (x1 + (x1 * ((6.0 * t_1) - 4.0))))
	elif x1 <= -170.0:
		tmp = x1 + (t_6 + (x1 + (t_4 + ((3.0 * t_0) + (t_2 * (((x1 * x1) * ((t_3 * 4.0) - 6.0)) + ((t_3 - 3.0) * t_5)))))))
	elif x1 <= 2300000.0:
		tmp = x1 + ((x1 * (t_7 - 2.0)) + (x2 * -6.0))
	elif x1 <= 1.35e+154:
		tmp = x1 + (t_6 + (x1 + (t_4 + ((t_0 * t_3) + (t_2 * ((t_5 * (-1.0 / x1)) + ((x1 * x1) * ((4.0 * (3.0 - (1.0 / x1))) - 6.0))))))))
	else:
		tmp = x1 + (x1 * (1.0 + t_7))
	return tmp

Julia

function code(x1, x2)
	t_0 = Float64(x1 * Float64(x1 * 3.0))
	t_1 = Float64(Float64(2.0 * x2) - 3.0)
	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(x1 * Float64(x1 * x1))
	t_5 = Float64(Float64(x1 * 2.0) * 3.0)
	t_6 = Float64(3.0 * Float64(x2 * -2.0))
	t_7 = Float64(4.0 * Float64(x2 * t_1))
	tmp = 0.0
	if (x1 <= -1.9e+108)
		tmp = Float64(x1 + Float64(t_6 + Float64(x1 + Float64(x1 * Float64(Float64(6.0 * t_1) - 4.0)))));
	elseif (x1 <= -170.0)
		tmp = Float64(x1 + Float64(t_6 + Float64(x1 + Float64(t_4 + Float64(Float64(3.0 * t_0) + Float64(t_2 * Float64(Float64(Float64(x1 * x1) * Float64(Float64(t_3 * 4.0) - 6.0)) + Float64(Float64(t_3 - 3.0) * t_5))))))));
	elseif (x1 <= 2300000.0)
		tmp = Float64(x1 + Float64(Float64(x1 * Float64(t_7 - 2.0)) + Float64(x2 * -6.0)));
	elseif (x1 <= 1.35e+154)
		tmp = Float64(x1 + Float64(t_6 + Float64(x1 + Float64(t_4 + Float64(Float64(t_0 * t_3) + Float64(t_2 * Float64(Float64(t_5 * Float64(-1.0 / x1)) + Float64(Float64(x1 * x1) * Float64(Float64(4.0 * Float64(3.0 - Float64(1.0 / x1))) - 6.0)))))))));
	else
		tmp = Float64(x1 + Float64(x1 * Float64(1.0 + t_7)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x1, x2)
	t_0 = x1 * (x1 * 3.0);
	t_1 = (2.0 * x2) - 3.0;
	t_2 = (x1 * x1) + 1.0;
	t_3 = ((t_0 + (2.0 * x2)) - x1) / t_2;
	t_4 = x1 * (x1 * x1);
	t_5 = (x1 * 2.0) * 3.0;
	t_6 = 3.0 * (x2 * -2.0);
	t_7 = 4.0 * (x2 * t_1);
	tmp = 0.0;
	if (x1 <= -1.9e+108)
		tmp = x1 + (t_6 + (x1 + (x1 * ((6.0 * t_1) - 4.0))));
	elseif (x1 <= -170.0)
		tmp = x1 + (t_6 + (x1 + (t_4 + ((3.0 * t_0) + (t_2 * (((x1 * x1) * ((t_3 * 4.0) - 6.0)) + ((t_3 - 3.0) * t_5)))))));
	elseif (x1 <= 2300000.0)
		tmp = x1 + ((x1 * (t_7 - 2.0)) + (x2 * -6.0));
	elseif (x1 <= 1.35e+154)
		tmp = x1 + (t_6 + (x1 + (t_4 + ((t_0 * t_3) + (t_2 * ((t_5 * (-1.0 / x1)) + ((x1 * x1) * ((4.0 * (3.0 - (1.0 / x1))) - 6.0))))))));
	else
		tmp = x1 + (x1 * (1.0 + t_7));
	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[(2.0 * x2), $MachinePrecision] - 3.0), $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[(x1 * N[(x1 * x1), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$5 = N[(N[(x1 * 2.0), $MachinePrecision] * 3.0), $MachinePrecision]}, Block[{t$95$6 = N[(3.0 * N[(x2 * -2.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$7 = N[(4.0 * N[(x2 * t$95$1), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x1, -1.9e+108], N[(x1 + N[(t$95$6 + N[(x1 + N[(x1 * N[(N[(6.0 * t$95$1), $MachinePrecision] - 4.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, -170.0], N[(x1 + N[(t$95$6 + N[(x1 + N[(t$95$4 + N[(N[(3.0 * t$95$0), $MachinePrecision] + N[(t$95$2 * N[(N[(N[(x1 * x1), $MachinePrecision] * N[(N[(t$95$3 * 4.0), $MachinePrecision] - 6.0), $MachinePrecision]), $MachinePrecision] + N[(N[(t$95$3 - 3.0), $MachinePrecision] * t$95$5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, 2300000.0], N[(x1 + N[(N[(x1 * N[(t$95$7 - 2.0), $MachinePrecision]), $MachinePrecision] + N[(x2 * -6.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, 1.35e+154], N[(x1 + N[(t$95$6 + N[(x1 + N[(t$95$4 + N[(N[(t$95$0 * t$95$3), $MachinePrecision] + N[(t$95$2 * N[(N[(t$95$5 * N[(-1.0 / x1), $MachinePrecision]), $MachinePrecision] + N[(N[(x1 * x1), $MachinePrecision] * N[(N[(4.0 * N[(3.0 - N[(1.0 / x1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - 6.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x1 + N[(x1 * N[(1.0 + t$95$7), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]]]]]]]]

Derivation

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

Alternative 14: 68.8% accurate, 1.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := x1 \cdot \left(x1 \cdot 3\right)\\ t_1 := 2 \cdot x2 - 3\\ t_2 := x1 \cdot x1 + 1\\ t_3 := \frac{\left(t_0 + 2 \cdot x2\right) - x1}{t_2}\\ t_4 := t_0 \cdot t_3\\ t_5 := x1 \cdot \left(x1 \cdot x1\right)\\ t_6 := \left(x1 \cdot 2\right) \cdot 3\\ t_7 := 3 \cdot \left(x2 \cdot -2\right)\\ t_8 := 4 \cdot \left(x2 \cdot t_1\right)\\ \mathbf{if}\;x1 \leq -5.2 \cdot 10^{+104}:\\ \;\;\;\;x1 + \left(t_7 + \left(x1 + x1 \cdot \left(6 \cdot t_1 - 4\right)\right)\right)\\ \mathbf{elif}\;x1 \leq -4.4:\\ \;\;\;\;x1 + \left(t_7 + \left(x1 + \left(t_5 + \left(t_4 + t_2 \cdot \left(\left(x1 \cdot x1\right) \cdot \left(t_3 \cdot 4 - 6\right) + t_6 \cdot 0\right)\right)\right)\right)\right)\\ \mathbf{elif}\;x1 \leq 2300000:\\ \;\;\;\;x1 + \left(x1 \cdot \left(t_8 - 2\right) + x2 \cdot -6\right)\\ \mathbf{elif}\;x1 \leq 1.35 \cdot 10^{+154}:\\ \;\;\;\;x1 + \left(t_7 + \left(x1 + \left(t_5 + \left(t_4 + t_2 \cdot \left(t_6 \cdot \frac{-1}{x1} + \left(x1 \cdot x1\right) \cdot \left(4 \cdot \left(3 - \frac{1}{x1}\right) - 6\right)\right)\right)\right)\right)\right)\\ \mathbf{else}:\\ \;\;\;\;x1 + x1 \cdot \left(1 + t_8\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x1 x2)
 :precision binary64
 (let* ((t_0 (* x1 (* x1 3.0)))
        (t_1 (- (* 2.0 x2) 3.0))
        (t_2 (+ (* x1 x1) 1.0))
        (t_3 (/ (- (+ t_0 (* 2.0 x2)) x1) t_2))
        (t_4 (* t_0 t_3))
        (t_5 (* x1 (* x1 x1)))
        (t_6 (* (* x1 2.0) 3.0))
        (t_7 (* 3.0 (* x2 -2.0)))
        (t_8 (* 4.0 (* x2 t_1))))
   (if (<= x1 -5.2e+104)
     (+ x1 (+ t_7 (+ x1 (* x1 (- (* 6.0 t_1) 4.0)))))
     (if (<= x1 -4.4)
       (+
        x1
        (+
         t_7
         (+
          x1
          (+
           t_5
           (+
            t_4
            (* t_2 (+ (* (* x1 x1) (- (* t_3 4.0) 6.0)) (* t_6 0.0))))))))
       (if (<= x1 2300000.0)
         (+ x1 (+ (* x1 (- t_8 2.0)) (* x2 -6.0)))
         (if (<= x1 1.35e+154)
           (+
            x1
            (+
             t_7
             (+
              x1
              (+
               t_5
               (+
                t_4
                (*
                 t_2
                 (+
                  (* t_6 (/ -1.0 x1))
                  (* (* x1 x1) (- (* 4.0 (- 3.0 (/ 1.0 x1))) 6.0)))))))))
           (+ x1 (* x1 (+ 1.0 t_8)))))))))

C

double code(double x1, double x2) {
	double t_0 = x1 * (x1 * 3.0);
	double t_1 = (2.0 * x2) - 3.0;
	double t_2 = (x1 * x1) + 1.0;
	double t_3 = ((t_0 + (2.0 * x2)) - x1) / t_2;
	double t_4 = t_0 * t_3;
	double t_5 = x1 * (x1 * x1);
	double t_6 = (x1 * 2.0) * 3.0;
	double t_7 = 3.0 * (x2 * -2.0);
	double t_8 = 4.0 * (x2 * t_1);
	double tmp;
	if (x1 <= -5.2e+104) {
		tmp = x1 + (t_7 + (x1 + (x1 * ((6.0 * t_1) - 4.0))));
	} else if (x1 <= -4.4) {
		tmp = x1 + (t_7 + (x1 + (t_5 + (t_4 + (t_2 * (((x1 * x1) * ((t_3 * 4.0) - 6.0)) + (t_6 * 0.0)))))));
	} else if (x1 <= 2300000.0) {
		tmp = x1 + ((x1 * (t_8 - 2.0)) + (x2 * -6.0));
	} else if (x1 <= 1.35e+154) {
		tmp = x1 + (t_7 + (x1 + (t_5 + (t_4 + (t_2 * ((t_6 * (-1.0 / x1)) + ((x1 * x1) * ((4.0 * (3.0 - (1.0 / x1))) - 6.0))))))));
	} else {
		tmp = x1 + (x1 * (1.0 + 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 * 3.0d0)
    t_1 = (2.0d0 * x2) - 3.0d0
    t_2 = (x1 * x1) + 1.0d0
    t_3 = ((t_0 + (2.0d0 * x2)) - x1) / t_2
    t_4 = t_0 * t_3
    t_5 = x1 * (x1 * x1)
    t_6 = (x1 * 2.0d0) * 3.0d0
    t_7 = 3.0d0 * (x2 * (-2.0d0))
    t_8 = 4.0d0 * (x2 * t_1)
    if (x1 <= (-5.2d+104)) then
        tmp = x1 + (t_7 + (x1 + (x1 * ((6.0d0 * t_1) - 4.0d0))))
    else if (x1 <= (-4.4d0)) then
        tmp = x1 + (t_7 + (x1 + (t_5 + (t_4 + (t_2 * (((x1 * x1) * ((t_3 * 4.0d0) - 6.0d0)) + (t_6 * 0.0d0)))))))
    else if (x1 <= 2300000.0d0) then
        tmp = x1 + ((x1 * (t_8 - 2.0d0)) + (x2 * (-6.0d0)))
    else if (x1 <= 1.35d+154) then
        tmp = x1 + (t_7 + (x1 + (t_5 + (t_4 + (t_2 * ((t_6 * ((-1.0d0) / x1)) + ((x1 * x1) * ((4.0d0 * (3.0d0 - (1.0d0 / x1))) - 6.0d0))))))))
    else
        tmp = x1 + (x1 * (1.0d0 + t_8))
    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 = (2.0 * x2) - 3.0;
	double t_2 = (x1 * x1) + 1.0;
	double t_3 = ((t_0 + (2.0 * x2)) - x1) / t_2;
	double t_4 = t_0 * t_3;
	double t_5 = x1 * (x1 * x1);
	double t_6 = (x1 * 2.0) * 3.0;
	double t_7 = 3.0 * (x2 * -2.0);
	double t_8 = 4.0 * (x2 * t_1);
	double tmp;
	if (x1 <= -5.2e+104) {
		tmp = x1 + (t_7 + (x1 + (x1 * ((6.0 * t_1) - 4.0))));
	} else if (x1 <= -4.4) {
		tmp = x1 + (t_7 + (x1 + (t_5 + (t_4 + (t_2 * (((x1 * x1) * ((t_3 * 4.0) - 6.0)) + (t_6 * 0.0)))))));
	} else if (x1 <= 2300000.0) {
		tmp = x1 + ((x1 * (t_8 - 2.0)) + (x2 * -6.0));
	} else if (x1 <= 1.35e+154) {
		tmp = x1 + (t_7 + (x1 + (t_5 + (t_4 + (t_2 * ((t_6 * (-1.0 / x1)) + ((x1 * x1) * ((4.0 * (3.0 - (1.0 / x1))) - 6.0))))))));
	} else {
		tmp = x1 + (x1 * (1.0 + t_8));
	}
	return tmp;
}

Python

def code(x1, x2):
	t_0 = x1 * (x1 * 3.0)
	t_1 = (2.0 * x2) - 3.0
	t_2 = (x1 * x1) + 1.0
	t_3 = ((t_0 + (2.0 * x2)) - x1) / t_2
	t_4 = t_0 * t_3
	t_5 = x1 * (x1 * x1)
	t_6 = (x1 * 2.0) * 3.0
	t_7 = 3.0 * (x2 * -2.0)
	t_8 = 4.0 * (x2 * t_1)
	tmp = 0
	if x1 <= -5.2e+104:
		tmp = x1 + (t_7 + (x1 + (x1 * ((6.0 * t_1) - 4.0))))
	elif x1 <= -4.4:
		tmp = x1 + (t_7 + (x1 + (t_5 + (t_4 + (t_2 * (((x1 * x1) * ((t_3 * 4.0) - 6.0)) + (t_6 * 0.0)))))))
	elif x1 <= 2300000.0:
		tmp = x1 + ((x1 * (t_8 - 2.0)) + (x2 * -6.0))
	elif x1 <= 1.35e+154:
		tmp = x1 + (t_7 + (x1 + (t_5 + (t_4 + (t_2 * ((t_6 * (-1.0 / x1)) + ((x1 * x1) * ((4.0 * (3.0 - (1.0 / x1))) - 6.0))))))))
	else:
		tmp = x1 + (x1 * (1.0 + t_8))
	return tmp

Julia

function code(x1, x2)
	t_0 = Float64(x1 * Float64(x1 * 3.0))
	t_1 = Float64(Float64(2.0 * x2) - 3.0)
	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(t_0 * t_3)
	t_5 = Float64(x1 * Float64(x1 * x1))
	t_6 = Float64(Float64(x1 * 2.0) * 3.0)
	t_7 = Float64(3.0 * Float64(x2 * -2.0))
	t_8 = Float64(4.0 * Float64(x2 * t_1))
	tmp = 0.0
	if (x1 <= -5.2e+104)
		tmp = Float64(x1 + Float64(t_7 + Float64(x1 + Float64(x1 * Float64(Float64(6.0 * t_1) - 4.0)))));
	elseif (x1 <= -4.4)
		tmp = Float64(x1 + Float64(t_7 + Float64(x1 + Float64(t_5 + Float64(t_4 + Float64(t_2 * Float64(Float64(Float64(x1 * x1) * Float64(Float64(t_3 * 4.0) - 6.0)) + Float64(t_6 * 0.0))))))));
	elseif (x1 <= 2300000.0)
		tmp = Float64(x1 + Float64(Float64(x1 * Float64(t_8 - 2.0)) + Float64(x2 * -6.0)));
	elseif (x1 <= 1.35e+154)
		tmp = Float64(x1 + Float64(t_7 + Float64(x1 + Float64(t_5 + Float64(t_4 + Float64(t_2 * Float64(Float64(t_6 * Float64(-1.0 / x1)) + Float64(Float64(x1 * x1) * Float64(Float64(4.0 * Float64(3.0 - Float64(1.0 / x1))) - 6.0)))))))));
	else
		tmp = Float64(x1 + Float64(x1 * Float64(1.0 + t_8)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x1, x2)
	t_0 = x1 * (x1 * 3.0);
	t_1 = (2.0 * x2) - 3.0;
	t_2 = (x1 * x1) + 1.0;
	t_3 = ((t_0 + (2.0 * x2)) - x1) / t_2;
	t_4 = t_0 * t_3;
	t_5 = x1 * (x1 * x1);
	t_6 = (x1 * 2.0) * 3.0;
	t_7 = 3.0 * (x2 * -2.0);
	t_8 = 4.0 * (x2 * t_1);
	tmp = 0.0;
	if (x1 <= -5.2e+104)
		tmp = x1 + (t_7 + (x1 + (x1 * ((6.0 * t_1) - 4.0))));
	elseif (x1 <= -4.4)
		tmp = x1 + (t_7 + (x1 + (t_5 + (t_4 + (t_2 * (((x1 * x1) * ((t_3 * 4.0) - 6.0)) + (t_6 * 0.0)))))));
	elseif (x1 <= 2300000.0)
		tmp = x1 + ((x1 * (t_8 - 2.0)) + (x2 * -6.0));
	elseif (x1 <= 1.35e+154)
		tmp = x1 + (t_7 + (x1 + (t_5 + (t_4 + (t_2 * ((t_6 * (-1.0 / x1)) + ((x1 * x1) * ((4.0 * (3.0 - (1.0 / x1))) - 6.0))))))));
	else
		tmp = x1 + (x1 * (1.0 + t_8));
	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[(2.0 * x2), $MachinePrecision] - 3.0), $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[(t$95$0 * t$95$3), $MachinePrecision]}, Block[{t$95$5 = N[(x1 * N[(x1 * x1), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$6 = N[(N[(x1 * 2.0), $MachinePrecision] * 3.0), $MachinePrecision]}, Block[{t$95$7 = N[(3.0 * N[(x2 * -2.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$8 = N[(4.0 * N[(x2 * t$95$1), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x1, -5.2e+104], N[(x1 + N[(t$95$7 + N[(x1 + N[(x1 * N[(N[(6.0 * t$95$1), $MachinePrecision] - 4.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, -4.4], N[(x1 + N[(t$95$7 + N[(x1 + N[(t$95$5 + N[(t$95$4 + N[(t$95$2 * N[(N[(N[(x1 * x1), $MachinePrecision] * N[(N[(t$95$3 * 4.0), $MachinePrecision] - 6.0), $MachinePrecision]), $MachinePrecision] + N[(t$95$6 * 0.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, 2300000.0], N[(x1 + N[(N[(x1 * N[(t$95$8 - 2.0), $MachinePrecision]), $MachinePrecision] + N[(x2 * -6.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, 1.35e+154], N[(x1 + N[(t$95$7 + N[(x1 + N[(t$95$5 + N[(t$95$4 + N[(t$95$2 * N[(N[(t$95$6 * N[(-1.0 / x1), $MachinePrecision]), $MachinePrecision] + N[(N[(x1 * x1), $MachinePrecision] * N[(N[(4.0 * N[(3.0 - N[(1.0 / x1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - 6.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x1 + N[(x1 * N[(1.0 + t$95$8), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]]]]]]]]]

Derivation

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

Alternative 15: 68.4% accurate, 1.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := x1 \cdot \left(x1 \cdot 3\right)\\ t_1 := 2 \cdot x2 - 3\\ t_2 := x1 \cdot x1 + 1\\ t_3 := 3 \cdot \left(x2 \cdot -2\right)\\ t_4 := x1 + \left(t_3 + \left(x1 + \left(x1 \cdot \left(x1 \cdot x1\right) + \left(3 \cdot t_0 + t_2 \cdot \left(\left(\frac{\left(t_0 + 2 \cdot x2\right) - x1}{t_2} - 3\right) \cdot \left(\left(x1 \cdot 2\right) \cdot 3\right) + \left(x1 \cdot x1\right) \cdot \left(4 \cdot \left(3 - \frac{1}{x1}\right) - 6\right)\right)\right)\right)\right)\right)\\ t_5 := 4 \cdot \left(x2 \cdot t_1\right)\\ \mathbf{if}\;x1 \leq -5.6 \cdot 10^{+102}:\\ \;\;\;\;x1 + \left(t_3 + \left(x1 + x1 \cdot \left(6 \cdot t_1 - 4\right)\right)\right)\\ \mathbf{elif}\;x1 \leq -32:\\ \;\;\;\;t_4\\ \mathbf{elif}\;x1 \leq 2300000:\\ \;\;\;\;x1 + \left(x1 \cdot \left(t_5 - 2\right) + x2 \cdot -6\right)\\ \mathbf{elif}\;x1 \leq 1.35 \cdot 10^{+154}:\\ \;\;\;\;t_4\\ \mathbf{else}:\\ \;\;\;\;x1 + x1 \cdot \left(1 + t_5\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x1 x2)
 :precision binary64
 (let* ((t_0 (* x1 (* x1 3.0)))
        (t_1 (- (* 2.0 x2) 3.0))
        (t_2 (+ (* x1 x1) 1.0))
        (t_3 (* 3.0 (* x2 -2.0)))
        (t_4
         (+
          x1
          (+
           t_3
           (+
            x1
            (+
             (* x1 (* x1 x1))
             (+
              (* 3.0 t_0)
              (*
               t_2
               (+
                (*
                 (- (/ (- (+ t_0 (* 2.0 x2)) x1) t_2) 3.0)
                 (* (* x1 2.0) 3.0))
                (* (* x1 x1) (- (* 4.0 (- 3.0 (/ 1.0 x1))) 6.0))))))))))
        (t_5 (* 4.0 (* x2 t_1))))
   (if (<= x1 -5.6e+102)
     (+ x1 (+ t_3 (+ x1 (* x1 (- (* 6.0 t_1) 4.0)))))
     (if (<= x1 -32.0)
       t_4
       (if (<= x1 2300000.0)
         (+ x1 (+ (* x1 (- t_5 2.0)) (* x2 -6.0)))
         (if (<= x1 1.35e+154) t_4 (+ x1 (* x1 (+ 1.0 t_5)))))))))

C

double code(double x1, double x2) {
	double t_0 = x1 * (x1 * 3.0);
	double t_1 = (2.0 * x2) - 3.0;
	double t_2 = (x1 * x1) + 1.0;
	double t_3 = 3.0 * (x2 * -2.0);
	double t_4 = x1 + (t_3 + (x1 + ((x1 * (x1 * x1)) + ((3.0 * t_0) + (t_2 * ((((((t_0 + (2.0 * x2)) - x1) / t_2) - 3.0) * ((x1 * 2.0) * 3.0)) + ((x1 * x1) * ((4.0 * (3.0 - (1.0 / x1))) - 6.0))))))));
	double t_5 = 4.0 * (x2 * t_1);
	double tmp;
	if (x1 <= -5.6e+102) {
		tmp = x1 + (t_3 + (x1 + (x1 * ((6.0 * t_1) - 4.0))));
	} else if (x1 <= -32.0) {
		tmp = t_4;
	} else if (x1 <= 2300000.0) {
		tmp = x1 + ((x1 * (t_5 - 2.0)) + (x2 * -6.0));
	} else if (x1 <= 1.35e+154) {
		tmp = t_4;
	} 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 * 3.0d0)
    t_1 = (2.0d0 * x2) - 3.0d0
    t_2 = (x1 * x1) + 1.0d0
    t_3 = 3.0d0 * (x2 * (-2.0d0))
    t_4 = x1 + (t_3 + (x1 + ((x1 * (x1 * x1)) + ((3.0d0 * t_0) + (t_2 * ((((((t_0 + (2.0d0 * x2)) - x1) / t_2) - 3.0d0) * ((x1 * 2.0d0) * 3.0d0)) + ((x1 * x1) * ((4.0d0 * (3.0d0 - (1.0d0 / x1))) - 6.0d0))))))))
    t_5 = 4.0d0 * (x2 * t_1)
    if (x1 <= (-5.6d+102)) then
        tmp = x1 + (t_3 + (x1 + (x1 * ((6.0d0 * t_1) - 4.0d0))))
    else if (x1 <= (-32.0d0)) then
        tmp = t_4
    else if (x1 <= 2300000.0d0) then
        tmp = x1 + ((x1 * (t_5 - 2.0d0)) + (x2 * (-6.0d0)))
    else if (x1 <= 1.35d+154) then
        tmp = t_4
    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 * 3.0);
	double t_1 = (2.0 * x2) - 3.0;
	double t_2 = (x1 * x1) + 1.0;
	double t_3 = 3.0 * (x2 * -2.0);
	double t_4 = x1 + (t_3 + (x1 + ((x1 * (x1 * x1)) + ((3.0 * t_0) + (t_2 * ((((((t_0 + (2.0 * x2)) - x1) / t_2) - 3.0) * ((x1 * 2.0) * 3.0)) + ((x1 * x1) * ((4.0 * (3.0 - (1.0 / x1))) - 6.0))))))));
	double t_5 = 4.0 * (x2 * t_1);
	double tmp;
	if (x1 <= -5.6e+102) {
		tmp = x1 + (t_3 + (x1 + (x1 * ((6.0 * t_1) - 4.0))));
	} else if (x1 <= -32.0) {
		tmp = t_4;
	} else if (x1 <= 2300000.0) {
		tmp = x1 + ((x1 * (t_5 - 2.0)) + (x2 * -6.0));
	} else if (x1 <= 1.35e+154) {
		tmp = t_4;
	} else {
		tmp = x1 + (x1 * (1.0 + t_5));
	}
	return tmp;
}

Python

def code(x1, x2):
	t_0 = x1 * (x1 * 3.0)
	t_1 = (2.0 * x2) - 3.0
	t_2 = (x1 * x1) + 1.0
	t_3 = 3.0 * (x2 * -2.0)
	t_4 = x1 + (t_3 + (x1 + ((x1 * (x1 * x1)) + ((3.0 * t_0) + (t_2 * ((((((t_0 + (2.0 * x2)) - x1) / t_2) - 3.0) * ((x1 * 2.0) * 3.0)) + ((x1 * x1) * ((4.0 * (3.0 - (1.0 / x1))) - 6.0))))))))
	t_5 = 4.0 * (x2 * t_1)
	tmp = 0
	if x1 <= -5.6e+102:
		tmp = x1 + (t_3 + (x1 + (x1 * ((6.0 * t_1) - 4.0))))
	elif x1 <= -32.0:
		tmp = t_4
	elif x1 <= 2300000.0:
		tmp = x1 + ((x1 * (t_5 - 2.0)) + (x2 * -6.0))
	elif x1 <= 1.35e+154:
		tmp = t_4
	else:
		tmp = x1 + (x1 * (1.0 + t_5))
	return tmp

Julia

function code(x1, x2)
	t_0 = Float64(x1 * Float64(x1 * 3.0))
	t_1 = Float64(Float64(2.0 * x2) - 3.0)
	t_2 = Float64(Float64(x1 * x1) + 1.0)
	t_3 = Float64(3.0 * Float64(x2 * -2.0))
	t_4 = Float64(x1 + Float64(t_3 + Float64(x1 + Float64(Float64(x1 * Float64(x1 * x1)) + Float64(Float64(3.0 * t_0) + Float64(t_2 * Float64(Float64(Float64(Float64(Float64(Float64(t_0 + Float64(2.0 * x2)) - x1) / t_2) - 3.0) * Float64(Float64(x1 * 2.0) * 3.0)) + Float64(Float64(x1 * x1) * Float64(Float64(4.0 * Float64(3.0 - Float64(1.0 / x1))) - 6.0)))))))))
	t_5 = Float64(4.0 * Float64(x2 * t_1))
	tmp = 0.0
	if (x1 <= -5.6e+102)
		tmp = Float64(x1 + Float64(t_3 + Float64(x1 + Float64(x1 * Float64(Float64(6.0 * t_1) - 4.0)))));
	elseif (x1 <= -32.0)
		tmp = t_4;
	elseif (x1 <= 2300000.0)
		tmp = Float64(x1 + Float64(Float64(x1 * Float64(t_5 - 2.0)) + Float64(x2 * -6.0)));
	elseif (x1 <= 1.35e+154)
		tmp = t_4;
	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 * 3.0);
	t_1 = (2.0 * x2) - 3.0;
	t_2 = (x1 * x1) + 1.0;
	t_3 = 3.0 * (x2 * -2.0);
	t_4 = x1 + (t_3 + (x1 + ((x1 * (x1 * x1)) + ((3.0 * t_0) + (t_2 * ((((((t_0 + (2.0 * x2)) - x1) / t_2) - 3.0) * ((x1 * 2.0) * 3.0)) + ((x1 * x1) * ((4.0 * (3.0 - (1.0 / x1))) - 6.0))))))));
	t_5 = 4.0 * (x2 * t_1);
	tmp = 0.0;
	if (x1 <= -5.6e+102)
		tmp = x1 + (t_3 + (x1 + (x1 * ((6.0 * t_1) - 4.0))));
	elseif (x1 <= -32.0)
		tmp = t_4;
	elseif (x1 <= 2300000.0)
		tmp = x1 + ((x1 * (t_5 - 2.0)) + (x2 * -6.0));
	elseif (x1 <= 1.35e+154)
		tmp = t_4;
	else
		tmp = x1 + (x1 * (1.0 + t_5));
	end
	tmp_2 = tmp;
end

Wolfram

code[x1_, x2_] := Block[{t$95$0 = N[(x1 * N[(x1 * 3.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(N[(2.0 * x2), $MachinePrecision] - 3.0), $MachinePrecision]}, Block[{t$95$2 = N[(N[(x1 * x1), $MachinePrecision] + 1.0), $MachinePrecision]}, Block[{t$95$3 = N[(3.0 * N[(x2 * -2.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$4 = N[(x1 + N[(t$95$3 + N[(x1 + N[(N[(x1 * N[(x1 * x1), $MachinePrecision]), $MachinePrecision] + N[(N[(3.0 * t$95$0), $MachinePrecision] + N[(t$95$2 * N[(N[(N[(N[(N[(N[(t$95$0 + N[(2.0 * x2), $MachinePrecision]), $MachinePrecision] - x1), $MachinePrecision] / t$95$2), $MachinePrecision] - 3.0), $MachinePrecision] * N[(N[(x1 * 2.0), $MachinePrecision] * 3.0), $MachinePrecision]), $MachinePrecision] + N[(N[(x1 * x1), $MachinePrecision] * N[(N[(4.0 * N[(3.0 - N[(1.0 / x1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - 6.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$5 = N[(4.0 * N[(x2 * t$95$1), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x1, -5.6e+102], N[(x1 + N[(t$95$3 + N[(x1 + N[(x1 * N[(N[(6.0 * t$95$1), $MachinePrecision] - 4.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, -32.0], t$95$4, If[LessEqual[x1, 2300000.0], N[(x1 + N[(N[(x1 * N[(t$95$5 - 2.0), $MachinePrecision]), $MachinePrecision] + N[(x2 * -6.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, 1.35e+154], t$95$4, N[(x1 + N[(x1 * N[(1.0 + t$95$5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]]]]]]

Derivation

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

Alternative 16: 68.3% accurate, 1.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := x1 \cdot \left(x1 \cdot 3\right)\\ t_1 := 2 \cdot x2 - 3\\ t_2 := x1 \cdot x1 + 1\\ t_3 := 3 \cdot \left(x2 \cdot -2\right)\\ t_4 := \frac{\left(t_0 + 2 \cdot x2\right) - x1}{t_2}\\ t_5 := 4 \cdot \left(x2 \cdot t_1\right)\\ t_6 := \left(x1 \cdot 2\right) \cdot 3\\ t_7 := x1 \cdot \left(x1 \cdot x1\right)\\ t_8 := \left(x1 \cdot x1\right) \cdot \left(4 \cdot \left(3 - \frac{1}{x1}\right) - 6\right)\\ \mathbf{if}\;x1 \leq -5.6 \cdot 10^{+102}:\\ \;\;\;\;x1 + \left(t_3 + \left(x1 + x1 \cdot \left(6 \cdot t_1 - 4\right)\right)\right)\\ \mathbf{elif}\;x1 \leq -240:\\ \;\;\;\;x1 + \left(t_3 + \left(x1 + \left(t_7 + \left(3 \cdot t_0 + t_2 \cdot \left(\left(t_4 - 3\right) \cdot t_6 + t_8\right)\right)\right)\right)\right)\\ \mathbf{elif}\;x1 \leq 2700000:\\ \;\;\;\;x1 + \left(x1 \cdot \left(t_5 - 2\right) + x2 \cdot -6\right)\\ \mathbf{elif}\;x1 \leq 1.35 \cdot 10^{+154}:\\ \;\;\;\;x1 + \left(t_3 + \left(x1 + \left(t_7 + \left(t_0 \cdot t_4 + t_2 \cdot \left(t_6 \cdot \frac{-1}{x1} + t_8\right)\right)\right)\right)\right)\\ \mathbf{else}:\\ \;\;\;\;x1 + x1 \cdot \left(1 + t_5\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x1 x2)
 :precision binary64
 (let* ((t_0 (* x1 (* x1 3.0)))
        (t_1 (- (* 2.0 x2) 3.0))
        (t_2 (+ (* x1 x1) 1.0))
        (t_3 (* 3.0 (* x2 -2.0)))
        (t_4 (/ (- (+ t_0 (* 2.0 x2)) x1) t_2))
        (t_5 (* 4.0 (* x2 t_1)))
        (t_6 (* (* x1 2.0) 3.0))
        (t_7 (* x1 (* x1 x1)))
        (t_8 (* (* x1 x1) (- (* 4.0 (- 3.0 (/ 1.0 x1))) 6.0))))
   (if (<= x1 -5.6e+102)
     (+ x1 (+ t_3 (+ x1 (* x1 (- (* 6.0 t_1) 4.0)))))
     (if (<= x1 -240.0)
       (+
        x1
        (+
         t_3
         (+ x1 (+ t_7 (+ (* 3.0 t_0) (* t_2 (+ (* (- t_4 3.0) t_6) t_8)))))))
       (if (<= x1 2700000.0)
         (+ x1 (+ (* x1 (- t_5 2.0)) (* x2 -6.0)))
         (if (<= x1 1.35e+154)
           (+
            x1
            (+
             t_3
             (+
              x1
              (+ t_7 (+ (* t_0 t_4) (* t_2 (+ (* t_6 (/ -1.0 x1)) t_8)))))))
           (+ x1 (* x1 (+ 1.0 t_5)))))))))

C

double code(double x1, double x2) {
	double t_0 = x1 * (x1 * 3.0);
	double t_1 = (2.0 * x2) - 3.0;
	double t_2 = (x1 * x1) + 1.0;
	double t_3 = 3.0 * (x2 * -2.0);
	double t_4 = ((t_0 + (2.0 * x2)) - x1) / t_2;
	double t_5 = 4.0 * (x2 * t_1);
	double t_6 = (x1 * 2.0) * 3.0;
	double t_7 = x1 * (x1 * x1);
	double t_8 = (x1 * x1) * ((4.0 * (3.0 - (1.0 / x1))) - 6.0);
	double tmp;
	if (x1 <= -5.6e+102) {
		tmp = x1 + (t_3 + (x1 + (x1 * ((6.0 * t_1) - 4.0))));
	} else if (x1 <= -240.0) {
		tmp = x1 + (t_3 + (x1 + (t_7 + ((3.0 * t_0) + (t_2 * (((t_4 - 3.0) * t_6) + t_8))))));
	} else if (x1 <= 2700000.0) {
		tmp = x1 + ((x1 * (t_5 - 2.0)) + (x2 * -6.0));
	} else if (x1 <= 1.35e+154) {
		tmp = x1 + (t_3 + (x1 + (t_7 + ((t_0 * t_4) + (t_2 * ((t_6 * (-1.0 / x1)) + t_8))))));
	} 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) :: t_6
    real(8) :: t_7
    real(8) :: t_8
    real(8) :: tmp
    t_0 = x1 * (x1 * 3.0d0)
    t_1 = (2.0d0 * x2) - 3.0d0
    t_2 = (x1 * x1) + 1.0d0
    t_3 = 3.0d0 * (x2 * (-2.0d0))
    t_4 = ((t_0 + (2.0d0 * x2)) - x1) / t_2
    t_5 = 4.0d0 * (x2 * t_1)
    t_6 = (x1 * 2.0d0) * 3.0d0
    t_7 = x1 * (x1 * x1)
    t_8 = (x1 * x1) * ((4.0d0 * (3.0d0 - (1.0d0 / x1))) - 6.0d0)
    if (x1 <= (-5.6d+102)) then
        tmp = x1 + (t_3 + (x1 + (x1 * ((6.0d0 * t_1) - 4.0d0))))
    else if (x1 <= (-240.0d0)) then
        tmp = x1 + (t_3 + (x1 + (t_7 + ((3.0d0 * t_0) + (t_2 * (((t_4 - 3.0d0) * t_6) + t_8))))))
    else if (x1 <= 2700000.0d0) then
        tmp = x1 + ((x1 * (t_5 - 2.0d0)) + (x2 * (-6.0d0)))
    else if (x1 <= 1.35d+154) then
        tmp = x1 + (t_3 + (x1 + (t_7 + ((t_0 * t_4) + (t_2 * ((t_6 * ((-1.0d0) / x1)) + t_8))))))
    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 * 3.0);
	double t_1 = (2.0 * x2) - 3.0;
	double t_2 = (x1 * x1) + 1.0;
	double t_3 = 3.0 * (x2 * -2.0);
	double t_4 = ((t_0 + (2.0 * x2)) - x1) / t_2;
	double t_5 = 4.0 * (x2 * t_1);
	double t_6 = (x1 * 2.0) * 3.0;
	double t_7 = x1 * (x1 * x1);
	double t_8 = (x1 * x1) * ((4.0 * (3.0 - (1.0 / x1))) - 6.0);
	double tmp;
	if (x1 <= -5.6e+102) {
		tmp = x1 + (t_3 + (x1 + (x1 * ((6.0 * t_1) - 4.0))));
	} else if (x1 <= -240.0) {
		tmp = x1 + (t_3 + (x1 + (t_7 + ((3.0 * t_0) + (t_2 * (((t_4 - 3.0) * t_6) + t_8))))));
	} else if (x1 <= 2700000.0) {
		tmp = x1 + ((x1 * (t_5 - 2.0)) + (x2 * -6.0));
	} else if (x1 <= 1.35e+154) {
		tmp = x1 + (t_3 + (x1 + (t_7 + ((t_0 * t_4) + (t_2 * ((t_6 * (-1.0 / x1)) + t_8))))));
	} else {
		tmp = x1 + (x1 * (1.0 + t_5));
	}
	return tmp;
}

Python

def code(x1, x2):
	t_0 = x1 * (x1 * 3.0)
	t_1 = (2.0 * x2) - 3.0
	t_2 = (x1 * x1) + 1.0
	t_3 = 3.0 * (x2 * -2.0)
	t_4 = ((t_0 + (2.0 * x2)) - x1) / t_2
	t_5 = 4.0 * (x2 * t_1)
	t_6 = (x1 * 2.0) * 3.0
	t_7 = x1 * (x1 * x1)
	t_8 = (x1 * x1) * ((4.0 * (3.0 - (1.0 / x1))) - 6.0)
	tmp = 0
	if x1 <= -5.6e+102:
		tmp = x1 + (t_3 + (x1 + (x1 * ((6.0 * t_1) - 4.0))))
	elif x1 <= -240.0:
		tmp = x1 + (t_3 + (x1 + (t_7 + ((3.0 * t_0) + (t_2 * (((t_4 - 3.0) * t_6) + t_8))))))
	elif x1 <= 2700000.0:
		tmp = x1 + ((x1 * (t_5 - 2.0)) + (x2 * -6.0))
	elif x1 <= 1.35e+154:
		tmp = x1 + (t_3 + (x1 + (t_7 + ((t_0 * t_4) + (t_2 * ((t_6 * (-1.0 / x1)) + t_8))))))
	else:
		tmp = x1 + (x1 * (1.0 + t_5))
	return tmp

Julia

function code(x1, x2)
	t_0 = Float64(x1 * Float64(x1 * 3.0))
	t_1 = Float64(Float64(2.0 * x2) - 3.0)
	t_2 = Float64(Float64(x1 * x1) + 1.0)
	t_3 = Float64(3.0 * Float64(x2 * -2.0))
	t_4 = Float64(Float64(Float64(t_0 + Float64(2.0 * x2)) - x1) / t_2)
	t_5 = Float64(4.0 * Float64(x2 * t_1))
	t_6 = Float64(Float64(x1 * 2.0) * 3.0)
	t_7 = Float64(x1 * Float64(x1 * x1))
	t_8 = Float64(Float64(x1 * x1) * Float64(Float64(4.0 * Float64(3.0 - Float64(1.0 / x1))) - 6.0))
	tmp = 0.0
	if (x1 <= -5.6e+102)
		tmp = Float64(x1 + Float64(t_3 + Float64(x1 + Float64(x1 * Float64(Float64(6.0 * t_1) - 4.0)))));
	elseif (x1 <= -240.0)
		tmp = Float64(x1 + Float64(t_3 + Float64(x1 + Float64(t_7 + Float64(Float64(3.0 * t_0) + Float64(t_2 * Float64(Float64(Float64(t_4 - 3.0) * t_6) + t_8)))))));
	elseif (x1 <= 2700000.0)
		tmp = Float64(x1 + Float64(Float64(x1 * Float64(t_5 - 2.0)) + Float64(x2 * -6.0)));
	elseif (x1 <= 1.35e+154)
		tmp = Float64(x1 + Float64(t_3 + Float64(x1 + Float64(t_7 + Float64(Float64(t_0 * t_4) + Float64(t_2 * Float64(Float64(t_6 * Float64(-1.0 / x1)) + t_8)))))));
	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 * 3.0);
	t_1 = (2.0 * x2) - 3.0;
	t_2 = (x1 * x1) + 1.0;
	t_3 = 3.0 * (x2 * -2.0);
	t_4 = ((t_0 + (2.0 * x2)) - x1) / t_2;
	t_5 = 4.0 * (x2 * t_1);
	t_6 = (x1 * 2.0) * 3.0;
	t_7 = x1 * (x1 * x1);
	t_8 = (x1 * x1) * ((4.0 * (3.0 - (1.0 / x1))) - 6.0);
	tmp = 0.0;
	if (x1 <= -5.6e+102)
		tmp = x1 + (t_3 + (x1 + (x1 * ((6.0 * t_1) - 4.0))));
	elseif (x1 <= -240.0)
		tmp = x1 + (t_3 + (x1 + (t_7 + ((3.0 * t_0) + (t_2 * (((t_4 - 3.0) * t_6) + t_8))))));
	elseif (x1 <= 2700000.0)
		tmp = x1 + ((x1 * (t_5 - 2.0)) + (x2 * -6.0));
	elseif (x1 <= 1.35e+154)
		tmp = x1 + (t_3 + (x1 + (t_7 + ((t_0 * t_4) + (t_2 * ((t_6 * (-1.0 / x1)) + t_8))))));
	else
		tmp = x1 + (x1 * (1.0 + t_5));
	end
	tmp_2 = tmp;
end

Wolfram

code[x1_, x2_] := Block[{t$95$0 = N[(x1 * N[(x1 * 3.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(N[(2.0 * x2), $MachinePrecision] - 3.0), $MachinePrecision]}, Block[{t$95$2 = N[(N[(x1 * x1), $MachinePrecision] + 1.0), $MachinePrecision]}, Block[{t$95$3 = N[(3.0 * N[(x2 * -2.0), $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[(4.0 * N[(x2 * t$95$1), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$6 = N[(N[(x1 * 2.0), $MachinePrecision] * 3.0), $MachinePrecision]}, Block[{t$95$7 = N[(x1 * N[(x1 * x1), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$8 = N[(N[(x1 * x1), $MachinePrecision] * N[(N[(4.0 * N[(3.0 - N[(1.0 / x1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - 6.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x1, -5.6e+102], N[(x1 + N[(t$95$3 + N[(x1 + N[(x1 * N[(N[(6.0 * t$95$1), $MachinePrecision] - 4.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, -240.0], N[(x1 + N[(t$95$3 + N[(x1 + N[(t$95$7 + N[(N[(3.0 * t$95$0), $MachinePrecision] + N[(t$95$2 * N[(N[(N[(t$95$4 - 3.0), $MachinePrecision] * t$95$6), $MachinePrecision] + t$95$8), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, 2700000.0], N[(x1 + N[(N[(x1 * N[(t$95$5 - 2.0), $MachinePrecision]), $MachinePrecision] + N[(x2 * -6.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, 1.35e+154], N[(x1 + N[(t$95$3 + N[(x1 + N[(t$95$7 + N[(N[(t$95$0 * t$95$4), $MachinePrecision] + N[(t$95$2 * N[(N[(t$95$6 * N[(-1.0 / x1), $MachinePrecision]), $MachinePrecision] + t$95$8), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x1 + N[(x1 * N[(1.0 + t$95$5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]]]]]]]]]

Derivation

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

Alternative 17: 68.2% accurate, 1.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := x1 \cdot x1 + 1\\ t_1 := 2 \cdot x2 - 3\\ t_2 := 3 \cdot \left(x2 \cdot -2\right)\\ t_3 := x1 \cdot \left(x1 \cdot 3\right)\\ t_4 := \frac{\left(t_3 + 2 \cdot x2\right) - x1}{t_0}\\ t_5 := 4 \cdot \left(x2 \cdot t_1\right)\\ t_6 := \left(x1 \cdot 2\right) \cdot 3\\ t_7 := x1 \cdot \left(x1 \cdot x1\right)\\ t_8 := \left(x1 \cdot x1\right) \cdot \left(4 \cdot \left(3 - \frac{1}{x1}\right) - 6\right)\\ \mathbf{if}\;x1 \leq -5.6 \cdot 10^{+102}:\\ \;\;\;\;x1 + \left(t_2 + \left(x1 + x1 \cdot \left(6 \cdot t_1 - 4\right)\right)\right)\\ \mathbf{elif}\;x1 \leq -225:\\ \;\;\;\;x1 + \left(t_2 + \left(x1 + \left(t_7 + \left(t_0 \cdot \left(\left(t_4 - 3\right) \cdot t_6 + t_8\right) + t_3 \cdot \left(2 \cdot x2\right)\right)\right)\right)\right)\\ \mathbf{elif}\;x1 \leq 100000000:\\ \;\;\;\;x1 + \left(x1 \cdot \left(t_5 - 2\right) + x2 \cdot -6\right)\\ \mathbf{elif}\;x1 \leq 1.35 \cdot 10^{+154}:\\ \;\;\;\;x1 + \left(t_2 + \left(x1 + \left(t_7 + \left(t_3 \cdot t_4 + t_0 \cdot \left(t_6 \cdot \frac{-1}{x1} + t_8\right)\right)\right)\right)\right)\\ \mathbf{else}:\\ \;\;\;\;x1 + x1 \cdot \left(1 + t_5\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x1 x2)
 :precision binary64
 (let* ((t_0 (+ (* x1 x1) 1.0))
        (t_1 (- (* 2.0 x2) 3.0))
        (t_2 (* 3.0 (* x2 -2.0)))
        (t_3 (* x1 (* x1 3.0)))
        (t_4 (/ (- (+ t_3 (* 2.0 x2)) x1) t_0))
        (t_5 (* 4.0 (* x2 t_1)))
        (t_6 (* (* x1 2.0) 3.0))
        (t_7 (* x1 (* x1 x1)))
        (t_8 (* (* x1 x1) (- (* 4.0 (- 3.0 (/ 1.0 x1))) 6.0))))
   (if (<= x1 -5.6e+102)
     (+ x1 (+ t_2 (+ x1 (* x1 (- (* 6.0 t_1) 4.0)))))
     (if (<= x1 -225.0)
       (+
        x1
        (+
         t_2
         (+
          x1
          (+ t_7 (+ (* t_0 (+ (* (- t_4 3.0) t_6) t_8)) (* t_3 (* 2.0 x2)))))))
       (if (<= x1 100000000.0)
         (+ x1 (+ (* x1 (- t_5 2.0)) (* x2 -6.0)))
         (if (<= x1 1.35e+154)
           (+
            x1
            (+
             t_2
             (+
              x1
              (+ t_7 (+ (* t_3 t_4) (* t_0 (+ (* t_6 (/ -1.0 x1)) t_8)))))))
           (+ x1 (* x1 (+ 1.0 t_5)))))))))

C

double code(double x1, double x2) {
	double t_0 = (x1 * x1) + 1.0;
	double t_1 = (2.0 * x2) - 3.0;
	double t_2 = 3.0 * (x2 * -2.0);
	double t_3 = x1 * (x1 * 3.0);
	double t_4 = ((t_3 + (2.0 * x2)) - x1) / t_0;
	double t_5 = 4.0 * (x2 * t_1);
	double t_6 = (x1 * 2.0) * 3.0;
	double t_7 = x1 * (x1 * x1);
	double t_8 = (x1 * x1) * ((4.0 * (3.0 - (1.0 / x1))) - 6.0);
	double tmp;
	if (x1 <= -5.6e+102) {
		tmp = x1 + (t_2 + (x1 + (x1 * ((6.0 * t_1) - 4.0))));
	} else if (x1 <= -225.0) {
		tmp = x1 + (t_2 + (x1 + (t_7 + ((t_0 * (((t_4 - 3.0) * t_6) + t_8)) + (t_3 * (2.0 * x2))))));
	} else if (x1 <= 100000000.0) {
		tmp = x1 + ((x1 * (t_5 - 2.0)) + (x2 * -6.0));
	} else if (x1 <= 1.35e+154) {
		tmp = x1 + (t_2 + (x1 + (t_7 + ((t_3 * t_4) + (t_0 * ((t_6 * (-1.0 / x1)) + t_8))))));
	} 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) :: t_6
    real(8) :: t_7
    real(8) :: t_8
    real(8) :: tmp
    t_0 = (x1 * x1) + 1.0d0
    t_1 = (2.0d0 * x2) - 3.0d0
    t_2 = 3.0d0 * (x2 * (-2.0d0))
    t_3 = x1 * (x1 * 3.0d0)
    t_4 = ((t_3 + (2.0d0 * x2)) - x1) / t_0
    t_5 = 4.0d0 * (x2 * t_1)
    t_6 = (x1 * 2.0d0) * 3.0d0
    t_7 = x1 * (x1 * x1)
    t_8 = (x1 * x1) * ((4.0d0 * (3.0d0 - (1.0d0 / x1))) - 6.0d0)
    if (x1 <= (-5.6d+102)) then
        tmp = x1 + (t_2 + (x1 + (x1 * ((6.0d0 * t_1) - 4.0d0))))
    else if (x1 <= (-225.0d0)) then
        tmp = x1 + (t_2 + (x1 + (t_7 + ((t_0 * (((t_4 - 3.0d0) * t_6) + t_8)) + (t_3 * (2.0d0 * x2))))))
    else if (x1 <= 100000000.0d0) then
        tmp = x1 + ((x1 * (t_5 - 2.0d0)) + (x2 * (-6.0d0)))
    else if (x1 <= 1.35d+154) then
        tmp = x1 + (t_2 + (x1 + (t_7 + ((t_3 * t_4) + (t_0 * ((t_6 * ((-1.0d0) / x1)) + t_8))))))
    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 = (2.0 * x2) - 3.0;
	double t_2 = 3.0 * (x2 * -2.0);
	double t_3 = x1 * (x1 * 3.0);
	double t_4 = ((t_3 + (2.0 * x2)) - x1) / t_0;
	double t_5 = 4.0 * (x2 * t_1);
	double t_6 = (x1 * 2.0) * 3.0;
	double t_7 = x1 * (x1 * x1);
	double t_8 = (x1 * x1) * ((4.0 * (3.0 - (1.0 / x1))) - 6.0);
	double tmp;
	if (x1 <= -5.6e+102) {
		tmp = x1 + (t_2 + (x1 + (x1 * ((6.0 * t_1) - 4.0))));
	} else if (x1 <= -225.0) {
		tmp = x1 + (t_2 + (x1 + (t_7 + ((t_0 * (((t_4 - 3.0) * t_6) + t_8)) + (t_3 * (2.0 * x2))))));
	} else if (x1 <= 100000000.0) {
		tmp = x1 + ((x1 * (t_5 - 2.0)) + (x2 * -6.0));
	} else if (x1 <= 1.35e+154) {
		tmp = x1 + (t_2 + (x1 + (t_7 + ((t_3 * t_4) + (t_0 * ((t_6 * (-1.0 / x1)) + t_8))))));
	} else {
		tmp = x1 + (x1 * (1.0 + t_5));
	}
	return tmp;
}

Python

def code(x1, x2):
	t_0 = (x1 * x1) + 1.0
	t_1 = (2.0 * x2) - 3.0
	t_2 = 3.0 * (x2 * -2.0)
	t_3 = x1 * (x1 * 3.0)
	t_4 = ((t_3 + (2.0 * x2)) - x1) / t_0
	t_5 = 4.0 * (x2 * t_1)
	t_6 = (x1 * 2.0) * 3.0
	t_7 = x1 * (x1 * x1)
	t_8 = (x1 * x1) * ((4.0 * (3.0 - (1.0 / x1))) - 6.0)
	tmp = 0
	if x1 <= -5.6e+102:
		tmp = x1 + (t_2 + (x1 + (x1 * ((6.0 * t_1) - 4.0))))
	elif x1 <= -225.0:
		tmp = x1 + (t_2 + (x1 + (t_7 + ((t_0 * (((t_4 - 3.0) * t_6) + t_8)) + (t_3 * (2.0 * x2))))))
	elif x1 <= 100000000.0:
		tmp = x1 + ((x1 * (t_5 - 2.0)) + (x2 * -6.0))
	elif x1 <= 1.35e+154:
		tmp = x1 + (t_2 + (x1 + (t_7 + ((t_3 * t_4) + (t_0 * ((t_6 * (-1.0 / x1)) + t_8))))))
	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(Float64(2.0 * x2) - 3.0)
	t_2 = Float64(3.0 * Float64(x2 * -2.0))
	t_3 = Float64(x1 * Float64(x1 * 3.0))
	t_4 = Float64(Float64(Float64(t_3 + Float64(2.0 * x2)) - x1) / t_0)
	t_5 = Float64(4.0 * Float64(x2 * t_1))
	t_6 = Float64(Float64(x1 * 2.0) * 3.0)
	t_7 = Float64(x1 * Float64(x1 * x1))
	t_8 = Float64(Float64(x1 * x1) * Float64(Float64(4.0 * Float64(3.0 - Float64(1.0 / x1))) - 6.0))
	tmp = 0.0
	if (x1 <= -5.6e+102)
		tmp = Float64(x1 + Float64(t_2 + Float64(x1 + Float64(x1 * Float64(Float64(6.0 * t_1) - 4.0)))));
	elseif (x1 <= -225.0)
		tmp = Float64(x1 + Float64(t_2 + Float64(x1 + Float64(t_7 + Float64(Float64(t_0 * Float64(Float64(Float64(t_4 - 3.0) * t_6) + t_8)) + Float64(t_3 * Float64(2.0 * x2)))))));
	elseif (x1 <= 100000000.0)
		tmp = Float64(x1 + Float64(Float64(x1 * Float64(t_5 - 2.0)) + Float64(x2 * -6.0)));
	elseif (x1 <= 1.35e+154)
		tmp = Float64(x1 + Float64(t_2 + Float64(x1 + Float64(t_7 + Float64(Float64(t_3 * t_4) + Float64(t_0 * Float64(Float64(t_6 * Float64(-1.0 / x1)) + t_8)))))));
	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 = (2.0 * x2) - 3.0;
	t_2 = 3.0 * (x2 * -2.0);
	t_3 = x1 * (x1 * 3.0);
	t_4 = ((t_3 + (2.0 * x2)) - x1) / t_0;
	t_5 = 4.0 * (x2 * t_1);
	t_6 = (x1 * 2.0) * 3.0;
	t_7 = x1 * (x1 * x1);
	t_8 = (x1 * x1) * ((4.0 * (3.0 - (1.0 / x1))) - 6.0);
	tmp = 0.0;
	if (x1 <= -5.6e+102)
		tmp = x1 + (t_2 + (x1 + (x1 * ((6.0 * t_1) - 4.0))));
	elseif (x1 <= -225.0)
		tmp = x1 + (t_2 + (x1 + (t_7 + ((t_0 * (((t_4 - 3.0) * t_6) + t_8)) + (t_3 * (2.0 * x2))))));
	elseif (x1 <= 100000000.0)
		tmp = x1 + ((x1 * (t_5 - 2.0)) + (x2 * -6.0));
	elseif (x1 <= 1.35e+154)
		tmp = x1 + (t_2 + (x1 + (t_7 + ((t_3 * t_4) + (t_0 * ((t_6 * (-1.0 / x1)) + t_8))))));
	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[(N[(2.0 * x2), $MachinePrecision] - 3.0), $MachinePrecision]}, Block[{t$95$2 = N[(3.0 * N[(x2 * -2.0), $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$0), $MachinePrecision]}, Block[{t$95$5 = N[(4.0 * N[(x2 * t$95$1), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$6 = N[(N[(x1 * 2.0), $MachinePrecision] * 3.0), $MachinePrecision]}, Block[{t$95$7 = N[(x1 * N[(x1 * x1), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$8 = N[(N[(x1 * x1), $MachinePrecision] * N[(N[(4.0 * N[(3.0 - N[(1.0 / x1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - 6.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x1, -5.6e+102], N[(x1 + N[(t$95$2 + N[(x1 + N[(x1 * N[(N[(6.0 * t$95$1), $MachinePrecision] - 4.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, -225.0], N[(x1 + N[(t$95$2 + N[(x1 + N[(t$95$7 + N[(N[(t$95$0 * N[(N[(N[(t$95$4 - 3.0), $MachinePrecision] * t$95$6), $MachinePrecision] + t$95$8), $MachinePrecision]), $MachinePrecision] + N[(t$95$3 * N[(2.0 * x2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, 100000000.0], N[(x1 + N[(N[(x1 * N[(t$95$5 - 2.0), $MachinePrecision]), $MachinePrecision] + N[(x2 * -6.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x1, 1.35e+154], N[(x1 + N[(t$95$2 + N[(x1 + N[(t$95$7 + N[(N[(t$95$3 * t$95$4), $MachinePrecision] + N[(t$95$0 * N[(N[(t$95$6 * N[(-1.0 / x1), $MachinePrecision]), $MachinePrecision] + t$95$8), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x1 + N[(x1 * N[(1.0 + t$95$5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]]]]]]]]]

Derivation

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

Alternative 18: 57.6% accurate, 5.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := 2 \cdot x2 - 3\\ \mathbf{if}\;x1 \leq -1.05 \cdot 10^{+94}:\\ \;\;\;\;x1 + \left(3 \cdot \left(x2 \cdot -2\right) + \left(x1 + x1 \cdot \left(6 \cdot t_0 - 4\right)\right)\right)\\ \mathbf{else}:\\ \;\;\;\;x1 + \left(x1 \cdot \left(4 \cdot \left(x2 \cdot t_0\right) - 2\right) + x2 \cdot -6\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x1 x2)
 :precision binary64
 (let* ((t_0 (- (* 2.0 x2) 3.0)))
   (if (<= x1 -1.05e+94)
     (+ x1 (+ (* 3.0 (* x2 -2.0)) (+ x1 (* x1 (- (* 6.0 t_0) 4.0)))))
     (+ x1 (+ (* x1 (- (* 4.0 (* x2 t_0)) 2.0)) (* x2 -6.0))))))

C

double code(double x1, double x2) {
	double t_0 = (2.0 * x2) - 3.0;
	double tmp;
	if (x1 <= -1.05e+94) {
		tmp = x1 + ((3.0 * (x2 * -2.0)) + (x1 + (x1 * ((6.0 * t_0) - 4.0))));
	} else {
		tmp = x1 + ((x1 * ((4.0 * (x2 * t_0)) - 2.0)) + (x2 * -6.0));
	}
	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 = (2.0d0 * x2) - 3.0d0
    if (x1 <= (-1.05d+94)) then
        tmp = x1 + ((3.0d0 * (x2 * (-2.0d0))) + (x1 + (x1 * ((6.0d0 * t_0) - 4.0d0))))
    else
        tmp = x1 + ((x1 * ((4.0d0 * (x2 * t_0)) - 2.0d0)) + (x2 * (-6.0d0)))
    end if
    code = tmp
end function

Java

public static double code(double x1, double x2) {
	double t_0 = (2.0 * x2) - 3.0;
	double tmp;
	if (x1 <= -1.05e+94) {
		tmp = x1 + ((3.0 * (x2 * -2.0)) + (x1 + (x1 * ((6.0 * t_0) - 4.0))));
	} else {
		tmp = x1 + ((x1 * ((4.0 * (x2 * t_0)) - 2.0)) + (x2 * -6.0));
	}
	return tmp;
}

Python

def code(x1, x2):
	t_0 = (2.0 * x2) - 3.0
	tmp = 0
	if x1 <= -1.05e+94:
		tmp = x1 + ((3.0 * (x2 * -2.0)) + (x1 + (x1 * ((6.0 * t_0) - 4.0))))
	else:
		tmp = x1 + ((x1 * ((4.0 * (x2 * t_0)) - 2.0)) + (x2 * -6.0))
	return tmp

Julia

function code(x1, x2)
	t_0 = Float64(Float64(2.0 * x2) - 3.0)
	tmp = 0.0
	if (x1 <= -1.05e+94)
		tmp = Float64(x1 + Float64(Float64(3.0 * Float64(x2 * -2.0)) + Float64(x1 + Float64(x1 * Float64(Float64(6.0 * t_0) - 4.0)))));
	else
		tmp = Float64(x1 + Float64(Float64(x1 * Float64(Float64(4.0 * Float64(x2 * t_0)) - 2.0)) + Float64(x2 * -6.0)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x1, x2)
	t_0 = (2.0 * x2) - 3.0;
	tmp = 0.0;
	if (x1 <= -1.05e+94)
		tmp = x1 + ((3.0 * (x2 * -2.0)) + (x1 + (x1 * ((6.0 * t_0) - 4.0))));
	else
		tmp = x1 + ((x1 * ((4.0 * (x2 * t_0)) - 2.0)) + (x2 * -6.0));
	end
	tmp_2 = tmp;
end

Wolfram

code[x1_, x2_] := Block[{t$95$0 = N[(N[(2.0 * x2), $MachinePrecision] - 3.0), $MachinePrecision]}, If[LessEqual[x1, -1.05e+94], N[(x1 + N[(N[(3.0 * N[(x2 * -2.0), $MachinePrecision]), $MachinePrecision] + N[(x1 + N[(x1 * N[(N[(6.0 * t$95$0), $MachinePrecision] - 4.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x1 + N[(N[(x1 * N[(N[(4.0 * N[(x2 * t$95$0), $MachinePrecision]), $MachinePrecision] - 2.0), $MachinePrecision]), $MachinePrecision] + N[(x2 * -6.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

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

Alternative 19: 57.6% accurate, 6.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x1 \leq -2.35 \cdot 10^{+45}:\\ \;\;\;\;x1 + \left(x1 \cdot \left(x2 \cdot -12 - 2\right) + x2 \cdot -6\right)\\ \mathbf{else}:\\ \;\;\;\;x1 + \left(x1 \cdot \left(4 \cdot \left(x2 \cdot \left(2 \cdot x2 - 3\right)\right) - 2\right) + x2 \cdot -6\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x1 x2)
 :precision binary64
 (if (<= x1 -2.35e+45)
   (+ x1 (+ (* x1 (- (* x2 -12.0) 2.0)) (* x2 -6.0)))
   (+ x1 (+ (* x1 (- (* 4.0 (* x2 (- (* 2.0 x2) 3.0))) 2.0)) (* x2 -6.0)))))

C

double code(double x1, double x2) {
	double tmp;
	if (x1 <= -2.35e+45) {
		tmp = x1 + ((x1 * ((x2 * -12.0) - 2.0)) + (x2 * -6.0));
	} else {
		tmp = x1 + ((x1 * ((4.0 * (x2 * ((2.0 * x2) - 3.0))) - 2.0)) + (x2 * -6.0));
	}
	return tmp;
}

Fortran

real(8) function code(x1, x2)
    real(8), intent (in) :: x1
    real(8), intent (in) :: x2
    real(8) :: tmp
    if (x1 <= (-2.35d+45)) then
        tmp = x1 + ((x1 * ((x2 * (-12.0d0)) - 2.0d0)) + (x2 * (-6.0d0)))
    else
        tmp = x1 + ((x1 * ((4.0d0 * (x2 * ((2.0d0 * x2) - 3.0d0))) - 2.0d0)) + (x2 * (-6.0d0)))
    end if
    code = tmp
end function

Java

public static double code(double x1, double x2) {
	double tmp;
	if (x1 <= -2.35e+45) {
		tmp = x1 + ((x1 * ((x2 * -12.0) - 2.0)) + (x2 * -6.0));
	} else {
		tmp = x1 + ((x1 * ((4.0 * (x2 * ((2.0 * x2) - 3.0))) - 2.0)) + (x2 * -6.0));
	}
	return tmp;
}

Python

def code(x1, x2):
	tmp = 0
	if x1 <= -2.35e+45:
		tmp = x1 + ((x1 * ((x2 * -12.0) - 2.0)) + (x2 * -6.0))
	else:
		tmp = x1 + ((x1 * ((4.0 * (x2 * ((2.0 * x2) - 3.0))) - 2.0)) + (x2 * -6.0))
	return tmp

Julia

function code(x1, x2)
	tmp = 0.0
	if (x1 <= -2.35e+45)
		tmp = Float64(x1 + Float64(Float64(x1 * Float64(Float64(x2 * -12.0) - 2.0)) + Float64(x2 * -6.0)));
	else
		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)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x1, x2)
	tmp = 0.0;
	if (x1 <= -2.35e+45)
		tmp = x1 + ((x1 * ((x2 * -12.0) - 2.0)) + (x2 * -6.0));
	else
		tmp = x1 + ((x1 * ((4.0 * (x2 * ((2.0 * x2) - 3.0))) - 2.0)) + (x2 * -6.0));
	end
	tmp_2 = tmp;
end

Wolfram

code[x1_, x2_] := If[LessEqual[x1, -2.35e+45], N[(x1 + N[(N[(x1 * N[(N[(x2 * -12.0), $MachinePrecision] - 2.0), $MachinePrecision]), $MachinePrecision] + N[(x2 * -6.0), $MachinePrecision]), $MachinePrecision]), $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]]

Derivation

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

Alternative 20: 57.6% accurate, 6.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := 2 \cdot x2 - 3\\ \mathbf{if}\;x1 \leq -8.2 \cdot 10^{+94}:\\ \;\;\;\;x1 + \left(3 \cdot \left(x2 \cdot -2\right) + \left(x1 + 6 \cdot \left(x1 \cdot t_0\right)\right)\right)\\ \mathbf{else}:\\ \;\;\;\;x1 + \left(x1 \cdot \left(4 \cdot \left(x2 \cdot t_0\right) - 2\right) + x2 \cdot -6\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x1 x2)
 :precision binary64
 (let* ((t_0 (- (* 2.0 x2) 3.0)))
   (if (<= x1 -8.2e+94)
     (+ x1 (+ (* 3.0 (* x2 -2.0)) (+ x1 (* 6.0 (* x1 t_0)))))
     (+ x1 (+ (* x1 (- (* 4.0 (* x2 t_0)) 2.0)) (* x2 -6.0))))))

C

double code(double x1, double x2) {
	double t_0 = (2.0 * x2) - 3.0;
	double tmp;
	if (x1 <= -8.2e+94) {
		tmp = x1 + ((3.0 * (x2 * -2.0)) + (x1 + (6.0 * (x1 * t_0))));
	} else {
		tmp = x1 + ((x1 * ((4.0 * (x2 * t_0)) - 2.0)) + (x2 * -6.0));
	}
	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 = (2.0d0 * x2) - 3.0d0
    if (x1 <= (-8.2d+94)) then
        tmp = x1 + ((3.0d0 * (x2 * (-2.0d0))) + (x1 + (6.0d0 * (x1 * t_0))))
    else
        tmp = x1 + ((x1 * ((4.0d0 * (x2 * t_0)) - 2.0d0)) + (x2 * (-6.0d0)))
    end if
    code = tmp
end function

Java

public static double code(double x1, double x2) {
	double t_0 = (2.0 * x2) - 3.0;
	double tmp;
	if (x1 <= -8.2e+94) {
		tmp = x1 + ((3.0 * (x2 * -2.0)) + (x1 + (6.0 * (x1 * t_0))));
	} else {
		tmp = x1 + ((x1 * ((4.0 * (x2 * t_0)) - 2.0)) + (x2 * -6.0));
	}
	return tmp;
}

Python

def code(x1, x2):
	t_0 = (2.0 * x2) - 3.0
	tmp = 0
	if x1 <= -8.2e+94:
		tmp = x1 + ((3.0 * (x2 * -2.0)) + (x1 + (6.0 * (x1 * t_0))))
	else:
		tmp = x1 + ((x1 * ((4.0 * (x2 * t_0)) - 2.0)) + (x2 * -6.0))
	return tmp

Julia

function code(x1, x2)
	t_0 = Float64(Float64(2.0 * x2) - 3.0)
	tmp = 0.0
	if (x1 <= -8.2e+94)
		tmp = Float64(x1 + Float64(Float64(3.0 * Float64(x2 * -2.0)) + Float64(x1 + Float64(6.0 * Float64(x1 * t_0)))));
	else
		tmp = Float64(x1 + Float64(Float64(x1 * Float64(Float64(4.0 * Float64(x2 * t_0)) - 2.0)) + Float64(x2 * -6.0)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x1, x2)
	t_0 = (2.0 * x2) - 3.0;
	tmp = 0.0;
	if (x1 <= -8.2e+94)
		tmp = x1 + ((3.0 * (x2 * -2.0)) + (x1 + (6.0 * (x1 * t_0))));
	else
		tmp = x1 + ((x1 * ((4.0 * (x2 * t_0)) - 2.0)) + (x2 * -6.0));
	end
	tmp_2 = tmp;
end

Wolfram

code[x1_, x2_] := Block[{t$95$0 = N[(N[(2.0 * x2), $MachinePrecision] - 3.0), $MachinePrecision]}, If[LessEqual[x1, -8.2e+94], N[(x1 + N[(N[(3.0 * N[(x2 * -2.0), $MachinePrecision]), $MachinePrecision] + N[(x1 + N[(6.0 * N[(x1 * t$95$0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x1 + N[(N[(x1 * N[(N[(4.0 * N[(x2 * t$95$0), $MachinePrecision]), $MachinePrecision] - 2.0), $MachinePrecision]), $MachinePrecision] + N[(x2 * -6.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

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

Alternative 21: 51.2% accurate, 6.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x2 \leq -6.5 \cdot 10^{+203} \lor \neg \left(x2 \leq 10^{+194}\right):\\ \;\;\;\;x1 + x1 \cdot \left(1 + 4 \cdot \left(x2 \cdot \left(2 \cdot x2 - 3\right)\right)\right)\\ \mathbf{else}:\\ \;\;\;\;x1 + \left(x1 \cdot \left(x2 \cdot -12 - 2\right) + x2 \cdot -6\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x1 x2)
 :precision binary64
 (if (or (<= x2 -6.5e+203) (not (<= x2 1e+194)))
   (+ x1 (* x1 (+ 1.0 (* 4.0 (* x2 (- (* 2.0 x2) 3.0))))))
   (+ x1 (+ (* x1 (- (* x2 -12.0) 2.0)) (* x2 -6.0)))))

C

double code(double x1, double x2) {
	double tmp;
	if ((x2 <= -6.5e+203) || !(x2 <= 1e+194)) {
		tmp = x1 + (x1 * (1.0 + (4.0 * (x2 * ((2.0 * x2) - 3.0)))));
	} else {
		tmp = x1 + ((x1 * ((x2 * -12.0) - 2.0)) + (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 ((x2 <= (-6.5d+203)) .or. (.not. (x2 <= 1d+194))) then
        tmp = x1 + (x1 * (1.0d0 + (4.0d0 * (x2 * ((2.0d0 * x2) - 3.0d0)))))
    else
        tmp = x1 + ((x1 * ((x2 * (-12.0d0)) - 2.0d0)) + (x2 * (-6.0d0)))
    end if
    code = tmp
end function

Java

public static double code(double x1, double x2) {
	double tmp;
	if ((x2 <= -6.5e+203) || !(x2 <= 1e+194)) {
		tmp = x1 + (x1 * (1.0 + (4.0 * (x2 * ((2.0 * x2) - 3.0)))));
	} else {
		tmp = x1 + ((x1 * ((x2 * -12.0) - 2.0)) + (x2 * -6.0));
	}
	return tmp;
}

Python

def code(x1, x2):
	tmp = 0
	if (x2 <= -6.5e+203) or not (x2 <= 1e+194):
		tmp = x1 + (x1 * (1.0 + (4.0 * (x2 * ((2.0 * x2) - 3.0)))))
	else:
		tmp = x1 + ((x1 * ((x2 * -12.0) - 2.0)) + (x2 * -6.0))
	return tmp

Julia

function code(x1, x2)
	tmp = 0.0
	if ((x2 <= -6.5e+203) || !(x2 <= 1e+194))
		tmp = Float64(x1 + Float64(x1 * Float64(1.0 + Float64(4.0 * Float64(x2 * Float64(Float64(2.0 * x2) - 3.0))))));
	else
		tmp = Float64(x1 + Float64(Float64(x1 * Float64(Float64(x2 * -12.0) - 2.0)) + Float64(x2 * -6.0)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x1, x2)
	tmp = 0.0;
	if ((x2 <= -6.5e+203) || ~((x2 <= 1e+194)))
		tmp = x1 + (x1 * (1.0 + (4.0 * (x2 * ((2.0 * x2) - 3.0)))));
	else
		tmp = x1 + ((x1 * ((x2 * -12.0) - 2.0)) + (x2 * -6.0));
	end
	tmp_2 = tmp;
end

Wolfram

code[x1_, x2_] := If[Or[LessEqual[x2, -6.5e+203], N[Not[LessEqual[x2, 1e+194]], $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[(x1 + N[(N[(x1 * N[(N[(x2 * -12.0), $MachinePrecision] - 2.0), $MachinePrecision]), $MachinePrecision] + N[(x2 * -6.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 22: 45.1% accurate, 8.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := x1 \cdot \left(1 + x2 \cdot -12\right)\\ \mathbf{if}\;x1 \leq -1.4 \cdot 10^{+86}:\\ \;\;\;\;x1 + t_0\\ \mathbf{elif}\;x1 \leq 8500:\\ \;\;\;\;x2 \cdot -6 - x1\\ \mathbf{else}:\\ \;\;\;\;x1 + \left(t_0 + 9\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x1 x2)
 :precision binary64
 (let* ((t_0 (* x1 (+ 1.0 (* x2 -12.0)))))
   (if (<= x1 -1.4e+86)
     (+ x1 t_0)
     (if (<= x1 8500.0) (- (* x2 -6.0) x1) (+ x1 (+ t_0 9.0))))))

C

double code(double x1, double x2) {
	double t_0 = x1 * (1.0 + (x2 * -12.0));
	double tmp;
	if (x1 <= -1.4e+86) {
		tmp = x1 + t_0;
	} else if (x1 <= 8500.0) {
		tmp = (x2 * -6.0) - x1;
	} else {
		tmp = x1 + (t_0 + 9.0);
	}
	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 + (x2 * (-12.0d0)))
    if (x1 <= (-1.4d+86)) then
        tmp = x1 + t_0
    else if (x1 <= 8500.0d0) then
        tmp = (x2 * (-6.0d0)) - x1
    else
        tmp = x1 + (t_0 + 9.0d0)
    end if
    code = tmp
end function

Java

public static double code(double x1, double x2) {
	double t_0 = x1 * (1.0 + (x2 * -12.0));
	double tmp;
	if (x1 <= -1.4e+86) {
		tmp = x1 + t_0;
	} else if (x1 <= 8500.0) {
		tmp = (x2 * -6.0) - x1;
	} else {
		tmp = x1 + (t_0 + 9.0);
	}
	return tmp;
}

Python

def code(x1, x2):
	t_0 = x1 * (1.0 + (x2 * -12.0))
	tmp = 0
	if x1 <= -1.4e+86:
		tmp = x1 + t_0
	elif x1 <= 8500.0:
		tmp = (x2 * -6.0) - x1
	else:
		tmp = x1 + (t_0 + 9.0)
	return tmp

Julia

function code(x1, x2)
	t_0 = Float64(x1 * Float64(1.0 + Float64(x2 * -12.0)))
	tmp = 0.0
	if (x1 <= -1.4e+86)
		tmp = Float64(x1 + t_0);
	elseif (x1 <= 8500.0)
		tmp = Float64(Float64(x2 * -6.0) - x1);
	else
		tmp = Float64(x1 + Float64(t_0 + 9.0));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x1, x2)
	t_0 = x1 * (1.0 + (x2 * -12.0));
	tmp = 0.0;
	if (x1 <= -1.4e+86)
		tmp = x1 + t_0;
	elseif (x1 <= 8500.0)
		tmp = (x2 * -6.0) - x1;
	else
		tmp = x1 + (t_0 + 9.0);
	end
	tmp_2 = tmp;
end

Wolfram

code[x1_, x2_] := Block[{t$95$0 = N[(x1 * N[(1.0 + N[(x2 * -12.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x1, -1.4e+86], N[(x1 + t$95$0), $MachinePrecision], If[LessEqual[x1, 8500.0], N[(N[(x2 * -6.0), $MachinePrecision] - x1), $MachinePrecision], N[(x1 + N[(t$95$0 + 9.0), $MachinePrecision]), $MachinePrecision]]]]

Derivation

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

Alternative 23: 45.1% accurate, 9.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x1 \leq -3.2 \cdot 10^{+85} \lor \neg \left(x1 \leq 8500\right):\\ \;\;\;\;x1 + x1 \cdot \left(1 + x2 \cdot -12\right)\\ \mathbf{else}:\\ \;\;\;\;x2 \cdot -6 - x1\\ \end{array} \end{array} \]

FPCore

(FPCore (x1 x2)
 :precision binary64
 (if (or (<= x1 -3.2e+85) (not (<= x1 8500.0)))
   (+ x1 (* x1 (+ 1.0 (* x2 -12.0))))
   (- (* x2 -6.0) x1)))

C

double code(double x1, double x2) {
	double tmp;
	if ((x1 <= -3.2e+85) || !(x1 <= 8500.0)) {
		tmp = x1 + (x1 * (1.0 + (x2 * -12.0)));
	} else {
		tmp = (x2 * -6.0) - x1;
	}
	return tmp;
}

Fortran

real(8) function code(x1, x2)
    real(8), intent (in) :: x1
    real(8), intent (in) :: x2
    real(8) :: tmp
    if ((x1 <= (-3.2d+85)) .or. (.not. (x1 <= 8500.0d0))) then
        tmp = x1 + (x1 * (1.0d0 + (x2 * (-12.0d0))))
    else
        tmp = (x2 * (-6.0d0)) - x1
    end if
    code = tmp
end function

Java

public static double code(double x1, double x2) {
	double tmp;
	if ((x1 <= -3.2e+85) || !(x1 <= 8500.0)) {
		tmp = x1 + (x1 * (1.0 + (x2 * -12.0)));
	} else {
		tmp = (x2 * -6.0) - x1;
	}
	return tmp;
}

Python

def code(x1, x2):
	tmp = 0
	if (x1 <= -3.2e+85) or not (x1 <= 8500.0):
		tmp = x1 + (x1 * (1.0 + (x2 * -12.0)))
	else:
		tmp = (x2 * -6.0) - x1
	return tmp

Julia

function code(x1, x2)
	tmp = 0.0
	if ((x1 <= -3.2e+85) || !(x1 <= 8500.0))
		tmp = Float64(x1 + Float64(x1 * Float64(1.0 + Float64(x2 * -12.0))));
	else
		tmp = Float64(Float64(x2 * -6.0) - x1);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x1, x2)
	tmp = 0.0;
	if ((x1 <= -3.2e+85) || ~((x1 <= 8500.0)))
		tmp = x1 + (x1 * (1.0 + (x2 * -12.0)));
	else
		tmp = (x2 * -6.0) - x1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x1_, x2_] := If[Or[LessEqual[x1, -3.2e+85], N[Not[LessEqual[x1, 8500.0]], $MachinePrecision]], N[(x1 + N[(x1 * N[(1.0 + N[(x2 * -12.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(x2 * -6.0), $MachinePrecision] - x1), $MachinePrecision]]

Derivation

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

Alternative 24: 45.3% accurate, 9.8× speedup

Math

\[\begin{array}{l} \\ x1 + \left(x1 \cdot \left(x2 \cdot -12 - 2\right) + x2 \cdot -6\right) \end{array} \]

FPCore

(FPCore (x1 x2)
 :precision binary64
 (+ x1 (+ (* x1 (- (* x2 -12.0) 2.0)) (* x2 -6.0))))

C

double code(double x1, double x2) {
	return x1 + ((x1 * ((x2 * -12.0) - 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 * ((x2 * (-12.0d0)) - 2.0d0)) + (x2 * (-6.0d0)))
end function

Java

public static double code(double x1, double x2) {
	return x1 + ((x1 * ((x2 * -12.0) - 2.0)) + (x2 * -6.0));
}

Python

def code(x1, x2):
	return x1 + ((x1 * ((x2 * -12.0) - 2.0)) + (x2 * -6.0))

Julia

function code(x1, x2)
	return Float64(x1 + Float64(Float64(x1 * Float64(Float64(x2 * -12.0) - 2.0)) + Float64(x2 * -6.0)))
end

MATLAB

function tmp = code(x1, x2)
	tmp = x1 + ((x1 * ((x2 * -12.0) - 2.0)) + (x2 * -6.0));
end

Wolfram

code[x1_, x2_] := N[(x1 + N[(N[(x1 * N[(N[(x2 * -12.0), $MachinePrecision] - 2.0), $MachinePrecision]), $MachinePrecision] + N[(x2 * -6.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

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

Alternative 25: 32.4% accurate, 13.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x2 \leq -1.45 \cdot 10^{-156} \lor \neg \left(x2 \leq 5.8 \cdot 10^{-200}\right):\\ \;\;\;\;x1 + x2 \cdot -6\\ \mathbf{else}:\\ \;\;\;\;-x1\\ \end{array} \end{array} \]

FPCore

(FPCore (x1 x2)
 :precision binary64
 (if (or (<= x2 -1.45e-156) (not (<= x2 5.8e-200))) (+ x1 (* x2 -6.0)) (- x1)))

C

double code(double x1, double x2) {
	double tmp;
	if ((x2 <= -1.45e-156) || !(x2 <= 5.8e-200)) {
		tmp = x1 + (x2 * -6.0);
	} else {
		tmp = -x1;
	}
	return tmp;
}

Fortran

real(8) function code(x1, x2)
    real(8), intent (in) :: x1
    real(8), intent (in) :: x2
    real(8) :: tmp
    if ((x2 <= (-1.45d-156)) .or. (.not. (x2 <= 5.8d-200))) then
        tmp = x1 + (x2 * (-6.0d0))
    else
        tmp = -x1
    end if
    code = tmp
end function

Java

public static double code(double x1, double x2) {
	double tmp;
	if ((x2 <= -1.45e-156) || !(x2 <= 5.8e-200)) {
		tmp = x1 + (x2 * -6.0);
	} else {
		tmp = -x1;
	}
	return tmp;
}

Python

def code(x1, x2):
	tmp = 0
	if (x2 <= -1.45e-156) or not (x2 <= 5.8e-200):
		tmp = x1 + (x2 * -6.0)
	else:
		tmp = -x1
	return tmp

Julia

function code(x1, x2)
	tmp = 0.0
	if ((x2 <= -1.45e-156) || !(x2 <= 5.8e-200))
		tmp = Float64(x1 + Float64(x2 * -6.0));
	else
		tmp = Float64(-x1);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x1, x2)
	tmp = 0.0;
	if ((x2 <= -1.45e-156) || ~((x2 <= 5.8e-200)))
		tmp = x1 + (x2 * -6.0);
	else
		tmp = -x1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x1_, x2_] := If[Or[LessEqual[x2, -1.45e-156], N[Not[LessEqual[x2, 5.8e-200]], $MachinePrecision]], N[(x1 + N[(x2 * -6.0), $MachinePrecision]), $MachinePrecision], (-x1)]

Derivation

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

Alternative 26: 39.1% accurate, 25.4× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

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

Alternative 27: 14.4% accurate, 63.5× 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 Float64(-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 2023350 
(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))))))