x / (x^2 + 1)

Percentage Accurate: 76.5% → 100.0%

Time: 4.8s

Alternatives: 5

Speedup: 1.1×

Specification

Math

\[\begin{array}{l} \\ \frac{x}{x \cdot x + 1} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (/ x (+ (* x x) 1.0)))

C

double code(double x) {
	return x / ((x * x) + 1.0);
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = x / ((x * x) + 1.0d0)
end function

Java

public static double code(double x) {
	return x / ((x * x) + 1.0);
}

Python

def code(x):
	return x / ((x * x) + 1.0)

Julia

function code(x)
	return Float64(x / Float64(Float64(x * x) + 1.0))
end

MATLAB

function tmp = code(x)
	tmp = x / ((x * x) + 1.0);
end

Wolfram

code[x_] := N[(x / N[(N[(x * x), $MachinePrecision] + 1.0), $MachinePrecision]), $MachinePrecision]

Initial Program: 76.5% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{x}{x \cdot x + 1} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (/ x (+ (* x x) 1.0)))

C

double code(double x) {
	return x / ((x * x) + 1.0);
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = x / ((x * x) + 1.0d0)
end function

Java

public static double code(double x) {
	return x / ((x * x) + 1.0);
}

Python

def code(x):
	return x / ((x * x) + 1.0)

Julia

function code(x)
	return Float64(x / Float64(Float64(x * x) + 1.0))
end

MATLAB

function tmp = code(x)
	tmp = x / ((x * x) + 1.0);
end

Wolfram

code[x_] := N[(x / N[(N[(x * x), $MachinePrecision] + 1.0), $MachinePrecision]), $MachinePrecision]

Alternative 1: 100.0% accurate, 0.8× speedup

Math

\[\begin{array}{l} x\_m = \left|x\right| \\ x\_s = \mathsf{copysign}\left(1, x\right) \\ x\_s \cdot \begin{array}{l} \mathbf{if}\;x\_m \leq 50000000:\\ \;\;\;\;\frac{x\_m}{\mathsf{fma}\left(x\_m, x\_m, 1\right)}\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{x\_m}\\ \end{array} \end{array} \]

FPCore

x\_m = (fabs.f64 x)
x\_s = (copysign.f64 #s(literal 1 binary64) x)
(FPCore (x_s x_m)
 :precision binary64
 (* x_s (if (<= x_m 50000000.0) (/ x_m (fma x_m x_m 1.0)) (/ 1.0 x_m))))

C

x\_m = fabs(x);
x\_s = copysign(1.0, x);
double code(double x_s, double x_m) {
	double tmp;
	if (x_m <= 50000000.0) {
		tmp = x_m / fma(x_m, x_m, 1.0);
	} else {
		tmp = 1.0 / x_m;
	}
	return x_s * tmp;
}

Julia

x\_m = abs(x)
x\_s = copysign(1.0, x)
function code(x_s, x_m)
	tmp = 0.0
	if (x_m <= 50000000.0)
		tmp = Float64(x_m / fma(x_m, x_m, 1.0));
	else
		tmp = Float64(1.0 / x_m);
	end
	return Float64(x_s * tmp)
end

Wolfram

x\_m = N[Abs[x], $MachinePrecision]
x\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[x]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
code[x$95$s_, x$95$m_] := N[(x$95$s * If[LessEqual[x$95$m, 50000000.0], N[(x$95$m / N[(x$95$m * x$95$m + 1.0), $MachinePrecision]), $MachinePrecision], N[(1.0 / x$95$m), $MachinePrecision]]), $MachinePrecision]

Derivation

1. Split input into 2 regimes

2. if x < 5e7

   1. Initial program 85.4%

      \[\frac{x}{x \cdot x + 1} \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift-+.f64 N/A

         \[\leadsto \frac{x}{\color{blue}{x \cdot x + 1}} \]

      2. lift-*.f64 N/A

         \[\leadsto \frac{x}{\color{blue}{x \cdot x} + 1} \]

      3. lower-fma.f64 85.4

         \[\leadsto \frac{x}{\color{blue}{\mathsf{fma}\left(x, x, 1\right)}} \]

   4. Applied rewrites 85.4%

      \[\leadsto \frac{x}{\color{blue}{\mathsf{fma}\left(x, x, 1\right)}} \]

   if 5e7 < x

   1. Initial program 60.6%

      \[\frac{x}{x \cdot x + 1} \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

      \[\leadsto \color{blue}{\frac{1}{x}} \]
   4. Step-by-step derivation

      1. lower-/.f64 100.0

         \[\leadsto \color{blue}{\frac{1}{x}} \]

   5. Applied rewrites 100.0%

      \[\leadsto \color{blue}{\frac{1}{x}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 2: 99.3% accurate, 1.0× speedup

Math

\[\begin{array}{l} x\_m = \left|x\right| \\ x\_s = \mathsf{copysign}\left(1, x\right) \\ x\_s \cdot \begin{array}{l} \mathbf{if}\;x\_m \leq 0.86:\\ \;\;\;\;\left(-x\_m\right) \cdot \mathsf{fma}\left(x\_m, x\_m, -1\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{x\_m}\\ \end{array} \end{array} \]

FPCore

x\_m = (fabs.f64 x)
x\_s = (copysign.f64 #s(literal 1 binary64) x)
(FPCore (x_s x_m)
 :precision binary64
 (* x_s (if (<= x_m 0.86) (* (- x_m) (fma x_m x_m -1.0)) (/ 1.0 x_m))))

C

x\_m = fabs(x);
x\_s = copysign(1.0, x);
double code(double x_s, double x_m) {
	double tmp;
	if (x_m <= 0.86) {
		tmp = -x_m * fma(x_m, x_m, -1.0);
	} else {
		tmp = 1.0 / x_m;
	}
	return x_s * tmp;
}

Julia

x\_m = abs(x)
x\_s = copysign(1.0, x)
function code(x_s, x_m)
	tmp = 0.0
	if (x_m <= 0.86)
		tmp = Float64(Float64(-x_m) * fma(x_m, x_m, -1.0));
	else
		tmp = Float64(1.0 / x_m);
	end
	return Float64(x_s * tmp)
end

Wolfram

x\_m = N[Abs[x], $MachinePrecision]
x\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[x]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
code[x$95$s_, x$95$m_] := N[(x$95$s * If[LessEqual[x$95$m, 0.86], N[((-x$95$m) * N[(x$95$m * x$95$m + -1.0), $MachinePrecision]), $MachinePrecision], N[(1.0 / x$95$m), $MachinePrecision]]), $MachinePrecision]

Derivation

1. Split input into 2 regimes

2. if x < 0.859999999999999987

   1. Initial program 85.2%

      \[\frac{x}{x \cdot x + 1} \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \color{blue}{\frac{x}{x \cdot x + 1}} \]

      2. clear-num N/A

         \[\leadsto \color{blue}{\frac{1}{\frac{x \cdot x + 1}{x}}} \]

      3. frac-2neg N/A

         \[\leadsto \frac{1}{\color{blue}{\frac{\mathsf{neg}\left(\left(x \cdot x + 1\right)\right)}{\mathsf{neg}\left(x\right)}}} \]

      4. associate-/r/ N/A

         \[\leadsto \color{blue}{\frac{1}{\mathsf{neg}\left(\left(x \cdot x + 1\right)\right)} \cdot \left(\mathsf{neg}\left(x\right)\right)} \]

      5. lower-*.f64 N/A

         \[\leadsto \color{blue}{\frac{1}{\mathsf{neg}\left(\left(x \cdot x + 1\right)\right)} \cdot \left(\mathsf{neg}\left(x\right)\right)} \]

      6. metadata-eval N/A

         \[\leadsto \frac{\color{blue}{\mathsf{neg}\left(-1\right)}}{\mathsf{neg}\left(\left(x \cdot x + 1\right)\right)} \cdot \left(\mathsf{neg}\left(x\right)\right) \]

      7. frac-2neg N/A

         \[\leadsto \color{blue}{\frac{-1}{x \cdot x + 1}} \cdot \left(\mathsf{neg}\left(x\right)\right) \]

      8. lower-/.f64 N/A

         \[\leadsto \color{blue}{\frac{-1}{x \cdot x + 1}} \cdot \left(\mathsf{neg}\left(x\right)\right) \]

      9. lift-+.f64 N/A

         \[\leadsto \frac{-1}{\color{blue}{x \cdot x + 1}} \cdot \left(\mathsf{neg}\left(x\right)\right) \]

      10. lift-*.f64 N/A

          \[\leadsto \frac{-1}{\color{blue}{x \cdot x} + 1} \cdot \left(\mathsf{neg}\left(x\right)\right) \]

      11. lower-fma.f64 N/A

          \[\leadsto \frac{-1}{\color{blue}{\mathsf{fma}\left(x, x, 1\right)}} \cdot \left(\mathsf{neg}\left(x\right)\right) \]

      12. lower-neg.f64 85.1

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

   4. Applied rewrites 85.1%

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

   5. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\left({x}^{2} - 1\right)} \cdot \left(-x\right) \]
   6. Step-by-step derivation

      1. sub-neg N/A

         \[\leadsto \color{blue}{\left({x}^{2} + \left(\mathsf{neg}\left(1\right)\right)\right)} \cdot \left(-x\right) \]

      2. unpow2 N/A

         \[\leadsto \left(\color{blue}{x \cdot x} + \left(\mathsf{neg}\left(1\right)\right)\right) \cdot \left(-x\right) \]

      3. metadata-eval N/A

         \[\leadsto \left(x \cdot x + \color{blue}{-1}\right) \cdot \left(-x\right) \]

      4. lower-fma.f64 66.6

         \[\leadsto \color{blue}{\mathsf{fma}\left(x, x, -1\right)} \cdot \left(-x\right) \]

   7. Applied rewrites 66.6%

      \[\leadsto \color{blue}{\mathsf{fma}\left(x, x, -1\right)} \cdot \left(-x\right) \]

   if 0.859999999999999987 < x

   1. Initial program 63.2%

      \[\frac{x}{x \cdot x + 1} \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

      \[\leadsto \color{blue}{\frac{1}{x}} \]
   4. Step-by-step derivation

      1. lower-/.f64 96.7

         \[\leadsto \color{blue}{\frac{1}{x}} \]

   5. Applied rewrites 96.7%

      \[\leadsto \color{blue}{\frac{1}{x}} \]
3. Recombined 2 regimes into one program.

4. Final simplification 73.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq 0.86:\\ \;\;\;\;\left(-x\right) \cdot \mathsf{fma}\left(x, x, -1\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{x}\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 99.0% accurate, 1.1× speedup

Math

\[\begin{array}{l} x\_m = \left|x\right| \\ x\_s = \mathsf{copysign}\left(1, x\right) \\ x\_s \cdot \begin{array}{l} \mathbf{if}\;x\_m \leq 1:\\ \;\;\;\;-1 \cdot \left(-x\_m\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{x\_m}\\ \end{array} \end{array} \]

FPCore

x\_m = (fabs.f64 x)
x\_s = (copysign.f64 #s(literal 1 binary64) x)
(FPCore (x_s x_m)
 :precision binary64
 (* x_s (if (<= x_m 1.0) (* -1.0 (- x_m)) (/ 1.0 x_m))))

C

x\_m = fabs(x);
x\_s = copysign(1.0, x);
double code(double x_s, double x_m) {
	double tmp;
	if (x_m <= 1.0) {
		tmp = -1.0 * -x_m;
	} else {
		tmp = 1.0 / x_m;
	}
	return x_s * tmp;
}

Fortran

x\_m = abs(x)
x\_s = copysign(1.0d0, x)
real(8) function code(x_s, x_m)
    real(8), intent (in) :: x_s
    real(8), intent (in) :: x_m
    real(8) :: tmp
    if (x_m <= 1.0d0) then
        tmp = (-1.0d0) * -x_m
    else
        tmp = 1.0d0 / x_m
    end if
    code = x_s * tmp
end function

Java

x\_m = Math.abs(x);
x\_s = Math.copySign(1.0, x);
public static double code(double x_s, double x_m) {
	double tmp;
	if (x_m <= 1.0) {
		tmp = -1.0 * -x_m;
	} else {
		tmp = 1.0 / x_m;
	}
	return x_s * tmp;
}

Python

x\_m = math.fabs(x)
x\_s = math.copysign(1.0, x)
def code(x_s, x_m):
	tmp = 0
	if x_m <= 1.0:
		tmp = -1.0 * -x_m
	else:
		tmp = 1.0 / x_m
	return x_s * tmp

Julia

x\_m = abs(x)
x\_s = copysign(1.0, x)
function code(x_s, x_m)
	tmp = 0.0
	if (x_m <= 1.0)
		tmp = Float64(-1.0 * Float64(-x_m));
	else
		tmp = Float64(1.0 / x_m);
	end
	return Float64(x_s * tmp)
end

MATLAB

x\_m = abs(x);
x\_s = sign(x) * abs(1.0);
function tmp_2 = code(x_s, x_m)
	tmp = 0.0;
	if (x_m <= 1.0)
		tmp = -1.0 * -x_m;
	else
		tmp = 1.0 / x_m;
	end
	tmp_2 = x_s * tmp;
end

Wolfram

x\_m = N[Abs[x], $MachinePrecision]
x\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[x]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
code[x$95$s_, x$95$m_] := N[(x$95$s * If[LessEqual[x$95$m, 1.0], N[(-1.0 * (-x$95$m)), $MachinePrecision], N[(1.0 / x$95$m), $MachinePrecision]]), $MachinePrecision]

Derivation

1. Split input into 2 regimes

2. if x < 1

   1. Initial program 85.2%

      \[\frac{x}{x \cdot x + 1} \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \color{blue}{\frac{x}{x \cdot x + 1}} \]

      2. clear-num N/A

         \[\leadsto \color{blue}{\frac{1}{\frac{x \cdot x + 1}{x}}} \]

      3. frac-2neg N/A

         \[\leadsto \frac{1}{\color{blue}{\frac{\mathsf{neg}\left(\left(x \cdot x + 1\right)\right)}{\mathsf{neg}\left(x\right)}}} \]

      4. associate-/r/ N/A

         \[\leadsto \color{blue}{\frac{1}{\mathsf{neg}\left(\left(x \cdot x + 1\right)\right)} \cdot \left(\mathsf{neg}\left(x\right)\right)} \]

      5. lower-*.f64 N/A

         \[\leadsto \color{blue}{\frac{1}{\mathsf{neg}\left(\left(x \cdot x + 1\right)\right)} \cdot \left(\mathsf{neg}\left(x\right)\right)} \]

      6. metadata-eval N/A

         \[\leadsto \frac{\color{blue}{\mathsf{neg}\left(-1\right)}}{\mathsf{neg}\left(\left(x \cdot x + 1\right)\right)} \cdot \left(\mathsf{neg}\left(x\right)\right) \]

      7. frac-2neg N/A

         \[\leadsto \color{blue}{\frac{-1}{x \cdot x + 1}} \cdot \left(\mathsf{neg}\left(x\right)\right) \]

      8. lower-/.f64 N/A

         \[\leadsto \color{blue}{\frac{-1}{x \cdot x + 1}} \cdot \left(\mathsf{neg}\left(x\right)\right) \]

      9. lift-+.f64 N/A

         \[\leadsto \frac{-1}{\color{blue}{x \cdot x + 1}} \cdot \left(\mathsf{neg}\left(x\right)\right) \]

      10. lift-*.f64 N/A

          \[\leadsto \frac{-1}{\color{blue}{x \cdot x} + 1} \cdot \left(\mathsf{neg}\left(x\right)\right) \]

      11. lower-fma.f64 N/A

          \[\leadsto \frac{-1}{\color{blue}{\mathsf{fma}\left(x, x, 1\right)}} \cdot \left(\mathsf{neg}\left(x\right)\right) \]

      12. lower-neg.f64 85.1

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

   4. Applied rewrites 85.1%

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

   5. Taylor expanded in x around 0

      \[\leadsto \color{blue}{-1} \cdot \left(-x\right) \]
   6. Step-by-step derivation

   7. Applied rewrites 67.2%

      \[\leadsto \color{blue}{-1} \cdot \left(-x\right) \]

   if 1 < x

   1. Initial program 63.2%

      \[\frac{x}{x \cdot x + 1} \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

      \[\leadsto \color{blue}{\frac{1}{x}} \]
   4. Step-by-step derivation

      1. lower-/.f64 96.7

         \[\leadsto \color{blue}{\frac{1}{x}} \]

   5. Applied rewrites 96.7%

      \[\leadsto \color{blue}{\frac{1}{x}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 4: 51.4% accurate, 2.5× speedup

Math

\[\begin{array}{l} x\_m = \left|x\right| \\ x\_s = \mathsf{copysign}\left(1, x\right) \\ x\_s \cdot \left(-1 \cdot \left(-x\_m\right)\right) \end{array} \]

FPCore

x\_m = (fabs.f64 x)
x\_s = (copysign.f64 #s(literal 1 binary64) x)
(FPCore (x_s x_m) :precision binary64 (* x_s (* -1.0 (- x_m))))

C

x\_m = fabs(x);
x\_s = copysign(1.0, x);
double code(double x_s, double x_m) {
	return x_s * (-1.0 * -x_m);
}

Fortran

x\_m = abs(x)
x\_s = copysign(1.0d0, x)
real(8) function code(x_s, x_m)
    real(8), intent (in) :: x_s
    real(8), intent (in) :: x_m
    code = x_s * ((-1.0d0) * -x_m)
end function

Java

x\_m = Math.abs(x);
x\_s = Math.copySign(1.0, x);
public static double code(double x_s, double x_m) {
	return x_s * (-1.0 * -x_m);
}

Python

x\_m = math.fabs(x)
x\_s = math.copysign(1.0, x)
def code(x_s, x_m):
	return x_s * (-1.0 * -x_m)

Julia

x\_m = abs(x)
x\_s = copysign(1.0, x)
function code(x_s, x_m)
	return Float64(x_s * Float64(-1.0 * Float64(-x_m)))
end

MATLAB

x\_m = abs(x);
x\_s = sign(x) * abs(1.0);
function tmp = code(x_s, x_m)
	tmp = x_s * (-1.0 * -x_m);
end

Wolfram

x\_m = N[Abs[x], $MachinePrecision]
x\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[x]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
code[x$95$s_, x$95$m_] := N[(x$95$s * N[(-1.0 * (-x$95$m)), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 79.9%

   \[\frac{x}{x \cdot x + 1} \]
2. Add Preprocessing
3. Step-by-step derivation

   1. lift-/.f64 N/A

      \[\leadsto \color{blue}{\frac{x}{x \cdot x + 1}} \]

   2. clear-num N/A

      \[\leadsto \color{blue}{\frac{1}{\frac{x \cdot x + 1}{x}}} \]

   3. frac-2neg N/A

      \[\leadsto \frac{1}{\color{blue}{\frac{\mathsf{neg}\left(\left(x \cdot x + 1\right)\right)}{\mathsf{neg}\left(x\right)}}} \]

   4. associate-/r/ N/A

      \[\leadsto \color{blue}{\frac{1}{\mathsf{neg}\left(\left(x \cdot x + 1\right)\right)} \cdot \left(\mathsf{neg}\left(x\right)\right)} \]

   5. lower-*.f64 N/A

      \[\leadsto \color{blue}{\frac{1}{\mathsf{neg}\left(\left(x \cdot x + 1\right)\right)} \cdot \left(\mathsf{neg}\left(x\right)\right)} \]

   6. metadata-eval N/A

      \[\leadsto \frac{\color{blue}{\mathsf{neg}\left(-1\right)}}{\mathsf{neg}\left(\left(x \cdot x + 1\right)\right)} \cdot \left(\mathsf{neg}\left(x\right)\right) \]

   7. frac-2neg N/A

      \[\leadsto \color{blue}{\frac{-1}{x \cdot x + 1}} \cdot \left(\mathsf{neg}\left(x\right)\right) \]

   8. lower-/.f64 N/A

      \[\leadsto \color{blue}{\frac{-1}{x \cdot x + 1}} \cdot \left(\mathsf{neg}\left(x\right)\right) \]

   9. lift-+.f64 N/A

      \[\leadsto \frac{-1}{\color{blue}{x \cdot x + 1}} \cdot \left(\mathsf{neg}\left(x\right)\right) \]

   10. lift-*.f64 N/A

       \[\leadsto \frac{-1}{\color{blue}{x \cdot x} + 1} \cdot \left(\mathsf{neg}\left(x\right)\right) \]

   11. lower-fma.f64 N/A

       \[\leadsto \frac{-1}{\color{blue}{\mathsf{fma}\left(x, x, 1\right)}} \cdot \left(\mathsf{neg}\left(x\right)\right) \]

   12. lower-neg.f64 79.9

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

4. Applied rewrites 79.9%

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

5. Taylor expanded in x around 0

   \[\leadsto \color{blue}{-1} \cdot \left(-x\right) \]
6. Step-by-step derivation

7. Applied rewrites 52.3%

   \[\leadsto \color{blue}{-1} \cdot \left(-x\right) \]
8. Add Preprocessing

Alternative 5: 2.7% accurate, 6.7× speedup

Math

\[\begin{array}{l} x\_m = \left|x\right| \\ x\_s = \mathsf{copysign}\left(1, x\right) \\ x\_s \cdot \left(-x\_m\right) \end{array} \]

FPCore

x\_m = (fabs.f64 x)
x\_s = (copysign.f64 #s(literal 1 binary64) x)
(FPCore (x_s x_m) :precision binary64 (* x_s (- x_m)))

C

x\_m = fabs(x);
x\_s = copysign(1.0, x);
double code(double x_s, double x_m) {
	return x_s * -x_m;
}

Fortran

x\_m = abs(x)
x\_s = copysign(1.0d0, x)
real(8) function code(x_s, x_m)
    real(8), intent (in) :: x_s
    real(8), intent (in) :: x_m
    code = x_s * -x_m
end function

Java

x\_m = Math.abs(x);
x\_s = Math.copySign(1.0, x);
public static double code(double x_s, double x_m) {
	return x_s * -x_m;
}

Python

x\_m = math.fabs(x)
x\_s = math.copysign(1.0, x)
def code(x_s, x_m):
	return x_s * -x_m

Julia

x\_m = abs(x)
x\_s = copysign(1.0, x)
function code(x_s, x_m)
	return Float64(x_s * Float64(-x_m))
end

MATLAB

x\_m = abs(x);
x\_s = sign(x) * abs(1.0);
function tmp = code(x_s, x_m)
	tmp = x_s * -x_m;
end

Wolfram

x\_m = N[Abs[x], $MachinePrecision]
x\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[x]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
code[x$95$s_, x$95$m_] := N[(x$95$s * (-x$95$m)), $MachinePrecision]

Derivation

1. Initial program 79.9%

   \[\frac{x}{x \cdot x + 1} \]
2. Add Preprocessing
3. Step-by-step derivation

   1. lift-/.f64 N/A

      \[\leadsto \color{blue}{\frac{x}{x \cdot x + 1}} \]

   2. clear-num N/A

      \[\leadsto \color{blue}{\frac{1}{\frac{x \cdot x + 1}{x}}} \]

   3. frac-2neg N/A

      \[\leadsto \frac{1}{\color{blue}{\frac{\mathsf{neg}\left(\left(x \cdot x + 1\right)\right)}{\mathsf{neg}\left(x\right)}}} \]

   4. associate-/r/ N/A

      \[\leadsto \color{blue}{\frac{1}{\mathsf{neg}\left(\left(x \cdot x + 1\right)\right)} \cdot \left(\mathsf{neg}\left(x\right)\right)} \]

   5. lower-*.f64 N/A

      \[\leadsto \color{blue}{\frac{1}{\mathsf{neg}\left(\left(x \cdot x + 1\right)\right)} \cdot \left(\mathsf{neg}\left(x\right)\right)} \]

   6. metadata-eval N/A

      \[\leadsto \frac{\color{blue}{\mathsf{neg}\left(-1\right)}}{\mathsf{neg}\left(\left(x \cdot x + 1\right)\right)} \cdot \left(\mathsf{neg}\left(x\right)\right) \]

   7. frac-2neg N/A

      \[\leadsto \color{blue}{\frac{-1}{x \cdot x + 1}} \cdot \left(\mathsf{neg}\left(x\right)\right) \]

   8. lower-/.f64 N/A

      \[\leadsto \color{blue}{\frac{-1}{x \cdot x + 1}} \cdot \left(\mathsf{neg}\left(x\right)\right) \]

   9. lift-+.f64 N/A

      \[\leadsto \frac{-1}{\color{blue}{x \cdot x + 1}} \cdot \left(\mathsf{neg}\left(x\right)\right) \]

   10. lift-*.f64 N/A

       \[\leadsto \frac{-1}{\color{blue}{x \cdot x} + 1} \cdot \left(\mathsf{neg}\left(x\right)\right) \]

   11. lower-fma.f64 N/A

       \[\leadsto \frac{-1}{\color{blue}{\mathsf{fma}\left(x, x, 1\right)}} \cdot \left(\mathsf{neg}\left(x\right)\right) \]

   12. lower-neg.f64 79.9

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

4. Applied rewrites 79.9%

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

5. Taylor expanded in x around 0

   \[\leadsto \color{blue}{-1} \cdot \left(-x\right) \]
6. Step-by-step derivation

7. Applied rewrites 52.3%

   \[\leadsto \color{blue}{-1} \cdot \left(-x\right) \]
8. Step-by-step derivation

   1. lift-neg.f64 N/A

      \[\leadsto -1 \cdot \color{blue}{\left(\mathsf{neg}\left(x\right)\right)} \]

   2. neg-sub0 N/A

      \[\leadsto -1 \cdot \color{blue}{\left(0 - x\right)} \]

   3. flip3-- N/A

      \[\leadsto -1 \cdot \color{blue}{\frac{{0}^{3} - {x}^{3}}{0 \cdot 0 + \left(x \cdot x + 0 \cdot x\right)}} \]

   4. metadata-eval N/A

      \[\leadsto -1 \cdot \frac{\color{blue}{0} - {x}^{3}}{0 \cdot 0 + \left(x \cdot x + 0 \cdot x\right)} \]

9. Applied rewrites 2.9%

   \[\leadsto \color{blue}{-1 \cdot x} \]

10. Taylor expanded in x around 0

    \[\leadsto \color{blue}{-1 \cdot x} \]
11. Step-by-step derivation

    1. mul-1-neg N/A

       \[\leadsto \color{blue}{\mathsf{neg}\left(x\right)} \]

    2. lower-neg.f64 2.9

       \[\leadsto \color{blue}{-x} \]

12. Applied rewrites 2.9%

    \[\leadsto \color{blue}{-x} \]
13. Add Preprocessing

Developer Target 1: 99.8% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \frac{1}{x + \frac{1}{x}} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (/ 1.0 (+ x (/ 1.0 x))))

C

double code(double x) {
	return 1.0 / (x + (1.0 / x));
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = 1.0d0 / (x + (1.0d0 / x))
end function

Java

public static double code(double x) {
	return 1.0 / (x + (1.0 / x));
}

Python

def code(x):
	return 1.0 / (x + (1.0 / x))

Julia

function code(x)
	return Float64(1.0 / Float64(x + Float64(1.0 / x)))
end

MATLAB

function tmp = code(x)
	tmp = 1.0 / (x + (1.0 / x));
end

Wolfram

code[x_] := N[(1.0 / N[(x + N[(1.0 / x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Reproduce

herbie shell --seed 2024271 
(FPCore (x)
  :name "x / (x^2 + 1)"
  :precision binary64

  :alt
  (! :herbie-platform default (/ 1 (+ x (/ 1 x))))

  (/ x (+ (* x x) 1.0)))