3frac (problem 3.3.3)

Percentage Accurate: 69.6% → 99.8%

Time: 10.6s

Alternatives: 7

Speedup: 2.1×

Specification

\[\left|x\right| > 1\]

Math

\[\begin{array}{l} \\ \left(\frac{1}{x + 1} - \frac{2}{x}\right) + \frac{1}{x - 1} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (+ (- (/ 1.0 (+ x 1.0)) (/ 2.0 x)) (/ 1.0 (- x 1.0))))

C

double code(double x) {
	return ((1.0 / (x + 1.0)) - (2.0 / x)) + (1.0 / (x - 1.0));
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = ((1.0d0 / (x + 1.0d0)) - (2.0d0 / x)) + (1.0d0 / (x - 1.0d0))
end function

Java

public static double code(double x) {
	return ((1.0 / (x + 1.0)) - (2.0 / x)) + (1.0 / (x - 1.0));
}

Python

def code(x):
	return ((1.0 / (x + 1.0)) - (2.0 / x)) + (1.0 / (x - 1.0))

Julia

function code(x)
	return Float64(Float64(Float64(1.0 / Float64(x + 1.0)) - Float64(2.0 / x)) + Float64(1.0 / Float64(x - 1.0)))
end

MATLAB

function tmp = code(x)
	tmp = ((1.0 / (x + 1.0)) - (2.0 / x)) + (1.0 / (x - 1.0));
end

Wolfram

code[x_] := N[(N[(N[(1.0 / N[(x + 1.0), $MachinePrecision]), $MachinePrecision] - N[(2.0 / x), $MachinePrecision]), $MachinePrecision] + N[(1.0 / N[(x - 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Initial Program: 69.6% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \left(\frac{1}{x + 1} - \frac{2}{x}\right) + \frac{1}{x - 1} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (+ (- (/ 1.0 (+ x 1.0)) (/ 2.0 x)) (/ 1.0 (- x 1.0))))

C

double code(double x) {
	return ((1.0 / (x + 1.0)) - (2.0 / x)) + (1.0 / (x - 1.0));
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = ((1.0d0 / (x + 1.0d0)) - (2.0d0 / x)) + (1.0d0 / (x - 1.0d0))
end function

Java

public static double code(double x) {
	return ((1.0 / (x + 1.0)) - (2.0 / x)) + (1.0 / (x - 1.0));
}

Python

def code(x):
	return ((1.0 / (x + 1.0)) - (2.0 / x)) + (1.0 / (x - 1.0))

Julia

function code(x)
	return Float64(Float64(Float64(1.0 / Float64(x + 1.0)) - Float64(2.0 / x)) + Float64(1.0 / Float64(x - 1.0)))
end

MATLAB

function tmp = code(x)
	tmp = ((1.0 / (x + 1.0)) - (2.0 / x)) + (1.0 / (x - 1.0));
end

Wolfram

code[x_] := N[(N[(N[(1.0 / N[(x + 1.0), $MachinePrecision]), $MachinePrecision] - N[(2.0 / x), $MachinePrecision]), $MachinePrecision] + N[(1.0 / N[(x - 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Alternative 1: 99.8% accurate, 1.7× speedup

Math

\[\begin{array}{l} \\ \frac{\frac{2}{x \cdot x + -1}}{x} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (/ (/ 2.0 (+ (* x x) -1.0)) x))

C

double code(double x) {
	return (2.0 / ((x * x) + -1.0)) / x;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = (2.0d0 / ((x * x) + (-1.0d0))) / x
end function

Java

public static double code(double x) {
	return (2.0 / ((x * x) + -1.0)) / x;
}

Python

def code(x):
	return (2.0 / ((x * x) + -1.0)) / x

Julia

function code(x)
	return Float64(Float64(2.0 / Float64(Float64(x * x) + -1.0)) / x)
end

MATLAB

function tmp = code(x)
	tmp = (2.0 / ((x * x) + -1.0)) / x;
end

Wolfram

code[x_] := N[(N[(2.0 / N[(N[(x * x), $MachinePrecision] + -1.0), $MachinePrecision]), $MachinePrecision] / x), $MachinePrecision]

Derivation

1. Initial program 73.0%

   \[\left(\frac{1}{x + 1} - \frac{2}{x}\right) + \frac{1}{x - 1} \]
2. Step-by-step derivation

   1. sub-neg N/A

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

   2. associate-+l+ N/A

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

   3. +-lowering-+.f64 N/A

      \[\leadsto \mathsf{+.f64}\left(\left(\frac{1}{x + 1}\right), \color{blue}{\left(\left(\mathsf{neg}\left(\frac{2}{x}\right)\right) + \frac{1}{x - 1}\right)}\right) \]

   4. /-lowering-/.f64 N/A

      \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \left(x + 1\right)\right), \left(\color{blue}{\left(\mathsf{neg}\left(\frac{2}{x}\right)\right)} + \frac{1}{x - 1}\right)\right) \]

   5. +-commutative N/A

      \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \left(1 + x\right)\right), \left(\left(\mathsf{neg}\left(\frac{2}{x}\right)\right) + \frac{1}{x - 1}\right)\right) \]

   6. +-lowering-+.f64 N/A

      \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(1, x\right)\right), \left(\left(\mathsf{neg}\left(\frac{2}{x}\right)\right) + \frac{1}{x - 1}\right)\right) \]

   7. +-commutative N/A

      \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(1, x\right)\right), \left(\frac{1}{x - 1} + \color{blue}{\left(\mathsf{neg}\left(\frac{2}{x}\right)\right)}\right)\right) \]

   8. +-lowering-+.f64 N/A

      \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(1, x\right)\right), \mathsf{+.f64}\left(\left(\frac{1}{x - 1}\right), \color{blue}{\left(\mathsf{neg}\left(\frac{2}{x}\right)\right)}\right)\right) \]

   9. /-lowering-/.f64 N/A

      \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(1, x\right)\right), \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \left(x - 1\right)\right), \left(\mathsf{neg}\left(\color{blue}{\frac{2}{x}}\right)\right)\right)\right) \]

   10. sub-neg N/A

       \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(1, x\right)\right), \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \left(x + \left(\mathsf{neg}\left(1\right)\right)\right)\right), \left(\mathsf{neg}\left(\frac{2}{\color{blue}{x}}\right)\right)\right)\right) \]

   11. +-lowering-+.f64 N/A

       \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(1, x\right)\right), \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(x, \left(\mathsf{neg}\left(1\right)\right)\right)\right), \left(\mathsf{neg}\left(\frac{2}{\color{blue}{x}}\right)\right)\right)\right) \]

   12. metadata-eval N/A

       \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(1, x\right)\right), \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(x, -1\right)\right), \left(\mathsf{neg}\left(\frac{2}{x}\right)\right)\right)\right) \]

   13. distribute-neg-frac N/A

       \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(1, x\right)\right), \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(x, -1\right)\right), \left(\frac{\mathsf{neg}\left(2\right)}{\color{blue}{x}}\right)\right)\right) \]

   14. /-lowering-/.f64 N/A

       \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(1, x\right)\right), \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(x, -1\right)\right), \mathsf{/.f64}\left(\left(\mathsf{neg}\left(2\right)\right), \color{blue}{x}\right)\right)\right) \]

   15. metadata-eval 73.0%

       \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(1, x\right)\right), \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(x, -1\right)\right), \mathsf{/.f64}\left(-2, x\right)\right)\right) \]

3. Simplified 73.0%

   \[\leadsto \color{blue}{\frac{1}{1 + x} + \left(\frac{1}{x + -1} + \frac{-2}{x}\right)} \]
4. Add Preprocessing
5. Step-by-step derivation

   1. associate-+r+ N/A

      \[\leadsto \left(\frac{1}{1 + x} + \frac{1}{x + -1}\right) + \color{blue}{\frac{-2}{x}} \]

   2. frac-add N/A

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

   3. frac-add N/A

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

   4. +-commutative N/A

      \[\leadsto \frac{\left(1 \cdot \left(x + -1\right) + \left(1 + x\right) \cdot 1\right) \cdot x + \left(\left(1 + x\right) \cdot \left(x + -1\right)\right) \cdot -2}{\left(\left(x + 1\right) \cdot \left(x + -1\right)\right) \cdot x} \]

   5. *-lft-identity N/A

      \[\leadsto \frac{\left(1 \cdot \left(x + -1\right) + \left(1 + x\right) \cdot 1\right) \cdot x + \left(\left(1 + x\right) \cdot \left(x + -1\right)\right) \cdot -2}{\left(\left(x + 1\right) \cdot \left(1 \cdot x + -1\right)\right) \cdot x} \]

   6. fma-define N/A

      \[\leadsto \frac{\left(1 \cdot \left(x + -1\right) + \left(1 + x\right) \cdot 1\right) \cdot x + \left(\left(1 + x\right) \cdot \left(x + -1\right)\right) \cdot -2}{\left(\left(x + 1\right) \cdot \mathsf{fma}\left(1, x, -1\right)\right) \cdot x} \]

   7. metadata-eval N/A

      \[\leadsto \frac{\left(1 \cdot \left(x + -1\right) + \left(1 + x\right) \cdot 1\right) \cdot x + \left(\left(1 + x\right) \cdot \left(x + -1\right)\right) \cdot -2}{\left(\left(x + 1\right) \cdot \mathsf{fma}\left(1, x, \mathsf{neg}\left(1\right)\right)\right) \cdot x} \]

   8. fmm-def N/A

      \[\leadsto \frac{\left(1 \cdot \left(x + -1\right) + \left(1 + x\right) \cdot 1\right) \cdot x + \left(\left(1 + x\right) \cdot \left(x + -1\right)\right) \cdot -2}{\left(\left(x + 1\right) \cdot \left(1 \cdot x - 1\right)\right) \cdot x} \]

   9. *-lft-identity N/A

      \[\leadsto \frac{\left(1 \cdot \left(x + -1\right) + \left(1 + x\right) \cdot 1\right) \cdot x + \left(\left(1 + x\right) \cdot \left(x + -1\right)\right) \cdot -2}{\left(\left(x + 1\right) \cdot \left(x - 1\right)\right) \cdot x} \]

   10. difference-of-sqr-1 N/A

       \[\leadsto \frac{\left(1 \cdot \left(x + -1\right) + \left(1 + x\right) \cdot 1\right) \cdot x + \left(\left(1 + x\right) \cdot \left(x + -1\right)\right) \cdot -2}{\left(x \cdot x - 1\right) \cdot x} \]

   11. metadata-eval N/A

       \[\leadsto \frac{\left(1 \cdot \left(x + -1\right) + \left(1 + x\right) \cdot 1\right) \cdot x + \left(\left(1 + x\right) \cdot \left(x + -1\right)\right) \cdot -2}{\left(x \cdot x - -1 \cdot -1\right) \cdot x} \]

   12. /-lowering-/.f64 N/A

       \[\leadsto \mathsf{/.f64}\left(\left(\left(1 \cdot \left(x + -1\right) + \left(1 + x\right) \cdot 1\right) \cdot x + \left(\left(1 + x\right) \cdot \left(x + -1\right)\right) \cdot -2\right), \color{blue}{\left(\left(x \cdot x - -1 \cdot -1\right) \cdot x\right)}\right) \]

6. Applied egg-rr 20.0%

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

7. Taylor expanded in x around 0

   \[\leadsto \mathsf{/.f64}\left(\color{blue}{2}, \mathsf{*.f64}\left(\mathsf{+.f64}\left(\mathsf{*.f64}\left(x, x\right), -1\right), x\right)\right) \]
8. Step-by-step derivation

9. Simplified 99.5%

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

    1. associate-/r* N/A

       \[\leadsto \frac{\frac{2}{x \cdot x + -1}}{\color{blue}{x}} \]

    2. /-lowering-/.f64 N/A

       \[\leadsto \mathsf{/.f64}\left(\left(\frac{2}{x \cdot x + -1}\right), \color{blue}{x}\right) \]

    3. /-lowering-/.f64 N/A

       \[\leadsto \mathsf{/.f64}\left(\mathsf{/.f64}\left(2, \left(x \cdot x + -1\right)\right), x\right) \]

    4. +-lowering-+.f64 N/A

       \[\leadsto \mathsf{/.f64}\left(\mathsf{/.f64}\left(2, \mathsf{+.f64}\left(\left(x \cdot x\right), -1\right)\right), x\right) \]

    5. *-lowering-*.f64 99.8%

       \[\leadsto \mathsf{/.f64}\left(\mathsf{/.f64}\left(2, \mathsf{+.f64}\left(\mathsf{*.f64}\left(x, x\right), -1\right)\right), x\right) \]

11. Applied egg-rr 99.8%

    \[\leadsto \color{blue}{\frac{\frac{2}{x \cdot x + -1}}{x}} \]
12. Add Preprocessing

Alternative 2: 99.3% accurate, 1.7× speedup

Math

\[\begin{array}{l} \\ \frac{2}{x \cdot \left(x \cdot x\right) - x} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (/ 2.0 (- (* x (* x x)) x)))

C

double code(double x) {
	return 2.0 / ((x * (x * x)) - x);
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = 2.0d0 / ((x * (x * x)) - x)
end function

Java

public static double code(double x) {
	return 2.0 / ((x * (x * x)) - x);
}

Python

def code(x):
	return 2.0 / ((x * (x * x)) - x)

Julia

function code(x)
	return Float64(2.0 / Float64(Float64(x * Float64(x * x)) - x))
end

MATLAB

function tmp = code(x)
	tmp = 2.0 / ((x * (x * x)) - x);
end

Wolfram

code[x_] := N[(2.0 / N[(N[(x * N[(x * x), $MachinePrecision]), $MachinePrecision] - x), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 73.0%

   \[\left(\frac{1}{x + 1} - \frac{2}{x}\right) + \frac{1}{x - 1} \]
2. Step-by-step derivation

   1. sub-neg N/A

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

   2. associate-+l+ N/A

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

   3. +-lowering-+.f64 N/A

      \[\leadsto \mathsf{+.f64}\left(\left(\frac{1}{x + 1}\right), \color{blue}{\left(\left(\mathsf{neg}\left(\frac{2}{x}\right)\right) + \frac{1}{x - 1}\right)}\right) \]

   4. /-lowering-/.f64 N/A

      \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \left(x + 1\right)\right), \left(\color{blue}{\left(\mathsf{neg}\left(\frac{2}{x}\right)\right)} + \frac{1}{x - 1}\right)\right) \]

   5. +-commutative N/A

      \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \left(1 + x\right)\right), \left(\left(\mathsf{neg}\left(\frac{2}{x}\right)\right) + \frac{1}{x - 1}\right)\right) \]

   6. +-lowering-+.f64 N/A

      \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(1, x\right)\right), \left(\left(\mathsf{neg}\left(\frac{2}{x}\right)\right) + \frac{1}{x - 1}\right)\right) \]

   7. +-commutative N/A

      \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(1, x\right)\right), \left(\frac{1}{x - 1} + \color{blue}{\left(\mathsf{neg}\left(\frac{2}{x}\right)\right)}\right)\right) \]

   8. +-lowering-+.f64 N/A

      \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(1, x\right)\right), \mathsf{+.f64}\left(\left(\frac{1}{x - 1}\right), \color{blue}{\left(\mathsf{neg}\left(\frac{2}{x}\right)\right)}\right)\right) \]

   9. /-lowering-/.f64 N/A

      \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(1, x\right)\right), \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \left(x - 1\right)\right), \left(\mathsf{neg}\left(\color{blue}{\frac{2}{x}}\right)\right)\right)\right) \]

   10. sub-neg N/A

       \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(1, x\right)\right), \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \left(x + \left(\mathsf{neg}\left(1\right)\right)\right)\right), \left(\mathsf{neg}\left(\frac{2}{\color{blue}{x}}\right)\right)\right)\right) \]

   11. +-lowering-+.f64 N/A

       \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(1, x\right)\right), \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(x, \left(\mathsf{neg}\left(1\right)\right)\right)\right), \left(\mathsf{neg}\left(\frac{2}{\color{blue}{x}}\right)\right)\right)\right) \]

   12. metadata-eval N/A

       \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(1, x\right)\right), \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(x, -1\right)\right), \left(\mathsf{neg}\left(\frac{2}{x}\right)\right)\right)\right) \]

   13. distribute-neg-frac N/A

       \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(1, x\right)\right), \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(x, -1\right)\right), \left(\frac{\mathsf{neg}\left(2\right)}{\color{blue}{x}}\right)\right)\right) \]

   14. /-lowering-/.f64 N/A

       \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(1, x\right)\right), \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(x, -1\right)\right), \mathsf{/.f64}\left(\left(\mathsf{neg}\left(2\right)\right), \color{blue}{x}\right)\right)\right) \]

   15. metadata-eval 73.0%

       \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(1, x\right)\right), \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(x, -1\right)\right), \mathsf{/.f64}\left(-2, x\right)\right)\right) \]

3. Simplified 73.0%

   \[\leadsto \color{blue}{\frac{1}{1 + x} + \left(\frac{1}{x + -1} + \frac{-2}{x}\right)} \]
4. Add Preprocessing
5. Step-by-step derivation

   1. associate-+r+ N/A

      \[\leadsto \left(\frac{1}{1 + x} + \frac{1}{x + -1}\right) + \color{blue}{\frac{-2}{x}} \]

   2. frac-add N/A

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

   3. frac-add N/A

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

   4. +-commutative N/A

      \[\leadsto \frac{\left(1 \cdot \left(x + -1\right) + \left(1 + x\right) \cdot 1\right) \cdot x + \left(\left(1 + x\right) \cdot \left(x + -1\right)\right) \cdot -2}{\left(\left(x + 1\right) \cdot \left(x + -1\right)\right) \cdot x} \]

   5. *-lft-identity N/A

      \[\leadsto \frac{\left(1 \cdot \left(x + -1\right) + \left(1 + x\right) \cdot 1\right) \cdot x + \left(\left(1 + x\right) \cdot \left(x + -1\right)\right) \cdot -2}{\left(\left(x + 1\right) \cdot \left(1 \cdot x + -1\right)\right) \cdot x} \]

   6. fma-define N/A

      \[\leadsto \frac{\left(1 \cdot \left(x + -1\right) + \left(1 + x\right) \cdot 1\right) \cdot x + \left(\left(1 + x\right) \cdot \left(x + -1\right)\right) \cdot -2}{\left(\left(x + 1\right) \cdot \mathsf{fma}\left(1, x, -1\right)\right) \cdot x} \]

   7. metadata-eval N/A

      \[\leadsto \frac{\left(1 \cdot \left(x + -1\right) + \left(1 + x\right) \cdot 1\right) \cdot x + \left(\left(1 + x\right) \cdot \left(x + -1\right)\right) \cdot -2}{\left(\left(x + 1\right) \cdot \mathsf{fma}\left(1, x, \mathsf{neg}\left(1\right)\right)\right) \cdot x} \]

   8. fmm-def N/A

      \[\leadsto \frac{\left(1 \cdot \left(x + -1\right) + \left(1 + x\right) \cdot 1\right) \cdot x + \left(\left(1 + x\right) \cdot \left(x + -1\right)\right) \cdot -2}{\left(\left(x + 1\right) \cdot \left(1 \cdot x - 1\right)\right) \cdot x} \]

   9. *-lft-identity N/A

      \[\leadsto \frac{\left(1 \cdot \left(x + -1\right) + \left(1 + x\right) \cdot 1\right) \cdot x + \left(\left(1 + x\right) \cdot \left(x + -1\right)\right) \cdot -2}{\left(\left(x + 1\right) \cdot \left(x - 1\right)\right) \cdot x} \]

   10. difference-of-sqr-1 N/A

       \[\leadsto \frac{\left(1 \cdot \left(x + -1\right) + \left(1 + x\right) \cdot 1\right) \cdot x + \left(\left(1 + x\right) \cdot \left(x + -1\right)\right) \cdot -2}{\left(x \cdot x - 1\right) \cdot x} \]

   11. metadata-eval N/A

       \[\leadsto \frac{\left(1 \cdot \left(x + -1\right) + \left(1 + x\right) \cdot 1\right) \cdot x + \left(\left(1 + x\right) \cdot \left(x + -1\right)\right) \cdot -2}{\left(x \cdot x - -1 \cdot -1\right) \cdot x} \]

   12. /-lowering-/.f64 N/A

       \[\leadsto \mathsf{/.f64}\left(\left(\left(1 \cdot \left(x + -1\right) + \left(1 + x\right) \cdot 1\right) \cdot x + \left(\left(1 + x\right) \cdot \left(x + -1\right)\right) \cdot -2\right), \color{blue}{\left(\left(x \cdot x - -1 \cdot -1\right) \cdot x\right)}\right) \]

6. Applied egg-rr 20.0%

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

7. Taylor expanded in x around 0

   \[\leadsto \mathsf{/.f64}\left(\color{blue}{2}, \mathsf{*.f64}\left(\mathsf{+.f64}\left(\mathsf{*.f64}\left(x, x\right), -1\right), x\right)\right) \]
8. Step-by-step derivation

9. Simplified 99.5%

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

    1. *-commutative N/A

       \[\leadsto \mathsf{/.f64}\left(2, \left(x \cdot \color{blue}{\left(x \cdot x + -1\right)}\right)\right) \]

    2. distribute-rgt-in N/A

       \[\leadsto \mathsf{/.f64}\left(2, \left(\left(x \cdot x\right) \cdot x + \color{blue}{-1 \cdot x}\right)\right) \]

    3. associate-*r* N/A

       \[\leadsto \mathsf{/.f64}\left(2, \left(x \cdot \left(x \cdot x\right) + \color{blue}{-1} \cdot x\right)\right) \]

    4. neg-mul-1 N/A

       \[\leadsto \mathsf{/.f64}\left(2, \left(x \cdot \left(x \cdot x\right) + \left(\mathsf{neg}\left(x\right)\right)\right)\right) \]

    5. unsub-neg N/A

       \[\leadsto \mathsf{/.f64}\left(2, \left(x \cdot \left(x \cdot x\right) - \color{blue}{x}\right)\right) \]

    6. --lowering--.f64 N/A

       \[\leadsto \mathsf{/.f64}\left(2, \mathsf{\_.f64}\left(\left(x \cdot \left(x \cdot x\right)\right), \color{blue}{x}\right)\right) \]

    7. *-lowering-*.f64 N/A

       \[\leadsto \mathsf{/.f64}\left(2, \mathsf{\_.f64}\left(\mathsf{*.f64}\left(x, \left(x \cdot x\right)\right), x\right)\right) \]

    8. *-lowering-*.f64 99.5%

       \[\leadsto \mathsf{/.f64}\left(2, \mathsf{\_.f64}\left(\mathsf{*.f64}\left(x, \mathsf{*.f64}\left(x, x\right)\right), x\right)\right) \]

11. Applied egg-rr 99.5%

    \[\leadsto \frac{2}{\color{blue}{x \cdot \left(x \cdot x\right) - x}} \]
12. Add Preprocessing

Alternative 3: 99.3% accurate, 1.7× speedup

Math

\[\begin{array}{l} \\ \frac{2}{x \cdot \left(x \cdot x + -1\right)} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (/ 2.0 (* x (+ (* x x) -1.0))))

C

double code(double x) {
	return 2.0 / (x * ((x * x) + -1.0));
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = 2.0d0 / (x * ((x * x) + (-1.0d0)))
end function

Java

public static double code(double x) {
	return 2.0 / (x * ((x * x) + -1.0));
}

Python

def code(x):
	return 2.0 / (x * ((x * x) + -1.0))

Julia

function code(x)
	return Float64(2.0 / Float64(x * Float64(Float64(x * x) + -1.0)))
end

MATLAB

function tmp = code(x)
	tmp = 2.0 / (x * ((x * x) + -1.0));
end

Wolfram

code[x_] := N[(2.0 / N[(x * N[(N[(x * x), $MachinePrecision] + -1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 73.0%

   \[\left(\frac{1}{x + 1} - \frac{2}{x}\right) + \frac{1}{x - 1} \]
2. Step-by-step derivation

   1. sub-neg N/A

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

   2. associate-+l+ N/A

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

   3. +-lowering-+.f64 N/A

      \[\leadsto \mathsf{+.f64}\left(\left(\frac{1}{x + 1}\right), \color{blue}{\left(\left(\mathsf{neg}\left(\frac{2}{x}\right)\right) + \frac{1}{x - 1}\right)}\right) \]

   4. /-lowering-/.f64 N/A

      \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \left(x + 1\right)\right), \left(\color{blue}{\left(\mathsf{neg}\left(\frac{2}{x}\right)\right)} + \frac{1}{x - 1}\right)\right) \]

   5. +-commutative N/A

      \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \left(1 + x\right)\right), \left(\left(\mathsf{neg}\left(\frac{2}{x}\right)\right) + \frac{1}{x - 1}\right)\right) \]

   6. +-lowering-+.f64 N/A

      \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(1, x\right)\right), \left(\left(\mathsf{neg}\left(\frac{2}{x}\right)\right) + \frac{1}{x - 1}\right)\right) \]

   7. +-commutative N/A

      \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(1, x\right)\right), \left(\frac{1}{x - 1} + \color{blue}{\left(\mathsf{neg}\left(\frac{2}{x}\right)\right)}\right)\right) \]

   8. +-lowering-+.f64 N/A

      \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(1, x\right)\right), \mathsf{+.f64}\left(\left(\frac{1}{x - 1}\right), \color{blue}{\left(\mathsf{neg}\left(\frac{2}{x}\right)\right)}\right)\right) \]

   9. /-lowering-/.f64 N/A

      \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(1, x\right)\right), \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \left(x - 1\right)\right), \left(\mathsf{neg}\left(\color{blue}{\frac{2}{x}}\right)\right)\right)\right) \]

   10. sub-neg N/A

       \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(1, x\right)\right), \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \left(x + \left(\mathsf{neg}\left(1\right)\right)\right)\right), \left(\mathsf{neg}\left(\frac{2}{\color{blue}{x}}\right)\right)\right)\right) \]

   11. +-lowering-+.f64 N/A

       \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(1, x\right)\right), \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(x, \left(\mathsf{neg}\left(1\right)\right)\right)\right), \left(\mathsf{neg}\left(\frac{2}{\color{blue}{x}}\right)\right)\right)\right) \]

   12. metadata-eval N/A

       \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(1, x\right)\right), \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(x, -1\right)\right), \left(\mathsf{neg}\left(\frac{2}{x}\right)\right)\right)\right) \]

   13. distribute-neg-frac N/A

       \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(1, x\right)\right), \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(x, -1\right)\right), \left(\frac{\mathsf{neg}\left(2\right)}{\color{blue}{x}}\right)\right)\right) \]

   14. /-lowering-/.f64 N/A

       \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(1, x\right)\right), \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(x, -1\right)\right), \mathsf{/.f64}\left(\left(\mathsf{neg}\left(2\right)\right), \color{blue}{x}\right)\right)\right) \]

   15. metadata-eval 73.0%

       \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(1, x\right)\right), \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(x, -1\right)\right), \mathsf{/.f64}\left(-2, x\right)\right)\right) \]

3. Simplified 73.0%

   \[\leadsto \color{blue}{\frac{1}{1 + x} + \left(\frac{1}{x + -1} + \frac{-2}{x}\right)} \]
4. Add Preprocessing
5. Step-by-step derivation

   1. associate-+r+ N/A

      \[\leadsto \left(\frac{1}{1 + x} + \frac{1}{x + -1}\right) + \color{blue}{\frac{-2}{x}} \]

   2. frac-add N/A

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

   3. frac-add N/A

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

   4. +-commutative N/A

      \[\leadsto \frac{\left(1 \cdot \left(x + -1\right) + \left(1 + x\right) \cdot 1\right) \cdot x + \left(\left(1 + x\right) \cdot \left(x + -1\right)\right) \cdot -2}{\left(\left(x + 1\right) \cdot \left(x + -1\right)\right) \cdot x} \]

   5. *-lft-identity N/A

      \[\leadsto \frac{\left(1 \cdot \left(x + -1\right) + \left(1 + x\right) \cdot 1\right) \cdot x + \left(\left(1 + x\right) \cdot \left(x + -1\right)\right) \cdot -2}{\left(\left(x + 1\right) \cdot \left(1 \cdot x + -1\right)\right) \cdot x} \]

   6. fma-define N/A

      \[\leadsto \frac{\left(1 \cdot \left(x + -1\right) + \left(1 + x\right) \cdot 1\right) \cdot x + \left(\left(1 + x\right) \cdot \left(x + -1\right)\right) \cdot -2}{\left(\left(x + 1\right) \cdot \mathsf{fma}\left(1, x, -1\right)\right) \cdot x} \]

   7. metadata-eval N/A

      \[\leadsto \frac{\left(1 \cdot \left(x + -1\right) + \left(1 + x\right) \cdot 1\right) \cdot x + \left(\left(1 + x\right) \cdot \left(x + -1\right)\right) \cdot -2}{\left(\left(x + 1\right) \cdot \mathsf{fma}\left(1, x, \mathsf{neg}\left(1\right)\right)\right) \cdot x} \]

   8. fmm-def N/A

      \[\leadsto \frac{\left(1 \cdot \left(x + -1\right) + \left(1 + x\right) \cdot 1\right) \cdot x + \left(\left(1 + x\right) \cdot \left(x + -1\right)\right) \cdot -2}{\left(\left(x + 1\right) \cdot \left(1 \cdot x - 1\right)\right) \cdot x} \]

   9. *-lft-identity N/A

      \[\leadsto \frac{\left(1 \cdot \left(x + -1\right) + \left(1 + x\right) \cdot 1\right) \cdot x + \left(\left(1 + x\right) \cdot \left(x + -1\right)\right) \cdot -2}{\left(\left(x + 1\right) \cdot \left(x - 1\right)\right) \cdot x} \]

   10. difference-of-sqr-1 N/A

       \[\leadsto \frac{\left(1 \cdot \left(x + -1\right) + \left(1 + x\right) \cdot 1\right) \cdot x + \left(\left(1 + x\right) \cdot \left(x + -1\right)\right) \cdot -2}{\left(x \cdot x - 1\right) \cdot x} \]

   11. metadata-eval N/A

       \[\leadsto \frac{\left(1 \cdot \left(x + -1\right) + \left(1 + x\right) \cdot 1\right) \cdot x + \left(\left(1 + x\right) \cdot \left(x + -1\right)\right) \cdot -2}{\left(x \cdot x - -1 \cdot -1\right) \cdot x} \]

   12. /-lowering-/.f64 N/A

       \[\leadsto \mathsf{/.f64}\left(\left(\left(1 \cdot \left(x + -1\right) + \left(1 + x\right) \cdot 1\right) \cdot x + \left(\left(1 + x\right) \cdot \left(x + -1\right)\right) \cdot -2\right), \color{blue}{\left(\left(x \cdot x - -1 \cdot -1\right) \cdot x\right)}\right) \]

6. Applied egg-rr 20.0%

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

7. Taylor expanded in x around 0

   \[\leadsto \mathsf{/.f64}\left(\color{blue}{2}, \mathsf{*.f64}\left(\mathsf{+.f64}\left(\mathsf{*.f64}\left(x, x\right), -1\right), x\right)\right) \]
8. Step-by-step derivation

9. Simplified 99.5%

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

10. Final simplification 99.5%

    \[\leadsto \frac{2}{x \cdot \left(x \cdot x + -1\right)} \]
11. Add Preprocessing

Alternative 4: 98.8% accurate, 2.1× speedup

Math

\[\begin{array}{l} \\ \frac{\frac{2}{x \cdot x}}{x} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (/ (/ 2.0 (* x x)) x))

C

double code(double x) {
	return (2.0 / (x * x)) / x;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = (2.0d0 / (x * x)) / x
end function

Java

public static double code(double x) {
	return (2.0 / (x * x)) / x;
}

Python

def code(x):
	return (2.0 / (x * x)) / x

Julia

function code(x)
	return Float64(Float64(2.0 / Float64(x * x)) / x)
end

MATLAB

function tmp = code(x)
	tmp = (2.0 / (x * x)) / x;
end

Wolfram

code[x_] := N[(N[(2.0 / N[(x * x), $MachinePrecision]), $MachinePrecision] / x), $MachinePrecision]

Derivation

1. Initial program 73.0%

   \[\left(\frac{1}{x + 1} - \frac{2}{x}\right) + \frac{1}{x - 1} \]
2. Step-by-step derivation

   1. sub-neg N/A

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

   2. associate-+l+ N/A

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

   3. +-lowering-+.f64 N/A

      \[\leadsto \mathsf{+.f64}\left(\left(\frac{1}{x + 1}\right), \color{blue}{\left(\left(\mathsf{neg}\left(\frac{2}{x}\right)\right) + \frac{1}{x - 1}\right)}\right) \]

   4. /-lowering-/.f64 N/A

      \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \left(x + 1\right)\right), \left(\color{blue}{\left(\mathsf{neg}\left(\frac{2}{x}\right)\right)} + \frac{1}{x - 1}\right)\right) \]

   5. +-commutative N/A

      \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \left(1 + x\right)\right), \left(\left(\mathsf{neg}\left(\frac{2}{x}\right)\right) + \frac{1}{x - 1}\right)\right) \]

   6. +-lowering-+.f64 N/A

      \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(1, x\right)\right), \left(\left(\mathsf{neg}\left(\frac{2}{x}\right)\right) + \frac{1}{x - 1}\right)\right) \]

   7. +-commutative N/A

      \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(1, x\right)\right), \left(\frac{1}{x - 1} + \color{blue}{\left(\mathsf{neg}\left(\frac{2}{x}\right)\right)}\right)\right) \]

   8. +-lowering-+.f64 N/A

      \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(1, x\right)\right), \mathsf{+.f64}\left(\left(\frac{1}{x - 1}\right), \color{blue}{\left(\mathsf{neg}\left(\frac{2}{x}\right)\right)}\right)\right) \]

   9. /-lowering-/.f64 N/A

      \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(1, x\right)\right), \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \left(x - 1\right)\right), \left(\mathsf{neg}\left(\color{blue}{\frac{2}{x}}\right)\right)\right)\right) \]

   10. sub-neg N/A

       \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(1, x\right)\right), \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \left(x + \left(\mathsf{neg}\left(1\right)\right)\right)\right), \left(\mathsf{neg}\left(\frac{2}{\color{blue}{x}}\right)\right)\right)\right) \]

   11. +-lowering-+.f64 N/A

       \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(1, x\right)\right), \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(x, \left(\mathsf{neg}\left(1\right)\right)\right)\right), \left(\mathsf{neg}\left(\frac{2}{\color{blue}{x}}\right)\right)\right)\right) \]

   12. metadata-eval N/A

       \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(1, x\right)\right), \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(x, -1\right)\right), \left(\mathsf{neg}\left(\frac{2}{x}\right)\right)\right)\right) \]

   13. distribute-neg-frac N/A

       \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(1, x\right)\right), \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(x, -1\right)\right), \left(\frac{\mathsf{neg}\left(2\right)}{\color{blue}{x}}\right)\right)\right) \]

   14. /-lowering-/.f64 N/A

       \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(1, x\right)\right), \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(x, -1\right)\right), \mathsf{/.f64}\left(\left(\mathsf{neg}\left(2\right)\right), \color{blue}{x}\right)\right)\right) \]

   15. metadata-eval 73.0%

       \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(1, x\right)\right), \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(x, -1\right)\right), \mathsf{/.f64}\left(-2, x\right)\right)\right) \]

3. Simplified 73.0%

   \[\leadsto \color{blue}{\frac{1}{1 + x} + \left(\frac{1}{x + -1} + \frac{-2}{x}\right)} \]
4. Add Preprocessing

5. Taylor expanded in x around inf

   \[\leadsto \color{blue}{\frac{2}{{x}^{3}}} \]
6. Step-by-step derivation

   1. /-lowering-/.f64 N/A

      \[\leadsto \mathsf{/.f64}\left(2, \color{blue}{\left({x}^{3}\right)}\right) \]

   2. cube-mult N/A

      \[\leadsto \mathsf{/.f64}\left(2, \left(x \cdot \color{blue}{\left(x \cdot x\right)}\right)\right) \]

   3. unpow2 N/A

      \[\leadsto \mathsf{/.f64}\left(2, \left(x \cdot {x}^{\color{blue}{2}}\right)\right) \]

   4. *-lowering-*.f64 N/A

      \[\leadsto \mathsf{/.f64}\left(2, \mathsf{*.f64}\left(x, \color{blue}{\left({x}^{2}\right)}\right)\right) \]

   5. unpow2 N/A

      \[\leadsto \mathsf{/.f64}\left(2, \mathsf{*.f64}\left(x, \left(x \cdot \color{blue}{x}\right)\right)\right) \]

   6. *-lowering-*.f64 98.0%

      \[\leadsto \mathsf{/.f64}\left(2, \mathsf{*.f64}\left(x, \mathsf{*.f64}\left(x, \color{blue}{x}\right)\right)\right) \]

7. Simplified 98.0%

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

   1. associate-*r* N/A

      \[\leadsto \frac{2}{\left(x \cdot x\right) \cdot \color{blue}{x}} \]

   2. associate-/r* N/A

      \[\leadsto \frac{\frac{2}{x \cdot x}}{\color{blue}{x}} \]

   3. /-lowering-/.f64 N/A

      \[\leadsto \mathsf{/.f64}\left(\left(\frac{2}{x \cdot x}\right), \color{blue}{x}\right) \]

   4. /-lowering-/.f64 N/A

      \[\leadsto \mathsf{/.f64}\left(\mathsf{/.f64}\left(2, \left(x \cdot x\right)\right), x\right) \]

   5. *-lowering-*.f64 98.3%

      \[\leadsto \mathsf{/.f64}\left(\mathsf{/.f64}\left(2, \mathsf{*.f64}\left(x, x\right)\right), x\right) \]

9. Applied egg-rr 98.3%

   \[\leadsto \color{blue}{\frac{\frac{2}{x \cdot x}}{x}} \]
10. Add Preprocessing

Alternative 5: 98.8% accurate, 2.1× speedup

Math

\[\begin{array}{l} \\ \frac{\frac{2}{x}}{x \cdot x} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (/ (/ 2.0 x) (* x x)))

C

double code(double x) {
	return (2.0 / x) / (x * x);
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = (2.0d0 / x) / (x * x)
end function

Java

public static double code(double x) {
	return (2.0 / x) / (x * x);
}

Python

def code(x):
	return (2.0 / x) / (x * x)

Julia

function code(x)
	return Float64(Float64(2.0 / x) / Float64(x * x))
end

MATLAB

function tmp = code(x)
	tmp = (2.0 / x) / (x * x);
end

Wolfram

code[x_] := N[(N[(2.0 / x), $MachinePrecision] / N[(x * x), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 73.0%

   \[\left(\frac{1}{x + 1} - \frac{2}{x}\right) + \frac{1}{x - 1} \]
2. Step-by-step derivation

   1. sub-neg N/A

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

   2. associate-+l+ N/A

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

   3. +-lowering-+.f64 N/A

      \[\leadsto \mathsf{+.f64}\left(\left(\frac{1}{x + 1}\right), \color{blue}{\left(\left(\mathsf{neg}\left(\frac{2}{x}\right)\right) + \frac{1}{x - 1}\right)}\right) \]

   4. /-lowering-/.f64 N/A

      \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \left(x + 1\right)\right), \left(\color{blue}{\left(\mathsf{neg}\left(\frac{2}{x}\right)\right)} + \frac{1}{x - 1}\right)\right) \]

   5. +-commutative N/A

      \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \left(1 + x\right)\right), \left(\left(\mathsf{neg}\left(\frac{2}{x}\right)\right) + \frac{1}{x - 1}\right)\right) \]

   6. +-lowering-+.f64 N/A

      \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(1, x\right)\right), \left(\left(\mathsf{neg}\left(\frac{2}{x}\right)\right) + \frac{1}{x - 1}\right)\right) \]

   7. +-commutative N/A

      \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(1, x\right)\right), \left(\frac{1}{x - 1} + \color{blue}{\left(\mathsf{neg}\left(\frac{2}{x}\right)\right)}\right)\right) \]

   8. +-lowering-+.f64 N/A

      \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(1, x\right)\right), \mathsf{+.f64}\left(\left(\frac{1}{x - 1}\right), \color{blue}{\left(\mathsf{neg}\left(\frac{2}{x}\right)\right)}\right)\right) \]

   9. /-lowering-/.f64 N/A

      \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(1, x\right)\right), \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \left(x - 1\right)\right), \left(\mathsf{neg}\left(\color{blue}{\frac{2}{x}}\right)\right)\right)\right) \]

   10. sub-neg N/A

       \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(1, x\right)\right), \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \left(x + \left(\mathsf{neg}\left(1\right)\right)\right)\right), \left(\mathsf{neg}\left(\frac{2}{\color{blue}{x}}\right)\right)\right)\right) \]

   11. +-lowering-+.f64 N/A

       \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(1, x\right)\right), \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(x, \left(\mathsf{neg}\left(1\right)\right)\right)\right), \left(\mathsf{neg}\left(\frac{2}{\color{blue}{x}}\right)\right)\right)\right) \]

   12. metadata-eval N/A

       \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(1, x\right)\right), \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(x, -1\right)\right), \left(\mathsf{neg}\left(\frac{2}{x}\right)\right)\right)\right) \]

   13. distribute-neg-frac N/A

       \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(1, x\right)\right), \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(x, -1\right)\right), \left(\frac{\mathsf{neg}\left(2\right)}{\color{blue}{x}}\right)\right)\right) \]

   14. /-lowering-/.f64 N/A

       \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(1, x\right)\right), \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(x, -1\right)\right), \mathsf{/.f64}\left(\left(\mathsf{neg}\left(2\right)\right), \color{blue}{x}\right)\right)\right) \]

   15. metadata-eval 73.0%

       \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(1, x\right)\right), \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(x, -1\right)\right), \mathsf{/.f64}\left(-2, x\right)\right)\right) \]

3. Simplified 73.0%

   \[\leadsto \color{blue}{\frac{1}{1 + x} + \left(\frac{1}{x + -1} + \frac{-2}{x}\right)} \]
4. Add Preprocessing

5. Taylor expanded in x around inf

   \[\leadsto \color{blue}{\frac{2}{{x}^{3}}} \]
6. Step-by-step derivation

   1. /-lowering-/.f64 N/A

      \[\leadsto \mathsf{/.f64}\left(2, \color{blue}{\left({x}^{3}\right)}\right) \]

   2. cube-mult N/A

      \[\leadsto \mathsf{/.f64}\left(2, \left(x \cdot \color{blue}{\left(x \cdot x\right)}\right)\right) \]

   3. unpow2 N/A

      \[\leadsto \mathsf{/.f64}\left(2, \left(x \cdot {x}^{\color{blue}{2}}\right)\right) \]

   4. *-lowering-*.f64 N/A

      \[\leadsto \mathsf{/.f64}\left(2, \mathsf{*.f64}\left(x, \color{blue}{\left({x}^{2}\right)}\right)\right) \]

   5. unpow2 N/A

      \[\leadsto \mathsf{/.f64}\left(2, \mathsf{*.f64}\left(x, \left(x \cdot \color{blue}{x}\right)\right)\right) \]

   6. *-lowering-*.f64 98.0%

      \[\leadsto \mathsf{/.f64}\left(2, \mathsf{*.f64}\left(x, \mathsf{*.f64}\left(x, \color{blue}{x}\right)\right)\right) \]

7. Simplified 98.0%

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

   1. associate-/r* N/A

      \[\leadsto \frac{\frac{2}{x}}{\color{blue}{x \cdot x}} \]

   2. /-lowering-/.f64 N/A

      \[\leadsto \mathsf{/.f64}\left(\left(\frac{2}{x}\right), \color{blue}{\left(x \cdot x\right)}\right) \]

   3. /-lowering-/.f64 N/A

      \[\leadsto \mathsf{/.f64}\left(\mathsf{/.f64}\left(2, x\right), \left(\color{blue}{x} \cdot x\right)\right) \]

   4. *-lowering-*.f64 98.3%

      \[\leadsto \mathsf{/.f64}\left(\mathsf{/.f64}\left(2, x\right), \mathsf{*.f64}\left(x, \color{blue}{x}\right)\right) \]

9. Applied egg-rr 98.3%

   \[\leadsto \color{blue}{\frac{\frac{2}{x}}{x \cdot x}} \]
10. Add Preprocessing

Alternative 6: 98.3% accurate, 2.1× speedup

Math

\[\begin{array}{l} \\ \frac{2}{x \cdot \left(x \cdot x\right)} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (/ 2.0 (* x (* x x))))

C

double code(double x) {
	return 2.0 / (x * (x * x));
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = 2.0d0 / (x * (x * x))
end function

Java

public static double code(double x) {
	return 2.0 / (x * (x * x));
}

Python

def code(x):
	return 2.0 / (x * (x * x))

Julia

function code(x)
	return Float64(2.0 / Float64(x * Float64(x * x)))
end

MATLAB

function tmp = code(x)
	tmp = 2.0 / (x * (x * x));
end

Wolfram

code[x_] := N[(2.0 / N[(x * N[(x * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 73.0%

   \[\left(\frac{1}{x + 1} - \frac{2}{x}\right) + \frac{1}{x - 1} \]
2. Step-by-step derivation

   1. sub-neg N/A

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

   2. associate-+l+ N/A

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

   3. +-lowering-+.f64 N/A

      \[\leadsto \mathsf{+.f64}\left(\left(\frac{1}{x + 1}\right), \color{blue}{\left(\left(\mathsf{neg}\left(\frac{2}{x}\right)\right) + \frac{1}{x - 1}\right)}\right) \]

   4. /-lowering-/.f64 N/A

      \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \left(x + 1\right)\right), \left(\color{blue}{\left(\mathsf{neg}\left(\frac{2}{x}\right)\right)} + \frac{1}{x - 1}\right)\right) \]

   5. +-commutative N/A

      \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \left(1 + x\right)\right), \left(\left(\mathsf{neg}\left(\frac{2}{x}\right)\right) + \frac{1}{x - 1}\right)\right) \]

   6. +-lowering-+.f64 N/A

      \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(1, x\right)\right), \left(\left(\mathsf{neg}\left(\frac{2}{x}\right)\right) + \frac{1}{x - 1}\right)\right) \]

   7. +-commutative N/A

      \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(1, x\right)\right), \left(\frac{1}{x - 1} + \color{blue}{\left(\mathsf{neg}\left(\frac{2}{x}\right)\right)}\right)\right) \]

   8. +-lowering-+.f64 N/A

      \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(1, x\right)\right), \mathsf{+.f64}\left(\left(\frac{1}{x - 1}\right), \color{blue}{\left(\mathsf{neg}\left(\frac{2}{x}\right)\right)}\right)\right) \]

   9. /-lowering-/.f64 N/A

      \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(1, x\right)\right), \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \left(x - 1\right)\right), \left(\mathsf{neg}\left(\color{blue}{\frac{2}{x}}\right)\right)\right)\right) \]

   10. sub-neg N/A

       \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(1, x\right)\right), \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \left(x + \left(\mathsf{neg}\left(1\right)\right)\right)\right), \left(\mathsf{neg}\left(\frac{2}{\color{blue}{x}}\right)\right)\right)\right) \]

   11. +-lowering-+.f64 N/A

       \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(1, x\right)\right), \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(x, \left(\mathsf{neg}\left(1\right)\right)\right)\right), \left(\mathsf{neg}\left(\frac{2}{\color{blue}{x}}\right)\right)\right)\right) \]

   12. metadata-eval N/A

       \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(1, x\right)\right), \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(x, -1\right)\right), \left(\mathsf{neg}\left(\frac{2}{x}\right)\right)\right)\right) \]

   13. distribute-neg-frac N/A

       \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(1, x\right)\right), \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(x, -1\right)\right), \left(\frac{\mathsf{neg}\left(2\right)}{\color{blue}{x}}\right)\right)\right) \]

   14. /-lowering-/.f64 N/A

       \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(1, x\right)\right), \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(x, -1\right)\right), \mathsf{/.f64}\left(\left(\mathsf{neg}\left(2\right)\right), \color{blue}{x}\right)\right)\right) \]

   15. metadata-eval 73.0%

       \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(1, x\right)\right), \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(x, -1\right)\right), \mathsf{/.f64}\left(-2, x\right)\right)\right) \]

3. Simplified 73.0%

   \[\leadsto \color{blue}{\frac{1}{1 + x} + \left(\frac{1}{x + -1} + \frac{-2}{x}\right)} \]
4. Add Preprocessing

5. Taylor expanded in x around inf

   \[\leadsto \color{blue}{\frac{2}{{x}^{3}}} \]
6. Step-by-step derivation

   1. /-lowering-/.f64 N/A

      \[\leadsto \mathsf{/.f64}\left(2, \color{blue}{\left({x}^{3}\right)}\right) \]

   2. cube-mult N/A

      \[\leadsto \mathsf{/.f64}\left(2, \left(x \cdot \color{blue}{\left(x \cdot x\right)}\right)\right) \]

   3. unpow2 N/A

      \[\leadsto \mathsf{/.f64}\left(2, \left(x \cdot {x}^{\color{blue}{2}}\right)\right) \]

   4. *-lowering-*.f64 N/A

      \[\leadsto \mathsf{/.f64}\left(2, \mathsf{*.f64}\left(x, \color{blue}{\left({x}^{2}\right)}\right)\right) \]

   5. unpow2 N/A

      \[\leadsto \mathsf{/.f64}\left(2, \mathsf{*.f64}\left(x, \left(x \cdot \color{blue}{x}\right)\right)\right) \]

   6. *-lowering-*.f64 98.0%

      \[\leadsto \mathsf{/.f64}\left(2, \mathsf{*.f64}\left(x, \mathsf{*.f64}\left(x, \color{blue}{x}\right)\right)\right) \]

7. Simplified 98.0%

   \[\leadsto \color{blue}{\frac{2}{x \cdot \left(x \cdot x\right)}} \]
8. Add Preprocessing

Alternative 7: 5.1% accurate, 5.0× speedup

Math

\[\begin{array}{l} \\ \frac{-2}{x} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (/ -2.0 x))

C

double code(double x) {
	return -2.0 / x;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = (-2.0d0) / x
end function

Java

public static double code(double x) {
	return -2.0 / x;
}

Python

def code(x):
	return -2.0 / x

Julia

function code(x)
	return Float64(-2.0 / x)
end

MATLAB

function tmp = code(x)
	tmp = -2.0 / x;
end

Wolfram

code[x_] := N[(-2.0 / x), $MachinePrecision]

Derivation

1. Initial program 73.0%

   \[\left(\frac{1}{x + 1} - \frac{2}{x}\right) + \frac{1}{x - 1} \]
2. Step-by-step derivation

   1. sub-neg N/A

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

   2. associate-+l+ N/A

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

   3. +-lowering-+.f64 N/A

      \[\leadsto \mathsf{+.f64}\left(\left(\frac{1}{x + 1}\right), \color{blue}{\left(\left(\mathsf{neg}\left(\frac{2}{x}\right)\right) + \frac{1}{x - 1}\right)}\right) \]

   4. /-lowering-/.f64 N/A

      \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \left(x + 1\right)\right), \left(\color{blue}{\left(\mathsf{neg}\left(\frac{2}{x}\right)\right)} + \frac{1}{x - 1}\right)\right) \]

   5. +-commutative N/A

      \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \left(1 + x\right)\right), \left(\left(\mathsf{neg}\left(\frac{2}{x}\right)\right) + \frac{1}{x - 1}\right)\right) \]

   6. +-lowering-+.f64 N/A

      \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(1, x\right)\right), \left(\left(\mathsf{neg}\left(\frac{2}{x}\right)\right) + \frac{1}{x - 1}\right)\right) \]

   7. +-commutative N/A

      \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(1, x\right)\right), \left(\frac{1}{x - 1} + \color{blue}{\left(\mathsf{neg}\left(\frac{2}{x}\right)\right)}\right)\right) \]

   8. +-lowering-+.f64 N/A

      \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(1, x\right)\right), \mathsf{+.f64}\left(\left(\frac{1}{x - 1}\right), \color{blue}{\left(\mathsf{neg}\left(\frac{2}{x}\right)\right)}\right)\right) \]

   9. /-lowering-/.f64 N/A

      \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(1, x\right)\right), \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \left(x - 1\right)\right), \left(\mathsf{neg}\left(\color{blue}{\frac{2}{x}}\right)\right)\right)\right) \]

   10. sub-neg N/A

       \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(1, x\right)\right), \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \left(x + \left(\mathsf{neg}\left(1\right)\right)\right)\right), \left(\mathsf{neg}\left(\frac{2}{\color{blue}{x}}\right)\right)\right)\right) \]

   11. +-lowering-+.f64 N/A

       \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(1, x\right)\right), \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(x, \left(\mathsf{neg}\left(1\right)\right)\right)\right), \left(\mathsf{neg}\left(\frac{2}{\color{blue}{x}}\right)\right)\right)\right) \]

   12. metadata-eval N/A

       \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(1, x\right)\right), \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(x, -1\right)\right), \left(\mathsf{neg}\left(\frac{2}{x}\right)\right)\right)\right) \]

   13. distribute-neg-frac N/A

       \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(1, x\right)\right), \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(x, -1\right)\right), \left(\frac{\mathsf{neg}\left(2\right)}{\color{blue}{x}}\right)\right)\right) \]

   14. /-lowering-/.f64 N/A

       \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(1, x\right)\right), \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(x, -1\right)\right), \mathsf{/.f64}\left(\left(\mathsf{neg}\left(2\right)\right), \color{blue}{x}\right)\right)\right) \]

   15. metadata-eval 73.0%

       \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(1, x\right)\right), \mathsf{+.f64}\left(\mathsf{/.f64}\left(1, \mathsf{+.f64}\left(x, -1\right)\right), \mathsf{/.f64}\left(-2, x\right)\right)\right) \]

3. Simplified 73.0%

   \[\leadsto \color{blue}{\frac{1}{1 + x} + \left(\frac{1}{x + -1} + \frac{-2}{x}\right)} \]
4. Add Preprocessing

5. Taylor expanded in x around 0

   \[\leadsto \color{blue}{\frac{-2}{x}} \]
6. Step-by-step derivation

   1. /-lowering-/.f64 5.2%

      \[\leadsto \mathsf{/.f64}\left(-2, \color{blue}{x}\right) \]

7. Simplified 5.2%

   \[\leadsto \color{blue}{\frac{-2}{x}} \]
8. Add Preprocessing

Developer Target 1: 99.3% accurate, 1.7× speedup

Math

\[\begin{array}{l} \\ \frac{2}{x \cdot \left(x \cdot x - 1\right)} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (/ 2.0 (* x (- (* x x) 1.0))))

C

double code(double x) {
	return 2.0 / (x * ((x * x) - 1.0));
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = 2.0d0 / (x * ((x * x) - 1.0d0))
end function

Java

public static double code(double x) {
	return 2.0 / (x * ((x * x) - 1.0));
}

Python

def code(x):
	return 2.0 / (x * ((x * x) - 1.0))

Julia

function code(x)
	return Float64(2.0 / Float64(x * Float64(Float64(x * x) - 1.0)))
end

MATLAB

function tmp = code(x)
	tmp = 2.0 / (x * ((x * x) - 1.0));
end

Wolfram

code[x_] := N[(2.0 / N[(x * N[(N[(x * x), $MachinePrecision] - 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Reproduce

herbie shell --seed 2024150 
(FPCore (x)
  :name "3frac (problem 3.3.3)"
  :precision binary64
  :pre (> (fabs x) 1.0)

  :alt
  (! :herbie-platform default (/ 2 (* x (- (* x x) 1))))

  (+ (- (/ 1.0 (+ x 1.0)) (/ 2.0 x)) (/ 1.0 (- x 1.0))))