3frac (problem 3.3.3)

Percentage Accurate: 69.2% → 99.8%

Time: 11.2s

Alternatives: 8

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: 69.2% 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, 1.2× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 70.4%

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

   1. +-commutative N/A

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

   2. frac-sub N/A

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

   3. frac-add N/A

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

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

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

4. Applied egg-rr 22.3%

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

5. Taylor expanded in x around 0

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

7. Simplified 99.0%

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

   1. associate-/r* N/A

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

   2. +-commutative N/A

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

   3. metadata-eval N/A

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

   4. sub-neg N/A

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

   5. *-commutative N/A

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

   6. associate-/r* N/A

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

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

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

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

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

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

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

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

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

   11. sub-neg N/A

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

   12. metadata-eval N/A

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

   13. +-lowering-+.f64 99.8

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

9. Applied egg-rr 99.8%

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

Alternative 2: 99.8% accurate, 1.6× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 70.4%

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

   1. +-commutative N/A

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

   2. frac-sub N/A

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

   3. frac-add N/A

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

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

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

4. Applied egg-rr 22.3%

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

5. Taylor expanded in x around 0

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

7. Simplified 99.0%

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

   1. associate-/r* N/A

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

   2. +-commutative N/A

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

   3. metadata-eval N/A

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

   4. sub-neg N/A

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

   5. *-commutative N/A

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

   6. associate-/r* N/A

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

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

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

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

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

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

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

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

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

   11. sub-neg N/A

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

   12. metadata-eval N/A

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

   13. +-lowering-+.f64 99.8

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

9. Applied egg-rr 99.8%

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

    1. associate-/l/ N/A

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

    2. metadata-eval N/A

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

    3. sub-neg N/A

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

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

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

    5. metadata-eval N/A

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

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

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

    7. metadata-eval N/A

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

    8. sub-neg N/A

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

    9. metadata-eval N/A

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

    10. accelerator-lowering-fma.f64 99.8

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

11. Applied egg-rr 99.8%

    \[\leadsto \frac{\color{blue}{\frac{2}{\mathsf{fma}\left(x, x, -1\right)}}}{x} \]
12. Add Preprocessing

Alternative 3: 99.8% accurate, 1.6× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 70.4%

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

   1. +-commutative N/A

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

   2. frac-sub N/A

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

   3. frac-add N/A

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

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

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

4. Applied egg-rr 22.3%

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

5. Taylor expanded in x around 0

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

7. Simplified 99.0%

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

   1. associate-/r* N/A

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

   2. +-commutative N/A

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

   3. metadata-eval N/A

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

   4. sub-neg N/A

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

   5. *-commutative N/A

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

   6. associate-/r* N/A

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

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

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

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

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

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

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

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

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

   11. sub-neg N/A

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

   12. metadata-eval N/A

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

   13. +-lowering-+.f64 99.8

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

9. Applied egg-rr 99.8%

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

    1. div-inv N/A

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

    2. associate-/l/ N/A

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

    3. metadata-eval N/A

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

    4. sub-neg N/A

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

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

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

    6. metadata-eval N/A

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

    7. associate-*l/ N/A

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

    8. div-inv N/A

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

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

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

    10. /-lowering-/.f64 N/A

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

    11. metadata-eval N/A

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

    12. sub-neg N/A

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

    13. metadata-eval N/A

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

    14. accelerator-lowering-fma.f64 99.8

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

11. Applied egg-rr 99.8%

    \[\leadsto \color{blue}{\frac{\frac{2}{x}}{\mathsf{fma}\left(x, x, -1\right)}} \]
12. Add Preprocessing

Alternative 4: 99.2% accurate, 1.6× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 70.4%

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

   1. +-commutative N/A

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

   2. frac-sub N/A

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

   3. frac-add N/A

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

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

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

4. Applied egg-rr 22.3%

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

5. Taylor expanded in x around 0

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

7. Simplified 99.0%

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

8. Final simplification 99.0%

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

Alternative 5: 99.2% accurate, 2.0× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 70.4%

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

   1. +-commutative N/A

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

   2. frac-sub N/A

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

   3. frac-add N/A

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

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

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

4. Applied egg-rr 22.3%

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

5. Taylor expanded in x around 0

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

7. Simplified 99.0%

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

8. Taylor expanded in x around 0

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

   1. distribute-lft-out-- N/A

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

   2. unpow2 N/A

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

   3. cube-mult N/A

      \[\leadsto \frac{2}{\color{blue}{{x}^{3}} - x \cdot 1} \]

   4. *-rgt-identity N/A

      \[\leadsto \frac{2}{\color{blue}{{x}^{3} \cdot 1} - x \cdot 1} \]

   5. rgt-mult-inverse N/A

      \[\leadsto \frac{2}{{x}^{3} \cdot 1 - x \cdot \color{blue}{\left({x}^{2} \cdot \frac{1}{{x}^{2}}\right)}} \]

   6. associate-*l* N/A

      \[\leadsto \frac{2}{{x}^{3} \cdot 1 - \color{blue}{\left(x \cdot {x}^{2}\right) \cdot \frac{1}{{x}^{2}}}} \]

   7. unpow2 N/A

      \[\leadsto \frac{2}{{x}^{3} \cdot 1 - \left(x \cdot \color{blue}{\left(x \cdot x\right)}\right) \cdot \frac{1}{{x}^{2}}} \]

   8. cube-mult N/A

      \[\leadsto \frac{2}{{x}^{3} \cdot 1 - \color{blue}{{x}^{3}} \cdot \frac{1}{{x}^{2}}} \]

   9. distribute-lft-out-- N/A

      \[\leadsto \frac{2}{\color{blue}{{x}^{3} \cdot \left(1 - \frac{1}{{x}^{2}}\right)}} \]

   10. cube-mult N/A

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

   11. unpow2 N/A

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

   12. associate-*l* N/A

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

   13. *-commutative N/A

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

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

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

   15. *-commutative N/A

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

   16. sub-neg N/A

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

   17. distribute-neg-frac N/A

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

   18. metadata-eval N/A

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

   19. distribute-lft-in N/A

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

   20. metadata-eval N/A

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

   21. distribute-neg-frac N/A

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

10. Simplified 99.0%

    \[\leadsto \frac{2}{\color{blue}{x \cdot \mathsf{fma}\left(x, x, -1\right)}} \]
11. Add Preprocessing

Alternative 6: 98.2% 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 70.4%

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

3. Taylor expanded in x around inf

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

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

      \[\leadsto \color{blue}{\frac{2}{{x}^{3}}} \]

   2. cube-mult N/A

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

   3. unpow2 N/A

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

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

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

   5. unpow2 N/A

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

   6. *-lowering-*.f64 97.8

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

5. Simplified 97.8%

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

Alternative 7: 53.2% accurate, 2.7× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 70.4%

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

   1. frac-sub N/A

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

   2. associate-/r* N/A

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

   3. frac-add N/A

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

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

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

4. Applied egg-rr 70.6%

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

5. Taylor expanded in x around 0

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

7. Simplified 52.8%

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

8. Taylor expanded in x around inf

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

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

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

   2. unpow2 N/A

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

   3. *-lowering-*.f64 52.8

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

10. Simplified 52.8%

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

Alternative 8: 5.0% accurate, 3.8× 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 70.4%

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

3. Taylor expanded in x around 0

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

   1. /-lowering-/.f64 5.0

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

5. Simplified 5.0%

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

Developer Target 1: 99.2% accurate, 1.8× 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 2024205 
(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))))