3frac (problem 3.3.3)

Percentage Accurate: 70.2% → 99.8%

Time: 8.0s

Alternatives: 4

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: 70.2% 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, 1.6× 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 71.0%

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

   1. lift--.f64 N/A

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

   2. sub-neg N/A

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

   3. +-commutative N/A

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

   4. lift-/.f64 N/A

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

   5. distribute-neg-frac N/A

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

   6. lift-/.f64 N/A

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

   7. frac-add N/A

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

   8. *-commutative N/A

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

   9. lower-/.f64 N/A

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

   10. *-rgt-identity N/A

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

   11. lower-fma.f64 N/A

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

   12. metadata-eval N/A

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

   13. lower-*.f64 19.7

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

4. Applied rewrites 19.7%

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

   1. lift-+.f64 N/A

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

   2. lift-/.f64 N/A

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

   3. lift-/.f64 N/A

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

   4. frac-add N/A

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

   5. lower-/.f64 N/A

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

   6. *-commutative N/A

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

   7. *-rgt-identity N/A

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

   8. lower-fma.f64 N/A

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

   9. lift-fma.f64 N/A

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

   10. *-commutative N/A

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

   11. lower-fma.f64 N/A

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

   12. lift-+.f64 N/A

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

   13. +-commutative N/A

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

   14. lower-+.f64 N/A

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

   15. lift-*.f64 N/A

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

   16. *-commutative N/A

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

   17. lift-+.f64 N/A

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

   18. distribute-lft-in N/A

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

   19. *-rgt-identity N/A

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

   20. lower-fma.f64 N/A

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

6. Applied rewrites 19.6%

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

7. Taylor expanded in x around 0

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

9. Applied rewrites 99.1%

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

    1. lift-/.f64 N/A

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

    2. lift-*.f64 N/A

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

    3. associate-/r* N/A

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

    4. lift-fma.f64 N/A

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

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

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

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

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

    7. metadata-eval N/A

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

    8. associate-/l/ N/A

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

    9. associate-/r* N/A

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

    10. lower-/.f64 N/A

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

11. Applied rewrites 99.8%

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

Alternative 2: 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 71.0%

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

   1. lift--.f64 N/A

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

   2. sub-neg N/A

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

   3. +-commutative N/A

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

   4. lift-/.f64 N/A

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

   5. distribute-neg-frac N/A

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

   6. lift-/.f64 N/A

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

   7. frac-add N/A

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

   8. *-commutative N/A

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

   9. lower-/.f64 N/A

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

   10. *-rgt-identity N/A

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

   11. lower-fma.f64 N/A

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

   12. metadata-eval N/A

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

   13. lower-*.f64 19.7

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

4. Applied rewrites 19.7%

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

   1. lift-+.f64 N/A

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

   2. lift-/.f64 N/A

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

   3. lift-/.f64 N/A

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

   4. frac-add N/A

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

   5. lower-/.f64 N/A

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

   6. *-commutative N/A

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

   7. *-rgt-identity N/A

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

   8. lower-fma.f64 N/A

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

   9. lift-fma.f64 N/A

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

   10. *-commutative N/A

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

   11. lower-fma.f64 N/A

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

   12. lift-+.f64 N/A

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

   13. +-commutative N/A

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

   14. lower-+.f64 N/A

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

   15. lift-*.f64 N/A

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

   16. *-commutative N/A

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

   17. lift-+.f64 N/A

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

   18. distribute-lft-in N/A

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

   19. *-rgt-identity N/A

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

   20. lower-fma.f64 N/A

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

6. Applied rewrites 19.6%

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

7. Taylor expanded in x around 0

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

9. Applied rewrites 99.1%

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

Alternative 3: 98.2% accurate, 2.1× speedup

Math

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

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(2.0 / Float64(Float64(x * x) * x))
end

MATLAB

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

Wolfram

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

Derivation

1. Initial program 71.0%

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

3. Taylor expanded in x around inf

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

   1. lower-/.f64 N/A

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

   2. lower-pow.f64 98.4

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

5. Applied rewrites 98.4%

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

7. Applied rewrites 98.4%

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

Alternative 4: 5.1% 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 71.0%

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

3. Taylor expanded in x around 0

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

   1. lower-/.f64 5.4

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

5. Applied rewrites 5.4%

   \[\leadsto \color{blue}{\frac{-2}{x}} \]
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 2024307 
(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))))