3frac (problem 3.3.3)

Percentage Accurate: 69.3% → 99.8%

Time: 8.8s

Alternatives: 7

Speedup: 1.4×

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: 69.3% 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, 0.1× 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 69.0%

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

   1. sub-neg 69.0%

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

   2. distribute-neg-frac 69.0%

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

   3. metadata-eval 69.0%

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

   4. metadata-eval 69.0%

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

   5. metadata-eval 69.0%

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

   6. associate-/r* 69.0%

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

   7. metadata-eval 69.0%

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

   8. neg-mul-1 69.0%

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

   9. +-commutative 69.0%

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

   10. associate-+l+ 69.0%

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

   11. +-commutative 69.0%

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

   12. neg-mul-1 69.0%

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

   13. metadata-eval 69.0%

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

   14. associate-/r* 69.0%

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

   15. metadata-eval 69.0%

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

   16. metadata-eval 69.0%

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

   17. +-commutative 69.0%

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

   18. +-commutative 69.0%

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

   19. sub-neg 69.0%

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

   20. metadata-eval 69.0%

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

3. Simplified 69.0%

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

   1. +-commutative 69.0%

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

   2. frac-add 19.5%

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

   3. frac-add 22.2%

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

   4. *-un-lft-identity 22.2%

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

   5. *-rgt-identity 22.2%

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

   6. +-commutative 22.2%

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

   7. +-commutative 22.2%

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

   8. +-commutative 22.2%

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

   9. +-commutative 22.2%

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

6. Applied egg-rr 22.2%

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

7. Taylor expanded in x around 0 99.1%

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

   1. expm1-log1p-u 99.1%

      \[\leadsto \color{blue}{\mathsf{expm1}\left(\mathsf{log1p}\left(\frac{2}{\left(\left(x + 1\right) \cdot \left(x + -1\right)\right) \cdot x}\right)\right)} \]

   2. expm1-udef 68.6%

      \[\leadsto \color{blue}{e^{\mathsf{log1p}\left(\frac{2}{\left(\left(x + 1\right) \cdot \left(x + -1\right)\right) \cdot x}\right)} - 1} \]

   3. *-commutative 68.6%

      \[\leadsto e^{\mathsf{log1p}\left(\frac{2}{\color{blue}{x \cdot \left(\left(x + 1\right) \cdot \left(x + -1\right)\right)}}\right)} - 1 \]

   4. metadata-eval 68.6%

      \[\leadsto e^{\mathsf{log1p}\left(\frac{2}{x \cdot \left(\left(x + 1\right) \cdot \left(x + \color{blue}{\left(-1\right)}\right)\right)}\right)} - 1 \]

   5. sub-neg 68.6%

      \[\leadsto e^{\mathsf{log1p}\left(\frac{2}{x \cdot \left(\left(x + 1\right) \cdot \color{blue}{\left(x - 1\right)}\right)}\right)} - 1 \]

   6. difference-of-sqr-1 68.6%

      \[\leadsto e^{\mathsf{log1p}\left(\frac{2}{x \cdot \color{blue}{\left(x \cdot x - 1\right)}}\right)} - 1 \]

   7. fma-neg 68.6%

      \[\leadsto e^{\mathsf{log1p}\left(\frac{2}{x \cdot \color{blue}{\mathsf{fma}\left(x, x, -1\right)}}\right)} - 1 \]

   8. metadata-eval 68.6%

      \[\leadsto e^{\mathsf{log1p}\left(\frac{2}{x \cdot \mathsf{fma}\left(x, x, \color{blue}{-1}\right)}\right)} - 1 \]

9. Applied egg-rr 68.6%

   \[\leadsto \color{blue}{e^{\mathsf{log1p}\left(\frac{2}{x \cdot \mathsf{fma}\left(x, x, -1\right)}\right)} - 1} \]
10. Step-by-step derivation

    1. expm1-def 99.1%

       \[\leadsto \color{blue}{\mathsf{expm1}\left(\mathsf{log1p}\left(\frac{2}{x \cdot \mathsf{fma}\left(x, x, -1\right)}\right)\right)} \]

    2. expm1-log1p 99.1%

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

    3. associate-/r* 99.8%

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

11. Simplified 99.8%

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

12. Final simplification 99.8%

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

Alternative 2: 99.2% accurate, 1.4× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 69.0%

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

   1. sub-neg 69.0%

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

   2. distribute-neg-frac 69.0%

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

   3. metadata-eval 69.0%

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

   4. metadata-eval 69.0%

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

   5. metadata-eval 69.0%

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

   6. associate-/r* 69.0%

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

   7. metadata-eval 69.0%

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

   8. neg-mul-1 69.0%

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

   9. +-commutative 69.0%

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

   10. associate-+l+ 69.0%

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

   11. +-commutative 69.0%

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

   12. neg-mul-1 69.0%

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

   13. metadata-eval 69.0%

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

   14. associate-/r* 69.0%

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

   15. metadata-eval 69.0%

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

   16. metadata-eval 69.0%

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

   17. +-commutative 69.0%

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

   18. +-commutative 69.0%

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

   19. sub-neg 69.0%

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

   20. metadata-eval 69.0%

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

3. Simplified 69.0%

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

   1. +-commutative 69.0%

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

   2. frac-add 19.5%

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

   3. frac-add 22.2%

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

   4. *-un-lft-identity 22.2%

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

   5. *-rgt-identity 22.2%

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

   6. +-commutative 22.2%

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

   7. +-commutative 22.2%

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

   8. +-commutative 22.2%

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

   9. +-commutative 22.2%

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

6. Applied egg-rr 22.2%

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

7. Taylor expanded in x around 0 99.1%

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

8. Final simplification 99.1%

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

Alternative 3: 68.0% accurate, 1.7× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 69.0%

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

   1. sub-neg 69.0%

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

   2. distribute-neg-frac 69.0%

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

   3. metadata-eval 69.0%

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

   4. metadata-eval 69.0%

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

   5. metadata-eval 69.0%

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

   6. associate-/r* 69.0%

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

   7. metadata-eval 69.0%

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

   8. neg-mul-1 69.0%

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

   9. +-commutative 69.0%

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

   10. associate-+l+ 69.0%

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

   11. +-commutative 69.0%

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

   12. neg-mul-1 69.0%

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

   13. metadata-eval 69.0%

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

   14. associate-/r* 69.0%

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

   15. metadata-eval 69.0%

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

   16. metadata-eval 69.0%

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

   17. +-commutative 69.0%

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

   18. +-commutative 69.0%

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

   19. sub-neg 69.0%

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

   20. metadata-eval 69.0%

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

3. Simplified 69.0%

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

5. Taylor expanded in x around inf 67.8%

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

   1. expm1-log1p-u 67.8%

      \[\leadsto \color{blue}{\mathsf{expm1}\left(\mathsf{log1p}\left(\frac{-2}{x} + \left(\frac{1}{x} + \frac{1}{x + -1}\right)\right)\right)} \]

   2. expm1-udef 67.8%

      \[\leadsto \color{blue}{e^{\mathsf{log1p}\left(\frac{-2}{x} + \left(\frac{1}{x} + \frac{1}{x + -1}\right)\right)} - 1} \]

   3. associate-+r+ 67.8%

      \[\leadsto e^{\mathsf{log1p}\left(\color{blue}{\left(\frac{-2}{x} + \frac{1}{x}\right) + \frac{1}{x + -1}}\right)} - 1 \]

   4. div-inv 67.8%

      \[\leadsto e^{\mathsf{log1p}\left(\left(\color{blue}{-2 \cdot \frac{1}{x}} + \frac{1}{x}\right) + \frac{1}{x + -1}\right)} - 1 \]

   5. *-un-lft-identity 67.8%

      \[\leadsto e^{\mathsf{log1p}\left(\left(-2 \cdot \frac{1}{x} + \color{blue}{1 \cdot \frac{1}{x}}\right) + \frac{1}{x + -1}\right)} - 1 \]

   6. distribute-rgt-out 67.8%

      \[\leadsto e^{\mathsf{log1p}\left(\color{blue}{\frac{1}{x} \cdot \left(-2 + 1\right)} + \frac{1}{x + -1}\right)} - 1 \]

   7. metadata-eval 67.8%

      \[\leadsto e^{\mathsf{log1p}\left(\frac{1}{x} \cdot \color{blue}{-1} + \frac{1}{x + -1}\right)} - 1 \]

7. Applied egg-rr 67.8%

   \[\leadsto \color{blue}{e^{\mathsf{log1p}\left(\frac{1}{x} \cdot -1 + \frac{1}{x + -1}\right)} - 1} \]
8. Step-by-step derivation

   1. expm1-def 67.8%

      \[\leadsto \color{blue}{\mathsf{expm1}\left(\mathsf{log1p}\left(\frac{1}{x} \cdot -1 + \frac{1}{x + -1}\right)\right)} \]

   2. expm1-log1p 67.8%

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

   3. *-commutative 67.8%

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

   4. neg-mul-1 67.8%

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

   5. neg-sub0 67.8%

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

   6. associate--r- 67.8%

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

   7. sub-neg 67.8%

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

   8. associate--r+ 67.8%

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

   9. neg-sub0 67.8%

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

   10. distribute-neg-frac 67.8%

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

   11. metadata-eval 67.8%

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

   12. distribute-neg-frac 67.8%

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

   13. metadata-eval 67.8%

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

   14. +-commutative 67.8%

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

9. Simplified 67.8%

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

10. Final simplification 67.8%

    \[\leadsto \frac{-1}{x} - \frac{-1}{x + -1} \]
11. Add Preprocessing

Alternative 4: 67.8% accurate, 2.1× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 69.0%

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

   1. sub-neg 69.0%

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

   2. distribute-neg-frac 69.0%

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

   3. metadata-eval 69.0%

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

   4. metadata-eval 69.0%

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

   5. metadata-eval 69.0%

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

   6. associate-/r* 69.0%

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

   7. metadata-eval 69.0%

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

   8. neg-mul-1 69.0%

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

   9. +-commutative 69.0%

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

   10. associate-+l+ 69.0%

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

   11. +-commutative 69.0%

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

   12. neg-mul-1 69.0%

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

   13. metadata-eval 69.0%

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

   14. associate-/r* 69.0%

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

   15. metadata-eval 69.0%

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

   16. metadata-eval 69.0%

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

   17. +-commutative 69.0%

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

   18. +-commutative 69.0%

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

   19. sub-neg 69.0%

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

   20. metadata-eval 69.0%

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

3. Simplified 69.0%

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

5. Taylor expanded in x around inf 67.6%

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

6. Final simplification 67.6%

   \[\leadsto \frac{2}{x} + \frac{-2}{x} \]
7. Add Preprocessing

Alternative 5: 5.0% 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 69.0%

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

   1. sub-neg 69.0%

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

   2. distribute-neg-frac 69.0%

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

   3. metadata-eval 69.0%

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

   4. metadata-eval 69.0%

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

   5. metadata-eval 69.0%

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

   6. associate-/r* 69.0%

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

   7. metadata-eval 69.0%

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

   8. neg-mul-1 69.0%

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

   9. +-commutative 69.0%

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

   10. associate-+l+ 69.0%

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

   11. +-commutative 69.0%

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

   12. neg-mul-1 69.0%

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

   13. metadata-eval 69.0%

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

   14. associate-/r* 69.0%

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

   15. metadata-eval 69.0%

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

   16. metadata-eval 69.0%

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

   17. +-commutative 69.0%

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

   18. +-commutative 69.0%

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

   19. sub-neg 69.0%

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

   20. metadata-eval 69.0%

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

3. Simplified 69.0%

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

5. Taylor expanded in x around 0 5.0%

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

6. Final simplification 5.0%

   \[\leadsto \frac{-2}{x} \]
7. Add Preprocessing

Alternative 6: 5.1% accurate, 5.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

def code(x):
	return -1.0 / x

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 69.0%

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

   1. sub-neg 69.0%

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

   2. distribute-neg-frac 69.0%

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

   3. metadata-eval 69.0%

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

   4. metadata-eval 69.0%

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

   5. metadata-eval 69.0%

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

   6. associate-/r* 69.0%

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

   7. metadata-eval 69.0%

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

   8. neg-mul-1 69.0%

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

   9. +-commutative 69.0%

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

   10. associate-+l+ 69.0%

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

   11. +-commutative 69.0%

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

   12. neg-mul-1 69.0%

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

   13. metadata-eval 69.0%

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

   14. associate-/r* 69.0%

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

   15. metadata-eval 69.0%

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

   16. metadata-eval 69.0%

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

   17. +-commutative 69.0%

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

   18. +-commutative 69.0%

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

   19. sub-neg 69.0%

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

   20. metadata-eval 69.0%

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

3. Simplified 69.0%

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

5. Taylor expanded in x around inf 67.8%

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

6. Taylor expanded in x around 0 5.0%

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

7. Final simplification 5.0%

   \[\leadsto \frac{-1}{x} \]
8. Add Preprocessing

Alternative 7: 3.4% accurate, 15.0× speedup

Math

\[\begin{array}{l} \\ 1 \end{array} \]

FPCore

(FPCore (x) :precision binary64 1.0)

C

double code(double x) {
	return 1.0;
}

Fortran

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

Java

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

Python

def code(x):
	return 1.0

Julia

function code(x)
	return 1.0
end

MATLAB

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

Wolfram

code[x_] := 1.0

Derivation

1. Initial program 69.0%

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

   1. associate-+l- 69.0%

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

   2. sub-neg 69.0%

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

   3. +-commutative 69.0%

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

   4. neg-sub0 69.0%

      \[\leadsto \frac{1}{1 + x} + \color{blue}{\left(0 - \left(\frac{2}{x} - \frac{1}{x - 1}\right)\right)} \]

   5. associate-+l- 69.0%

      \[\leadsto \frac{1}{1 + x} + \color{blue}{\left(\left(0 - \frac{2}{x}\right) + \frac{1}{x - 1}\right)} \]

   6. neg-sub0 69.0%

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

   7. distribute-neg-frac 69.0%

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

   8. metadata-eval 69.0%

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

   9. sub-neg 69.0%

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

   10. metadata-eval 69.0%

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

3. Simplified 69.0%

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

5. Taylor expanded in x around 0 3.4%

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

6. Taylor expanded in x around inf 3.4%

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

7. Taylor expanded in x around inf 3.4%

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

8. Final simplification 3.4%

   \[\leadsto 1 \]
9. Add Preprocessing

Developer target: 99.2% 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 2024026 
(FPCore (x)
  :name "3frac (problem 3.3.3)"
  :precision binary64
  :pre (> (fabs x) 1.0)

  :herbie-target
  (/ 2.0 (* x (- (* x x) 1.0)))

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