2frac (problem 3.3.1)

Percentage Accurate: 78.1% → 99.4%

Time: 4.2s

Alternatives: 6

Speedup: 1.6×

Specification

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 78.1% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 99.4% accurate, 1.6× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 82.8%

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

   1. lift--.f64 N/A

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

   2. lift-/.f64 N/A

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

   3. lift-/.f64 N/A

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

   4. frac-sub N/A

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

   5. lower-/.f64 N/A

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

   6. *-lft-identity N/A

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

   7. *-rgt-identity N/A

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

   8. lift-+.f64 N/A

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

   9. associate--r+ N/A

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

   10. lower--.f64 N/A

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

   11. lower--.f64 N/A

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

   12. lift-+.f64 N/A

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

   13. distribute-lft1-in N/A

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

   14. lower-fma.f64 99.5

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

4. Applied rewrites 99.5%

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

5. Taylor expanded in x around 0

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

7. Applied rewrites 99.5%

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

Alternative 2: 98.1% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1 \lor \neg \left(x \leq 1\right):\\ \;\;\;\;\frac{-1}{x \cdot x}\\ \mathbf{else}:\\ \;\;\;\;\frac{x - 1}{x} - x\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (if (or (<= x -1.0) (not (<= x 1.0))) (/ -1.0 (* x x)) (- (/ (- x 1.0) x) x)))

C

double code(double x) {
	double tmp;
	if ((x <= -1.0) || !(x <= 1.0)) {
		tmp = -1.0 / (x * x);
	} else {
		tmp = ((x - 1.0) / x) - x;
	}
	return tmp;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    real(8) :: tmp
    if ((x <= (-1.0d0)) .or. (.not. (x <= 1.0d0))) then
        tmp = (-1.0d0) / (x * x)
    else
        tmp = ((x - 1.0d0) / x) - x
    end if
    code = tmp
end function

Java

public static double code(double x) {
	double tmp;
	if ((x <= -1.0) || !(x <= 1.0)) {
		tmp = -1.0 / (x * x);
	} else {
		tmp = ((x - 1.0) / x) - x;
	}
	return tmp;
}

Python

def code(x):
	tmp = 0
	if (x <= -1.0) or not (x <= 1.0):
		tmp = -1.0 / (x * x)
	else:
		tmp = ((x - 1.0) / x) - x
	return tmp

Julia

function code(x)
	tmp = 0.0
	if ((x <= -1.0) || !(x <= 1.0))
		tmp = Float64(-1.0 / Float64(x * x));
	else
		tmp = Float64(Float64(Float64(x - 1.0) / x) - x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x)
	tmp = 0.0;
	if ((x <= -1.0) || ~((x <= 1.0)))
		tmp = -1.0 / (x * x);
	else
		tmp = ((x - 1.0) / x) - x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_] := If[Or[LessEqual[x, -1.0], N[Not[LessEqual[x, 1.0]], $MachinePrecision]], N[(-1.0 / N[(x * x), $MachinePrecision]), $MachinePrecision], N[(N[(N[(x - 1.0), $MachinePrecision] / x), $MachinePrecision] - x), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < -1 or 1 < x

   1. Initial program 62.9%

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

   3. Taylor expanded in x around inf

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

      1. unpow2 N/A

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

      2. associate-/r* N/A

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

      3. metadata-eval N/A

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

      4. distribute-neg-frac N/A

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

      5. lower-/.f64 N/A

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

      6. distribute-neg-frac N/A

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

      7. metadata-eval N/A

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

      8. lower-/.f64 98.2

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

   5. Applied rewrites 98.2%

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

   7. Applied rewrites 97.3%

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

   if -1 < x < 1

   1. Initial program 100.0%

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

   3. Taylor expanded in x around 0

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

      1. div-sub N/A

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

      2. sub-neg N/A

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

      3. *-commutative N/A

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

      4. associate-/l* N/A

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

      5. *-inverses N/A

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

      6. *-rgt-identity N/A

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

      7. +-commutative N/A

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

      8. associate-+r+ N/A

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

      9. neg-sub0 N/A

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

      10. associate-+l- N/A

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

      11. neg-sub0 N/A

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

      12. mul-1-neg N/A

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

      13. mul-1-neg N/A

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

      14. sub-neg N/A

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

      15. lower--.f64 N/A

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

      16. mul-1-neg N/A

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

      17. neg-sub0 N/A

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

      18. associate-+l- N/A

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

      19. neg-sub0 N/A

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

      20. +-commutative N/A

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

      21. sub-neg N/A

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

      22. *-inverses N/A

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

      23. div-sub N/A

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

      24. lower-/.f64 N/A

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

      25. lower--.f64 100.0

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

   5. Applied rewrites 100.0%

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

4. Final simplification 98.7%

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

Alternative 3: 97.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1 \lor \neg \left(x \leq 0.76\right):\\ \;\;\;\;\frac{-1}{x \cdot x}\\ \mathbf{else}:\\ \;\;\;\;\frac{x - 1}{x}\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (if (or (<= x -1.0) (not (<= x 0.76))) (/ -1.0 (* x x)) (/ (- x 1.0) x)))

C

double code(double x) {
	double tmp;
	if ((x <= -1.0) || !(x <= 0.76)) {
		tmp = -1.0 / (x * x);
	} else {
		tmp = (x - 1.0) / x;
	}
	return tmp;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    real(8) :: tmp
    if ((x <= (-1.0d0)) .or. (.not. (x <= 0.76d0))) then
        tmp = (-1.0d0) / (x * x)
    else
        tmp = (x - 1.0d0) / x
    end if
    code = tmp
end function

Java

public static double code(double x) {
	double tmp;
	if ((x <= -1.0) || !(x <= 0.76)) {
		tmp = -1.0 / (x * x);
	} else {
		tmp = (x - 1.0) / x;
	}
	return tmp;
}

Python

def code(x):
	tmp = 0
	if (x <= -1.0) or not (x <= 0.76):
		tmp = -1.0 / (x * x)
	else:
		tmp = (x - 1.0) / x
	return tmp

Julia

function code(x)
	tmp = 0.0
	if ((x <= -1.0) || !(x <= 0.76))
		tmp = Float64(-1.0 / Float64(x * x));
	else
		tmp = Float64(Float64(x - 1.0) / x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x)
	tmp = 0.0;
	if ((x <= -1.0) || ~((x <= 0.76)))
		tmp = -1.0 / (x * x);
	else
		tmp = (x - 1.0) / x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_] := If[Or[LessEqual[x, -1.0], N[Not[LessEqual[x, 0.76]], $MachinePrecision]], N[(-1.0 / N[(x * x), $MachinePrecision]), $MachinePrecision], N[(N[(x - 1.0), $MachinePrecision] / x), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < -1 or 0.76000000000000001 < x

   1. Initial program 62.9%

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

   3. Taylor expanded in x around inf

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

      1. unpow2 N/A

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

      2. associate-/r* N/A

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

      3. metadata-eval N/A

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

      4. distribute-neg-frac N/A

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

      5. lower-/.f64 N/A

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

      6. distribute-neg-frac N/A

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

      7. metadata-eval N/A

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

      8. lower-/.f64 98.2

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

   5. Applied rewrites 98.2%

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

   7. Applied rewrites 97.3%

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

   if -1 < x < 0.76000000000000001

   1. Initial program 100.0%

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

   3. Taylor expanded in x around 0

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

      1. lower-/.f64 N/A

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

      2. lower--.f64 99.8

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

   5. Applied rewrites 99.8%

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

4. Final simplification 98.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -1 \lor \neg \left(x \leq 0.76\right):\\ \;\;\;\;\frac{-1}{x \cdot x}\\ \mathbf{else}:\\ \;\;\;\;\frac{x - 1}{x}\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 52.0% accurate, 2.4× 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 82.8%

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

3. Taylor expanded in x around 0

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

   1. lower-/.f64 55.7

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

5. Applied rewrites 55.7%

   \[\leadsto \color{blue}{\frac{-1}{x}} \]
6. Add Preprocessing

Alternative 5: 3.2% accurate, 9.7× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

def code(x):
	return -x

Julia

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

MATLAB

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

Wolfram

code[x_] := (-x)

Derivation

1. Initial program 82.8%

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

3. Taylor expanded in x around 0

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

   1. div-sub N/A

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

   2. sub-neg N/A

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

   3. *-commutative N/A

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

   4. associate-/l* N/A

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

   5. *-inverses N/A

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

   6. *-rgt-identity N/A

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

   7. +-commutative N/A

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

   8. associate-+r+ N/A

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

   9. neg-sub0 N/A

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

   10. associate-+l- N/A

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

   11. neg-sub0 N/A

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

   12. mul-1-neg N/A

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

   13. mul-1-neg N/A

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

   14. sub-neg N/A

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

   15. lower--.f64 N/A

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

   16. mul-1-neg N/A

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

   17. neg-sub0 N/A

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

   18. associate-+l- N/A

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

   19. neg-sub0 N/A

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

   20. +-commutative N/A

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

   21. sub-neg N/A

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

   22. *-inverses N/A

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

   23. div-sub N/A

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

   24. lower-/.f64 N/A

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

   25. lower--.f64 54.6

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

5. Applied rewrites 54.6%

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

6. Taylor expanded in x around inf

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

8. Applied rewrites 3.0%

   \[\leadsto -x \]
9. Add Preprocessing

Alternative 6: 2.9% accurate, 29.0× 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 82.8%

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

3. Taylor expanded in x around 0

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

   1. lower-/.f64 N/A

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

   2. lower--.f64 54.7

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

5. Applied rewrites 54.7%

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

6. Taylor expanded in x around inf

   \[\leadsto 1 \]
7. Step-by-step derivation

8. Applied rewrites 2.8%

   \[\leadsto 1 \]
9. Add Preprocessing

Developer Target 1: 99.4% accurate, 1.5× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Reproduce

herbie shell --seed 2024318 
(FPCore (x)
  :name "2frac (problem 3.3.1)"
  :precision binary64

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

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