2frac (problem 3.3.1)

Percentage Accurate: 77.4% → 99.9%

Time: 5.7s

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: 77.4% 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: 99.9% accurate, 1.1× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 73.0%

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

4. Applied rewrites 99.9%

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

   1. lift-neg.f64 N/A

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

   2. lift-/.f64 N/A

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

   3. distribute-neg-frac N/A

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

   4. lift--.f64 N/A

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

   5. lift--.f64 N/A

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

   6. +-inverses N/A

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

   7. metadata-eval N/A

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

   8. metadata-eval N/A

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

   9. lower-/.f64 99.9

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

6. Applied rewrites 99.9%

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

Alternative 2: 98.0% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{1}{x + 1} - \frac{1}{x}\\ t_1 := \frac{x - 1}{x} - x\\ \mathbf{if}\;t\_0 \leq -5:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;t\_0 \leq 0:\\ \;\;\;\;\frac{-1}{x \cdot x}\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (let* ((t_0 (- (/ 1.0 (+ x 1.0)) (/ 1.0 x))) (t_1 (- (/ (- x 1.0) x) x)))
   (if (<= t_0 -5.0) t_1 (if (<= t_0 0.0) (/ -1.0 (* x x)) t_1))))

C

double code(double x) {
	double t_0 = (1.0 / (x + 1.0)) - (1.0 / x);
	double t_1 = ((x - 1.0) / x) - x;
	double tmp;
	if (t_0 <= -5.0) {
		tmp = t_1;
	} else if (t_0 <= 0.0) {
		tmp = -1.0 / (x * x);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    real(8) :: t_0
    real(8) :: t_1
    real(8) :: tmp
    t_0 = (1.0d0 / (x + 1.0d0)) - (1.0d0 / x)
    t_1 = ((x - 1.0d0) / x) - x
    if (t_0 <= (-5.0d0)) then
        tmp = t_1
    else if (t_0 <= 0.0d0) then
        tmp = (-1.0d0) / (x * x)
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x) {
	double t_0 = (1.0 / (x + 1.0)) - (1.0 / x);
	double t_1 = ((x - 1.0) / x) - x;
	double tmp;
	if (t_0 <= -5.0) {
		tmp = t_1;
	} else if (t_0 <= 0.0) {
		tmp = -1.0 / (x * x);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x):
	t_0 = (1.0 / (x + 1.0)) - (1.0 / x)
	t_1 = ((x - 1.0) / x) - x
	tmp = 0
	if t_0 <= -5.0:
		tmp = t_1
	elif t_0 <= 0.0:
		tmp = -1.0 / (x * x)
	else:
		tmp = t_1
	return tmp

Julia

function code(x)
	t_0 = Float64(Float64(1.0 / Float64(x + 1.0)) - Float64(1.0 / x))
	t_1 = Float64(Float64(Float64(x - 1.0) / x) - x)
	tmp = 0.0
	if (t_0 <= -5.0)
		tmp = t_1;
	elseif (t_0 <= 0.0)
		tmp = Float64(-1.0 / Float64(x * x));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x)
	t_0 = (1.0 / (x + 1.0)) - (1.0 / x);
	t_1 = ((x - 1.0) / x) - x;
	tmp = 0.0;
	if (t_0 <= -5.0)
		tmp = t_1;
	elseif (t_0 <= 0.0)
		tmp = -1.0 / (x * x);
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_] := Block[{t$95$0 = N[(N[(1.0 / N[(x + 1.0), $MachinePrecision]), $MachinePrecision] - N[(1.0 / x), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(N[(N[(x - 1.0), $MachinePrecision] / x), $MachinePrecision] - x), $MachinePrecision]}, If[LessEqual[t$95$0, -5.0], t$95$1, If[LessEqual[t$95$0, 0.0], N[(-1.0 / N[(x * x), $MachinePrecision]), $MachinePrecision], t$95$1]]]]

Derivation

1. Split input into 2 regimes

2. if (-.f64 (/.f64 #s(literal 1 binary64) (+.f64 x #s(literal 1 binary64))) (/.f64 #s(literal 1 binary64) x)) < -5 or 0.0 < (-.f64 (/.f64 #s(literal 1 binary64) (+.f64 x #s(literal 1 binary64))) (/.f64 #s(literal 1 binary64) x))

   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. *-inverses N/A

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

      22. sub-neg N/A

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

      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 99.2

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

   5. Applied rewrites 99.2%

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

   if -5 < (-.f64 (/.f64 #s(literal 1 binary64) (+.f64 x #s(literal 1 binary64))) (/.f64 #s(literal 1 binary64) x)) < 0.0

   1. Initial program 53.3%

      \[\frac{1}{x + 1} - \frac{1}{x} \]
   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 98.6

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

   5. Applied rewrites 98.6%

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

   7. Applied rewrites 97.1%

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

Alternative 3: 97.9% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{1}{x + 1} - \frac{1}{x}\\ t_1 := \frac{x - 1}{x}\\ \mathbf{if}\;t\_0 \leq -5:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;t\_0 \leq 0:\\ \;\;\;\;\frac{-1}{x \cdot x}\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (let* ((t_0 (- (/ 1.0 (+ x 1.0)) (/ 1.0 x))) (t_1 (/ (- x 1.0) x)))
   (if (<= t_0 -5.0) t_1 (if (<= t_0 0.0) (/ -1.0 (* x x)) t_1))))

C

double code(double x) {
	double t_0 = (1.0 / (x + 1.0)) - (1.0 / x);
	double t_1 = (x - 1.0) / x;
	double tmp;
	if (t_0 <= -5.0) {
		tmp = t_1;
	} else if (t_0 <= 0.0) {
		tmp = -1.0 / (x * x);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    real(8) :: t_0
    real(8) :: t_1
    real(8) :: tmp
    t_0 = (1.0d0 / (x + 1.0d0)) - (1.0d0 / x)
    t_1 = (x - 1.0d0) / x
    if (t_0 <= (-5.0d0)) then
        tmp = t_1
    else if (t_0 <= 0.0d0) then
        tmp = (-1.0d0) / (x * x)
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x) {
	double t_0 = (1.0 / (x + 1.0)) - (1.0 / x);
	double t_1 = (x - 1.0) / x;
	double tmp;
	if (t_0 <= -5.0) {
		tmp = t_1;
	} else if (t_0 <= 0.0) {
		tmp = -1.0 / (x * x);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x):
	t_0 = (1.0 / (x + 1.0)) - (1.0 / x)
	t_1 = (x - 1.0) / x
	tmp = 0
	if t_0 <= -5.0:
		tmp = t_1
	elif t_0 <= 0.0:
		tmp = -1.0 / (x * x)
	else:
		tmp = t_1
	return tmp

Julia

function code(x)
	t_0 = Float64(Float64(1.0 / Float64(x + 1.0)) - Float64(1.0 / x))
	t_1 = Float64(Float64(x - 1.0) / x)
	tmp = 0.0
	if (t_0 <= -5.0)
		tmp = t_1;
	elseif (t_0 <= 0.0)
		tmp = Float64(-1.0 / Float64(x * x));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x)
	t_0 = (1.0 / (x + 1.0)) - (1.0 / x);
	t_1 = (x - 1.0) / x;
	tmp = 0.0;
	if (t_0 <= -5.0)
		tmp = t_1;
	elseif (t_0 <= 0.0)
		tmp = -1.0 / (x * x);
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_] := Block[{t$95$0 = N[(N[(1.0 / N[(x + 1.0), $MachinePrecision]), $MachinePrecision] - N[(1.0 / x), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(N[(x - 1.0), $MachinePrecision] / x), $MachinePrecision]}, If[LessEqual[t$95$0, -5.0], t$95$1, If[LessEqual[t$95$0, 0.0], N[(-1.0 / N[(x * x), $MachinePrecision]), $MachinePrecision], t$95$1]]]]

Derivation

1. Split input into 2 regimes

2. if (-.f64 (/.f64 #s(literal 1 binary64) (+.f64 x #s(literal 1 binary64))) (/.f64 #s(literal 1 binary64) x)) < -5 or 0.0 < (-.f64 (/.f64 #s(literal 1 binary64) (+.f64 x #s(literal 1 binary64))) (/.f64 #s(literal 1 binary64) x))

   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 98.4

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

   5. Applied rewrites 98.4%

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

   if -5 < (-.f64 (/.f64 #s(literal 1 binary64) (+.f64 x #s(literal 1 binary64))) (/.f64 #s(literal 1 binary64) x)) < 0.0

   1. Initial program 53.3%

      \[\frac{1}{x + 1} - \frac{1}{x} \]
   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 98.6

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

   5. Applied rewrites 98.6%

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

   7. Applied rewrites 97.1%

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

Alternative 4: 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 73.0%

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

4. Applied rewrites 99.9%

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

   1. lift-/.f64 N/A

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

   2. div-inv N/A

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

   3. lift-neg.f64 N/A

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

   4. lift-/.f64 N/A

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

   5. distribute-neg-frac N/A

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

   6. lift--.f64 N/A

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

   7. lift--.f64 N/A

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

   8. +-inverses N/A

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

   9. metadata-eval N/A

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

   10. metadata-eval N/A

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

   11. associate-*l/ N/A

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

   12. lift--.f64 N/A

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

   13. sub-neg N/A

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

   14. metadata-eval N/A

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

   15. distribute-neg-in N/A

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

   16. +-commutative N/A

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

   17. neg-mul-1 N/A

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

   18. times-frac N/A

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

   19. metadata-eval N/A

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

   20. div-inv N/A

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

   21. associate-*l/ N/A

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

6. Applied rewrites 99.0%

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

Alternative 5: 51.9% 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 73.0%

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

3. Taylor expanded in x around 0

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

   1. lower-/.f64 44.2

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

5. Applied rewrites 44.2%

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

Alternative 6: 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 73.0%

   \[\frac{1}{x + 1} - \frac{1}{x} \]
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. *-inverses N/A

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

   22. sub-neg N/A

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

   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 43.2

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

5. Applied rewrites 43.2%

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

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

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 2024255 
(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)))