2frac (problem 3.3.1)

Percentage Accurate: 77.2% → 99.4%

Time: 4.9s

Alternatives: 5

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: 77.2% 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 78.1%

   \[\frac{1}{x + 1} - \frac{1}{x} \]
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 99.2

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

4. Applied rewrites 99.2%

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

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 99.2%

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

Alternative 2: 97.6% accurate, 0.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := {\left(x + 1\right)}^{-1} - {x}^{-1}\\ \mathbf{if}\;t\_0 \leq -4:\\ \;\;\;\;\left(1 - x\right) - {x}^{-1}\\ \mathbf{elif}\;t\_0 \leq 0:\\ \;\;\;\;\frac{-1}{x \cdot x}\\ \mathbf{else}:\\ \;\;\;\;\frac{-1}{x}\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (let* ((t_0 (- (pow (+ x 1.0) -1.0) (pow x -1.0))))
   (if (<= t_0 -4.0)
     (- (- 1.0 x) (pow x -1.0))
     (if (<= t_0 0.0) (/ -1.0 (* x x)) (/ -1.0 x)))))

C

double code(double x) {
	double t_0 = pow((x + 1.0), -1.0) - pow(x, -1.0);
	double tmp;
	if (t_0 <= -4.0) {
		tmp = (1.0 - x) - pow(x, -1.0);
	} else if (t_0 <= 0.0) {
		tmp = -1.0 / (x * x);
	} else {
		tmp = -1.0 / x;
	}
	return tmp;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    real(8) :: t_0
    real(8) :: tmp
    t_0 = ((x + 1.0d0) ** (-1.0d0)) - (x ** (-1.0d0))
    if (t_0 <= (-4.0d0)) then
        tmp = (1.0d0 - x) - (x ** (-1.0d0))
    else if (t_0 <= 0.0d0) then
        tmp = (-1.0d0) / (x * x)
    else
        tmp = (-1.0d0) / x
    end if
    code = tmp
end function

Java

public static double code(double x) {
	double t_0 = Math.pow((x + 1.0), -1.0) - Math.pow(x, -1.0);
	double tmp;
	if (t_0 <= -4.0) {
		tmp = (1.0 - x) - Math.pow(x, -1.0);
	} else if (t_0 <= 0.0) {
		tmp = -1.0 / (x * x);
	} else {
		tmp = -1.0 / x;
	}
	return tmp;
}

Python

def code(x):
	t_0 = math.pow((x + 1.0), -1.0) - math.pow(x, -1.0)
	tmp = 0
	if t_0 <= -4.0:
		tmp = (1.0 - x) - math.pow(x, -1.0)
	elif t_0 <= 0.0:
		tmp = -1.0 / (x * x)
	else:
		tmp = -1.0 / x
	return tmp

Julia

function code(x)
	t_0 = Float64((Float64(x + 1.0) ^ -1.0) - (x ^ -1.0))
	tmp = 0.0
	if (t_0 <= -4.0)
		tmp = Float64(Float64(1.0 - x) - (x ^ -1.0));
	elseif (t_0 <= 0.0)
		tmp = Float64(-1.0 / Float64(x * x));
	else
		tmp = Float64(-1.0 / x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x)
	t_0 = ((x + 1.0) ^ -1.0) - (x ^ -1.0);
	tmp = 0.0;
	if (t_0 <= -4.0)
		tmp = (1.0 - x) - (x ^ -1.0);
	elseif (t_0 <= 0.0)
		tmp = -1.0 / (x * x);
	else
		tmp = -1.0 / x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_] := Block[{t$95$0 = N[(N[Power[N[(x + 1.0), $MachinePrecision], -1.0], $MachinePrecision] - N[Power[x, -1.0], $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$0, -4.0], N[(N[(1.0 - x), $MachinePrecision] - N[Power[x, -1.0], $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$0, 0.0], N[(-1.0 / N[(x * x), $MachinePrecision]), $MachinePrecision], N[(-1.0 / x), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if (-.f64 (/.f64 #s(literal 1 binary64) (+.f64 x #s(literal 1 binary64))) (/.f64 #s(literal 1 binary64) x)) < -4

   1. Initial program 100.0%

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

   3. Taylor expanded in x around 0

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

      1. mul-1-neg N/A

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

      2. unsub-neg N/A

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

      3. lower--.f64 98.6

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

   5. Applied rewrites 98.6%

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

   if -4 < (-.f64 (/.f64 #s(literal 1 binary64) (+.f64 x #s(literal 1 binary64))) (/.f64 #s(literal 1 binary64) x)) < 0.0

   1. Initial program 55.9%

      \[\frac{1}{x + 1} - \frac{1}{x} \]
   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 98.5

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

   4. Applied rewrites 98.5%

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

   5. Taylor expanded in x around inf

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

      1. lower-/.f64 N/A

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

      2. unpow2 N/A

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

      3. lower-*.f64 97.3

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

   7. Applied rewrites 97.3%

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

   if 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

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

      1. lower-/.f64 100.0

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

   5. Applied rewrites 100.0%

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

4. Final simplification 98.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;{\left(x + 1\right)}^{-1} - {x}^{-1} \leq -4:\\ \;\;\;\;\left(1 - x\right) - {x}^{-1}\\ \mathbf{elif}\;{\left(x + 1\right)}^{-1} - {x}^{-1} \leq 0:\\ \;\;\;\;\frac{-1}{x \cdot x}\\ \mathbf{else}:\\ \;\;\;\;\frac{-1}{x}\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 97.5% accurate, 0.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := {\left(x + 1\right)}^{-1} - {x}^{-1}\\ \mathbf{if}\;t\_0 \leq -4:\\ \;\;\;\;\frac{x - 1}{x}\\ \mathbf{elif}\;t\_0 \leq 0:\\ \;\;\;\;\frac{-1}{x \cdot x}\\ \mathbf{else}:\\ \;\;\;\;\frac{-1}{x}\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (let* ((t_0 (- (pow (+ x 1.0) -1.0) (pow x -1.0))))
   (if (<= t_0 -4.0)
     (/ (- x 1.0) x)
     (if (<= t_0 0.0) (/ -1.0 (* x x)) (/ -1.0 x)))))

C

double code(double x) {
	double t_0 = pow((x + 1.0), -1.0) - pow(x, -1.0);
	double tmp;
	if (t_0 <= -4.0) {
		tmp = (x - 1.0) / x;
	} else if (t_0 <= 0.0) {
		tmp = -1.0 / (x * x);
	} else {
		tmp = -1.0 / x;
	}
	return tmp;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    real(8) :: t_0
    real(8) :: tmp
    t_0 = ((x + 1.0d0) ** (-1.0d0)) - (x ** (-1.0d0))
    if (t_0 <= (-4.0d0)) then
        tmp = (x - 1.0d0) / x
    else if (t_0 <= 0.0d0) then
        tmp = (-1.0d0) / (x * x)
    else
        tmp = (-1.0d0) / x
    end if
    code = tmp
end function

Java

public static double code(double x) {
	double t_0 = Math.pow((x + 1.0), -1.0) - Math.pow(x, -1.0);
	double tmp;
	if (t_0 <= -4.0) {
		tmp = (x - 1.0) / x;
	} else if (t_0 <= 0.0) {
		tmp = -1.0 / (x * x);
	} else {
		tmp = -1.0 / x;
	}
	return tmp;
}

Python

def code(x):
	t_0 = math.pow((x + 1.0), -1.0) - math.pow(x, -1.0)
	tmp = 0
	if t_0 <= -4.0:
		tmp = (x - 1.0) / x
	elif t_0 <= 0.0:
		tmp = -1.0 / (x * x)
	else:
		tmp = -1.0 / x
	return tmp

Julia

function code(x)
	t_0 = Float64((Float64(x + 1.0) ^ -1.0) - (x ^ -1.0))
	tmp = 0.0
	if (t_0 <= -4.0)
		tmp = Float64(Float64(x - 1.0) / x);
	elseif (t_0 <= 0.0)
		tmp = Float64(-1.0 / Float64(x * x));
	else
		tmp = Float64(-1.0 / x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x)
	t_0 = ((x + 1.0) ^ -1.0) - (x ^ -1.0);
	tmp = 0.0;
	if (t_0 <= -4.0)
		tmp = (x - 1.0) / x;
	elseif (t_0 <= 0.0)
		tmp = -1.0 / (x * x);
	else
		tmp = -1.0 / x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_] := Block[{t$95$0 = N[(N[Power[N[(x + 1.0), $MachinePrecision], -1.0], $MachinePrecision] - N[Power[x, -1.0], $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$0, -4.0], N[(N[(x - 1.0), $MachinePrecision] / x), $MachinePrecision], If[LessEqual[t$95$0, 0.0], N[(-1.0 / N[(x * x), $MachinePrecision]), $MachinePrecision], N[(-1.0 / x), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if (-.f64 (/.f64 #s(literal 1 binary64) (+.f64 x #s(literal 1 binary64))) (/.f64 #s(literal 1 binary64) x)) < -4

   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 98.1

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

   5. Applied rewrites 98.1%

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

   if -4 < (-.f64 (/.f64 #s(literal 1 binary64) (+.f64 x #s(literal 1 binary64))) (/.f64 #s(literal 1 binary64) x)) < 0.0

   1. Initial program 55.9%

      \[\frac{1}{x + 1} - \frac{1}{x} \]
   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 98.5

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

   4. Applied rewrites 98.5%

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

   5. Taylor expanded in x around inf

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

      1. lower-/.f64 N/A

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

      2. unpow2 N/A

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

      3. lower-*.f64 97.3

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

   7. Applied rewrites 97.3%

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

   if 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

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

      1. lower-/.f64 100.0

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

   5. Applied rewrites 100.0%

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

4. Final simplification 98.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;{\left(x + 1\right)}^{-1} - {x}^{-1} \leq -4:\\ \;\;\;\;\frac{x - 1}{x}\\ \mathbf{elif}\;{\left(x + 1\right)}^{-1} - {x}^{-1} \leq 0:\\ \;\;\;\;\frac{-1}{x \cdot x}\\ \mathbf{else}:\\ \;\;\;\;\frac{-1}{x}\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 52.1% 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 78.1%

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

3. Taylor expanded in x around 0

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

   1. lower-/.f64 52.7

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

5. Applied rewrites 52.7%

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

6. Final simplification 52.7%

   \[\leadsto \frac{-1}{x} \]
7. 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 78.1%

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

3. Taylor expanded in x around 0

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

   1. div-sub N/A

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

   2. sub-neg N/A

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

   3. *-commutative N/A

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

   4. associate-/l* N/A

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

   5. *-inverses N/A

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

   6. *-rgt-identity N/A

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

   7. +-commutative N/A

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

   8. associate-+r+ N/A

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

   9. neg-sub0 N/A

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

   10. associate-+l- N/A

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

   11. neg-sub0 N/A

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

   12. sub-neg N/A

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

   13. remove-double-neg N/A

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

   14. mul-1-neg N/A

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

   15. distribute-neg-in N/A

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

   16. +-commutative N/A

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

   17. distribute-rgt-neg-in N/A

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

   18. distribute-neg-in N/A

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

5. Applied rewrites 50.9%

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

6. Taylor expanded in x around inf

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

8. Applied rewrites 2.7%

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

9. Taylor expanded in x around 0

   \[\leadsto -1 \cdot x \]
10. Step-by-step derivation

11. Applied rewrites 3.1%

    \[\leadsto -x \]

12. Final simplification 3.1%

    \[\leadsto -x \]
13. 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 2024314 
(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)))