2frac (problem 3.3.1)

Percentage Accurate: 77.4% → 99.9%

Time: 4.5s

Alternatives: 6

Speedup: 1.3×

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, 1.3× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 76.6%

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

   1. frac-sub 77.3%

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

   2. div-inv 77.3%

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

   3. *-un-lft-identity 77.3%

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

   4. *-rgt-identity 77.3%

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

   5. +-commutative 77.3%

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

   6. metadata-eval 77.3%

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

   7. frac-times 77.4%

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

   8. clear-num 77.4%

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

   9. associate-*l/ 77.4%

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

   10. *-un-lft-identity 77.4%

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

   11. div-inv 77.4%

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

   12. metadata-eval 77.4%

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

   13. *-rgt-identity 77.4%

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

   14. +-commutative 77.4%

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

4. Applied egg-rr 77.4%

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

5. Taylor expanded in x around 0 99.9%

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

   1. associate-*r/ 99.9%

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

   2. div-inv 99.9%

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

7. Applied egg-rr 99.9%

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

8. Final simplification 99.9%

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

Alternative 2: 96.5% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{-1}{x} + \frac{1}{x + 1}\\ \mathbf{if}\;t\_0 \leq -2 \cdot 10^{+28}:\\ \;\;\;\;\frac{-1}{x}\\ \mathbf{elif}\;t\_0 \leq 0:\\ \;\;\;\;\frac{\frac{-1}{x}}{x}\\ \mathbf{else}:\\ \;\;\;\;\frac{-1}{x} + 1\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (let* ((t_0 (+ (/ -1.0 x) (/ 1.0 (+ x 1.0)))))
   (if (<= t_0 -2e+28)
     (/ -1.0 x)
     (if (<= t_0 0.0) (/ (/ -1.0 x) x) (+ (/ -1.0 x) 1.0)))))

C

double code(double x) {
	double t_0 = (-1.0 / x) + (1.0 / (x + 1.0));
	double tmp;
	if (t_0 <= -2e+28) {
		tmp = -1.0 / x;
	} else if (t_0 <= 0.0) {
		tmp = (-1.0 / x) / x;
	} else {
		tmp = (-1.0 / x) + 1.0;
	}
	return tmp;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    real(8) :: t_0
    real(8) :: tmp
    t_0 = ((-1.0d0) / x) + (1.0d0 / (x + 1.0d0))
    if (t_0 <= (-2d+28)) then
        tmp = (-1.0d0) / x
    else if (t_0 <= 0.0d0) then
        tmp = ((-1.0d0) / x) / x
    else
        tmp = ((-1.0d0) / x) + 1.0d0
    end if
    code = tmp
end function

Java

public static double code(double x) {
	double t_0 = (-1.0 / x) + (1.0 / (x + 1.0));
	double tmp;
	if (t_0 <= -2e+28) {
		tmp = -1.0 / x;
	} else if (t_0 <= 0.0) {
		tmp = (-1.0 / x) / x;
	} else {
		tmp = (-1.0 / x) + 1.0;
	}
	return tmp;
}

Python

def code(x):
	t_0 = (-1.0 / x) + (1.0 / (x + 1.0))
	tmp = 0
	if t_0 <= -2e+28:
		tmp = -1.0 / x
	elif t_0 <= 0.0:
		tmp = (-1.0 / x) / x
	else:
		tmp = (-1.0 / x) + 1.0
	return tmp

Julia

function code(x)
	t_0 = Float64(Float64(-1.0 / x) + Float64(1.0 / Float64(x + 1.0)))
	tmp = 0.0
	if (t_0 <= -2e+28)
		tmp = Float64(-1.0 / x);
	elseif (t_0 <= 0.0)
		tmp = Float64(Float64(-1.0 / x) / x);
	else
		tmp = Float64(Float64(-1.0 / x) + 1.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x)
	t_0 = (-1.0 / x) + (1.0 / (x + 1.0));
	tmp = 0.0;
	if (t_0 <= -2e+28)
		tmp = -1.0 / x;
	elseif (t_0 <= 0.0)
		tmp = (-1.0 / x) / x;
	else
		tmp = (-1.0 / x) + 1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_] := Block[{t$95$0 = N[(N[(-1.0 / x), $MachinePrecision] + N[(1.0 / N[(x + 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$0, -2e+28], N[(-1.0 / x), $MachinePrecision], If[LessEqual[t$95$0, 0.0], N[(N[(-1.0 / x), $MachinePrecision] / x), $MachinePrecision], N[(N[(-1.0 / x), $MachinePrecision] + 1.0), $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)) < -1.99999999999999992e28

   1. Initial program 100.0%

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

   3. Taylor expanded in x around 0 100.0%

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

   if -1.99999999999999992e28 < (-.f64 (/.f64 #s(literal 1 binary64) (+.f64 x #s(literal 1 binary64))) (/.f64 #s(literal 1 binary64) x)) < 0.0

   1. Initial program 53.9%

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

   3. Taylor expanded in x around inf 96.1%

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

      1. unpow2 96.1%

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

      2. associate-/r* 97.1%

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

      3. *-lft-identity 97.1%

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

      4. associate-*l/ 96.9%

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

      5. metadata-eval 96.9%

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

      6. distribute-neg-frac 96.9%

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

      7. distribute-rgt-neg-out 96.9%

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

      8. unpow-1 96.9%

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

      9. unpow-1 96.9%

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

      10. pow-sqr 97.3%

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

      11. metadata-eval 97.3%

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

   5. Simplified 97.3%

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

      1. add-sqr-sqrt 45.4%

         \[\leadsto \color{blue}{\sqrt{-{x}^{-2}} \cdot \sqrt{-{x}^{-2}}} \]

      2. sqrt-unprod 49.7%

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

      3. sqr-neg 49.7%

         \[\leadsto \sqrt{\color{blue}{{x}^{-2} \cdot {x}^{-2}}} \]

      4. sqrt-unprod 49.4%

         \[\leadsto \color{blue}{\sqrt{{x}^{-2}} \cdot \sqrt{{x}^{-2}}} \]

      5. pow2 49.4%

         \[\leadsto \color{blue}{{\left(\sqrt{{x}^{-2}}\right)}^{2}} \]

      6. sqrt-pow1 49.4%

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

      7. metadata-eval 49.4%

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

      8. inv-pow 49.4%

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

      9. pow2 49.4%

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

      10. un-div-inv 49.4%

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

   7. Applied egg-rr 97.1%

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

   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 0 100.0%

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

4. Final simplification 98.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{-1}{x} + \frac{1}{x + 1} \leq -2 \cdot 10^{+28}:\\ \;\;\;\;\frac{-1}{x}\\ \mathbf{elif}\;\frac{-1}{x} + \frac{1}{x + 1} \leq 0:\\ \;\;\;\;\frac{\frac{-1}{x}}{x}\\ \mathbf{else}:\\ \;\;\;\;\frac{-1}{x} + 1\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 96.0% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{-1}{x} + \frac{1}{x + 1}\\ \mathbf{if}\;t\_0 \leq -2 \cdot 10^{+28}:\\ \;\;\;\;\frac{-1}{x}\\ \mathbf{elif}\;t\_0 \leq 0:\\ \;\;\;\;\frac{-1}{x \cdot x}\\ \mathbf{else}:\\ \;\;\;\;\frac{-1}{x} + 1\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (let* ((t_0 (+ (/ -1.0 x) (/ 1.0 (+ x 1.0)))))
   (if (<= t_0 -2e+28)
     (/ -1.0 x)
     (if (<= t_0 0.0) (/ -1.0 (* x x)) (+ (/ -1.0 x) 1.0)))))

C

double code(double x) {
	double t_0 = (-1.0 / x) + (1.0 / (x + 1.0));
	double tmp;
	if (t_0 <= -2e+28) {
		tmp = -1.0 / x;
	} else if (t_0 <= 0.0) {
		tmp = -1.0 / (x * x);
	} else {
		tmp = (-1.0 / x) + 1.0;
	}
	return tmp;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    real(8) :: t_0
    real(8) :: tmp
    t_0 = ((-1.0d0) / x) + (1.0d0 / (x + 1.0d0))
    if (t_0 <= (-2d+28)) then
        tmp = (-1.0d0) / x
    else if (t_0 <= 0.0d0) then
        tmp = (-1.0d0) / (x * x)
    else
        tmp = ((-1.0d0) / x) + 1.0d0
    end if
    code = tmp
end function

Java

public static double code(double x) {
	double t_0 = (-1.0 / x) + (1.0 / (x + 1.0));
	double tmp;
	if (t_0 <= -2e+28) {
		tmp = -1.0 / x;
	} else if (t_0 <= 0.0) {
		tmp = -1.0 / (x * x);
	} else {
		tmp = (-1.0 / x) + 1.0;
	}
	return tmp;
}

Python

def code(x):
	t_0 = (-1.0 / x) + (1.0 / (x + 1.0))
	tmp = 0
	if t_0 <= -2e+28:
		tmp = -1.0 / x
	elif t_0 <= 0.0:
		tmp = -1.0 / (x * x)
	else:
		tmp = (-1.0 / x) + 1.0
	return tmp

Julia

function code(x)
	t_0 = Float64(Float64(-1.0 / x) + Float64(1.0 / Float64(x + 1.0)))
	tmp = 0.0
	if (t_0 <= -2e+28)
		tmp = Float64(-1.0 / x);
	elseif (t_0 <= 0.0)
		tmp = Float64(-1.0 / Float64(x * x));
	else
		tmp = Float64(Float64(-1.0 / x) + 1.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x)
	t_0 = (-1.0 / x) + (1.0 / (x + 1.0));
	tmp = 0.0;
	if (t_0 <= -2e+28)
		tmp = -1.0 / x;
	elseif (t_0 <= 0.0)
		tmp = -1.0 / (x * x);
	else
		tmp = (-1.0 / x) + 1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_] := Block[{t$95$0 = N[(N[(-1.0 / x), $MachinePrecision] + N[(1.0 / N[(x + 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$0, -2e+28], N[(-1.0 / x), $MachinePrecision], If[LessEqual[t$95$0, 0.0], N[(-1.0 / N[(x * x), $MachinePrecision]), $MachinePrecision], N[(N[(-1.0 / x), $MachinePrecision] + 1.0), $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)) < -1.99999999999999992e28

   1. Initial program 100.0%

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

   3. Taylor expanded in x around 0 100.0%

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

   if -1.99999999999999992e28 < (-.f64 (/.f64 #s(literal 1 binary64) (+.f64 x #s(literal 1 binary64))) (/.f64 #s(literal 1 binary64) x)) < 0.0

   1. Initial program 53.9%

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

      1. clear-num 53.9%

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

      2. frac-sub 55.4%

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

      3. *-commutative 55.4%

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

      4. *-un-lft-identity 55.4%

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

      5. *-un-lft-identity 55.4%

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

      6. div-inv 55.4%

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

      7. metadata-eval 55.4%

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

      8. *-rgt-identity 55.4%

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

      9. +-commutative 55.4%

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

      10. *-commutative 55.4%

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

      11. div-inv 55.4%

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

      12. metadata-eval 55.4%

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

      13. *-rgt-identity 55.4%

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

      14. +-commutative 55.4%

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

   4. Applied egg-rr 55.4%

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

   5. Taylor expanded in x around 0 98.8%

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

   6. Taylor expanded in x around inf 96.1%

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

   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 0 100.0%

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

4. Final simplification 98.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{-1}{x} + \frac{1}{x + 1} \leq -2 \cdot 10^{+28}:\\ \;\;\;\;\frac{-1}{x}\\ \mathbf{elif}\;\frac{-1}{x} + \frac{1}{x + 1} \leq 0:\\ \;\;\;\;\frac{-1}{x \cdot x}\\ \mathbf{else}:\\ \;\;\;\;\frac{-1}{x} + 1\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 75.2% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1:\\ \;\;\;\;0\\ \mathbf{elif}\;x \leq 4.5 \cdot 10^{+102}:\\ \;\;\;\;\frac{-1}{x}\\ \mathbf{else}:\\ \;\;\;\;0\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (if (<= x -1.0) 0.0 (if (<= x 4.5e+102) (/ -1.0 x) 0.0)))

C

double code(double x) {
	double tmp;
	if (x <= -1.0) {
		tmp = 0.0;
	} else if (x <= 4.5e+102) {
		tmp = -1.0 / x;
	} else {
		tmp = 0.0;
	}
	return tmp;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    real(8) :: tmp
    if (x <= (-1.0d0)) then
        tmp = 0.0d0
    else if (x <= 4.5d+102) then
        tmp = (-1.0d0) / x
    else
        tmp = 0.0d0
    end if
    code = tmp
end function

Java

public static double code(double x) {
	double tmp;
	if (x <= -1.0) {
		tmp = 0.0;
	} else if (x <= 4.5e+102) {
		tmp = -1.0 / x;
	} else {
		tmp = 0.0;
	}
	return tmp;
}

Python

def code(x):
	tmp = 0
	if x <= -1.0:
		tmp = 0.0
	elif x <= 4.5e+102:
		tmp = -1.0 / x
	else:
		tmp = 0.0
	return tmp

Julia

function code(x)
	tmp = 0.0
	if (x <= -1.0)
		tmp = 0.0;
	elseif (x <= 4.5e+102)
		tmp = Float64(-1.0 / x);
	else
		tmp = 0.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x)
	tmp = 0.0;
	if (x <= -1.0)
		tmp = 0.0;
	elseif (x <= 4.5e+102)
		tmp = -1.0 / x;
	else
		tmp = 0.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_] := If[LessEqual[x, -1.0], 0.0, If[LessEqual[x, 4.5e+102], N[(-1.0 / x), $MachinePrecision], 0.0]]

Derivation

1. Split input into 2 regimes

2. if x < -1 or 4.50000000000000021e102 < x

   1. Initial program 65.7%

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

   3. Taylor expanded in x around inf 62.9%

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

   4. Taylor expanded in x around 0 62.9%

      \[\leadsto \color{blue}{0} \]

   if -1 < x < 4.50000000000000021e102

   1. Initial program 83.8%

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

   3. Taylor expanded in x around 0 82.9%

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

Alternative 5: 99.4% accurate, 1.3× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 76.6%

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

   1. clear-num 76.6%

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

   2. frac-sub 77.3%

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

   3. *-commutative 77.3%

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

   4. *-un-lft-identity 77.3%

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

   5. *-un-lft-identity 77.3%

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

   6. div-inv 77.3%

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

   7. metadata-eval 77.3%

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

   8. *-rgt-identity 77.3%

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

   9. +-commutative 77.3%

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

   10. *-commutative 77.3%

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

   11. div-inv 77.3%

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

   12. metadata-eval 77.3%

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

   13. *-rgt-identity 77.3%

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

   14. +-commutative 77.3%

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

4. Applied egg-rr 77.3%

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

5. Taylor expanded in x around 0 99.4%

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

6. Final simplification 99.4%

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

Alternative 6: 26.9% accurate, 9.0× speedup

Math

\[\begin{array}{l} \\ 0 \end{array} \]

FPCore

(FPCore (x) :precision binary64 0.0)

C

double code(double x) {
	return 0.0;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = 0.0d0
end function

Java

public static double code(double x) {
	return 0.0;
}

Python

def code(x):
	return 0.0

Julia

function code(x)
	return 0.0
end

MATLAB

function tmp = code(x)
	tmp = 0.0;
end

Wolfram

code[x_] := 0.0

Derivation

1. Initial program 76.6%

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

3. Taylor expanded in x around inf 26.6%

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

4. Taylor expanded in x around 0 26.6%

   \[\leadsto \color{blue}{0} \]
5. Add Preprocessing

Developer Target 1: 99.9% accurate, 1.3× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Developer Target 2: 99.4% accurate, 1.3× 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 2024191 
(FPCore (x)
  :name "2frac (problem 3.3.1)"
  :precision binary64

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

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

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