2frac (problem 3.3.1)

Percentage Accurate: 77.3% → 99.9%

Time: 5.9s

Alternatives: 8

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: 77.3% 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

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

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 76.3%

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

   1. frac-sub 76.7%

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

   2. *-rgt-identity 76.7%

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

   3. metadata-eval 76.7%

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

   4. div-inv 76.7%

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

   5. associate-/r* 76.7%

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

   6. *-un-lft-identity 76.7%

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

   7. *-rgt-identity 76.7%

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

   8. +-commutative 76.7%

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

   9. div-inv 76.7%

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

   10. metadata-eval 76.7%

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

   11. *-rgt-identity 76.7%

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

   12. +-commutative 76.7%

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

4. Applied egg-rr 76.7%

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

   1. frac-2neg 76.7%

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

   2. div-inv 76.7%

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

   3. +-commutative 76.7%

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

   4. distribute-neg-in 76.7%

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

   5. metadata-eval 76.7%

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

6. Applied egg-rr 76.7%

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

   1. associate-*r/ 76.7%

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

   2. *-rgt-identity 76.7%

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

   3. neg-sub0 76.7%

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

   4. associate--r- 76.7%

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

   5. neg-sub0 76.7%

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

   6. +-commutative 76.7%

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

   7. metadata-eval 76.7%

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

   8. remove-double-neg 76.7%

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

   9. distribute-neg-in 76.7%

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

   10. distribute-neg-in 76.7%

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

   11. +-commutative 76.7%

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

   12. unsub-neg 76.7%

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

   13. associate-+l- 99.9%

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

   14. +-inverses 99.9%

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

   15. metadata-eval 99.9%

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

   16. metadata-eval 99.9%

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

   17. unsub-neg 99.9%

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

8. Simplified 99.9%

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

Alternative 2: 98.2% accurate, 0.5× speedup

Math

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

FPCore

(FPCore (x)
 :precision binary64
 (if (<= x -1.0)
   (/ -1.0 (* x x))
   (if (<= x 1.0) (- (- 1.0 x) (/ 1.0 x)) (/ (/ -1.0 x) x))))

C

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

Fortran

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

Java

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

Python

def code(x):
	tmp = 0
	if x <= -1.0:
		tmp = -1.0 / (x * x)
	elif x <= 1.0:
		tmp = (1.0 - x) - (1.0 / x)
	else:
		tmp = (-1.0 / x) / x
	return tmp

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Split input into 3 regimes

2. if x < -1

   1. Initial program 53.7%

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

   3. Taylor expanded in x around inf 98.0%

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

      1. unpow2 98.0%

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

   5. Applied egg-rr 98.0%

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

   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 99.2%

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

      1. neg-mul-1 99.2%

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

      2. sub-neg 99.2%

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

   5. Simplified 99.2%

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

   if 1 < x

   1. Initial program 52.9%

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

      1. frac-sub 53.2%

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

      2. *-rgt-identity 53.2%

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

      3. metadata-eval 53.2%

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

      4. div-inv 53.2%

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

      5. associate-/r* 53.1%

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

      6. *-un-lft-identity 53.1%

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

      7. *-rgt-identity 53.1%

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

      8. +-commutative 53.1%

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

      9. div-inv 53.1%

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

      10. metadata-eval 53.1%

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

      11. *-rgt-identity 53.1%

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

      12. +-commutative 53.1%

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

   4. Applied egg-rr 53.1%

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

      1. frac-2neg 53.1%

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

      2. div-inv 53.1%

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

      3. +-commutative 53.1%

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

      4. distribute-neg-in 53.1%

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

      5. metadata-eval 53.1%

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

   6. Applied egg-rr 53.1%

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

      1. associate-*r/ 53.1%

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

      2. *-rgt-identity 53.1%

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

      3. neg-sub0 53.1%

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

      4. associate--r- 53.1%

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

      5. neg-sub0 53.1%

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

      6. +-commutative 53.1%

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

      7. metadata-eval 53.1%

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

      8. remove-double-neg 53.1%

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

      9. distribute-neg-in 53.1%

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

      10. distribute-neg-in 53.1%

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

      11. +-commutative 53.1%

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

      12. unsub-neg 53.1%

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

      13. associate-+l- 99.7%

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

      14. +-inverses 99.7%

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

      15. metadata-eval 99.7%

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

      16. metadata-eval 99.7%

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

      17. unsub-neg 99.7%

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

   8. Simplified 99.7%

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

   9. Taylor expanded in x around inf 98.6%

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

       1. mul-1-neg 98.6%

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

   11. Simplified 98.6%

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

4. Final simplification 98.7%

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

Alternative 3: 97.8% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1 \lor \neg \left(x \leq 0.75\right):\\ \;\;\;\;\frac{-1}{x \cdot x}\\ \mathbf{else}:\\ \;\;\;\;1 + \frac{-1}{x}\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (if (or (<= x -1.0) (not (<= x 0.75))) (/ -1.0 (* x x)) (+ 1.0 (/ -1.0 x))))

C

double code(double x) {
	double tmp;
	if ((x <= -1.0) || !(x <= 0.75)) {
		tmp = -1.0 / (x * x);
	} else {
		tmp = 1.0 + (-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 <= 0.75d0))) then
        tmp = (-1.0d0) / (x * x)
    else
        tmp = 1.0d0 + ((-1.0d0) / x)
    end if
    code = tmp
end function

Java

public static double code(double x) {
	double tmp;
	if ((x <= -1.0) || !(x <= 0.75)) {
		tmp = -1.0 / (x * x);
	} else {
		tmp = 1.0 + (-1.0 / x);
	}
	return tmp;
}

Python

def code(x):
	tmp = 0
	if (x <= -1.0) or not (x <= 0.75):
		tmp = -1.0 / (x * x)
	else:
		tmp = 1.0 + (-1.0 / x)
	return tmp

Julia

function code(x)
	tmp = 0.0
	if ((x <= -1.0) || !(x <= 0.75))
		tmp = Float64(-1.0 / Float64(x * x));
	else
		tmp = Float64(1.0 + Float64(-1.0 / x));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x)
	tmp = 0.0;
	if ((x <= -1.0) || ~((x <= 0.75)))
		tmp = -1.0 / (x * x);
	else
		tmp = 1.0 + (-1.0 / x);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_] := If[Or[LessEqual[x, -1.0], N[Not[LessEqual[x, 0.75]], $MachinePrecision]], N[(-1.0 / N[(x * x), $MachinePrecision]), $MachinePrecision], N[(1.0 + N[(-1.0 / x), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < -1 or 0.75 < x

   1. Initial program 53.3%

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

   3. Taylor expanded in x around inf 96.7%

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

      1. unpow2 96.7%

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

   5. Applied egg-rr 96.7%

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

   if -1 < x < 0.75

   1. Initial program 100.0%

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

   3. Taylor expanded in x around 0 99.0%

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

4. Final simplification 97.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -1 \lor \neg \left(x \leq 0.75\right):\\ \;\;\;\;\frac{-1}{x \cdot x}\\ \mathbf{else}:\\ \;\;\;\;1 + \frac{-1}{x}\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 98.0% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1:\\ \;\;\;\;\frac{-1}{x \cdot x}\\ \mathbf{elif}\;x \leq 0.75:\\ \;\;\;\;1 + \frac{-1}{x}\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{-1}{x}}{x}\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (if (<= x -1.0)
   (/ -1.0 (* x x))
   (if (<= x 0.75) (+ 1.0 (/ -1.0 x)) (/ (/ -1.0 x) x))))

C

double code(double x) {
	double tmp;
	if (x <= -1.0) {
		tmp = -1.0 / (x * x);
	} else if (x <= 0.75) {
		tmp = 1.0 + (-1.0 / x);
	} else {
		tmp = (-1.0 / x) / x;
	}
	return tmp;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    real(8) :: tmp
    if (x <= (-1.0d0)) then
        tmp = (-1.0d0) / (x * x)
    else if (x <= 0.75d0) then
        tmp = 1.0d0 + ((-1.0d0) / x)
    else
        tmp = ((-1.0d0) / x) / x
    end if
    code = tmp
end function

Java

public static double code(double x) {
	double tmp;
	if (x <= -1.0) {
		tmp = -1.0 / (x * x);
	} else if (x <= 0.75) {
		tmp = 1.0 + (-1.0 / x);
	} else {
		tmp = (-1.0 / x) / x;
	}
	return tmp;
}

Python

def code(x):
	tmp = 0
	if x <= -1.0:
		tmp = -1.0 / (x * x)
	elif x <= 0.75:
		tmp = 1.0 + (-1.0 / x)
	else:
		tmp = (-1.0 / x) / x
	return tmp

Julia

function code(x)
	tmp = 0.0
	if (x <= -1.0)
		tmp = Float64(-1.0 / Float64(x * x));
	elseif (x <= 0.75)
		tmp = Float64(1.0 + Float64(-1.0 / x));
	else
		tmp = Float64(Float64(-1.0 / x) / x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x)
	tmp = 0.0;
	if (x <= -1.0)
		tmp = -1.0 / (x * x);
	elseif (x <= 0.75)
		tmp = 1.0 + (-1.0 / x);
	else
		tmp = (-1.0 / x) / x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_] := If[LessEqual[x, -1.0], N[(-1.0 / N[(x * x), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 0.75], N[(1.0 + N[(-1.0 / x), $MachinePrecision]), $MachinePrecision], N[(N[(-1.0 / x), $MachinePrecision] / x), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if x < -1

   1. Initial program 53.7%

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

   3. Taylor expanded in x around inf 98.0%

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

      1. unpow2 98.0%

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

   5. Applied egg-rr 98.0%

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

   if -1 < x < 0.75

   1. Initial program 100.0%

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

   3. Taylor expanded in x around 0 99.0%

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

   if 0.75 < x

   1. Initial program 52.9%

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

      1. frac-sub 53.2%

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

      2. *-rgt-identity 53.2%

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

      3. metadata-eval 53.2%

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

      4. div-inv 53.2%

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

      5. associate-/r* 53.1%

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

      6. *-un-lft-identity 53.1%

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

      7. *-rgt-identity 53.1%

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

      8. +-commutative 53.1%

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

      9. div-inv 53.1%

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

      10. metadata-eval 53.1%

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

      11. *-rgt-identity 53.1%

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

      12. +-commutative 53.1%

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

   4. Applied egg-rr 53.1%

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

      1. frac-2neg 53.1%

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

      2. div-inv 53.1%

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

      3. +-commutative 53.1%

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

      4. distribute-neg-in 53.1%

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

      5. metadata-eval 53.1%

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

   6. Applied egg-rr 53.1%

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

      1. associate-*r/ 53.1%

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

      2. *-rgt-identity 53.1%

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

      3. neg-sub0 53.1%

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

      4. associate--r- 53.1%

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

      5. neg-sub0 53.1%

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

      6. +-commutative 53.1%

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

      7. metadata-eval 53.1%

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

      8. remove-double-neg 53.1%

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

      9. distribute-neg-in 53.1%

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

      10. distribute-neg-in 53.1%

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

      11. +-commutative 53.1%

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

      12. unsub-neg 53.1%

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

      13. associate-+l- 99.7%

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

      14. +-inverses 99.7%

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

      15. metadata-eval 99.7%

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

      16. metadata-eval 99.7%

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

      17. unsub-neg 99.7%

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

   8. Simplified 99.7%

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

   9. Taylor expanded in x around inf 98.6%

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

       1. mul-1-neg 98.6%

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

   11. Simplified 98.6%

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

4. Final simplification 98.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -1:\\ \;\;\;\;\frac{-1}{x \cdot x}\\ \mathbf{elif}\;x \leq 0.75:\\ \;\;\;\;1 + \frac{-1}{x}\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{-1}{x}}{x}\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 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]

Derivation

1. Initial program 76.3%

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

   1. clear-num 76.3%

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

   2. frac-sub 76.7%

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

   3. *-un-lft-identity 76.7%

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

   4. div-inv 76.7%

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

   5. metadata-eval 76.7%

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

   6. *-rgt-identity 76.7%

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

   7. *-rgt-identity 76.7%

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

   8. +-commutative 76.7%

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

   9. *-commutative 76.7%

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

   10. div-inv 76.7%

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

   11. metadata-eval 76.7%

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

   12. *-rgt-identity 76.7%

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

   13. +-commutative 76.7%

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

4. Applied egg-rr 76.7%

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

5. Taylor expanded in x around 0 99.1%

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

Alternative 6: 51.9% accurate, 3.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

def code(x):
	return -1.0 / x

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 76.3%

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

3. Taylor expanded in x around 0 51.0%

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

Alternative 7: 3.1% accurate, 4.5× 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 76.3%

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

3. Taylor expanded in x around 0 50.1%

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

   1. neg-mul-1 50.1%

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

   2. sub-neg 50.1%

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

5. Simplified 50.1%

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

6. Taylor expanded in x around inf 3.2%

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

   1. neg-mul-1 3.2%

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

8. Simplified 3.2%

   \[\leadsto \color{blue}{-x} \]
9. Add Preprocessing

Alternative 8: 3.0% accurate, 9.0× 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 76.3%

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

3. Taylor expanded in x around 0 50.1%

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

4. Taylor expanded in x around inf 2.9%

   \[\leadsto \color{blue}{1} \]
5. Add Preprocessing

Reproduce

herbie shell --seed 2024113 
(FPCore (x)
  :name "2frac (problem 3.3.1)"
  :precision binary64
  (- (/ 1.0 (+ x 1.0)) (/ 1.0 x)))