2frac (problem 3.3.1)

Percentage Accurate: 77.1% → 99.9%

Time: 4.6s

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: 77.1% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 99.9% accurate, 1.3× speedup

Math

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

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(Float64(-1.0 / x) / Float64(1.0 + x))
end

MATLAB

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

Wolfram

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

Derivation

1. Initial program 78.9%

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

   1. frac-sub 80.2%

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

   2. div-inv 80.2%

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

   3. *-un-lft-identity 80.2%

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

   4. *-rgt-identity 80.2%

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

   5. +-commutative 80.2%

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

   6. metadata-eval 80.2%

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

   7. frac-times 80.1%

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

   8. clear-num 80.1%

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

   9. associate-*l/ 80.2%

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

   10. *-un-lft-identity 80.2%

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

   11. div-inv 80.2%

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

   12. metadata-eval 80.2%

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

   13. *-rgt-identity 80.2%

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

   14. +-commutative 80.2%

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

3. Applied egg-rr 80.2%

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

4. Taylor expanded in x around 0 99.9%

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

5. Final simplification 99.9%

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

Alternative 2: 97.9% accurate, 1.0× speedup

Math

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

FPCore

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

C

double code(double x) {
	double tmp;
	if ((x <= -1.0) || !(x <= 0.76)) {
		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 <= 0.76d0))) 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 <= 0.76)) {
		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 <= 0.76):
		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 <= 0.76))
		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 <= 0.76)))
		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, 0.76]], $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 0.76000000000000001 < x

   1. Initial program 56.5%

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

   2. Taylor expanded in x around inf 97.3%

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

      1. unpow2 97.3%

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

   4. Simplified 97.3%

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

   if -1 < x < 0.76000000000000001

   1. Initial program 100.0%

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

   2. Taylor expanded in x around 0 97.6%

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

4. Final simplification 97.4%

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

Alternative 3: 98.1% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1:\\ \;\;\;\;\frac{-1}{x \cdot x}\\ \mathbf{elif}\;x \leq 0.76:\\ \;\;\;\;1 + \frac{-1}{x}\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{-1}{x}}{x}\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (if (<= x -1.0)
   (/ -1.0 (* x x))
   (if (<= x 0.76) (+ 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 <= 0.76) {
		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 <= 0.76d0) 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 <= 0.76) {
		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 <= 0.76:
		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 <= 0.76)
		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 <= 0.76)
		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, 0.76], 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 56.8%

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

   2. Taylor expanded in x around inf 97.7%

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

      1. unpow2 97.7%

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

   4. Simplified 97.7%

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

   if -1 < x < 0.76000000000000001

   1. Initial program 100.0%

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

   2. Taylor expanded in x around 0 97.6%

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

   if 0.76000000000000001 < x

   1. Initial program 56.1%

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

      1. sub-neg 56.1%

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

      2. +-commutative 56.1%

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

      3. distribute-neg-frac 56.1%

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

      4. metadata-eval 56.1%

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

   3. Applied egg-rr 56.1%

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

   5. Simplified 98.4%

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

      1. distribute-rgt-in 98.4%

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

      2. *-un-lft-identity 98.4%

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

   7. Applied egg-rr 98.4%

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

   8. Taylor expanded in x around inf 96.9%

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

      1. unpow2 96.9%

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

      2. associate-/r* 98.3%

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

   10. Simplified 98.3%

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

4. Final simplification 97.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -1:\\ \;\;\;\;\frac{-1}{x \cdot x}\\ \mathbf{elif}\;x \leq 0.76:\\ \;\;\;\;1 + \frac{-1}{x}\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{-1}{x}}{x}\\ \end{array} \]

Alternative 4: 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 78.9%

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

   1. sub-neg 78.9%

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

   2. +-commutative 78.9%

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

   3. distribute-neg-frac 78.9%

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

   4. metadata-eval 78.9%

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

3. Applied egg-rr 78.9%

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

5. Simplified 99.6%

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

6. Final simplification 99.6%

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

Alternative 5: 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 78.9%

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

   1. frac-sub 80.2%

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

   2. *-rgt-identity 80.2%

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

   3. metadata-eval 80.2%

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

   4. div-inv 80.2%

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

   5. associate-/r* 80.1%

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

   6. *-un-lft-identity 80.1%

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

   7. *-rgt-identity 80.1%

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

   8. +-commutative 80.1%

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

   9. div-inv 80.1%

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

   10. metadata-eval 80.1%

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

   11. *-rgt-identity 80.1%

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

   12. +-commutative 80.1%

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

3. Applied egg-rr 80.1%

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

   1. frac-2neg 80.1%

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

   2. div-inv 80.1%

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

   3. +-commutative 80.1%

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

   4. +-commutative 80.1%

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

   5. distribute-neg-in 80.1%

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

   6. neg-mul-1 80.1%

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

   7. metadata-eval 80.1%

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

   8. fma-def 80.1%

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

5. Applied egg-rr 80.1%

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

   1. associate-*r/ 80.1%

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

   2. *-rgt-identity 80.1%

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

   3. distribute-neg-frac 80.1%

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

   4. neg-mul-1 80.1%

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

   5. associate--r+ 99.9%

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

   6. +-inverses 99.9%

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

   7. metadata-eval 99.9%

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

   8. associate-*r/ 99.9%

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

   9. metadata-eval 99.9%

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

   10. fma-udef 99.9%

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

   11. neg-mul-1 99.9%

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

   12. +-commutative 99.9%

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

   13. unsub-neg 99.9%

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

7. Simplified 99.9%

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

8. Final simplification 99.9%

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

Alternative 6: 52.0% 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 78.9%

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

2. Taylor expanded in x around 0 52.7%

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

3. Final simplification 52.7%

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

Reproduce

herbie shell --seed 2023192 
(FPCore (x)
  :name "2frac (problem 3.3.1)"
  :precision binary64
  (- (/ 1.0 (+ x 1.0)) (/ 1.0 x)))