2frac (problem 3.3.1)

Average Accuracy: 77.8% → 99.9%

Time: 3.2s

Precision: binary64

Cost: 448.00

Math

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

↓

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

FPCore

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

↓

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

C

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

↓

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)) - (1.0d0 / x)
end function

↓

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)) - (1.0 / x);
}

↓

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

Python

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

↓

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

Julia

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

↓

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)) - (1.0 / x);
end

↓

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

Wolfram

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

↓

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

Derivation

1. Initial program 77.8%

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

2. Applied egg-rr 78.8%

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

3. Simplified 78.8%

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

   [Start] 78.8	\[ \frac{\left(-x\right) + \left(1 + x\right)}{x \cdot \left(-1 - x\right)} \]
   associate-+r+ [=>] 78.8	\[ \frac{\color{blue}{\left(\left(-x\right) + 1\right) + x}}{x \cdot \left(-1 - x\right)} \]
   +-commutative [<=] 78.8	\[ \frac{\color{blue}{\left(1 + \left(-x\right)\right)} + x}{x \cdot \left(-1 - x\right)} \]
   sub-neg [<=] 78.8	\[ \frac{\color{blue}{\left(1 - x\right)} + x}{x \cdot \left(-1 - x\right)} \]

4. Applied egg-rr 99.5%

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

5. Simplified 99.9%

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

   [Start] 99.5	\[ \frac{1}{x \cdot \left(-1 - x\right)} - \frac{0}{x \cdot \left(-1 - x\right)} \]
   div0 [=>] 99.5	\[ \frac{1}{x \cdot \left(-1 - x\right)} - \color{blue}{0} \]
   --rgt-identity [=>] 99.5	\[ \color{blue}{\frac{1}{x \cdot \left(-1 - x\right)}} \]
   associate-/r* [=>] 99.9	\[ \color{blue}{\frac{\frac{1}{x}}{-1 - x}} \]

6. Final simplification 99.9%

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

Alternatives

Alternative 1
Accuracy	98.0%
Cost	713.00

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

Alternative 2
Accuracy	97.9%
Cost	585.00

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

Alternative 3
Accuracy	99.5%
Cost	448.00

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

Alternative 4
Accuracy	51.9%
Cost	192.00

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

Alternative 5
Accuracy	3.2%
Cost	128.00

\[-x \]

Reproduce

herbie shell --seed 2023096 
(FPCore (x)
  :name "2frac (problem 3.3.1)"
  :precision binary64
  (- (/ 1.0 (+ x 1.0)) (/ 1.0 x)))