2frac (problem 3.3.1)

Average Error: 14.4 → 0.5

Time: 10.5s

Precision: binary64

Cost: 28104

Math

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

↓

\[\begin{array}{l} t_0 := \frac{1}{x + 1} - \frac{1}{x}\\ \mathbf{if}\;t_0 \leq -0.0005:\\ \;\;\;\;t_0\\ \mathbf{elif}\;t_0 \leq 0:\\ \;\;\;\;\left(\frac{1}{{x}^{5}} + \frac{1}{{x}^{3}}\right) - \left(\frac{1}{{x}^{4}} + \frac{1}{{x}^{2}}\right)\\ \mathbf{else}:\\ \;\;\;\;\left(1 + \left(-x\right)\right) - \frac{1}{x}\\ \end{array} \]

FPCore

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

↓

(FPCore (x)
 :precision binary64
 (let* ((t_0 (- (/ 1.0 (+ x 1.0)) (/ 1.0 x))))
   (if (<= t_0 -0.0005)
     t_0
     (if (<= t_0 0.0)
       (-
        (+ (/ 1.0 (pow x 5.0)) (/ 1.0 (pow x 3.0)))
        (+ (/ 1.0 (pow x 4.0)) (/ 1.0 (pow x 2.0))))
       (- (+ 1.0 (- x)) (/ 1.0 x))))))

C

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

↓

double code(double x) {
	double t_0 = (1.0 / (x + 1.0)) - (1.0 / x);
	double tmp;
	if (t_0 <= -0.0005) {
		tmp = t_0;
	} else if (t_0 <= 0.0) {
		tmp = ((1.0 / pow(x, 5.0)) + (1.0 / pow(x, 3.0))) - ((1.0 / pow(x, 4.0)) + (1.0 / pow(x, 2.0)));
	} else {
		tmp = (1.0 + -x) - (1.0 / x);
	}
	return tmp;
}

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
    real(8) :: t_0
    real(8) :: tmp
    t_0 = (1.0d0 / (x + 1.0d0)) - (1.0d0 / x)
    if (t_0 <= (-0.0005d0)) then
        tmp = t_0
    else if (t_0 <= 0.0d0) then
        tmp = ((1.0d0 / (x ** 5.0d0)) + (1.0d0 / (x ** 3.0d0))) - ((1.0d0 / (x ** 4.0d0)) + (1.0d0 / (x ** 2.0d0)))
    else
        tmp = (1.0d0 + -x) - (1.0d0 / x)
    end if
    code = tmp
end function

Java

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

↓

public static double code(double x) {
	double t_0 = (1.0 / (x + 1.0)) - (1.0 / x);
	double tmp;
	if (t_0 <= -0.0005) {
		tmp = t_0;
	} else if (t_0 <= 0.0) {
		tmp = ((1.0 / Math.pow(x, 5.0)) + (1.0 / Math.pow(x, 3.0))) - ((1.0 / Math.pow(x, 4.0)) + (1.0 / Math.pow(x, 2.0)));
	} else {
		tmp = (1.0 + -x) - (1.0 / x);
	}
	return tmp;
}

Python

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

↓

def code(x):
	t_0 = (1.0 / (x + 1.0)) - (1.0 / x)
	tmp = 0
	if t_0 <= -0.0005:
		tmp = t_0
	elif t_0 <= 0.0:
		tmp = ((1.0 / math.pow(x, 5.0)) + (1.0 / math.pow(x, 3.0))) - ((1.0 / math.pow(x, 4.0)) + (1.0 / math.pow(x, 2.0)))
	else:
		tmp = (1.0 + -x) - (1.0 / x)
	return tmp

Julia

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

↓

function code(x)
	t_0 = Float64(Float64(1.0 / Float64(x + 1.0)) - Float64(1.0 / x))
	tmp = 0.0
	if (t_0 <= -0.0005)
		tmp = t_0;
	elseif (t_0 <= 0.0)
		tmp = Float64(Float64(Float64(1.0 / (x ^ 5.0)) + Float64(1.0 / (x ^ 3.0))) - Float64(Float64(1.0 / (x ^ 4.0)) + Float64(1.0 / (x ^ 2.0))));
	else
		tmp = Float64(Float64(1.0 + Float64(-x)) - Float64(1.0 / x));
	end
	return tmp
end

MATLAB

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

↓

function tmp_2 = code(x)
	t_0 = (1.0 / (x + 1.0)) - (1.0 / x);
	tmp = 0.0;
	if (t_0 <= -0.0005)
		tmp = t_0;
	elseif (t_0 <= 0.0)
		tmp = ((1.0 / (x ^ 5.0)) + (1.0 / (x ^ 3.0))) - ((1.0 / (x ^ 4.0)) + (1.0 / (x ^ 2.0)));
	else
		tmp = (1.0 + -x) - (1.0 / x);
	end
	tmp_2 = tmp;
end

Wolfram

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

↓

code[x_] := Block[{t$95$0 = N[(N[(1.0 / N[(x + 1.0), $MachinePrecision]), $MachinePrecision] - N[(1.0 / x), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$0, -0.0005], t$95$0, If[LessEqual[t$95$0, 0.0], N[(N[(N[(1.0 / N[Power[x, 5.0], $MachinePrecision]), $MachinePrecision] + N[(1.0 / N[Power[x, 3.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(N[(1.0 / N[Power[x, 4.0], $MachinePrecision]), $MachinePrecision] + N[(1.0 / N[Power[x, 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(1.0 + (-x)), $MachinePrecision] - N[(1.0 / x), $MachinePrecision]), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if (-.f64 (/.f64 1 (+.f64 x 1)) (/.f64 1 x)) < -5.0000000000000001e-4

   1. Initial program 0.0

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

   if -5.0000000000000001e-4 < (-.f64 (/.f64 1 (+.f64 x 1)) (/.f64 1 x)) < 0.0

   1. Initial program 29.2

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

   2. Taylor expanded in x around inf 0.9

      \[\leadsto \color{blue}{\left(\frac{1}{{x}^{5}} + \frac{1}{{x}^{3}}\right) - \left(\frac{1}{{x}^{4}} + \frac{1}{{x}^{2}}\right)} \]

   if 0.0 < (-.f64 (/.f64 1 (+.f64 x 1)) (/.f64 1 x))

   1. Initial program 0.0

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

   2. Taylor expanded in x around 0 0.4

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

   3. Simplified 0.4

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

      [Start] 0.4	\[ \left(1 + -1 \cdot x\right) - \frac{1}{x} \]
      rational_best-simplify-2 [=>] 0.4	\[ \left(1 + \color{blue}{x \cdot -1}\right) - \frac{1}{x} \]
      rational_best-simplify-12 [=>] 0.4	\[ \left(1 + \color{blue}{\left(-x\right)}\right) - \frac{1}{x} \]

3. Recombined 3 regimes into one program.

4. Final simplification 0.5

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{1}{x + 1} - \frac{1}{x} \leq -0.0005:\\ \;\;\;\;\frac{1}{x + 1} - \frac{1}{x}\\ \mathbf{elif}\;\frac{1}{x + 1} - \frac{1}{x} \leq 0:\\ \;\;\;\;\left(\frac{1}{{x}^{5}} + \frac{1}{{x}^{3}}\right) - \left(\frac{1}{{x}^{4}} + \frac{1}{{x}^{2}}\right)\\ \mathbf{else}:\\ \;\;\;\;\left(1 + \left(-x\right)\right) - \frac{1}{x}\\ \end{array} \]

Alternatives

Alternative 1
Error	0.6
Cost	21384

\[\begin{array}{l} t_0 := \frac{1}{x + 1} - \frac{1}{x}\\ \mathbf{if}\;t_0 \leq -0.0005:\\ \;\;\;\;t_0\\ \mathbf{elif}\;t_0 \leq 0:\\ \;\;\;\;\frac{1}{{x}^{3}} - \left(\frac{1}{{x}^{4}} + \frac{1}{{x}^{2}}\right)\\ \mathbf{else}:\\ \;\;\;\;\left(1 + \left(-x\right)\right) - \frac{1}{x}\\ \end{array} \]

Alternative 2
Error	0.6
Cost	14664

\[\begin{array}{l} t_0 := \frac{1}{x + 1} - \frac{1}{x}\\ \mathbf{if}\;t_0 \leq -4 \cdot 10^{-11}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;t_0 \leq 0:\\ \;\;\;\;\frac{1}{{x}^{3}} - \frac{1}{{x}^{2}}\\ \mathbf{else}:\\ \;\;\;\;\left(1 + \left(-x\right)\right) - \frac{1}{x}\\ \end{array} \]

Alternative 3
Error	0.8
Cost	7944

\[\begin{array}{l} t_0 := \frac{1}{x + 1} - \frac{1}{x}\\ \mathbf{if}\;t_0 \leq -2 \cdot 10^{-15}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;t_0 \leq 0:\\ \;\;\;\;\frac{-1}{{x}^{2}}\\ \mathbf{else}:\\ \;\;\;\;\left(1 + \left(-x\right)\right) - \frac{1}{x}\\ \end{array} \]

Alternative 4
Error	15.6
Cost	776

\[\begin{array}{l} t_0 := \frac{1}{x} - \frac{1}{x}\\ \mathbf{if}\;x \leq -1:\\ \;\;\;\;t_0\\ \mathbf{elif}\;x \leq 3.8 \cdot 10^{+61}:\\ \;\;\;\;\left(1 + \left(-x\right)\right) - \frac{1}{x}\\ \mathbf{else}:\\ \;\;\;\;t_0\\ \end{array} \]

Alternative 5
Error	15.8
Cost	712

\[\begin{array}{l} t_0 := \frac{1}{x} - \frac{1}{x}\\ \mathbf{if}\;x \leq -1:\\ \;\;\;\;t_0\\ \mathbf{elif}\;x \leq 1:\\ \;\;\;\;1 - \frac{1}{x}\\ \mathbf{else}:\\ \;\;\;\;t_0\\ \end{array} \]

Alternative 6
Error	14.4
Cost	576

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

Alternative 7
Error	30.5
Cost	192

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

Reproduce

herbie shell --seed 2023092 
(FPCore (x)
  :name "2frac (problem 3.3.1)"
  :precision binary64
  (- (/ 1.0 (+ x 1.0)) (/ 1.0 x)))