Numeric.Log:$clog1p from log-domain-0.10.2.1, B

Average Error: 0.2 → 0.2

Time: 2.8s

Precision: binary64

Cost: 6852

Math

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

↓

\[\begin{array}{l} \mathbf{if}\;x \leq 6.8 \cdot 10^{-6}:\\ \;\;\;\;-0.125 \cdot \left(x \cdot x\right) + x \cdot 0.5\\ \mathbf{else}:\\ \;\;\;\;\sqrt{x + 1} + -1\\ \end{array} \]

FPCore

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

↓

(FPCore (x)
 :precision binary64
 (if (<= x 6.8e-6) (+ (* -0.125 (* x x)) (* x 0.5)) (+ (sqrt (+ x 1.0)) -1.0)))

C

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

↓

double code(double x) {
	double tmp;
	if (x <= 6.8e-6) {
		tmp = (-0.125 * (x * x)) + (x * 0.5);
	} else {
		tmp = sqrt((x + 1.0)) + -1.0;
	}
	return tmp;
}

Fortran

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

↓

real(8) function code(x)
    real(8), intent (in) :: x
    real(8) :: tmp
    if (x <= 6.8d-6) then
        tmp = ((-0.125d0) * (x * x)) + (x * 0.5d0)
    else
        tmp = sqrt((x + 1.0d0)) + (-1.0d0)
    end if
    code = tmp
end function

Java

public static double code(double x) {
	return x / (1.0 + Math.sqrt((x + 1.0)));
}

↓

public static double code(double x) {
	double tmp;
	if (x <= 6.8e-6) {
		tmp = (-0.125 * (x * x)) + (x * 0.5);
	} else {
		tmp = Math.sqrt((x + 1.0)) + -1.0;
	}
	return tmp;
}

Python

def code(x):
	return x / (1.0 + math.sqrt((x + 1.0)))

↓

def code(x):
	tmp = 0
	if x <= 6.8e-6:
		tmp = (-0.125 * (x * x)) + (x * 0.5)
	else:
		tmp = math.sqrt((x + 1.0)) + -1.0
	return tmp

Julia

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

↓

function code(x)
	tmp = 0.0
	if (x <= 6.8e-6)
		tmp = Float64(Float64(-0.125 * Float64(x * x)) + Float64(x * 0.5));
	else
		tmp = Float64(sqrt(Float64(x + 1.0)) + -1.0);
	end
	return tmp
end

MATLAB

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

↓

function tmp_2 = code(x)
	tmp = 0.0;
	if (x <= 6.8e-6)
		tmp = (-0.125 * (x * x)) + (x * 0.5);
	else
		tmp = sqrt((x + 1.0)) + -1.0;
	end
	tmp_2 = tmp;
end

Wolfram

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

↓

code[x_] := If[LessEqual[x, 6.8e-6], N[(N[(-0.125 * N[(x * x), $MachinePrecision]), $MachinePrecision] + N[(x * 0.5), $MachinePrecision]), $MachinePrecision], N[(N[Sqrt[N[(x + 1.0), $MachinePrecision]], $MachinePrecision] + -1.0), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < 6.80000000000000012e-6

   1. Initial program 0.0

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

   2. Taylor expanded in x around 0 0.3

      \[\leadsto \color{blue}{-0.125 \cdot {x}^{2} + 0.5 \cdot x} \]

   3. Simplified 0.3

      \[\leadsto \color{blue}{x \cdot \left(0.5 + x \cdot -0.125\right)} \]
      Proof

      [Start] 0.3	\[ -0.125 \cdot {x}^{2} + 0.5 \cdot x \]
      +-commutative [=>] 0.3	\[ \color{blue}{0.5 \cdot x + -0.125 \cdot {x}^{2}} \]
      *-commutative [=>] 0.3	\[ \color{blue}{x \cdot 0.5} + -0.125 \cdot {x}^{2} \]
      *-commutative [=>] 0.3	\[ x \cdot 0.5 + \color{blue}{{x}^{2} \cdot -0.125} \]
      unpow2 [=>] 0.3	\[ x \cdot 0.5 + \color{blue}{\left(x \cdot x\right)} \cdot -0.125 \]
      associate-*l* [=>] 0.3	\[ x \cdot 0.5 + \color{blue}{x \cdot \left(x \cdot -0.125\right)} \]
      distribute-lft-out [=>] 0.3	\[ \color{blue}{x \cdot \left(0.5 + x \cdot -0.125\right)} \]

   4. Applied egg-rr 0.3

      \[\leadsto \color{blue}{x \cdot 0.5 + -0.125 \cdot \left(x \cdot x\right)} \]

   if 6.80000000000000012e-6 < x

   1. Initial program 0.5

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

   2. Applied egg-rr 0.1

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

   3. Simplified 0.1

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

      [Start] 0.1	\[ \frac{x}{-x} \cdot 1 + \frac{x}{-x} \cdot \left(-\sqrt{x + 1}\right) \]
      distribute-lft-out [=>] 0.1	\[ \color{blue}{\frac{x}{-x} \cdot \left(1 + \left(-\sqrt{x + 1}\right)\right)} \]
      remove-double-neg [<=] 0.1	\[ \frac{\color{blue}{-\left(-x\right)}}{-x} \cdot \left(1 + \left(-\sqrt{x + 1}\right)\right) \]
      distribute-frac-neg [=>] 0.1	\[ \color{blue}{\left(-\frac{-x}{-x}\right)} \cdot \left(1 + \left(-\sqrt{x + 1}\right)\right) \]
      *-inverses [=>] 0.1	\[ \left(-\color{blue}{1}\right) \cdot \left(1 + \left(-\sqrt{x + 1}\right)\right) \]
      metadata-eval [=>] 0.1	\[ \color{blue}{-1} \cdot \left(1 + \left(-\sqrt{x + 1}\right)\right) \]
      sub-neg [<=] 0.1	\[ -1 \cdot \color{blue}{\left(1 - \sqrt{x + 1}\right)} \]
      neg-mul-1 [<=] 0.1	\[ \color{blue}{-\left(1 - \sqrt{x + 1}\right)} \]
      neg-sub0 [=>] 0.1	\[ \color{blue}{0 - \left(1 - \sqrt{x + 1}\right)} \]
      associate--r- [=>] 0.1	\[ \color{blue}{\left(0 - 1\right) + \sqrt{x + 1}} \]
      metadata-eval [=>] 0.1	\[ \color{blue}{-1} + \sqrt{x + 1} \]
      +-commutative [<=] 0.1	\[ \color{blue}{\sqrt{x + 1} + -1} \]

3. Recombined 2 regimes into one program.

4. Final simplification 0.2

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq 6.8 \cdot 10^{-6}:\\ \;\;\;\;-0.125 \cdot \left(x \cdot x\right) + x \cdot 0.5\\ \mathbf{else}:\\ \;\;\;\;\sqrt{x + 1} + -1\\ \end{array} \]

Alternatives

Alternative 1
Error	0.2
Cost	6848

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

Alternative 2
Error	20.3
Cost	448

\[\frac{x}{x \cdot 0.5 + 2} \]

Alternative 3
Error	20.7
Cost	192

\[\frac{x}{2} \]

Alternative 4
Error	60.9
Cost	64

\[2 \]

Reproduce

herbie shell --seed 2023083 
(FPCore (x)
  :name "Numeric.Log:$clog1p from log-domain-0.10.2.1, B"
  :precision binary64
  (/ x (+ 1.0 (sqrt (+ x 1.0)))))