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

Percentage Accurate: 99.7% → 99.7%

Time: 5.1s

Alternatives: 7

Speedup: 1.0×

Specification

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 99.7% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 99.7% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 99.7%

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

   1. clear-num 99.6%

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

   2. associate-/r/ 99.8%

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

4. Applied egg-rr 99.8%

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

5. Final simplification 99.8%

   \[\leadsto x \cdot \frac{1}{1 + \sqrt{1 + x}} \]
6. Add Preprocessing

Alternative 2: 99.5% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq 0.00028:\\ \;\;\;\;\frac{1}{0.5 + \left(x \cdot -0.125 + 2 \cdot \frac{1}{x}\right)}\\ \mathbf{else}:\\ \;\;\;\;\sqrt{1 + x} + -1\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (if (<= x 0.00028)
   (/ 1.0 (+ 0.5 (+ (* x -0.125) (* 2.0 (/ 1.0 x)))))
   (+ (sqrt (+ 1.0 x)) -1.0)))

C

double code(double x) {
	double tmp;
	if (x <= 0.00028) {
		tmp = 1.0 / (0.5 + ((x * -0.125) + (2.0 * (1.0 / x))));
	} else {
		tmp = sqrt((1.0 + x)) + -1.0;
	}
	return tmp;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    real(8) :: tmp
    if (x <= 0.00028d0) then
        tmp = 1.0d0 / (0.5d0 + ((x * (-0.125d0)) + (2.0d0 * (1.0d0 / x))))
    else
        tmp = sqrt((1.0d0 + x)) + (-1.0d0)
    end if
    code = tmp
end function

Java

public static double code(double x) {
	double tmp;
	if (x <= 0.00028) {
		tmp = 1.0 / (0.5 + ((x * -0.125) + (2.0 * (1.0 / x))));
	} else {
		tmp = Math.sqrt((1.0 + x)) + -1.0;
	}
	return tmp;
}

Python

def code(x):
	tmp = 0
	if x <= 0.00028:
		tmp = 1.0 / (0.5 + ((x * -0.125) + (2.0 * (1.0 / x))))
	else:
		tmp = math.sqrt((1.0 + x)) + -1.0
	return tmp

Julia

function code(x)
	tmp = 0.0
	if (x <= 0.00028)
		tmp = Float64(1.0 / Float64(0.5 + Float64(Float64(x * -0.125) + Float64(2.0 * Float64(1.0 / x)))));
	else
		tmp = Float64(sqrt(Float64(1.0 + x)) + -1.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x)
	tmp = 0.0;
	if (x <= 0.00028)
		tmp = 1.0 / (0.5 + ((x * -0.125) + (2.0 * (1.0 / x))));
	else
		tmp = sqrt((1.0 + x)) + -1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_] := If[LessEqual[x, 0.00028], N[(1.0 / N[(0.5 + N[(N[(x * -0.125), $MachinePrecision] + N[(2.0 * N[(1.0 / x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[Sqrt[N[(1.0 + x), $MachinePrecision]], $MachinePrecision] + -1.0), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < 2.7999999999999998e-4

   1. Initial program 100.0%

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

      1. clear-num 99.7%

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

      2. associate-/r/ 100.0%

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

   4. Applied egg-rr 100.0%

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

      1. associate-*l/ 100.0%

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

      2. associate-/l* 99.7%

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

      3. +-commutative 99.7%

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

   6. Applied egg-rr 99.7%

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

   7. Taylor expanded in x around 0 99.0%

      \[\leadsto \frac{1}{\color{blue}{0.5 + \left(-0.125 \cdot x + 2 \cdot \frac{1}{x}\right)}} \]

   if 2.7999999999999998e-4 < x

   1. Initial program 99.3%

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

      1. flip-+ 99.3%

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

      2. metadata-eval 99.3%

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

      3. add-sqr-sqrt 99.8%

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

      4. +-commutative 99.8%

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

      5. associate--r+ 99.8%

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

      6. metadata-eval 99.8%

         \[\leadsto \frac{x}{\frac{\color{blue}{0} - x}{1 - \sqrt{x + 1}}} \]

      7. neg-sub0 99.8%

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

      8. associate-/r/ 99.8%

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

   4. Applied egg-rr 99.8%

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

      1. remove-double-neg 99.8%

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

      2. distribute-frac-neg 99.8%

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

      3. *-inverses 99.8%

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

      4. metadata-eval 99.8%

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

      5. neg-mul-1 99.8%

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

      6. neg-sub0 99.8%

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

      7. associate--r- 99.8%

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

      8. metadata-eval 99.8%

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

   6. Simplified 99.8%

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

4. Final simplification 99.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq 0.00028:\\ \;\;\;\;\frac{1}{0.5 + \left(x \cdot -0.125 + 2 \cdot \frac{1}{x}\right)}\\ \mathbf{else}:\\ \;\;\;\;\sqrt{1 + x} + -1\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 99.7% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 99.7%

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

3. Final simplification 99.7%

   \[\leadsto \frac{x}{1 + \sqrt{1 + x}} \]
4. Add Preprocessing

Alternative 4: 68.5% accurate, 15.3× speedup

Math

\[\begin{array}{l} \\ \frac{1}{0.5 + \frac{2}{x}} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (/ 1.0 (+ 0.5 (/ 2.0 x))))

C

double code(double x) {
	return 1.0 / (0.5 + (2.0 / x));
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = 1.0d0 / (0.5d0 + (2.0d0 / x))
end function

Java

public static double code(double x) {
	return 1.0 / (0.5 + (2.0 / x));
}

Python

def code(x):
	return 1.0 / (0.5 + (2.0 / x))

Julia

function code(x)
	return Float64(1.0 / Float64(0.5 + Float64(2.0 / x)))
end

MATLAB

function tmp = code(x)
	tmp = 1.0 / (0.5 + (2.0 / x));
end

Wolfram

code[x_] := N[(1.0 / N[(0.5 + N[(2.0 / x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.7%

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

   1. clear-num 99.6%

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

   2. associate-/r/ 99.8%

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

4. Applied egg-rr 99.8%

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

   1. associate-*l/ 99.7%

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

   2. associate-/l* 99.6%

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

   3. +-commutative 99.6%

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

6. Applied egg-rr 99.6%

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

7. Taylor expanded in x around 0 67.7%

   \[\leadsto \frac{1}{\color{blue}{0.5 + 2 \cdot \frac{1}{x}}} \]
8. Step-by-step derivation

   1. associate-*r/ 67.7%

      \[\leadsto \frac{1}{0.5 + \color{blue}{\frac{2 \cdot 1}{x}}} \]

   2. metadata-eval 67.7%

      \[\leadsto \frac{1}{0.5 + \frac{\color{blue}{2}}{x}} \]

9. Simplified 67.7%

   \[\leadsto \frac{1}{\color{blue}{0.5 + \frac{2}{x}}} \]

10. Final simplification 67.7%

    \[\leadsto \frac{1}{0.5 + \frac{2}{x}} \]
11. Add Preprocessing

Alternative 5: 68.7% accurate, 15.3× speedup

Math

\[\begin{array}{l} \\ \frac{x}{2 + x \cdot 0.5} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (/ x (+ 2.0 (* x 0.5))))

C

double code(double x) {
	return x / (2.0 + (x * 0.5));
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = x / (2.0d0 + (x * 0.5d0))
end function

Java

public static double code(double x) {
	return x / (2.0 + (x * 0.5));
}

Python

def code(x):
	return x / (2.0 + (x * 0.5))

Julia

function code(x)
	return Float64(x / Float64(2.0 + Float64(x * 0.5)))
end

MATLAB

function tmp = code(x)
	tmp = x / (2.0 + (x * 0.5));
end

Wolfram

code[x_] := N[(x / N[(2.0 + N[(x * 0.5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.7%

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

3. Taylor expanded in x around 0 67.9%

   \[\leadsto \frac{x}{\color{blue}{2 + 0.5 \cdot x}} \]
4. Step-by-step derivation

   1. +-commutative 67.9%

      \[\leadsto \frac{x}{\color{blue}{0.5 \cdot x + 2}} \]

5. Simplified 67.9%

   \[\leadsto \frac{x}{\color{blue}{0.5 \cdot x + 2}} \]

6. Final simplification 67.9%

   \[\leadsto \frac{x}{2 + x \cdot 0.5} \]
7. Add Preprocessing

Alternative 6: 68.0% accurate, 35.7× speedup

Math

\[\begin{array}{l} \\ \frac{x}{2} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (/ x 2.0))

C

double code(double x) {
	return x / 2.0;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = x / 2.0d0
end function

Java

public static double code(double x) {
	return x / 2.0;
}

Python

def code(x):
	return x / 2.0

Julia

function code(x)
	return Float64(x / 2.0)
end

MATLAB

function tmp = code(x)
	tmp = x / 2.0;
end

Wolfram

code[x_] := N[(x / 2.0), $MachinePrecision]

Derivation

1. Initial program 99.7%

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

3. Taylor expanded in x around 0 66.8%

   \[\leadsto \frac{x}{\color{blue}{2}} \]

4. Final simplification 66.8%

   \[\leadsto \frac{x}{2} \]
5. Add Preprocessing

Alternative 7: 4.9% accurate, 107.0× speedup

Math

\[\begin{array}{l} \\ 2 \end{array} \]

FPCore

(FPCore (x) :precision binary64 2.0)

C

double code(double x) {
	return 2.0;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = 2.0d0
end function

Java

public static double code(double x) {
	return 2.0;
}

Python

def code(x):
	return 2.0

Julia

function code(x)
	return 2.0
end

MATLAB

function tmp = code(x)
	tmp = 2.0;
end

Wolfram

code[x_] := 2.0

Derivation

1. Initial program 99.7%

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

3. Taylor expanded in x around 0 67.9%

   \[\leadsto \frac{x}{\color{blue}{2 + 0.5 \cdot x}} \]
4. Step-by-step derivation

   1. +-commutative 67.9%

      \[\leadsto \frac{x}{\color{blue}{0.5 \cdot x + 2}} \]

5. Simplified 67.9%

   \[\leadsto \frac{x}{\color{blue}{0.5 \cdot x + 2}} \]

6. Taylor expanded in x around inf 5.0%

   \[\leadsto \color{blue}{2} \]

7. Final simplification 5.0%

   \[\leadsto 2 \]
8. Add Preprocessing

Reproduce

herbie shell --seed 2024026 
(FPCore (x)
  :name "Numeric.Log:$clog1p from log-domain-0.10.2.1, B"
  :precision binary64
  (/ x (+ 1.0 (sqrt (+ x 1.0)))))