sqrtexp (problem 3.4.4)

Percentage Accurate: 35.4% → 100.0%

Time: 7.5s

Alternatives: 6

Speedup: 3.1×

Specification

Math

\[\begin{array}{l} \\ \sqrt{\frac{e^{2 \cdot x} - 1}{e^{x} - 1}} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (sqrt (/ (- (exp (* 2.0 x)) 1.0) (- (exp x) 1.0))))

C

double code(double x) {
	return sqrt(((exp((2.0 * x)) - 1.0) / (exp(x) - 1.0)));
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = sqrt(((exp((2.0d0 * x)) - 1.0d0) / (exp(x) - 1.0d0)))
end function

Java

public static double code(double x) {
	return Math.sqrt(((Math.exp((2.0 * x)) - 1.0) / (Math.exp(x) - 1.0)));
}

Python

def code(x):
	return math.sqrt(((math.exp((2.0 * x)) - 1.0) / (math.exp(x) - 1.0)))

Julia

function code(x)
	return sqrt(Float64(Float64(exp(Float64(2.0 * x)) - 1.0) / Float64(exp(x) - 1.0)))
end

MATLAB

function tmp = code(x)
	tmp = sqrt(((exp((2.0 * x)) - 1.0) / (exp(x) - 1.0)));
end

Wolfram

code[x_] := N[Sqrt[N[(N[(N[Exp[N[(2.0 * x), $MachinePrecision]], $MachinePrecision] - 1.0), $MachinePrecision] / N[(N[Exp[x], $MachinePrecision] - 1.0), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]

Initial Program: 35.4% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \sqrt{\frac{e^{2 \cdot x} - 1}{e^{x} - 1}} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (sqrt (/ (- (exp (* 2.0 x)) 1.0) (- (exp x) 1.0))))

C

double code(double x) {
	return sqrt(((exp((2.0 * x)) - 1.0) / (exp(x) - 1.0)));
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = sqrt(((exp((2.0d0 * x)) - 1.0d0) / (exp(x) - 1.0d0)))
end function

Java

public static double code(double x) {
	return Math.sqrt(((Math.exp((2.0 * x)) - 1.0) / (Math.exp(x) - 1.0)));
}

Python

def code(x):
	return math.sqrt(((math.exp((2.0 * x)) - 1.0) / (math.exp(x) - 1.0)))

Julia

function code(x)
	return sqrt(Float64(Float64(exp(Float64(2.0 * x)) - 1.0) / Float64(exp(x) - 1.0)))
end

MATLAB

function tmp = code(x)
	tmp = sqrt(((exp((2.0 * x)) - 1.0) / (exp(x) - 1.0)));
end

Wolfram

code[x_] := N[Sqrt[N[(N[(N[Exp[N[(2.0 * x), $MachinePrecision]], $MachinePrecision] - 1.0), $MachinePrecision] / N[(N[Exp[x], $MachinePrecision] - 1.0), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]

Alternative 1: 100.0% accurate, 1.5× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 30.0%

   \[\sqrt{\frac{e^{2 \cdot x} - 1}{e^{x} - 1}} \]
2. Step-by-step derivation

   1. *-commutative 30.0%

      \[\leadsto \sqrt{\frac{e^{\color{blue}{x \cdot 2}} - 1}{e^{x} - 1}} \]

   2. exp-lft-sqr 30.2%

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

   3. difference-of-sqr-1 31.2%

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

   4. associate-*r/ 31.2%

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

   5. *-inverses 100.0%

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

   6. *-rgt-identity 100.0%

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

   7. +-commutative 100.0%

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

3. Simplified 100.0%

   \[\leadsto \color{blue}{\sqrt{1 + e^{x}}} \]
4. Add Preprocessing
5. Add Preprocessing

Alternative 2: 72.8% accurate, 2.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -5.2:\\ \;\;\;\;\sqrt{2}\\ \mathbf{else}:\\ \;\;\;\;\mathsf{hypot}\left(1, 1 + x \cdot \left(0.5 + x \cdot \left(0.125 + x \cdot 0.020833333333333332\right)\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (if (<= x -5.2)
   (sqrt 2.0)
   (hypot
    1.0
    (+ 1.0 (* x (+ 0.5 (* x (+ 0.125 (* x 0.020833333333333332)))))))))

C

double code(double x) {
	double tmp;
	if (x <= -5.2) {
		tmp = sqrt(2.0);
	} else {
		tmp = hypot(1.0, (1.0 + (x * (0.5 + (x * (0.125 + (x * 0.020833333333333332)))))));
	}
	return tmp;
}

Java

public static double code(double x) {
	double tmp;
	if (x <= -5.2) {
		tmp = Math.sqrt(2.0);
	} else {
		tmp = Math.hypot(1.0, (1.0 + (x * (0.5 + (x * (0.125 + (x * 0.020833333333333332)))))));
	}
	return tmp;
}

Python

def code(x):
	tmp = 0
	if x <= -5.2:
		tmp = math.sqrt(2.0)
	else:
		tmp = math.hypot(1.0, (1.0 + (x * (0.5 + (x * (0.125 + (x * 0.020833333333333332)))))))
	return tmp

Julia

function code(x)
	tmp = 0.0
	if (x <= -5.2)
		tmp = sqrt(2.0);
	else
		tmp = hypot(1.0, Float64(1.0 + Float64(x * Float64(0.5 + Float64(x * Float64(0.125 + Float64(x * 0.020833333333333332)))))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x)
	tmp = 0.0;
	if (x <= -5.2)
		tmp = sqrt(2.0);
	else
		tmp = hypot(1.0, (1.0 + (x * (0.5 + (x * (0.125 + (x * 0.020833333333333332)))))));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_] := If[LessEqual[x, -5.2], N[Sqrt[2.0], $MachinePrecision], N[Sqrt[1.0 ^ 2 + N[(1.0 + N[(x * N[(0.5 + N[(x * N[(0.125 + N[(x * 0.020833333333333332), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] ^ 2], $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < -5.20000000000000018

   1. Initial program 100.0%

      \[\sqrt{\frac{e^{2 \cdot x} - 1}{e^{x} - 1}} \]
   2. Step-by-step derivation

      1. *-commutative 100.0%

         \[\leadsto \sqrt{\frac{e^{\color{blue}{x \cdot 2}} - 1}{e^{x} - 1}} \]

      2. exp-lft-sqr 100.0%

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

      3. difference-of-sqr-1 100.0%

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

      4. associate-*r/ 100.0%

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

      5. *-inverses 100.0%

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

      6. *-rgt-identity 100.0%

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

      7. +-commutative 100.0%

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

   3. Simplified 100.0%

      \[\leadsto \color{blue}{\sqrt{1 + e^{x}}} \]
   4. Add Preprocessing

   5. Taylor expanded in x around 0 20.7%

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

   if -5.20000000000000018 < x

   1. Initial program 6.2%

      \[\sqrt{\frac{e^{2 \cdot x} - 1}{e^{x} - 1}} \]
   2. Step-by-step derivation

      1. *-commutative 6.2%

         \[\leadsto \sqrt{\frac{e^{\color{blue}{x \cdot 2}} - 1}{e^{x} - 1}} \]

      2. exp-lft-sqr 6.5%

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

      3. difference-of-sqr-1 7.8%

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

      4. associate-*r/ 7.8%

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

      5. *-inverses 100.0%

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

      6. *-rgt-identity 100.0%

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

      7. +-commutative 100.0%

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

   3. Simplified 100.0%

      \[\leadsto \color{blue}{\sqrt{1 + e^{x}}} \]
   4. Add Preprocessing
   5. Step-by-step derivation

      1. add-log-exp 99.5%

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

      2. *-un-lft-identity 99.5%

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

      3. log-prod 99.5%

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

      4. metadata-eval 99.5%

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

      5. add-log-exp 100.0%

         \[\leadsto 0 + \color{blue}{\sqrt{1 + e^{x}}} \]

      6. add-sqr-sqrt 100.0%

         \[\leadsto 0 + \sqrt{1 + \color{blue}{\sqrt{e^{x}} \cdot \sqrt{e^{x}}}} \]

      7. hypot-1-def 100.0%

         \[\leadsto 0 + \color{blue}{\mathsf{hypot}\left(1, \sqrt{e^{x}}\right)} \]

   6. Applied egg-rr 100.0%

      \[\leadsto \color{blue}{0 + \mathsf{hypot}\left(1, \sqrt{e^{x}}\right)} \]
   7. Step-by-step derivation

      1. +-lft-identity 100.0%

         \[\leadsto \color{blue}{\mathsf{hypot}\left(1, \sqrt{e^{x}}\right)} \]

   8. Simplified 100.0%

      \[\leadsto \color{blue}{\mathsf{hypot}\left(1, \sqrt{e^{x}}\right)} \]

   9. Taylor expanded in x around 0 98.1%

      \[\leadsto \mathsf{hypot}\left(1, \color{blue}{1 + x \cdot \left(0.5 + x \cdot \left(0.125 + 0.020833333333333332 \cdot x\right)\right)}\right) \]
   10. Step-by-step derivation

       1. *-commutative 98.1%

          \[\leadsto \mathsf{hypot}\left(1, 1 + x \cdot \left(0.5 + x \cdot \left(0.125 + \color{blue}{x \cdot 0.020833333333333332}\right)\right)\right) \]

   11. Simplified 98.1%

       \[\leadsto \mathsf{hypot}\left(1, \color{blue}{1 + x \cdot \left(0.5 + x \cdot \left(0.125 + x \cdot 0.020833333333333332\right)\right)}\right) \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 3: 71.8% accurate, 3.1× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

def code(x):
	return math.sqrt(2.0)

Julia

function code(x)
	return sqrt(2.0)
end

MATLAB

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

Wolfram

code[x_] := N[Sqrt[2.0], $MachinePrecision]

Derivation

1. Initial program 30.0%

   \[\sqrt{\frac{e^{2 \cdot x} - 1}{e^{x} - 1}} \]
2. Step-by-step derivation

   1. *-commutative 30.0%

      \[\leadsto \sqrt{\frac{e^{\color{blue}{x \cdot 2}} - 1}{e^{x} - 1}} \]

   2. exp-lft-sqr 30.2%

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

   3. difference-of-sqr-1 31.2%

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

   4. associate-*r/ 31.2%

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

   5. *-inverses 100.0%

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

   6. *-rgt-identity 100.0%

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

   7. +-commutative 100.0%

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

3. Simplified 100.0%

   \[\leadsto \color{blue}{\sqrt{1 + e^{x}}} \]
4. Add Preprocessing

5. Taylor expanded in x around 0 76.9%

   \[\leadsto \color{blue}{\sqrt{2}} \]
6. Add Preprocessing

Alternative 4: 14.1% accurate, 44.1× speedup

Math

\[\begin{array}{l} \\ x \cdot \left(0.5 + \frac{1}{x}\right) \end{array} \]

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 30.0%

   \[\sqrt{\frac{e^{2 \cdot x} - 1}{e^{x} - 1}} \]
2. Step-by-step derivation

   1. *-commutative 30.0%

      \[\leadsto \sqrt{\frac{e^{\color{blue}{x \cdot 2}} - 1}{e^{x} - 1}} \]

   2. exp-lft-sqr 30.2%

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

   3. difference-of-sqr-1 31.2%

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

   4. associate-*r/ 31.2%

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

   5. *-inverses 100.0%

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

   6. *-rgt-identity 100.0%

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

   7. +-commutative 100.0%

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

3. Simplified 100.0%

   \[\leadsto \color{blue}{\sqrt{1 + e^{x}}} \]
4. Add Preprocessing
5. Step-by-step derivation

   1. add-log-exp 99.6%

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

   2. *-un-lft-identity 99.6%

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

   3. log-prod 99.6%

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

   4. metadata-eval 99.6%

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

   5. add-log-exp 100.0%

      \[\leadsto 0 + \color{blue}{\sqrt{1 + e^{x}}} \]

   6. add-sqr-sqrt 100.0%

      \[\leadsto 0 + \sqrt{1 + \color{blue}{\sqrt{e^{x}} \cdot \sqrt{e^{x}}}} \]

   7. hypot-1-def 100.0%

      \[\leadsto 0 + \color{blue}{\mathsf{hypot}\left(1, \sqrt{e^{x}}\right)} \]

6. Applied egg-rr 100.0%

   \[\leadsto \color{blue}{0 + \mathsf{hypot}\left(1, \sqrt{e^{x}}\right)} \]
7. Step-by-step derivation

   1. +-lft-identity 100.0%

      \[\leadsto \color{blue}{\mathsf{hypot}\left(1, \sqrt{e^{x}}\right)} \]

8. Simplified 100.0%

   \[\leadsto \color{blue}{\mathsf{hypot}\left(1, \sqrt{e^{x}}\right)} \]

9. Taylor expanded in x around 0 74.1%

   \[\leadsto \mathsf{hypot}\left(1, \color{blue}{1 + 0.5 \cdot x}\right) \]

10. Taylor expanded in x around inf 15.5%

    \[\leadsto \color{blue}{x \cdot \left(0.5 + \frac{1}{x}\right)} \]
11. Add Preprocessing

Alternative 5: 4.3% accurate, 103.0× speedup

Math

\[\begin{array}{l} \\ x \cdot -0.5 \end{array} \]

FPCore

(FPCore (x) :precision binary64 (* x -0.5))

C

double code(double x) {
	return x * -0.5;
}

Fortran

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

Java

public static double code(double x) {
	return x * -0.5;
}

Python

def code(x):
	return x * -0.5

Julia

function code(x)
	return Float64(x * -0.5)
end

MATLAB

function tmp = code(x)
	tmp = x * -0.5;
end

Wolfram

code[x_] := N[(x * -0.5), $MachinePrecision]

Derivation

1. Initial program 30.0%

   \[\sqrt{\frac{e^{2 \cdot x} - 1}{e^{x} - 1}} \]
2. Step-by-step derivation

   1. *-commutative 30.0%

      \[\leadsto \sqrt{\frac{e^{\color{blue}{x \cdot 2}} - 1}{e^{x} - 1}} \]

   2. exp-lft-sqr 30.2%

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

   3. difference-of-sqr-1 31.2%

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

   4. associate-*r/ 31.2%

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

   5. *-inverses 100.0%

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

   6. *-rgt-identity 100.0%

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

   7. +-commutative 100.0%

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

3. Simplified 100.0%

   \[\leadsto \color{blue}{\sqrt{1 + e^{x}}} \]
4. Add Preprocessing
5. Step-by-step derivation

   1. add-log-exp 99.6%

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

   2. *-un-lft-identity 99.6%

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

   3. log-prod 99.6%

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

   4. metadata-eval 99.6%

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

   5. add-log-exp 100.0%

      \[\leadsto 0 + \color{blue}{\sqrt{1 + e^{x}}} \]

   6. add-sqr-sqrt 100.0%

      \[\leadsto 0 + \sqrt{1 + \color{blue}{\sqrt{e^{x}} \cdot \sqrt{e^{x}}}} \]

   7. hypot-1-def 100.0%

      \[\leadsto 0 + \color{blue}{\mathsf{hypot}\left(1, \sqrt{e^{x}}\right)} \]

6. Applied egg-rr 100.0%

   \[\leadsto \color{blue}{0 + \mathsf{hypot}\left(1, \sqrt{e^{x}}\right)} \]
7. Step-by-step derivation

   1. +-lft-identity 100.0%

      \[\leadsto \color{blue}{\mathsf{hypot}\left(1, \sqrt{e^{x}}\right)} \]

8. Simplified 100.0%

   \[\leadsto \color{blue}{\mathsf{hypot}\left(1, \sqrt{e^{x}}\right)} \]

9. Taylor expanded in x around 0 74.1%

   \[\leadsto \mathsf{hypot}\left(1, \color{blue}{1 + 0.5 \cdot x}\right) \]

10. Taylor expanded in x around -inf 4.1%

    \[\leadsto \color{blue}{-0.5 \cdot x} \]
11. Step-by-step derivation

    1. *-commutative 4.1%

       \[\leadsto \color{blue}{x \cdot -0.5} \]

12. Simplified 4.1%

    \[\leadsto \color{blue}{x \cdot -0.5} \]
13. Add Preprocessing

Alternative 6: 2.9% accurate, 103.0× speedup

Math

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

FPCore

(FPCore (x) :precision binary64 (* x 0.5))

C

double code(double x) {
	return x * 0.5;
}

Fortran

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

Java

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

Python

def code(x):
	return x * 0.5

Julia

function code(x)
	return Float64(x * 0.5)
end

MATLAB

function tmp = code(x)
	tmp = x * 0.5;
end

Wolfram

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

Derivation

1. Initial program 30.0%

   \[\sqrt{\frac{e^{2 \cdot x} - 1}{e^{x} - 1}} \]
2. Step-by-step derivation

   1. *-commutative 30.0%

      \[\leadsto \sqrt{\frac{e^{\color{blue}{x \cdot 2}} - 1}{e^{x} - 1}} \]

   2. exp-lft-sqr 30.2%

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

   3. difference-of-sqr-1 31.2%

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

   4. associate-*r/ 31.2%

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

   5. *-inverses 100.0%

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

   6. *-rgt-identity 100.0%

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

   7. +-commutative 100.0%

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

3. Simplified 100.0%

   \[\leadsto \color{blue}{\sqrt{1 + e^{x}}} \]
4. Add Preprocessing
5. Step-by-step derivation

   1. add-log-exp 99.6%

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

   2. *-un-lft-identity 99.6%

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

   3. log-prod 99.6%

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

   4. metadata-eval 99.6%

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

   5. add-log-exp 100.0%

      \[\leadsto 0 + \color{blue}{\sqrt{1 + e^{x}}} \]

   6. add-sqr-sqrt 100.0%

      \[\leadsto 0 + \sqrt{1 + \color{blue}{\sqrt{e^{x}} \cdot \sqrt{e^{x}}}} \]

   7. hypot-1-def 100.0%

      \[\leadsto 0 + \color{blue}{\mathsf{hypot}\left(1, \sqrt{e^{x}}\right)} \]

6. Applied egg-rr 100.0%

   \[\leadsto \color{blue}{0 + \mathsf{hypot}\left(1, \sqrt{e^{x}}\right)} \]
7. Step-by-step derivation

   1. +-lft-identity 100.0%

      \[\leadsto \color{blue}{\mathsf{hypot}\left(1, \sqrt{e^{x}}\right)} \]

8. Simplified 100.0%

   \[\leadsto \color{blue}{\mathsf{hypot}\left(1, \sqrt{e^{x}}\right)} \]

9. Taylor expanded in x around 0 74.1%

   \[\leadsto \mathsf{hypot}\left(1, \color{blue}{1 + 0.5 \cdot x}\right) \]

10. Taylor expanded in x around inf 3.1%

    \[\leadsto \color{blue}{0.5 \cdot x} \]

11. Final simplification 3.1%

    \[\leadsto x \cdot 0.5 \]
12. Add Preprocessing

Reproduce

herbie shell --seed 2024086 
(FPCore (x)
  :name "sqrtexp (problem 3.4.4)"
  :precision binary64
  (sqrt (/ (- (exp (* 2.0 x)) 1.0) (- (exp x) 1.0))))