Rust f64::acosh

Percentage Accurate: 51.1% → 99.9%

Time: 4.3s

Alternatives: 4

Speedup: 2.0×

Specification

\[x \geq 1\]

Math

\[\begin{array}{l} \\ \cosh^{-1} x \end{array} \]

FPCore

(FPCore (x) :precision binary64 (acosh x))

C

double code(double x) {
	return acosh(x);
}

Python

def code(x):
	return math.acosh(x)

Julia

function code(x)
	return acosh(x)
end

MATLAB

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

Wolfram

code[x_] := N[ArcCosh[x], $MachinePrecision]

Initial Program: 51.1% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \log \left(x + \sqrt{x \cdot x - 1}\right) \end{array} \]

FPCore

(FPCore (x) :precision binary64 (log (+ x (sqrt (- (* x x) 1.0)))))

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 99.9% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \log \left(x + \sqrt{x + 1} \cdot \sqrt{x + -1}\right) \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (log (+ x (* (sqrt (+ x 1.0)) (sqrt (+ x -1.0))))))

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 50.1%

   \[\log \left(x + \sqrt{x \cdot x - 1}\right) \]
2. Add Preprocessing
3. Step-by-step derivation

   1. difference-of-sqr-1 50.1%

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

   2. sqrt-prod 98.9%

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

   3. sub-neg 98.9%

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

   4. metadata-eval 98.9%

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

4. Applied egg-rr 98.9%

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

5. Final simplification 98.9%

   \[\leadsto \log \left(x + \sqrt{x + 1} \cdot \sqrt{x + -1}\right) \]
6. Add Preprocessing

Alternative 2: 98.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \log 2 + \log x \end{array} \]

FPCore

(FPCore (x) :precision binary64 (+ (log 2.0) (log x)))

C

double code(double x) {
	return log(2.0) + log(x);
}

Fortran

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

Java

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

Python

def code(x):
	return math.log(2.0) + math.log(x)

Julia

function code(x)
	return Float64(log(2.0) + log(x))
end

MATLAB

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

Wolfram

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

Derivation

1. Initial program 50.1%

   \[\log \left(x + \sqrt{x \cdot x - 1}\right) \]
2. Add Preprocessing

3. Taylor expanded in x around inf 98.8%

   \[\leadsto \color{blue}{\log 2 + -1 \cdot \log \left(\frac{1}{x}\right)} \]
4. Step-by-step derivation

   1. mul-1-neg 98.8%

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

   2. log-rec 98.8%

      \[\leadsto \log 2 + \left(-\color{blue}{\left(-\log x\right)}\right) \]

   3. remove-double-neg 98.8%

      \[\leadsto \log 2 + \color{blue}{\log x} \]

5. Simplified 98.8%

   \[\leadsto \color{blue}{\log 2 + \log x} \]

6. Final simplification 98.8%

   \[\leadsto \log 2 + \log x \]
7. Add Preprocessing

Alternative 3: 99.1% accurate, 2.0× speedup

Math

\[\begin{array}{l} \\ \log \left(x + x\right) \end{array} \]

FPCore

(FPCore (x) :precision binary64 (log (+ x x)))

C

double code(double x) {
	return log((x + x));
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = log((x + x))
end function

Java

public static double code(double x) {
	return Math.log((x + x));
}

Python

def code(x):
	return math.log((x + x))

Julia

function code(x)
	return log(Float64(x + x))
end

MATLAB

function tmp = code(x)
	tmp = log((x + x));
end

Wolfram

code[x_] := N[Log[N[(x + x), $MachinePrecision]], $MachinePrecision]

Derivation

1. Initial program 50.1%

   \[\log \left(x + \sqrt{x \cdot x - 1}\right) \]
2. Add Preprocessing

3. Taylor expanded in x around inf 97.9%

   \[\leadsto \log \left(x + \color{blue}{x}\right) \]

4. Final simplification 97.9%

   \[\leadsto \log \left(x + x\right) \]
5. Add Preprocessing

Alternative 4: 3.1% accurate, 207.0× speedup

Math

\[\begin{array}{l} \\ 0 \end{array} \]

FPCore

(FPCore (x) :precision binary64 0.0)

C

double code(double x) {
	return 0.0;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = 0.0d0
end function

Java

public static double code(double x) {
	return 0.0;
}

Python

def code(x):
	return 0.0

Julia

function code(x)
	return 0.0
end

MATLAB

function tmp = code(x)
	tmp = 0.0;
end

Wolfram

code[x_] := 0.0

Derivation

1. Initial program 50.1%

   \[\log \left(x + \sqrt{x \cdot x - 1}\right) \]
2. Add Preprocessing

3. Taylor expanded in x around inf 97.9%

   \[\leadsto \log \left(x + \color{blue}{x}\right) \]
4. Step-by-step derivation

   1. add-sqr-sqrt 97.9%

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

   2. log-prod 97.9%

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

   3. flip-+ 0.0%

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

   4. difference-of-squares 0.0%

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

   5. associate-*r/ 0.0%

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

   6. +-inverses 0.0%

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

   7. +-inverses 0.0%

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

   8. flip-+ 11.3%

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

   9. sqrt-unprod 19.8%

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

   10. add-sqr-sqrt 19.8%

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

   11. flip-+ 0.0%

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

   12. +-inverses 0.0%

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

   13. +-inverses 0.0%

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

   14. flip-+ 0.0%

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

   15. difference-of-squares 0.0%

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

   16. associate-*r/ 0.0%

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

   17. +-inverses 0.0%

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

   18. +-inverses 0.0%

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

   19. flip-+ 0.0%

       \[\leadsto \log \left(\frac{0}{0}\right) + \log \left(\sqrt{\left(x + x\right) \cdot \color{blue}{\left(x + x\right)}}\right) \]

5. Applied egg-rr 0.0%

   \[\leadsto \color{blue}{\log \left(\frac{0}{0}\right) + \log \left(\frac{0}{0}\right)} \]

7. Simplified 3.1%

   \[\leadsto \color{blue}{0} \]

8. Final simplification 3.1%

   \[\leadsto 0 \]
9. Add Preprocessing

Developer target: 99.9% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \log \left(x + \sqrt{x - 1} \cdot \sqrt{x + 1}\right) \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (log (+ x (* (sqrt (- x 1.0)) (sqrt (+ x 1.0))))))

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Reproduce

herbie shell --seed 2024066 
(FPCore (x)
  :name "Rust f64::acosh"
  :precision binary64
  :pre (>= x 1.0)

  :alt
  (log (+ x (* (sqrt (- x 1.0)) (sqrt (+ x 1.0)))))

  (log (+ x (sqrt (- (* x x) 1.0)))))