Hyperbolic arc-cosine

Percentage Accurate: 51.7% → 99.5%
Time: 4.8s
Alternatives: 9
Speedup: 2.0×

Specification

?
\[\begin{array}{l} \\ \log \left(x + \sqrt{x \cdot x - 1}\right) \end{array} \]
(FPCore (x) :precision binary64 (log (+ x (sqrt (- (* x x) 1.0)))))
double code(double x) {
	return log((x + sqrt(((x * x) - 1.0))));
}

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

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

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

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

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

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

\begin{array}{l}

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

Sampling outcomes in binary64 precision:

Local Percentage Accuracy vs ?

The average percentage accuracy by input value. Horizontal axis shows value of an input variable; the variable is choosen in the title. Vertical axis is accuracy; higher is better. Red represent the original program, while blue represents Herbie's suggestion. These can be toggled with buttons below the plot. The line is an average while dots represent individual samples.

Accuracy vs Speed?

Herbie found 9 alternatives:

AlternativeAccuracySpeedup
The accuracy (vertical axis) and speed (horizontal axis) of each alternatives. Up and to the right is better. The red square shows the initial program, and each blue circle shows an alternative.The line shows the best available speed-accuracy tradeoffs.

Initial Program: 51.7% accurate, 1.0× speedup?

\[\begin{array}{l} \\ \log \left(x + \sqrt{x \cdot x - 1}\right) \end{array} \]
(FPCore (x) :precision binary64 (log (+ x (sqrt (- (* x x) 1.0)))))
double code(double x) {
	return log((x + sqrt(((x * x) - 1.0))));
}

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

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

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

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

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

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

\begin{array}{l}

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

Alternative 1: 99.5% accurate, 1.9× speedup?

\[\begin{array}{l} \\ \log \left(x \cdot 2 - \frac{0.5}{x}\right) \end{array} \]
(FPCore (x) :precision binary64 (log (- (* x 2.0) (/ 0.5 x))))
double code(double x) {
	return log(((x * 2.0) - (0.5 / x)));
}

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

public static double code(double x) {
	return Math.log(((x * 2.0) - (0.5 / x)));
}

def code(x):
	return math.log(((x * 2.0) - (0.5 / x)))

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

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

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

\begin{array}{l}

\\
\log \left(x \cdot 2 - \frac{0.5}{x}\right)
\end{array}
Derivation
    &prev;&pcontext;&pcontext2;&ctx;
  1. Add Preprocessing

Alternative 2: 99.0% accurate, 2.0× speedup?

\[\begin{array}{l} \\ \log \left(x + x\right) \end{array} \]
(FPCore (x) :precision binary64 (log (+ x x)))
double code(double x) {
	return log((x + x));
}

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

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

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

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

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

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

\begin{array}{l}

\\
\log \left(x + x\right)
\end{array}
Derivation
    &prev;&pcontext;&pcontext2;&ctx;
  1. Add Preprocessing

Alternative 3: 1.6% accurate, 207.0× speedup?

\[\begin{array}{l} \\ -1 \end{array} \]
(FPCore (x) :precision binary64 -1.0)
double code(double x) {
	return -1.0;
}

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

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

def code(x):
	return -1.0

function code(x)
	return -1.0
end

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

code[x_] := -1.0

\begin{array}{l}

\\
-1
\end{array}
Derivation
    &prev;&pcontext;&pcontext2;&ctx;
  1. Add Preprocessing

Alternative 4: 14.6% accurate, 207.0× speedup?

\[\begin{array}{l} \\ 3 \end{array} \]
(FPCore (x) :precision binary64 3.0)
double code(double x) {
	return 3.0;
}

real(8) function code(x)
    real(8), intent (in) :: x
    code = 3.0d0
end function

public static double code(double x) {
	return 3.0;
}

def code(x):
	return 3.0

function code(x)
	return 3.0
end

function tmp = code(x)
	tmp = 3.0;
end

code[x_] := 3.0

\begin{array}{l}

\\
3
\end{array}
Derivation
    &prev;&pcontext;&pcontext2;&ctx;
  1. Add Preprocessing

Alternative 5: 14.8% accurate, 207.0× speedup?

\[\begin{array}{l} \\ 4 \end{array} \]
(FPCore (x) :precision binary64 4.0)
double code(double x) {
	return 4.0;
}

real(8) function code(x)
    real(8), intent (in) :: x
    code = 4.0d0
end function

public static double code(double x) {
	return 4.0;
}

def code(x):
	return 4.0

function code(x)
	return 4.0
end

function tmp = code(x)
	tmp = 4.0;
end

code[x_] := 4.0

\begin{array}{l}

\\
4
\end{array}
Derivation
    &prev;&pcontext;&pcontext2;&ctx;
  1. Add Preprocessing

Alternative 6: 15.3% accurate, 207.0× speedup?

\[\begin{array}{l} \\ 9 \end{array} \]
(FPCore (x) :precision binary64 9.0)
double code(double x) {
	return 9.0;
}

real(8) function code(x)
    real(8), intent (in) :: x
    code = 9.0d0
end function

public static double code(double x) {
	return 9.0;
}

def code(x):
	return 9.0

function code(x)
	return 9.0
end

function tmp = code(x)
	tmp = 9.0;
end

code[x_] := 9.0

\begin{array}{l}

\\
9
\end{array}
Derivation
    &prev;&pcontext;&pcontext2;&ctx;
  1. Add Preprocessing

Alternative 7: 15.8% accurate, 207.0× speedup?

\[\begin{array}{l} \\ 16 \end{array} \]
(FPCore (x) :precision binary64 16.0)
double code(double x) {
	return 16.0;
}

real(8) function code(x)
    real(8), intent (in) :: x
    code = 16.0d0
end function

public static double code(double x) {
	return 16.0;
}

def code(x):
	return 16.0

function code(x)
	return 16.0
end

function tmp = code(x)
	tmp = 16.0;
end

code[x_] := 16.0

\begin{array}{l}

\\
16
\end{array}
Derivation
    &prev;&pcontext;&pcontext2;&ctx;
  1. Add Preprocessing

Alternative 8: 16.3% accurate, 207.0× speedup?

\[\begin{array}{l} \\ 27 \end{array} \]
(FPCore (x) :precision binary64 27.0)
double code(double x) {
	return 27.0;
}

real(8) function code(x)
    real(8), intent (in) :: x
    code = 27.0d0
end function

public static double code(double x) {
	return 27.0;
}

def code(x):
	return 27.0

function code(x)
	return 27.0
end

function tmp = code(x)
	tmp = 27.0;
end

code[x_] := 27.0

\begin{array}{l}

\\
27
\end{array}
Derivation
    &prev;&pcontext;&pcontext2;&ctx;
  1. Add Preprocessing

Alternative 9: 17.4% accurate, 207.0× speedup?

\[\begin{array}{l} \\ 64 \end{array} \]
(FPCore (x) :precision binary64 64.0)
double code(double x) {
	return 64.0;
}

real(8) function code(x)
    real(8), intent (in) :: x
    code = 64.0d0
end function

public static double code(double x) {
	return 64.0;
}

def code(x):
	return 64.0

function code(x)
	return 64.0
end

function tmp = code(x)
	tmp = 64.0;
end

code[x_] := 64.0

\begin{array}{l}

\\
64
\end{array}
Derivation
    &prev;&pcontext;&pcontext2;&ctx;
  1. Add Preprocessing

Reproduce

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herbie shell --seed 2024010 
(FPCore (x)
  :name "Hyperbolic arc-cosine"
  :precision binary64
  (log (+ x (sqrt (- (* x x) 1.0)))))