Does Python optimize low-power functions?
oscar.j.benjamin at gmail.com
Fri Dec 6 21:57:49 CET 2013
On 6 December 2013 18:16, John Ladasky <john_ladasky at sbcglobal.net> wrote:
> The following two functions return the same result:
> But they may be computed in different ways. The first choice can accommodate non-integer powers and so it would logically proceed by taking a logarithm, multiplying by the power (in this case, 2), and then taking the anti-logarithm. But for a trivial value for the power like 2, this is clearly a wasteful choice. Just multiply x by itself, and skip the expensive log and anti-log steps.
> My question is, what do Python interpreters do with power operators where the power is a small constant, like 2? Do they know to take the shortcut?
As mentioned this will depend on the interpreter and on the type of x.
Python's integer arithmetic is exact and unbounded so switching to
floating point and using approximate logarithms is a no go if x is an
For CPython specifically, you can see here:
that for floats x**2 will be equivalent to x**2.0 and will be handled
by the pow function from the underlying C math library. If you read
the comments around that line you'll see that different inconsistent
math libraries can do things very differently leading to all kinds of
For CPython if x is an int (long) then as mentioned before it is
handled by the HAC algorithm:
For CPython if x is a complex then it is handled roughly as you say:
for x**n if n is between -100 and 100 then multiplication is performed
using the "bit-mask exponentiation" algorithm. Otherwise it is
computed by converting to polar exponential form and using logs (see
also the two functions above this one):
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