Issue #13263 has been updated by Jabari Zakiya.
A further simplification can be done for ``numb = root**n``
root = bit_mask = 1 << (b = (num.bit_length - 1)/n)
numb = root**n
numb = root << b*(n-1)
numb = (1 << b) << b*(n-1)
numb = 1 << (b + b*(n-1))
numb = 1 << b*(1 + (n-1))
numb = 1 << b*n
Which means **root** and **numb** can be done in parallel now by the compiler.
Also, because **root** and **bit_mask** are the same size, only one **word_count**
variable is needed, to track which word of **root** is being worked on, not one for each.
You also only need on word_count variable for the size of **numb** and **num**.
Then the comparisons **numb == num** and **numb > num** can be done on a word-by-word
basis, starting from each MSword. If the MSword of **numb** is > or < than that for **num**
then it's "true" or "false"; if equal continue with next lower Mswords as necessary.
This reduces the implementations to just bit manipulations, with 1|2 bit twiddles and one shift
operation per loop, and one real arithmetic operation, the ``**`` exponentiation inside the loop.
```
root = bit_mask = 1 << (b = (num.bit_length - 1)/n)
numb = 1 << b*n # fast cpu level root**n
until ((bit_mask >>= 1) == 0) || numb == num
root |= bit_mask
root ^= bit_mask if (numb = root**n) > num
end
```
----------------------------------------
Feature #13263: Add companion integer nth-root method to recent Integer#isqrt
https://bugs.ruby-lang.org/issues/13263#change-63364
* Author: Jabari Zakiya
* Status: Open
* Priority: Normal
* Assignee:
* Target version:
----------------------------------------
Following the heels of adding the method ``Integer#isqrt``, to create exact integer
squareroot values for arbitrary sized integers, based on the following threads:
https://bugs.ruby-lang.org/issues/13219
https://bugs.ruby-lang.org/issues/13250
I also request adding its companion method to compute any integer nth-root too.
Below are sample methods of high level Ruby code that compute exact results.
https://en.wikipedia.org/wiki/Nth_root_algorithm
The Newton's code is a Python version I tweaked to make it look like ``Integer#isqrt``'s form.
Benchmarks show the **bbm** method is generally faster, especially as the roots become larger,
than using Newton's method, with an added benefits its simpler to code/understand, and has a lower
sensitivity to the initial root value, and handling of small numbers.
```
class Integer
def irootn(n) # binary bit method (bbm) for nth root
return nil if self < 0 && n.even?
raise "root n is < 2 or not an Integer" unless n.is_a?(Integer) && n > 1
num = self.abs
bits_shift = (num.bit_length - 1)/n + 1 # add 1 for initial loop >>= 1
root, bitn_mask = 0, (1 << bits_shift)
until (bitn_mask >>= 1) == 0
root |= bitn_mask
root ^= bitn_mask if root**n > num
end
root *= self < 0 ? -1 : 1
end
def irootn1(n) # Newton's method for nth root
return nil if self < 0 && n.even?
raise "root n is < 2 or not an Integer" unless n.is_a?(Integer) && n > 1
return self if self == 0 || (self == -1 && n.odd?)
num = self.abs
b = num.bit_length
e, u, x = n-1, (x = 1 << (b-1)/(n-1)), x+1
while u < x
x = u
t = e * x + num / x ** e
u = t / n
end
x *= self < 0 ? -1 : 1
end
def irootn2(n) # Newton's restructured coded method for nth root
return nil if self < 0 && n.even?
raise "root n is < 2 or not an Integer" unless n.is_a?(Integer) && n > 1
return self if self == 0 || (self == -1 && n.odd?)
num = self.abs
b = num.bit_length
e, x = n-1, 1 << (b-1)/(n-1) + 1
while t = (e * x + num / x ** e)/n < x
x = (e * x + num / x ** e)/n
end
x *= self < 0 ? -1 : 1
end
end
require "benchmark/ips"
[50, 500, 1000, 2000, 4000, 5000].each do |exp|
[3, 4, 7, 13, 25, 33]. each do |k|
Benchmark.ips do |x|
n = 10**exp
puts "integer root tests for root #{k} of n = 10**#{exp}"
x.report("bbm" ) { n.irootn(k) }
x.report("newton1" ) { n.irootn1(k) }
x.report("newton2" ) { n.irootn2(k) }
x.compare!
end
end
end
```
Here are results.
```
def tm; t=Time.now; yield; Time.now-t end
2.4.0 :022 > exp = 111; n = 10**exp; r = 10; puts n, "#{ tm{ puts n.irootn(r)} }", "#{ tm{ puts n.irootn1(r)} }", "#{ tm{ puts n.irootn2(r)} }"
125892541179
125892541179
125892541179
1000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
4.6673e-05
6.5506e-05
0.000121357
=> nil
2.4.0 :023 > exp = 150; n = 10**exp; r = 50; puts n, "#{tm{ puts n.irootn(r)}}", "#{ tm{ puts n.irootn1(r)}}", "#{ tm{ puts n.irootn2(r)} }"
1000
1000
1000
1000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
2.28e-05
1.8762e-05
0.000128852
=> nil
2.4.0 :024 >
```
The benchmarks show that ``irootn2`` is the slowest but it has the same
form as ``Integer#isqt`` in the numeric.c and bignum.c files in trunk.
It probably can be tweaked to make it faster.
bignum.c, starting at line 6772
https://bugs.ruby-lang.org/projects/ruby-trunk/repository/revisions/57705/entry/bignum.c
numeric.c, starting at line 5131
https://bugs.ruby-lang.org/projects/ruby-trunk/repository/revisions/57705/entry/numeric.c
Thus, a hybrid method could be created that swtiches between the two.
```
def isqrt(num=self)
b = num.bit_length
x = 1 << (b-1)/2 | num >> (b/2 + 1) # optimum first root extimate
while (t = num / x) < x
x = ((x + t) >> 1)
end
x
end
def irootn2(n)
b = num.bit_length
e, x = n-1, 1 << (b-1)/(n-1) + 1 # optimum first root estimate(?)
while t = (e * x + num / x ** e)/n < x
x = (e * x + num / x ** e)/n
end
x
end
def irtn(n) # possible hybrid combination for all nth-roots
b = num.bit_length
if 2 < n # for squareroot
x = 1 << (b-1)/2 | num >> (b/2 + 1)
while (t = num / x) < x
x = ((x + t) >> 1)
end
else # for roots > 2
e, x = n-1, 1 << (b-1)/(n-1) + 1
while t = (e * x + num / x ** e)/n < x
x = (e * x + num / x ** e)/n
end
end
x *= if self < 0 ? -1 : 1
end
```
So with just a little more work, a highly performant nth-root method can be added
to the std lib, as with ``Integer#isqrt``, to take care of all the exact integer roots
for arbitrary sized integers, by whatever name that is preferable.
This will enhance Ruby's use even more in fields like number theory, advanced math, cryptography,
etc, to have fast primitive standard methods to compute these use case values.
--
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