```Issue #13263 has been updated by jzakiya (Jabari Zakiya).

Just FYI.

I simplified Newton's general nth-root method from the original implementation I posted.
It's faster, and seems to produce the correct results all the time (from the tests I've run).
For some roots (mostly smallish) of certain numbers it's faster than **bbm** by some
percentage difference, but in general **bbm** is still faster, by whole number factors, across the board.

```
original implementation of Newton's general nth-root method

def irootn2(n)
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       # optimum first root estimate(?)
while t = (e * x + num / x ** e)/n < x
x = (e * x + num / x ** e)/n
end
x
end

simpler/faster implementation of Newton's general nth-root method

def irootn2(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, x = n-1, 1 << (b-1)/(n-1) + 1       # optimum first root estimate(?)
while (t = e * x + num / x ** e)/n < x
x = t/n
end
x *= self < 0 ? -1 : 1
end

----------------------------------------
Feature #13263: Add companion integer nth-root method to recent Integer#isqrt
https://bugs.ruby-lang.org/issues/13263#change-63573

* Author: jzakiya (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 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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```