What is the longest known sequence that repeats in Pi? (homelab)
The article explores repeating sequences in Pi's decimal representation, detailing the transition from Python to C for efficiency, successful identification of sequences, and plans for further extensive searches.
Read original articleThe article discusses the exploration of repeating sequences in the decimal representation of Pi, focusing on computational number theory. It highlights the discovery of various repeating sequences, such as single digits and longer sequences, and references the Online Encyclopedia of Integer Sequences (OEIS) for known sequences. The author describes the challenges of searching for these sequences, particularly as the length of the sequences increases, necessitating the use of extensive computational resources. A Python algorithm is initially employed to identify these sequences, but due to memory limitations, the author transitions to a C implementation for better efficiency. The C program utilizes a hash table to store candidate numbers and their positions in Pi, allowing for faster searches. The author successfully finds several sequences, including the 19th and 20th entries, and discusses the need for further optimization and memory management. The search for the 21st entry is also initiated, with plans to explore up to 200 billion digits of Pi. The article emphasizes the complexity and resource demands of computational searches in number theory, particularly when dealing with large datasets like the digits of Pi.
- The article explores repeating sequences in the decimal representation of Pi.
- Initial searches were conducted using Python, later optimized with a C implementation.
- A hash table structure is used to efficiently store and search for candidate numbers.
- The author successfully identifies several repeating sequences, including the 19th and 20th entries.
- Future searches aim to find longer sequences, requiring extensive computational resources.
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Very much appreciate the amazing effort that is OEIS!
I thought I should mention it to raise awareness: https://oeis.org/
The sequence in the article: https://oeis.org/A197123
filters = {}
candidates = []
for i in range(len(pi)):
block = pi[i:i+10]
hash_prefix = block[:4]
try:
bloom = filters[hash_prefix]
except KeyError:
filters[hash_prefix] = BloomFilter().add(block)
candidates.append((i, block))
continue
if block in bloom:
candidates.append((i, block))
else:
bloom.add(block)
Basically, make one pass through to build a list of statistically likely candidates to evaluate later. Then use full string matching on those candidates to find the real matching sequences.That might be helpful here because Bloom filters can say "we might have seen this string before" or "we definitely have not seen it before". That may greatly reduce the amount of storage you need.
Finding random numbers repeating is a simple brute force problem. It is cool, but slightly boring.
Related to this, what I cannot wrap my head across, is the Infinite monkey theorem. Is it not possible that we keep expanding the numbers and never reach a complex enough set of values?
[1] https://github.com/lifthrasiir/remote-pi-reader/
[2] https://storage.googleapis.com/pi100t/index.html (used to be `pi50t` back then)
Do N passes over the data, and at each pass only put in your hashmap the values that mod N equal the current iteration. If you take N~100, the runtime would probably be in the same ballpark, since the only thing that increase is streaming all the data N times. With a fast SSD, that's not that much.
Seriously, I find the most interesting works self-published in people’s personal blogs and I often wonder if there’s a paper in there and why it didn’t make it into a paper.
Maybe there’s a paper in finding the largest repeating sequence in Pi. There have certainly been more niche papers than that.
I _think_ you can but my brain doesn't have the bandwidth to think past that.
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