
TL;DR
This paper proposes a probabilistic approach to the 3x+1 problem, suggesting that by analyzing the stopping times and their distribution, the conjecture can be effectively addressed using computational methods.
Contribution
It introduces a novel method based on probabilistic analysis of stopping times, offering a new perspective for solving the 3x+1 conjecture with computational support.
Findings
The approach indicates the decreasing likelihood of counterexamples as calculations increase.
It provides a framework for using large-scale computations to verify the conjecture.
The method suggests the conjecture could be proven by demonstrating the diminishing size of problematic cases.
Abstract
The 3x+1 problem is one of the most classical problems in computer science, related to many fields. As it is thought by scientists a highly hard problem, resolving it successfully not only can improve the research in many relating fields, but also be meaningful to the method study. By deep analyzing the 3x+1 calculation process with the input positive integer becoming greater, we find a useful way for solving this problem with high probability. By making use of the greater calculating ability of great computers and the internet, our way is a valid and powerful way for utterly solving the 3x+1 problem. This way can be expressed in three points: 1) If we can find a positive integer N, for any positive integer less than 2N, the times of dividing 2 out of its stopping time is less than or equal to N, then the 3x+1 conjecture is true; 2) This N may be big, so the calculation may be too big.…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsBenford’s Law and Fraud Detection · Computability, Logic, AI Algorithms · Algorithms and Data Compression
