Theoretical algorithms lack real speedup
Problem
The article claims a faster shortest‑path algorithm, but commenters note that the practical speedup is marginal, citing large constant factors and unintuitive complexity expressions, which raises doubts about the real‑world value of such theoretical improvements.
Pain points
- Theoretical improvements fail to deliver substantive speed gains
- Complexity expressions are unintuitive (e.g., using m and n instead of |V| and |E|)
- Large constant factors result in no practical speedup
Questions to verify
- Have you encountered similar algorithmic performance bottlenecks in real projects?
- Would you be willing to provide benchmark data to evaluate the algorithm's actual acceleration effect?
- Which type of improvement are you more interested in: reducing constant factors, enhancing theoretical readability, or enabling direct deployment?
Evidence
quote · @Anonymous
If I'm reading correctly, this is one of those theoretical improvements that doesn't put a whole lot of practical speedup on the table.
quote · @Anonymous
The time complexities given in this article would be easier to read if they used |V| and |E| instead of m and n.
quote · @Anonymous
I don’t want to rain on the parade here, but this is a typical issue with AI slop maths or CS. It just builds complexity upon complexity to generate diminishing improvements (this case doesn’t lead to any practical speedup).
Disclaimer: this card is distilled by AI from community discussions as “pain point evidence”. It is a starting point for need analysis and validation, not a “validated market need”. AI has analytical limits; please open the original discussion above to manually verify before key decisions.