Possible solution to large data sets?
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- Dominant language
- C++
- Stars
- 278
- Forks
- 36
- PR merge metrics
- No merged PRs in 30d
Description
This has already been mentioned in another issue, but I thought it merits its own thread, and besides the other one hasn't been updated for quite a while.
Since the algorithm complexity is $O(n^3)$, the computation can get quite costly quite quickly, both time-wise and memory-wise. Are there any solutions or approximation algorithms to address this problem?
In the other thread a data size of 55K was already considered a big data set, and I have a data set that is thousands of times its size... Any hope?
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First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reading this issue and the referenced issue #7 to understand the existing large-data discussion. Investigate whether the requested result is an approximation algorithm, a more scalable implementation, or guidance for large inputs; done would require a decided approach with clear performance expectations and documented or tested behavior.
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Assessment
- Tech stack
- cpp, python
- Domain
- performance
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 15/100