A Gentle Introduction to Computational Complexity Through an Examination of Noodle Making
Bibliographic record
Abstract
Computational complexity is regarded by many Computer Science students as extremely difficult and as a topic to be avoided. However, the concepts of an algorithm and of computational complexity as a means of characterising the resource consumption of algorithms are fundamental in Computer Science and are included in all curricula for it. To better motivate students and to increase their interest in computational complexity, this paper suggests introducing it by examining algorithms, a.k.a. recipes, for making noodles. This paper describes several traditional algorithms for making Chinese and Italian noodles and classifies each according to its computational complexity. It compares the power of the algorithms. It considers the nature of variations of the traditional algorithms. It examines machines that implement some of the algorithms. It cites a world speed record for making a large number of noodles using the algorithm with the maximal complexity. It shows how computational thinking and other topics can be introduced in the same manner. It concludes by mentioning avenues for further studies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".