Bibliographic record
Abstract
Dynamic programming is another powerful tool for solving optimization problems. Just like recursive backtracking, it has as a key component a recurrence relation that says how to find an optimal solution for one instance of the problem from optimal solutions for some number of smaller instances of the same problem. Instead of re-cursing on these subinstances, dynamic programming iteratively fills in a table with an optimal solution for each, so that each only needs to be solved once. Dynamic programming provides polynomial-time algorithms for many important and practical problem. Personally, I do not like the name “dynamic programming.” It is true that dynamic programming algorithms have a program of subinstances to solve. But these subinstances are chosen in a fixed prescheduled order, not dynamically. In contrast, in recursive backtracking algorithms, the subinstances are constructed dynamically. One way to design a dynamic programming algorithm is to start by guessing the set of subinstances that need to be solved. However, I feel that it is easier to start by designing the recurrence relation, and the easiest way to do this is to first design a recursive backtracking algorithm for the problem. Once you have done this, you can use a technique referred to as memoization to mechanically convert this recursive backtracking algorithm into a dynamic programming algorithm. Start by Developing a Recursive Backtracking This section reviews the recommended steps for developing a recursive backtracking algorithm.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.051 | 0.023 |
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".