Solving traveling salesman problems via a parallel fully connected ising machine
Bibliographic record
Abstract
Annealing-based Ising machines have shown promising results in solving combinatorial optimization problems. As a typical class of these problems, however, traveling salesman problems (TSPs) are very challenging to solve due to the constraints imposed on the solution. This article proposes a parallel annealing algorithm for a fully connected Ising machine that significantly improves the accuracy and performance in solving constrained combinatorial optimization problems such as the TSP. Unlike previous parallel annealing algorithms, this improved parallel annealing (IPA) algorithm efficiently solves TSPs using an exponential temperature function with a dynamic offset. Compared with digital annealing (DA) and momentum annealing (MA), the IPA reduces the run time by 44.4 times and 19.9 times for a 14-city TSP, respectively. Large scale TSPs can be more efficiently solved by taking a k-medoids clustering approach that decreases the average travel distance of a 22-city TSP by 51.8% compared with DA and by 42.0% compared with MA. This approach groups neighboring cities into clusters to form a reduced TSP, which is then solved in a hierarchical manner by using the IPA 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".