{"paper":{"title":"Characterisation of the probabilistic travelling salesman problem","license":"","headline":"","cross_cats":["physics.gen-ph"],"primary_cat":"physics.comp-ph","authors_text":"Neill E. Bowler, Robin C. Ball, Thomas M. Fink","submitted_at":"2000-11-13T10:45:36Z","abstract_excerpt":"We show that Stochastic Annealing can be successfully applied to gain new results on the Probabilistic Traveling Salesman Problem (PTSP). The probabilistic \"traveling salesman\" must decide on an a priori order in which to visit n cities (randomly distributed over a unit square) before learning that some cities can be omitted. We find the optimized average length of the pruned tour follows E(\\bar{L}_{pruned}) = \\sqrt{np} (0.872-0.105p) f(np) where p is the probability of a city needing to be visited, and f(np) -> 1 as np -> infinity. The average length of the a priori tour (before omitting any "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"physics/0011023","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}