{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:7HE3YBWJNCNBAWRYH2JLAGAFCQ","short_pith_number":"pith:7HE3YBWJ","schema_version":"1.0","canonical_sha256":"f9c9bc06c9689a105a383e92b018051402fb728d4270881b891bbe4b23252f60","source":{"kind":"arxiv","id":"1406.7282","version":2},"attestation_state":"computed","paper":{"title":"An interacting replica approach applied to the traveling salesman problem","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","physics.data-an"],"primary_cat":"cond-mat.stat-mech","authors_text":"Blake Leonard, Bo Sun, Peter Ronhovde, Zohar Nussinov","submitted_at":"2014-06-27T19:35:35Z","abstract_excerpt":"We present a physics inspired heuristic method for solving combinatorial optimization problems. Our approach is specifically motivated by the desire to avoid trapping in metastable local minima- a common occurrence in hard problems with multiple extrema. Our method involves (i) coupling otherwise independent simulations of a system (\"replicas\") via geometrical distances as well as (ii) probabilistic inference applied to the solutions found by individual replicas. The {\\it ensemble} of replicas evolves as to maximize the inter-replica correlation while simultaneously minimize the local intra-re"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1406.7282","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.stat-mech","submitted_at":"2014-06-27T19:35:35Z","cross_cats_sorted":["cs.AI","physics.data-an"],"title_canon_sha256":"bbc2aa4a9fa4d39962f05e652e1d1173ba8668cb461b9f24954c1aea62f281ac","abstract_canon_sha256":"071fa252cef99a1a803dbff7387517bcabcda4646e1e0918aaffe72aafe38dfa"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:19:14.406440Z","signature_b64":"cWyeWlUajwW0xDvigsa6wRC8XvvpaEpuQyqL0bqIMvi9mar305CKRezmiyma21YtHiyvoWUkKiKJyaLk7NmbDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f9c9bc06c9689a105a383e92b018051402fb728d4270881b891bbe4b23252f60","last_reissued_at":"2026-05-18T01:19:14.405733Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:19:14.405733Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"An interacting replica approach applied to the traveling salesman problem","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","physics.data-an"],"primary_cat":"cond-mat.stat-mech","authors_text":"Blake Leonard, Bo Sun, Peter Ronhovde, Zohar Nussinov","submitted_at":"2014-06-27T19:35:35Z","abstract_excerpt":"We present a physics inspired heuristic method for solving combinatorial optimization problems. Our approach is specifically motivated by the desire to avoid trapping in metastable local minima- a common occurrence in hard problems with multiple extrema. Our method involves (i) coupling otherwise independent simulations of a system (\"replicas\") via geometrical distances as well as (ii) probabilistic inference applied to the solutions found by individual replicas. The {\\it ensemble} of replicas evolves as to maximize the inter-replica correlation while simultaneously minimize the local intra-re"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1406.7282","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1406.7282","created_at":"2026-05-18T01:19:14.405839+00:00"},{"alias_kind":"arxiv_version","alias_value":"1406.7282v2","created_at":"2026-05-18T01:19:14.405839+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1406.7282","created_at":"2026-05-18T01:19:14.405839+00:00"},{"alias_kind":"pith_short_12","alias_value":"7HE3YBWJNCNB","created_at":"2026-05-18T12:28:16.859392+00:00"},{"alias_kind":"pith_short_16","alias_value":"7HE3YBWJNCNBAWRY","created_at":"2026-05-18T12:28:16.859392+00:00"},{"alias_kind":"pith_short_8","alias_value":"7HE3YBWJ","created_at":"2026-05-18T12:28:16.859392+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7HE3YBWJNCNBAWRYH2JLAGAFCQ","json":"https://pith.science/pith/7HE3YBWJNCNBAWRYH2JLAGAFCQ.json","graph_json":"https://pith.science/api/pith-number/7HE3YBWJNCNBAWRYH2JLAGAFCQ/graph.json","events_json":"https://pith.science/api/pith-number/7HE3YBWJNCNBAWRYH2JLAGAFCQ/events.json","paper":"https://pith.science/paper/7HE3YBWJ"},"agent_actions":{"view_html":"https://pith.science/pith/7HE3YBWJNCNBAWRYH2JLAGAFCQ","download_json":"https://pith.science/pith/7HE3YBWJNCNBAWRYH2JLAGAFCQ.json","view_paper":"https://pith.science/paper/7HE3YBWJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1406.7282&json=true","fetch_graph":"https://pith.science/api/pith-number/7HE3YBWJNCNBAWRYH2JLAGAFCQ/graph.json","fetch_events":"https://pith.science/api/pith-number/7HE3YBWJNCNBAWRYH2JLAGAFCQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7HE3YBWJNCNBAWRYH2JLAGAFCQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7HE3YBWJNCNBAWRYH2JLAGAFCQ/action/storage_attestation","attest_author":"https://pith.science/pith/7HE3YBWJNCNBAWRYH2JLAGAFCQ/action/author_attestation","sign_citation":"https://pith.science/pith/7HE3YBWJNCNBAWRYH2JLAGAFCQ/action/citation_signature","submit_replication":"https://pith.science/pith/7HE3YBWJNCNBAWRYH2JLAGAFCQ/action/replication_record"}},"created_at":"2026-05-18T01:19:14.405839+00:00","updated_at":"2026-05-18T01:19:14.405839+00:00"}