{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:HWIFR3TGNMXTRCRLI4JZ6QFMU6","short_pith_number":"pith:HWIFR3TG","schema_version":"1.0","canonical_sha256":"3d9058ee666b2f388a2b47139f40aca78f66739913e8b7534a574bed93b2409a","source":{"kind":"arxiv","id":"2006.05490","version":1},"attestation_state":"computed","paper":{"title":"Sublinear Algorithms and Lower Bounds for Metric TSP Cost Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DS","authors_text":"Sampath Kannan, Sanjeev Khanna, Yu Chen","submitted_at":"2020-06-09T20:08:09Z","abstract_excerpt":"We consider the problem of designing sublinear time algorithms for estimating the cost of a minimum metric traveling salesman (TSP) tour. Specifically, given access to a $n \\times n$ distance matrix $D$ that specifies pairwise distances between $n$ points, the goal is to estimate the TSP cost by performing only sublinear (in the size of $D$) queries. For the closely related problem of estimating the weight of a metric minimum spanning tree (MST), it is known that for any $\\varepsilon > 0$, there exists an $\\tilde{O}(n/\\varepsilon^{O(1)})$ time algorithm that returns a $(1 + \\varepsilon)$-appro"},"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":"2006.05490","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DS","submitted_at":"2020-06-09T20:08:09Z","cross_cats_sorted":[],"title_canon_sha256":"4d3894e67698ade01b96110b08ec921d3a9e45e1afab6c4e2f1a3003120f9cda","abstract_canon_sha256":"f5ffec9fd0910e73bfae37476e64ece8faea7d250521efaa0a0844ea916836e8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:09:14.117211Z","signature_b64":"KOxInwQiTL+G3nqfjSNsMmg9EUXwrJzxgCG3P76utWtkP18+sBafTQ+yFJ7y0YXlsZjMPpHqJ5M4kWCfU9cwAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3d9058ee666b2f388a2b47139f40aca78f66739913e8b7534a574bed93b2409a","last_reissued_at":"2026-07-05T01:09:14.116834Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:09:14.116834Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Sublinear Algorithms and Lower Bounds for Metric TSP Cost Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DS","authors_text":"Sampath Kannan, Sanjeev Khanna, Yu Chen","submitted_at":"2020-06-09T20:08:09Z","abstract_excerpt":"We consider the problem of designing sublinear time algorithms for estimating the cost of a minimum metric traveling salesman (TSP) tour. Specifically, given access to a $n \\times n$ distance matrix $D$ that specifies pairwise distances between $n$ points, the goal is to estimate the TSP cost by performing only sublinear (in the size of $D$) queries. For the closely related problem of estimating the weight of a metric minimum spanning tree (MST), it is known that for any $\\varepsilon > 0$, there exists an $\\tilde{O}(n/\\varepsilon^{O(1)})$ time algorithm that returns a $(1 + \\varepsilon)$-appro"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2006.05490","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2006.05490/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2006.05490","created_at":"2026-07-05T01:09:14.116889+00:00"},{"alias_kind":"arxiv_version","alias_value":"2006.05490v1","created_at":"2026-07-05T01:09:14.116889+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2006.05490","created_at":"2026-07-05T01:09:14.116889+00:00"},{"alias_kind":"pith_short_12","alias_value":"HWIFR3TGNMXT","created_at":"2026-07-05T01:09:14.116889+00:00"},{"alias_kind":"pith_short_16","alias_value":"HWIFR3TGNMXTRCRL","created_at":"2026-07-05T01:09:14.116889+00:00"},{"alias_kind":"pith_short_8","alias_value":"HWIFR3TG","created_at":"2026-07-05T01:09:14.116889+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.01669","citing_title":"A 0.51-Approximation of Maximum Matching in Sublinear $n^{1.5}$ Time","ref_index":9,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HWIFR3TGNMXTRCRLI4JZ6QFMU6","json":"https://pith.science/pith/HWIFR3TGNMXTRCRLI4JZ6QFMU6.json","graph_json":"https://pith.science/api/pith-number/HWIFR3TGNMXTRCRLI4JZ6QFMU6/graph.json","events_json":"https://pith.science/api/pith-number/HWIFR3TGNMXTRCRLI4JZ6QFMU6/events.json","paper":"https://pith.science/paper/HWIFR3TG"},"agent_actions":{"view_html":"https://pith.science/pith/HWIFR3TGNMXTRCRLI4JZ6QFMU6","download_json":"https://pith.science/pith/HWIFR3TGNMXTRCRLI4JZ6QFMU6.json","view_paper":"https://pith.science/paper/HWIFR3TG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2006.05490&json=true","fetch_graph":"https://pith.science/api/pith-number/HWIFR3TGNMXTRCRLI4JZ6QFMU6/graph.json","fetch_events":"https://pith.science/api/pith-number/HWIFR3TGNMXTRCRLI4JZ6QFMU6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HWIFR3TGNMXTRCRLI4JZ6QFMU6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HWIFR3TGNMXTRCRLI4JZ6QFMU6/action/storage_attestation","attest_author":"https://pith.science/pith/HWIFR3TGNMXTRCRLI4JZ6QFMU6/action/author_attestation","sign_citation":"https://pith.science/pith/HWIFR3TGNMXTRCRLI4JZ6QFMU6/action/citation_signature","submit_replication":"https://pith.science/pith/HWIFR3TGNMXTRCRLI4JZ6QFMU6/action/replication_record"}},"created_at":"2026-07-05T01:09:14.116889+00:00","updated_at":"2026-07-05T01:09:14.116889+00:00"}