{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:4IPVFPZTVKSNG2PWD4LCQC53TP","short_pith_number":"pith:4IPVFPZT","schema_version":"1.0","canonical_sha256":"e21f52bf33aaa4d369f61f16280bbb9bd00fc6b27e1dd42986a218e778d8aa08","source":{"kind":"arxiv","id":"1710.10037","version":1},"attestation_state":"computed","paper":{"title":"Rapidly Mixing Markov Chain Monte Carlo Technique for Matching Problems with Global Utility Function","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DM","authors_text":"Madhu N. Belur, Prasanna Chaporkar, Shana Moothedath","submitted_at":"2017-10-27T09:16:32Z","abstract_excerpt":"This paper deals with a complete bipartite matching problem with the objective of finding an optimal matching that maximizes a certain generic predefined utility function on the set of all matchings. After proving the NP-hardness of the problem using reduction from the 3-SAT problem, we propose a randomized algorithm based on Markov Chain Monte Carlo (MCMC) technique for solving this. We sample from Gibb's distribution and construct a reversible positive recurrent discrete time Markov chain (DTMC) that has the steady state distribution same as the Gibb's distribution. In one of our key contrib"},"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":"1710.10037","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DM","submitted_at":"2017-10-27T09:16:32Z","cross_cats_sorted":[],"title_canon_sha256":"ad02590f84a0cfd3033058ce1948fe3ae365c5301dbfb8a50486f691ee042594","abstract_canon_sha256":"09d2ee5113d2f33e680f37f1beb95efd6468ac0a88d94290f8d5b577d486baee"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:31:54.909911Z","signature_b64":"aR+ygN9StO0U2IMc3D24cyxnnQi4myTkerq/2L1jZRKI/JWVRgtVpquZV9Qns8U9HM/mnT88tgL9nVsLtmGnCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e21f52bf33aaa4d369f61f16280bbb9bd00fc6b27e1dd42986a218e778d8aa08","last_reissued_at":"2026-05-18T00:31:54.909142Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:31:54.909142Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Rapidly Mixing Markov Chain Monte Carlo Technique for Matching Problems with Global Utility Function","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DM","authors_text":"Madhu N. Belur, Prasanna Chaporkar, Shana Moothedath","submitted_at":"2017-10-27T09:16:32Z","abstract_excerpt":"This paper deals with a complete bipartite matching problem with the objective of finding an optimal matching that maximizes a certain generic predefined utility function on the set of all matchings. After proving the NP-hardness of the problem using reduction from the 3-SAT problem, we propose a randomized algorithm based on Markov Chain Monte Carlo (MCMC) technique for solving this. We sample from Gibb's distribution and construct a reversible positive recurrent discrete time Markov chain (DTMC) that has the steady state distribution same as the Gibb's distribution. In one of our key contrib"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.10037","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":""},"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":"1710.10037","created_at":"2026-05-18T00:31:54.909268+00:00"},{"alias_kind":"arxiv_version","alias_value":"1710.10037v1","created_at":"2026-05-18T00:31:54.909268+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.10037","created_at":"2026-05-18T00:31:54.909268+00:00"},{"alias_kind":"pith_short_12","alias_value":"4IPVFPZTVKSN","created_at":"2026-05-18T12:31:00.734936+00:00"},{"alias_kind":"pith_short_16","alias_value":"4IPVFPZTVKSNG2PW","created_at":"2026-05-18T12:31:00.734936+00:00"},{"alias_kind":"pith_short_8","alias_value":"4IPVFPZT","created_at":"2026-05-18T12:31:00.734936+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/4IPVFPZTVKSNG2PWD4LCQC53TP","json":"https://pith.science/pith/4IPVFPZTVKSNG2PWD4LCQC53TP.json","graph_json":"https://pith.science/api/pith-number/4IPVFPZTVKSNG2PWD4LCQC53TP/graph.json","events_json":"https://pith.science/api/pith-number/4IPVFPZTVKSNG2PWD4LCQC53TP/events.json","paper":"https://pith.science/paper/4IPVFPZT"},"agent_actions":{"view_html":"https://pith.science/pith/4IPVFPZTVKSNG2PWD4LCQC53TP","download_json":"https://pith.science/pith/4IPVFPZTVKSNG2PWD4LCQC53TP.json","view_paper":"https://pith.science/paper/4IPVFPZT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1710.10037&json=true","fetch_graph":"https://pith.science/api/pith-number/4IPVFPZTVKSNG2PWD4LCQC53TP/graph.json","fetch_events":"https://pith.science/api/pith-number/4IPVFPZTVKSNG2PWD4LCQC53TP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4IPVFPZTVKSNG2PWD4LCQC53TP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4IPVFPZTVKSNG2PWD4LCQC53TP/action/storage_attestation","attest_author":"https://pith.science/pith/4IPVFPZTVKSNG2PWD4LCQC53TP/action/author_attestation","sign_citation":"https://pith.science/pith/4IPVFPZTVKSNG2PWD4LCQC53TP/action/citation_signature","submit_replication":"https://pith.science/pith/4IPVFPZTVKSNG2PWD4LCQC53TP/action/replication_record"}},"created_at":"2026-05-18T00:31:54.909268+00:00","updated_at":"2026-05-18T00:31:54.909268+00:00"}