{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:H5FAPEJSMTNO6NF42DXDJ2DQVL","short_pith_number":"pith:H5FAPEJS","schema_version":"1.0","canonical_sha256":"3f4a07913264daef34bcd0ee34e870aacdb3ed84b401af45ba9af44a11f7e1a8","source":{"kind":"arxiv","id":"1403.7721","version":1},"attestation_state":"computed","paper":{"title":"Maximum Quadratic Assignment Problem: Reduction from Maximum Label Cover and LP-based Approximation Algorithm","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DS"],"primary_cat":"cs.CC","authors_text":"Konstantin Makarychev, Maxim Sviridenko, Rajsekar Manokaran","submitted_at":"2014-03-30T09:13:49Z","abstract_excerpt":"We show that for every positive $\\epsilon > 0$, unless NP $\\subset$ BPQP, it is impossible to approximate the maximum quadratic assignment problem within a factor better than $2^{\\log^{1-\\epsilon} n}$ by a reduction from the maximum label cover problem. Our result also implies that Approximate Graph Isomorphism is not robust and is in fact, $1 - \\epsilon$ vs $\\epsilon$ hard assuming the Unique Games Conjecture.\n  Then, we present an $O(\\sqrt{n})$-approximation algorithm for the problem based on rounding of the linear programming relaxation often used in the state of the art exact algorithms."},"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":"1403.7721","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CC","submitted_at":"2014-03-30T09:13:49Z","cross_cats_sorted":["cs.DS"],"title_canon_sha256":"c3778323d4f41eebb1bf7a36e0a9298f40a44241170af2358efa370c2a2350f8","abstract_canon_sha256":"478e4af9f8632fa1b2d59721ddf22a7ae385cd7a588823e5eef3af15d087b5b2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:55:12.571883Z","signature_b64":"1iRy/xHD7PNTu1zoOqKxbNxFh7xY4Yy1UULfn5oS1XSGPAFm1YGvXzM0tOD7aUvt+GlrbWSz8QIEH1pspcr4DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3f4a07913264daef34bcd0ee34e870aacdb3ed84b401af45ba9af44a11f7e1a8","last_reissued_at":"2026-05-18T02:55:12.571455Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:55:12.571455Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Maximum Quadratic Assignment Problem: Reduction from Maximum Label Cover and LP-based Approximation Algorithm","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DS"],"primary_cat":"cs.CC","authors_text":"Konstantin Makarychev, Maxim Sviridenko, Rajsekar Manokaran","submitted_at":"2014-03-30T09:13:49Z","abstract_excerpt":"We show that for every positive $\\epsilon > 0$, unless NP $\\subset$ BPQP, it is impossible to approximate the maximum quadratic assignment problem within a factor better than $2^{\\log^{1-\\epsilon} n}$ by a reduction from the maximum label cover problem. Our result also implies that Approximate Graph Isomorphism is not robust and is in fact, $1 - \\epsilon$ vs $\\epsilon$ hard assuming the Unique Games Conjecture.\n  Then, we present an $O(\\sqrt{n})$-approximation algorithm for the problem based on rounding of the linear programming relaxation often used in the state of the art exact algorithms."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1403.7721","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":"1403.7721","created_at":"2026-05-18T02:55:12.571516+00:00"},{"alias_kind":"arxiv_version","alias_value":"1403.7721v1","created_at":"2026-05-18T02:55:12.571516+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1403.7721","created_at":"2026-05-18T02:55:12.571516+00:00"},{"alias_kind":"pith_short_12","alias_value":"H5FAPEJSMTNO","created_at":"2026-05-18T12:28:30.664211+00:00"},{"alias_kind":"pith_short_16","alias_value":"H5FAPEJSMTNO6NF4","created_at":"2026-05-18T12:28:30.664211+00:00"},{"alias_kind":"pith_short_8","alias_value":"H5FAPEJS","created_at":"2026-05-18T12:28:30.664211+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/H5FAPEJSMTNO6NF42DXDJ2DQVL","json":"https://pith.science/pith/H5FAPEJSMTNO6NF42DXDJ2DQVL.json","graph_json":"https://pith.science/api/pith-number/H5FAPEJSMTNO6NF42DXDJ2DQVL/graph.json","events_json":"https://pith.science/api/pith-number/H5FAPEJSMTNO6NF42DXDJ2DQVL/events.json","paper":"https://pith.science/paper/H5FAPEJS"},"agent_actions":{"view_html":"https://pith.science/pith/H5FAPEJSMTNO6NF42DXDJ2DQVL","download_json":"https://pith.science/pith/H5FAPEJSMTNO6NF42DXDJ2DQVL.json","view_paper":"https://pith.science/paper/H5FAPEJS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1403.7721&json=true","fetch_graph":"https://pith.science/api/pith-number/H5FAPEJSMTNO6NF42DXDJ2DQVL/graph.json","fetch_events":"https://pith.science/api/pith-number/H5FAPEJSMTNO6NF42DXDJ2DQVL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/H5FAPEJSMTNO6NF42DXDJ2DQVL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/H5FAPEJSMTNO6NF42DXDJ2DQVL/action/storage_attestation","attest_author":"https://pith.science/pith/H5FAPEJSMTNO6NF42DXDJ2DQVL/action/author_attestation","sign_citation":"https://pith.science/pith/H5FAPEJSMTNO6NF42DXDJ2DQVL/action/citation_signature","submit_replication":"https://pith.science/pith/H5FAPEJSMTNO6NF42DXDJ2DQVL/action/replication_record"}},"created_at":"2026-05-18T02:55:12.571516+00:00","updated_at":"2026-05-18T02:55:12.571516+00:00"}