{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:3LCNSQA4VGSWOFMSIVQHVZCWJW","short_pith_number":"pith:3LCNSQA4","schema_version":"1.0","canonical_sha256":"dac4d9401ca9a567159245607ae4564da6b170f9cca1fd8e48f03aa276c7d44e","source":{"kind":"arxiv","id":"1610.02820","version":1},"attestation_state":"computed","paper":{"title":"Redundancies in Linear Systems with two Variables per Inequality","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DS","authors_text":"Komei Fukuda, May Szedlak","submitted_at":"2016-10-10T09:30:23Z","abstract_excerpt":"The problem of detecting and removing redundant constraints is fundamental in optimization. We focus on the case of linear programs (LPs), given by $d$ variables with $n$ inequality constraints. A constraint is called \\emph{redundant}, if after its removal, the LP still has the same feasible region. The currently fastest method to detect all redundancies is due to Clarkson: it solves $n$ linear programs, but each of them has at most $s$ constraints, where $s$ is the number of nonredundant constraints.\n  In this paper, we study the special case where every constraint has at most two variables w"},"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":"1610.02820","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DS","submitted_at":"2016-10-10T09:30:23Z","cross_cats_sorted":[],"title_canon_sha256":"101bf2f3bf3ca9f016f994fb9e5e911af857fbbc30aecb7701f07141d472eb6a","abstract_canon_sha256":"609abb78374a8a2718256ea18f7bea9b94390a2e8da21d43f8cfbc4ccb8b99b5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:02:50.630049Z","signature_b64":"nSeGHXKy9rZB0NLCciMQDUOZk9uAnbNUzLlk1qGgUAwgD9P9PcUFs9qStg1YWTrsKTjNFPeUaG/IvCT9X4SLDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dac4d9401ca9a567159245607ae4564da6b170f9cca1fd8e48f03aa276c7d44e","last_reissued_at":"2026-05-18T01:02:50.629579Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:02:50.629579Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Redundancies in Linear Systems with two Variables per Inequality","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DS","authors_text":"Komei Fukuda, May Szedlak","submitted_at":"2016-10-10T09:30:23Z","abstract_excerpt":"The problem of detecting and removing redundant constraints is fundamental in optimization. We focus on the case of linear programs (LPs), given by $d$ variables with $n$ inequality constraints. A constraint is called \\emph{redundant}, if after its removal, the LP still has the same feasible region. The currently fastest method to detect all redundancies is due to Clarkson: it solves $n$ linear programs, but each of them has at most $s$ constraints, where $s$ is the number of nonredundant constraints.\n  In this paper, we study the special case where every constraint has at most two variables w"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1610.02820","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":"1610.02820","created_at":"2026-05-18T01:02:50.629659+00:00"},{"alias_kind":"arxiv_version","alias_value":"1610.02820v1","created_at":"2026-05-18T01:02:50.629659+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1610.02820","created_at":"2026-05-18T01:02:50.629659+00:00"},{"alias_kind":"pith_short_12","alias_value":"3LCNSQA4VGSW","created_at":"2026-05-18T12:29:55.572404+00:00"},{"alias_kind":"pith_short_16","alias_value":"3LCNSQA4VGSWOFMS","created_at":"2026-05-18T12:29:55.572404+00:00"},{"alias_kind":"pith_short_8","alias_value":"3LCNSQA4","created_at":"2026-05-18T12:29:55.572404+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/3LCNSQA4VGSWOFMSIVQHVZCWJW","json":"https://pith.science/pith/3LCNSQA4VGSWOFMSIVQHVZCWJW.json","graph_json":"https://pith.science/api/pith-number/3LCNSQA4VGSWOFMSIVQHVZCWJW/graph.json","events_json":"https://pith.science/api/pith-number/3LCNSQA4VGSWOFMSIVQHVZCWJW/events.json","paper":"https://pith.science/paper/3LCNSQA4"},"agent_actions":{"view_html":"https://pith.science/pith/3LCNSQA4VGSWOFMSIVQHVZCWJW","download_json":"https://pith.science/pith/3LCNSQA4VGSWOFMSIVQHVZCWJW.json","view_paper":"https://pith.science/paper/3LCNSQA4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1610.02820&json=true","fetch_graph":"https://pith.science/api/pith-number/3LCNSQA4VGSWOFMSIVQHVZCWJW/graph.json","fetch_events":"https://pith.science/api/pith-number/3LCNSQA4VGSWOFMSIVQHVZCWJW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3LCNSQA4VGSWOFMSIVQHVZCWJW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3LCNSQA4VGSWOFMSIVQHVZCWJW/action/storage_attestation","attest_author":"https://pith.science/pith/3LCNSQA4VGSWOFMSIVQHVZCWJW/action/author_attestation","sign_citation":"https://pith.science/pith/3LCNSQA4VGSWOFMSIVQHVZCWJW/action/citation_signature","submit_replication":"https://pith.science/pith/3LCNSQA4VGSWOFMSIVQHVZCWJW/action/replication_record"}},"created_at":"2026-05-18T01:02:50.629659+00:00","updated_at":"2026-05-18T01:02:50.629659+00:00"}