{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:2LPYBMIGUFFKRORJWO7LAWWY2M","short_pith_number":"pith:2LPYBMIG","schema_version":"1.0","canonical_sha256":"d2df80b106a14aa8ba29b3beb05ad8d30e2bd38745be111fbe9a85f948fd595c","source":{"kind":"arxiv","id":"2107.00251","version":3},"attestation_state":"computed","paper":{"title":"$L_p$ Isotonic Regression Algorithms Using an $L_0$ Approach","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.DS","authors_text":"Quentin F. Stout","submitted_at":"2021-07-01T07:04:58Z","abstract_excerpt":"Significant advances in flow algorithms have changed the relative performance of various approaches to algorithms for $L_p$ isotonic regression. We show a simple plug-in method to systematically incorporate such advances, and advances in determining violator dags, with no assumptions about the algorithms' structures. The method is based on the standard algorithm for $L_0$ (Hamming distance) isotonic regression (by finding anti-chains in a violator dag), coupled with partitioning based on binary $L_1$ isotonic regression. For several important classes of graphs the algorithms are already faster"},"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":"2107.00251","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.DS","submitted_at":"2021-07-01T07:04:58Z","cross_cats_sorted":[],"title_canon_sha256":"e0e7fbcbf0743d0638bd3d09a3877f9311369f58dad4bcb84f7983f612b854ae","abstract_canon_sha256":"81cc7163b89573e063ae16e732e7270544eb1643e072b8b7c56cc96944544377"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:26:14.889472Z","signature_b64":"U+2dbI5spPbfmxiWGChTWlPTr/hu1Edb3A8rb4m4DJ98nTyZtIr/M9D8XrnqGMXKzFwy50AO++vML78M92PCDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d2df80b106a14aa8ba29b3beb05ad8d30e2bd38745be111fbe9a85f948fd595c","last_reissued_at":"2026-07-05T06:26:14.888969Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:26:14.888969Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"$L_p$ Isotonic Regression Algorithms Using an $L_0$ Approach","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.DS","authors_text":"Quentin F. Stout","submitted_at":"2021-07-01T07:04:58Z","abstract_excerpt":"Significant advances in flow algorithms have changed the relative performance of various approaches to algorithms for $L_p$ isotonic regression. We show a simple plug-in method to systematically incorporate such advances, and advances in determining violator dags, with no assumptions about the algorithms' structures. The method is based on the standard algorithm for $L_0$ (Hamming distance) isotonic regression (by finding anti-chains in a violator dag), coupled with partitioning based on binary $L_1$ isotonic regression. For several important classes of graphs the algorithms are already faster"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2107.00251","kind":"arxiv","version":3},"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/2107.00251/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":"2107.00251","created_at":"2026-07-05T06:26:14.889026+00:00"},{"alias_kind":"arxiv_version","alias_value":"2107.00251v3","created_at":"2026-07-05T06:26:14.889026+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2107.00251","created_at":"2026-07-05T06:26:14.889026+00:00"},{"alias_kind":"pith_short_12","alias_value":"2LPYBMIGUFFK","created_at":"2026-07-05T06:26:14.889026+00:00"},{"alias_kind":"pith_short_16","alias_value":"2LPYBMIGUFFKRORJ","created_at":"2026-07-05T06:26:14.889026+00:00"},{"alias_kind":"pith_short_8","alias_value":"2LPYBMIG","created_at":"2026-07-05T06:26:14.889026+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/2LPYBMIGUFFKRORJWO7LAWWY2M","json":"https://pith.science/pith/2LPYBMIGUFFKRORJWO7LAWWY2M.json","graph_json":"https://pith.science/api/pith-number/2LPYBMIGUFFKRORJWO7LAWWY2M/graph.json","events_json":"https://pith.science/api/pith-number/2LPYBMIGUFFKRORJWO7LAWWY2M/events.json","paper":"https://pith.science/paper/2LPYBMIG"},"agent_actions":{"view_html":"https://pith.science/pith/2LPYBMIGUFFKRORJWO7LAWWY2M","download_json":"https://pith.science/pith/2LPYBMIGUFFKRORJWO7LAWWY2M.json","view_paper":"https://pith.science/paper/2LPYBMIG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2107.00251&json=true","fetch_graph":"https://pith.science/api/pith-number/2LPYBMIGUFFKRORJWO7LAWWY2M/graph.json","fetch_events":"https://pith.science/api/pith-number/2LPYBMIGUFFKRORJWO7LAWWY2M/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2LPYBMIGUFFKRORJWO7LAWWY2M/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2LPYBMIGUFFKRORJWO7LAWWY2M/action/storage_attestation","attest_author":"https://pith.science/pith/2LPYBMIGUFFKRORJWO7LAWWY2M/action/author_attestation","sign_citation":"https://pith.science/pith/2LPYBMIGUFFKRORJWO7LAWWY2M/action/citation_signature","submit_replication":"https://pith.science/pith/2LPYBMIGUFFKRORJWO7LAWWY2M/action/replication_record"}},"created_at":"2026-07-05T06:26:14.889026+00:00","updated_at":"2026-07-05T06:26:14.889026+00:00"}