{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:GU4RTILSCLO4J5S5PDSDFQ2KI6","short_pith_number":"pith:GU4RTILS","schema_version":"1.0","canonical_sha256":"353919a17212ddc4f65d78e432c34a479478f5769c78bcb3bf845e3fafd42178","source":{"kind":"arxiv","id":"2508.18199","version":1},"attestation_state":"computed","paper":{"title":"Sparse Polynomial Regression under Anomalous Data","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Maryam Dehghani, Mohammad Reza Hesamzadeh, Roozbeh Abolpour","submitted_at":"2025-08-25T17:00:56Z","abstract_excerpt":"This paper starts with the general form of the polynomial regression model. We reformulate the Sparse Polynomial Regression Model (SPRM) with anomalous data filtering as Mixed-Integer Linear Program (MILP). This MILP is then converted to a non-convex Quadratically Constrained Quadratic Program (QCQP). Through a proposed mapping, the derived QCQP is reformulated as a Fractional Program (FP). We theoretically show that the reformulated FP has better computational properties than the original QCQP. We then suggest a conic-relaxation-based algorithm to solve the proposed FP. A Two-Step Convex Rela"},"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":"2508.18199","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2025-08-25T17:00:56Z","cross_cats_sorted":[],"title_canon_sha256":"9dfb8f889c05945933eeb0786df7236d8b167552fb70154f629aa67f7e22a8ff","abstract_canon_sha256":"36412bd47401e36c9290c84cdc2e4b45bde0fdff986e2032bf5990de45557f69"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:58:55.334274Z","signature_b64":"V5G6WZcyp0/je0W20ApPEaTWImSbeTw4poAQmMZSLhP2ySG44ufIg3byF9/mjj+WxSR2SPhT4ckVOO/Nb1guBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"353919a17212ddc4f65d78e432c34a479478f5769c78bcb3bf845e3fafd42178","last_reissued_at":"2026-07-05T11:58:55.333776Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:58:55.333776Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Sparse Polynomial Regression under Anomalous Data","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Maryam Dehghani, Mohammad Reza Hesamzadeh, Roozbeh Abolpour","submitted_at":"2025-08-25T17:00:56Z","abstract_excerpt":"This paper starts with the general form of the polynomial regression model. We reformulate the Sparse Polynomial Regression Model (SPRM) with anomalous data filtering as Mixed-Integer Linear Program (MILP). This MILP is then converted to a non-convex Quadratically Constrained Quadratic Program (QCQP). Through a proposed mapping, the derived QCQP is reformulated as a Fractional Program (FP). We theoretically show that the reformulated FP has better computational properties than the original QCQP. We then suggest a conic-relaxation-based algorithm to solve the proposed FP. A Two-Step Convex Rela"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.18199","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/2508.18199/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":"2508.18199","created_at":"2026-07-05T11:58:55.333845+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.18199v1","created_at":"2026-07-05T11:58:55.333845+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.18199","created_at":"2026-07-05T11:58:55.333845+00:00"},{"alias_kind":"pith_short_12","alias_value":"GU4RTILSCLO4","created_at":"2026-07-05T11:58:55.333845+00:00"},{"alias_kind":"pith_short_16","alias_value":"GU4RTILSCLO4J5S5","created_at":"2026-07-05T11:58:55.333845+00:00"},{"alias_kind":"pith_short_8","alias_value":"GU4RTILS","created_at":"2026-07-05T11:58:55.333845+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/GU4RTILSCLO4J5S5PDSDFQ2KI6","json":"https://pith.science/pith/GU4RTILSCLO4J5S5PDSDFQ2KI6.json","graph_json":"https://pith.science/api/pith-number/GU4RTILSCLO4J5S5PDSDFQ2KI6/graph.json","events_json":"https://pith.science/api/pith-number/GU4RTILSCLO4J5S5PDSDFQ2KI6/events.json","paper":"https://pith.science/paper/GU4RTILS"},"agent_actions":{"view_html":"https://pith.science/pith/GU4RTILSCLO4J5S5PDSDFQ2KI6","download_json":"https://pith.science/pith/GU4RTILSCLO4J5S5PDSDFQ2KI6.json","view_paper":"https://pith.science/paper/GU4RTILS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.18199&json=true","fetch_graph":"https://pith.science/api/pith-number/GU4RTILSCLO4J5S5PDSDFQ2KI6/graph.json","fetch_events":"https://pith.science/api/pith-number/GU4RTILSCLO4J5S5PDSDFQ2KI6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GU4RTILSCLO4J5S5PDSDFQ2KI6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GU4RTILSCLO4J5S5PDSDFQ2KI6/action/storage_attestation","attest_author":"https://pith.science/pith/GU4RTILSCLO4J5S5PDSDFQ2KI6/action/author_attestation","sign_citation":"https://pith.science/pith/GU4RTILSCLO4J5S5PDSDFQ2KI6/action/citation_signature","submit_replication":"https://pith.science/pith/GU4RTILSCLO4J5S5PDSDFQ2KI6/action/replication_record"}},"created_at":"2026-07-05T11:58:55.333845+00:00","updated_at":"2026-07-05T11:58:55.333845+00:00"}