{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:GOMUNOPY6J5GYG5763R2XSM2FS","short_pith_number":"pith:GOMUNOPY","schema_version":"1.0","canonical_sha256":"339946b9f8f27a6c1bbff6e3abc99a2c9cfd7a887816f4be504f02624873a7bf","source":{"kind":"arxiv","id":"2404.14524","version":2},"attestation_state":"computed","paper":{"title":"Randomized Nystr\\\"om Preconditioned Interior Point-Proximal Method of Multipliers","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Luiz-Rafael Santos, Madeleine Udell, Ya-Chi Chu","submitted_at":"2024-04-22T18:46:35Z","abstract_excerpt":"We present a new algorithm for convex separable quadratic programming (QP) called Nys-IP-PMM, a regularized interior-point solver that uses low-rank structure to accelerate solution of the Newton system. The algorithm combines the interior point proximal method of multipliers (IP-PMM) with the randomized Nystr\\\"om preconditioned conjugate gradient method as the inner linear system solver. Our algorithm is matrix-free: it accesses the input matrices solely through matrix-vector products, as opposed to methods involving matrix factorization. It works particularly well for separable QP instances "},"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":"2404.14524","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2024-04-22T18:46:35Z","cross_cats_sorted":[],"title_canon_sha256":"8cf922f11d153c3716ae18f8e19e0f5e19cf28d936796c8a414bb482e63385b6","abstract_canon_sha256":"fb90c29db0078e512da6d6a8821969a4641ab1535d4524472f5a610ebf4bcf36"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:00:36.300370Z","signature_b64":"ng3Wxuzo+HZg8pf9nBQh/HQ3iYAGgwQATB/72BkecTgWCDnijpG8CqdQLQDosixivHOiE/64t1wziveUJOmOAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"339946b9f8f27a6c1bbff6e3abc99a2c9cfd7a887816f4be504f02624873a7bf","last_reissued_at":"2026-07-05T10:00:36.299914Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:00:36.299914Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Randomized Nystr\\\"om Preconditioned Interior Point-Proximal Method of Multipliers","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Luiz-Rafael Santos, Madeleine Udell, Ya-Chi Chu","submitted_at":"2024-04-22T18:46:35Z","abstract_excerpt":"We present a new algorithm for convex separable quadratic programming (QP) called Nys-IP-PMM, a regularized interior-point solver that uses low-rank structure to accelerate solution of the Newton system. The algorithm combines the interior point proximal method of multipliers (IP-PMM) with the randomized Nystr\\\"om preconditioned conjugate gradient method as the inner linear system solver. Our algorithm is matrix-free: it accesses the input matrices solely through matrix-vector products, as opposed to methods involving matrix factorization. It works particularly well for separable QP instances "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.14524","kind":"arxiv","version":2},"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/2404.14524/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":"2404.14524","created_at":"2026-07-05T10:00:36.299970+00:00"},{"alias_kind":"arxiv_version","alias_value":"2404.14524v2","created_at":"2026-07-05T10:00:36.299970+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.14524","created_at":"2026-07-05T10:00:36.299970+00:00"},{"alias_kind":"pith_short_12","alias_value":"GOMUNOPY6J5G","created_at":"2026-07-05T10:00:36.299970+00:00"},{"alias_kind":"pith_short_16","alias_value":"GOMUNOPY6J5GYG57","created_at":"2026-07-05T10:00:36.299970+00:00"},{"alias_kind":"pith_short_8","alias_value":"GOMUNOPY","created_at":"2026-07-05T10:00:36.299970+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2510.07929","citing_title":"Scalable Kernel Quantile Regression: A Preconditioned Augmented Lagrangian Method","ref_index":5,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/GOMUNOPY6J5GYG5763R2XSM2FS","json":"https://pith.science/pith/GOMUNOPY6J5GYG5763R2XSM2FS.json","graph_json":"https://pith.science/api/pith-number/GOMUNOPY6J5GYG5763R2XSM2FS/graph.json","events_json":"https://pith.science/api/pith-number/GOMUNOPY6J5GYG5763R2XSM2FS/events.json","paper":"https://pith.science/paper/GOMUNOPY"},"agent_actions":{"view_html":"https://pith.science/pith/GOMUNOPY6J5GYG5763R2XSM2FS","download_json":"https://pith.science/pith/GOMUNOPY6J5GYG5763R2XSM2FS.json","view_paper":"https://pith.science/paper/GOMUNOPY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2404.14524&json=true","fetch_graph":"https://pith.science/api/pith-number/GOMUNOPY6J5GYG5763R2XSM2FS/graph.json","fetch_events":"https://pith.science/api/pith-number/GOMUNOPY6J5GYG5763R2XSM2FS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GOMUNOPY6J5GYG5763R2XSM2FS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GOMUNOPY6J5GYG5763R2XSM2FS/action/storage_attestation","attest_author":"https://pith.science/pith/GOMUNOPY6J5GYG5763R2XSM2FS/action/author_attestation","sign_citation":"https://pith.science/pith/GOMUNOPY6J5GYG5763R2XSM2FS/action/citation_signature","submit_replication":"https://pith.science/pith/GOMUNOPY6J5GYG5763R2XSM2FS/action/replication_record"}},"created_at":"2026-07-05T10:00:36.299970+00:00","updated_at":"2026-07-05T10:00:36.299970+00:00"}