{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:Q42FFGXPR3PYX2C4XNEOWC733E","short_pith_number":"pith:Q42FFGXP","schema_version":"1.0","canonical_sha256":"8734529aef8edf8be85cbb48eb0bfbd92c870b190e3cd6d08c6e18adc015da95","source":{"kind":"arxiv","id":"2405.11400","version":1},"attestation_state":"computed","paper":{"title":"On the Convergence of Interior-Point Methods for Bound-Constrained Nonlinear Optimization Problems with Noise","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Andreas W\\\"achter, Shima Dezfulian","submitted_at":"2024-05-18T21:58:40Z","abstract_excerpt":"We analyze the convergence properties of a modified barrier method for solving bound-constrained optimization problems where evaluations of the objective function and its derivatives are affected by bounded and non-diminishing noise. The only modification compared to a standard barrier method is a relaxation of the Armijo line-search condition. We prove that the algorithm generates iterates at which the size of the barrier function gradient eventually falls below a threshold that converges to zero if the noise level converges to zero. Based on this result, we propose a practical stopping test "},"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":"2405.11400","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2024-05-18T21:58:40Z","cross_cats_sorted":[],"title_canon_sha256":"c64640f6cca05473c0f6e037e71aa43dea9f3dbe0b2876a12513697eafe75351","abstract_canon_sha256":"1ee6a9577e5e08a1fc33442a7ecdf10da703b79a9ebe7c2417ac8e10a471528f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:20:38.780666Z","signature_b64":"UGZ+eM3DFDOBSXws1zRD8k5nWIY+RkreoYNC/4gDFzraSLhowfGMzXvl6VLiDIqbsObXqx4OJNyoBfuH/nJeCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8734529aef8edf8be85cbb48eb0bfbd92c870b190e3cd6d08c6e18adc015da95","last_reissued_at":"2026-07-05T08:20:38.780261Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:20:38.780261Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"On the Convergence of Interior-Point Methods for Bound-Constrained Nonlinear Optimization Problems with Noise","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Andreas W\\\"achter, Shima Dezfulian","submitted_at":"2024-05-18T21:58:40Z","abstract_excerpt":"We analyze the convergence properties of a modified barrier method for solving bound-constrained optimization problems where evaluations of the objective function and its derivatives are affected by bounded and non-diminishing noise. The only modification compared to a standard barrier method is a relaxation of the Armijo line-search condition. We prove that the algorithm generates iterates at which the size of the barrier function gradient eventually falls below a threshold that converges to zero if the noise level converges to zero. Based on this result, we propose a practical stopping test "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.11400","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/2405.11400/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":"2405.11400","created_at":"2026-07-05T08:20:38.780322+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.11400v1","created_at":"2026-07-05T08:20:38.780322+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.11400","created_at":"2026-07-05T08:20:38.780322+00:00"},{"alias_kind":"pith_short_12","alias_value":"Q42FFGXPR3PY","created_at":"2026-07-05T08:20:38.780322+00:00"},{"alias_kind":"pith_short_16","alias_value":"Q42FFGXPR3PYX2C4","created_at":"2026-07-05T08:20:38.780322+00:00"},{"alias_kind":"pith_short_8","alias_value":"Q42FFGXP","created_at":"2026-07-05T08:20:38.780322+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2509.00888","citing_title":"Active-Set Identification in Noisy and Stochastic Optimization","ref_index":17,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/Q42FFGXPR3PYX2C4XNEOWC733E","json":"https://pith.science/pith/Q42FFGXPR3PYX2C4XNEOWC733E.json","graph_json":"https://pith.science/api/pith-number/Q42FFGXPR3PYX2C4XNEOWC733E/graph.json","events_json":"https://pith.science/api/pith-number/Q42FFGXPR3PYX2C4XNEOWC733E/events.json","paper":"https://pith.science/paper/Q42FFGXP"},"agent_actions":{"view_html":"https://pith.science/pith/Q42FFGXPR3PYX2C4XNEOWC733E","download_json":"https://pith.science/pith/Q42FFGXPR3PYX2C4XNEOWC733E.json","view_paper":"https://pith.science/paper/Q42FFGXP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.11400&json=true","fetch_graph":"https://pith.science/api/pith-number/Q42FFGXPR3PYX2C4XNEOWC733E/graph.json","fetch_events":"https://pith.science/api/pith-number/Q42FFGXPR3PYX2C4XNEOWC733E/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Q42FFGXPR3PYX2C4XNEOWC733E/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Q42FFGXPR3PYX2C4XNEOWC733E/action/storage_attestation","attest_author":"https://pith.science/pith/Q42FFGXPR3PYX2C4XNEOWC733E/action/author_attestation","sign_citation":"https://pith.science/pith/Q42FFGXPR3PYX2C4XNEOWC733E/action/citation_signature","submit_replication":"https://pith.science/pith/Q42FFGXPR3PYX2C4XNEOWC733E/action/replication_record"}},"created_at":"2026-07-05T08:20:38.780322+00:00","updated_at":"2026-07-05T08:20:38.780322+00:00"}