{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:OCMTZXGRMXNTUHUHYCUUSTKOO2","short_pith_number":"pith:OCMTZXGR","schema_version":"1.0","canonical_sha256":"70993cdcd165db3a1e87c0a9494d4e76a451fd08ecf307e258dd2f6c62f158cd","source":{"kind":"arxiv","id":"1904.08397","version":1},"attestation_state":"computed","paper":{"title":"SACOBRA with Online Whitening for Solving Optimization Problems with High Conditioning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","math.OC"],"primary_cat":"stat.ML","authors_text":"Samineh Bagheri, Thomas B\\\"ack, Wolfgang Konen","submitted_at":"2019-04-17T17:53:42Z","abstract_excerpt":"Real-world optimization problems often have expensive objective functions in terms of cost and time. It is desirable to find near-optimal solutions with very few function evaluations. Surrogate-assisted optimizers tend to reduce the required number of function evaluations by replacing the real function with an efficient mathematical model built on few evaluated points. Problems with a high condition number are a challenge for many surrogate-assisted optimizers including SACOBRA. To address such problems we propose a new online whitening operating in the black-box optimization paradigm. We show"},"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":"1904.08397","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-04-17T17:53:42Z","cross_cats_sorted":["cs.LG","math.OC"],"title_canon_sha256":"57cced0639c73cfebb3519da42d80134b46fb45bc2589858229ce231e4ff471c","abstract_canon_sha256":"deb8867b1e320e3c79ba28829d316c899cef5cff6bbdaa48958d6ab538d48396"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:48:17.799228Z","signature_b64":"e0vXW6GvV6Gl79zGQVl4Dnqbfu3PoSp0mUfuwqPxzc9XgcK67YMEfe4HJ4vBCJVxynFcjJW5fkxT4PRWlQnyDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"70993cdcd165db3a1e87c0a9494d4e76a451fd08ecf307e258dd2f6c62f158cd","last_reissued_at":"2026-05-17T23:48:17.798628Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:48:17.798628Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SACOBRA with Online Whitening for Solving Optimization Problems with High Conditioning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","math.OC"],"primary_cat":"stat.ML","authors_text":"Samineh Bagheri, Thomas B\\\"ack, Wolfgang Konen","submitted_at":"2019-04-17T17:53:42Z","abstract_excerpt":"Real-world optimization problems often have expensive objective functions in terms of cost and time. It is desirable to find near-optimal solutions with very few function evaluations. Surrogate-assisted optimizers tend to reduce the required number of function evaluations by replacing the real function with an efficient mathematical model built on few evaluated points. Problems with a high condition number are a challenge for many surrogate-assisted optimizers including SACOBRA. To address such problems we propose a new online whitening operating in the black-box optimization paradigm. We show"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.08397","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":"1904.08397","created_at":"2026-05-17T23:48:17.798737+00:00"},{"alias_kind":"arxiv_version","alias_value":"1904.08397v1","created_at":"2026-05-17T23:48:17.798737+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.08397","created_at":"2026-05-17T23:48:17.798737+00:00"},{"alias_kind":"pith_short_12","alias_value":"OCMTZXGRMXNT","created_at":"2026-05-18T12:33:24.271573+00:00"},{"alias_kind":"pith_short_16","alias_value":"OCMTZXGRMXNTUHUH","created_at":"2026-05-18T12:33:24.271573+00:00"},{"alias_kind":"pith_short_8","alias_value":"OCMTZXGR","created_at":"2026-05-18T12:33:24.271573+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/OCMTZXGRMXNTUHUHYCUUSTKOO2","json":"https://pith.science/pith/OCMTZXGRMXNTUHUHYCUUSTKOO2.json","graph_json":"https://pith.science/api/pith-number/OCMTZXGRMXNTUHUHYCUUSTKOO2/graph.json","events_json":"https://pith.science/api/pith-number/OCMTZXGRMXNTUHUHYCUUSTKOO2/events.json","paper":"https://pith.science/paper/OCMTZXGR"},"agent_actions":{"view_html":"https://pith.science/pith/OCMTZXGRMXNTUHUHYCUUSTKOO2","download_json":"https://pith.science/pith/OCMTZXGRMXNTUHUHYCUUSTKOO2.json","view_paper":"https://pith.science/paper/OCMTZXGR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1904.08397&json=true","fetch_graph":"https://pith.science/api/pith-number/OCMTZXGRMXNTUHUHYCUUSTKOO2/graph.json","fetch_events":"https://pith.science/api/pith-number/OCMTZXGRMXNTUHUHYCUUSTKOO2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OCMTZXGRMXNTUHUHYCUUSTKOO2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OCMTZXGRMXNTUHUHYCUUSTKOO2/action/storage_attestation","attest_author":"https://pith.science/pith/OCMTZXGRMXNTUHUHYCUUSTKOO2/action/author_attestation","sign_citation":"https://pith.science/pith/OCMTZXGRMXNTUHUHYCUUSTKOO2/action/citation_signature","submit_replication":"https://pith.science/pith/OCMTZXGRMXNTUHUHYCUUSTKOO2/action/replication_record"}},"created_at":"2026-05-17T23:48:17.798737+00:00","updated_at":"2026-05-17T23:48:17.798737+00:00"}