{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:CRQIITHE6RH6YPORPKPO2SHQBE","short_pith_number":"pith:CRQIITHE","schema_version":"1.0","canonical_sha256":"1460844ce4f44fec3dd17a9eed48f00904dce7bba998cb9eeb471ccdc2505524","source":{"kind":"arxiv","id":"1903.08742","version":1},"attestation_state":"computed","paper":{"title":"Noisy Accelerated Power Method for Eigenproblems with Applications","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","eess.SP"],"primary_cat":"math.OC","authors_text":"Mikael Johansson, Vien V. Mai","submitted_at":"2019-03-20T21:05:42Z","abstract_excerpt":"This paper introduces an efficient algorithm for finding the dominant generalized eigenvectors of a pair of symmetric matrices. Combining tools from approximation theory and convex optimization, we develop a simple scalable algorithm with strong theoretical performance guarantees. More precisely, the algorithm retains the simplicity of the well-known power method but enjoys the asymptotic iteration complexity of the powerful Lanczos method. Unlike these classic techniques, our algorithm is designed to decompose the overall problem into a series of subproblems that only need to be solved approx"},"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":"1903.08742","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2019-03-20T21:05:42Z","cross_cats_sorted":["cs.LG","eess.SP"],"title_canon_sha256":"d56b3ad6e0c40001907add385fb5f8c850b4090c1fa4018e7417fe304489252c","abstract_canon_sha256":"cdd9a4fa09e66c87c5301f566dfe245e1c1d5d1928fb69f3a1b3df36e3c5b814"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:42:20.498657Z","signature_b64":"YmRPUPMyUIczePr7hScrG/HXUgkJss/uzr0zUHre2aT1w2QJbgCC1mNnfK7jnZFNBeAAffGpAiLTP054zlWkBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1460844ce4f44fec3dd17a9eed48f00904dce7bba998cb9eeb471ccdc2505524","last_reissued_at":"2026-05-17T23:42:20.497963Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:42:20.497963Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Noisy Accelerated Power Method for Eigenproblems with Applications","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","eess.SP"],"primary_cat":"math.OC","authors_text":"Mikael Johansson, Vien V. Mai","submitted_at":"2019-03-20T21:05:42Z","abstract_excerpt":"This paper introduces an efficient algorithm for finding the dominant generalized eigenvectors of a pair of symmetric matrices. Combining tools from approximation theory and convex optimization, we develop a simple scalable algorithm with strong theoretical performance guarantees. More precisely, the algorithm retains the simplicity of the well-known power method but enjoys the asymptotic iteration complexity of the powerful Lanczos method. Unlike these classic techniques, our algorithm is designed to decompose the overall problem into a series of subproblems that only need to be solved approx"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.08742","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":"1903.08742","created_at":"2026-05-17T23:42:20.498058+00:00"},{"alias_kind":"arxiv_version","alias_value":"1903.08742v1","created_at":"2026-05-17T23:42:20.498058+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.08742","created_at":"2026-05-17T23:42:20.498058+00:00"},{"alias_kind":"pith_short_12","alias_value":"CRQIITHE6RH6","created_at":"2026-05-18T12:33:15.570797+00:00"},{"alias_kind":"pith_short_16","alias_value":"CRQIITHE6RH6YPOR","created_at":"2026-05-18T12:33:15.570797+00:00"},{"alias_kind":"pith_short_8","alias_value":"CRQIITHE","created_at":"2026-05-18T12:33:15.570797+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/CRQIITHE6RH6YPORPKPO2SHQBE","json":"https://pith.science/pith/CRQIITHE6RH6YPORPKPO2SHQBE.json","graph_json":"https://pith.science/api/pith-number/CRQIITHE6RH6YPORPKPO2SHQBE/graph.json","events_json":"https://pith.science/api/pith-number/CRQIITHE6RH6YPORPKPO2SHQBE/events.json","paper":"https://pith.science/paper/CRQIITHE"},"agent_actions":{"view_html":"https://pith.science/pith/CRQIITHE6RH6YPORPKPO2SHQBE","download_json":"https://pith.science/pith/CRQIITHE6RH6YPORPKPO2SHQBE.json","view_paper":"https://pith.science/paper/CRQIITHE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1903.08742&json=true","fetch_graph":"https://pith.science/api/pith-number/CRQIITHE6RH6YPORPKPO2SHQBE/graph.json","fetch_events":"https://pith.science/api/pith-number/CRQIITHE6RH6YPORPKPO2SHQBE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CRQIITHE6RH6YPORPKPO2SHQBE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CRQIITHE6RH6YPORPKPO2SHQBE/action/storage_attestation","attest_author":"https://pith.science/pith/CRQIITHE6RH6YPORPKPO2SHQBE/action/author_attestation","sign_citation":"https://pith.science/pith/CRQIITHE6RH6YPORPKPO2SHQBE/action/citation_signature","submit_replication":"https://pith.science/pith/CRQIITHE6RH6YPORPKPO2SHQBE/action/replication_record"}},"created_at":"2026-05-17T23:42:20.498058+00:00","updated_at":"2026-05-17T23:42:20.498058+00:00"}