{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:J3LK6KMW55Z4TNWSVACUXOKXU6","short_pith_number":"pith:J3LK6KMW","schema_version":"1.0","canonical_sha256":"4ed6af2996ef73c9b6d2a8054bb957a79b6b9bbed4a5e3540575f5675e78fda5","source":{"kind":"arxiv","id":"1401.2288","version":3},"attestation_state":"computed","paper":{"title":"Extension of Sparse Randomized Kaczmarz Algorithm for Multiple Measurement Vectors","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.NA","authors_text":"Angshul Majumdar, Hemant Kumar Aggarwal","submitted_at":"2014-01-10T11:24:35Z","abstract_excerpt":"The Kaczmarz algorithm is popular for iteratively solving an overdetermined system of linear equations. The traditional Kaczmarz algorithm can approximate the solution in few sweeps through the equations but a randomized version of the Kaczmarz algorithm was shown to converge exponentially and independent of number of equations. Recently an algorithm for finding sparse solution to a linear system of equations has been proposed based on weighted randomized Kaczmarz algorithm. These algorithms solves single measurement vector problem; however there are applications were multiple-measurements are"},"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":"1401.2288","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NA","submitted_at":"2014-01-10T11:24:35Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"b68747e8d7eaf888ca6fdd2580c63fa8fc1a35d20bf7f09e35de47c670ce6ad8","abstract_canon_sha256":"33ec1c7e08e5d8179e64bde3f412b551d2c8b4d56810e2f858eef7163f7a2e22"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:00:25.303773Z","signature_b64":"4gaICtIdWmhqfHPjx/dgP5EE2PwJGpli26fZ9mPvUa6ZfatAyU2uSuQ/GV0uVyeWcemOOCn4cUwMZgmmnl3XAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4ed6af2996ef73c9b6d2a8054bb957a79b6b9bbed4a5e3540575f5675e78fda5","last_reissued_at":"2026-05-18T03:00:25.303012Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:00:25.303012Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Extension of Sparse Randomized Kaczmarz Algorithm for Multiple Measurement Vectors","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.NA","authors_text":"Angshul Majumdar, Hemant Kumar Aggarwal","submitted_at":"2014-01-10T11:24:35Z","abstract_excerpt":"The Kaczmarz algorithm is popular for iteratively solving an overdetermined system of linear equations. The traditional Kaczmarz algorithm can approximate the solution in few sweeps through the equations but a randomized version of the Kaczmarz algorithm was shown to converge exponentially and independent of number of equations. Recently an algorithm for finding sparse solution to a linear system of equations has been proposed based on weighted randomized Kaczmarz algorithm. These algorithms solves single measurement vector problem; however there are applications were multiple-measurements are"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1401.2288","kind":"arxiv","version":3},"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":"1401.2288","created_at":"2026-05-18T03:00:25.303162+00:00"},{"alias_kind":"arxiv_version","alias_value":"1401.2288v3","created_at":"2026-05-18T03:00:25.303162+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1401.2288","created_at":"2026-05-18T03:00:25.303162+00:00"},{"alias_kind":"pith_short_12","alias_value":"J3LK6KMW55Z4","created_at":"2026-05-18T12:28:33.132498+00:00"},{"alias_kind":"pith_short_16","alias_value":"J3LK6KMW55Z4TNWS","created_at":"2026-05-18T12:28:33.132498+00:00"},{"alias_kind":"pith_short_8","alias_value":"J3LK6KMW","created_at":"2026-05-18T12:28:33.132498+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/J3LK6KMW55Z4TNWSVACUXOKXU6","json":"https://pith.science/pith/J3LK6KMW55Z4TNWSVACUXOKXU6.json","graph_json":"https://pith.science/api/pith-number/J3LK6KMW55Z4TNWSVACUXOKXU6/graph.json","events_json":"https://pith.science/api/pith-number/J3LK6KMW55Z4TNWSVACUXOKXU6/events.json","paper":"https://pith.science/paper/J3LK6KMW"},"agent_actions":{"view_html":"https://pith.science/pith/J3LK6KMW55Z4TNWSVACUXOKXU6","download_json":"https://pith.science/pith/J3LK6KMW55Z4TNWSVACUXOKXU6.json","view_paper":"https://pith.science/paper/J3LK6KMW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1401.2288&json=true","fetch_graph":"https://pith.science/api/pith-number/J3LK6KMW55Z4TNWSVACUXOKXU6/graph.json","fetch_events":"https://pith.science/api/pith-number/J3LK6KMW55Z4TNWSVACUXOKXU6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/J3LK6KMW55Z4TNWSVACUXOKXU6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/J3LK6KMW55Z4TNWSVACUXOKXU6/action/storage_attestation","attest_author":"https://pith.science/pith/J3LK6KMW55Z4TNWSVACUXOKXU6/action/author_attestation","sign_citation":"https://pith.science/pith/J3LK6KMW55Z4TNWSVACUXOKXU6/action/citation_signature","submit_replication":"https://pith.science/pith/J3LK6KMW55Z4TNWSVACUXOKXU6/action/replication_record"}},"created_at":"2026-05-18T03:00:25.303162+00:00","updated_at":"2026-05-18T03:00:25.303162+00:00"}