{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:OSRNRUN36QF3HQ4S2MXBRDCTHG","short_pith_number":"pith:OSRNRUN3","schema_version":"1.0","canonical_sha256":"74a2d8d1bbf40bb3c392d32e188c5339a23e8df0b931f0a1a828f3a568451a83","source":{"kind":"arxiv","id":"1812.01307","version":1},"attestation_state":"computed","paper":{"title":"BSGD-TV: A parallel algorithm solving total variation constrained image reconstruction problems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT"],"primary_cat":"cs.IT","authors_text":"Thomas Blumensath, Yushan Gao","submitted_at":"2018-12-04T10:08:25Z","abstract_excerpt":"We propose a parallel reconstruction algorithm to solve large scale TV constrained linear inverse problems. We provide a convergence proof and show numerically that our method is significantly faster than the main competitor, block ADMM."},"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":"1812.01307","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2018-12-04T10:08:25Z","cross_cats_sorted":["math.IT"],"title_canon_sha256":"0c8e32e102bf9ddc5abe4ca438c54855e5eec72c465383664079cd3f9334e2b1","abstract_canon_sha256":"81522ad871e52a60b41a0186d497f257216e2e7c0d364159352c22bf49263ef3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:59:12.836075Z","signature_b64":"oQWTBJ9Uu9nE4JCafmZg8UvFxgP42gD6uDMAs/u7jD6X7TejlMJ5WdydHQmUlBpz80WtsxYf/ZLVlCWLWfB2Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"74a2d8d1bbf40bb3c392d32e188c5339a23e8df0b931f0a1a828f3a568451a83","last_reissued_at":"2026-05-17T23:59:12.835568Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:59:12.835568Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"BSGD-TV: A parallel algorithm solving total variation constrained image reconstruction problems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT"],"primary_cat":"cs.IT","authors_text":"Thomas Blumensath, Yushan Gao","submitted_at":"2018-12-04T10:08:25Z","abstract_excerpt":"We propose a parallel reconstruction algorithm to solve large scale TV constrained linear inverse problems. We provide a convergence proof and show numerically that our method is significantly faster than the main competitor, block ADMM."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.01307","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":"1812.01307","created_at":"2026-05-17T23:59:12.835646+00:00"},{"alias_kind":"arxiv_version","alias_value":"1812.01307v1","created_at":"2026-05-17T23:59:12.835646+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.01307","created_at":"2026-05-17T23:59:12.835646+00:00"},{"alias_kind":"pith_short_12","alias_value":"OSRNRUN36QF3","created_at":"2026-05-18T12:32:43.782077+00:00"},{"alias_kind":"pith_short_16","alias_value":"OSRNRUN36QF3HQ4S","created_at":"2026-05-18T12:32:43.782077+00:00"},{"alias_kind":"pith_short_8","alias_value":"OSRNRUN3","created_at":"2026-05-18T12:32:43.782077+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/OSRNRUN36QF3HQ4S2MXBRDCTHG","json":"https://pith.science/pith/OSRNRUN36QF3HQ4S2MXBRDCTHG.json","graph_json":"https://pith.science/api/pith-number/OSRNRUN36QF3HQ4S2MXBRDCTHG/graph.json","events_json":"https://pith.science/api/pith-number/OSRNRUN36QF3HQ4S2MXBRDCTHG/events.json","paper":"https://pith.science/paper/OSRNRUN3"},"agent_actions":{"view_html":"https://pith.science/pith/OSRNRUN36QF3HQ4S2MXBRDCTHG","download_json":"https://pith.science/pith/OSRNRUN36QF3HQ4S2MXBRDCTHG.json","view_paper":"https://pith.science/paper/OSRNRUN3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1812.01307&json=true","fetch_graph":"https://pith.science/api/pith-number/OSRNRUN36QF3HQ4S2MXBRDCTHG/graph.json","fetch_events":"https://pith.science/api/pith-number/OSRNRUN36QF3HQ4S2MXBRDCTHG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OSRNRUN36QF3HQ4S2MXBRDCTHG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OSRNRUN36QF3HQ4S2MXBRDCTHG/action/storage_attestation","attest_author":"https://pith.science/pith/OSRNRUN36QF3HQ4S2MXBRDCTHG/action/author_attestation","sign_citation":"https://pith.science/pith/OSRNRUN36QF3HQ4S2MXBRDCTHG/action/citation_signature","submit_replication":"https://pith.science/pith/OSRNRUN36QF3HQ4S2MXBRDCTHG/action/replication_record"}},"created_at":"2026-05-17T23:59:12.835646+00:00","updated_at":"2026-05-17T23:59:12.835646+00:00"}