{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2013:CSNNBBLOLYEODPZGUNZHSPUKJV","short_pith_number":"pith:CSNNBBLO","canonical_record":{"source":{"id":"1309.6976","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2013-09-26T17:14:32Z","cross_cats_sorted":[],"title_canon_sha256":"b6f1a856c344f6ff22ad424b6b93bf778a5318ef3bafb22f4b6e13c4a9979766","abstract_canon_sha256":"6845199cd4b38cf6520ff3419cf3e1d948762c5c25729f90aedfb24fffacac4d"},"schema_version":"1.0"},"canonical_sha256":"149ad0856e5e08e1bf26a372793e8a4d4ec1ccaa2cd1be1fd5342e4382e191fa","source":{"kind":"arxiv","id":"1309.6976","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1309.6976","created_at":"2026-05-18T03:12:09Z"},{"alias_kind":"arxiv_version","alias_value":"1309.6976v1","created_at":"2026-05-18T03:12:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1309.6976","created_at":"2026-05-18T03:12:09Z"},{"alias_kind":"pith_short_12","alias_value":"CSNNBBLOLYEO","created_at":"2026-05-18T12:27:40Z"},{"alias_kind":"pith_short_16","alias_value":"CSNNBBLOLYEODPZG","created_at":"2026-05-18T12:27:40Z"},{"alias_kind":"pith_short_8","alias_value":"CSNNBBLO","created_at":"2026-05-18T12:27:40Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2013:CSNNBBLOLYEODPZGUNZHSPUKJV","target":"record","payload":{"canonical_record":{"source":{"id":"1309.6976","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2013-09-26T17:14:32Z","cross_cats_sorted":[],"title_canon_sha256":"b6f1a856c344f6ff22ad424b6b93bf778a5318ef3bafb22f4b6e13c4a9979766","abstract_canon_sha256":"6845199cd4b38cf6520ff3419cf3e1d948762c5c25729f90aedfb24fffacac4d"},"schema_version":"1.0"},"canonical_sha256":"149ad0856e5e08e1bf26a372793e8a4d4ec1ccaa2cd1be1fd5342e4382e191fa","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:12:09.597612Z","signature_b64":"s3XT9Pwli9NVHvxu0+Z/OXy5eM0fLaTrRb7mcRc/aFRQ02yPX6+j4ryWlaY2JugWZEqxPdYS4vi5LX+Bn584Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"149ad0856e5e08e1bf26a372793e8a4d4ec1ccaa2cd1be1fd5342e4382e191fa","last_reissued_at":"2026-05-18T03:12:09.596899Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:12:09.596899Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1309.6976","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T03:12:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TS8D/b4zDGVdG7ns05CVnsYsIN4kscO20LS9KX+aFg1dIt/V/wFeP28R0XbIUg4KoXUWasqPMz3eMTOju4T0CA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-22T08:31:12.536443Z"},"content_sha256":"c47f512f451dd5d398f6b2f714bf80b88559385ec39bfd62195caf45891aa215","schema_version":"1.0","event_id":"sha256:c47f512f451dd5d398f6b2f714bf80b88559385ec39bfd62195caf45891aa215"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2013:CSNNBBLOLYEODPZGUNZHSPUKJV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Efficient Algorithms for Robust and Stable Principal Component Pursuit Problems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Donald Goldfarb, Necdet Serhat Aybat, Shiqian Ma","submitted_at":"2013-09-26T17:14:32Z","abstract_excerpt":"The problem of recovering a low-rank matrix from a set of observations corrupted with gross sparse error is known as the robust principal component analysis (RPCA) and has many applications in computer vision, image processing and web data ranking. It has been shown that under certain conditions, the solution to the NP-hard RPCA problem can be obtained by solving a convex optimization problem, namely the robust principal component pursuit (RPCP). Moreover, if the observed data matrix has also been corrupted by a dense noise matrix in addition to gross sparse error, then the stable principal co"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1309.6976","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T03:12:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ekJQ51XzGjkFeIaSpiK73jjtD379sd6h7sS3pw7+/oFayT9c3A9pqOEa+Ev1DTsBnfchGqkI0jgjle0mcJiZAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-22T08:31:12.537126Z"},"content_sha256":"e8be6ca60f5f71c31d12cfd55b30f19d1af447508b1f6e15b09a1e92feb2e46d","schema_version":"1.0","event_id":"sha256:e8be6ca60f5f71c31d12cfd55b30f19d1af447508b1f6e15b09a1e92feb2e46d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CSNNBBLOLYEODPZGUNZHSPUKJV/bundle.json","state_url":"https://pith.science/pith/CSNNBBLOLYEODPZGUNZHSPUKJV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CSNNBBLOLYEODPZGUNZHSPUKJV/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-22T08:31:12Z","links":{"resolver":"https://pith.science/pith/CSNNBBLOLYEODPZGUNZHSPUKJV","bundle":"https://pith.science/pith/CSNNBBLOLYEODPZGUNZHSPUKJV/bundle.json","state":"https://pith.science/pith/CSNNBBLOLYEODPZGUNZHSPUKJV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CSNNBBLOLYEODPZGUNZHSPUKJV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2013:CSNNBBLOLYEODPZGUNZHSPUKJV","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"6845199cd4b38cf6520ff3419cf3e1d948762c5c25729f90aedfb24fffacac4d","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2013-09-26T17:14:32Z","title_canon_sha256":"b6f1a856c344f6ff22ad424b6b93bf778a5318ef3bafb22f4b6e13c4a9979766"},"schema_version":"1.0","source":{"id":"1309.6976","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1309.6976","created_at":"2026-05-18T03:12:09Z"},{"alias_kind":"arxiv_version","alias_value":"1309.6976v1","created_at":"2026-05-18T03:12:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1309.6976","created_at":"2026-05-18T03:12:09Z"},{"alias_kind":"pith_short_12","alias_value":"CSNNBBLOLYEO","created_at":"2026-05-18T12:27:40Z"},{"alias_kind":"pith_short_16","alias_value":"CSNNBBLOLYEODPZG","created_at":"2026-05-18T12:27:40Z"},{"alias_kind":"pith_short_8","alias_value":"CSNNBBLO","created_at":"2026-05-18T12:27:40Z"}],"graph_snapshots":[{"event_id":"sha256:e8be6ca60f5f71c31d12cfd55b30f19d1af447508b1f6e15b09a1e92feb2e46d","target":"graph","created_at":"2026-05-18T03:12:09Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"The problem of recovering a low-rank matrix from a set of observations corrupted with gross sparse error is known as the robust principal component analysis (RPCA) and has many applications in computer vision, image processing and web data ranking. It has been shown that under certain conditions, the solution to the NP-hard RPCA problem can be obtained by solving a convex optimization problem, namely the robust principal component pursuit (RPCP). Moreover, if the observed data matrix has also been corrupted by a dense noise matrix in addition to gross sparse error, then the stable principal co","authors_text":"Donald Goldfarb, Necdet Serhat Aybat, Shiqian Ma","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2013-09-26T17:14:32Z","title":"Efficient Algorithms for Robust and Stable Principal Component Pursuit Problems"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1309.6976","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:c47f512f451dd5d398f6b2f714bf80b88559385ec39bfd62195caf45891aa215","target":"record","created_at":"2026-05-18T03:12:09Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"6845199cd4b38cf6520ff3419cf3e1d948762c5c25729f90aedfb24fffacac4d","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2013-09-26T17:14:32Z","title_canon_sha256":"b6f1a856c344f6ff22ad424b6b93bf778a5318ef3bafb22f4b6e13c4a9979766"},"schema_version":"1.0","source":{"id":"1309.6976","kind":"arxiv","version":1}},"canonical_sha256":"149ad0856e5e08e1bf26a372793e8a4d4ec1ccaa2cd1be1fd5342e4382e191fa","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"149ad0856e5e08e1bf26a372793e8a4d4ec1ccaa2cd1be1fd5342e4382e191fa","first_computed_at":"2026-05-18T03:12:09.596899Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T03:12:09.596899Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"s3XT9Pwli9NVHvxu0+Z/OXy5eM0fLaTrRb7mcRc/aFRQ02yPX6+j4ryWlaY2JugWZEqxPdYS4vi5LX+Bn584Cg==","signature_status":"signed_v1","signed_at":"2026-05-18T03:12:09.597612Z","signed_message":"canonical_sha256_bytes"},"source_id":"1309.6976","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c47f512f451dd5d398f6b2f714bf80b88559385ec39bfd62195caf45891aa215","sha256:e8be6ca60f5f71c31d12cfd55b30f19d1af447508b1f6e15b09a1e92feb2e46d"],"state_sha256":"6bc7182f87f6ceb98a3d87173db67d127dd546e478e94eee94974aa9692a1b0a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2Hle1fqxpgBNm87zrYsyglzdNOmLDlbXGUmyY/x0Z1ppn/hc9b4A9+Miiz3BRJK9m3C8GTUQSpWAAj63R1OgBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-22T08:31:12.540792Z","bundle_sha256":"8d1bcc28b6b2b39088e500f439822797d937d4730cabb9427fe7a3eda8554527"}}