{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:VRC4E5OORQZU7LKSF6XRCNKDHN","short_pith_number":"pith:VRC4E5OO","canonical_record":{"source":{"id":"1603.00284","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-03-01T14:33:12Z","cross_cats_sorted":["cs.CV","math.OC"],"title_canon_sha256":"7c9c0d7ac74ce0d51a30aa84f79ba4b7a8b2060bae9421f6f25c88c4d6372733","abstract_canon_sha256":"d9397abf5d97cf4a1429a5a4c7cf9c179c80e7816cfdc5b3d191149e52cc89c9"},"schema_version":"1.0"},"canonical_sha256":"ac45c275ce8c334fad522faf1135433b4556636a0984718f95b63fa001449aeb","source":{"kind":"arxiv","id":"1603.00284","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1603.00284","created_at":"2026-05-18T01:19:45Z"},{"alias_kind":"arxiv_version","alias_value":"1603.00284v1","created_at":"2026-05-18T01:19:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1603.00284","created_at":"2026-05-18T01:19:45Z"},{"alias_kind":"pith_short_12","alias_value":"VRC4E5OORQZU","created_at":"2026-05-18T12:30:48Z"},{"alias_kind":"pith_short_16","alias_value":"VRC4E5OORQZU7LKS","created_at":"2026-05-18T12:30:48Z"},{"alias_kind":"pith_short_8","alias_value":"VRC4E5OO","created_at":"2026-05-18T12:30:48Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:VRC4E5OORQZU7LKSF6XRCNKDHN","target":"record","payload":{"canonical_record":{"source":{"id":"1603.00284","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-03-01T14:33:12Z","cross_cats_sorted":["cs.CV","math.OC"],"title_canon_sha256":"7c9c0d7ac74ce0d51a30aa84f79ba4b7a8b2060bae9421f6f25c88c4d6372733","abstract_canon_sha256":"d9397abf5d97cf4a1429a5a4c7cf9c179c80e7816cfdc5b3d191149e52cc89c9"},"schema_version":"1.0"},"canonical_sha256":"ac45c275ce8c334fad522faf1135433b4556636a0984718f95b63fa001449aeb","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:19:45.436481Z","signature_b64":"4Gh9sskzIVrHn6Wm4ErSl10My5PB+VYnycoPXBM4jgi6dqox+0lbUQp/PzQwCtKIHfw3CCQvrTfvjAnqbPmRAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ac45c275ce8c334fad522faf1135433b4556636a0984718f95b63fa001449aeb","last_reissued_at":"2026-05-18T01:19:45.435951Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:19:45.435951Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1603.00284","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":"2026-05-18T01:19:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GjQLNopte10k+BcNqw+WFR8bo/Q37KNArZ20TVB8HeNO2yTX9hRba5FMsIwekpyYs6ok9D53oFHPU6gRNLUACg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T01:27:41.905982Z"},"content_sha256":"a73449185a72cc7391744b7d95e5f2795a6dbfb5e3e7a3a0dfd8974a0a907b9b","schema_version":"1.0","event_id":"sha256:a73449185a72cc7391744b7d95e5f2795a6dbfb5e3e7a3a0dfd8974a0a907b9b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:VRC4E5OORQZU7LKSF6XRCNKDHN","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Dual Smoothing and Level Set Techniques for Variational Matrix Decomposition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","math.OC"],"primary_cat":"stat.ML","authors_text":"Aleksandr Y. Aravkin, Stephen Becker","submitted_at":"2016-03-01T14:33:12Z","abstract_excerpt":"We focus on the robust principal component analysis (RPCA) problem, and review a range of old and new convex formulations for the problem and its variants. We then review dual smoothing and level set techniques in convex optimization, present several novel theoretical results, and apply the techniques on the RPCA problem. In the final sections, we show a range of numerical experiments for simulated and real-world problems."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1603.00284","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":"2026-05-18T01:19:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RmkDH9R95zvNsoidH84la2/fCZTCI7k2DrojayzWh2UzfKGqajH9vQ8E9VlM6B6NYWnyTUcE8VZs3r/0lGyrBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T01:27:41.906344Z"},"content_sha256":"3666006c0ce485591f2b9dda498709f69f031f67cfcc993a49e65aa93cc1f85a","schema_version":"1.0","event_id":"sha256:3666006c0ce485591f2b9dda498709f69f031f67cfcc993a49e65aa93cc1f85a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/VRC4E5OORQZU7LKSF6XRCNKDHN/bundle.json","state_url":"https://pith.science/pith/VRC4E5OORQZU7LKSF6XRCNKDHN/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/VRC4E5OORQZU7LKSF6XRCNKDHN/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":"2026-06-02T01:27:41Z","links":{"resolver":"https://pith.science/pith/VRC4E5OORQZU7LKSF6XRCNKDHN","bundle":"https://pith.science/pith/VRC4E5OORQZU7LKSF6XRCNKDHN/bundle.json","state":"https://pith.science/pith/VRC4E5OORQZU7LKSF6XRCNKDHN/state.json","well_known_bundle":"https://pith.science/.well-known/pith/VRC4E5OORQZU7LKSF6XRCNKDHN/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:VRC4E5OORQZU7LKSF6XRCNKDHN","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":"d9397abf5d97cf4a1429a5a4c7cf9c179c80e7816cfdc5b3d191149e52cc89c9","cross_cats_sorted":["cs.CV","math.OC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-03-01T14:33:12Z","title_canon_sha256":"7c9c0d7ac74ce0d51a30aa84f79ba4b7a8b2060bae9421f6f25c88c4d6372733"},"schema_version":"1.0","source":{"id":"1603.00284","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1603.00284","created_at":"2026-05-18T01:19:45Z"},{"alias_kind":"arxiv_version","alias_value":"1603.00284v1","created_at":"2026-05-18T01:19:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1603.00284","created_at":"2026-05-18T01:19:45Z"},{"alias_kind":"pith_short_12","alias_value":"VRC4E5OORQZU","created_at":"2026-05-18T12:30:48Z"},{"alias_kind":"pith_short_16","alias_value":"VRC4E5OORQZU7LKS","created_at":"2026-05-18T12:30:48Z"},{"alias_kind":"pith_short_8","alias_value":"VRC4E5OO","created_at":"2026-05-18T12:30:48Z"}],"graph_snapshots":[{"event_id":"sha256:3666006c0ce485591f2b9dda498709f69f031f67cfcc993a49e65aa93cc1f85a","target":"graph","created_at":"2026-05-18T01:19:45Z","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":"We focus on the robust principal component analysis (RPCA) problem, and review a range of old and new convex formulations for the problem and its variants. We then review dual smoothing and level set techniques in convex optimization, present several novel theoretical results, and apply the techniques on the RPCA problem. In the final sections, we show a range of numerical experiments for simulated and real-world problems.","authors_text":"Aleksandr Y. Aravkin, Stephen Becker","cross_cats":["cs.CV","math.OC"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-03-01T14:33:12Z","title":"Dual Smoothing and Level Set Techniques for Variational Matrix Decomposition"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1603.00284","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:a73449185a72cc7391744b7d95e5f2795a6dbfb5e3e7a3a0dfd8974a0a907b9b","target":"record","created_at":"2026-05-18T01:19:45Z","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":"d9397abf5d97cf4a1429a5a4c7cf9c179c80e7816cfdc5b3d191149e52cc89c9","cross_cats_sorted":["cs.CV","math.OC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-03-01T14:33:12Z","title_canon_sha256":"7c9c0d7ac74ce0d51a30aa84f79ba4b7a8b2060bae9421f6f25c88c4d6372733"},"schema_version":"1.0","source":{"id":"1603.00284","kind":"arxiv","version":1}},"canonical_sha256":"ac45c275ce8c334fad522faf1135433b4556636a0984718f95b63fa001449aeb","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ac45c275ce8c334fad522faf1135433b4556636a0984718f95b63fa001449aeb","first_computed_at":"2026-05-18T01:19:45.435951Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:19:45.435951Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"4Gh9sskzIVrHn6Wm4ErSl10My5PB+VYnycoPXBM4jgi6dqox+0lbUQp/PzQwCtKIHfw3CCQvrTfvjAnqbPmRAw==","signature_status":"signed_v1","signed_at":"2026-05-18T01:19:45.436481Z","signed_message":"canonical_sha256_bytes"},"source_id":"1603.00284","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a73449185a72cc7391744b7d95e5f2795a6dbfb5e3e7a3a0dfd8974a0a907b9b","sha256:3666006c0ce485591f2b9dda498709f69f031f67cfcc993a49e65aa93cc1f85a"],"state_sha256":"640f2dba78cdfbc3f3cf4f7056ce34b7b4fae0846cb2c5ec349ddb051e54dadc"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yut+WGw/SPsORMxEBO9tPedD/c38ddBwfZGOnrWRIMTHCFg7uPZgCHUv4sSFWFyPLVX4FQjbAtq5KxGLwwRIAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-02T01:27:41.908250Z","bundle_sha256":"96c1f199d34feb36c4ce9e927f57a4d740b125bdc963ed36d2e7af7ce2b8ef40"}}