{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2014:OCW2PDFCBNG3C5XMRKSL6PBLVM","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":"5aa6672c51a612df9d6f00c191114d0f719564f310065e96d3075ccc0e0883f2","cross_cats_sorted":["cs.CV","cs.NA","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2014-03-29T04:04:43Z","title_canon_sha256":"3793ee18e4eaba9dc276aa2a84b49eac0f7348ded054d0e660ccff2b2f22fb04"},"schema_version":"1.0","source":{"id":"1403.7588","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1403.7588","created_at":"2026-05-18T00:43:31Z"},{"alias_kind":"arxiv_version","alias_value":"1403.7588v2","created_at":"2026-05-18T00:43:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1403.7588","created_at":"2026-05-18T00:43:31Z"},{"alias_kind":"pith_short_12","alias_value":"OCW2PDFCBNG3","created_at":"2026-05-18T12:28:41Z"},{"alias_kind":"pith_short_16","alias_value":"OCW2PDFCBNG3C5XM","created_at":"2026-05-18T12:28:41Z"},{"alias_kind":"pith_short_8","alias_value":"OCW2PDFC","created_at":"2026-05-18T12:28:41Z"}],"graph_snapshots":[{"event_id":"sha256:02753732e6af19f54b803e472469ada4550f34e981848a05b93e33826829f569","target":"graph","created_at":"2026-05-18T00:43:31Z","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":"Recovering matrices from compressive and grossly corrupted observations is a fundamental problem in robust statistics, with rich applications in computer vision and machine learning. In theory, under certain conditions, this problem can be solved in polynomial time via a natural convex relaxation, known as Compressive Principal Component Pursuit (CPCP). However, all existing provable algorithms for CPCP suffer from superlinear per-iteration cost, which severely limits their applicability to large scale problems. In this paper, we propose provable, scalable and efficient methods to solve CPCP w","authors_text":"Cun Mu, Donald Goldfarb, John Wright, Yuqian Zhang","cross_cats":["cs.CV","cs.NA","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2014-03-29T04:04:43Z","title":"Scalable Robust Matrix Recovery: Frank-Wolfe Meets Proximal Methods"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1403.7588","kind":"arxiv","version":2},"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":"sha256:8104c291476b2cd1a1ca1daf35a082bba91e5f5f7c29eb43b9356d47bf05e984","target":"record","created_at":"2026-05-18T00:43:31Z","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":"5aa6672c51a612df9d6f00c191114d0f719564f310065e96d3075ccc0e0883f2","cross_cats_sorted":["cs.CV","cs.NA","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2014-03-29T04:04:43Z","title_canon_sha256":"3793ee18e4eaba9dc276aa2a84b49eac0f7348ded054d0e660ccff2b2f22fb04"},"schema_version":"1.0","source":{"id":"1403.7588","kind":"arxiv","version":2}},"canonical_sha256":"70ada78ca20b4db176ec8aa4bf3c2bab1516b2fd14305ead8e1440a52b084556","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"70ada78ca20b4db176ec8aa4bf3c2bab1516b2fd14305ead8e1440a52b084556","first_computed_at":"2026-05-18T00:43:31.454114Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:43:31.454114Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"L0N1AtVeC+qCxny2qFDQu3BE4EiroXoGweRJiTymmIzG05Zo4m+Nqp+kbp3c4Tl+3eG1ji82JUuj6TdBnDbpBg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:43:31.454522Z","signed_message":"canonical_sha256_bytes"},"source_id":"1403.7588","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8104c291476b2cd1a1ca1daf35a082bba91e5f5f7c29eb43b9356d47bf05e984","sha256:02753732e6af19f54b803e472469ada4550f34e981848a05b93e33826829f569"],"state_sha256":"0f8935cd869aec6d865fedac9298d2a08f32a017a2fa68e0cae9e353580a8de4"}