{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2012:NZIS3ZTAZ3DUTOU5OJ3FQIAJIN","short_pith_number":"pith:NZIS3ZTA","schema_version":"1.0","canonical_sha256":"6e512de660cec749ba9d72765820094369099c77a747a32ef98e9b76a53c680e","source":{"kind":"arxiv","id":"1203.1570","version":1},"attestation_state":"computed","paper":{"title":"In-network Sparsity-regularized Rank Minimization: Algorithms and Applications","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IT","cs.NI","math.IT","stat.ML"],"primary_cat":"cs.MA","authors_text":"Georgios B. Giannakis, Gonzalo Mateos, Morteza Mardani","submitted_at":"2012-03-07T19:14:32Z","abstract_excerpt":"Given a limited number of entries from the superposition of a low-rank matrix plus the product of a known fat compression matrix times a sparse matrix, recovery of the low-rank and sparse components is a fundamental task subsuming compressed sensing, matrix completion, and principal components pursuit. This paper develops algorithms for distributed sparsity-regularized rank minimization over networks, when the nuclear- and $\\ell_1$-norm are used as surrogates to the rank and nonzero entry counts of the sought matrices, respectively. While nuclear-norm minimization has well-documented merits wh"},"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":"1203.1570","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.MA","submitted_at":"2012-03-07T19:14:32Z","cross_cats_sorted":["cs.IT","cs.NI","math.IT","stat.ML"],"title_canon_sha256":"50c1ccd42d951d4ed41f0d9f1e5e1062b2167ab991b9b4bef468954ffb6d0b85","abstract_canon_sha256":"855ea92c001037ad82b00241e0342acd948489cd9314753082aea0d6b884bdf2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:11:58.290479Z","signature_b64":"1305AydEVZvuVO5t+am2W9poU7XiZhATpUuruOu2Pvr42U7itZHDxvtyA0pjW6+4RiKO8F0qCQgqVITo/WocCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6e512de660cec749ba9d72765820094369099c77a747a32ef98e9b76a53c680e","last_reissued_at":"2026-05-18T03:11:58.289724Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:11:58.289724Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"In-network Sparsity-regularized Rank Minimization: Algorithms and Applications","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IT","cs.NI","math.IT","stat.ML"],"primary_cat":"cs.MA","authors_text":"Georgios B. Giannakis, Gonzalo Mateos, Morteza Mardani","submitted_at":"2012-03-07T19:14:32Z","abstract_excerpt":"Given a limited number of entries from the superposition of a low-rank matrix plus the product of a known fat compression matrix times a sparse matrix, recovery of the low-rank and sparse components is a fundamental task subsuming compressed sensing, matrix completion, and principal components pursuit. This paper develops algorithms for distributed sparsity-regularized rank minimization over networks, when the nuclear- and $\\ell_1$-norm are used as surrogates to the rank and nonzero entry counts of the sought matrices, respectively. While nuclear-norm minimization has well-documented merits wh"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1203.1570","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":"1203.1570","created_at":"2026-05-18T03:11:58.289835+00:00"},{"alias_kind":"arxiv_version","alias_value":"1203.1570v1","created_at":"2026-05-18T03:11:58.289835+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1203.1570","created_at":"2026-05-18T03:11:58.289835+00:00"},{"alias_kind":"pith_short_12","alias_value":"NZIS3ZTAZ3DU","created_at":"2026-05-18T12:27:16.716162+00:00"},{"alias_kind":"pith_short_16","alias_value":"NZIS3ZTAZ3DUTOU5","created_at":"2026-05-18T12:27:16.716162+00:00"},{"alias_kind":"pith_short_8","alias_value":"NZIS3ZTA","created_at":"2026-05-18T12:27:16.716162+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.00442","citing_title":"SLoG-Net: Algorithm Unrolling for Source Localization on Graphs","ref_index":22,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/NZIS3ZTAZ3DUTOU5OJ3FQIAJIN","json":"https://pith.science/pith/NZIS3ZTAZ3DUTOU5OJ3FQIAJIN.json","graph_json":"https://pith.science/api/pith-number/NZIS3ZTAZ3DUTOU5OJ3FQIAJIN/graph.json","events_json":"https://pith.science/api/pith-number/NZIS3ZTAZ3DUTOU5OJ3FQIAJIN/events.json","paper":"https://pith.science/paper/NZIS3ZTA"},"agent_actions":{"view_html":"https://pith.science/pith/NZIS3ZTAZ3DUTOU5OJ3FQIAJIN","download_json":"https://pith.science/pith/NZIS3ZTAZ3DUTOU5OJ3FQIAJIN.json","view_paper":"https://pith.science/paper/NZIS3ZTA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1203.1570&json=true","fetch_graph":"https://pith.science/api/pith-number/NZIS3ZTAZ3DUTOU5OJ3FQIAJIN/graph.json","fetch_events":"https://pith.science/api/pith-number/NZIS3ZTAZ3DUTOU5OJ3FQIAJIN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NZIS3ZTAZ3DUTOU5OJ3FQIAJIN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NZIS3ZTAZ3DUTOU5OJ3FQIAJIN/action/storage_attestation","attest_author":"https://pith.science/pith/NZIS3ZTAZ3DUTOU5OJ3FQIAJIN/action/author_attestation","sign_citation":"https://pith.science/pith/NZIS3ZTAZ3DUTOU5OJ3FQIAJIN/action/citation_signature","submit_replication":"https://pith.science/pith/NZIS3ZTAZ3DUTOU5OJ3FQIAJIN/action/replication_record"}},"created_at":"2026-05-18T03:11:58.289835+00:00","updated_at":"2026-05-18T03:11:58.289835+00:00"}