{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2011:72SWKFCHVYVZTIU5HBSXCTRRFM","short_pith_number":"pith:72SWKFCH","schema_version":"1.0","canonical_sha256":"fea5651447ae2b99a29d3865714e312b2b1c6383643bf1fde017fb499d62f4c3","source":{"kind":"arxiv","id":"1102.3289","version":1},"attestation_state":"computed","paper":{"title":"Belief propagation for joint sparse recovery","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT"],"primary_cat":"cs.IT","authors_text":"Bangchul Jung, Dror Baron, Jong Chul Ye, Jongmin Kim, Woohyuk Chang","submitted_at":"2011-02-16T10:28:29Z","abstract_excerpt":"Compressed sensing (CS) demonstrates that sparse signals can be recovered from underdetermined linear measurements. We focus on the joint sparse recovery problem where multiple signals share the same common sparse support sets, and they are measured through the same sensing matrix. Leveraging a recent information theoretic characterization of single signal CS, we formulate the optimal minimum mean square error (MMSE) estimation problem, and derive a belief propagation algorithm, its relaxed version, for the joint sparse recovery problem and an approximate message passing algorithm. In addition"},"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":"1102.3289","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2011-02-16T10:28:29Z","cross_cats_sorted":["math.IT"],"title_canon_sha256":"4f32a2613e0ced90e2e6c0c82bab202b8b5c6614023cc83ef05f82acf7cfd8df","abstract_canon_sha256":"84e886f000deb4a10244dc52b4f4bbbabe64bfca6e9647e2f8cbc9447e4b2b45"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T04:28:36.698085Z","signature_b64":"IxgEKFAmr6vkaL9wkwyv2ZovapqRCPZfMwZBsmKZFhUassxDkdWgYwgUHG1SypojAi7OvonHd8JX+8aH4IlrBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fea5651447ae2b99a29d3865714e312b2b1c6383643bf1fde017fb499d62f4c3","last_reissued_at":"2026-05-18T04:28:36.697426Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T04:28:36.697426Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Belief propagation for joint sparse recovery","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT"],"primary_cat":"cs.IT","authors_text":"Bangchul Jung, Dror Baron, Jong Chul Ye, Jongmin Kim, Woohyuk Chang","submitted_at":"2011-02-16T10:28:29Z","abstract_excerpt":"Compressed sensing (CS) demonstrates that sparse signals can be recovered from underdetermined linear measurements. We focus on the joint sparse recovery problem where multiple signals share the same common sparse support sets, and they are measured through the same sensing matrix. Leveraging a recent information theoretic characterization of single signal CS, we formulate the optimal minimum mean square error (MMSE) estimation problem, and derive a belief propagation algorithm, its relaxed version, for the joint sparse recovery problem and an approximate message passing algorithm. In addition"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1102.3289","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":"1102.3289","created_at":"2026-05-18T04:28:36.697527+00:00"},{"alias_kind":"arxiv_version","alias_value":"1102.3289v1","created_at":"2026-05-18T04:28:36.697527+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1102.3289","created_at":"2026-05-18T04:28:36.697527+00:00"},{"alias_kind":"pith_short_12","alias_value":"72SWKFCHVYVZ","created_at":"2026-05-18T12:26:22.705136+00:00"},{"alias_kind":"pith_short_16","alias_value":"72SWKFCHVYVZTIU5","created_at":"2026-05-18T12:26:22.705136+00:00"},{"alias_kind":"pith_short_8","alias_value":"72SWKFCH","created_at":"2026-05-18T12:26:22.705136+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"1906.09494","citing_title":"Multi-Cell Sparse Activity Detection for Massive Random Access: Massive MIMO versus Cooperative MIMO","ref_index":24,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/72SWKFCHVYVZTIU5HBSXCTRRFM","json":"https://pith.science/pith/72SWKFCHVYVZTIU5HBSXCTRRFM.json","graph_json":"https://pith.science/api/pith-number/72SWKFCHVYVZTIU5HBSXCTRRFM/graph.json","events_json":"https://pith.science/api/pith-number/72SWKFCHVYVZTIU5HBSXCTRRFM/events.json","paper":"https://pith.science/paper/72SWKFCH"},"agent_actions":{"view_html":"https://pith.science/pith/72SWKFCHVYVZTIU5HBSXCTRRFM","download_json":"https://pith.science/pith/72SWKFCHVYVZTIU5HBSXCTRRFM.json","view_paper":"https://pith.science/paper/72SWKFCH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1102.3289&json=true","fetch_graph":"https://pith.science/api/pith-number/72SWKFCHVYVZTIU5HBSXCTRRFM/graph.json","fetch_events":"https://pith.science/api/pith-number/72SWKFCHVYVZTIU5HBSXCTRRFM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/72SWKFCHVYVZTIU5HBSXCTRRFM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/72SWKFCHVYVZTIU5HBSXCTRRFM/action/storage_attestation","attest_author":"https://pith.science/pith/72SWKFCHVYVZTIU5HBSXCTRRFM/action/author_attestation","sign_citation":"https://pith.science/pith/72SWKFCHVYVZTIU5HBSXCTRRFM/action/citation_signature","submit_replication":"https://pith.science/pith/72SWKFCHVYVZTIU5HBSXCTRRFM/action/replication_record"}},"created_at":"2026-05-18T04:28:36.697527+00:00","updated_at":"2026-05-18T04:28:36.697527+00:00"}