{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:ADJIYA4Y4XC4ZXN4DM7L2VPYWK","short_pith_number":"pith:ADJIYA4Y","schema_version":"1.0","canonical_sha256":"00d28c0398e5c5ccddbc1b3ebd55f8b28a05aefa9bdf9c6db75658ec9dc66ef4","source":{"kind":"arxiv","id":"1706.06054","version":1},"attestation_state":"computed","paper":{"title":"Rigorous Dynamics and Consistent Estimation in Arbitrarily Conditioned Linear Systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","math.IT"],"primary_cat":"cs.IT","authors_text":"Alyson K. Fletcher, Mojtaba Sahraee-Ardakan, Philip Schniter, Sundeep Rangan","submitted_at":"2017-06-19T16:57:11Z","abstract_excerpt":"The problem of estimating a random vector x from noisy linear measurements y = A x + w with unknown parameters on the distributions of x and w, which must also be learned, arises in a wide range of statistical learning and linear inverse problems. We show that a computationally simple iterative message-passing algorithm can provably obtain asymptotically consistent estimates in a certain high-dimensional large-system limit (LSL) under very general parameterizations. Previous message passing techniques have required i.i.d. sub-Gaussian A matrices and often fail when the matrix is ill-conditione"},"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":"1706.06054","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2017-06-19T16:57:11Z","cross_cats_sorted":["cs.LG","math.IT"],"title_canon_sha256":"df3ccbdaacddee58ad773e57f5c5b9342271287bb093e835a93dc400b154db2c","abstract_canon_sha256":"377221cde8576d27a7f5ce1f7dd641ffa8fbe6f9f1ec11dcd18fc45192b17f5f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:42:08.203020Z","signature_b64":"p4ezfG2WwgIa1/FXlim0WoemhL3fs6iKGzlDSk4V8QJyCdYrjUfuOL7Gc7tBwo8wO9IiIvAXQUm0j+T7EAl7DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"00d28c0398e5c5ccddbc1b3ebd55f8b28a05aefa9bdf9c6db75658ec9dc66ef4","last_reissued_at":"2026-05-18T00:42:08.202606Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:42:08.202606Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Rigorous Dynamics and Consistent Estimation in Arbitrarily Conditioned Linear Systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","math.IT"],"primary_cat":"cs.IT","authors_text":"Alyson K. Fletcher, Mojtaba Sahraee-Ardakan, Philip Schniter, Sundeep Rangan","submitted_at":"2017-06-19T16:57:11Z","abstract_excerpt":"The problem of estimating a random vector x from noisy linear measurements y = A x + w with unknown parameters on the distributions of x and w, which must also be learned, arises in a wide range of statistical learning and linear inverse problems. We show that a computationally simple iterative message-passing algorithm can provably obtain asymptotically consistent estimates in a certain high-dimensional large-system limit (LSL) under very general parameterizations. Previous message passing techniques have required i.i.d. sub-Gaussian A matrices and often fail when the matrix is ill-conditione"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.06054","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":"1706.06054","created_at":"2026-05-18T00:42:08.202669+00:00"},{"alias_kind":"arxiv_version","alias_value":"1706.06054v1","created_at":"2026-05-18T00:42:08.202669+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.06054","created_at":"2026-05-18T00:42:08.202669+00:00"},{"alias_kind":"pith_short_12","alias_value":"ADJIYA4Y4XC4","created_at":"2026-05-18T12:31:05.417338+00:00"},{"alias_kind":"pith_short_16","alias_value":"ADJIYA4Y4XC4ZXN4","created_at":"2026-05-18T12:31:05.417338+00:00"},{"alias_kind":"pith_short_8","alias_value":"ADJIYA4Y","created_at":"2026-05-18T12:31:05.417338+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ADJIYA4Y4XC4ZXN4DM7L2VPYWK","json":"https://pith.science/pith/ADJIYA4Y4XC4ZXN4DM7L2VPYWK.json","graph_json":"https://pith.science/api/pith-number/ADJIYA4Y4XC4ZXN4DM7L2VPYWK/graph.json","events_json":"https://pith.science/api/pith-number/ADJIYA4Y4XC4ZXN4DM7L2VPYWK/events.json","paper":"https://pith.science/paper/ADJIYA4Y"},"agent_actions":{"view_html":"https://pith.science/pith/ADJIYA4Y4XC4ZXN4DM7L2VPYWK","download_json":"https://pith.science/pith/ADJIYA4Y4XC4ZXN4DM7L2VPYWK.json","view_paper":"https://pith.science/paper/ADJIYA4Y","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1706.06054&json=true","fetch_graph":"https://pith.science/api/pith-number/ADJIYA4Y4XC4ZXN4DM7L2VPYWK/graph.json","fetch_events":"https://pith.science/api/pith-number/ADJIYA4Y4XC4ZXN4DM7L2VPYWK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ADJIYA4Y4XC4ZXN4DM7L2VPYWK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ADJIYA4Y4XC4ZXN4DM7L2VPYWK/action/storage_attestation","attest_author":"https://pith.science/pith/ADJIYA4Y4XC4ZXN4DM7L2VPYWK/action/author_attestation","sign_citation":"https://pith.science/pith/ADJIYA4Y4XC4ZXN4DM7L2VPYWK/action/citation_signature","submit_replication":"https://pith.science/pith/ADJIYA4Y4XC4ZXN4DM7L2VPYWK/action/replication_record"}},"created_at":"2026-05-18T00:42:08.202669+00:00","updated_at":"2026-05-18T00:42:08.202669+00:00"}