{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:7UG7J4DVG2YIJCNZVZQUH6MWCB","short_pith_number":"pith:7UG7J4DV","schema_version":"1.0","canonical_sha256":"fd0df4f07536b08489b9ae6143f9961043141160110d4b79d18a7591377a0a60","source":{"kind":"arxiv","id":"1810.11749","version":1},"attestation_state":"computed","paper":{"title":"Iterative Hard Thresholding for Low-Rank Recovery from Rank-One Projections","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.NA","authors_text":"Simon Foucart, Srinivas Subramanian","submitted_at":"2018-10-28T02:25:19Z","abstract_excerpt":"A novel algorithm for the recovery of low-rank matrices acquired via compressive linear measurements is proposed and analyzed. The algorithm, a variation on the iterative hard thresholding algorithm for low-rank recovery, is designed to succeed in situations where the standard rank-restricted isometry property fails, e.g. in case of subexponential unstructured measurements or of subgaussian rank-one measurements. The stability and robustness of the algorithm are established based on distinctive matrix-analytic ingredients and its performance is substantiated numerically."},"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":"1810.11749","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2018-10-28T02:25:19Z","cross_cats_sorted":[],"title_canon_sha256":"82125ee83e945a4fb9bf5ec5517d2eb3f2ea87d4fbd51e6cbd0b0d345a9d66be","abstract_canon_sha256":"afe17a5fdacae88305da07ad1b737e80366e224f826fccdfb202dc49a0e2c24f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:02:07.530058Z","signature_b64":"mDGq2OPLqEawbMQF7WA4IYbYTvmApxuuKlOZt6hWatCdWzyx+orQMqnSr2l57XBDYPr08j6JXnubJeYdzHY9Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fd0df4f07536b08489b9ae6143f9961043141160110d4b79d18a7591377a0a60","last_reissued_at":"2026-05-18T00:02:07.529471Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:02:07.529471Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Iterative Hard Thresholding for Low-Rank Recovery from Rank-One Projections","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.NA","authors_text":"Simon Foucart, Srinivas Subramanian","submitted_at":"2018-10-28T02:25:19Z","abstract_excerpt":"A novel algorithm for the recovery of low-rank matrices acquired via compressive linear measurements is proposed and analyzed. The algorithm, a variation on the iterative hard thresholding algorithm for low-rank recovery, is designed to succeed in situations where the standard rank-restricted isometry property fails, e.g. in case of subexponential unstructured measurements or of subgaussian rank-one measurements. The stability and robustness of the algorithm are established based on distinctive matrix-analytic ingredients and its performance is substantiated numerically."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.11749","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":"1810.11749","created_at":"2026-05-18T00:02:07.529569+00:00"},{"alias_kind":"arxiv_version","alias_value":"1810.11749v1","created_at":"2026-05-18T00:02:07.529569+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.11749","created_at":"2026-05-18T00:02:07.529569+00:00"},{"alias_kind":"pith_short_12","alias_value":"7UG7J4DVG2YI","created_at":"2026-05-18T12:32:11.075285+00:00"},{"alias_kind":"pith_short_16","alias_value":"7UG7J4DVG2YIJCNZ","created_at":"2026-05-18T12:32:11.075285+00:00"},{"alias_kind":"pith_short_8","alias_value":"7UG7J4DV","created_at":"2026-05-18T12:32:11.075285+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/7UG7J4DVG2YIJCNZVZQUH6MWCB","json":"https://pith.science/pith/7UG7J4DVG2YIJCNZVZQUH6MWCB.json","graph_json":"https://pith.science/api/pith-number/7UG7J4DVG2YIJCNZVZQUH6MWCB/graph.json","events_json":"https://pith.science/api/pith-number/7UG7J4DVG2YIJCNZVZQUH6MWCB/events.json","paper":"https://pith.science/paper/7UG7J4DV"},"agent_actions":{"view_html":"https://pith.science/pith/7UG7J4DVG2YIJCNZVZQUH6MWCB","download_json":"https://pith.science/pith/7UG7J4DVG2YIJCNZVZQUH6MWCB.json","view_paper":"https://pith.science/paper/7UG7J4DV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1810.11749&json=true","fetch_graph":"https://pith.science/api/pith-number/7UG7J4DVG2YIJCNZVZQUH6MWCB/graph.json","fetch_events":"https://pith.science/api/pith-number/7UG7J4DVG2YIJCNZVZQUH6MWCB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7UG7J4DVG2YIJCNZVZQUH6MWCB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7UG7J4DVG2YIJCNZVZQUH6MWCB/action/storage_attestation","attest_author":"https://pith.science/pith/7UG7J4DVG2YIJCNZVZQUH6MWCB/action/author_attestation","sign_citation":"https://pith.science/pith/7UG7J4DVG2YIJCNZVZQUH6MWCB/action/citation_signature","submit_replication":"https://pith.science/pith/7UG7J4DVG2YIJCNZVZQUH6MWCB/action/replication_record"}},"created_at":"2026-05-18T00:02:07.529569+00:00","updated_at":"2026-05-18T00:02:07.529569+00:00"}