{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:5QNK237TK5XGCOAOUAQQSZKDGY","short_pith_number":"pith:5QNK237T","schema_version":"1.0","canonical_sha256":"ec1aad6ff3576e61380ea021096543360d177af299ca24ad988bd6b7ebd4cfac","source":{"kind":"arxiv","id":"1905.05436","version":2},"attestation_state":"computed","paper":{"title":"Nonconvex fraction function recovery sparse signal by convex optimization algorithm","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Angang Cui, Haiyang Li, Jigen Peng, Meng Wen","submitted_at":"2019-05-14T08:00:09Z","abstract_excerpt":"In this paper, we will generate a convex iterative FP thresholding algorithm to solve the problem $(FP^{\\lambda}_{a})$. Two schemes of convex iterative FP thresholding algorithms are generated. One is convex iterative FP thresholding algorithm-Scheme 1 and the other is convex iterative FP thresholding algorithm-Scheme 2. A global convergence theorem is proved for the convex iterative FP thresholding algorithm-Scheme 1. Under an adaptive rule, the convex iterative FP thresholding algorithm-Scheme 2 will be adaptive both for the choice of the regularized parameter $\\lambda$ and parameter $a$. Th"},"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":"1905.05436","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2019-05-14T08:00:09Z","cross_cats_sorted":[],"title_canon_sha256":"39487b1d711e4f937a83b6ba705372a186cad64a111092139ee6d3529e1c4c9a","abstract_canon_sha256":"7b9e111b0194352dab196c2f2be311641e847ce82722c3ee4550fb13c589da57"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:44:53.912308Z","signature_b64":"ZaD0aUcwBNuU5FdKbcf7NXQtI3mmzgZyb+v8m+zpqIfY6MI75al3EuzuAiTWHe44Np4zSLywEbinYMQS70TpDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ec1aad6ff3576e61380ea021096543360d177af299ca24ad988bd6b7ebd4cfac","last_reissued_at":"2026-05-17T23:44:53.911848Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:44:53.911848Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Nonconvex fraction function recovery sparse signal by convex optimization algorithm","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Angang Cui, Haiyang Li, Jigen Peng, Meng Wen","submitted_at":"2019-05-14T08:00:09Z","abstract_excerpt":"In this paper, we will generate a convex iterative FP thresholding algorithm to solve the problem $(FP^{\\lambda}_{a})$. Two schemes of convex iterative FP thresholding algorithms are generated. One is convex iterative FP thresholding algorithm-Scheme 1 and the other is convex iterative FP thresholding algorithm-Scheme 2. A global convergence theorem is proved for the convex iterative FP thresholding algorithm-Scheme 1. Under an adaptive rule, the convex iterative FP thresholding algorithm-Scheme 2 will be adaptive both for the choice of the regularized parameter $\\lambda$ and parameter $a$. Th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.05436","kind":"arxiv","version":2},"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":"1905.05436","created_at":"2026-05-17T23:44:53.911921+00:00"},{"alias_kind":"arxiv_version","alias_value":"1905.05436v2","created_at":"2026-05-17T23:44:53.911921+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.05436","created_at":"2026-05-17T23:44:53.911921+00:00"},{"alias_kind":"pith_short_12","alias_value":"5QNK237TK5XG","created_at":"2026-05-18T12:33:10.108867+00:00"},{"alias_kind":"pith_short_16","alias_value":"5QNK237TK5XGCOAO","created_at":"2026-05-18T12:33:10.108867+00:00"},{"alias_kind":"pith_short_8","alias_value":"5QNK237T","created_at":"2026-05-18T12:33:10.108867+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/5QNK237TK5XGCOAOUAQQSZKDGY","json":"https://pith.science/pith/5QNK237TK5XGCOAOUAQQSZKDGY.json","graph_json":"https://pith.science/api/pith-number/5QNK237TK5XGCOAOUAQQSZKDGY/graph.json","events_json":"https://pith.science/api/pith-number/5QNK237TK5XGCOAOUAQQSZKDGY/events.json","paper":"https://pith.science/paper/5QNK237T"},"agent_actions":{"view_html":"https://pith.science/pith/5QNK237TK5XGCOAOUAQQSZKDGY","download_json":"https://pith.science/pith/5QNK237TK5XGCOAOUAQQSZKDGY.json","view_paper":"https://pith.science/paper/5QNK237T","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1905.05436&json=true","fetch_graph":"https://pith.science/api/pith-number/5QNK237TK5XGCOAOUAQQSZKDGY/graph.json","fetch_events":"https://pith.science/api/pith-number/5QNK237TK5XGCOAOUAQQSZKDGY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5QNK237TK5XGCOAOUAQQSZKDGY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5QNK237TK5XGCOAOUAQQSZKDGY/action/storage_attestation","attest_author":"https://pith.science/pith/5QNK237TK5XGCOAOUAQQSZKDGY/action/author_attestation","sign_citation":"https://pith.science/pith/5QNK237TK5XGCOAOUAQQSZKDGY/action/citation_signature","submit_replication":"https://pith.science/pith/5QNK237TK5XGCOAOUAQQSZKDGY/action/replication_record"}},"created_at":"2026-05-17T23:44:53.911921+00:00","updated_at":"2026-05-17T23:44:53.911921+00:00"}