{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:IHEXSVWH6ERWIORYX6SJDTEH5X","short_pith_number":"pith:IHEXSVWH","schema_version":"1.0","canonical_sha256":"41c97956c7f123643a38bfa491cc87ede79645c7d6cd9381d9a08d5d060864df","source":{"kind":"arxiv","id":"1402.5744","version":3},"attestation_state":"computed","paper":{"title":"Sparse Regularization: Convergence Of Iterative Jumping Thresholding Algorithm","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Jinshan Zeng, Shaobo Lin, Zongben Xu","submitted_at":"2014-02-24T08:18:53Z","abstract_excerpt":"In recent studies on sparse modeling, non-convex penalties have received considerable attentions due to their superiorities on sparsity-inducing over the convex counterparts. Compared with the convex optimization approaches, however, the non-convex approaches have more challenging convergence analysis. In this paper, we study the convergence of a non-convex iterative thresholding algorithm for solving sparse recovery problems with a certain class of non-convex penalties, whose corresponding thresholding functions are discontinuous with jump discontinuities. Therefore, we call the algorithm the"},"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":"1402.5744","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2014-02-24T08:18:53Z","cross_cats_sorted":[],"title_canon_sha256":"e0b1b8b5b298ea038a4d037c5267013cee53797a6d322c24ab2e78ded37a726f","abstract_canon_sha256":"f4d579288dab67b3f3ee17bfd9e100a2f5eeac51f686d5f4e84e6a0114190344"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:36:20.864192Z","signature_b64":"/OAAUxYROcgQu7CPFtaoyoFW8SQKZPa96o7xK8vJLvhUs/AZzr1wyDs8YMXODIIGrOL3f7egQfB1dsQd7TwwCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"41c97956c7f123643a38bfa491cc87ede79645c7d6cd9381d9a08d5d060864df","last_reissued_at":"2026-05-18T01:36:20.863729Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:36:20.863729Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Sparse Regularization: Convergence Of Iterative Jumping Thresholding Algorithm","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Jinshan Zeng, Shaobo Lin, Zongben Xu","submitted_at":"2014-02-24T08:18:53Z","abstract_excerpt":"In recent studies on sparse modeling, non-convex penalties have received considerable attentions due to their superiorities on sparsity-inducing over the convex counterparts. Compared with the convex optimization approaches, however, the non-convex approaches have more challenging convergence analysis. In this paper, we study the convergence of a non-convex iterative thresholding algorithm for solving sparse recovery problems with a certain class of non-convex penalties, whose corresponding thresholding functions are discontinuous with jump discontinuities. Therefore, we call the algorithm the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1402.5744","kind":"arxiv","version":3},"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":"1402.5744","created_at":"2026-05-18T01:36:20.863792+00:00"},{"alias_kind":"arxiv_version","alias_value":"1402.5744v3","created_at":"2026-05-18T01:36:20.863792+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1402.5744","created_at":"2026-05-18T01:36:20.863792+00:00"},{"alias_kind":"pith_short_12","alias_value":"IHEXSVWH6ERW","created_at":"2026-05-18T12:28:33.132498+00:00"},{"alias_kind":"pith_short_16","alias_value":"IHEXSVWH6ERWIORY","created_at":"2026-05-18T12:28:33.132498+00:00"},{"alias_kind":"pith_short_8","alias_value":"IHEXSVWH","created_at":"2026-05-18T12:28:33.132498+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/IHEXSVWH6ERWIORYX6SJDTEH5X","json":"https://pith.science/pith/IHEXSVWH6ERWIORYX6SJDTEH5X.json","graph_json":"https://pith.science/api/pith-number/IHEXSVWH6ERWIORYX6SJDTEH5X/graph.json","events_json":"https://pith.science/api/pith-number/IHEXSVWH6ERWIORYX6SJDTEH5X/events.json","paper":"https://pith.science/paper/IHEXSVWH"},"agent_actions":{"view_html":"https://pith.science/pith/IHEXSVWH6ERWIORYX6SJDTEH5X","download_json":"https://pith.science/pith/IHEXSVWH6ERWIORYX6SJDTEH5X.json","view_paper":"https://pith.science/paper/IHEXSVWH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1402.5744&json=true","fetch_graph":"https://pith.science/api/pith-number/IHEXSVWH6ERWIORYX6SJDTEH5X/graph.json","fetch_events":"https://pith.science/api/pith-number/IHEXSVWH6ERWIORYX6SJDTEH5X/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IHEXSVWH6ERWIORYX6SJDTEH5X/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IHEXSVWH6ERWIORYX6SJDTEH5X/action/storage_attestation","attest_author":"https://pith.science/pith/IHEXSVWH6ERWIORYX6SJDTEH5X/action/author_attestation","sign_citation":"https://pith.science/pith/IHEXSVWH6ERWIORYX6SJDTEH5X/action/citation_signature","submit_replication":"https://pith.science/pith/IHEXSVWH6ERWIORYX6SJDTEH5X/action/replication_record"}},"created_at":"2026-05-18T01:36:20.863792+00:00","updated_at":"2026-05-18T01:36:20.863792+00:00"}