{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:5TPJTAPPDRX53WY2QATEZPSQTS","short_pith_number":"pith:5TPJTAPP","schema_version":"1.0","canonical_sha256":"ecde9981ef1c6fdddb1a80264cbe509c9c9c339dd2afff1066e06bd818146a55","source":{"kind":"arxiv","id":"2208.12432","version":2},"attestation_state":"computed","paper":{"title":"A proximal subgradient algorithm with extrapolation for structured nonconvex nonsmooth problems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Guoyin Li, Minh N. Dao, Nargiz Sultanova, Rakibuzzaman Shah, Syed Islam, Tan Nhat Pham","submitted_at":"2022-08-26T04:25:22Z","abstract_excerpt":"In this paper, we consider a class of structured nonconvex nonsmooth optimization problems, in which the objective function is formed by the sum of a possibly nonsmooth nonconvex function and a differentiable function whose gradient is Lipschitz continuous, subtracted by a weakly convex function. This type of structured problems has many practical applications in machine learning and statistics such as compressed sensing, signal recovery, sparse dictionary learning, clustering, matrix factorization, and others. We develop a flexible extrapolated proximal subgradient algorithm for solving these"},"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":"2208.12432","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2022-08-26T04:25:22Z","cross_cats_sorted":[],"title_canon_sha256":"420c2539e62d6d4f4082798e2673ffbe2c4393e97479dda85b7fe4c220ddfdb6","abstract_canon_sha256":"c13e99238db382ac02ab86014fa187792601f253339163526028214d99f3794f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:13:32.409090Z","signature_b64":"Yn6IqU2NztIZSViHRqIspy4fA1Reut2KG7tmbYkZVo0FH/1WtYmroK5Gcwzx4XSaBMm/MErvxAQFfSTW/0cNBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ecde9981ef1c6fdddb1a80264cbe509c9c9c339dd2afff1066e06bd818146a55","last_reissued_at":"2026-07-05T09:13:32.408697Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:13:32.408697Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A proximal subgradient algorithm with extrapolation for structured nonconvex nonsmooth problems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Guoyin Li, Minh N. Dao, Nargiz Sultanova, Rakibuzzaman Shah, Syed Islam, Tan Nhat Pham","submitted_at":"2022-08-26T04:25:22Z","abstract_excerpt":"In this paper, we consider a class of structured nonconvex nonsmooth optimization problems, in which the objective function is formed by the sum of a possibly nonsmooth nonconvex function and a differentiable function whose gradient is Lipschitz continuous, subtracted by a weakly convex function. This type of structured problems has many practical applications in machine learning and statistics such as compressed sensing, signal recovery, sparse dictionary learning, clustering, matrix factorization, and others. We develop a flexible extrapolated proximal subgradient algorithm for solving these"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2208.12432","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2208.12432/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2208.12432","created_at":"2026-07-05T09:13:32.408757+00:00"},{"alias_kind":"arxiv_version","alias_value":"2208.12432v2","created_at":"2026-07-05T09:13:32.408757+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2208.12432","created_at":"2026-07-05T09:13:32.408757+00:00"},{"alias_kind":"pith_short_12","alias_value":"5TPJTAPPDRX5","created_at":"2026-07-05T09:13:32.408757+00:00"},{"alias_kind":"pith_short_16","alias_value":"5TPJTAPPDRX53WY2","created_at":"2026-07-05T09:13:32.408757+00:00"},{"alias_kind":"pith_short_8","alias_value":"5TPJTAPP","created_at":"2026-07-05T09:13:32.408757+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/5TPJTAPPDRX53WY2QATEZPSQTS","json":"https://pith.science/pith/5TPJTAPPDRX53WY2QATEZPSQTS.json","graph_json":"https://pith.science/api/pith-number/5TPJTAPPDRX53WY2QATEZPSQTS/graph.json","events_json":"https://pith.science/api/pith-number/5TPJTAPPDRX53WY2QATEZPSQTS/events.json","paper":"https://pith.science/paper/5TPJTAPP"},"agent_actions":{"view_html":"https://pith.science/pith/5TPJTAPPDRX53WY2QATEZPSQTS","download_json":"https://pith.science/pith/5TPJTAPPDRX53WY2QATEZPSQTS.json","view_paper":"https://pith.science/paper/5TPJTAPP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2208.12432&json=true","fetch_graph":"https://pith.science/api/pith-number/5TPJTAPPDRX53WY2QATEZPSQTS/graph.json","fetch_events":"https://pith.science/api/pith-number/5TPJTAPPDRX53WY2QATEZPSQTS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5TPJTAPPDRX53WY2QATEZPSQTS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5TPJTAPPDRX53WY2QATEZPSQTS/action/storage_attestation","attest_author":"https://pith.science/pith/5TPJTAPPDRX53WY2QATEZPSQTS/action/author_attestation","sign_citation":"https://pith.science/pith/5TPJTAPPDRX53WY2QATEZPSQTS/action/citation_signature","submit_replication":"https://pith.science/pith/5TPJTAPPDRX53WY2QATEZPSQTS/action/replication_record"}},"created_at":"2026-07-05T09:13:32.408757+00:00","updated_at":"2026-07-05T09:13:32.408757+00:00"}