{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:FSBFXCRWIXGZQHZQIR4YXWI7JB","short_pith_number":"pith:FSBFXCRW","schema_version":"1.0","canonical_sha256":"2c825b8a3645cd981f3044798bd91f48569be743554a7c39bafa7db17fcdfadd","source":{"kind":"arxiv","id":"2303.17192","version":1},"attestation_state":"computed","paper":{"title":"Sublinear Convergence Rates of Extragradient-Type Methods: A Survey on Classical and Recent Developments","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"math.OC","authors_text":"Quoc Tran-Dinh","submitted_at":"2023-03-30T07:04:22Z","abstract_excerpt":"The extragradient (EG), introduced by G. M. Korpelevich in 1976, is a well-known method to approximate solutions of saddle-point problems and their extensions such as variational inequalities and monotone inclusions. Over the years, numerous variants of EG have been proposed and studied in the literature. Recently, these methods have gained popularity due to new applications in machine learning and robust optimization. In this work, we survey the latest developments in the EG method and its variants for approximating solutions of nonlinear equations and inclusions, with a focus on the monotoni"},"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":"2303.17192","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2023-03-30T07:04:22Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"d160b1c83028fa8a583fdf028390ddbfa8cd445661c6b55faae814edc9240c57","abstract_canon_sha256":"8a3d1a5b9b54df339079a2637078af76ff791f07a31a7f4d4038f26c5cebe9ba"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:56:20.617717Z","signature_b64":"Kw9dMr6MUdTT+hqIy1OKaJJV+TSHEBJpRfTN/1kFO/cBJ2ekeNuw7U8lTN91IsSCvKqx3LDUwSK6JMQ5ijbKDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2c825b8a3645cd981f3044798bd91f48569be743554a7c39bafa7db17fcdfadd","last_reissued_at":"2026-07-05T05:56:20.617122Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:56:20.617122Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Sublinear Convergence Rates of Extragradient-Type Methods: A Survey on Classical and Recent Developments","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"math.OC","authors_text":"Quoc Tran-Dinh","submitted_at":"2023-03-30T07:04:22Z","abstract_excerpt":"The extragradient (EG), introduced by G. M. Korpelevich in 1976, is a well-known method to approximate solutions of saddle-point problems and their extensions such as variational inequalities and monotone inclusions. Over the years, numerous variants of EG have been proposed and studied in the literature. Recently, these methods have gained popularity due to new applications in machine learning and robust optimization. In this work, we survey the latest developments in the EG method and its variants for approximating solutions of nonlinear equations and inclusions, with a focus on the monotoni"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.17192","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2303.17192/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":"2303.17192","created_at":"2026-07-05T05:56:20.617192+00:00"},{"alias_kind":"arxiv_version","alias_value":"2303.17192v1","created_at":"2026-07-05T05:56:20.617192+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.17192","created_at":"2026-07-05T05:56:20.617192+00:00"},{"alias_kind":"pith_short_12","alias_value":"FSBFXCRWIXGZ","created_at":"2026-07-05T05:56:20.617192+00:00"},{"alias_kind":"pith_short_16","alias_value":"FSBFXCRWIXGZQHZQ","created_at":"2026-07-05T05:56:20.617192+00:00"},{"alias_kind":"pith_short_8","alias_value":"FSBFXCRW","created_at":"2026-07-05T05:56:20.617192+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.07585","citing_title":"Convergence Analysis of the Restarted Moving-Anchored Extra-Gradient Method in the Absence of Local Lipschitz Continuity","ref_index":37,"is_internal_anchor":true},{"citing_arxiv_id":"2606.22392","citing_title":"Convergence Rates of Tseng's Splitting Method and Its Acceleration Schemes for Monotone Inclusion Problem with a Sum of H\\\"older Continuous Operators","ref_index":41,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FSBFXCRWIXGZQHZQIR4YXWI7JB","json":"https://pith.science/pith/FSBFXCRWIXGZQHZQIR4YXWI7JB.json","graph_json":"https://pith.science/api/pith-number/FSBFXCRWIXGZQHZQIR4YXWI7JB/graph.json","events_json":"https://pith.science/api/pith-number/FSBFXCRWIXGZQHZQIR4YXWI7JB/events.json","paper":"https://pith.science/paper/FSBFXCRW"},"agent_actions":{"view_html":"https://pith.science/pith/FSBFXCRWIXGZQHZQIR4YXWI7JB","download_json":"https://pith.science/pith/FSBFXCRWIXGZQHZQIR4YXWI7JB.json","view_paper":"https://pith.science/paper/FSBFXCRW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2303.17192&json=true","fetch_graph":"https://pith.science/api/pith-number/FSBFXCRWIXGZQHZQIR4YXWI7JB/graph.json","fetch_events":"https://pith.science/api/pith-number/FSBFXCRWIXGZQHZQIR4YXWI7JB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FSBFXCRWIXGZQHZQIR4YXWI7JB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FSBFXCRWIXGZQHZQIR4YXWI7JB/action/storage_attestation","attest_author":"https://pith.science/pith/FSBFXCRWIXGZQHZQIR4YXWI7JB/action/author_attestation","sign_citation":"https://pith.science/pith/FSBFXCRWIXGZQHZQIR4YXWI7JB/action/citation_signature","submit_replication":"https://pith.science/pith/FSBFXCRWIXGZQHZQIR4YXWI7JB/action/replication_record"}},"created_at":"2026-07-05T05:56:20.617192+00:00","updated_at":"2026-07-05T05:56:20.617192+00:00"}