{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:JRKHZI6UW3LDPNN66SI7CAJHEW","short_pith_number":"pith:JRKHZI6U","schema_version":"1.0","canonical_sha256":"4c547ca3d4b6d637b5bef491f10127258ef605557ee52be775e73e438e27d95c","source":{"kind":"arxiv","id":"2402.00311","version":1},"attestation_state":"computed","paper":{"title":"A Tuning-Free Primal-Dual Splitting Algorithm for Large-Scale Semidefinite Programming","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Haixiang Lan, Yinjun Wang, Yinyu Ye","submitted_at":"2024-02-01T03:50:12Z","abstract_excerpt":"This paper proposes and analyzes a tuning-free variant of Primal-Dual Hybrid Gradient (PDHG), and investigates its effectiveness for solving large-scale semidefinite programming (SDP). The core idea is based on the combination of two seemingly unrelated results: (1) the equivalence of PDHG and Douglas-Rachford splitting (DRS); (2) the asymptotic convergence of non-stationary DRS. This combination provides a unified approach to analyze the convergence of generic adaptive PDHG, including the proposed tuning-free algorithm and various existing ones. Numerical experiments are conducted to show 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":"2402.00311","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2024-02-01T03:50:12Z","cross_cats_sorted":[],"title_canon_sha256":"7766eaaf2e0b4eec24440ba4fd5339d398f14c576bb02c5143857040f2175c9f","abstract_canon_sha256":"f066b05911e5276643cb9a9b8a67f640af1d500dbc3b7dc5e265ee0b60dbe5cb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:40:09.899354Z","signature_b64":"kHQJtVO3QBUkuiVcEIsGmgYrUjy4I8oAikqTlLzJSHaHM1KkzZ2YVQigDQyKACJj9J3WWwlecNrfm5r2sJ1CAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4c547ca3d4b6d637b5bef491f10127258ef605557ee52be775e73e438e27d95c","last_reissued_at":"2026-07-05T07:40:09.898854Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:40:09.898854Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Tuning-Free Primal-Dual Splitting Algorithm for Large-Scale Semidefinite Programming","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Haixiang Lan, Yinjun Wang, Yinyu Ye","submitted_at":"2024-02-01T03:50:12Z","abstract_excerpt":"This paper proposes and analyzes a tuning-free variant of Primal-Dual Hybrid Gradient (PDHG), and investigates its effectiveness for solving large-scale semidefinite programming (SDP). The core idea is based on the combination of two seemingly unrelated results: (1) the equivalence of PDHG and Douglas-Rachford splitting (DRS); (2) the asymptotic convergence of non-stationary DRS. This combination provides a unified approach to analyze the convergence of generic adaptive PDHG, including the proposed tuning-free algorithm and various existing ones. Numerical experiments are conducted to show the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.00311","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/2402.00311/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":"2402.00311","created_at":"2026-07-05T07:40:09.898915+00:00"},{"alias_kind":"arxiv_version","alias_value":"2402.00311v1","created_at":"2026-07-05T07:40:09.898915+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.00311","created_at":"2026-07-05T07:40:09.898915+00:00"},{"alias_kind":"pith_short_12","alias_value":"JRKHZI6UW3LD","created_at":"2026-07-05T07:40:09.898915+00:00"},{"alias_kind":"pith_short_16","alias_value":"JRKHZI6UW3LDPNN6","created_at":"2026-07-05T07:40:09.898915+00:00"},{"alias_kind":"pith_short_8","alias_value":"JRKHZI6U","created_at":"2026-07-05T07:40:09.898915+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.08035","citing_title":"Local Linear Convergence of the Primal-Dual Hybrid Gradient Method for Semidefinite Programming","ref_index":60,"is_internal_anchor":true},{"citing_arxiv_id":"2605.07113","citing_title":"Solving Max-Cut to Global Optimality via Feasibility-Preserving Graph Neural Networks","ref_index":81,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JRKHZI6UW3LDPNN66SI7CAJHEW","json":"https://pith.science/pith/JRKHZI6UW3LDPNN66SI7CAJHEW.json","graph_json":"https://pith.science/api/pith-number/JRKHZI6UW3LDPNN66SI7CAJHEW/graph.json","events_json":"https://pith.science/api/pith-number/JRKHZI6UW3LDPNN66SI7CAJHEW/events.json","paper":"https://pith.science/paper/JRKHZI6U"},"agent_actions":{"view_html":"https://pith.science/pith/JRKHZI6UW3LDPNN66SI7CAJHEW","download_json":"https://pith.science/pith/JRKHZI6UW3LDPNN66SI7CAJHEW.json","view_paper":"https://pith.science/paper/JRKHZI6U","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2402.00311&json=true","fetch_graph":"https://pith.science/api/pith-number/JRKHZI6UW3LDPNN66SI7CAJHEW/graph.json","fetch_events":"https://pith.science/api/pith-number/JRKHZI6UW3LDPNN66SI7CAJHEW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JRKHZI6UW3LDPNN66SI7CAJHEW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JRKHZI6UW3LDPNN66SI7CAJHEW/action/storage_attestation","attest_author":"https://pith.science/pith/JRKHZI6UW3LDPNN66SI7CAJHEW/action/author_attestation","sign_citation":"https://pith.science/pith/JRKHZI6UW3LDPNN66SI7CAJHEW/action/citation_signature","submit_replication":"https://pith.science/pith/JRKHZI6UW3LDPNN66SI7CAJHEW/action/replication_record"}},"created_at":"2026-07-05T07:40:09.898915+00:00","updated_at":"2026-07-05T07:40:09.898915+00:00"}