{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:VKUARX3IF2TBHDMJKYIWKVIFF6","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"a2e466fd6fb454350b3f15f246aecc117a24d5aa40962656306ec4011d841888","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2024-12-02T02:22:44Z","title_canon_sha256":"edc36745faa4a71170fdb135fe7bc22a451a9c17ce5b01a9ea85449c5b1ce9b2"},"schema_version":"1.0","source":{"id":"2412.01051","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.01051","created_at":"2026-07-05T09:42:52Z"},{"alias_kind":"arxiv_version","alias_value":"2412.01051v1","created_at":"2026-07-05T09:42:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.01051","created_at":"2026-07-05T09:42:52Z"},{"alias_kind":"pith_short_12","alias_value":"VKUARX3IF2TB","created_at":"2026-07-05T09:42:52Z"},{"alias_kind":"pith_short_16","alias_value":"VKUARX3IF2TBHDMJ","created_at":"2026-07-05T09:42:52Z"},{"alias_kind":"pith_short_8","alias_value":"VKUARX3I","created_at":"2026-07-05T09:42:52Z"}],"graph_snapshots":[{"event_id":"sha256:6d32c48d5b715c93f4de4edd892709981bf705469196eb97979a158667037902","target":"graph","created_at":"2026-07-05T09:42:52Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2412.01051/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Quadratic programs (QPs) arise in various domains such as machine learning, finance, and control. Recently, learning-enhanced primal-dual hybrid gradient (PDHG) methods have shown great potential in addressing large-scale linear programs; however, this approach has not been extended to QPs. In this work, we focus on unrolling \"PDQP\", a PDHG algorithm specialized for convex QPs. Specifically, we propose a neural network model called \"PDQP-net\" to learn optimal QP solutions. Theoretically, we demonstrate that a PDQP-net of polynomial size can align with the PDQP algorithm, returning optimal prim","authors_text":"Akang Wang, Bingheng Li, Jianghua Wu, Jiliang Tang, Linxin Yang, Ruoyu Sun, Tian Ding, Xiaodong Luo, Yuyi Wang","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2024-12-02T02:22:44Z","title":"An Efficient Unsupervised Framework for Convex Quadratic Programs via Deep Unrolling"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.01051","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:e85ac0c7998d680c3aaeb971966735a098dd7202853db300657a3056d5516b66","target":"record","created_at":"2026-07-05T09:42:52Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"a2e466fd6fb454350b3f15f246aecc117a24d5aa40962656306ec4011d841888","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2024-12-02T02:22:44Z","title_canon_sha256":"edc36745faa4a71170fdb135fe7bc22a451a9c17ce5b01a9ea85449c5b1ce9b2"},"schema_version":"1.0","source":{"id":"2412.01051","kind":"arxiv","version":1}},"canonical_sha256":"aaa808df682ea6138d8956116555052f9b2e249d6990f89d67d90ad51b2cb97a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"aaa808df682ea6138d8956116555052f9b2e249d6990f89d67d90ad51b2cb97a","first_computed_at":"2026-07-05T09:42:52.864417Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:42:52.864417Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"zct43I6mAdWNYp5tmXvJY0XZywUEJWhOOsQ1d7lulPGRFBsolgEk9ud/WtOtGLfHXOZ/0Ws57NDynfCejrD/Aw==","signature_status":"signed_v1","signed_at":"2026-07-05T09:42:52.864916Z","signed_message":"canonical_sha256_bytes"},"source_id":"2412.01051","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e85ac0c7998d680c3aaeb971966735a098dd7202853db300657a3056d5516b66","sha256:6d32c48d5b715c93f4de4edd892709981bf705469196eb97979a158667037902"],"state_sha256":"b44f8dac6957a56be05274b6afc6800d2a2f10ff20f572369fc796840c07e34f"}