{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:4AOARMB5WJ653HGPCULYTDX4VS","short_pith_number":"pith:4AOARMB5","schema_version":"1.0","canonical_sha256":"e01c08b03db27ddd9ccf1517898efcac86924f06fd47db0bc48a22139a3b9aa9","source":{"kind":"arxiv","id":"2407.15049","version":3},"attestation_state":"computed","paper":{"title":"Accelerating Low-Rank Factorization-Based Semidefinite Programming Algorithms on GPU","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Caihua Chen, Dongdong Ge, Hanwen Liu, Qi Deng, Qiushi Han, Yinyu Ye, Zhenwei Lin","submitted_at":"2024-07-21T04:21:40Z","abstract_excerpt":"In this paper, we address a long-standing challenge: how to achieve both efficiency and scalability in solving semidefinite programming problems. We propose breakthrough acceleration techniques for a wide range of low-rank factorization-based first-order methods using GPUs, making the computation much more efficient and scalable. To illustrate the idea and effectiveness of our approach, we use the low-rank factorization-based SDP solver, LoRADS, as an example, which involves both the classic Burer-Monterio method and a novel splitting scheme with a starting logarithmic rank. Our numerical resu"},"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":"2407.15049","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2024-07-21T04:21:40Z","cross_cats_sorted":[],"title_canon_sha256":"9704cc309d879e954041791b878b64fdca9fc8dcb69dab9798eee426feaaf7da","abstract_canon_sha256":"fb5b739642cb8c9e80cabfd0c5660bda32828fdce469d81bd7c88fcdd8a4c0e8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:58:45.679976Z","signature_b64":"OQ/9o65BCY61J9qBaaiEdgxya9ZscAwA/k8nQF4pOyobli6Ws3TOqudaVXDme4N5/aXU6wpzut2bapoB9SbXCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e01c08b03db27ddd9ccf1517898efcac86924f06fd47db0bc48a22139a3b9aa9","last_reissued_at":"2026-07-05T08:58:45.679319Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:58:45.679319Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Accelerating Low-Rank Factorization-Based Semidefinite Programming Algorithms on GPU","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Caihua Chen, Dongdong Ge, Hanwen Liu, Qi Deng, Qiushi Han, Yinyu Ye, Zhenwei Lin","submitted_at":"2024-07-21T04:21:40Z","abstract_excerpt":"In this paper, we address a long-standing challenge: how to achieve both efficiency and scalability in solving semidefinite programming problems. We propose breakthrough acceleration techniques for a wide range of low-rank factorization-based first-order methods using GPUs, making the computation much more efficient and scalable. To illustrate the idea and effectiveness of our approach, we use the low-rank factorization-based SDP solver, LoRADS, as an example, which involves both the classic Burer-Monterio method and a novel splitting scheme with a starting logarithmic rank. Our numerical resu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.15049","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2407.15049/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":"2407.15049","created_at":"2026-07-05T08:58:45.679398+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.15049v3","created_at":"2026-07-05T08:58:45.679398+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.15049","created_at":"2026-07-05T08:58:45.679398+00:00"},{"alias_kind":"pith_short_12","alias_value":"4AOARMB5WJ65","created_at":"2026-07-05T08:58:45.679398+00:00"},{"alias_kind":"pith_short_16","alias_value":"4AOARMB5WJ653HGP","created_at":"2026-07-05T08:58:45.679398+00:00"},{"alias_kind":"pith_short_8","alias_value":"4AOARMB5","created_at":"2026-07-05T08:58:45.679398+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.22188","citing_title":"From Sequential Nodes to GPU Batches: Parallel Branch and Bound for Optimal $k$-Sparse GLMs","ref_index":30,"is_internal_anchor":false},{"citing_arxiv_id":"2605.17883","citing_title":"On the convergence of doubly stochastic Primal-Dual Hybrid Gradient Method","ref_index":15,"is_internal_anchor":false},{"citing_arxiv_id":"2602.02394","citing_title":"On the Practical Implementation of a Sequential Quadratic Programming Algorithm for Nonconvex Sum-of-squares Problems","ref_index":12,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4AOARMB5WJ653HGPCULYTDX4VS","json":"https://pith.science/pith/4AOARMB5WJ653HGPCULYTDX4VS.json","graph_json":"https://pith.science/api/pith-number/4AOARMB5WJ653HGPCULYTDX4VS/graph.json","events_json":"https://pith.science/api/pith-number/4AOARMB5WJ653HGPCULYTDX4VS/events.json","paper":"https://pith.science/paper/4AOARMB5"},"agent_actions":{"view_html":"https://pith.science/pith/4AOARMB5WJ653HGPCULYTDX4VS","download_json":"https://pith.science/pith/4AOARMB5WJ653HGPCULYTDX4VS.json","view_paper":"https://pith.science/paper/4AOARMB5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.15049&json=true","fetch_graph":"https://pith.science/api/pith-number/4AOARMB5WJ653HGPCULYTDX4VS/graph.json","fetch_events":"https://pith.science/api/pith-number/4AOARMB5WJ653HGPCULYTDX4VS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4AOARMB5WJ653HGPCULYTDX4VS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4AOARMB5WJ653HGPCULYTDX4VS/action/storage_attestation","attest_author":"https://pith.science/pith/4AOARMB5WJ653HGPCULYTDX4VS/action/author_attestation","sign_citation":"https://pith.science/pith/4AOARMB5WJ653HGPCULYTDX4VS/action/citation_signature","submit_replication":"https://pith.science/pith/4AOARMB5WJ653HGPCULYTDX4VS/action/replication_record"}},"created_at":"2026-07-05T08:58:45.679398+00:00","updated_at":"2026-07-05T08:58:45.679398+00:00"}