{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:TAOPHMTGDCZECLD33OW7B4DGJF","short_pith_number":"pith:TAOPHMTG","schema_version":"1.0","canonical_sha256":"981cf3b26618b2412c7bdbadf0f06649756c6ccaa71465fbdd9489b1426c8970","source":{"kind":"arxiv","id":"2607.03627","version":1},"attestation_state":"computed","paper":{"title":"Rapid Concurrent GPU-CPU Solvers for Scalable Unit Commitment in Large Power Grids","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Hussein Sharadga, Javad Mohammadi, Yuhan Du","submitted_at":"2026-07-03T23:01:18Z","abstract_excerpt":"This paper presents an accelerated solver for the unit commitment problem in large-scale power systems. The approach is based on the concurrent execution of GPU- and CPU-based optimization solvers on a single machine, with the solver that converges first terminating the other to minimize overall runtime. This strategy effectively harnesses the complementary strengths of different solvers. Convergence is further accelerated through a systematic and aggressive presolve approach. Numerical experiments on a 6,049-bus system with millions of decision variables and constraints demonstrate speedups r"},"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":"2607.03627","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2026-07-03T23:01:18Z","cross_cats_sorted":[],"title_canon_sha256":"d16e8b29a8d82929c05b0d97243f00692054e0ee439117d71bef15b3a0d9b355","abstract_canon_sha256":"a206ea3a10446d22ea09121d8fe2da6ef99292939a0ba3cc1a98e4a33a5befa6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-07T02:17:58.131021Z","signature_b64":"lyKp/oqHXT3Zrh5m8farWbGrWSarmnoIzDjrtkPLQAo+fQW7CeJKUTkOi7KecAbh4nWHc+TpgDCrtArp6V3ZCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"981cf3b26618b2412c7bdbadf0f06649756c6ccaa71465fbdd9489b1426c8970","last_reissued_at":"2026-07-07T02:17:58.130230Z","signature_status":"signed_v1","first_computed_at":"2026-07-07T02:17:58.130230Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Rapid Concurrent GPU-CPU Solvers for Scalable Unit Commitment in Large Power Grids","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Hussein Sharadga, Javad Mohammadi, Yuhan Du","submitted_at":"2026-07-03T23:01:18Z","abstract_excerpt":"This paper presents an accelerated solver for the unit commitment problem in large-scale power systems. The approach is based on the concurrent execution of GPU- and CPU-based optimization solvers on a single machine, with the solver that converges first terminating the other to minimize overall runtime. This strategy effectively harnesses the complementary strengths of different solvers. Convergence is further accelerated through a systematic and aggressive presolve approach. Numerical experiments on a 6,049-bus system with millions of decision variables and constraints demonstrate speedups r"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.03627","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/2607.03627/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":"2607.03627","created_at":"2026-07-07T02:17:58.130373+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.03627v1","created_at":"2026-07-07T02:17:58.130373+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.03627","created_at":"2026-07-07T02:17:58.130373+00:00"},{"alias_kind":"pith_short_12","alias_value":"TAOPHMTGDCZE","created_at":"2026-07-07T02:17:58.130373+00:00"},{"alias_kind":"pith_short_16","alias_value":"TAOPHMTGDCZECLD3","created_at":"2026-07-07T02:17:58.130373+00:00"},{"alias_kind":"pith_short_8","alias_value":"TAOPHMTG","created_at":"2026-07-07T02:17:58.130373+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/TAOPHMTGDCZECLD33OW7B4DGJF","json":"https://pith.science/pith/TAOPHMTGDCZECLD33OW7B4DGJF.json","graph_json":"https://pith.science/api/pith-number/TAOPHMTGDCZECLD33OW7B4DGJF/graph.json","events_json":"https://pith.science/api/pith-number/TAOPHMTGDCZECLD33OW7B4DGJF/events.json","paper":"https://pith.science/paper/TAOPHMTG"},"agent_actions":{"view_html":"https://pith.science/pith/TAOPHMTGDCZECLD33OW7B4DGJF","download_json":"https://pith.science/pith/TAOPHMTGDCZECLD33OW7B4DGJF.json","view_paper":"https://pith.science/paper/TAOPHMTG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.03627&json=true","fetch_graph":"https://pith.science/api/pith-number/TAOPHMTGDCZECLD33OW7B4DGJF/graph.json","fetch_events":"https://pith.science/api/pith-number/TAOPHMTGDCZECLD33OW7B4DGJF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TAOPHMTGDCZECLD33OW7B4DGJF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TAOPHMTGDCZECLD33OW7B4DGJF/action/storage_attestation","attest_author":"https://pith.science/pith/TAOPHMTGDCZECLD33OW7B4DGJF/action/author_attestation","sign_citation":"https://pith.science/pith/TAOPHMTGDCZECLD33OW7B4DGJF/action/citation_signature","submit_replication":"https://pith.science/pith/TAOPHMTGDCZECLD33OW7B4DGJF/action/replication_record"}},"created_at":"2026-07-07T02:17:58.130373+00:00","updated_at":"2026-07-07T02:17:58.130373+00:00"}