{"as_of":"2026-08-10T06:21:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:70ab2b8526b0ab2985682dbec06d39b3e9a5d6fa94f71c963a59b692bdace855","coverage":[{"denominator":25,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":25,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-02T14:51:05.939664Z","state":"measured"},{"denominator":25,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":25,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2605.04605/citation-record","integrity":"/paper/2605.04605/integrity","json":"/paper/2605.04605/citation-record.json","paper":"/paper/2605.04605"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T14:51:05.843926Z","title":"Formalizing best practice for ene rgy system optimization modelling","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2605.04605","last_updated":"2026-07-23T13:19:50Z","snapshot_observed_at":"2026-08-02T22:21:28.608656Z","submitted_at":"2026-05-06T07:53:55Z","title":"Computational acceleration strategies for large-scale energy system optimization: a comparative study of GPU-accelerated and distributed-memory solvers","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-02T14:51:05.843926Z"},"links":{"citing_paper":"/paper/2605.04605"},"observation_digest":"sha256:754b48f486997643dec2fdcd94a0084e11a8cd92c5dc25f8477559a513bfe188","observation_id":"f75d3f11-7571-4e86-bdb1-1fb211f0b599","resolution":{"observed_at":"2026-08-02T14:51:05.843926Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T14:51:05.848741Z","title":"A review of approaches to uncertainty asses sment in energy system optimiza- tion models","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2605.04605","last_updated":"2026-07-23T13:19:50Z","snapshot_observed_at":"2026-08-02T22:21:28.608656Z","submitted_at":"2026-05-06T07:53:55Z","title":"Computational acceleration strategies for large-scale energy system optimization: a comparative study of GPU-accelerated and distributed-memory solvers","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-02T14:51:05.848741Z"},"links":{"citing_paper":"/paper/2605.04605"},"observation_digest":"sha256:0376c314b0e0eaca8fbfb429a9115f57ff4fe1df8ecf6899c96f60794196438f","observation_id":"08f08655-b7ba-49f8-b867-1689477b50b7","resolution":{"observed_at":"2026-08-02T14:51:05.848741Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T14:51:05.852372Z","title":"Chances and barriers for Germany’s low carbon transition – Quantifying uncertainties in key inﬂuential factors","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2605.04605","last_updated":"2026-07-23T13:19:50Z","snapshot_observed_at":"2026-08-02T22:21:28.608656Z","submitted_at":"2026-05-06T07:53:55Z","title":"Computational acceleration strategies for large-scale energy system optimization: a comparative study of GPU-accelerated and distributed-memory solvers","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-02T14:51:05.852372Z"},"links":{"citing_paper":"/paper/2605.04605"},"observation_digest":"sha256:d44bef2d562af90f203b9117d3d66cca336a7ef72e16b3cb840b439b6ba074c3","observation_id":"d7325a34-0a48-4af0-bdba-044c837efa18","resolution":{"observed_at":"2026-08-02T14:51:05.852372Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T14:51:05.856266Z","title":"Demand uncertainty in energy systems: scenario catalogs vs. integrated robust optimization","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.04605","last_updated":"2026-07-23T13:19:50Z","snapshot_observed_at":"2026-08-02T22:21:28.608656Z","submitted_at":"2026-05-06T07:53:55Z","title":"Computational acceleration strategies for large-scale energy system optimization: a comparative study of GPU-accelerated and distributed-memory solvers","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-02T14:51:05.856266Z"},"links":{"citing_paper":"/paper/2605.04605"},"observation_digest":"sha256:55f67366b9f830fa51d1bf9254f80130ceb59769cf82765b21aabb3adcd74e10","observation_id":"f3a77786-16d1-4918-87ec-1de60efb3e40","resolution":{"observed_at":"2026-08-02T14:51:05.856266Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T14:51:05.860245Z","title":"Sensitivity analysis of the energy tr ansition path in the Berlin-Brandenburg area to uncertainties in operational and investment costs o f diverse energy production tech- nologies","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.04605","last_updated":"2026-07-23T13:19:50Z","snapshot_observed_at":"2026-08-02T22:21:28.608656Z","submitted_at":"2026-05-06T07:53:55Z","title":"Computational acceleration strategies for large-scale energy system optimization: a comparative study of GPU-accelerated and distributed-memory solvers","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-02T14:51:05.860245Z"},"links":{"citing_paper":"/paper/2605.04605"},"observation_digest":"sha256:8ed7c610e92a4c6b3255bba395cb9db0c3a1b1dd39ebd53a8dbf3cfcbb7b35a5","observation_id":"e29c2e4d-9a64-483e-b21d-6aed8545bf45","resolution":{"observed_at":"2026-08-02T14:51:05.860245Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T14:51:05.864224Z","title":"Progress in mathematical programming sol vers from 2001 to 2020","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2605.04605","last_updated":"2026-07-23T13:19:50Z","snapshot_observed_at":"2026-08-02T22:21:28.608656Z","submitted_at":"2026-05-06T07:53:55Z","title":"Computational acceleration strategies for large-scale energy system optimization: a comparative study of GPU-accelerated and distributed-memory solvers","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-02T14:51:05.864224Z"},"links":{"citing_paper":"/paper/2605.04605"},"observation_digest":"sha256:f7e95dd3bd5f160492aca2de6dd1c8fb009d48a77af336ea7f343447463edc58","observation_id":"1387ce85-7002-4c7f-9a51-073834acb54c","resolution":{"observed_at":"2026-08-02T14:51:05.864224Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T14:51:05.869511Z","title":"Classiﬁcation and Evaluation of Concep ts for Improving the Performance of Applied Energy System Optimization Models","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2605.04605","last_updated":"2026-07-23T13:19:50Z","snapshot_observed_at":"2026-08-02T22:21:28.608656Z","submitted_at":"2026-05-06T07:53:55Z","title":"Computational acceleration strategies for large-scale energy system optimization: a comparative study of GPU-accelerated and distributed-memory solvers","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-02T14:51:05.869511Z"},"links":{"citing_paper":"/paper/2605.04605"},"observation_digest":"sha256:f387a2cd1fb31f0c21270a8dc1ef487dae3c917ab8d60f03302ee87f23f8b6e1","observation_id":"9400c543-9485-451a-9a77-87f5efe65a36","resolution":{"observed_at":"2026-08-02T14:51:05.869511Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T14:51:05.873328Z","title":"A modeler’s guide to handle complexity i n energy systems optimization","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2605.04605","last_updated":"2026-07-23T13:19:50Z","snapshot_observed_at":"2026-08-02T22:21:28.608656Z","submitted_at":"2026-05-06T07:53:55Z","title":"Computational acceleration strategies for large-scale energy system optimization: a comparative study of GPU-accelerated and distributed-memory solvers","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-02T14:51:05.873328Z"},"links":{"citing_paper":"/paper/2605.04605"},"observation_digest":"sha256:230b635d74ce20652d45ccfce9dca4caf440e4e610797a40089611d752775f40","observation_id":"3886d7b0-331e-490d-b59a-cc99b40f37af","resolution":{"observed_at":"2026-08-02T14:51:05.873328Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T14:51:05.877351Z","title":"Impact of different time series aggrega tion methods on optimal energy sys- tem design","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2605.04605","last_updated":"2026-07-23T13:19:50Z","snapshot_observed_at":"2026-08-02T22:21:28.608656Z","submitted_at":"2026-05-06T07:53:55Z","title":"Computational acceleration strategies for large-scale energy system optimization: a comparative study of GPU-accelerated and distributed-memory solvers","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-02T14:51:05.877351Z"},"links":{"citing_paper":"/paper/2605.04605"},"observation_digest":"sha256:e018773700711836d1ad8bbf0ba6ca1f45f54c01b57a7285fbcf5fb496087f79","observation_id":"8025cedb-71eb-4af5-903c-3da90c19a8f0","resolution":{"observed_at":"2026-08-02T14:51:05.877351Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T14:51:05.881924Z","title":"A massively parallel interior-poin t solver for LPs with generalized ar- rowhead structure, and applications to energy system model s","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2605.04605","last_updated":"2026-07-23T13:19:50Z","snapshot_observed_at":"2026-08-02T22:21:28.608656Z","submitted_at":"2026-05-06T07:53:55Z","title":"Computational acceleration strategies for large-scale energy system optimization: a comparative study of GPU-accelerated and distributed-memory solvers","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-02T14:51:05.881924Z"},"links":{"citing_paper":"/paper/2605.04605"},"observation_digest":"sha256:d4e7fcf412b44191fd3023626021780b6b6a56527a53ce33c8713c405a911ac1","observation_id":"2c524c50-eba5-4e28-96d0-1e9dbe53881e","resolution":{"observed_at":"2026-08-02T14:51:05.881924Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.07731","last_updated":"2026-04-09T08:16:15Z","snapshot_observed_at":"2026-07-06T20:04:48.422043Z","submitted_at":"2024-12-10T18:27:09Z","title":"A Massively Parallel Interior-Point Method for Arrowhead Linear Programs with Local Linking Structure","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.07731","snapshot_observed_at":"2026-08-02T14:51:05.885564Z","title":"A Massively Par allel Interior-Point Method for Arrowhead Linear Programs","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.04605","last_updated":"2026-07-23T13:19:50Z","snapshot_observed_at":"2026-08-02T22:21:28.608656Z","submitted_at":"2026-05-06T07:53:55Z","title":"Computational acceleration strategies for large-scale energy system optimization: a comparative study of GPU-accelerated and distributed-memory solvers","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-02T14:51:05.885564Z"},"links":{"cited_paper":"/paper/2412.07731","citing_paper":"/paper/2605.04605"},"observation_digest":"sha256:fd380de869abbc68078b04360a2139cab5f50dbbd5d4e96e0ff859cd5c2669e1","observation_id":"647921f6-2565-4054-a8b6-a8d1fdd03240","resolution":{"observed_at":"2026-08-02T14:51:05.885564Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T14:51:05.889734Z","title":"Wetzel, K.-K","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.04605","last_updated":"2026-07-23T13:19:50Z","snapshot_observed_at":"2026-08-02T22:21:28.608656Z","submitted_at":"2026-05-06T07:53:55Z","title":"Computational acceleration strategies for large-scale energy system optimization: a comparative study of GPU-accelerated and distributed-memory solvers","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-02T14:51:05.889734Z"},"links":{"citing_paper":"/paper/2605.04605"},"observation_digest":"sha256:00d174f05b46a657ade4bb7eba3c2d73335c1f5d51c31a5f8b6724fd34f6425d","observation_id":"5b1c6ba5-79a8-4dd1-ad2e-d957e3275909","resolution":{"observed_at":"2026-08-02T14:51:05.889734Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T14:51:05.893388Z","title":"High-Performance Robust Energy System P lanning with Storage: A Single- LP Approach","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2605.04605","last_updated":"2026-07-23T13:19:50Z","snapshot_observed_at":"2026-08-02T22:21:28.608656Z","submitted_at":"2026-05-06T07:53:55Z","title":"Computational acceleration strategies for large-scale energy system optimization: a comparative study of GPU-accelerated and distributed-memory solvers","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-02T14:51:05.893388Z"},"links":{"citing_paper":"/paper/2605.04605"},"observation_digest":"sha256:97ee5267fe4cfdc49104a6f520096a40eab4ee3b0f14f92e895f9aa5eb789bf6","observation_id":"fbbc4a71-3b51-4c8e-8991-259e841127a0","resolution":{"observed_at":"2026-08-02T14:51:05.893388Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T14:51:05.897426Z","title":"PDLP: A Practical First-Order Meth od for Large-Scale Linear Pro- gramming","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.04605","last_updated":"2026-07-23T13:19:50Z","snapshot_observed_at":"2026-08-02T22:21:28.608656Z","submitted_at":"2026-05-06T07:53:55Z","title":"Computational acceleration strategies for large-scale energy system optimization: a comparative study of GPU-accelerated and distributed-memory solvers","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-02T14:51:05.897426Z"},"links":{"citing_paper":"/paper/2605.04605"},"observation_digest":"sha256:5dd6084a3db49ecd2fd95026a17ecb186b30f56f40e506da5f4d781147edf0dc","observation_id":"dd6eb38e-0a6d-41f6-be82-b1badfb11c56","resolution":{"observed_at":"2026-08-02T14:51:05.897426Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T14:51:05.901422Z","title":"HPR-LP: An implementation of an HPR metho d for solving linear program- ming","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.04605","last_updated":"2026-07-23T13:19:50Z","snapshot_observed_at":"2026-08-02T22:21:28.608656Z","submitted_at":"2026-05-06T07:53:55Z","title":"Computational acceleration strategies for large-scale energy system optimization: a comparative study of GPU-accelerated and distributed-memory solvers","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-02T14:51:05.901422Z"},"links":{"citing_paper":"/paper/2605.04605"},"observation_digest":"sha256:d3cfe429b332ce583d9eeb64590f995b3f68624aefa9f73c5d68c573e58efab6","observation_id":"688e29f0-fb26-4947-ae7f-d2fbae932618","resolution":{"observed_at":"2026-08-02T14:51:05.901422Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.17996","last_updated":"2025-04-03T01:55:49Z","snapshot_observed_at":"2026-08-10T04:23:58.925882Z","submitted_at":"2025-01-29T21:07:29Z","title":"Solving Large Multicommodity Network Flow Problems on GPUs","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.17996","snapshot_observed_at":"2026-08-02T14:51:05.905399Z","title":"Solving Large Multicommodity Net work Flow Problems on GPUs","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.04605","last_updated":"2026-07-23T13:19:50Z","snapshot_observed_at":"2026-08-02T22:21:28.608656Z","submitted_at":"2026-05-06T07:53:55Z","title":"Computational acceleration strategies for large-scale energy system optimization: a comparative study of GPU-accelerated and distributed-memory solvers","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-02T14:51:05.905399Z"},"links":{"cited_paper":"/paper/2501.17996","citing_paper":"/paper/2605.04605"},"observation_digest":"sha256:bddf4109397894cfcb27ac5aa1b4dc7b4d4b0cda3428dfbb14703a5a809c6728","observation_id":"4844ee0a-c9c0-46be-bc61-5e679d04eafe","resolution":{"observed_at":"2026-08-02T14:51:05.905399Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T14:51:05.909519Z","title":"Low-precision ﬁrst-order me thod-based ﬁx-and-propagate heuristics for large-scale mixed-integer linear optimization","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.04605","last_updated":"2026-07-23T13:19:50Z","snapshot_observed_at":"2026-08-02T22:21:28.608656Z","submitted_at":"2026-05-06T07:53:55Z","title":"Computational acceleration strategies for large-scale energy system optimization: a comparative study of GPU-accelerated and distributed-memory solvers","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-02T14:51:05.909519Z"},"links":{"citing_paper":"/paper/2605.04605"},"observation_digest":"sha256:39975b08fe83569bb71efe53bd0b946a84c53727b5df1f46072b1c1391b8bc75","observation_id":"465fe754-84f1-4152-8b91-fc355abef0e5","resolution":{"observed_at":"2026-08-02T14:51:05.909519Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T14:51:05.913503Z","title":"Parallelizing the dual revised s implex method","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2605.04605","last_updated":"2026-07-23T13:19:50Z","snapshot_observed_at":"2026-08-02T22:21:28.608656Z","submitted_at":"2026-05-06T07:53:55Z","title":"Computational acceleration strategies for large-scale energy system optimization: a comparative study of GPU-accelerated and distributed-memory solvers","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-02T14:51:05.913503Z"},"links":{"citing_paper":"/paper/2605.04605"},"observation_digest":"sha256:691971efbf99203a63dc89fc10f895a5ec25f6f36cb56a2087254fd414b79c52","observation_id":"f5e9c01a-0896-4bc9-98d3-969192023b95","resolution":{"observed_at":"2026-08-02T14:51:05.913503Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T14:51:05.917038Z","title":"Available at https://github.com/NVIDIA/cuopt, [accessed 26.03.2026]","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2605.04605","last_updated":"2026-07-23T13:19:50Z","snapshot_observed_at":"2026-08-02T22:21:28.608656Z","submitted_at":"2026-05-06T07:53:55Z","title":"Computational acceleration strategies for large-scale energy system optimization: a comparative study of GPU-accelerated and distributed-memory solvers","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-02T14:51:05.917038Z"},"links":{"citing_paper":"/paper/2605.04605"},"observation_digest":"sha256:b8f1e4307497e1482c252413582383fd218a62f90804c97995caf2634f270c5d","observation_id":"807995ec-0579-4319-9b69-a989570aa8fb","resolution":{"observed_at":"2026-08-02T14:51:05.917038Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T14:51:05.921156Z","title":"The Ubiquity Generator Framework: 7 Y ears of Progress in Parallelizing Branch- and-Bound","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2605.04605","last_updated":"2026-07-23T13:19:50Z","snapshot_observed_at":"2026-08-02T22:21:28.608656Z","submitted_at":"2026-05-06T07:53:55Z","title":"Computational acceleration strategies for large-scale energy system optimization: a comparative study of GPU-accelerated and distributed-memory solvers","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-02T14:51:05.921156Z"},"links":{"citing_paper":"/paper/2605.04605"},"observation_digest":"sha256:d80a25dc0e5bd43834d2baf257a53626440bbb8edaa154ce583f5a5b2d3ff012","observation_id":"ee476d3a-1dc4-4a1e-904e-2ab0b45948bb","resolution":{"observed_at":"2026-08-02T14:51:05.921156Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T14:51:05.925583Z","title":"Zittel et al","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2605.04605","last_updated":"2026-07-23T13:19:50Z","snapshot_observed_at":"2026-08-02T22:21:28.608656Z","submitted_at":"2026-05-06T07:53:55Z","title":"Computational acceleration strategies for large-scale energy system optimization: a comparative study of GPU-accelerated and distributed-memory solvers","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-02T14:51:05.925583Z"},"links":{"citing_paper":"/paper/2605.04605"},"observation_digest":"sha256:a0c0b8a770e87b13f2bfda977cb6e054536332879470154cb0f64be78e6eabb3","observation_id":"a00fe85c-9793-4131-8cfe-87c0f6006136","resolution":{"observed_at":"2026-08-02T14:51:05.925583Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T14:51:05.929102Z","title":null,"venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2605.04605","last_updated":"2026-07-23T13:19:50Z","snapshot_observed_at":"2026-08-02T22:21:28.608656Z","submitted_at":"2026-05-06T07:53:55Z","title":"Computational acceleration strategies for large-scale energy system optimization: a comparative study of GPU-accelerated and distributed-memory solvers","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-02T14:51:05.929102Z"},"links":{"citing_paper":"/paper/2605.04605"},"observation_digest":"sha256:e432f57674d67e7bcd549aba98f6c0b26f8e274f715a873f05eae3658a702d76","observation_id":"78bbf564-cbf1-483f-b796-b1d5b812dbd2","resolution":{"observed_at":"2026-08-02T14:51:05.929102Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T14:51:05.932307Z","title":"Scalable high-quality hypergr aph partitioning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.04605","last_updated":"2026-07-23T13:19:50Z","snapshot_observed_at":"2026-08-02T22:21:28.608656Z","submitted_at":"2026-05-06T07:53:55Z","title":"Computational acceleration strategies for large-scale energy system optimization: a comparative study of GPU-accelerated and distributed-memory solvers","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-02T14:51:05.932307Z"},"links":{"citing_paper":"/paper/2605.04605"},"observation_digest":"sha256:62a4cc96242169f4bfa973bc616c6b29a7e95a24d6581c6eb93488524d78b0d4","observation_id":"ff65bf14-5505-4ee2-94d1-32b44619e8f3","resolution":{"observed_at":"2026-08-02T14:51:05.932307Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T14:51:05.935810Z","title":"Solving unsymmetric sparse systems of linear equations with PARDISO","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2605.04605","last_updated":"2026-07-23T13:19:50Z","snapshot_observed_at":"2026-08-02T22:21:28.608656Z","submitted_at":"2026-05-06T07:53:55Z","title":"Computational acceleration strategies for large-scale energy system optimization: a comparative study of GPU-accelerated and distributed-memory solvers","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-02T14:51:05.935810Z"},"links":{"citing_paper":"/paper/2605.04605"},"observation_digest":"sha256:11fb36c3b6154e0647c060acad299a9409c3834c39048b396a8fada3a5cecd4b","observation_id":"40c27db9-e1fd-47c1-8a68-69a4ed316e64","resolution":{"observed_at":"2026-08-02T14:51:05.935810Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T14:51:05.939664Z","title":"MA57—a code for the solution of sparse symme tric deﬁnite and indeﬁnite sys- tems","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2605.04605","last_updated":"2026-07-23T13:19:50Z","snapshot_observed_at":"2026-08-02T22:21:28.608656Z","submitted_at":"2026-05-06T07:53:55Z","title":"Computational acceleration strategies for large-scale energy system optimization: a comparative study of GPU-accelerated and distributed-memory solvers","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-02T14:51:05.939664Z"},"links":{"citing_paper":"/paper/2605.04605"},"observation_digest":"sha256:176ff699d6b3ab681add6a3c669baeb3976a9c77250103e3b8f92c3a5fefc49d","observation_id":"79131e79-39bf-41e8-8b0d-cd63131ca2ad","resolution":{"observed_at":"2026-08-02T14:51:05.939664Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2605.04605","last_updated":"2026-07-23T13:19:50Z","latest_version":2,"primary_category":"math.OC","snapshot_observed_at":"2026-08-02T22:21:28.608656Z","submitted_at":"2026-05-06T07:53:55Z","title":"Computational acceleration strategies for large-scale energy system optimization: a comparative study of GPU-accelerated and distributed-memory solvers"},"reference_resolution":{"displayed":25,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":24,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":25},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 0 inbound Pith citation observations for arXiv:2605.04605."}