{"as_of":"2026-08-10T23:17:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e4884b11bebb94a65107ef959439429e726ea4b99d335f0c90faf0ddf99d4cf1","coverage":[{"denominator":35,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":35,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-04T21:04:57.235709Z","state":"measured"},{"denominator":36,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":36,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-20T04:55:27.851849Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-20T04:58:05.087047Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2509.08256","last_updated":"2025-09-10T03:25:56Z","snapshot_observed_at":"2026-08-10T01:21:15.509381Z","submitted_at":"2025-09-10T03:25:56Z","title":"Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy","version":1},"cited_work":{"arxiv_id":"2509.08256","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2509.08256","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"He and P","venue":null,"work_id":"717136a6-884e-416d-bbb2-2e3d14d15d65","year":2025},"citing_paper":{"arxiv_id":"2605.19396","last_updated":"2026-05-19T05:44:34Z","snapshot_observed_at":"2026-07-06T23:30:06.876413Z","submitted_at":"2026-05-19T05:44:34Z","title":"Distributed Gradient-Regularized Newton Method: Scheduled Consensus and O(epsilon^{-1}) Global Iteration Complexity","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-05-20T04:55:27.851849Z"},"links":{"cited_paper":"/paper/2509.08256","citing_paper":"/paper/2605.19396"},"observation_digest":"sha256:4f1e032082764096be7edb956db13221c69689871925b0d730de4bfe72e27899","observation_id":"0da372c3-27f6-46d8-8972-34f7c985171f","resolution":{"observed_at":"2026-05-20T04:58:05.090559Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2509.08256/citation-record","integrity":"/paper/2509.08256/integrity","json":"/paper/2509.08256/citation-record.json","paper":"/paper/2509.08256"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:04:57.145509Z","title":"Gheribi, Michael Kokkolaras, and S´ ebastien Le Digabel","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.08256","last_updated":"2025-09-10T03:25:56Z","snapshot_observed_at":"2026-08-10T01:21:15.509381Z","submitted_at":"2025-09-10T03:25:56Z","title":"Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-04T21:04:57.145509Z"},"links":{"citing_paper":"/paper/2509.08256"},"observation_digest":"sha256:afb628b4c698a6e285991771b4905c48e81dcf188eac7ed8529f8468c415dff5","observation_id":"6cc2486e-a34c-4128-8b17-9a53a2fe1be8","resolution":{"observed_at":"2026-08-04T21:04:57.145509Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:04:57.605594Z","title":"Quadratic Models","venue":null,"work_id":"5149a283-094a-415a-87df-b93d61e4d15a","year":2017},"citing_paper":{"arxiv_id":"2509.08256","last_updated":"2025-09-10T03:25:56Z","snapshot_observed_at":"2026-08-10T01:21:15.509381Z","submitted_at":"2025-09-10T03:25:56Z","title":"Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-04T21:04:57.148629Z"},"links":{"citing_paper":"/paper/2509.08256"},"observation_digest":"sha256:979cf22f94892f0dc989b706bb7cbc6369f6042827097a08853bc9c9a2b1b1dd","observation_id":"dac601a2-5d3d-46b1-b38b-426f3cd55b7a","resolution":{"observed_at":"2026-08-04T21:04:57.608573Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/s10107-022-01836-1","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Scalable subspace methods for derivative- free nonlinear least-squares optimization.Math","venue":"Mathematical Programming","work_id":"2662a6a9-487f-4e7e-b779-9525b44063bf","year":2023},"citing_paper":{"arxiv_id":"2509.08256","last_updated":"2025-09-10T03:25:56Z","snapshot_observed_at":"2026-08-10T01:21:15.509381Z","submitted_at":"2025-09-10T03:25:56Z","title":"Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-04T21:04:57.151373Z"},"links":{"citing_paper":"/paper/2509.08256"},"observation_digest":"sha256:204798e9588f696acd354dce235e1aa1acee59a4095e4f4e8c4468daf3c41814","observation_id":"de29ae58-4f7f-4f05-8890-9202f342d76a","resolution":{"observed_at":"2026-08-04T21:04:57.341811Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:04:57.154257Z","title":"Adaptive cubic regularisation methods for unconstrained optimization","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2509.08256","last_updated":"2025-09-10T03:25:56Z","snapshot_observed_at":"2026-08-10T01:21:15.509381Z","submitted_at":"2025-09-10T03:25:56Z","title":"Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-04T21:04:57.154257Z"},"links":{"citing_paper":"/paper/2509.08256"},"observation_digest":"sha256:20c3701df699c1fadee9fe3c4adc20afa0bd834223f4952d032179beb0ba7fbf","observation_id":"355dd6b0-1b9e-4094-b6fa-16a2cc9a12f0","resolution":{"observed_at":"2026-08-04T21:04:57.154257Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2211.05727","last_updated":"2022-11-10T17:51:08Z","snapshot_observed_at":"2026-08-10T01:20:42.043105Z","submitted_at":"2022-11-10T17:51:08Z","title":"A Randomised Subspace Gauss-Newton Method for Nonlinear Least-Squares","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.05727","snapshot_observed_at":"2026-08-04T21:04:57.157152Z","title":"A randomised subspace gauss-newton method for nonlinear least-squares, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.08256","last_updated":"2025-09-10T03:25:56Z","snapshot_observed_at":"2026-08-10T01:21:15.509381Z","submitted_at":"2025-09-10T03:25:56Z","title":"Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-04T21:04:57.157152Z"},"links":{"cited_paper":"/paper/2211.05727","citing_paper":"/paper/2509.08256"},"observation_digest":"sha256:476d99fcc247a14783b973ef716088c723b4dd97118f619c3521a0b6c4f8663b","observation_id":"4744d7c8-ca1a-495c-93d5-f2aa57307284","resolution":{"observed_at":"2026-08-04T21:04:57.157152Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.09734","last_updated":"2025-01-16T18:37:59Z","snapshot_observed_at":"2026-08-10T19:39:56.759991Z","submitted_at":"2025-01-16T18:37:59Z","title":"Random Subspace Cubic-Regularization Methods, with Applications to Low-Rank Functions","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.09734","snapshot_observed_at":"2026-08-04T21:04:57.160160Z","title":"Random subspace cubic-regularization methods, with applications to low-rank functions, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.08256","last_updated":"2025-09-10T03:25:56Z","snapshot_observed_at":"2026-08-10T01:21:15.509381Z","submitted_at":"2025-09-10T03:25:56Z","title":"Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-04T21:04:57.160160Z"},"links":{"cited_paper":"/paper/2501.09734","citing_paper":"/paper/2509.08256"},"observation_digest":"sha256:c3af186861fa8670ae591cf385d3fc4c6244abbae44235f7910bdeff496420ff","observation_id":"1a0d238c-cc6a-4e8a-966c-e949194e1c46","resolution":{"observed_at":"2026-08-04T21:04:57.160160Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:04:57.596802Z","title":"Q-fully quadratic modeling and its applica- tion in a random subspace derivative-free method.Comput","venue":null,"work_id":"4091ca7a-1a54-40be-a63e-b53194981c2d","year":2024},"citing_paper":{"arxiv_id":"2509.08256","last_updated":"2025-09-10T03:25:56Z","snapshot_observed_at":"2026-08-10T01:21:15.509381Z","submitted_at":"2025-09-10T03:25:56Z","title":"Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-04T21:04:57.163291Z"},"links":{"citing_paper":"/paper/2509.08256"},"observation_digest":"sha256:d177709cc48a8d3333295eff4256289988301be2910163aaffcaf0dbea900952","observation_id":"ad4e5be9-fccc-478b-b3cf-a1c82d901544","resolution":{"observed_at":"2026-08-04T21:04:57.599574Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:04:57.588481Z","title":null,"venue":null,"work_id":"a774605e-db7d-4c1d-bf86-2569c6c7780e","year":1996},"citing_paper":{"arxiv_id":"2509.08256","last_updated":"2025-09-10T03:25:56Z","snapshot_observed_at":"2026-08-10T01:21:15.509381Z","submitted_at":"2025-09-10T03:25:56Z","title":"Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-04T21:04:57.165723Z"},"links":{"citing_paper":"/paper/2509.08256"},"observation_digest":"sha256:742282d65a9515d8892db33d3d2705072628c48fad4e4d1abafb69b1ceb8133d","observation_id":"bc4d7f74-37e6-40d3-92c3-277532543a21","resolution":{"observed_at":"2026-08-04T21:04:57.591188Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:04:57.580016Z","title":"Conn, Katya Scheinberg, and Lu ´ ıs N","venue":null,"work_id":"8024cbb3-3ea6-41bf-83f8-8eb02df2bcdc","year":null},"citing_paper":{"arxiv_id":"2509.08256","last_updated":"2025-09-10T03:25:56Z","snapshot_observed_at":"2026-08-10T01:21:15.509381Z","submitted_at":"2025-09-10T03:25:56Z","title":"Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-04T21:04:57.168200Z"},"links":{"citing_paper":"/paper/2509.08256"},"observation_digest":"sha256:200f41b428401482627d274d66f3c6e3a336c677a1f6bdd6af2c99b5e669ddcf","observation_id":"bae87734-75bf-4083-b88a-3741b82920af","resolution":{"observed_at":"2026-08-04T21:04:57.583122Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:04:57.173369Z","title":"Dolan and Jorge J","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2509.08256","last_updated":"2025-09-10T03:25:56Z","snapshot_observed_at":"2026-08-10T01:21:15.509381Z","submitted_at":"2025-09-10T03:25:56Z","title":"Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-04T21:04:57.173369Z"},"links":{"citing_paper":"/paper/2509.08256"},"observation_digest":"sha256:853ff205023ffaed986e13dd4d9845d7ae7260ea9643d5529a5a926c9f7b8237","observation_id":"fb087e48-edd3-4742-beb8-7ef4f170d38b","resolution":{"observed_at":"2026-08-04T21:04:57.173369Z","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":"2507.10992","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:04:57.406316Z","title":"Larson, Matt Menickelly, and Stefan M","venue":null,"work_id":"e1d81a42-6d6d-4f93-8ff1-b891feb6236f","year":2025},"citing_paper":{"arxiv_id":"2509.08256","last_updated":"2025-09-10T03:25:56Z","snapshot_observed_at":"2026-08-10T01:21:15.509381Z","submitted_at":"2025-09-10T03:25:56Z","title":"Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-04T21:04:57.176179Z"},"links":{"citing_paper":"/paper/2509.08256"},"observation_digest":"sha256:6e1f8cea7a5b08ba2da056178cb355f81faa9a8b86cc927ba25ab0840ccde48b","observation_id":"9b63ba96-acd8-4bf1-96ac-bda8f4755dc5","resolution":{"observed_at":"2026-08-04T21:04:57.410392Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:04:57.491502Z","title":"Wild, and Pengcheng Xie","venue":null,"work_id":"d03e2482-3154-480a-8572-053338c5822c","year":2025},"citing_paper":{"arxiv_id":"2509.08256","last_updated":"2025-09-10T03:25:56Z","snapshot_observed_at":"2026-08-10T01:21:15.509381Z","submitted_at":"2025-09-10T03:25:56Z","title":"Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-04T21:04:57.178573Z"},"links":{"citing_paper":"/paper/2509.08256"},"observation_digest":"sha256:7c004817b34f0d2a98a549b4753b7907826331d9e183d59b11428216583413df","observation_id":"b768a6c4-791e-469f-b82a-0084e5823f82","resolution":{"observed_at":"2026-08-04T21:04:57.494770Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1002/9781118723203.ch5","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"John Wiley & Sons, Ltd, USA, 2000","venue":null,"work_id":"d195defb-e313-4dcb-88fa-c0025e316cef","year":2000},"citing_paper":{"arxiv_id":"2509.08256","last_updated":"2025-09-10T03:25:56Z","snapshot_observed_at":"2026-08-10T01:21:15.509381Z","submitted_at":"2025-09-10T03:25:56Z","title":"Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-04T21:04:57.181033Z"},"links":{"citing_paper":"/paper/2509.08256"},"observation_digest":"sha256:cd655118424c559a6fccb0dfdd1fdb9cd98800676c8e696cdd6a9c23238459f0","observation_id":"052cee0b-27d5-493f-b46f-b1363c220721","resolution":{"observed_at":"2026-08-04T21:04:57.318844Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:04:57.482758Z","title":"Stochastic first- and zeroth-order methods for non- convex stochastic programming.SIAM J","venue":null,"work_id":"9377bfb1-736a-4100-a508-0dd857cdc482","year":2013},"citing_paper":{"arxiv_id":"2509.08256","last_updated":"2025-09-10T03:25:56Z","snapshot_observed_at":"2026-08-10T01:21:15.509381Z","submitted_at":"2025-09-10T03:25:56Z","title":"Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-04T21:04:57.183865Z"},"links":{"citing_paper":"/paper/2509.08256"},"observation_digest":"sha256:8982942b06c1dd71cccf9ff0f003d045782d6251eab4d0298f3cf34287ba410f","observation_id":"b8a1d724-e970-4484-93e5-f66017712245","resolution":{"observed_at":"2026-08-04T21:04:57.485686Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1905.10874","last_updated":"2019-10-03T09:42:43Z","snapshot_observed_at":"2026-07-06T07:55:38.610348Z","submitted_at":"2019-05-26T20:33:51Z","title":"RSN: Randomized Subspace Newton","version":2},"cited_work":{"arxiv_id":"1905.10874","doi":null,"metadata_source":"pith","pith_arxiv_id":"1905.10874","snapshot_observed_at":"2026-08-04T21:04:57.396001Z","title":"RSN: Randomized Subspace Newton","venue":"math.OC","work_id":"dc5fe146-6c28-4db6-b73b-4fe9c0a0612f","year":2019},"citing_paper":{"arxiv_id":"2509.08256","last_updated":"2025-09-10T03:25:56Z","snapshot_observed_at":"2026-08-10T01:21:15.509381Z","submitted_at":"2025-09-10T03:25:56Z","title":"Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-04T21:04:57.186294Z"},"links":{"cited_paper":"/paper/1905.10874","citing_paper":"/paper/2509.08256"},"observation_digest":"sha256:781e0ea0ca184a446b69ab2ea5312b3f03f0b0fc1de36e8264babdfc75fd6ad6","observation_id":"0f626e23-6c9a-48a9-84e8-8124781d674a","resolution":{"observed_at":"2026-08-04T21:04:57.398846Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:04:57.473279Z","title":"Expected decrease for derivative-free algorithms using random subspaces.Math","venue":null,"work_id":"dce318d2-413d-4eff-b5fa-030147c6ff28","year":2025},"citing_paper":{"arxiv_id":"2509.08256","last_updated":"2025-09-10T03:25:56Z","snapshot_observed_at":"2026-08-10T01:21:15.509381Z","submitted_at":"2025-09-10T03:25:56Z","title":"Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-04T21:04:57.188983Z"},"links":{"citing_paper":"/paper/2509.08256"},"observation_digest":"sha256:4d2c37f763d7c61cd9284cb96a3ad94e1900c2d490197f5a8c9312538d9a5223","observation_id":"79eec735-a9e2-4661-b72b-963f4f488162","resolution":{"observed_at":"2026-08-04T21:04:57.476781Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:04:57.464737Z","title":"New subspace method for unconstrained derivative-free optimization.ACM Trans","venue":null,"work_id":"7d3cd734-580d-4bfd-bf01-01656d66dcb4","year":null},"citing_paper":{"arxiv_id":"2509.08256","last_updated":"2025-09-10T03:25:56Z","snapshot_observed_at":"2026-08-10T01:21:15.509381Z","submitted_at":"2025-09-10T03:25:56Z","title":"Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-04T21:04:57.191514Z"},"links":{"citing_paper":"/paper/2509.08256"},"observation_digest":"sha256:71c24df739bc375eadd8a43fee156fc093183d6ecbd09339e71ad46d825d8747","observation_id":"ee7d38a5-fe3e-4185-8aee-cd9616f940e2","resolution":{"observed_at":"2026-08-04T21:04:57.467565Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:04:57.456691Z","title":"Liu and Jorge Nocedal","venue":null,"work_id":"f3930ad1-dda2-419d-a278-556bc946c1b3","year":1989},"citing_paper":{"arxiv_id":"2509.08256","last_updated":"2025-09-10T03:25:56Z","snapshot_observed_at":"2026-08-10T01:21:15.509381Z","submitted_at":"2025-09-10T03:25:56Z","title":"Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-04T21:04:57.196835Z"},"links":{"citing_paper":"/paper/2509.08256"},"observation_digest":"sha256:253884d8769daa27242da199e5d70b40d7ab40b1b4c2f47a21011cfff149d78e","observation_id":"1ac483ad-f804-4741-80e7-585f45f584a0","resolution":{"observed_at":"2026-08-04T21:04:57.459507Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/bf01593790","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":null,"venue":"Mathematical Programming","work_id":"ecb35d2e-4a44-41d4-a22e-6155348df6bd","year":1977},"citing_paper":{"arxiv_id":"2509.08256","last_updated":"2025-09-10T03:25:56Z","snapshot_observed_at":"2026-08-10T01:21:15.509381Z","submitted_at":"2025-09-10T03:25:56Z","title":"Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-04T21:04:57.199134Z"},"links":{"citing_paper":"/paper/2509.08256"},"observation_digest":"sha256:f6efc1cf6945f71756020b2830d16b2760939b2b01a1620c9dd5daa4322e2d0b","observation_id":"8590bc50-343e-44fd-a650-f13bd7e4fb95","resolution":{"observed_at":"2026-08-04T21:04:57.305742Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:04:57.201653Z","title":null,"venue":null,"work_id":null,"year":1982},"citing_paper":{"arxiv_id":"2509.08256","last_updated":"2025-09-10T03:25:56Z","snapshot_observed_at":"2026-08-10T01:21:15.509381Z","submitted_at":"2025-09-10T03:25:56Z","title":"Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-04T21:04:57.201653Z"},"links":{"citing_paper":"/paper/2509.08256"},"observation_digest":"sha256:ab7c6e6f09537fa7846abc773c9ab6807482c685d3e5885f9c496fbca0268ba4","observation_id":"7e55bef9-c4a0-40b6-941a-fea085da5f76","resolution":{"observed_at":"2026-08-04T21:04:57.201653Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.03718","last_updated":"2025-01-08T11:52:40Z","snapshot_observed_at":"2026-08-10T21:46:16.616187Z","submitted_at":"2025-01-07T11:58:10Z","title":"Scalable Second-Order Optimization Algorithms for Minimizing Low-rank Functions","version":2},"cited_work":{"arxiv_id":"2501.03718","doi":null,"metadata_source":"pith","pith_arxiv_id":"2501.03718","snapshot_observed_at":"2026-08-04T21:04:57.384506Z","title":"Scalable Second-Order Optimization Algorithms for Minimizing Low-rank Functions","venue":"math.OC","work_id":"1547f69d-136a-454c-82d5-ccab8763e236","year":2025},"citing_paper":{"arxiv_id":"2509.08256","last_updated":"2025-09-10T03:25:56Z","snapshot_observed_at":"2026-08-10T01:21:15.509381Z","submitted_at":"2025-09-10T03:25:56Z","title":"Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-04T21:04:57.204325Z"},"links":{"cited_paper":"/paper/2501.03718","citing_paper":"/paper/2509.08256"},"observation_digest":"sha256:413090b716bf1c6cf9f597e1c13760b58ac25424ba17061a0c32619523ae6371","observation_id":"b2150d6c-adbf-459f-a382-be25995a4238","resolution":{"observed_at":"2026-08-04T21:04:57.387819Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1137/0717025","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":null,"venue":"SIAM Journal on Numerical Analysis","work_id":"b96b774b-91a0-43bf-942c-6ad932d037e5","year":1980},"citing_paper":{"arxiv_id":"2509.08256","last_updated":"2025-09-10T03:25:56Z","snapshot_observed_at":"2026-08-10T01:21:15.509381Z","submitted_at":"2025-09-10T03:25:56Z","title":"Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-04T21:04:57.207224Z"},"links":{"citing_paper":"/paper/2509.08256"},"observation_digest":"sha256:4c02927794a1f2e7af8840a5b9278cc6ccda6235b9e73fd1fe1edd4f3bf323b3","observation_id":"30f7007e-7597-4ca5-80cd-f236447695bd","resolution":{"observed_at":"2026-08-04T21:04:57.292838Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:04:57.209646Z","title":"Woodruff","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2509.08256","last_updated":"2025-09-10T03:25:56Z","snapshot_observed_at":"2026-08-10T01:21:15.509381Z","submitted_at":"2025-09-10T03:25:56Z","title":"Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-04T21:04:57.209646Z"},"links":{"citing_paper":"/paper/2509.08256"},"observation_digest":"sha256:2c830c2ac6bc7e4381c406a989348c11f65301f420f5cbc1ffbc8046529e1911","observation_id":"f19c45f5-a14f-442b-bacb-f750f7e006ea","resolution":{"observed_at":"2026-08-04T21:04:57.209646Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.03606","last_updated":"2025-04-04T17:24:04Z","snapshot_observed_at":"2026-08-07T16:08:39.274825Z","submitted_at":"2025-04-04T17:24:04Z","title":"ReMU: Regional Minimal Updating for Model-Based Derivative-Free Optimization","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.03606","snapshot_observed_at":"2026-08-04T21:04:57.212048Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.08256","last_updated":"2025-09-10T03:25:56Z","snapshot_observed_at":"2026-08-10T01:21:15.509381Z","submitted_at":"2025-09-10T03:25:56Z","title":"Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-04T21:04:57.212048Z"},"links":{"cited_paper":"/paper/2504.03606","citing_paper":"/paper/2509.08256"},"observation_digest":"sha256:8bf7570d8453198170fe5aa08bd4bc75abb8424ebaf91b8ad2821180884ede26","observation_id":"ccea7b31-70cb-4e5f-9f41-e2b3cbd516fc","resolution":{"observed_at":"2026-08-04T21:04:57.212048Z","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":"2026-08-04T21:04:57.215172Z","title":"Least H2 norm updating of quadratic interpolation models for derivative-free trust-region algorithms.IMA Journal of Numerical Analysis, page drae106, 03 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.08256","last_updated":"2025-09-10T03:25:56Z","snapshot_observed_at":"2026-08-10T01:21:15.509381Z","submitted_at":"2025-09-10T03:25:56Z","title":"Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-04T21:04:57.215172Z"},"links":{"citing_paper":"/paper/2509.08256"},"observation_digest":"sha256:f5d84931a2b4cea1b079a320434389cbfa1ea63d66d9a108e5a74809d715cc56","observation_id":"758df8cd-d044-4ddd-a1ab-ec24bcfe238f","resolution":{"observed_at":"2026-08-04T21:04:57.215172Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.14855","last_updated":"2024-01-02T09:07:33Z","snapshot_observed_at":"2026-08-10T16:31:13.969547Z","submitted_at":"2023-09-26T11:33:00Z","title":"A New Two-dimensional Model-based Subspace Method for Large-scale Unconstrained Derivative-free Optimization: 2D-MoSub","version":3},"cited_work":{"arxiv_id":"2309.14855","doi":null,"metadata_source":"pith","pith_arxiv_id":"2309.14855","snapshot_observed_at":"2026-08-04T21:04:57.365041Z","title":"A New Two-dimensional Model-based Subspace Method for Large-scale Unconstrained Derivative-free Optimization: 2D-MoSub","venue":"math.OC","work_id":"816a8807-d26a-4558-89b9-0b55a100c5db","year":2023},"citing_paper":{"arxiv_id":"2509.08256","last_updated":"2025-09-10T03:25:56Z","snapshot_observed_at":"2026-08-10T01:21:15.509381Z","submitted_at":"2025-09-10T03:25:56Z","title":"Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-04T21:04:57.218100Z"},"links":{"cited_paper":"/paper/2309.14855","citing_paper":"/paper/2509.08256"},"observation_digest":"sha256:76f274adb04c318194c1e9c3a83d22053d28da7786f5cee76d5c898b6f665164","observation_id":"56a5cd5f-77d0-4622-bab0-45bef493e367","resolution":{"observed_at":"2026-08-04T21:04:57.368198Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:04:57.220794Z","title":"Derivative-free optimization with transformed ob- jective functions (DFOTO) and the algorithm based on the least Frobenius norm up- dating quadratic model.J","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.08256","last_updated":"2025-09-10T03:25:56Z","snapshot_observed_at":"2026-08-10T01:21:15.509381Z","submitted_at":"2025-09-10T03:25:56Z","title":"Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-04T21:04:57.220794Z"},"links":{"citing_paper":"/paper/2509.08256"},"observation_digest":"sha256:e85cfff91c1265d37277a0910bf55d671e1faf94ba1f3e0c14f60815a3ba6d90","observation_id":"44958967-f415-4633-8301-37361bb7e10a","resolution":{"observed_at":"2026-08-04T21:04:57.220794Z","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":"2026-08-04T21:04:57.223281Z","title":"A derivative-free method using a new under- determined quadratic interpolation model.SIAM J","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.08256","last_updated":"2025-09-10T03:25:56Z","snapshot_observed_at":"2026-08-10T01:21:15.509381Z","submitted_at":"2025-09-10T03:25:56Z","title":"Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-04T21:04:57.223281Z"},"links":{"citing_paper":"/paper/2509.08256"},"observation_digest":"sha256:2295478b3a2b60fcb2f6813a650636562d5af2531010549ca0ba4e275c2ef050","observation_id":"c686db85-755e-4f63-96fb-b953ad0834fc","resolution":{"observed_at":"2026-08-04T21:04:57.223281Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:04:57.448051Z","title":"Robinson, and Rene Vidal","venue":null,"work_id":"ac67b3ec-962b-4b94-9e02-d37f86f09aac","year":2016},"citing_paper":{"arxiv_id":"2509.08256","last_updated":"2025-09-10T03:25:56Z","snapshot_observed_at":"2026-08-10T01:21:15.509381Z","submitted_at":"2025-09-10T03:25:56Z","title":"Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-04T21:04:57.225850Z"},"links":{"citing_paper":"/paper/2509.08256"},"observation_digest":"sha256:4e2661c1332f54064f5d2ccb32ed577a38fb7e3c46ac5640ccb07e739e7be4b5","observation_id":"7cfb9c79-ed45-4470-8458-7a1de144a5b5","resolution":{"observed_at":"2026-08-04T21:04:57.450734Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/s101079900126","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:04:57.258612Z","title":"On the truncated conjugate gradient method.Math","venue":null,"work_id":"db4d514f-7a58-49dd-85ff-b1c1a57313e7","year":2000},"citing_paper":{"arxiv_id":"2509.08256","last_updated":"2025-09-10T03:25:56Z","snapshot_observed_at":"2026-08-10T01:21:15.509381Z","submitted_at":"2025-09-10T03:25:56Z","title":"Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-04T21:04:57.228231Z"},"links":{"citing_paper":"/paper/2509.08256"},"observation_digest":"sha256:6c267e72bd66a9e4fbf407b653d407f88f27e56e71da952f584157f9f2eb8239","observation_id":"7b8f92e3-0c3b-4d16-ba30-fcba1f288160","resolution":{"observed_at":"2026-08-04T21:04:57.263502Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:04:57.439538Z","title":null,"venue":null,"work_id":"b3d5cc75-5acd-47c4-8cfd-dd47f675173b","year":1995},"citing_paper":{"arxiv_id":"2509.08256","last_updated":"2025-09-10T03:25:56Z","snapshot_observed_at":"2026-08-10T01:21:15.509381Z","submitted_at":"2025-09-10T03:25:56Z","title":"Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-04T21:04:57.230758Z"},"links":{"citing_paper":"/paper/2509.08256"},"observation_digest":"sha256:f7030d478a8735c6349b8383bb923e6eb2da7308114fa9071cb15920fc5f2150","observation_id":"8830ac2b-28d8-437f-b82d-5d980b1d724d","resolution":{"observed_at":"2026-08-04T21:04:57.442229Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2208.00208","last_updated":"2023-07-02T16:11:27Z","snapshot_observed_at":"2026-07-06T13:36:58.879342Z","submitted_at":"2022-07-30T13:05:01Z","title":"DRSOM: A Dimension Reduced Second-Order Method","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2208.00208","snapshot_observed_at":"2026-08-04T21:04:57.233115Z","title":"DR- SOM: A dimension reduced second-order method, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.08256","last_updated":"2025-09-10T03:25:56Z","snapshot_observed_at":"2026-08-10T01:21:15.509381Z","submitted_at":"2025-09-10T03:25:56Z","title":"Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-04T21:04:57.233115Z"},"links":{"cited_paper":"/paper/2208.00208","citing_paper":"/paper/2509.08256"},"observation_digest":"sha256:541f7dc2e4b3505aa76faca7443261f3e020ae07c76f67a2a2e5b583a97a1a64","observation_id":"f7e923e5-eb43-42d7-9d43-f74b0839c9ab","resolution":{"observed_at":"2026-08-04T21:04:57.233115Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.04536","last_updated":"2025-01-08T14:36:22Z","snapshot_observed_at":"2026-08-10T21:27:55.061765Z","submitted_at":"2025-01-08T14:36:22Z","title":"Scalable Derivative-Free Optimization Algorithms with Low-Dimensional Subspace Techniques","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.04536","snapshot_observed_at":"2026-08-04T21:04:57.235709Z","title":"Scalable derivative-free optimization algorithms with low-dimensional sub- space techniques, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.08256","last_updated":"2025-09-10T03:25:56Z","snapshot_observed_at":"2026-08-10T01:21:15.509381Z","submitted_at":"2025-09-10T03:25:56Z","title":"Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-04T21:04:57.235709Z"},"links":{"cited_paper":"/paper/2501.04536","citing_paper":"/paper/2509.08256"},"observation_digest":"sha256:7ea9c12819de426e9df2ec297b2b503042b7ad289a4745672c7cbff23927952b","observation_id":"47b63c24-0954-4931-8f97-fe6073b40254","resolution":{"observed_at":"2026-08-04T21:04:57.235709Z","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":"2026-08-04T21:04:57.170663Z","title":"URLhttp://epubs.siam.org/doi/book/10.1137/ 1.9780898718768","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.08256","last_updated":"2025-09-10T03:25:56Z","snapshot_observed_at":"2026-08-10T01:21:15.509381Z","submitted_at":"2025-09-10T03:25:56Z","title":"Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy","version":1},"reference_index":2009,"source":"pdf_text","source_observed_at":"2026-08-04T21:04:57.170663Z"},"links":{"citing_paper":"/paper/2509.08256"},"observation_digest":"sha256:703f9663762f3063fc64761fccc70475e86f73271199822303d6b88f2c5b24ea","observation_id":"89b1eee1-31e2-4054-98ac-e861a7a49a99","resolution":{"observed_at":"2026-08-04T21:04:57.170663Z","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":"2026-08-04T21:04:57.194008Z","title":"doi:10.1145/3618297","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.08256","last_updated":"2025-09-10T03:25:56Z","snapshot_observed_at":"2026-08-10T01:21:15.509381Z","submitted_at":"2025-09-10T03:25:56Z","title":"Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-04T21:04:57.194008Z"},"links":{"citing_paper":"/paper/2509.08256"},"observation_digest":"sha256:f9eb62406aad93aec0a8472e35c440ac758908b7e63dcfc5df311b5d28ecf1b5","observation_id":"3b64e6a6-3beb-4d58-b66e-377b487bc672","resolution":{"observed_at":"2026-08-04T21:04:57.194008Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2509.08256","last_updated":"2025-09-10T03:25:56Z","latest_version":1,"primary_category":"math.OC","snapshot_observed_at":"2026-08-10T01:21:15.509381Z","submitted_at":"2025-09-10T03:25:56Z","title":"Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy"},"reference_resolution":{"displayed":35,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":17,"verified_exact":8,"verified_fuzzy":9},"total_outbound_references":35},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 1 inbound Pith citation observation for arXiv:2509.08256."}