{"as_of":"2026-08-08T11:42:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f1fb07aabe892c395f3a2a45de1159d0232a44cfab6dd8bf96f190792629b547","coverage":[{"denominator":15,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":15,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-01T03:01:05.028964Z","state":"measured"},{"denominator":15,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":15,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+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/2607.25272/citation-record","integrity":"/paper/2607.25272/integrity","json":"/paper/2607.25272/citation-record.json","paper":"/paper/2607.25272"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T03:01:02.791694Z","title":"Blendenpik: supercharging LAPACK’s least-squares solver","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2607.25272","last_updated":"2026-07-28T04:19:49Z","snapshot_observed_at":"2026-08-01T03:01:01.187945Z","submitted_at":"2026-07-28T04:19:49Z","title":"Sequential Preconditioned Conjugate Gradient Method for Linear Statistical Models","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-01T03:01:02.791694Z"},"links":{"citing_paper":"/paper/2607.25272"},"observation_digest":"sha256:8437db52a45c6c68d8e984a2280c89dba6b7f67b5ed05c6b15c672e6fa922b90","observation_id":"e69b3d25-ec5e-4bed-a4f3-53a563e2710d","resolution":{"observed_at":"2026-08-01T03:01:02.791694Z","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":"2026-08-01T03:01:02.929405Z","title":"Asymptotics for sketching in least squares regression, in: Advances in Neural Information Processing Systems, pp","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.25272","last_updated":"2026-07-28T04:19:49Z","snapshot_observed_at":"2026-08-01T03:01:01.187945Z","submitted_at":"2026-07-28T04:19:49Z","title":"Sequential Preconditioned Conjugate Gradient Method for Linear Statistical Models","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-01T03:01:02.929405Z"},"links":{"citing_paper":"/paper/2607.25272"},"observation_digest":"sha256:454cbf0044e23feb5f2729f9f85919f918b6f611c8c92287c9762dee9fcfc830","observation_id":"0561a720-0807-4308-909f-6653fdad5172","resolution":{"observed_at":"2026-08-01T03:01:02.929405Z","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":"2026-08-01T03:01:03.053103Z","title":"RandNLA: randomized numerical linear algebra","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2607.25272","last_updated":"2026-07-28T04:19:49Z","snapshot_observed_at":"2026-08-01T03:01:01.187945Z","submitted_at":"2026-07-28T04:19:49Z","title":"Sequential Preconditioned Conjugate Gradient Method for Linear Statistical Models","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-01T03:01:03.053103Z"},"links":{"citing_paper":"/paper/2607.25272"},"observation_digest":"sha256:c6a0c3ba1d47a084188e6a9a2da7e5dca40557fbaf06f1b8598ee4ab57313625","observation_id":"e551ad09-fa8b-4c4a-839d-45baba70bf76","resolution":{"observed_at":"2026-08-01T03:01:03.053103Z","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":"2026-08-01T03:01:03.191168Z","title":"Matrix Computations","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2607.25272","last_updated":"2026-07-28T04:19:49Z","snapshot_observed_at":"2026-08-01T03:01:01.187945Z","submitted_at":"2026-07-28T04:19:49Z","title":"Sequential Preconditioned Conjugate Gradient Method for Linear Statistical Models","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-01T03:01:03.191168Z"},"links":{"citing_paper":"/paper/2607.25272"},"observation_digest":"sha256:10ce161291914893b80eb46384723ab9026077cedea3e5b26c4a2facd059cc5d","observation_id":"45523274-35a4-4c05-b49b-d92aca8e85c5","resolution":{"observed_at":"2026-08-01T03:01:03.191168Z","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":"2026-08-01T03:01:03.295972Z","title":"Optimal randomized first-order methods for least-squares problems, in: International Conference on Machine Learning, PMLR","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.25272","last_updated":"2026-07-28T04:19:49Z","snapshot_observed_at":"2026-08-01T03:01:01.187945Z","submitted_at":"2026-07-28T04:19:49Z","title":"Sequential Preconditioned Conjugate Gradient Method for Linear Statistical Models","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-01T03:01:03.295972Z"},"links":{"citing_paper":"/paper/2607.25272"},"observation_digest":"sha256:6ccc717c3a35bc65b65f4a35f11907de9bf93333a0017d9b65481cbd13003c86","observation_id":"24733256-d133-4f20-b9b5-aed3e9ad2ca6","resolution":{"observed_at":"2026-08-01T03:01:03.295972Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2104.14101","last_updated":"2021-04-29T04:36:41Z","snapshot_observed_at":"2026-08-05T15:39:46.147883Z","submitted_at":"2021-04-29T04:36:41Z","title":"Fast Convex Quadratic Optimization Solvers with Adaptive Sketching-based Preconditioners","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2104.14101","snapshot_observed_at":"2026-08-01T03:01:03.438590Z","title":"Fast convex quadratic optimization solvers with adaptive sketching-based preconditioners","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.25272","last_updated":"2026-07-28T04:19:49Z","snapshot_observed_at":"2026-08-01T03:01:01.187945Z","submitted_at":"2026-07-28T04:19:49Z","title":"Sequential Preconditioned Conjugate Gradient Method for Linear Statistical Models","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-01T03:01:03.438590Z"},"links":{"cited_paper":"/paper/2104.14101","citing_paper":"/paper/2607.25272"},"observation_digest":"sha256:f0353a6a7acc0fcb6768a9d84edaa0efe0a62bb32126a5168acb38ff88454b4f","observation_id":"7a04fe5c-d56f-454d-8970-4092bc21542b","resolution":{"observed_at":"2026-08-01T03:01:03.438590Z","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":"2026-08-01T03:01:03.627850Z","title":"Randomized numerical linear algebra: foundations and algorithms","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.25272","last_updated":"2026-07-28T04:19:49Z","snapshot_observed_at":"2026-08-01T03:01:01.187945Z","submitted_at":"2026-07-28T04:19:49Z","title":"Sequential Preconditioned Conjugate Gradient Method for Linear Statistical Models","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-01T03:01:03.627850Z"},"links":{"citing_paper":"/paper/2607.25272"},"observation_digest":"sha256:18e091494dba13704e5d08bcd918ce7bff0fb044f4c1c0bb85007492462f2a59","observation_id":"dcee8d63-2e27-4a71-8b0b-159e2aadc4c7","resolution":{"observed_at":"2026-08-01T03:01:03.627850Z","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":"2026-08-01T03:01:03.776344Z","title":"LSRN: a parallel iterative solver for strongly over- or underdetermined systems","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2607.25272","last_updated":"2026-07-28T04:19:49Z","snapshot_observed_at":"2026-08-01T03:01:01.187945Z","submitted_at":"2026-07-28T04:19:49Z","title":"Sequential Preconditioned Conjugate Gradient Method for Linear Statistical Models","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-01T03:01:03.776344Z"},"links":{"citing_paper":"/paper/2607.25272"},"observation_digest":"sha256:7a026d0bae4d9f86b8bc0f2a455bfc0b3f7dc03c316293ce1cbdaf442a245575","observation_id":"b402a8ff-0681-4d4c-9f65-ad73ba9f2a38","resolution":{"observed_at":"2026-08-01T03:01:03.776344Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.11474","last_updated":"2023-04-12T20:56:40Z","snapshot_observed_at":"2026-07-06T14:54:41.337108Z","submitted_at":"2023-02-22T16:21:37Z","title":"Randomized Numerical Linear Algebra : A Perspective on the Field With an Eye to Software","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.11474","snapshot_observed_at":"2026-08-01T03:01:03.938335Z","title":"Randomized numerical linear algebra: a perspective on the field with an eye to software","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.25272","last_updated":"2026-07-28T04:19:49Z","snapshot_observed_at":"2026-08-01T03:01:01.187945Z","submitted_at":"2026-07-28T04:19:49Z","title":"Sequential Preconditioned Conjugate Gradient Method for Linear Statistical Models","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-01T03:01:03.938335Z"},"links":{"cited_paper":"/paper/2302.11474","citing_paper":"/paper/2607.25272"},"observation_digest":"sha256:2652e0538d56ce465a450151f6e54ae214b2027279d1b4511da3371b169900d5","observation_id":"bb8f102e-ebbd-4898-8c36-0a52f74c7234","resolution":{"observed_at":"2026-08-01T03:01:03.938335Z","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":"2026-08-01T03:01:04.105712Z","title":"Numerical Optimization","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2607.25272","last_updated":"2026-07-28T04:19:49Z","snapshot_observed_at":"2026-08-01T03:01:01.187945Z","submitted_at":"2026-07-28T04:19:49Z","title":"Sequential Preconditioned Conjugate Gradient Method for Linear Statistical Models","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-01T03:01:04.105712Z"},"links":{"citing_paper":"/paper/2607.25272"},"observation_digest":"sha256:d87ca10e73b9033765444c22b8c01e24cf4b6f1d45b35b29bb07dc5d60e4d46e","observation_id":"5187e659-6145-4578-9e4d-7d115322a01e","resolution":{"observed_at":"2026-08-01T03:01:04.105712Z","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":"2026-08-01T03:01:04.258195Z","title":"Iterative Hessian sketch: fast and accurate solution approximation for constrained least-squares","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2607.25272","last_updated":"2026-07-28T04:19:49Z","snapshot_observed_at":"2026-08-01T03:01:01.187945Z","submitted_at":"2026-07-28T04:19:49Z","title":"Sequential Preconditioned Conjugate Gradient Method for Linear Statistical Models","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-01T03:01:04.258195Z"},"links":{"citing_paper":"/paper/2607.25272"},"observation_digest":"sha256:2343188535a71f114f56f1407f394c78dcedf032d925466cd4cff616e1333d83","observation_id":"b0efcfa9-7eaf-4a71-923c-7ab75917ef61","resolution":{"observed_at":"2026-08-01T03:01:04.258195Z","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":"2026-08-01T03:01:04.419845Z","title":"A statistical perspective on randomized sketching for ordinary least-squares","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2607.25272","last_updated":"2026-07-28T04:19:49Z","snapshot_observed_at":"2026-08-01T03:01:01.187945Z","submitted_at":"2026-07-28T04:19:49Z","title":"Sequential Preconditioned Conjugate Gradient Method for Linear Statistical Models","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-01T03:01:04.419845Z"},"links":{"citing_paper":"/paper/2607.25272"},"observation_digest":"sha256:ac052a588aea885c21bba967e0330e81007b89c1b45ba34b902f5417fda5ed09","observation_id":"b2fc00e9-eb1e-40f6-8bfc-af63cd2ffd08","resolution":{"observed_at":"2026-08-01T03:01:04.419845Z","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":"2026-08-01T03:01:04.580895Z","title":"A First Course in Probability","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.25272","last_updated":"2026-07-28T04:19:49Z","snapshot_observed_at":"2026-08-01T03:01:01.187945Z","submitted_at":"2026-07-28T04:19:49Z","title":"Sequential Preconditioned Conjugate Gradient Method for Linear Statistical Models","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-01T03:01:04.580895Z"},"links":{"citing_paper":"/paper/2607.25272"},"observation_digest":"sha256:d1b0d6818f3471be326462677b744ba0ff345997973fa59d92af5f7a862dbc0f","observation_id":"e129388b-23d7-4745-8c6d-d2d517457400","resolution":{"observed_at":"2026-08-01T03:01:04.580895Z","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":"2026-08-01T03:01:04.816908Z","title":"Iterative double sketching for faster least-squares optimization, in: International Conference on Machine Learning, PMLR","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.25272","last_updated":"2026-07-28T04:19:49Z","snapshot_observed_at":"2026-08-01T03:01:01.187945Z","submitted_at":"2026-07-28T04:19:49Z","title":"Sequential Preconditioned Conjugate Gradient Method for Linear Statistical Models","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-01T03:01:04.816908Z"},"links":{"citing_paper":"/paper/2607.25272"},"observation_digest":"sha256:60caf2afb697a64eb14d77cf2219b4851a6555b13c796431f515e154bb7d914a","observation_id":"23db5a1f-b0bd-4cf5-96c1-90ba4994b4a0","resolution":{"observed_at":"2026-08-01T03:01:04.816908Z","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":"2026-08-01T03:01:05.028964Z","title":"Sketching as a tool for numerical linear algebra","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2607.25272","last_updated":"2026-07-28T04:19:49Z","snapshot_observed_at":"2026-08-01T03:01:01.187945Z","submitted_at":"2026-07-28T04:19:49Z","title":"Sequential Preconditioned Conjugate Gradient Method for Linear Statistical Models","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-01T03:01:05.028964Z"},"links":{"citing_paper":"/paper/2607.25272"},"observation_digest":"sha256:fe4b9d61c8339c01420b0a508a339a8aaaff06963f14f65272e094974145a5e6","observation_id":"6e5ae4c1-d8b6-4652-bdca-c5a11948b208","resolution":{"observed_at":"2026-08-01T03:01:05.028964Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2607.25272","last_updated":"2026-07-28T04:19:49Z","latest_version":1,"primary_category":"math.NA","snapshot_observed_at":"2026-08-01T03:01:01.187945Z","submitted_at":"2026-07-28T04:19:49Z","title":"Sequential Preconditioned Conjugate Gradient Method for Linear Statistical Models"},"reference_resolution":{"displayed":15,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":15,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":15},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 0 inbound Pith citation observations for arXiv:2607.25272."}