{"as_of":"2026-08-15T06:56:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:4fc25120eff7f46cd91a6e274bee489415b5aa8080e5c9dbd6e6a3b37e500aa5","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":4,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":4,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-15T06:32:42.880941+00:00","state":"measured"},{"denominator":4,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":4,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-14T15:09:34.269736Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-14T10:22:26.168422Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1707.03505","last_updated":"2018-09-18T03:46:41Z","snapshot_observed_at":"2026-08-14T20:48:15.068733Z","submitted_at":"2017-07-12T00:35:43Z","title":"Proximally Guided Stochastic Subgradient Method for Nonsmooth, Nonconvex Problems","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1707.03505","snapshot_observed_at":"2026-08-14T15:09:34.269736Z","title":"Davis and B","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"1908.01871","last_updated":"2023-03-23T04:05:54Z","snapshot_observed_at":"2026-08-15T01:15:27.282812Z","submitted_at":"2019-08-05T21:39:24Z","title":"Quadratically Regularized Subgradient Methods for Weakly Convex Optimization with Weakly Convex Constraints","version":3},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-14T15:09:34.269736Z"},"links":{"cited_paper":"/paper/1707.03505","citing_paper":"/paper/1908.01871"},"observation_digest":"sha256:6b2f2826be25a0ad7da3d95ed1016c35de78e7c3a3f220ad293616092b072552","observation_id":"92ecae7b-a186-4b1c-b0c7-7a77a159c83b","resolution":{"observed_at":"2026-08-14T15:09:34.269736Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1707.03505","last_updated":"2018-09-18T03:46:41Z","snapshot_observed_at":"2026-08-14T20:48:15.068733Z","submitted_at":"2017-07-12T00:35:43Z","title":"Proximally Guided Stochastic Subgradient Method for Nonsmooth, Nonconvex Problems","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1707.03505","snapshot_observed_at":"2026-08-14T14:45:51.701312Z","title":"Proximally guided stochastic subgradient method for nons- mooth, nonconvex problems","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"1908.02734","last_updated":"2022-01-27T00:38:36Z","snapshot_observed_at":"2026-08-15T06:56:27.244087Z","submitted_at":"2019-08-07T17:10:52Z","title":"Stochastic First-order Methods for Convex and Nonconvex Functional Constrained Optimization","version":4},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-14T14:45:51.701312Z"},"links":{"cited_paper":"/paper/1707.03505","citing_paper":"/paper/1908.02734"},"observation_digest":"sha256:e0b4dfeca13ad43922acbf3968475e9a16f0c33e41fd88acb10ccb26c59dee37","observation_id":"af68026e-6f70-40ed-b78b-cd1d87de2cda","resolution":{"observed_at":"2026-08-14T14:45:51.701312Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1707.03505","last_updated":"2018-09-18T03:46:41Z","snapshot_observed_at":"2026-08-14T20:48:15.068733Z","submitted_at":"2017-07-12T00:35:43Z","title":"Proximally Guided Stochastic Subgradient Method for Nonsmooth, Nonconvex Problems","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1707.03505","snapshot_observed_at":"2026-08-14T11:05:32.264931Z","title":"and Grimmer, B","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"1908.09941","last_updated":"2020-07-13T20:16:48Z","snapshot_observed_at":"2026-08-15T06:35:31.911411Z","submitted_at":"2019-08-26T22:28:53Z","title":"Stochastic Optimization for Non-convex Inf-Projection Problems","version":2},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-14T11:05:32.264931Z"},"links":{"cited_paper":"/paper/1707.03505","citing_paper":"/paper/1908.09941"},"observation_digest":"sha256:7f27f558b93ebc0c250cde7d104e20da55e05ff8932e87df4f15c19458628434","observation_id":"93e42503-622e-49c3-ae72-58f75aa9b6c8","resolution":{"observed_at":"2026-08-14T11:05:32.264931Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1707.03505","last_updated":"2018-09-18T03:46:41Z","snapshot_observed_at":"2026-08-14T20:48:15.068733Z","submitted_at":"2017-07-12T00:35:43Z","title":"Proximally Guided Stochastic Subgradient Method for Nonsmooth, Nonconvex Problems","version":5},"cited_work":{"arxiv_id":"1707.03505","doi":null,"metadata_source":"pith","pith_arxiv_id":"1707.03505","snapshot_observed_at":"2026-08-14T10:22:26.168422Z","title":"Proximally Guided Stochastic Subgradient Method for Nonsmooth, Nonconvex Problems","venue":"math.OC","work_id":"7181135c-513e-475d-b1de-a18facc4f464","year":2017},"citing_paper":{"arxiv_id":"1908.11518","last_updated":"2020-12-01T14:23:29Z","snapshot_observed_at":"2026-08-15T01:15:42.120675Z","submitted_at":"2019-08-30T03:33:53Z","title":"Inexact Proximal-Point Penalty Methods for Constrained Non-Convex Optimization","version":4},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-14T10:22:25.516493Z"},"links":{"cited_paper":"/paper/1707.03505","citing_paper":"/paper/1908.11518"},"observation_digest":"sha256:cf5e4cf5c37fa2d1da63f593b91aa0e3b2ef03cbba37cc742e5f07e3c4ec0f84","observation_id":"93aa0c77-258c-4899-b281-3f3591c44be4","resolution":{"observed_at":"2026-08-14T10:22:26.172338Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/1707.03505/citation-record","integrity":"/paper/1707.03505/integrity","json":"/paper/1707.03505/citation-record.json","paper":"/paper/1707.03505"},"outbound":[],"paper":{"arxiv_id":"1707.03505","last_updated":"2018-09-18T03:46:41Z","latest_version":5,"primary_category":"math.OC","snapshot_observed_at":"2026-08-14T20:48:15.068733Z","submitted_at":"2017-07-12T00:35:43Z","title":"Proximally Guided Stochastic Subgradient Method for Nonsmooth, Nonconvex Problems"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:1707.03505."}