{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:BIBEGFEDT6IHZF4JGQZ72S25UT","short_pith_number":"pith:BIBEGFED","schema_version":"1.0","canonical_sha256":"0a024314839f907c97893433fd4b5da4f9c669d558beaa1fac2422d5f1ec08e6","source":{"kind":"arxiv","id":"2508.20645","version":1},"attestation_state":"computed","paper":{"title":"A Hybrid Stochastic Gradient Tracking Method for Distributed Online Optimization Over Time-Varying Directed Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.DC","math.OC"],"primary_cat":"cs.LG","authors_text":"Guanghui Wen, Longkang Zhu, Xingxing Yuan, Xinli Shi","submitted_at":"2025-08-28T10:47:18Z","abstract_excerpt":"With the increasing scale and dynamics of data, distributed online optimization has become essential for real-time decision-making in various applications. However, existing algorithms often rely on bounded gradient assumptions and overlook the impact of stochastic gradients, especially in time-varying directed networks. This study proposes a novel Time-Varying Hybrid Stochastic Gradient Tracking algorithm named TV-HSGT, based on hybrid stochastic gradient tracking and variance reduction mechanisms. Specifically, TV-HSGT integrates row-stochastic and column-stochastic communication schemes ove"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2508.20645","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-08-28T10:47:18Z","cross_cats_sorted":["cs.DC","math.OC"],"title_canon_sha256":"dc9556174e1daee5a8498ce78361cc39f62fb798e391f424de45b6fae39ae3f2","abstract_canon_sha256":"6d82aadc3f173c21576ed81019d143db2ff60ca111fc0b13d269de8e107b4cf6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:00:56.688483Z","signature_b64":"FofnudmFuImEHixUsvbDGH6ccvIqGAie55A7klstmkxr3eF/EsbBEvxZvNkUoPcwccrpsZeQNpBzSVueEkfICw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0a024314839f907c97893433fd4b5da4f9c669d558beaa1fac2422d5f1ec08e6","last_reissued_at":"2026-07-05T12:00:56.687949Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:00:56.687949Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Hybrid Stochastic Gradient Tracking Method for Distributed Online Optimization Over Time-Varying Directed Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.DC","math.OC"],"primary_cat":"cs.LG","authors_text":"Guanghui Wen, Longkang Zhu, Xingxing Yuan, Xinli Shi","submitted_at":"2025-08-28T10:47:18Z","abstract_excerpt":"With the increasing scale and dynamics of data, distributed online optimization has become essential for real-time decision-making in various applications. However, existing algorithms often rely on bounded gradient assumptions and overlook the impact of stochastic gradients, especially in time-varying directed networks. This study proposes a novel Time-Varying Hybrid Stochastic Gradient Tracking algorithm named TV-HSGT, based on hybrid stochastic gradient tracking and variance reduction mechanisms. Specifically, TV-HSGT integrates row-stochastic and column-stochastic communication schemes ove"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.20645","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2508.20645/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2508.20645","created_at":"2026-07-05T12:00:56.688025+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.20645v1","created_at":"2026-07-05T12:00:56.688025+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.20645","created_at":"2026-07-05T12:00:56.688025+00:00"},{"alias_kind":"pith_short_12","alias_value":"BIBEGFEDT6IH","created_at":"2026-07-05T12:00:56.688025+00:00"},{"alias_kind":"pith_short_16","alias_value":"BIBEGFEDT6IHZF4J","created_at":"2026-07-05T12:00:56.688025+00:00"},{"alias_kind":"pith_short_8","alias_value":"BIBEGFED","created_at":"2026-07-05T12:00:56.688025+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/BIBEGFEDT6IHZF4JGQZ72S25UT","json":"https://pith.science/pith/BIBEGFEDT6IHZF4JGQZ72S25UT.json","graph_json":"https://pith.science/api/pith-number/BIBEGFEDT6IHZF4JGQZ72S25UT/graph.json","events_json":"https://pith.science/api/pith-number/BIBEGFEDT6IHZF4JGQZ72S25UT/events.json","paper":"https://pith.science/paper/BIBEGFED"},"agent_actions":{"view_html":"https://pith.science/pith/BIBEGFEDT6IHZF4JGQZ72S25UT","download_json":"https://pith.science/pith/BIBEGFEDT6IHZF4JGQZ72S25UT.json","view_paper":"https://pith.science/paper/BIBEGFED","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.20645&json=true","fetch_graph":"https://pith.science/api/pith-number/BIBEGFEDT6IHZF4JGQZ72S25UT/graph.json","fetch_events":"https://pith.science/api/pith-number/BIBEGFEDT6IHZF4JGQZ72S25UT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BIBEGFEDT6IHZF4JGQZ72S25UT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BIBEGFEDT6IHZF4JGQZ72S25UT/action/storage_attestation","attest_author":"https://pith.science/pith/BIBEGFEDT6IHZF4JGQZ72S25UT/action/author_attestation","sign_citation":"https://pith.science/pith/BIBEGFEDT6IHZF4JGQZ72S25UT/action/citation_signature","submit_replication":"https://pith.science/pith/BIBEGFEDT6IHZF4JGQZ72S25UT/action/replication_record"}},"created_at":"2026-07-05T12:00:56.688025+00:00","updated_at":"2026-07-05T12:00:56.688025+00:00"}