{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:FW6BONCBXN366ILWGU2N2WU46T","short_pith_number":"pith:FW6BONCB","schema_version":"1.0","canonical_sha256":"2dbc173441bb77ef21763534dd5a9cf4e2d6cd194952a81218cc37ed52de99a8","source":{"kind":"arxiv","id":"2103.14392","version":1},"attestation_state":"computed","paper":{"title":"An Accelerated Second-Order Method for Distributed Stochastic Optimization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Aleksandr Lukashevich, Alexander Gasnikov, Amir Daneshmand, Artem Agafonov, Dmitry Kamzolov, Gesualdo Scutari, Pavel Dvurechensky","submitted_at":"2021-03-26T10:53:01Z","abstract_excerpt":"We consider distributed stochastic optimization problems that are solved with master/workers computation architecture. Statistical arguments allow to exploit statistical similarity and approximate this problem by a finite-sum problem, for which we propose an inexact accelerated cubic-regularized Newton's method that achieves lower communication complexity bound for this setting and improves upon existing upper bound. We further exploit this algorithm to obtain convergence rate bounds for the original stochastic optimization problem and compare our bounds with the existing bounds in several reg"},"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":"2103.14392","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2021-03-26T10:53:01Z","cross_cats_sorted":[],"title_canon_sha256":"7a3ea83c34a4458f80aa5cf2c5db9d0a5ed3d6bbe27ca26bdb1d06732f0ba110","abstract_canon_sha256":"032ccb10280f3317741a25619d3e57e28ebe4b82f93e20de9603b7a34db10463"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:26:33.182956Z","signature_b64":"EKb8b/B6EepfFK8WkPk10QBszWI9+DRvORhbe35GxqIRGjqwiPmcCSmslFkRNPmEToXDbXRS1mUDZf6fxXHzBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2dbc173441bb77ef21763534dd5a9cf4e2d6cd194952a81218cc37ed52de99a8","last_reissued_at":"2026-07-05T02:26:33.182576Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:26:33.182576Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"An Accelerated Second-Order Method for Distributed Stochastic Optimization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Aleksandr Lukashevich, Alexander Gasnikov, Amir Daneshmand, Artem Agafonov, Dmitry Kamzolov, Gesualdo Scutari, Pavel Dvurechensky","submitted_at":"2021-03-26T10:53:01Z","abstract_excerpt":"We consider distributed stochastic optimization problems that are solved with master/workers computation architecture. Statistical arguments allow to exploit statistical similarity and approximate this problem by a finite-sum problem, for which we propose an inexact accelerated cubic-regularized Newton's method that achieves lower communication complexity bound for this setting and improves upon existing upper bound. We further exploit this algorithm to obtain convergence rate bounds for the original stochastic optimization problem and compare our bounds with the existing bounds in several reg"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.14392","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/2103.14392/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":"2103.14392","created_at":"2026-07-05T02:26:33.182632+00:00"},{"alias_kind":"arxiv_version","alias_value":"2103.14392v1","created_at":"2026-07-05T02:26:33.182632+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.14392","created_at":"2026-07-05T02:26:33.182632+00:00"},{"alias_kind":"pith_short_12","alias_value":"FW6BONCBXN36","created_at":"2026-07-05T02:26:33.182632+00:00"},{"alias_kind":"pith_short_16","alias_value":"FW6BONCBXN366ILW","created_at":"2026-07-05T02:26:33.182632+00:00"},{"alias_kind":"pith_short_8","alias_value":"FW6BONCB","created_at":"2026-07-05T02:26:33.182632+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/FW6BONCBXN366ILWGU2N2WU46T","json":"https://pith.science/pith/FW6BONCBXN366ILWGU2N2WU46T.json","graph_json":"https://pith.science/api/pith-number/FW6BONCBXN366ILWGU2N2WU46T/graph.json","events_json":"https://pith.science/api/pith-number/FW6BONCBXN366ILWGU2N2WU46T/events.json","paper":"https://pith.science/paper/FW6BONCB"},"agent_actions":{"view_html":"https://pith.science/pith/FW6BONCBXN366ILWGU2N2WU46T","download_json":"https://pith.science/pith/FW6BONCBXN366ILWGU2N2WU46T.json","view_paper":"https://pith.science/paper/FW6BONCB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2103.14392&json=true","fetch_graph":"https://pith.science/api/pith-number/FW6BONCBXN366ILWGU2N2WU46T/graph.json","fetch_events":"https://pith.science/api/pith-number/FW6BONCBXN366ILWGU2N2WU46T/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FW6BONCBXN366ILWGU2N2WU46T/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FW6BONCBXN366ILWGU2N2WU46T/action/storage_attestation","attest_author":"https://pith.science/pith/FW6BONCBXN366ILWGU2N2WU46T/action/author_attestation","sign_citation":"https://pith.science/pith/FW6BONCBXN366ILWGU2N2WU46T/action/citation_signature","submit_replication":"https://pith.science/pith/FW6BONCBXN366ILWGU2N2WU46T/action/replication_record"}},"created_at":"2026-07-05T02:26:33.182632+00:00","updated_at":"2026-07-05T02:26:33.182632+00:00"}