{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:MZ2KXQYEWM57XAOZG3D3PZD4AM","short_pith_number":"pith:MZ2KXQYE","schema_version":"1.0","canonical_sha256":"6674abc304b33bfb81d936c7b7e47c030c7ed4176d02c5f964d4d56868e8d73c","source":{"kind":"arxiv","id":"1808.07252","version":2},"attestation_state":"computed","paper":{"title":"Distributed Big-Data Optimization via Block-wise Gradient Tracking","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DC","cs.MA"],"primary_cat":"math.OC","authors_text":"Gesualdo Scutari, Giuseppe Notarstefano, Ivano Notarnicola, Ying Sun","submitted_at":"2018-08-22T07:30:06Z","abstract_excerpt":"We study distributed big-data nonconvex optimization in multi-agent networks. We consider the (constrained) minimization of the sum of a smooth (possibly) nonconvex function, i.e., the agents' sum-utility, plus a convex (possibly) nonsmooth regularizer. Our interest is on big-data problems in which there is a large number of variables to optimize. If treated by means of standard distributed optimization algorithms, these large-scale problems may be intractable due to the prohibitive local computation and communication burden at each node. We propose a novel distributed solution method where, a"},"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":"1808.07252","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2018-08-22T07:30:06Z","cross_cats_sorted":["cs.DC","cs.MA"],"title_canon_sha256":"783ef0166d1a5f3d710322cb6f6fdbd6863389ae9a3090ba91ca1c400c30e5eb","abstract_canon_sha256":"d53f448dd9e5daa77c558e4cf7290cd37e17ba46b148b8fcbf33bbb7ef902110"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:06:45.747854Z","signature_b64":"Rhf4H64zvVcqQznh7IvPiNSt+0VjLyOnoEN4c3K+PwDjUN8T2R9qHZygIsE4m5ksM1TqnWlcIEMaxF6zgo+GDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6674abc304b33bfb81d936c7b7e47c030c7ed4176d02c5f964d4d56868e8d73c","last_reissued_at":"2026-05-18T00:06:45.747137Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:06:45.747137Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Distributed Big-Data Optimization via Block-wise Gradient Tracking","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DC","cs.MA"],"primary_cat":"math.OC","authors_text":"Gesualdo Scutari, Giuseppe Notarstefano, Ivano Notarnicola, Ying Sun","submitted_at":"2018-08-22T07:30:06Z","abstract_excerpt":"We study distributed big-data nonconvex optimization in multi-agent networks. We consider the (constrained) minimization of the sum of a smooth (possibly) nonconvex function, i.e., the agents' sum-utility, plus a convex (possibly) nonsmooth regularizer. Our interest is on big-data problems in which there is a large number of variables to optimize. If treated by means of standard distributed optimization algorithms, these large-scale problems may be intractable due to the prohibitive local computation and communication burden at each node. We propose a novel distributed solution method where, a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.07252","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"1808.07252","created_at":"2026-05-18T00:06:45.747238+00:00"},{"alias_kind":"arxiv_version","alias_value":"1808.07252v2","created_at":"2026-05-18T00:06:45.747238+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.07252","created_at":"2026-05-18T00:06:45.747238+00:00"},{"alias_kind":"pith_short_12","alias_value":"MZ2KXQYEWM57","created_at":"2026-05-18T12:32:40.477152+00:00"},{"alias_kind":"pith_short_16","alias_value":"MZ2KXQYEWM57XAOZ","created_at":"2026-05-18T12:32:40.477152+00:00"},{"alias_kind":"pith_short_8","alias_value":"MZ2KXQYE","created_at":"2026-05-18T12:32:40.477152+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/MZ2KXQYEWM57XAOZG3D3PZD4AM","json":"https://pith.science/pith/MZ2KXQYEWM57XAOZG3D3PZD4AM.json","graph_json":"https://pith.science/api/pith-number/MZ2KXQYEWM57XAOZG3D3PZD4AM/graph.json","events_json":"https://pith.science/api/pith-number/MZ2KXQYEWM57XAOZG3D3PZD4AM/events.json","paper":"https://pith.science/paper/MZ2KXQYE"},"agent_actions":{"view_html":"https://pith.science/pith/MZ2KXQYEWM57XAOZG3D3PZD4AM","download_json":"https://pith.science/pith/MZ2KXQYEWM57XAOZG3D3PZD4AM.json","view_paper":"https://pith.science/paper/MZ2KXQYE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1808.07252&json=true","fetch_graph":"https://pith.science/api/pith-number/MZ2KXQYEWM57XAOZG3D3PZD4AM/graph.json","fetch_events":"https://pith.science/api/pith-number/MZ2KXQYEWM57XAOZG3D3PZD4AM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MZ2KXQYEWM57XAOZG3D3PZD4AM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MZ2KXQYEWM57XAOZG3D3PZD4AM/action/storage_attestation","attest_author":"https://pith.science/pith/MZ2KXQYEWM57XAOZG3D3PZD4AM/action/author_attestation","sign_citation":"https://pith.science/pith/MZ2KXQYEWM57XAOZG3D3PZD4AM/action/citation_signature","submit_replication":"https://pith.science/pith/MZ2KXQYEWM57XAOZG3D3PZD4AM/action/replication_record"}},"created_at":"2026-05-18T00:06:45.747238+00:00","updated_at":"2026-05-18T00:06:45.747238+00:00"}