{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:YM7IULHSUE3XVAU2AWUSBKCHAK","short_pith_number":"pith:YM7IULHS","schema_version":"1.0","canonical_sha256":"c33e8a2cf2a1377a829a05a920a84702ab928ba2aed4b600c5e71369e88f8296","source":{"kind":"arxiv","id":"1711.05762","version":1},"attestation_state":"computed","paper":{"title":"Random gradient extrapolation for distributed and stochastic optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CC","cs.LG","stat.ML"],"primary_cat":"math.OC","authors_text":"Guanghui Lan, Yi Zhou","submitted_at":"2017-11-15T19:18:59Z","abstract_excerpt":"In this paper, we consider a class of finite-sum convex optimization problems defined over a distributed multiagent network with $m$ agents connected to a central server. In particular, the objective function consists of the average of $m$ ($\\ge 1$) smooth components associated with each network agent together with a strongly convex term. Our major contribution is to develop a new randomized incremental gradient algorithm, namely random gradient extrapolation method (RGEM), which does not require any exact gradient evaluation even for the initial point, but can achieve the optimal ${\\cal O}(\\l"},"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":"1711.05762","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2017-11-15T19:18:59Z","cross_cats_sorted":["cs.CC","cs.LG","stat.ML"],"title_canon_sha256":"2672580564070fc58498f61017ddbc8ada6d051f005a6768ea174e256cf64acc","abstract_canon_sha256":"a0deb5ee76f62f241fe808b65aae57d867709eaf05fc4b3b812d267bc48ae9ea"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:30:24.215098Z","signature_b64":"ORY7+mB4puN9T1KSyQxqxWeOi45XavnliPJjVG7P6Da/IbA5Vu+eVAOWPAPfOFzW/KQ4IvAJDmDHa1zyrO35DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c33e8a2cf2a1377a829a05a920a84702ab928ba2aed4b600c5e71369e88f8296","last_reissued_at":"2026-05-18T00:30:24.214423Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:30:24.214423Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Random gradient extrapolation for distributed and stochastic optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CC","cs.LG","stat.ML"],"primary_cat":"math.OC","authors_text":"Guanghui Lan, Yi Zhou","submitted_at":"2017-11-15T19:18:59Z","abstract_excerpt":"In this paper, we consider a class of finite-sum convex optimization problems defined over a distributed multiagent network with $m$ agents connected to a central server. In particular, the objective function consists of the average of $m$ ($\\ge 1$) smooth components associated with each network agent together with a strongly convex term. Our major contribution is to develop a new randomized incremental gradient algorithm, namely random gradient extrapolation method (RGEM), which does not require any exact gradient evaluation even for the initial point, but can achieve the optimal ${\\cal O}(\\l"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.05762","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":""},"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":"1711.05762","created_at":"2026-05-18T00:30:24.214524+00:00"},{"alias_kind":"arxiv_version","alias_value":"1711.05762v1","created_at":"2026-05-18T00:30:24.214524+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.05762","created_at":"2026-05-18T00:30:24.214524+00:00"},{"alias_kind":"pith_short_12","alias_value":"YM7IULHSUE3X","created_at":"2026-05-18T12:31:56.362134+00:00"},{"alias_kind":"pith_short_16","alias_value":"YM7IULHSUE3XVAU2","created_at":"2026-05-18T12:31:56.362134+00:00"},{"alias_kind":"pith_short_8","alias_value":"YM7IULHS","created_at":"2026-05-18T12:31:56.362134+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/YM7IULHSUE3XVAU2AWUSBKCHAK","json":"https://pith.science/pith/YM7IULHSUE3XVAU2AWUSBKCHAK.json","graph_json":"https://pith.science/api/pith-number/YM7IULHSUE3XVAU2AWUSBKCHAK/graph.json","events_json":"https://pith.science/api/pith-number/YM7IULHSUE3XVAU2AWUSBKCHAK/events.json","paper":"https://pith.science/paper/YM7IULHS"},"agent_actions":{"view_html":"https://pith.science/pith/YM7IULHSUE3XVAU2AWUSBKCHAK","download_json":"https://pith.science/pith/YM7IULHSUE3XVAU2AWUSBKCHAK.json","view_paper":"https://pith.science/paper/YM7IULHS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1711.05762&json=true","fetch_graph":"https://pith.science/api/pith-number/YM7IULHSUE3XVAU2AWUSBKCHAK/graph.json","fetch_events":"https://pith.science/api/pith-number/YM7IULHSUE3XVAU2AWUSBKCHAK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YM7IULHSUE3XVAU2AWUSBKCHAK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YM7IULHSUE3XVAU2AWUSBKCHAK/action/storage_attestation","attest_author":"https://pith.science/pith/YM7IULHSUE3XVAU2AWUSBKCHAK/action/author_attestation","sign_citation":"https://pith.science/pith/YM7IULHSUE3XVAU2AWUSBKCHAK/action/citation_signature","submit_replication":"https://pith.science/pith/YM7IULHSUE3XVAU2AWUSBKCHAK/action/replication_record"}},"created_at":"2026-05-18T00:30:24.214524+00:00","updated_at":"2026-05-18T00:30:24.214524+00:00"}