{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:C5Z5BHX64BXNU3SIA5F2LEQLDF","short_pith_number":"pith:C5Z5BHX6","schema_version":"1.0","canonical_sha256":"1773d09efee06eda6e48074ba5920b1973dc608105d34dda49efe3c38af1b59f","source":{"kind":"arxiv","id":"1705.04138","version":2},"attestation_state":"computed","paper":{"title":"Fast Stochastic Variance Reduced ADMM for Stochastic Composition Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Longbo Huang, Yue Yu","submitted_at":"2017-05-11T12:50:23Z","abstract_excerpt":"We consider the stochastic composition optimization problem proposed in \\cite{wang2017stochastic}, which has applications ranging from estimation to statistical and machine learning. We propose the first ADMM-based algorithm named com-SVR-ADMM, and show that com-SVR-ADMM converges linearly for strongly convex and Lipschitz smooth objectives, and has a convergence rate of $O( \\log S/S)$, which improves upon the $O(S^{-4/9})$ rate in \\cite{wang2016accelerating} when the objective is convex and Lipschitz smooth. Moreover, com-SVR-ADMM possesses a rate of $O(1/\\sqrt{S})$ when the objective is conv"},"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":"1705.04138","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-05-11T12:50:23Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"5f59e789ec935247669ec0bdbcbcb6fc432755ea224d099de8b3186dd90fd34a","abstract_canon_sha256":"95db4dabfdb6475adbb8a981f0f80a9b2d247f5930fa1af1c2fb5a0994b52f5e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:44:06.630604Z","signature_b64":"4JbDIN2i4maGSh5mL6IGvqsUvRVGzYJT4yRqL8BA0/Cl3VtybFj23OfR5f/GTtCcsZosG++9WqARwmbCIMbZCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1773d09efee06eda6e48074ba5920b1973dc608105d34dda49efe3c38af1b59f","last_reissued_at":"2026-05-18T00:44:06.630144Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:44:06.630144Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Fast Stochastic Variance Reduced ADMM for Stochastic Composition Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Longbo Huang, Yue Yu","submitted_at":"2017-05-11T12:50:23Z","abstract_excerpt":"We consider the stochastic composition optimization problem proposed in \\cite{wang2017stochastic}, which has applications ranging from estimation to statistical and machine learning. We propose the first ADMM-based algorithm named com-SVR-ADMM, and show that com-SVR-ADMM converges linearly for strongly convex and Lipschitz smooth objectives, and has a convergence rate of $O( \\log S/S)$, which improves upon the $O(S^{-4/9})$ rate in \\cite{wang2016accelerating} when the objective is convex and Lipschitz smooth. Moreover, com-SVR-ADMM possesses a rate of $O(1/\\sqrt{S})$ when the objective is conv"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.04138","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":"1705.04138","created_at":"2026-05-18T00:44:06.630212+00:00"},{"alias_kind":"arxiv_version","alias_value":"1705.04138v2","created_at":"2026-05-18T00:44:06.630212+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.04138","created_at":"2026-05-18T00:44:06.630212+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/C5Z5BHX64BXNU3SIA5F2LEQLDF","json":"https://pith.science/pith/C5Z5BHX64BXNU3SIA5F2LEQLDF.json","graph_json":"https://pith.science/api/pith-number/C5Z5BHX64BXNU3SIA5F2LEQLDF/graph.json","events_json":"https://pith.science/api/pith-number/C5Z5BHX64BXNU3SIA5F2LEQLDF/events.json","paper":"https://pith.science/paper/C5Z5BHX6"},"agent_actions":{"view_html":"https://pith.science/pith/C5Z5BHX64BXNU3SIA5F2LEQLDF","download_json":"https://pith.science/pith/C5Z5BHX64BXNU3SIA5F2LEQLDF.json","view_paper":"https://pith.science/paper/C5Z5BHX6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1705.04138&json=true","fetch_graph":"https://pith.science/api/pith-number/C5Z5BHX64BXNU3SIA5F2LEQLDF/graph.json","fetch_events":"https://pith.science/api/pith-number/C5Z5BHX64BXNU3SIA5F2LEQLDF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/C5Z5BHX64BXNU3SIA5F2LEQLDF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/C5Z5BHX64BXNU3SIA5F2LEQLDF/action/storage_attestation","attest_author":"https://pith.science/pith/C5Z5BHX64BXNU3SIA5F2LEQLDF/action/author_attestation","sign_citation":"https://pith.science/pith/C5Z5BHX64BXNU3SIA5F2LEQLDF/action/citation_signature","submit_replication":"https://pith.science/pith/C5Z5BHX64BXNU3SIA5F2LEQLDF/action/replication_record"}},"created_at":"2026-05-18T00:44:06.630212+00:00","updated_at":"2026-05-18T00:44:06.630212+00:00"}