{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2010:QEIFP2IO67S3RJ6ME5JD2L2Q5W","short_pith_number":"pith:QEIFP2IO","schema_version":"1.0","canonical_sha256":"811057e90ef7e5b8a7cc27523d2f50ed87da548cbbbb718f85f9f64827ef6eda","source":{"kind":"arxiv","id":"1008.5204","version":2},"attestation_state":"computed","paper":{"title":"A Smoothing Stochastic Gradient Method for Composite Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"math.OC","authors_text":"Javier Pena, Qihang Lin, Xi Chen","submitted_at":"2010-08-31T02:42:32Z","abstract_excerpt":"We consider the unconstrained optimization problem whose objective function is composed of a smooth and a non-smooth conponents where the smooth component is the expectation a random function. This type of problem arises in some interesting applications in machine learning. We propose a stochastic gradient descent algorithm for this class of optimization problem. When the non-smooth component has a particular structure, we propose another stochastic gradient descent algorithm by incorporating a smoothing method into our first algorithm. The proofs of the convergence rates of these two algorith"},"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":"1008.5204","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2010-08-31T02:42:32Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"4a469f0cfdb5949032d2ca7b291d16f484529f203c43718be73239702e4386ce","abstract_canon_sha256":"88d20c684eeeccb64e7ed24e22051b0a94e8d3381bbbb3f3109a0baf9be74c09"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T04:19:04.404452Z","signature_b64":"UWGmQ6Y021HmmGXAtdBfR5736EFIVermMmctbK0bq2EzbHvzwVi6SWeE8XsQo0YMlE1GmE5kVx1jCacNebWUBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"811057e90ef7e5b8a7cc27523d2f50ed87da548cbbbb718f85f9f64827ef6eda","last_reissued_at":"2026-05-18T04:19:04.404004Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T04:19:04.404004Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Smoothing Stochastic Gradient Method for Composite Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"math.OC","authors_text":"Javier Pena, Qihang Lin, Xi Chen","submitted_at":"2010-08-31T02:42:32Z","abstract_excerpt":"We consider the unconstrained optimization problem whose objective function is composed of a smooth and a non-smooth conponents where the smooth component is the expectation a random function. This type of problem arises in some interesting applications in machine learning. We propose a stochastic gradient descent algorithm for this class of optimization problem. When the non-smooth component has a particular structure, we propose another stochastic gradient descent algorithm by incorporating a smoothing method into our first algorithm. The proofs of the convergence rates of these two algorith"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1008.5204","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":"1008.5204","created_at":"2026-05-18T04:19:04.404070+00:00"},{"alias_kind":"arxiv_version","alias_value":"1008.5204v2","created_at":"2026-05-18T04:19:04.404070+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1008.5204","created_at":"2026-05-18T04:19:04.404070+00:00"},{"alias_kind":"pith_short_12","alias_value":"QEIFP2IO67S3","created_at":"2026-05-18T12:26:12.377268+00:00"},{"alias_kind":"pith_short_16","alias_value":"QEIFP2IO67S3RJ6M","created_at":"2026-05-18T12:26:12.377268+00:00"},{"alias_kind":"pith_short_8","alias_value":"QEIFP2IO","created_at":"2026-05-18T12:26:12.377268+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/QEIFP2IO67S3RJ6ME5JD2L2Q5W","json":"https://pith.science/pith/QEIFP2IO67S3RJ6ME5JD2L2Q5W.json","graph_json":"https://pith.science/api/pith-number/QEIFP2IO67S3RJ6ME5JD2L2Q5W/graph.json","events_json":"https://pith.science/api/pith-number/QEIFP2IO67S3RJ6ME5JD2L2Q5W/events.json","paper":"https://pith.science/paper/QEIFP2IO"},"agent_actions":{"view_html":"https://pith.science/pith/QEIFP2IO67S3RJ6ME5JD2L2Q5W","download_json":"https://pith.science/pith/QEIFP2IO67S3RJ6ME5JD2L2Q5W.json","view_paper":"https://pith.science/paper/QEIFP2IO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1008.5204&json=true","fetch_graph":"https://pith.science/api/pith-number/QEIFP2IO67S3RJ6ME5JD2L2Q5W/graph.json","fetch_events":"https://pith.science/api/pith-number/QEIFP2IO67S3RJ6ME5JD2L2Q5W/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QEIFP2IO67S3RJ6ME5JD2L2Q5W/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QEIFP2IO67S3RJ6ME5JD2L2Q5W/action/storage_attestation","attest_author":"https://pith.science/pith/QEIFP2IO67S3RJ6ME5JD2L2Q5W/action/author_attestation","sign_citation":"https://pith.science/pith/QEIFP2IO67S3RJ6ME5JD2L2Q5W/action/citation_signature","submit_replication":"https://pith.science/pith/QEIFP2IO67S3RJ6ME5JD2L2Q5W/action/replication_record"}},"created_at":"2026-05-18T04:19:04.404070+00:00","updated_at":"2026-05-18T04:19:04.404070+00:00"}