{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2013:7V2QSEA6NWZNQ66NGDAJ6CPUG2","short_pith_number":"pith:7V2QSEA6","schema_version":"1.0","canonical_sha256":"fd7509101e6db2d87bcd30c09f09f436b7f0594a2db26df5566598f6867bcc41","source":{"kind":"arxiv","id":"1301.4666","version":6},"attestation_state":"computed","paper":{"title":"A Linearly Convergent Conditional Gradient Algorithm with Applications to Online and Stochastic Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.OC","stat.ML"],"primary_cat":"cs.LG","authors_text":"Dan Garber, Elad Hazan","submitted_at":"2013-01-20T15:54:22Z","abstract_excerpt":"Linear optimization is many times algorithmically simpler than non-linear convex optimization. Linear optimization over matroid polytopes, matching polytopes and path polytopes are example of problems for which we have simple and efficient combinatorial algorithms, but whose non-linear convex counterpart is harder and admits significantly less efficient algorithms. This motivates the computational model of convex optimization, including the offline, online and stochastic settings, using a linear optimization oracle. In this computational model we give several new results that improve over the "},"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":"1301.4666","kind":"arxiv","version":6},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2013-01-20T15:54:22Z","cross_cats_sorted":["math.OC","stat.ML"],"title_canon_sha256":"5f91f2b07560e9d5a4416ffca4a7500f767570b95d5e47888a4efa2b1285ef5b","abstract_canon_sha256":"9eef44cee142a47d46acac032b8324cb1343589b8028631891d8ee879441bdea"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:35:20.927041Z","signature_b64":"zncPLVOLJmD3f/3w1Jd53TurhWykJthEVq/a6QVRjh4fujWjlTc3b/R/Q8s6KgQtKLMcXAixK5dFsWIXOhtQDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fd7509101e6db2d87bcd30c09f09f436b7f0594a2db26df5566598f6867bcc41","last_reissued_at":"2026-05-18T01:35:20.926381Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:35:20.926381Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Linearly Convergent Conditional Gradient Algorithm with Applications to Online and Stochastic Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.OC","stat.ML"],"primary_cat":"cs.LG","authors_text":"Dan Garber, Elad Hazan","submitted_at":"2013-01-20T15:54:22Z","abstract_excerpt":"Linear optimization is many times algorithmically simpler than non-linear convex optimization. Linear optimization over matroid polytopes, matching polytopes and path polytopes are example of problems for which we have simple and efficient combinatorial algorithms, but whose non-linear convex counterpart is harder and admits significantly less efficient algorithms. This motivates the computational model of convex optimization, including the offline, online and stochastic settings, using a linear optimization oracle. In this computational model we give several new results that improve over the "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1301.4666","kind":"arxiv","version":6},"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":"1301.4666","created_at":"2026-05-18T01:35:20.926510+00:00"},{"alias_kind":"arxiv_version","alias_value":"1301.4666v6","created_at":"2026-05-18T01:35:20.926510+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1301.4666","created_at":"2026-05-18T01:35:20.926510+00:00"},{"alias_kind":"pith_short_12","alias_value":"7V2QSEA6NWZN","created_at":"2026-05-18T12:27:38.830355+00:00"},{"alias_kind":"pith_short_16","alias_value":"7V2QSEA6NWZNQ66N","created_at":"2026-05-18T12:27:38.830355+00:00"},{"alias_kind":"pith_short_8","alias_value":"7V2QSEA6","created_at":"2026-05-18T12:27:38.830355+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/7V2QSEA6NWZNQ66NGDAJ6CPUG2","json":"https://pith.science/pith/7V2QSEA6NWZNQ66NGDAJ6CPUG2.json","graph_json":"https://pith.science/api/pith-number/7V2QSEA6NWZNQ66NGDAJ6CPUG2/graph.json","events_json":"https://pith.science/api/pith-number/7V2QSEA6NWZNQ66NGDAJ6CPUG2/events.json","paper":"https://pith.science/paper/7V2QSEA6"},"agent_actions":{"view_html":"https://pith.science/pith/7V2QSEA6NWZNQ66NGDAJ6CPUG2","download_json":"https://pith.science/pith/7V2QSEA6NWZNQ66NGDAJ6CPUG2.json","view_paper":"https://pith.science/paper/7V2QSEA6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1301.4666&json=true","fetch_graph":"https://pith.science/api/pith-number/7V2QSEA6NWZNQ66NGDAJ6CPUG2/graph.json","fetch_events":"https://pith.science/api/pith-number/7V2QSEA6NWZNQ66NGDAJ6CPUG2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7V2QSEA6NWZNQ66NGDAJ6CPUG2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7V2QSEA6NWZNQ66NGDAJ6CPUG2/action/storage_attestation","attest_author":"https://pith.science/pith/7V2QSEA6NWZNQ66NGDAJ6CPUG2/action/author_attestation","sign_citation":"https://pith.science/pith/7V2QSEA6NWZNQ66NGDAJ6CPUG2/action/citation_signature","submit_replication":"https://pith.science/pith/7V2QSEA6NWZNQ66NGDAJ6CPUG2/action/replication_record"}},"created_at":"2026-05-18T01:35:20.926510+00:00","updated_at":"2026-05-18T01:35:20.926510+00:00"}