{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:BMBSDPHQJNCIF5VKZWFWJRZGI3","short_pith_number":"pith:BMBSDPHQ","schema_version":"1.0","canonical_sha256":"0b0321bcf04b4482f6aacd8b64c72646e6ee7e4182099ea5dba12775104393ff","source":{"kind":"arxiv","id":"1602.00961","version":2},"attestation_state":"computed","paper":{"title":"Conditional gradient type methods for composite nonlinear and stochastic optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Saeed Ghadimi","submitted_at":"2016-02-02T15:05:29Z","abstract_excerpt":"In this paper, we present a conditional gradient type (CGT) method for solving a class of composite optimization problems where the objective function consists of a (weakly) smooth term and a (strongly) convex regularization term. While including a strongly convex term in the subproblems of the classical conditional gradient (CG) method improves its rate of convergence, it does not cost per iteration as much as general proximal type algorithms. More specifically, we present a unified analysis for the CGT method in the sense that it achieves the best-known rate of convergence when the weakly sm"},"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":"1602.00961","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2016-02-02T15:05:29Z","cross_cats_sorted":[],"title_canon_sha256":"3c6f56aa91572300eaf76481d6e232297137d4d28a5cbc1e4fa457a6501da88f","abstract_canon_sha256":"795ab2a7f150076f72738ae8d673d5a8b1ea80b0d6f4640d1ef6302fc2bef984"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:26:57.950081Z","signature_b64":"E1tHvU57qwasFuOX9i/fwnGe+HOuIbwd//wyGweHniEv/WoXk8lqRbq/XvpSLaq2NiiajGXH6sxoP4LzP+beDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0b0321bcf04b4482f6aacd8b64c72646e6ee7e4182099ea5dba12775104393ff","last_reissued_at":"2026-05-18T00:26:57.949161Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:26:57.949161Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Conditional gradient type methods for composite nonlinear and stochastic optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Saeed Ghadimi","submitted_at":"2016-02-02T15:05:29Z","abstract_excerpt":"In this paper, we present a conditional gradient type (CGT) method for solving a class of composite optimization problems where the objective function consists of a (weakly) smooth term and a (strongly) convex regularization term. While including a strongly convex term in the subproblems of the classical conditional gradient (CG) method improves its rate of convergence, it does not cost per iteration as much as general proximal type algorithms. More specifically, we present a unified analysis for the CGT method in the sense that it achieves the best-known rate of convergence when the weakly sm"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1602.00961","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":"1602.00961","created_at":"2026-05-18T00:26:57.949334+00:00"},{"alias_kind":"arxiv_version","alias_value":"1602.00961v2","created_at":"2026-05-18T00:26:57.949334+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1602.00961","created_at":"2026-05-18T00:26:57.949334+00:00"},{"alias_kind":"pith_short_12","alias_value":"BMBSDPHQJNCI","created_at":"2026-05-18T12:30:07.202191+00:00"},{"alias_kind":"pith_short_16","alias_value":"BMBSDPHQJNCIF5VK","created_at":"2026-05-18T12:30:07.202191+00:00"},{"alias_kind":"pith_short_8","alias_value":"BMBSDPHQ","created_at":"2026-05-18T12:30:07.202191+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/BMBSDPHQJNCIF5VKZWFWJRZGI3","json":"https://pith.science/pith/BMBSDPHQJNCIF5VKZWFWJRZGI3.json","graph_json":"https://pith.science/api/pith-number/BMBSDPHQJNCIF5VKZWFWJRZGI3/graph.json","events_json":"https://pith.science/api/pith-number/BMBSDPHQJNCIF5VKZWFWJRZGI3/events.json","paper":"https://pith.science/paper/BMBSDPHQ"},"agent_actions":{"view_html":"https://pith.science/pith/BMBSDPHQJNCIF5VKZWFWJRZGI3","download_json":"https://pith.science/pith/BMBSDPHQJNCIF5VKZWFWJRZGI3.json","view_paper":"https://pith.science/paper/BMBSDPHQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1602.00961&json=true","fetch_graph":"https://pith.science/api/pith-number/BMBSDPHQJNCIF5VKZWFWJRZGI3/graph.json","fetch_events":"https://pith.science/api/pith-number/BMBSDPHQJNCIF5VKZWFWJRZGI3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BMBSDPHQJNCIF5VKZWFWJRZGI3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BMBSDPHQJNCIF5VKZWFWJRZGI3/action/storage_attestation","attest_author":"https://pith.science/pith/BMBSDPHQJNCIF5VKZWFWJRZGI3/action/author_attestation","sign_citation":"https://pith.science/pith/BMBSDPHQJNCIF5VKZWFWJRZGI3/action/citation_signature","submit_replication":"https://pith.science/pith/BMBSDPHQJNCIF5VKZWFWJRZGI3/action/replication_record"}},"created_at":"2026-05-18T00:26:57.949334+00:00","updated_at":"2026-05-18T00:26:57.949334+00:00"}