{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:73CLGOPJTHQKOJVXKJ6MHIQEXS","short_pith_number":"pith:73CLGOPJ","schema_version":"1.0","canonical_sha256":"fec4b339e999e0a726b7527cc3a204bca36a5805e6beb7d3db66b70ade2378f1","source":{"kind":"arxiv","id":"1410.4062","version":1},"attestation_state":"computed","paper":{"title":"Complexity Issues and Randomization Strategies in Frank-Wolfe Algorithms for Machine Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.NA","math.OC"],"primary_cat":"stat.ML","authors_text":"Emanuele Frandi, Johan Suykens, Ricardo Nanculef","submitted_at":"2014-10-15T13:50:34Z","abstract_excerpt":"Frank-Wolfe algorithms for convex minimization have recently gained considerable attention from the Optimization and Machine Learning communities, as their properties make them a suitable choice in a variety of applications. However, as each iteration requires to optimize a linear model, a clever implementation is crucial to make such algorithms viable on large-scale datasets. For this purpose, approximation strategies based on a random sampling have been proposed by several researchers. In this work, we perform an experimental study on the effectiveness of these techniques, analyze possible a"},"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":"1410.4062","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2014-10-15T13:50:34Z","cross_cats_sorted":["cs.LG","cs.NA","math.OC"],"title_canon_sha256":"6aa52a6b5a5bc61a9f99a86acbea99a874f0d5f57283051620a78ff6bfc98450","abstract_canon_sha256":"dccdd5177380e47b0536494b8f36b8909c28996b0febd17169a4f588f092c88b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:29:21.594865Z","signature_b64":"ZsfLRPfarivThM7FOZqYLkXDlQlH0VoKY3RMPx4VcWg8VTgc5uDYdOrepdtwznp3fc+Ao59nRprr0IPWRRn6Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fec4b339e999e0a726b7527cc3a204bca36a5805e6beb7d3db66b70ade2378f1","last_reissued_at":"2026-05-18T01:29:21.594155Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:29:21.594155Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Complexity Issues and Randomization Strategies in Frank-Wolfe Algorithms for Machine Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.NA","math.OC"],"primary_cat":"stat.ML","authors_text":"Emanuele Frandi, Johan Suykens, Ricardo Nanculef","submitted_at":"2014-10-15T13:50:34Z","abstract_excerpt":"Frank-Wolfe algorithms for convex minimization have recently gained considerable attention from the Optimization and Machine Learning communities, as their properties make them a suitable choice in a variety of applications. However, as each iteration requires to optimize a linear model, a clever implementation is crucial to make such algorithms viable on large-scale datasets. For this purpose, approximation strategies based on a random sampling have been proposed by several researchers. In this work, we perform an experimental study on the effectiveness of these techniques, analyze possible a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1410.4062","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":"1410.4062","created_at":"2026-05-18T01:29:21.594272+00:00"},{"alias_kind":"arxiv_version","alias_value":"1410.4062v1","created_at":"2026-05-18T01:29:21.594272+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1410.4062","created_at":"2026-05-18T01:29:21.594272+00:00"},{"alias_kind":"pith_short_12","alias_value":"73CLGOPJTHQK","created_at":"2026-05-18T12:28:16.859392+00:00"},{"alias_kind":"pith_short_16","alias_value":"73CLGOPJTHQKOJVX","created_at":"2026-05-18T12:28:16.859392+00:00"},{"alias_kind":"pith_short_8","alias_value":"73CLGOPJ","created_at":"2026-05-18T12:28:16.859392+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/73CLGOPJTHQKOJVXKJ6MHIQEXS","json":"https://pith.science/pith/73CLGOPJTHQKOJVXKJ6MHIQEXS.json","graph_json":"https://pith.science/api/pith-number/73CLGOPJTHQKOJVXKJ6MHIQEXS/graph.json","events_json":"https://pith.science/api/pith-number/73CLGOPJTHQKOJVXKJ6MHIQEXS/events.json","paper":"https://pith.science/paper/73CLGOPJ"},"agent_actions":{"view_html":"https://pith.science/pith/73CLGOPJTHQKOJVXKJ6MHIQEXS","download_json":"https://pith.science/pith/73CLGOPJTHQKOJVXKJ6MHIQEXS.json","view_paper":"https://pith.science/paper/73CLGOPJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1410.4062&json=true","fetch_graph":"https://pith.science/api/pith-number/73CLGOPJTHQKOJVXKJ6MHIQEXS/graph.json","fetch_events":"https://pith.science/api/pith-number/73CLGOPJTHQKOJVXKJ6MHIQEXS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/73CLGOPJTHQKOJVXKJ6MHIQEXS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/73CLGOPJTHQKOJVXKJ6MHIQEXS/action/storage_attestation","attest_author":"https://pith.science/pith/73CLGOPJTHQKOJVXKJ6MHIQEXS/action/author_attestation","sign_citation":"https://pith.science/pith/73CLGOPJTHQKOJVXKJ6MHIQEXS/action/citation_signature","submit_replication":"https://pith.science/pith/73CLGOPJTHQKOJVXKJ6MHIQEXS/action/replication_record"}},"created_at":"2026-05-18T01:29:21.594272+00:00","updated_at":"2026-05-18T01:29:21.594272+00:00"}