{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2013:ALUGBXFWWBCZJMUKLTYZM4FROJ","short_pith_number":"pith:ALUGBXFW","schema_version":"1.0","canonical_sha256":"02e860dcb6b04594b28a5cf19670b1727853091efeeb2e11f3861e13d70064ad","source":{"kind":"arxiv","id":"1309.5885","version":1},"attestation_state":"computed","paper":{"title":"Smooth minimization of nonsmooth functions with parallel coordinate descent methods","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.OC","stat.ML"],"primary_cat":"cs.DC","authors_text":"Olivier Fercoq, Peter Richt\\'arik","submitted_at":"2013-09-23T17:24:00Z","abstract_excerpt":"We study the performance of a family of randomized parallel coordinate descent methods for minimizing the sum of a nonsmooth and separable convex functions. The problem class includes as a special case L1-regularized L1 regression and the minimization of the exponential loss (\"AdaBoost problem\"). We assume the input data defining the loss function is contained in a sparse $m\\times n$ matrix $A$ with at most $\\omega$ nonzeros in each row. Our methods need $O(n \\beta/\\tau)$ iterations to find an approximate solution with high probability, where $\\tau$ is the number of processors and $\\beta = 1 +"},"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":"1309.5885","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2013-09-23T17:24:00Z","cross_cats_sorted":["math.OC","stat.ML"],"title_canon_sha256":"054d85d80c550922610b0cf8d77fd6d2aafb0d12ffca542bb3d4eea2c6dfe301","abstract_canon_sha256":"53482f6184661072754e3afc898c5a7a625d6f26da5f066bad201126ee8de778"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:48:02.640617Z","signature_b64":"svohOrV3JFH24hcD0n5UTFJFqvbX6JaFHAFGGHb9hz+OwjAZU7tg7xwkC1HZ1X/HDbDVuvKZp0khsmH8NhkoDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"02e860dcb6b04594b28a5cf19670b1727853091efeeb2e11f3861e13d70064ad","last_reissued_at":"2026-05-17T23:48:02.639978Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:48:02.639978Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Smooth minimization of nonsmooth functions with parallel coordinate descent methods","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.OC","stat.ML"],"primary_cat":"cs.DC","authors_text":"Olivier Fercoq, Peter Richt\\'arik","submitted_at":"2013-09-23T17:24:00Z","abstract_excerpt":"We study the performance of a family of randomized parallel coordinate descent methods for minimizing the sum of a nonsmooth and separable convex functions. The problem class includes as a special case L1-regularized L1 regression and the minimization of the exponential loss (\"AdaBoost problem\"). We assume the input data defining the loss function is contained in a sparse $m\\times n$ matrix $A$ with at most $\\omega$ nonzeros in each row. Our methods need $O(n \\beta/\\tau)$ iterations to find an approximate solution with high probability, where $\\tau$ is the number of processors and $\\beta = 1 +"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1309.5885","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":"1309.5885","created_at":"2026-05-17T23:48:02.640068+00:00"},{"alias_kind":"arxiv_version","alias_value":"1309.5885v1","created_at":"2026-05-17T23:48:02.640068+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1309.5885","created_at":"2026-05-17T23:48:02.640068+00:00"},{"alias_kind":"pith_short_12","alias_value":"ALUGBXFWWBCZ","created_at":"2026-05-18T12:27:38.830355+00:00"},{"alias_kind":"pith_short_16","alias_value":"ALUGBXFWWBCZJMUK","created_at":"2026-05-18T12:27:38.830355+00:00"},{"alias_kind":"pith_short_8","alias_value":"ALUGBXFW","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/ALUGBXFWWBCZJMUKLTYZM4FROJ","json":"https://pith.science/pith/ALUGBXFWWBCZJMUKLTYZM4FROJ.json","graph_json":"https://pith.science/api/pith-number/ALUGBXFWWBCZJMUKLTYZM4FROJ/graph.json","events_json":"https://pith.science/api/pith-number/ALUGBXFWWBCZJMUKLTYZM4FROJ/events.json","paper":"https://pith.science/paper/ALUGBXFW"},"agent_actions":{"view_html":"https://pith.science/pith/ALUGBXFWWBCZJMUKLTYZM4FROJ","download_json":"https://pith.science/pith/ALUGBXFWWBCZJMUKLTYZM4FROJ.json","view_paper":"https://pith.science/paper/ALUGBXFW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1309.5885&json=true","fetch_graph":"https://pith.science/api/pith-number/ALUGBXFWWBCZJMUKLTYZM4FROJ/graph.json","fetch_events":"https://pith.science/api/pith-number/ALUGBXFWWBCZJMUKLTYZM4FROJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ALUGBXFWWBCZJMUKLTYZM4FROJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ALUGBXFWWBCZJMUKLTYZM4FROJ/action/storage_attestation","attest_author":"https://pith.science/pith/ALUGBXFWWBCZJMUKLTYZM4FROJ/action/author_attestation","sign_citation":"https://pith.science/pith/ALUGBXFWWBCZJMUKLTYZM4FROJ/action/citation_signature","submit_replication":"https://pith.science/pith/ALUGBXFWWBCZJMUKLTYZM4FROJ/action/replication_record"}},"created_at":"2026-05-17T23:48:02.640068+00:00","updated_at":"2026-05-17T23:48:02.640068+00:00"}