{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:ZLNGGLUOMK6Z45FPSG2F3S5VMK","short_pith_number":"pith:ZLNGGLUO","schema_version":"1.0","canonical_sha256":"cada632e8e62bd9e74af91b45dcbb562b1069500f8cb9bcc87bc5d5ee1cdf3ea","source":{"kind":"arxiv","id":"1412.8060","version":2},"attestation_state":"computed","paper":{"title":"Coordinate Descent with Arbitrary Sampling I: Algorithms and Complexity","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.NA","math.NA"],"primary_cat":"math.OC","authors_text":"Peter Richt\\'arik, Zheng Qu","submitted_at":"2014-12-27T15:28:26Z","abstract_excerpt":"We study the problem of minimizing the sum of a smooth convex function and a convex block-separable regularizer and propose a new randomized coordinate descent method, which we call ALPHA. Our method at every iteration updates a random subset of coordinates, following an arbitrary distribution. No coordinate descent methods capable to handle an arbitrary sampling have been studied in the literature before for this problem. ALPHA is a remarkably flexible algorithm: in special cases, it reduces to deterministic and randomized methods such as gradient descent, coordinate descent, parallel coordin"},"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":"1412.8060","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2014-12-27T15:28:26Z","cross_cats_sorted":["cs.LG","cs.NA","math.NA"],"title_canon_sha256":"a2a8eecd916df610e2081a3160a07c22fe56f347b748488835659a3844387070","abstract_canon_sha256":"31704e52986a18817d0417e124e2a1d7af53514e20bcb2370d3d92a7779eec2f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:49:31.328521Z","signature_b64":"HIF5CsYluCcAwcOHHL2mL9Rx6oWRxbJoSWt6c/3nAfRRLVfnOSvShe8SRf5MkjfSyo+8uoaS8mmvozKGIbqXBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cada632e8e62bd9e74af91b45dcbb562b1069500f8cb9bcc87bc5d5ee1cdf3ea","last_reissued_at":"2026-05-18T01:49:31.328062Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:49:31.328062Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Coordinate Descent with Arbitrary Sampling I: Algorithms and Complexity","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.NA","math.NA"],"primary_cat":"math.OC","authors_text":"Peter Richt\\'arik, Zheng Qu","submitted_at":"2014-12-27T15:28:26Z","abstract_excerpt":"We study the problem of minimizing the sum of a smooth convex function and a convex block-separable regularizer and propose a new randomized coordinate descent method, which we call ALPHA. Our method at every iteration updates a random subset of coordinates, following an arbitrary distribution. No coordinate descent methods capable to handle an arbitrary sampling have been studied in the literature before for this problem. ALPHA is a remarkably flexible algorithm: in special cases, it reduces to deterministic and randomized methods such as gradient descent, coordinate descent, parallel coordin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1412.8060","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":"1412.8060","created_at":"2026-05-18T01:49:31.328137+00:00"},{"alias_kind":"arxiv_version","alias_value":"1412.8060v2","created_at":"2026-05-18T01:49:31.328137+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1412.8060","created_at":"2026-05-18T01:49:31.328137+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZLNGGLUOMK6Z","created_at":"2026-05-18T12:28:59.999130+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZLNGGLUOMK6Z45FP","created_at":"2026-05-18T12:28:59.999130+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZLNGGLUO","created_at":"2026-05-18T12:28:59.999130+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/ZLNGGLUOMK6Z45FPSG2F3S5VMK","json":"https://pith.science/pith/ZLNGGLUOMK6Z45FPSG2F3S5VMK.json","graph_json":"https://pith.science/api/pith-number/ZLNGGLUOMK6Z45FPSG2F3S5VMK/graph.json","events_json":"https://pith.science/api/pith-number/ZLNGGLUOMK6Z45FPSG2F3S5VMK/events.json","paper":"https://pith.science/paper/ZLNGGLUO"},"agent_actions":{"view_html":"https://pith.science/pith/ZLNGGLUOMK6Z45FPSG2F3S5VMK","download_json":"https://pith.science/pith/ZLNGGLUOMK6Z45FPSG2F3S5VMK.json","view_paper":"https://pith.science/paper/ZLNGGLUO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1412.8060&json=true","fetch_graph":"https://pith.science/api/pith-number/ZLNGGLUOMK6Z45FPSG2F3S5VMK/graph.json","fetch_events":"https://pith.science/api/pith-number/ZLNGGLUOMK6Z45FPSG2F3S5VMK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZLNGGLUOMK6Z45FPSG2F3S5VMK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZLNGGLUOMK6Z45FPSG2F3S5VMK/action/storage_attestation","attest_author":"https://pith.science/pith/ZLNGGLUOMK6Z45FPSG2F3S5VMK/action/author_attestation","sign_citation":"https://pith.science/pith/ZLNGGLUOMK6Z45FPSG2F3S5VMK/action/citation_signature","submit_replication":"https://pith.science/pith/ZLNGGLUOMK6Z45FPSG2F3S5VMK/action/replication_record"}},"created_at":"2026-05-18T01:49:31.328137+00:00","updated_at":"2026-05-18T01:49:31.328137+00:00"}