{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2013:AXFEZJRLFLZBBGSVC2GV3AURIB","short_pith_number":"pith:AXFEZJRL","schema_version":"1.0","canonical_sha256":"05ca4ca62b2af2109a55168d5d8291405e30801eef0c5531bf15cb0dcd934761","source":{"kind":"arxiv","id":"1302.2349","version":2},"attestation_state":"computed","paper":{"title":"Dual subgradient algorithms for large-scale nonsmooth learning problems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Anatoli Juditsky, Arkadi Nemirovski, Bruce Cox","submitted_at":"2013-02-10T18:04:20Z","abstract_excerpt":"\"Classical\" First Order (FO) algorithms of convex optimization, such as Mirror Descent algorithm or Nesterov's optimal algorithm of smooth convex optimization, are well known to have optimal (theoretical) complexity estimates which do not depend on the problem dimension. However, to attain the optimality, the domain of the problem should admit a \"good proximal setup\". The latter essentially means that 1) the problem domain should satisfy certain geometric conditions of \"favorable geometry\", and 2) the practical use of these methods is conditioned by our ability to compute at a moderate cost {\\"},"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":"1302.2349","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2013-02-10T18:04:20Z","cross_cats_sorted":[],"title_canon_sha256":"1bf0565cebeac3ff761d99770013cd7cd50d206d60fef69c948a9a7c02abd4be","abstract_canon_sha256":"7e4329c0d6530b99dc8f53ea0ce39d480ec33efd1ad42c36d4086089d7f4c2a6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:15:06.195935Z","signature_b64":"81SMX5x7sBgSRluzzwpshh31sXJm1HeH4xY4mLoxmeiFuOtRdYhc8eQVn7ZZbvd/eND0ikSa3bATHKBqpa7BAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"05ca4ca62b2af2109a55168d5d8291405e30801eef0c5531bf15cb0dcd934761","last_reissued_at":"2026-05-18T03:15:06.195123Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:15:06.195123Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Dual subgradient algorithms for large-scale nonsmooth learning problems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Anatoli Juditsky, Arkadi Nemirovski, Bruce Cox","submitted_at":"2013-02-10T18:04:20Z","abstract_excerpt":"\"Classical\" First Order (FO) algorithms of convex optimization, such as Mirror Descent algorithm or Nesterov's optimal algorithm of smooth convex optimization, are well known to have optimal (theoretical) complexity estimates which do not depend on the problem dimension. However, to attain the optimality, the domain of the problem should admit a \"good proximal setup\". The latter essentially means that 1) the problem domain should satisfy certain geometric conditions of \"favorable geometry\", and 2) the practical use of these methods is conditioned by our ability to compute at a moderate cost {\\"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1302.2349","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":"1302.2349","created_at":"2026-05-18T03:15:06.195226+00:00"},{"alias_kind":"arxiv_version","alias_value":"1302.2349v2","created_at":"2026-05-18T03:15:06.195226+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1302.2349","created_at":"2026-05-18T03:15:06.195226+00:00"},{"alias_kind":"pith_short_12","alias_value":"AXFEZJRLFLZB","created_at":"2026-05-18T12:27:38.830355+00:00"},{"alias_kind":"pith_short_16","alias_value":"AXFEZJRLFLZBBGSV","created_at":"2026-05-18T12:27:38.830355+00:00"},{"alias_kind":"pith_short_8","alias_value":"AXFEZJRL","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/AXFEZJRLFLZBBGSVC2GV3AURIB","json":"https://pith.science/pith/AXFEZJRLFLZBBGSVC2GV3AURIB.json","graph_json":"https://pith.science/api/pith-number/AXFEZJRLFLZBBGSVC2GV3AURIB/graph.json","events_json":"https://pith.science/api/pith-number/AXFEZJRLFLZBBGSVC2GV3AURIB/events.json","paper":"https://pith.science/paper/AXFEZJRL"},"agent_actions":{"view_html":"https://pith.science/pith/AXFEZJRLFLZBBGSVC2GV3AURIB","download_json":"https://pith.science/pith/AXFEZJRLFLZBBGSVC2GV3AURIB.json","view_paper":"https://pith.science/paper/AXFEZJRL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1302.2349&json=true","fetch_graph":"https://pith.science/api/pith-number/AXFEZJRLFLZBBGSVC2GV3AURIB/graph.json","fetch_events":"https://pith.science/api/pith-number/AXFEZJRLFLZBBGSVC2GV3AURIB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AXFEZJRLFLZBBGSVC2GV3AURIB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AXFEZJRLFLZBBGSVC2GV3AURIB/action/storage_attestation","attest_author":"https://pith.science/pith/AXFEZJRLFLZBBGSVC2GV3AURIB/action/author_attestation","sign_citation":"https://pith.science/pith/AXFEZJRLFLZBBGSVC2GV3AURIB/action/citation_signature","submit_replication":"https://pith.science/pith/AXFEZJRLFLZBBGSVC2GV3AURIB/action/replication_record"}},"created_at":"2026-05-18T03:15:06.195226+00:00","updated_at":"2026-05-18T03:15:06.195226+00:00"}