{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:KRIOWY4J2EUXMP2MVS77JXQT3S","short_pith_number":"pith:KRIOWY4J","schema_version":"1.0","canonical_sha256":"5450eb6389d129763f4cacbff4de13dca0cfe3771b64700aaa230c6e79342795","source":{"kind":"arxiv","id":"1410.0390","version":2},"attestation_state":"computed","paper":{"title":"Simple Complexity Analysis of Simplified Direct Search","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CC"],"primary_cat":"math.OC","authors_text":"Jakub Kone\\v{c}n\\'y, Peter Richt\\'arik","submitted_at":"2014-10-01T21:42:04Z","abstract_excerpt":"We consider the problem of unconstrained minimization of a smooth function in the derivative-free setting using. In particular, we propose and study a simplified variant of the direct search method (of direction type), which we call simplified direct search (SDS). Unlike standard direct search methods, which depend on a large number of parameters that need to be tuned, SDS depends on a single scalar parameter only.\n  Despite relevant research activity in direct search methods spanning several decades, complexity guarantees---bounds on the number of function evaluations needed to find an approx"},"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":"1410.0390","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2014-10-01T21:42:04Z","cross_cats_sorted":["cs.CC"],"title_canon_sha256":"321d609c4a60cd28ac815f7a6859bb1fcd295065f8c7dc766d50c245570a63d2","abstract_canon_sha256":"a6e07b56dd3f37a037b3e497f6194485a34633dd7b21a634292f0ce020d146fb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:37:44.241303Z","signature_b64":"q/9jkPcUPT0Nn27nTbZmWwbXp59g73tfnz+lKhlHpGgHIurutW8x4pbx1tjHYwFfEBRNSt6XiEHj9Vorx9v4Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5450eb6389d129763f4cacbff4de13dca0cfe3771b64700aaa230c6e79342795","last_reissued_at":"2026-05-18T02:37:44.240842Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:37:44.240842Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Simple Complexity Analysis of Simplified Direct Search","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CC"],"primary_cat":"math.OC","authors_text":"Jakub Kone\\v{c}n\\'y, Peter Richt\\'arik","submitted_at":"2014-10-01T21:42:04Z","abstract_excerpt":"We consider the problem of unconstrained minimization of a smooth function in the derivative-free setting using. In particular, we propose and study a simplified variant of the direct search method (of direction type), which we call simplified direct search (SDS). Unlike standard direct search methods, which depend on a large number of parameters that need to be tuned, SDS depends on a single scalar parameter only.\n  Despite relevant research activity in direct search methods spanning several decades, complexity guarantees---bounds on the number of function evaluations needed to find an approx"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1410.0390","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":"1410.0390","created_at":"2026-05-18T02:37:44.240914+00:00"},{"alias_kind":"arxiv_version","alias_value":"1410.0390v2","created_at":"2026-05-18T02:37:44.240914+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1410.0390","created_at":"2026-05-18T02:37:44.240914+00:00"},{"alias_kind":"pith_short_12","alias_value":"KRIOWY4J2EUX","created_at":"2026-05-18T12:28:35.611951+00:00"},{"alias_kind":"pith_short_16","alias_value":"KRIOWY4J2EUXMP2M","created_at":"2026-05-18T12:28:35.611951+00:00"},{"alias_kind":"pith_short_8","alias_value":"KRIOWY4J","created_at":"2026-05-18T12:28:35.611951+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2605.13434","citing_title":"Rescaled Asynchronous SGD: Optimal Distributed Optimization under Data and System Heterogeneity","ref_index":61,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KRIOWY4J2EUXMP2MVS77JXQT3S","json":"https://pith.science/pith/KRIOWY4J2EUXMP2MVS77JXQT3S.json","graph_json":"https://pith.science/api/pith-number/KRIOWY4J2EUXMP2MVS77JXQT3S/graph.json","events_json":"https://pith.science/api/pith-number/KRIOWY4J2EUXMP2MVS77JXQT3S/events.json","paper":"https://pith.science/paper/KRIOWY4J"},"agent_actions":{"view_html":"https://pith.science/pith/KRIOWY4J2EUXMP2MVS77JXQT3S","download_json":"https://pith.science/pith/KRIOWY4J2EUXMP2MVS77JXQT3S.json","view_paper":"https://pith.science/paper/KRIOWY4J","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1410.0390&json=true","fetch_graph":"https://pith.science/api/pith-number/KRIOWY4J2EUXMP2MVS77JXQT3S/graph.json","fetch_events":"https://pith.science/api/pith-number/KRIOWY4J2EUXMP2MVS77JXQT3S/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KRIOWY4J2EUXMP2MVS77JXQT3S/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KRIOWY4J2EUXMP2MVS77JXQT3S/action/storage_attestation","attest_author":"https://pith.science/pith/KRIOWY4J2EUXMP2MVS77JXQT3S/action/author_attestation","sign_citation":"https://pith.science/pith/KRIOWY4J2EUXMP2MVS77JXQT3S/action/citation_signature","submit_replication":"https://pith.science/pith/KRIOWY4J2EUXMP2MVS77JXQT3S/action/replication_record"}},"created_at":"2026-05-18T02:37:44.240914+00:00","updated_at":"2026-05-18T02:37:44.240914+00:00"}