{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2012:W6LDR7X46A6ONEBC3GT5DRJ5YE","short_pith_number":"pith:W6LDR7X4","schema_version":"1.0","canonical_sha256":"b79638fefcf03ce69022d9a7d1c53dc131cbebfd8c02adbfbbbadbd5eb1f1a9b","source":{"kind":"arxiv","id":"1210.5114","version":3},"attestation_state":"computed","paper":{"title":"Variable Metric Random Pursuit","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Bernd G\\\"artner, Christian L. M\\\"uller, Sebastian U. Stich","submitted_at":"2012-10-18T13:32:15Z","abstract_excerpt":"We consider unconstrained randomized optimization of smooth convex objective functions in the gradient-free setting. We analyze Random Pursuit (RP) algorithms with fixed (F-RP) and variable metric (V-RP). The algorithms only use zeroth-order information about the objective function and compute an approximate solution by repeated optimization over randomly chosen one-dimensional subspaces. The distribution of search directions is dictated by the chosen metric.\n  Variable Metric RP uses novel variants of a randomized zeroth-order Hessian approximation scheme recently introduced by Leventhal and "},"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":"1210.5114","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2012-10-18T13:32:15Z","cross_cats_sorted":[],"title_canon_sha256":"e12c655895dd0e34a403611759d4bf05fcaef58c05bff3fbcc4f80b88657b87a","abstract_canon_sha256":"54f59f674b697837836ea26b41a40c1f30223d28ddd4fc828bf4301d1b2104b5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:41:18.927518Z","signature_b64":"R+7cHbCnIphSOh1EN0q7zSi4voM9U5AEdr9WFmWLISROssRVECKkv21+2LmhtBJ6ziTWbVP3d5sDg3lM5RS2AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b79638fefcf03ce69022d9a7d1c53dc131cbebfd8c02adbfbbbadbd5eb1f1a9b","last_reissued_at":"2026-05-18T02:41:18.926959Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:41:18.926959Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Variable Metric Random Pursuit","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Bernd G\\\"artner, Christian L. M\\\"uller, Sebastian U. Stich","submitted_at":"2012-10-18T13:32:15Z","abstract_excerpt":"We consider unconstrained randomized optimization of smooth convex objective functions in the gradient-free setting. We analyze Random Pursuit (RP) algorithms with fixed (F-RP) and variable metric (V-RP). The algorithms only use zeroth-order information about the objective function and compute an approximate solution by repeated optimization over randomly chosen one-dimensional subspaces. The distribution of search directions is dictated by the chosen metric.\n  Variable Metric RP uses novel variants of a randomized zeroth-order Hessian approximation scheme recently introduced by Leventhal and "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1210.5114","kind":"arxiv","version":3},"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":"1210.5114","created_at":"2026-05-18T02:41:18.927065+00:00"},{"alias_kind":"arxiv_version","alias_value":"1210.5114v3","created_at":"2026-05-18T02:41:18.927065+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1210.5114","created_at":"2026-05-18T02:41:18.927065+00:00"},{"alias_kind":"pith_short_12","alias_value":"W6LDR7X46A6O","created_at":"2026-05-18T12:27:25.539911+00:00"},{"alias_kind":"pith_short_16","alias_value":"W6LDR7X46A6ONEBC","created_at":"2026-05-18T12:27:25.539911+00:00"},{"alias_kind":"pith_short_8","alias_value":"W6LDR7X4","created_at":"2026-05-18T12:27:25.539911+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/W6LDR7X46A6ONEBC3GT5DRJ5YE","json":"https://pith.science/pith/W6LDR7X46A6ONEBC3GT5DRJ5YE.json","graph_json":"https://pith.science/api/pith-number/W6LDR7X46A6ONEBC3GT5DRJ5YE/graph.json","events_json":"https://pith.science/api/pith-number/W6LDR7X46A6ONEBC3GT5DRJ5YE/events.json","paper":"https://pith.science/paper/W6LDR7X4"},"agent_actions":{"view_html":"https://pith.science/pith/W6LDR7X46A6ONEBC3GT5DRJ5YE","download_json":"https://pith.science/pith/W6LDR7X46A6ONEBC3GT5DRJ5YE.json","view_paper":"https://pith.science/paper/W6LDR7X4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1210.5114&json=true","fetch_graph":"https://pith.science/api/pith-number/W6LDR7X46A6ONEBC3GT5DRJ5YE/graph.json","fetch_events":"https://pith.science/api/pith-number/W6LDR7X46A6ONEBC3GT5DRJ5YE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/W6LDR7X46A6ONEBC3GT5DRJ5YE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/W6LDR7X46A6ONEBC3GT5DRJ5YE/action/storage_attestation","attest_author":"https://pith.science/pith/W6LDR7X46A6ONEBC3GT5DRJ5YE/action/author_attestation","sign_citation":"https://pith.science/pith/W6LDR7X46A6ONEBC3GT5DRJ5YE/action/citation_signature","submit_replication":"https://pith.science/pith/W6LDR7X46A6ONEBC3GT5DRJ5YE/action/replication_record"}},"created_at":"2026-05-18T02:41:18.927065+00:00","updated_at":"2026-05-18T02:41:18.927065+00:00"}