{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:BFH6FZZHMF52EIZKDSAMKT3MXZ","short_pith_number":"pith:BFH6FZZH","canonical_record":{"source":{"id":"1909.09249","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2019-09-19T22:13:03Z","cross_cats_sorted":[],"title_canon_sha256":"0184f3ef8872bc8774d1ad59047a3b8deaa87344f09079d62c94bcc94355bbb7","abstract_canon_sha256":"52648ffd6b6f0c24f29fd25a00adbabdb9424a3ce863764de5c95e8cb7e6cb44"},"schema_version":"1.0"},"canonical_sha256":"094fe2e727617ba2232a1c80c54f6cbe7e2ed433e4b483bc06f730efbc4cd9e3","source":{"kind":"arxiv","id":"1909.09249","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1909.09249","created_at":"2026-07-05T00:45:52Z"},{"alias_kind":"arxiv_version","alias_value":"1909.09249v2","created_at":"2026-07-05T00:45:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1909.09249","created_at":"2026-07-05T00:45:52Z"},{"alias_kind":"pith_short_12","alias_value":"BFH6FZZHMF52","created_at":"2026-07-05T00:45:52Z"},{"alias_kind":"pith_short_16","alias_value":"BFH6FZZHMF52EIZK","created_at":"2026-07-05T00:45:52Z"},{"alias_kind":"pith_short_8","alias_value":"BFH6FZZH","created_at":"2026-07-05T00:45:52Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:BFH6FZZHMF52EIZKDSAMKT3MXZ","target":"record","payload":{"canonical_record":{"source":{"id":"1909.09249","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2019-09-19T22:13:03Z","cross_cats_sorted":[],"title_canon_sha256":"0184f3ef8872bc8774d1ad59047a3b8deaa87344f09079d62c94bcc94355bbb7","abstract_canon_sha256":"52648ffd6b6f0c24f29fd25a00adbabdb9424a3ce863764de5c95e8cb7e6cb44"},"schema_version":"1.0"},"canonical_sha256":"094fe2e727617ba2232a1c80c54f6cbe7e2ed433e4b483bc06f730efbc4cd9e3","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:45:52.350358Z","signature_b64":"1ILBJldZaYeIhzUxwtElpyXwSLx3QcU9MwJ4PooUhalnTCJOQmTvezhLyC4YQLP9S/eoR/Es1p5z2jeHuSVVDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"094fe2e727617ba2232a1c80c54f6cbe7e2ed433e4b483bc06f730efbc4cd9e3","last_reissued_at":"2026-07-05T00:45:52.349885Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:45:52.349885Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1909.09249","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T00:45:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kozZjCqzOMS/Kqd84oe03EIjSV/aC/J7vg792fYHa/Up2fcRYkv9HDkP8RzDxIcV1/3+R5eHK7MS/e+p8PcpCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-20T15:25:59.040719Z"},"content_sha256":"f8a4ea65e872acfac1f76ab45ec27007bcdd69cc690fcd72f58c644ade908745","schema_version":"1.0","event_id":"sha256:f8a4ea65e872acfac1f76ab45ec27007bcdd69cc690fcd72f58c644ade908745"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:BFH6FZZHMF52EIZKDSAMKT3MXZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A consensus-based global optimization method for high dimensional machine learning problems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Jos\\'e A. Carrillo, Lei Li, Shi Jin, Yuhua Zhu","submitted_at":"2019-09-19T22:13:03Z","abstract_excerpt":"We improve recently introduced consensus-based optimization method, proposed in [R. Pinnau, C. Totzeck, O. Tse and S. Martin, Math. Models Methods Appl. Sci., 27(01):183--204, 2017], which is a gradient-free optimization method for general non-convex functions. We first replace the isotropic geometric Brownian motion by the component-wise one, thus removing the dimensionality dependence of the drift rate, making the method more competitive for high dimensional optimization problems. Secondly, we utilize the random mini-batch ideas to reduce the computational cost of calculating the weighted av"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1909.09249","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1909.09249/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T00:45:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rnLr3AgX3U7zB93zt8Moov87LVccor0p/ms5T/olIWIivHHO+Ji3yMGBk4/tTL8OSi4VswXJ/nuQIv7j6i30CA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-20T15:25:59.041104Z"},"content_sha256":"0f3367e3d674991d437d11c58b1abf67c8ee1d3b4554733d7de86b68b615d6e0","schema_version":"1.0","event_id":"sha256:0f3367e3d674991d437d11c58b1abf67c8ee1d3b4554733d7de86b68b615d6e0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BFH6FZZHMF52EIZKDSAMKT3MXZ/bundle.json","state_url":"https://pith.science/pith/BFH6FZZHMF52EIZKDSAMKT3MXZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BFH6FZZHMF52EIZKDSAMKT3MXZ/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-20T15:25:59Z","links":{"resolver":"https://pith.science/pith/BFH6FZZHMF52EIZKDSAMKT3MXZ","bundle":"https://pith.science/pith/BFH6FZZHMF52EIZKDSAMKT3MXZ/bundle.json","state":"https://pith.science/pith/BFH6FZZHMF52EIZKDSAMKT3MXZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BFH6FZZHMF52EIZKDSAMKT3MXZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:BFH6FZZHMF52EIZKDSAMKT3MXZ","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"52648ffd6b6f0c24f29fd25a00adbabdb9424a3ce863764de5c95e8cb7e6cb44","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2019-09-19T22:13:03Z","title_canon_sha256":"0184f3ef8872bc8774d1ad59047a3b8deaa87344f09079d62c94bcc94355bbb7"},"schema_version":"1.0","source":{"id":"1909.09249","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1909.09249","created_at":"2026-07-05T00:45:52Z"},{"alias_kind":"arxiv_version","alias_value":"1909.09249v2","created_at":"2026-07-05T00:45:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1909.09249","created_at":"2026-07-05T00:45:52Z"},{"alias_kind":"pith_short_12","alias_value":"BFH6FZZHMF52","created_at":"2026-07-05T00:45:52Z"},{"alias_kind":"pith_short_16","alias_value":"BFH6FZZHMF52EIZK","created_at":"2026-07-05T00:45:52Z"},{"alias_kind":"pith_short_8","alias_value":"BFH6FZZH","created_at":"2026-07-05T00:45:52Z"}],"graph_snapshots":[{"event_id":"sha256:0f3367e3d674991d437d11c58b1abf67c8ee1d3b4554733d7de86b68b615d6e0","target":"graph","created_at":"2026-07-05T00:45:52Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/1909.09249/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We improve recently introduced consensus-based optimization method, proposed in [R. Pinnau, C. Totzeck, O. Tse and S. Martin, Math. Models Methods Appl. Sci., 27(01):183--204, 2017], which is a gradient-free optimization method for general non-convex functions. We first replace the isotropic geometric Brownian motion by the component-wise one, thus removing the dimensionality dependence of the drift rate, making the method more competitive for high dimensional optimization problems. Secondly, we utilize the random mini-batch ideas to reduce the computational cost of calculating the weighted av","authors_text":"Jos\\'e A. Carrillo, Lei Li, Shi Jin, Yuhua Zhu","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2019-09-19T22:13:03Z","title":"A consensus-based global optimization method for high dimensional machine learning problems"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1909.09249","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:f8a4ea65e872acfac1f76ab45ec27007bcdd69cc690fcd72f58c644ade908745","target":"record","created_at":"2026-07-05T00:45:52Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"52648ffd6b6f0c24f29fd25a00adbabdb9424a3ce863764de5c95e8cb7e6cb44","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2019-09-19T22:13:03Z","title_canon_sha256":"0184f3ef8872bc8774d1ad59047a3b8deaa87344f09079d62c94bcc94355bbb7"},"schema_version":"1.0","source":{"id":"1909.09249","kind":"arxiv","version":2}},"canonical_sha256":"094fe2e727617ba2232a1c80c54f6cbe7e2ed433e4b483bc06f730efbc4cd9e3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"094fe2e727617ba2232a1c80c54f6cbe7e2ed433e4b483bc06f730efbc4cd9e3","first_computed_at":"2026-07-05T00:45:52.349885Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:45:52.349885Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"1ILBJldZaYeIhzUxwtElpyXwSLx3QcU9MwJ4PooUhalnTCJOQmTvezhLyC4YQLP9S/eoR/Es1p5z2jeHuSVVDA==","signature_status":"signed_v1","signed_at":"2026-07-05T00:45:52.350358Z","signed_message":"canonical_sha256_bytes"},"source_id":"1909.09249","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f8a4ea65e872acfac1f76ab45ec27007bcdd69cc690fcd72f58c644ade908745","sha256:0f3367e3d674991d437d11c58b1abf67c8ee1d3b4554733d7de86b68b615d6e0"],"state_sha256":"9c6d6418fce672d0ad067a39cc07b4291b6391b6d39e34ff189335167da08d00"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JOQzTwVDjjvs/L1WCq95Qe50sg1fdycACVMt6E4mTea2RkaNvzCai+qL4UqE6E3+gxvqnZfg6TTeD5b3V6qRCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-20T15:25:59.043462Z","bundle_sha256":"27ebb5e963c2e66f73b61dccfc8ffc382d71e87762f38d89448243812e6974be"}}