{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:QERWYFEXJVBBNYAGOTKJW4OUEE","short_pith_number":"pith:QERWYFEX","canonical_record":{"source":{"id":"2110.14985","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-10-28T09:50:29Z","cross_cats_sorted":["math.OC"],"title_canon_sha256":"2f4e8e8e4340876beff5594b32027a4960fa923639077adc6bb0e4bb80737b29","abstract_canon_sha256":"ba3896b054ffe3d6a2dc4f4a03ff304e6a9752511f3f3b77750fcc926e1f720d"},"schema_version":"1.0"},"canonical_sha256":"81236c14974d4216e00674d49b71d421377ef894b2cfb320f752d7616abf1a5d","source":{"kind":"arxiv","id":"2110.14985","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2110.14985","created_at":"2026-07-05T05:12:49Z"},{"alias_kind":"arxiv_version","alias_value":"2110.14985v2","created_at":"2026-07-05T05:12:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.14985","created_at":"2026-07-05T05:12:49Z"},{"alias_kind":"pith_short_12","alias_value":"QERWYFEXJVBB","created_at":"2026-07-05T05:12:49Z"},{"alias_kind":"pith_short_16","alias_value":"QERWYFEXJVBBNYAG","created_at":"2026-07-05T05:12:49Z"},{"alias_kind":"pith_short_8","alias_value":"QERWYFEX","created_at":"2026-07-05T05:12:49Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:QERWYFEXJVBBNYAGOTKJW4OUEE","target":"record","payload":{"canonical_record":{"source":{"id":"2110.14985","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-10-28T09:50:29Z","cross_cats_sorted":["math.OC"],"title_canon_sha256":"2f4e8e8e4340876beff5594b32027a4960fa923639077adc6bb0e4bb80737b29","abstract_canon_sha256":"ba3896b054ffe3d6a2dc4f4a03ff304e6a9752511f3f3b77750fcc926e1f720d"},"schema_version":"1.0"},"canonical_sha256":"81236c14974d4216e00674d49b71d421377ef894b2cfb320f752d7616abf1a5d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:12:49.319025Z","signature_b64":"Vk9MQGw/2sHGwc9lfz0QVMp9K1Ijbc7pMhGoASZGhqEd4pGJMl2/l8Rz6vpbbG0ADm8ib6vPBA1VfPms+BCFAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"81236c14974d4216e00674d49b71d421377ef894b2cfb320f752d7616abf1a5d","last_reissued_at":"2026-07-05T05:12:49.318517Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:12:49.318517Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2110.14985","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":"2026-07-05T05:12:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"i+bTrdMA0pb8HeJnU1Ux07BI4zIPran+rPib6aJiNzkUzLKuaURkwMd6H73xU4AUO4nXhUFwoIkO4HpSc2fiBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T16:45:22.190843Z"},"content_sha256":"9a566f2ea53d8c3e937cde11e4d83b1153a6d1e1e0edc19bd5cfefe992dfc161","schema_version":"1.0","event_id":"sha256:9a566f2ea53d8c3e937cde11e4d83b1153a6d1e1e0edc19bd5cfefe992dfc161"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:QERWYFEXJVBBNYAGOTKJW4OUEE","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A machine learning approach for fighting the curse of dimensionality in global optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.OC"],"primary_cat":"cs.LG","authors_text":"Alejandro M. Arag\\'on, Julian F. Schumann","submitted_at":"2021-10-28T09:50:29Z","abstract_excerpt":"Finding global optima in high-dimensional optimization problems is extremely challenging since the number of function evaluations required to sufficiently explore the search space increases exponentially with its dimensionality. Furthermore, multimodal cost functions render local gradient-based search techniques ineffective. To overcome these difficulties, we propose to trim uninteresting regions of the search space where global optima are unlikely to be found by means of autoencoders, exploiting the lower intrinsic dimensionality of certain cost functions; optima are then searched over lower-"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.14985","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/2110.14985/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":"2026-07-05T05:12:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pcjAIHHqVSQdjAcvIQnL8kWKMMOIMcaKMHkLa3CUaz/9pXjl20I1rmfLRyMaxqqdNSIjgYzIw5yddLWPN/84BQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T16:45:22.191807Z"},"content_sha256":"a75f7bf68211c4cc99dadfa1167805b2dffe04e908c8287b6bea96dc33aa037f","schema_version":"1.0","event_id":"sha256:a75f7bf68211c4cc99dadfa1167805b2dffe04e908c8287b6bea96dc33aa037f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QERWYFEXJVBBNYAGOTKJW4OUEE/bundle.json","state_url":"https://pith.science/pith/QERWYFEXJVBBNYAGOTKJW4OUEE/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QERWYFEXJVBBNYAGOTKJW4OUEE/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":"2026-08-03T16:45:22Z","links":{"resolver":"https://pith.science/pith/QERWYFEXJVBBNYAGOTKJW4OUEE","bundle":"https://pith.science/pith/QERWYFEXJVBBNYAGOTKJW4OUEE/bundle.json","state":"https://pith.science/pith/QERWYFEXJVBBNYAGOTKJW4OUEE/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QERWYFEXJVBBNYAGOTKJW4OUEE/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:QERWYFEXJVBBNYAGOTKJW4OUEE","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":"ba3896b054ffe3d6a2dc4f4a03ff304e6a9752511f3f3b77750fcc926e1f720d","cross_cats_sorted":["math.OC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-10-28T09:50:29Z","title_canon_sha256":"2f4e8e8e4340876beff5594b32027a4960fa923639077adc6bb0e4bb80737b29"},"schema_version":"1.0","source":{"id":"2110.14985","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2110.14985","created_at":"2026-07-05T05:12:49Z"},{"alias_kind":"arxiv_version","alias_value":"2110.14985v2","created_at":"2026-07-05T05:12:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.14985","created_at":"2026-07-05T05:12:49Z"},{"alias_kind":"pith_short_12","alias_value":"QERWYFEXJVBB","created_at":"2026-07-05T05:12:49Z"},{"alias_kind":"pith_short_16","alias_value":"QERWYFEXJVBBNYAG","created_at":"2026-07-05T05:12:49Z"},{"alias_kind":"pith_short_8","alias_value":"QERWYFEX","created_at":"2026-07-05T05:12:49Z"}],"graph_snapshots":[{"event_id":"sha256:a75f7bf68211c4cc99dadfa1167805b2dffe04e908c8287b6bea96dc33aa037f","target":"graph","created_at":"2026-07-05T05:12:49Z","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/2110.14985/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Finding global optima in high-dimensional optimization problems is extremely challenging since the number of function evaluations required to sufficiently explore the search space increases exponentially with its dimensionality. Furthermore, multimodal cost functions render local gradient-based search techniques ineffective. To overcome these difficulties, we propose to trim uninteresting regions of the search space where global optima are unlikely to be found by means of autoencoders, exploiting the lower intrinsic dimensionality of certain cost functions; optima are then searched over lower-","authors_text":"Alejandro M. Arag\\'on, Julian F. Schumann","cross_cats":["math.OC"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-10-28T09:50:29Z","title":"A machine learning approach for fighting the curse of dimensionality in global optimization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.14985","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":"sha256:9a566f2ea53d8c3e937cde11e4d83b1153a6d1e1e0edc19bd5cfefe992dfc161","target":"record","created_at":"2026-07-05T05:12:49Z","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":"ba3896b054ffe3d6a2dc4f4a03ff304e6a9752511f3f3b77750fcc926e1f720d","cross_cats_sorted":["math.OC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-10-28T09:50:29Z","title_canon_sha256":"2f4e8e8e4340876beff5594b32027a4960fa923639077adc6bb0e4bb80737b29"},"schema_version":"1.0","source":{"id":"2110.14985","kind":"arxiv","version":2}},"canonical_sha256":"81236c14974d4216e00674d49b71d421377ef894b2cfb320f752d7616abf1a5d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"81236c14974d4216e00674d49b71d421377ef894b2cfb320f752d7616abf1a5d","first_computed_at":"2026-07-05T05:12:49.318517Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:12:49.318517Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Vk9MQGw/2sHGwc9lfz0QVMp9K1Ijbc7pMhGoASZGhqEd4pGJMl2/l8Rz6vpbbG0ADm8ib6vPBA1VfPms+BCFAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T05:12:49.319025Z","signed_message":"canonical_sha256_bytes"},"source_id":"2110.14985","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9a566f2ea53d8c3e937cde11e4d83b1153a6d1e1e0edc19bd5cfefe992dfc161","sha256:a75f7bf68211c4cc99dadfa1167805b2dffe04e908c8287b6bea96dc33aa037f"],"state_sha256":"a7c2491ae48a5989d67798951241d88c0d4a80fde24f7ca47d77c4d387740cb2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zp4Wnz2f2bOZ5t1mij9eI3Wn8h83KY2Mmo9Kl3yr/xZ7cndFWe8LgyeAJYLdU5eMagJHRZcj6EiCrbCSQvVeAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-03T16:45:22.198673Z","bundle_sha256":"a3d9e1fa37cc733dc4b5939485c79b072bf7ebc20b6429192d6b7c4f17f64d98"}}