{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:7ISFPLWTNBRRP6LN3MSAAFSMMP","short_pith_number":"pith:7ISFPLWT","schema_version":"1.0","canonical_sha256":"fa2457aed3686317f96ddb2400164c63c8af691f0263ad9dfeecbbe82e57d32c","source":{"kind":"arxiv","id":"2003.09673","version":1},"attestation_state":"computed","paper":{"title":"A dimensionality reduction technique for unconstrained global optimization of functions with low effective dimensionality","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Adilet Otemissov, Coralia Cartis","submitted_at":"2020-03-21T14:45:43Z","abstract_excerpt":"We investigate the unconstrained global optimization of functions with low effective dimensionality, that are constant along certain (unknown) linear subspaces. Extending the technique of random subspace embeddings in [Wang et al., Bayesian optimization in a billion dimensions via random embeddings. JAIR, 55(1): 361--387, 2016], we study a generic Random Embeddings for Global Optimization (REGO) framework that is compatible with any global minimization algorithm. Instead of the original, potentially large-scale optimization problem, within REGO, a Gaussian random, low-dimensional problem with "},"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":"2003.09673","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2020-03-21T14:45:43Z","cross_cats_sorted":[],"title_canon_sha256":"59812d02c732bbbdd2f4ff62d44a237b14478ab47deb867a10fc6079e9789fa5","abstract_canon_sha256":"6b441558d72de8b1c7b071130ea3d2f766995607254f45c9cff2da33a1ce7594"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:49:46.003498Z","signature_b64":"yvLXHP9Wrx91I84t9p/ZFabkJiE02EFGchsIo+O8JO3c/+9NZwWVDrXq8AjAj1/ItuT7bY6p+sasr6kRitTPBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fa2457aed3686317f96ddb2400164c63c8af691f0263ad9dfeecbbe82e57d32c","last_reissued_at":"2026-07-05T00:49:46.003137Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:49:46.003137Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A dimensionality reduction technique for unconstrained global optimization of functions with low effective dimensionality","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Adilet Otemissov, Coralia Cartis","submitted_at":"2020-03-21T14:45:43Z","abstract_excerpt":"We investigate the unconstrained global optimization of functions with low effective dimensionality, that are constant along certain (unknown) linear subspaces. Extending the technique of random subspace embeddings in [Wang et al., Bayesian optimization in a billion dimensions via random embeddings. JAIR, 55(1): 361--387, 2016], we study a generic Random Embeddings for Global Optimization (REGO) framework that is compatible with any global minimization algorithm. Instead of the original, potentially large-scale optimization problem, within REGO, a Gaussian random, low-dimensional problem with "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2003.09673","kind":"arxiv","version":1},"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/2003.09673/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2003.09673","created_at":"2026-07-05T00:49:46.003197+00:00"},{"alias_kind":"arxiv_version","alias_value":"2003.09673v1","created_at":"2026-07-05T00:49:46.003197+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2003.09673","created_at":"2026-07-05T00:49:46.003197+00:00"},{"alias_kind":"pith_short_12","alias_value":"7ISFPLWTNBRR","created_at":"2026-07-05T00:49:46.003197+00:00"},{"alias_kind":"pith_short_16","alias_value":"7ISFPLWTNBRRP6LN","created_at":"2026-07-05T00:49:46.003197+00:00"},{"alias_kind":"pith_short_8","alias_value":"7ISFPLWT","created_at":"2026-07-05T00:49:46.003197+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.09183","citing_title":"Dimensionality Reduction Techniques for Global Bayesian Optimisation","ref_index":4,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7ISFPLWTNBRRP6LN3MSAAFSMMP","json":"https://pith.science/pith/7ISFPLWTNBRRP6LN3MSAAFSMMP.json","graph_json":"https://pith.science/api/pith-number/7ISFPLWTNBRRP6LN3MSAAFSMMP/graph.json","events_json":"https://pith.science/api/pith-number/7ISFPLWTNBRRP6LN3MSAAFSMMP/events.json","paper":"https://pith.science/paper/7ISFPLWT"},"agent_actions":{"view_html":"https://pith.science/pith/7ISFPLWTNBRRP6LN3MSAAFSMMP","download_json":"https://pith.science/pith/7ISFPLWTNBRRP6LN3MSAAFSMMP.json","view_paper":"https://pith.science/paper/7ISFPLWT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2003.09673&json=true","fetch_graph":"https://pith.science/api/pith-number/7ISFPLWTNBRRP6LN3MSAAFSMMP/graph.json","fetch_events":"https://pith.science/api/pith-number/7ISFPLWTNBRRP6LN3MSAAFSMMP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7ISFPLWTNBRRP6LN3MSAAFSMMP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7ISFPLWTNBRRP6LN3MSAAFSMMP/action/storage_attestation","attest_author":"https://pith.science/pith/7ISFPLWTNBRRP6LN3MSAAFSMMP/action/author_attestation","sign_citation":"https://pith.science/pith/7ISFPLWTNBRRP6LN3MSAAFSMMP/action/citation_signature","submit_replication":"https://pith.science/pith/7ISFPLWTNBRRP6LN3MSAAFSMMP/action/replication_record"}},"created_at":"2026-07-05T00:49:46.003197+00:00","updated_at":"2026-07-05T00:49:46.003197+00:00"}