{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:JUBH4GR44WFLU7RDDALQ4Z425C","short_pith_number":"pith:JUBH4GR4","schema_version":"1.0","canonical_sha256":"4d027e1a3ce58aba7e2318170e679ae8bac269b974b93ceed03d83696a3be1f9","source":{"kind":"arxiv","id":"1606.07407","version":4},"attestation_state":"computed","paper":{"title":"High-Dimensional Sparse Fourier Algorithms","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NA"],"primary_cat":"math.NA","authors_text":"Andrew Christlieb, Bosu Choi, Yang Wang","submitted_at":"2016-06-23T18:56:06Z","abstract_excerpt":"In this paper, we discuss the development of a sublinear sparse Fourier algorithm for high-dimensional data. In ``Adaptive Sublinear Time Fourier Algorithm\" by D. Lawlor, Y. Wang and A. Christlieb (2013), an efficient algorithm with $\\Theta(k\\log k)$ average-case runtime and $\\Theta(k)$ average-case sampling complexity for the one-dimensional sparse FFT was developed for signals of bandwidth $N$, where $k$ is the number of significant modes such that $k\\ll N$.\n  In this work we develop an efficient algorithm for sparse FFT for higher dimensional signals, extending some of the ideas in the pape"},"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":"1606.07407","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2016-06-23T18:56:06Z","cross_cats_sorted":["cs.NA"],"title_canon_sha256":"50b5c1b5f9f19940e5b5d30efda569c5b7e14013b98005002f7d18cbff1cba20","abstract_canon_sha256":"1065c028419be28c1accd1661813616efda8dff382672e94f54a99963ebc17be"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:41:56.879193Z","signature_b64":"3kGppW2yW5UD7SRJURYSpERma7bFj+8Sd+KpNP3YKdwA1n1bp5SEzuR4w+ZNm3w7zzhuGzh7gcAhX+KQY3mSAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4d027e1a3ce58aba7e2318170e679ae8bac269b974b93ceed03d83696a3be1f9","last_reissued_at":"2026-05-17T23:41:56.878484Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:41:56.878484Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"High-Dimensional Sparse Fourier Algorithms","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NA"],"primary_cat":"math.NA","authors_text":"Andrew Christlieb, Bosu Choi, Yang Wang","submitted_at":"2016-06-23T18:56:06Z","abstract_excerpt":"In this paper, we discuss the development of a sublinear sparse Fourier algorithm for high-dimensional data. In ``Adaptive Sublinear Time Fourier Algorithm\" by D. Lawlor, Y. Wang and A. Christlieb (2013), an efficient algorithm with $\\Theta(k\\log k)$ average-case runtime and $\\Theta(k)$ average-case sampling complexity for the one-dimensional sparse FFT was developed for signals of bandwidth $N$, where $k$ is the number of significant modes such that $k\\ll N$.\n  In this work we develop an efficient algorithm for sparse FFT for higher dimensional signals, extending some of the ideas in the pape"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1606.07407","kind":"arxiv","version":4},"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":"1606.07407","created_at":"2026-05-17T23:41:56.878604+00:00"},{"alias_kind":"arxiv_version","alias_value":"1606.07407v4","created_at":"2026-05-17T23:41:56.878604+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1606.07407","created_at":"2026-05-17T23:41:56.878604+00:00"},{"alias_kind":"pith_short_12","alias_value":"JUBH4GR44WFL","created_at":"2026-05-18T12:30:25.849896+00:00"},{"alias_kind":"pith_short_16","alias_value":"JUBH4GR44WFLU7RD","created_at":"2026-05-18T12:30:25.849896+00:00"},{"alias_kind":"pith_short_8","alias_value":"JUBH4GR4","created_at":"2026-05-18T12:30:25.849896+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/JUBH4GR44WFLU7RDDALQ4Z425C","json":"https://pith.science/pith/JUBH4GR44WFLU7RDDALQ4Z425C.json","graph_json":"https://pith.science/api/pith-number/JUBH4GR44WFLU7RDDALQ4Z425C/graph.json","events_json":"https://pith.science/api/pith-number/JUBH4GR44WFLU7RDDALQ4Z425C/events.json","paper":"https://pith.science/paper/JUBH4GR4"},"agent_actions":{"view_html":"https://pith.science/pith/JUBH4GR44WFLU7RDDALQ4Z425C","download_json":"https://pith.science/pith/JUBH4GR44WFLU7RDDALQ4Z425C.json","view_paper":"https://pith.science/paper/JUBH4GR4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1606.07407&json=true","fetch_graph":"https://pith.science/api/pith-number/JUBH4GR44WFLU7RDDALQ4Z425C/graph.json","fetch_events":"https://pith.science/api/pith-number/JUBH4GR44WFLU7RDDALQ4Z425C/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JUBH4GR44WFLU7RDDALQ4Z425C/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JUBH4GR44WFLU7RDDALQ4Z425C/action/storage_attestation","attest_author":"https://pith.science/pith/JUBH4GR44WFLU7RDDALQ4Z425C/action/author_attestation","sign_citation":"https://pith.science/pith/JUBH4GR44WFLU7RDDALQ4Z425C/action/citation_signature","submit_replication":"https://pith.science/pith/JUBH4GR44WFLU7RDDALQ4Z425C/action/replication_record"}},"created_at":"2026-05-17T23:41:56.878604+00:00","updated_at":"2026-05-17T23:41:56.878604+00:00"}