{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:OJQLPUPS6APQCABUFLD4HDXPK6","short_pith_number":"pith:OJQLPUPS","schema_version":"1.0","canonical_sha256":"7260b7d1f2f01f0100342ac7c38eef57a66866a70713740629f00b64743b563b","source":{"kind":"arxiv","id":"2311.14873","version":1},"attestation_state":"computed","paper":{"title":"RTSMS: Randomized Tucker with single-mode sketching","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NA"],"primary_cat":"math.NA","authors_text":"Behnam Hashemi, Yuji Nakatsukasa","submitted_at":"2023-11-24T23:50:43Z","abstract_excerpt":"We propose RTSMS (Randomized Tucker via Single-Mode-Sketching), a randomized algorithm for approximately computing a low-rank Tucker decomposition of a given tensor. It uses sketching and least-squares to compute the Tucker decomposition in a sequentially truncated manner. The algorithm only sketches one mode at a time, so the sketch matrices are significantly smaller than alternative approaches. The algorithm is demonstrated to be competitive with existing methods, sometimes outperforming them by a large margin."},"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":"2311.14873","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2023-11-24T23:50:43Z","cross_cats_sorted":["cs.NA"],"title_canon_sha256":"5fd4453418d2c4b2a1d7f4eb4c9348c738c888d6760b8ebc5a82fb257b4d03f9","abstract_canon_sha256":"feecb6b9d7f778f4fc127a060f4e706215f256a18994bfb9ab0c84494835c665"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:16:54.900580Z","signature_b64":"yjAN8b2xGHUddvsN6Fv3yERSgYVyP49FRfxPZRmklpsH45xkcleccXolTnClo1GnKWwzKnX8DhErKGLOevBeCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7260b7d1f2f01f0100342ac7c38eef57a66866a70713740629f00b64743b563b","last_reissued_at":"2026-07-05T07:16:54.900162Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:16:54.900162Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"RTSMS: Randomized Tucker with single-mode sketching","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NA"],"primary_cat":"math.NA","authors_text":"Behnam Hashemi, Yuji Nakatsukasa","submitted_at":"2023-11-24T23:50:43Z","abstract_excerpt":"We propose RTSMS (Randomized Tucker via Single-Mode-Sketching), a randomized algorithm for approximately computing a low-rank Tucker decomposition of a given tensor. It uses sketching and least-squares to compute the Tucker decomposition in a sequentially truncated manner. The algorithm only sketches one mode at a time, so the sketch matrices are significantly smaller than alternative approaches. The algorithm is demonstrated to be competitive with existing methods, sometimes outperforming them by a large margin."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.14873","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/2311.14873/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":"2311.14873","created_at":"2026-07-05T07:16:54.900218+00:00"},{"alias_kind":"arxiv_version","alias_value":"2311.14873v1","created_at":"2026-07-05T07:16:54.900218+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.14873","created_at":"2026-07-05T07:16:54.900218+00:00"},{"alias_kind":"pith_short_12","alias_value":"OJQLPUPS6APQ","created_at":"2026-07-05T07:16:54.900218+00:00"},{"alias_kind":"pith_short_16","alias_value":"OJQLPUPS6APQCABU","created_at":"2026-07-05T07:16:54.900218+00:00"},{"alias_kind":"pith_short_8","alias_value":"OJQLPUPS","created_at":"2026-07-05T07:16:54.900218+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.04840","citing_title":"Efficient randomized algorithms for the fixed Tucker-rank problem of Tucker decomposition with adaptive shifts","ref_index":28,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OJQLPUPS6APQCABUFLD4HDXPK6","json":"https://pith.science/pith/OJQLPUPS6APQCABUFLD4HDXPK6.json","graph_json":"https://pith.science/api/pith-number/OJQLPUPS6APQCABUFLD4HDXPK6/graph.json","events_json":"https://pith.science/api/pith-number/OJQLPUPS6APQCABUFLD4HDXPK6/events.json","paper":"https://pith.science/paper/OJQLPUPS"},"agent_actions":{"view_html":"https://pith.science/pith/OJQLPUPS6APQCABUFLD4HDXPK6","download_json":"https://pith.science/pith/OJQLPUPS6APQCABUFLD4HDXPK6.json","view_paper":"https://pith.science/paper/OJQLPUPS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2311.14873&json=true","fetch_graph":"https://pith.science/api/pith-number/OJQLPUPS6APQCABUFLD4HDXPK6/graph.json","fetch_events":"https://pith.science/api/pith-number/OJQLPUPS6APQCABUFLD4HDXPK6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OJQLPUPS6APQCABUFLD4HDXPK6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OJQLPUPS6APQCABUFLD4HDXPK6/action/storage_attestation","attest_author":"https://pith.science/pith/OJQLPUPS6APQCABUFLD4HDXPK6/action/author_attestation","sign_citation":"https://pith.science/pith/OJQLPUPS6APQCABUFLD4HDXPK6/action/citation_signature","submit_replication":"https://pith.science/pith/OJQLPUPS6APQCABUFLD4HDXPK6/action/replication_record"}},"created_at":"2026-07-05T07:16:54.900218+00:00","updated_at":"2026-07-05T07:16:54.900218+00:00"}