{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:SWZ74JCEYVZHDESMV5TSWR3DKY","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":"1b87efe37776597cbb11fd2f388a05efb030d0a55aa4ad14b51526996c5456c0","cross_cats_sorted":["math.NA","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-06-01T10:17:12Z","title_canon_sha256":"c76b9fd5a02c1e848a2e5b2ec2635ee8daca8ce3af7107035c413e7f402d566f"},"schema_version":"1.0","source":{"id":"1706.00241","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1706.00241","created_at":"2026-05-18T00:43:14Z"},{"alias_kind":"arxiv_version","alias_value":"1706.00241v1","created_at":"2026-05-18T00:43:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.00241","created_at":"2026-05-18T00:43:14Z"},{"alias_kind":"pith_short_12","alias_value":"SWZ74JCEYVZH","created_at":"2026-05-18T12:31:43Z"},{"alias_kind":"pith_short_16","alias_value":"SWZ74JCEYVZHDESM","created_at":"2026-05-18T12:31:43Z"},{"alias_kind":"pith_short_8","alias_value":"SWZ74JCE","created_at":"2026-05-18T12:31:43Z"}],"graph_snapshots":[{"event_id":"sha256:55f56fcce96bf31d6a6b22ec07ec4f74460286444105ba1534e9c2bc926c4792","target":"graph","created_at":"2026-05-18T00:43:14Z","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"},"paper":{"abstract_excerpt":"Solving symmetric positive definite linear problems is a fundamental computational task in machine learning. The exact solution, famously, is cubicly expensive in the size of the matrix. To alleviate this problem, several linear-time approximations, such as spectral and inducing-point methods, have been suggested and are now in wide use. These are low-rank approximations that choose the low-rank space a priori and do not refine it over time. While this allows linear cost in the data-set size, it also causes a finite, uncorrected approximation error. Authors from numerical linear algebra have e","authors_text":"Filip de Roos, Philipp Hennig","cross_cats":["math.NA","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-06-01T10:17:12Z","title":"Krylov Subspace Recycling for Fast Iterative Least-Squares in Machine Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.00241","kind":"arxiv","version":1},"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":"sha256:29a930d37bd76d571c3bf41a8fa4e2f6cdf5c1bb1ae55f99079e0fccb82fa803","target":"record","created_at":"2026-05-18T00:43:14Z","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":"1b87efe37776597cbb11fd2f388a05efb030d0a55aa4ad14b51526996c5456c0","cross_cats_sorted":["math.NA","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-06-01T10:17:12Z","title_canon_sha256":"c76b9fd5a02c1e848a2e5b2ec2635ee8daca8ce3af7107035c413e7f402d566f"},"schema_version":"1.0","source":{"id":"1706.00241","kind":"arxiv","version":1}},"canonical_sha256":"95b3fe2444c57271924caf672b47635605fb423ce5b6058f74e39a7084918111","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"95b3fe2444c57271924caf672b47635605fb423ce5b6058f74e39a7084918111","first_computed_at":"2026-05-18T00:43:14.645672Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:43:14.645672Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"FMSCWP10hqq4Tqm2fD2tDRE3bQ14+aUXotKf/XCF7kPar64Gvn8kPUDBB1gB9hxjbVhvR4yQZM56xEDLGdGqBA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:43:14.646363Z","signed_message":"canonical_sha256_bytes"},"source_id":"1706.00241","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:29a930d37bd76d571c3bf41a8fa4e2f6cdf5c1bb1ae55f99079e0fccb82fa803","sha256:55f56fcce96bf31d6a6b22ec07ec4f74460286444105ba1534e9c2bc926c4792"],"state_sha256":"bc1b45a0d4aff1013f455dee600946b1a51dfd63616512bb03b240a9b33e935d"}