{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2008:JT6SC5PKG52N6I3Q7WJLNOGM34","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":"0e4f2137c5ac4a59fc7ecb6ebe1a3d9ff28c003cad9cf6f1c9498cdd5407f0d3","cross_cats_sorted":["cs.IT","cs.NA","math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NA","submitted_at":"2008-03-06T08:04:13Z","title_canon_sha256":"aec33e522f038cae9d8dcaffe9eb6e553a730c3677cae4399dbb1ca53ca0e1fb"},"schema_version":"1.0","source":{"id":"0803.0811","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"0803.0811","created_at":"2026-06-03T22:06:04Z"},{"alias_kind":"arxiv_version","alias_value":"0803.0811v3","created_at":"2026-06-03T22:06:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.0803.0811","created_at":"2026-06-03T22:06:04Z"},{"alias_kind":"pith_short_12","alias_value":"JT6SC5PKG52N","created_at":"2026-06-03T22:06:04Z"},{"alias_kind":"pith_short_16","alias_value":"JT6SC5PKG52N6I3Q","created_at":"2026-06-03T22:06:04Z"},{"alias_kind":"pith_short_8","alias_value":"JT6SC5PK","created_at":"2026-06-03T22:06:04Z"}],"graph_snapshots":[{"event_id":"sha256:07c6354da92dd99c7189b69dbf79fe5ca5a4d70c6025eabcfa994b07d029e9b7","target":"graph","created_at":"2026-06-03T22:06:04Z","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/0803.0811/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We propose a new method for reconstruction of sparse signals with and without noisy perturbations, termed the subspace pursuit algorithm. The algorithm has two important characteristics: low computational complexity, comparable to that of orthogonal matching pursuit techniques when applied to very sparse signals, and reconstruction accuracy of the same order as that of LP optimization methods. The presented analysis shows that in the noiseless setting, the proposed algorithm can exactly reconstruct arbitrary sparse signals provided that the sensing matrix satisfies the restricted isometry prop","authors_text":"Olgica Milenkovic, Wei Dai","cross_cats":["cs.IT","cs.NA","math.IT"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NA","submitted_at":"2008-03-06T08:04:13Z","title":"Subspace Pursuit for Compressive Sensing Signal Reconstruction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"0803.0811","kind":"arxiv","version":3},"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:d8c7224907e76e4b50032f309403f020c9726442ea4028c31fbe2d64b7f6eceb","target":"record","created_at":"2026-06-03T22:06:04Z","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":"0e4f2137c5ac4a59fc7ecb6ebe1a3d9ff28c003cad9cf6f1c9498cdd5407f0d3","cross_cats_sorted":["cs.IT","cs.NA","math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NA","submitted_at":"2008-03-06T08:04:13Z","title_canon_sha256":"aec33e522f038cae9d8dcaffe9eb6e553a730c3677cae4399dbb1ca53ca0e1fb"},"schema_version":"1.0","source":{"id":"0803.0811","kind":"arxiv","version":3}},"canonical_sha256":"4cfd2175ea3774df2370fd92b6b8ccdf3d761bd3ec9e1f46914d3b394c6b0435","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4cfd2175ea3774df2370fd92b6b8ccdf3d761bd3ec9e1f46914d3b394c6b0435","first_computed_at":"2026-06-03T22:06:04.847353Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-03T22:06:04.847353Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ruHlsSdnNKMThbG8u8GyCVLd1B1IY0xaRmeFirzjnFdgYawdc6QeNr6MxfUIpQBTCE+Y6tTLKKiwkrA9V1JUAA==","signature_status":"signed_v1","signed_at":"2026-06-03T22:06:04.847787Z","signed_message":"canonical_sha256_bytes"},"source_id":"0803.0811","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d8c7224907e76e4b50032f309403f020c9726442ea4028c31fbe2d64b7f6eceb","sha256:07c6354da92dd99c7189b69dbf79fe5ca5a4d70c6025eabcfa994b07d029e9b7"],"state_sha256":"519101e8c31767e36eec0216cebfed0490c5ac497ec54e6fa9b8eaa269a8f02d"}