{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:D34ZAY36YETOMJS3OPLT4IBASC","short_pith_number":"pith:D34ZAY36","canonical_record":{"source":{"id":"1810.11115","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2018-10-25T21:33:08Z","cross_cats_sorted":["cs.IT","math.IT"],"title_canon_sha256":"297779dae5caf5bf6d967cc0136548d662baed137cd64a232c83dcb3a0bda194","abstract_canon_sha256":"db4d9b5e0c1466d23e84d9c4b6b0f5e2b04a36da35d9e9ed1f2f554ec8130696"},"schema_version":"1.0"},"canonical_sha256":"1ef990637ec126e6265b73d73e202090a035e160185d3682b5c6cf353067e5db","source":{"kind":"arxiv","id":"1810.11115","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1810.11115","created_at":"2026-05-17T23:47:08Z"},{"alias_kind":"arxiv_version","alias_value":"1810.11115v2","created_at":"2026-05-17T23:47:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.11115","created_at":"2026-05-17T23:47:08Z"},{"alias_kind":"pith_short_12","alias_value":"D34ZAY36YETO","created_at":"2026-05-18T12:32:19Z"},{"alias_kind":"pith_short_16","alias_value":"D34ZAY36YETOMJS3","created_at":"2026-05-18T12:32:19Z"},{"alias_kind":"pith_short_8","alias_value":"D34ZAY36","created_at":"2026-05-18T12:32:19Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:D34ZAY36YETOMJS3OPLT4IBASC","target":"record","payload":{"canonical_record":{"source":{"id":"1810.11115","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2018-10-25T21:33:08Z","cross_cats_sorted":["cs.IT","math.IT"],"title_canon_sha256":"297779dae5caf5bf6d967cc0136548d662baed137cd64a232c83dcb3a0bda194","abstract_canon_sha256":"db4d9b5e0c1466d23e84d9c4b6b0f5e2b04a36da35d9e9ed1f2f554ec8130696"},"schema_version":"1.0"},"canonical_sha256":"1ef990637ec126e6265b73d73e202090a035e160185d3682b5c6cf353067e5db","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:47:08.620883Z","signature_b64":"+gnWfaDuG33WXWQZE5jueDnJUZbSMOX/1bSBQiCPt26DlYXvFGuBC8Ejcfl3BlQKdTuWV6yuLMZfEurLMQ3DDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1ef990637ec126e6265b73d73e202090a035e160185d3682b5c6cf353067e5db","last_reissued_at":"2026-05-17T23:47:08.620257Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:47:08.620257Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1810.11115","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:47:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3Wy6cQGWW3nfNCIaH+NrcqFjsd5k3ZUxR1XoShMklSpmflhP3dik49nrDkXEMfbXZoQRaeyj6Chpw5adhEwJDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-10T23:31:24.995132Z"},"content_sha256":"5fbbe1d038d74a098b951c812ea9bc45aae428c90a23325cdd6b20c4bb781651","schema_version":"1.0","event_id":"sha256:5fbbe1d038d74a098b951c812ea9bc45aae428c90a23325cdd6b20c4bb781651"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:D34ZAY36YETOMJS3OPLT4IBASC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Sparse approximation of multivariate functions from small datasets via weighted orthogonal matching pursuit","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IT","math.IT"],"primary_cat":"math.NA","authors_text":"Ben Adcock, Simone Brugiapaglia","submitted_at":"2018-10-25T21:33:08Z","abstract_excerpt":"We show the potential of greedy recovery strategies for the sparse approximation of multivariate functions from a small dataset of pointwise evaluations by considering an extension of the orthogonal matching pursuit to the setting of weighted sparsity. The proposed recovery strategy is based on a formal derivation of the greedy index selection rule. Numerical experiments show that the proposed weighted orthogonal matching pursuit algorithm is able to reach accuracy levels similar to those of weighted $\\ell^1$ minimization programs while considerably improving the computational efficiency for s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.11115","kind":"arxiv","version":2},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:47:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wozFXWNf/II8kdpdJK38K70CZSGzbdy02zdQuvBTzho2WiOssYkPEMZDVPfl1oSt4G18FypcbTPNRLmCLxL2Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-10T23:31:24.995726Z"},"content_sha256":"be4f26cd04508aea561892d6465c5ebceef0da7d5cdab6d1ef80c04645bb749a","schema_version":"1.0","event_id":"sha256:be4f26cd04508aea561892d6465c5ebceef0da7d5cdab6d1ef80c04645bb749a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/D34ZAY36YETOMJS3OPLT4IBASC/bundle.json","state_url":"https://pith.science/pith/D34ZAY36YETOMJS3OPLT4IBASC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/D34ZAY36YETOMJS3OPLT4IBASC/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-10T23:31:24Z","links":{"resolver":"https://pith.science/pith/D34ZAY36YETOMJS3OPLT4IBASC","bundle":"https://pith.science/pith/D34ZAY36YETOMJS3OPLT4IBASC/bundle.json","state":"https://pith.science/pith/D34ZAY36YETOMJS3OPLT4IBASC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/D34ZAY36YETOMJS3OPLT4IBASC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:D34ZAY36YETOMJS3OPLT4IBASC","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":"db4d9b5e0c1466d23e84d9c4b6b0f5e2b04a36da35d9e9ed1f2f554ec8130696","cross_cats_sorted":["cs.IT","math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2018-10-25T21:33:08Z","title_canon_sha256":"297779dae5caf5bf6d967cc0136548d662baed137cd64a232c83dcb3a0bda194"},"schema_version":"1.0","source":{"id":"1810.11115","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1810.11115","created_at":"2026-05-17T23:47:08Z"},{"alias_kind":"arxiv_version","alias_value":"1810.11115v2","created_at":"2026-05-17T23:47:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.11115","created_at":"2026-05-17T23:47:08Z"},{"alias_kind":"pith_short_12","alias_value":"D34ZAY36YETO","created_at":"2026-05-18T12:32:19Z"},{"alias_kind":"pith_short_16","alias_value":"D34ZAY36YETOMJS3","created_at":"2026-05-18T12:32:19Z"},{"alias_kind":"pith_short_8","alias_value":"D34ZAY36","created_at":"2026-05-18T12:32:19Z"}],"graph_snapshots":[{"event_id":"sha256:be4f26cd04508aea561892d6465c5ebceef0da7d5cdab6d1ef80c04645bb749a","target":"graph","created_at":"2026-05-17T23:47:08Z","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":"We show the potential of greedy recovery strategies for the sparse approximation of multivariate functions from a small dataset of pointwise evaluations by considering an extension of the orthogonal matching pursuit to the setting of weighted sparsity. The proposed recovery strategy is based on a formal derivation of the greedy index selection rule. Numerical experiments show that the proposed weighted orthogonal matching pursuit algorithm is able to reach accuracy levels similar to those of weighted $\\ell^1$ minimization programs while considerably improving the computational efficiency for s","authors_text":"Ben Adcock, Simone Brugiapaglia","cross_cats":["cs.IT","math.IT"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2018-10-25T21:33:08Z","title":"Sparse approximation of multivariate functions from small datasets via weighted orthogonal matching pursuit"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.11115","kind":"arxiv","version":2},"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":"sha256:5fbbe1d038d74a098b951c812ea9bc45aae428c90a23325cdd6b20c4bb781651","target":"record","created_at":"2026-05-17T23:47:08Z","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":"db4d9b5e0c1466d23e84d9c4b6b0f5e2b04a36da35d9e9ed1f2f554ec8130696","cross_cats_sorted":["cs.IT","math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2018-10-25T21:33:08Z","title_canon_sha256":"297779dae5caf5bf6d967cc0136548d662baed137cd64a232c83dcb3a0bda194"},"schema_version":"1.0","source":{"id":"1810.11115","kind":"arxiv","version":2}},"canonical_sha256":"1ef990637ec126e6265b73d73e202090a035e160185d3682b5c6cf353067e5db","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1ef990637ec126e6265b73d73e202090a035e160185d3682b5c6cf353067e5db","first_computed_at":"2026-05-17T23:47:08.620257Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:47:08.620257Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"+gnWfaDuG33WXWQZE5jueDnJUZbSMOX/1bSBQiCPt26DlYXvFGuBC8Ejcfl3BlQKdTuWV6yuLMZfEurLMQ3DDw==","signature_status":"signed_v1","signed_at":"2026-05-17T23:47:08.620883Z","signed_message":"canonical_sha256_bytes"},"source_id":"1810.11115","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5fbbe1d038d74a098b951c812ea9bc45aae428c90a23325cdd6b20c4bb781651","sha256:be4f26cd04508aea561892d6465c5ebceef0da7d5cdab6d1ef80c04645bb749a"],"state_sha256":"cc759024058310af36181b1a352c5ce9f4e25cee1039959d663eadfeb2b54202"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Zro2SlcqAxvOqooxa79sipuiNrmnLq72p23mDftHDdnLJM4j17AJAO1DhjyNP7DqLDXdrw706Uhpia3nPxSGAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-10T23:31:24.999090Z","bundle_sha256":"a48e5161284546c9115f58796e769b12c15878a010f351617063f9d9d721b000"}}