{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:RPPRG6ZSMVDREBBYSK34ZY7HPU","short_pith_number":"pith:RPPRG6ZS","canonical_record":{"source":{"id":"2104.13107","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2021-04-27T11:03:15Z","cross_cats_sorted":[],"title_canon_sha256":"c581876f5bbca2206d7360fa84e016174b4cd9e78373a9cccb099c4e95bf6c7f","abstract_canon_sha256":"c854e4e384048040e8c2ad03f16267ad2fbba0680e2bedc8f8da54927a844808"},"schema_version":"1.0"},"canonical_sha256":"8bdf137b32654712043892b7cce3e77d3d4a00fadd5ef7444defa983c0bae452","source":{"kind":"arxiv","id":"2104.13107","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2104.13107","created_at":"2026-07-05T02:35:45Z"},{"alias_kind":"arxiv_version","alias_value":"2104.13107v1","created_at":"2026-07-05T02:35:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2104.13107","created_at":"2026-07-05T02:35:45Z"},{"alias_kind":"pith_short_12","alias_value":"RPPRG6ZSMVDR","created_at":"2026-07-05T02:35:45Z"},{"alias_kind":"pith_short_16","alias_value":"RPPRG6ZSMVDREBBY","created_at":"2026-07-05T02:35:45Z"},{"alias_kind":"pith_short_8","alias_value":"RPPRG6ZS","created_at":"2026-07-05T02:35:45Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:RPPRG6ZSMVDREBBYSK34ZY7HPU","target":"record","payload":{"canonical_record":{"source":{"id":"2104.13107","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2021-04-27T11:03:15Z","cross_cats_sorted":[],"title_canon_sha256":"c581876f5bbca2206d7360fa84e016174b4cd9e78373a9cccb099c4e95bf6c7f","abstract_canon_sha256":"c854e4e384048040e8c2ad03f16267ad2fbba0680e2bedc8f8da54927a844808"},"schema_version":"1.0"},"canonical_sha256":"8bdf137b32654712043892b7cce3e77d3d4a00fadd5ef7444defa983c0bae452","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:35:45.580774Z","signature_b64":"8zfDXTNw1sndUMx635KSsTgnaKvUKFUnwz5NoxTB2wNJD7v+mKULK4yMwqs+qEc2KUd18R8wHM1AdAtZRX26AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8bdf137b32654712043892b7cce3e77d3d4a00fadd5ef7444defa983c0bae452","last_reissued_at":"2026-07-05T02:35:45.580432Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:35:45.580432Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2104.13107","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T02:35:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"61CkpB/C35aWzGt8JU8xZhuRM52l90xpyiB2dcAjC83NZvXVO12757PgjxlJHPcU4pUsGcwJGBE7DvUQAbb+BQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T03:08:51.970749Z"},"content_sha256":"1c485603822aa845d5d178015f8b3d321c6219e965ee4f5ceb7df4b1743df6ef","schema_version":"1.0","event_id":"sha256:1c485603822aa845d5d178015f8b3d321c6219e965ee4f5ceb7df4b1743df6ef"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:RPPRG6ZSMVDREBBYSK34ZY7HPU","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Smoothing fast iterative hard thresholding algorithm for $\\ell_0$ regularized nonsmooth convex regression problem","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Fan Wu, Wei Bian, Xiaoping Xue","submitted_at":"2021-04-27T11:03:15Z","abstract_excerpt":"We investigate a class of constrained sparse regression problem with cardinality penalty, where the feasible set is defined by box constraint, and the loss function is convex, but not necessarily smooth. First, we put forward a smoothing fast iterative hard thresholding (SFIHT) algorithm for solving such optimization problems, which combines smoothing approximations, extrapolation techniques and iterative hard thresholding methods. The extrapolation coefficients can be chosen to satisfy $\\sup_k \\beta_k=1$ in the proposed algorithm. We discuss the convergence behavior of the algorithm with diff"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2104.13107","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/2104.13107/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T02:35:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FAYEM4PwCQmCpAJ8D9GjObzxPhIZC1DHbGUe4aWbTINOpEq77G6CT1RCmmJ2Ak5O7q443ksn3d0u+lN34wBwAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T03:08:51.971215Z"},"content_sha256":"2125f96ce8251b7131f586ed3f0a2e7f62979fec879ede7946f67276c0a78d19","schema_version":"1.0","event_id":"sha256:2125f96ce8251b7131f586ed3f0a2e7f62979fec879ede7946f67276c0a78d19"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/RPPRG6ZSMVDREBBYSK34ZY7HPU/bundle.json","state_url":"https://pith.science/pith/RPPRG6ZSMVDREBBYSK34ZY7HPU/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/RPPRG6ZSMVDREBBYSK34ZY7HPU/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":"2026-08-06T03:08:51Z","links":{"resolver":"https://pith.science/pith/RPPRG6ZSMVDREBBYSK34ZY7HPU","bundle":"https://pith.science/pith/RPPRG6ZSMVDREBBYSK34ZY7HPU/bundle.json","state":"https://pith.science/pith/RPPRG6ZSMVDREBBYSK34ZY7HPU/state.json","well_known_bundle":"https://pith.science/.well-known/pith/RPPRG6ZSMVDREBBYSK34ZY7HPU/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:RPPRG6ZSMVDREBBYSK34ZY7HPU","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":"c854e4e384048040e8c2ad03f16267ad2fbba0680e2bedc8f8da54927a844808","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2021-04-27T11:03:15Z","title_canon_sha256":"c581876f5bbca2206d7360fa84e016174b4cd9e78373a9cccb099c4e95bf6c7f"},"schema_version":"1.0","source":{"id":"2104.13107","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2104.13107","created_at":"2026-07-05T02:35:45Z"},{"alias_kind":"arxiv_version","alias_value":"2104.13107v1","created_at":"2026-07-05T02:35:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2104.13107","created_at":"2026-07-05T02:35:45Z"},{"alias_kind":"pith_short_12","alias_value":"RPPRG6ZSMVDR","created_at":"2026-07-05T02:35:45Z"},{"alias_kind":"pith_short_16","alias_value":"RPPRG6ZSMVDREBBY","created_at":"2026-07-05T02:35:45Z"},{"alias_kind":"pith_short_8","alias_value":"RPPRG6ZS","created_at":"2026-07-05T02:35:45Z"}],"graph_snapshots":[{"event_id":"sha256:2125f96ce8251b7131f586ed3f0a2e7f62979fec879ede7946f67276c0a78d19","target":"graph","created_at":"2026-07-05T02:35:45Z","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/2104.13107/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We investigate a class of constrained sparse regression problem with cardinality penalty, where the feasible set is defined by box constraint, and the loss function is convex, but not necessarily smooth. First, we put forward a smoothing fast iterative hard thresholding (SFIHT) algorithm for solving such optimization problems, which combines smoothing approximations, extrapolation techniques and iterative hard thresholding methods. The extrapolation coefficients can be chosen to satisfy $\\sup_k \\beta_k=1$ in the proposed algorithm. We discuss the convergence behavior of the algorithm with diff","authors_text":"Fan Wu, Wei Bian, Xiaoping Xue","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2021-04-27T11:03:15Z","title":"Smoothing fast iterative hard thresholding algorithm for $\\ell_0$ regularized nonsmooth convex regression problem"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2104.13107","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":"sha256:1c485603822aa845d5d178015f8b3d321c6219e965ee4f5ceb7df4b1743df6ef","target":"record","created_at":"2026-07-05T02:35:45Z","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":"c854e4e384048040e8c2ad03f16267ad2fbba0680e2bedc8f8da54927a844808","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2021-04-27T11:03:15Z","title_canon_sha256":"c581876f5bbca2206d7360fa84e016174b4cd9e78373a9cccb099c4e95bf6c7f"},"schema_version":"1.0","source":{"id":"2104.13107","kind":"arxiv","version":1}},"canonical_sha256":"8bdf137b32654712043892b7cce3e77d3d4a00fadd5ef7444defa983c0bae452","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8bdf137b32654712043892b7cce3e77d3d4a00fadd5ef7444defa983c0bae452","first_computed_at":"2026-07-05T02:35:45.580432Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:35:45.580432Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"8zfDXTNw1sndUMx635KSsTgnaKvUKFUnwz5NoxTB2wNJD7v+mKULK4yMwqs+qEc2KUd18R8wHM1AdAtZRX26AA==","signature_status":"signed_v1","signed_at":"2026-07-05T02:35:45.580774Z","signed_message":"canonical_sha256_bytes"},"source_id":"2104.13107","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1c485603822aa845d5d178015f8b3d321c6219e965ee4f5ceb7df4b1743df6ef","sha256:2125f96ce8251b7131f586ed3f0a2e7f62979fec879ede7946f67276c0a78d19"],"state_sha256":"d43faad9a406e726d81ca20b19c022e9e9100d7d086c4bae3ff67ddc31325560"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tbkQvyUZF7UKUlASMCIl0BtRlrU0fkK4p5ZNPa7e696llcPgyL7lpL/n3Kbzzn8MZZ1QkMO+Nq5AmEtjnYktBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T03:08:51.974610Z","bundle_sha256":"d75ae9e251e762a552cd083868fbea5886fd5d2398f8ab0c31b8a8bdd72d8953"}}