{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:YQ5YHSEMY6ZFZ5MDVGXZBZVVFJ","short_pith_number":"pith:YQ5YHSEM","canonical_record":{"source":{"id":"1502.02613","kind":"arxiv","version":6},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2015-02-09T19:19:46Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"82b463e9d35197c12202f00af5a64f3aa395743dfaf865061ec937d96a47ea73","abstract_canon_sha256":"cf2a4e469ab91b7e6e1fd998d9223eb47221578f484a48572fa0e97ec84c1d44"},"schema_version":"1.0"},"canonical_sha256":"c43b83c88cc7b25cf583a9af90e6b52a4d7b22c8aeeb3661a943022d666eab1e","source":{"kind":"arxiv","id":"1502.02613","version":6},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1502.02613","created_at":"2026-05-18T00:44:58Z"},{"alias_kind":"arxiv_version","alias_value":"1502.02613v6","created_at":"2026-05-18T00:44:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1502.02613","created_at":"2026-05-18T00:44:58Z"},{"alias_kind":"pith_short_12","alias_value":"YQ5YHSEMY6ZF","created_at":"2026-05-18T12:29:50Z"},{"alias_kind":"pith_short_16","alias_value":"YQ5YHSEMY6ZFZ5MD","created_at":"2026-05-18T12:29:50Z"},{"alias_kind":"pith_short_8","alias_value":"YQ5YHSEM","created_at":"2026-05-18T12:29:50Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:YQ5YHSEMY6ZFZ5MDVGXZBZVVFJ","target":"record","payload":{"canonical_record":{"source":{"id":"1502.02613","kind":"arxiv","version":6},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2015-02-09T19:19:46Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"82b463e9d35197c12202f00af5a64f3aa395743dfaf865061ec937d96a47ea73","abstract_canon_sha256":"cf2a4e469ab91b7e6e1fd998d9223eb47221578f484a48572fa0e97ec84c1d44"},"schema_version":"1.0"},"canonical_sha256":"c43b83c88cc7b25cf583a9af90e6b52a4d7b22c8aeeb3661a943022d666eab1e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:44:58.379611Z","signature_b64":"vaxYC5RI4QuUxk8U3H9mzJZySnNTRssaUbpZRNYBRINPp349zPrUz9ijZcTjOfXTYa5A6CIUGMFF0r1K56cRDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c43b83c88cc7b25cf583a9af90e6b52a4d7b22c8aeeb3661a943022d666eab1e","last_reissued_at":"2026-05-18T00:44:58.379181Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:44:58.379181Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1502.02613","source_version":6,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:44:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7Dd9VbaDyK0/3xZrdGmehR+V0wL02T65z4vufvr6JAH+bCg6Rz0ZbwvFE3dtBDzx3IHxre5zcukO9FCKnpkKCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T06:04:01.171622Z"},"content_sha256":"56490263d0a28e2b70e321cd63acb6064390c68c6db6948ee51843493f761989","schema_version":"1.0","event_id":"sha256:56490263d0a28e2b70e321cd63acb6064390c68c6db6948ee51843493f761989"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:YQ5YHSEMY6ZFZ5MDVGXZBZVVFJ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Projected Nesterov's Proximal-Gradient Algorithm for Sparse Signal Reconstruction with a Convex Constraint","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"stat.CO","authors_text":"Aleksandar Dogand\\v{z}i\\'c, Renliang Gu","submitted_at":"2015-02-09T19:19:46Z","abstract_excerpt":"We develop a projected Nesterov's proximal-gradient (PNPG) approach for sparse signal reconstruction that combines adaptive step size with Nesterov's momentum acceleration. The objective function that we wish to minimize is the sum of a convex differentiable data-fidelity (negative log-likelihood (NLL)) term and a convex regularization term. We apply sparse signal regularization where the signal belongs to a closed convex set within the closure of the domain of the NLL; the convex-set constraint facilitates flexible NLL domains and accurate signal recovery. Signal sparsity is imposed using the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1502.02613","kind":"arxiv","version":6},"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":"2026-05-18T00:44:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4GB495c7XxpWWQE2Z8kxxbrI7kkdpLwUr+jU2OB3CvxxJrdVZIk9xhC4PjWlh3OD74frR73/V1oJbmZ4wtnRAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T06:04:01.172181Z"},"content_sha256":"f2d9fa7c459f890078bb48e65b5b7c2c22ae11902b5a1b4b55780e81fc9534bf","schema_version":"1.0","event_id":"sha256:f2d9fa7c459f890078bb48e65b5b7c2c22ae11902b5a1b4b55780e81fc9534bf"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YQ5YHSEMY6ZFZ5MDVGXZBZVVFJ/bundle.json","state_url":"https://pith.science/pith/YQ5YHSEMY6ZFZ5MDVGXZBZVVFJ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YQ5YHSEMY6ZFZ5MDVGXZBZVVFJ/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":"2026-06-07T06:04:01Z","links":{"resolver":"https://pith.science/pith/YQ5YHSEMY6ZFZ5MDVGXZBZVVFJ","bundle":"https://pith.science/pith/YQ5YHSEMY6ZFZ5MDVGXZBZVVFJ/bundle.json","state":"https://pith.science/pith/YQ5YHSEMY6ZFZ5MDVGXZBZVVFJ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YQ5YHSEMY6ZFZ5MDVGXZBZVVFJ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:YQ5YHSEMY6ZFZ5MDVGXZBZVVFJ","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":"cf2a4e469ab91b7e6e1fd998d9223eb47221578f484a48572fa0e97ec84c1d44","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2015-02-09T19:19:46Z","title_canon_sha256":"82b463e9d35197c12202f00af5a64f3aa395743dfaf865061ec937d96a47ea73"},"schema_version":"1.0","source":{"id":"1502.02613","kind":"arxiv","version":6}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1502.02613","created_at":"2026-05-18T00:44:58Z"},{"alias_kind":"arxiv_version","alias_value":"1502.02613v6","created_at":"2026-05-18T00:44:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1502.02613","created_at":"2026-05-18T00:44:58Z"},{"alias_kind":"pith_short_12","alias_value":"YQ5YHSEMY6ZF","created_at":"2026-05-18T12:29:50Z"},{"alias_kind":"pith_short_16","alias_value":"YQ5YHSEMY6ZFZ5MD","created_at":"2026-05-18T12:29:50Z"},{"alias_kind":"pith_short_8","alias_value":"YQ5YHSEM","created_at":"2026-05-18T12:29:50Z"}],"graph_snapshots":[{"event_id":"sha256:f2d9fa7c459f890078bb48e65b5b7c2c22ae11902b5a1b4b55780e81fc9534bf","target":"graph","created_at":"2026-05-18T00:44:58Z","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 develop a projected Nesterov's proximal-gradient (PNPG) approach for sparse signal reconstruction that combines adaptive step size with Nesterov's momentum acceleration. The objective function that we wish to minimize is the sum of a convex differentiable data-fidelity (negative log-likelihood (NLL)) term and a convex regularization term. We apply sparse signal regularization where the signal belongs to a closed convex set within the closure of the domain of the NLL; the convex-set constraint facilitates flexible NLL domains and accurate signal recovery. Signal sparsity is imposed using the","authors_text":"Aleksandar Dogand\\v{z}i\\'c, Renliang Gu","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2015-02-09T19:19:46Z","title":"Projected Nesterov's Proximal-Gradient Algorithm for Sparse Signal Reconstruction with a Convex Constraint"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1502.02613","kind":"arxiv","version":6},"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":"sha256:56490263d0a28e2b70e321cd63acb6064390c68c6db6948ee51843493f761989","target":"record","created_at":"2026-05-18T00:44:58Z","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":"cf2a4e469ab91b7e6e1fd998d9223eb47221578f484a48572fa0e97ec84c1d44","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2015-02-09T19:19:46Z","title_canon_sha256":"82b463e9d35197c12202f00af5a64f3aa395743dfaf865061ec937d96a47ea73"},"schema_version":"1.0","source":{"id":"1502.02613","kind":"arxiv","version":6}},"canonical_sha256":"c43b83c88cc7b25cf583a9af90e6b52a4d7b22c8aeeb3661a943022d666eab1e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c43b83c88cc7b25cf583a9af90e6b52a4d7b22c8aeeb3661a943022d666eab1e","first_computed_at":"2026-05-18T00:44:58.379181Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:44:58.379181Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"vaxYC5RI4QuUxk8U3H9mzJZySnNTRssaUbpZRNYBRINPp349zPrUz9ijZcTjOfXTYa5A6CIUGMFF0r1K56cRDQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:44:58.379611Z","signed_message":"canonical_sha256_bytes"},"source_id":"1502.02613","source_kind":"arxiv","source_version":6}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:56490263d0a28e2b70e321cd63acb6064390c68c6db6948ee51843493f761989","sha256:f2d9fa7c459f890078bb48e65b5b7c2c22ae11902b5a1b4b55780e81fc9534bf"],"state_sha256":"da7dfb700ec89b64e6369e04bdd6045fc9770cb984098a62a1eaec5b06c91c64"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"X/XIXEMTut6X5UUjAhwIrDme0O11KatuET90VP+mvYxtBVtrIPFtbb6iHex48vjABP8HHyUMdvHa/Q4Ikp9SBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-07T06:04:01.175568Z","bundle_sha256":"58087ad47ff50dc41b36c53e23d3cc3f4da00b0c6e4443383d61f73d2a11f488"}}