{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:5QCGA6DPIF664MOPQ2EGRIWRT7","short_pith_number":"pith:5QCGA6DP","canonical_record":{"source":{"id":"1608.02227","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2016-08-07T15:45:21Z","cross_cats_sorted":[],"title_canon_sha256":"0661b3934864c9174aeb9815f58a41e93a5165d4a9e3d3dac76e65c21da4ce77","abstract_canon_sha256":"c6921fd060d6f26b40888f5ea57cbc4ee956aae5353abc3dd2018a94961b177f"},"schema_version":"1.0"},"canonical_sha256":"ec0460786f417dee31cf868868a2d19fefc31645e1b15b5a1f7bc61480531d37","source":{"kind":"arxiv","id":"1608.02227","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1608.02227","created_at":"2026-05-18T01:09:40Z"},{"alias_kind":"arxiv_version","alias_value":"1608.02227v1","created_at":"2026-05-18T01:09:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1608.02227","created_at":"2026-05-18T01:09:40Z"},{"alias_kind":"pith_short_12","alias_value":"5QCGA6DPIF66","created_at":"2026-05-18T12:30:01Z"},{"alias_kind":"pith_short_16","alias_value":"5QCGA6DPIF664MOP","created_at":"2026-05-18T12:30:01Z"},{"alias_kind":"pith_short_8","alias_value":"5QCGA6DP","created_at":"2026-05-18T12:30:01Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:5QCGA6DPIF664MOPQ2EGRIWRT7","target":"record","payload":{"canonical_record":{"source":{"id":"1608.02227","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2016-08-07T15:45:21Z","cross_cats_sorted":[],"title_canon_sha256":"0661b3934864c9174aeb9815f58a41e93a5165d4a9e3d3dac76e65c21da4ce77","abstract_canon_sha256":"c6921fd060d6f26b40888f5ea57cbc4ee956aae5353abc3dd2018a94961b177f"},"schema_version":"1.0"},"canonical_sha256":"ec0460786f417dee31cf868868a2d19fefc31645e1b15b5a1f7bc61480531d37","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:09:40.697311Z","signature_b64":"P5/StKmPFJVFlAYpq463P5aNQdYrGLUqzEwEG8TJXIdO1E1UHvSlMsNYF/tX3KA/dO229TVUmITwLAn/PpgfCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ec0460786f417dee31cf868868a2d19fefc31645e1b15b5a1f7bc61480531d37","last_reissued_at":"2026-05-18T01:09:40.696729Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:09:40.696729Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1608.02227","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":"2026-05-18T01:09:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TedwSD+X5dBa+dREw0VSvd+fKR76VNpiuknkBM8R+uOn/cbiKFcS+WskKnJ7jqDCxYXakn6deqyL0c0UPUJ0CQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-21T14:49:38.475845Z"},"content_sha256":"31ad0047b06fb3b154d4f70fe6263387697cd6553973d920fb27451ce4844df4","schema_version":"1.0","event_id":"sha256:31ad0047b06fb3b154d4f70fe6263387697cd6553973d920fb27451ce4844df4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:5QCGA6DPIF664MOPQ2EGRIWRT7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Parallelizable Dual Smoothing Method for Large Scale Convex Regression Problems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Necdet Serhat Aybat, Zi Wang","submitted_at":"2016-08-07T15:45:21Z","abstract_excerpt":"Convex regression (CR) is an approach for fitting a convex function to a finite number of observations. It arises in various applications from diverse fields such as statistics, operations research, economics, and electrical engineering. The least squares (LS) estimator, which can be computed via solving a quadratic program (QP), is an intuitive method for convex regression with already established strong theoretical guarantees. On the other hand, since the number of constraints in the QP formulation increases quadratically in the number of observed data points, the QP quickly becomes impracti"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1608.02227","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":""},"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":"2026-05-18T01:09:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mNQo8/bwi30xlRVD0fPNqXnm/zHlE+cqrXoB6tPFMDwMVkr1KvlunCsmIOJJ3s6pSWjU0M9ewVOp0jNgnDFCDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-21T14:49:38.476488Z"},"content_sha256":"1794c6e93e0d24c424ccf7dcda915be891ad4ede8bf2941b6d19e894b3f6312b","schema_version":"1.0","event_id":"sha256:1794c6e93e0d24c424ccf7dcda915be891ad4ede8bf2941b6d19e894b3f6312b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/5QCGA6DPIF664MOPQ2EGRIWRT7/bundle.json","state_url":"https://pith.science/pith/5QCGA6DPIF664MOPQ2EGRIWRT7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/5QCGA6DPIF664MOPQ2EGRIWRT7/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":"2026-05-21T14:49:38Z","links":{"resolver":"https://pith.science/pith/5QCGA6DPIF664MOPQ2EGRIWRT7","bundle":"https://pith.science/pith/5QCGA6DPIF664MOPQ2EGRIWRT7/bundle.json","state":"https://pith.science/pith/5QCGA6DPIF664MOPQ2EGRIWRT7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/5QCGA6DPIF664MOPQ2EGRIWRT7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:5QCGA6DPIF664MOPQ2EGRIWRT7","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":"c6921fd060d6f26b40888f5ea57cbc4ee956aae5353abc3dd2018a94961b177f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2016-08-07T15:45:21Z","title_canon_sha256":"0661b3934864c9174aeb9815f58a41e93a5165d4a9e3d3dac76e65c21da4ce77"},"schema_version":"1.0","source":{"id":"1608.02227","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1608.02227","created_at":"2026-05-18T01:09:40Z"},{"alias_kind":"arxiv_version","alias_value":"1608.02227v1","created_at":"2026-05-18T01:09:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1608.02227","created_at":"2026-05-18T01:09:40Z"},{"alias_kind":"pith_short_12","alias_value":"5QCGA6DPIF66","created_at":"2026-05-18T12:30:01Z"},{"alias_kind":"pith_short_16","alias_value":"5QCGA6DPIF664MOP","created_at":"2026-05-18T12:30:01Z"},{"alias_kind":"pith_short_8","alias_value":"5QCGA6DP","created_at":"2026-05-18T12:30:01Z"}],"graph_snapshots":[{"event_id":"sha256:1794c6e93e0d24c424ccf7dcda915be891ad4ede8bf2941b6d19e894b3f6312b","target":"graph","created_at":"2026-05-18T01:09:40Z","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":"Convex regression (CR) is an approach for fitting a convex function to a finite number of observations. It arises in various applications from diverse fields such as statistics, operations research, economics, and electrical engineering. The least squares (LS) estimator, which can be computed via solving a quadratic program (QP), is an intuitive method for convex regression with already established strong theoretical guarantees. On the other hand, since the number of constraints in the QP formulation increases quadratically in the number of observed data points, the QP quickly becomes impracti","authors_text":"Necdet Serhat Aybat, Zi Wang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2016-08-07T15:45:21Z","title":"A Parallelizable Dual Smoothing Method for Large Scale Convex Regression Problems"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1608.02227","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":"sha256:31ad0047b06fb3b154d4f70fe6263387697cd6553973d920fb27451ce4844df4","target":"record","created_at":"2026-05-18T01:09:40Z","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":"c6921fd060d6f26b40888f5ea57cbc4ee956aae5353abc3dd2018a94961b177f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2016-08-07T15:45:21Z","title_canon_sha256":"0661b3934864c9174aeb9815f58a41e93a5165d4a9e3d3dac76e65c21da4ce77"},"schema_version":"1.0","source":{"id":"1608.02227","kind":"arxiv","version":1}},"canonical_sha256":"ec0460786f417dee31cf868868a2d19fefc31645e1b15b5a1f7bc61480531d37","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ec0460786f417dee31cf868868a2d19fefc31645e1b15b5a1f7bc61480531d37","first_computed_at":"2026-05-18T01:09:40.696729Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:09:40.696729Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"P5/StKmPFJVFlAYpq463P5aNQdYrGLUqzEwEG8TJXIdO1E1UHvSlMsNYF/tX3KA/dO229TVUmITwLAn/PpgfCQ==","signature_status":"signed_v1","signed_at":"2026-05-18T01:09:40.697311Z","signed_message":"canonical_sha256_bytes"},"source_id":"1608.02227","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:31ad0047b06fb3b154d4f70fe6263387697cd6553973d920fb27451ce4844df4","sha256:1794c6e93e0d24c424ccf7dcda915be891ad4ede8bf2941b6d19e894b3f6312b"],"state_sha256":"d9721409fc913e02222440f477bda0920118f9bccf90fed16302b3ee8d108f4c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nxZXR25tZzWH9lvg9Dx2toL+PXemA+A7GexOFiDZMvkZkf+8B+WnaKOisQS7rTBhtEJ7PLWbUFKJ8M5v2LkbBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-21T14:49:38.479565Z","bundle_sha256":"c2347ad74c3bbdd130fdaca8432e14525c577cc6f0584d09415bf3652a5f09af"}}