{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:J3YVBXZGVVZVKRU5W6G3RRFUOW","short_pith_number":"pith:J3YVBXZG","canonical_record":{"source":{"id":"1809.06304","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-09-17T16:14:55Z","cross_cats_sorted":["cs.IT","cs.LG","math.IT"],"title_canon_sha256":"015fd96cdd129be1a01b30d1d360ac9442770443c3ad1ca6ade2aedb57cd0f45","abstract_canon_sha256":"85c19def7038c6e1a9003bd27e474bb82d0b16ef48a529562841845bf8c1e6a0"},"schema_version":"1.0"},"canonical_sha256":"4ef150df26ad7355469db78db8c4b475953a4252d32d7cbd1fbe3cd1dc86116e","source":{"kind":"arxiv","id":"1809.06304","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1809.06304","created_at":"2026-05-18T00:05:35Z"},{"alias_kind":"arxiv_version","alias_value":"1809.06304v1","created_at":"2026-05-18T00:05:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.06304","created_at":"2026-05-18T00:05:35Z"},{"alias_kind":"pith_short_12","alias_value":"J3YVBXZGVVZV","created_at":"2026-05-18T12:32:31Z"},{"alias_kind":"pith_short_16","alias_value":"J3YVBXZGVVZVKRU5","created_at":"2026-05-18T12:32:31Z"},{"alias_kind":"pith_short_8","alias_value":"J3YVBXZG","created_at":"2026-05-18T12:32:31Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:J3YVBXZGVVZVKRU5W6G3RRFUOW","target":"record","payload":{"canonical_record":{"source":{"id":"1809.06304","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-09-17T16:14:55Z","cross_cats_sorted":["cs.IT","cs.LG","math.IT"],"title_canon_sha256":"015fd96cdd129be1a01b30d1d360ac9442770443c3ad1ca6ade2aedb57cd0f45","abstract_canon_sha256":"85c19def7038c6e1a9003bd27e474bb82d0b16ef48a529562841845bf8c1e6a0"},"schema_version":"1.0"},"canonical_sha256":"4ef150df26ad7355469db78db8c4b475953a4252d32d7cbd1fbe3cd1dc86116e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:05:35.068624Z","signature_b64":"oYG+g+en91aYJZzRZaEMecXmnHTdBv56ab3aDU0exvS/QVaU/D2QBR2S48bpsJq46bhX0+z+0F/bI2fKu0odCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4ef150df26ad7355469db78db8c4b475953a4252d32d7cbd1fbe3cd1dc86116e","last_reissued_at":"2026-05-18T00:05:35.068217Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:05:35.068217Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1809.06304","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":"2026-05-18T00:05:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rm9p772RpbfOq7XNeO/c1DmdStLcUClGvU7DEB3jLVX8j5R6XOwn4VclYKmMJLCdhjDdrSZbjwDB+Xp9txjHBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-05T01:56:40.896830Z"},"content_sha256":"09d1a64528004f3ee19b9c95a351d99bd98cb921a0b4fdd4853cea9e5883f20a","schema_version":"1.0","event_id":"sha256:09d1a64528004f3ee19b9c95a351d99bd98cb921a0b4fdd4853cea9e5883f20a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:J3YVBXZGVVZVKRU5W6G3RRFUOW","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Approximate message-passing for convex optimization with non-separable penalties","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IT","cs.LG","math.IT"],"primary_cat":"stat.ML","authors_text":"Andre Manoel, Bertrand Thirion, Florent Krzakala, Ga\\\"el Varoquaux, Lenka Zdeborov\\'a","submitted_at":"2018-09-17T16:14:55Z","abstract_excerpt":"We introduce an iterative optimization scheme for convex objectives consisting of a linear loss and a non-separable penalty, based on the expectation-consistent approximation and the vector approximate message-passing (VAMP) algorithm. Specifically, the penalties we approach are convex on a linear transformation of the variable to be determined, a notable example being total variation (TV). We describe the connection between message-passing algorithms -- typically used for approximate inference -- and proximal methods for optimization, and show that our scheme is, as VAMP, similar in nature to"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.06304","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":"2026-05-18T00:05:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8znTe+fl+apGn9qC1FT8yjE6e1rY+YLdgHdZvvrL5FoGXHzFSHabCqgBPb2XyodjtoLigsVAax11ksDcHiqDAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-05T01:56:40.897190Z"},"content_sha256":"fe895274e24ef847b3948bcba0498fd97251e31eeb278f3cf7b268f83138ec61","schema_version":"1.0","event_id":"sha256:fe895274e24ef847b3948bcba0498fd97251e31eeb278f3cf7b268f83138ec61"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/J3YVBXZGVVZVKRU5W6G3RRFUOW/bundle.json","state_url":"https://pith.science/pith/J3YVBXZGVVZVKRU5W6G3RRFUOW/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/J3YVBXZGVVZVKRU5W6G3RRFUOW/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":"2026-06-05T01:56:40Z","links":{"resolver":"https://pith.science/pith/J3YVBXZGVVZVKRU5W6G3RRFUOW","bundle":"https://pith.science/pith/J3YVBXZGVVZVKRU5W6G3RRFUOW/bundle.json","state":"https://pith.science/pith/J3YVBXZGVVZVKRU5W6G3RRFUOW/state.json","well_known_bundle":"https://pith.science/.well-known/pith/J3YVBXZGVVZVKRU5W6G3RRFUOW/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:J3YVBXZGVVZVKRU5W6G3RRFUOW","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":"85c19def7038c6e1a9003bd27e474bb82d0b16ef48a529562841845bf8c1e6a0","cross_cats_sorted":["cs.IT","cs.LG","math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-09-17T16:14:55Z","title_canon_sha256":"015fd96cdd129be1a01b30d1d360ac9442770443c3ad1ca6ade2aedb57cd0f45"},"schema_version":"1.0","source":{"id":"1809.06304","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1809.06304","created_at":"2026-05-18T00:05:35Z"},{"alias_kind":"arxiv_version","alias_value":"1809.06304v1","created_at":"2026-05-18T00:05:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.06304","created_at":"2026-05-18T00:05:35Z"},{"alias_kind":"pith_short_12","alias_value":"J3YVBXZGVVZV","created_at":"2026-05-18T12:32:31Z"},{"alias_kind":"pith_short_16","alias_value":"J3YVBXZGVVZVKRU5","created_at":"2026-05-18T12:32:31Z"},{"alias_kind":"pith_short_8","alias_value":"J3YVBXZG","created_at":"2026-05-18T12:32:31Z"}],"graph_snapshots":[{"event_id":"sha256:fe895274e24ef847b3948bcba0498fd97251e31eeb278f3cf7b268f83138ec61","target":"graph","created_at":"2026-05-18T00:05:35Z","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 introduce an iterative optimization scheme for convex objectives consisting of a linear loss and a non-separable penalty, based on the expectation-consistent approximation and the vector approximate message-passing (VAMP) algorithm. Specifically, the penalties we approach are convex on a linear transformation of the variable to be determined, a notable example being total variation (TV). We describe the connection between message-passing algorithms -- typically used for approximate inference -- and proximal methods for optimization, and show that our scheme is, as VAMP, similar in nature to","authors_text":"Andre Manoel, Bertrand Thirion, Florent Krzakala, Ga\\\"el Varoquaux, Lenka Zdeborov\\'a","cross_cats":["cs.IT","cs.LG","math.IT"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-09-17T16:14:55Z","title":"Approximate message-passing for convex optimization with non-separable penalties"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.06304","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":"sha256:09d1a64528004f3ee19b9c95a351d99bd98cb921a0b4fdd4853cea9e5883f20a","target":"record","created_at":"2026-05-18T00:05:35Z","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":"85c19def7038c6e1a9003bd27e474bb82d0b16ef48a529562841845bf8c1e6a0","cross_cats_sorted":["cs.IT","cs.LG","math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-09-17T16:14:55Z","title_canon_sha256":"015fd96cdd129be1a01b30d1d360ac9442770443c3ad1ca6ade2aedb57cd0f45"},"schema_version":"1.0","source":{"id":"1809.06304","kind":"arxiv","version":1}},"canonical_sha256":"4ef150df26ad7355469db78db8c4b475953a4252d32d7cbd1fbe3cd1dc86116e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4ef150df26ad7355469db78db8c4b475953a4252d32d7cbd1fbe3cd1dc86116e","first_computed_at":"2026-05-18T00:05:35.068217Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:05:35.068217Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"oYG+g+en91aYJZzRZaEMecXmnHTdBv56ab3aDU0exvS/QVaU/D2QBR2S48bpsJq46bhX0+z+0F/bI2fKu0odCw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:05:35.068624Z","signed_message":"canonical_sha256_bytes"},"source_id":"1809.06304","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:09d1a64528004f3ee19b9c95a351d99bd98cb921a0b4fdd4853cea9e5883f20a","sha256:fe895274e24ef847b3948bcba0498fd97251e31eeb278f3cf7b268f83138ec61"],"state_sha256":"2bb2d9a7d6c1ea58e54400ea9be04a065a01a0d44a5fd02a5a995525a4921f34"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iG75bLW9qh2a1uFD4HpVXyoeliMJP2ENhUR/RBBn1AEAOKaTA8UPotWiOtLCLxnMvgYoyJUV9WfEOXv72KU7CQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-05T01:56:40.899766Z","bundle_sha256":"b1076326b688c05f502271d8c383f7d38326ba97ea814b39f198c535261bdb64"}}